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Mobile App Research: The Ultimate Guide for Your App Success

Mobile App Development

 / December 18, 2023

Picture this: You’ve invested your passion and creativity into an innovative app idea, dreaming of millions eagerly clicking “download.” The concept seems promising, but a vital question remains: Does your mobile app idea align with your audience’s needs? Don’t dive into development without clarity on this. Understand your potential users’ desires first. This is where mobile app market research becomes indispensable.

If you crave a thorough guide to embark on mobile app market research, keep reading this article. This guide will enlighten you about the advantages of market research for app development, the significance of choosing the right platforms for your apps, and the latest technologies in the field. Consider this guide your essential partner in mastering mobile app research. Let’s start this enlightening journey together! 

Why Conduct Mobile App Market Research?

Market research is an integral contributor to your app success because it helps reveal what your users crave and what the market truly needs.

In other words, mobile app research helps answer these questions when you learn about your target audience and the market:

• Who are your ideal users? (Age, location, interests, or mobile usage habits)
• What are their needs and pain points? What problems do they face with existing apps?
• What features would they find most valuable? What would make their lives easier or more enjoyable?
• How much are they willing to pay for a solution? What are their spending habits and preferences?
• What platforms do they use most? iOS, Android, or something else?

By answering these questions and many more, you can unlock the following benefits of mobile app research:

Validate App Ideas

Research helps you identify if your app concept is a diamond or a lump of coal. This helps avoid pouring resources into ideas with low market potential and discover hidden gems that users will flock to.

Understand Your Target Audience

Ever tried designing a shirt without knowing your friend’s favorite color or style? It wouldn’t exactly be a hit, right? Similarly, building an app without understanding your target audience is a recipe for disaster. Research paints a vivid picture of your ideal users – their demographics, tech savviness, mobile habits, and even their deepest pain points. This empathy-driven approach ensures your app speaks their language and solves their problems seamlessly.

Analyze Competition

The mobile app landscape is a competitive battlefield, but knowledge is your ultimate weapon. Research unveils the strengths and weaknesses of your competitors, allowing you to learn from the best and carve your unique path. Besides, you can identify untapped market segments, avoid their pitfalls, and create an app so unique and compelling that it leaves them in the dust.

Reduce Risk

Mobile app research serves as a magic shield against costly mistakes. By making informed decisions about features, platforms, and monetization strategies based on real data, you minimize the risk of wasted resources and development dead ends. This ensures your app is set on a path to sustainable success.

A 6-Step Guide to Powerful Mobile App Research

You’ve learned about the importance of market research for your mobile apps. So now, let’s navigate the six actionable steps to conduct mobile app research effectively.

Step 1: Define Your Research Objectives and Questions

Defining your research objectives and questions is the foundation upon which successful mobile app research is built. This step is crucial as it sets the direction for your entire research process. It helps you to focus on what you want to learn and the specific questions you want to answer.

Ask yourself the following questions to help identify your research objectives:

• How big is the market for my app idea ?
• Who are my competitors, and what are their strengths and weaknesses? Can I identify gaps in their offerings or capitalize on their blind spots?
• What are the top frustrations my potential users face in similar apps?
• What features do users value most?
• What pricing model would be most profitable?

By answering these questions, you can formulate clear and concise research objectives. For this reason, you can avoid wasting time and resources on searching irrelevant or unnecessary information. Furthermore, having clear research objectives and questions can help guide your data collection and analysis, ensuring that your research findings are relevant and actionable.

Step 2: Choose Your Research Methods and Tools

Now that you’ve charted your course with clear research objectives, it’s time to grab suitable methods and tools for your research. This step determines how you will collect and analyze the data needed to answer your research questions. Accordingly, choosing the right methods and tools helps you target your research data and increase the reliability of your findings.

There are two main types of research methods you can use: primary research and secondary research.

Primary research involves collecting new data that has not been collected before. For example, surveys, interviews, and observations are all types of primary research. You can use online survey tools, such as SurveyMonkey or Google Forms, to create and distribute surveys to your target audience.

Secondary research, on the other hand, involves using existing data that has been collected by someone else. This can include reports, market research studies, and articles. You can use web search tools, such as Google Trends, Statista, or Pew Research, to find relevant and trustworthy sources of secondary data.

Depending on your objectives and questions, you can use different methods and tools to collect and analyze data. For instance, if you want to understand the size and growth of your market, you might use secondary research to find existing reports and studies. If you want to understand the pain points of your customers, you might use primary research to conduct surveys or interviews.

Step 3: Collect and Organize Your Data

With your research tools in hand, it’s time to embark on excavating your data for mobile app research, by both qualitative and quantitative methods. When collecting data, you should consider various aspects of your target market. These include: 

• Demographics: Age, location, income, and education – these basic details paint a picture of your target audience.
• Psychographics: Values, interests, and lifestyle choices offer deeper insights into user motivations and aspirations.
• Online behavior: Web browsing habits, social media engagement, and even app usage patterns reveal valuable clues about user preferences.
• Mobile app usage habits: How do users interact with apps? What features do they value? Or how often do they use them? These questions help you better understand app usage patterns. Such information is essential for creating an engaging and user-friendly experience later.

After you have collected data, it’s important to record and organize your data in a clear and consistent way. This could involve using spreadsheets, charts, or tables to keep track of your data. Tools like Microsoft Excel or Google Sheets can be particularly helpful for data management and visualization.

Organizing your data effectively can make the subsequent steps of analyzing and interpreting your data much easier. It can help you to see patterns and trends in your data, and it can make it easier to share your findings with others.

Step 4: Analyze and Interpret Your Data

Once you have collected and organized your data, you can start analyzing and interpreting it to find patterns, insights, and answers to your research questions. 

But in the age of data, caution is key. The 2021 Report by Remesh revealed that 56% of agencies are becoming more cautious about how they receive and analyze data. This underscores the importance of responsible data handling in mobile app research.

Data analysis involves looking at your data from different angles and having in-depth insights into these aspects:

• Existing apps in your niche: Understand the landscape, their strengths, weaknesses, and feature sets. This helps you avoid reinventing the wheel and carve out your unique space.
• Market trends and user insights: Stay ahead of the curve by identifying emerging technologies, user behavior shifts, and evolving market demands. This ensures your app remains relevant and competitive.

Remember, analysis isn’t just about dull numbers and texts. It’s about storytelling. You should use data analysis platforms (Power BI, Google Analytics) to transform complex data into interactive dashboards and insightful reports. Besides, consider visualization tools (Graphic_art, Tableau) to create compelling charts, graphs, and diagrams to tell the story hidden within your data. These tools support you in telling a persuasive, thrilling story that all stakeholders can understand.

Step 5: Assess the Risks

Besides opportunities, data analysis helps you spot potential risks linked to your app and the market. Through thorough risk assessments in mobile app research, you are well-equipped to anticipate problems and develop solutions in advance. This way, you can reduce the likelihood of failure when developing and launching the app to market. 

Common Risks Faced by Mobile Apps

During market research, you may discover the following risks the app may encounter:

Market Saturation: Is your niche already overflowing with similar apps? Can you carve out a unique space or offer a differentiated value proposition? Trusted data helps you answer these questions to check the saturation level of the market you plan to jump into. Then determine the demand for your app and prevent you from launching into a sea of red faces (and low downloads).

Competition Intensity: The intensity of competition can impact the success of your app. If there are many apps with similar features, you may need to find unique ways to differentiate your app. So to evaluate this risk, ask yourself:

• Are established players dominating the market? 
• How can you stand out from the crowd and attract your target audience?

Monetization Potential: Assessing the monetization potential of your app is crucial. This involves understanding how your app can generate revenue, whether through in-app purchases, advertising, subscriptions, or other means.

Technical Feasibility: Does your app concept align with current technological limitations? Can you develop it within your budget and timeframe? These questions help you better assess the technical feasibility of your app. Ensure you grasp the technical requirements of your app and have the resources and capabilities to meet these requirements. 

Techniques in Risk Assessment

There are several tools and techniques that can assist risk assessment:

SWOT Analysis: This tool can help you understand the strengths, weaknesses, opportunities, and threats associated with your app.

Competitor Pricing Models: Understanding how your competitors price their apps can give you insights into how to price your own app.

User Acquisition Cost Analysis: This involves understanding how much it costs to acquire a new user for your app. This can help you to determine the profitability of your app.

Developing Mitigation Strategies

Based on your risk assessment, you should develop strategies to mitigate the identified risks. This could involve adjusting your app’s features, changing your marketing strategy, or even reconsidering your entire app concept.

Step 6: Report and Apply Your Findings

After meticulous data analysis and risk assessment, you can report and apply your findings to your mobile app development and marketing strategy. 

Use tools like Microsoft Word or Google Docs to create and share your research report, which should include an executive summary, an introduction, a methodology, a results section, a discussion section, a conclusion, and a list of references. Besides, such tools as Microsoft PowerPoint or Google Slides prove helpful in creating and presenting your research findings to your stakeholders, such as investors, partners, or customers.

Platform Selection: iOS vs. Android and Beyond

The platform selection is an extension of your app research. Mobile app research findings help you not only identify the right development direction for your app but also choose the right platform to reach its full potential. But why does platform selection matter? And what are the key considerations in selecting a platform? Read on to find the answers.

Why Choose the Right Mobile App Platforms?

The platform you choose determines the reach of your app. Each platform comes with its own demographic characteristics, and understanding these can help you tailor your app to your target audience. Moreover, different platforms support different features and have varying development costs and timelines. Therefore, choosing the right platform is crucial for maximizing your reach, minimizing your risks, and setting your app up for success.

Factors to Consider

When selecting a platform, consider the following factors:

Target Audience: Where are your ideal users hanging out? Do they dominate the iOS ecosystem or rule the Android kingdom? Understanding their platform preferences is key to helping you identify which platforms they use the most.

App Features: Does your app require specific functionalities or advanced capabilities? Some platforms, like iOS, are known for their high-end features, while others, like KaiOS, cater to simpler devices. Not to mention some features may only be available on certain platforms. So, make sure the platform you choose supports all the features you want to include in your app.

Budget and Development Time: Building for multiple platforms can be a resource-hungry beast. Consider your budget and development timeline to choose the platform that delivers the most impact on your investment. 

Choosing Your Launchpad: Android vs. iOS and the Niche Alternatives

Android and iOS are the two major players in the mobile app market. Android, with a market share of over 70%, is the most widely used mobile operating system. Its open-source nature allows for high customization, but this can also lead to fragmentation and security issues.

On the other hand, iOS, with a market share of nearly 30%, is known for its uniformity across devices and its stringent app review process, which enhances security and user experience but can also lengthen the app approval time.

While iOS and Android dominate the market, niche platforms like Samsung’s Tizen, KaiOS, Windows, and Linux also exist. These platforms may not have as wide a reach as Android or iOS, but they cater to specific audiences and can offer unique benefits. For instance, KaiOS is popular in emerging markets, and Linux is favored for its open-source nature. Exploring these platforms could open up new opportunities for your app.

However, the ideal platform isn’t always a single choice. Consider a hybrid approach, leveraging cross-platform development tools to reach a wider audience while maintaining a native feel. 

Top 3 Emerging Technologies in Mobile App Market Research

Technology is transforming the landscape of mobile app research. Tech-based solutions offer quicker and more affordable insights at a larger scale. Therefore, they’re increasingly being adopted by organizations, replacing traditional research methodologies. In this section, let’s explore the top four emerging technologies together.

Artificial Intelligence (AI) Integration

AI is increasingly becoming a cornerstone in the field of market research. A significant 80% of industry professionals believe that AI will have a positive impact on market research, primarily due to its potential to enhance data quality. Furthermore, 75% of researchers predict that the data generated through AI will surpass today’s accuracy levels.

These statistics underscore the expanding role of AI in market research tasks. From sentiment analysis and predictive analysis to demand forecasting, AI integration is becoming more prevalent. 

This trend is evident in the growing number of companies introducing AI-powered market research tools to support mobile app research. Tools such as SEMRush Market Explorer, Poll the People, AI Persona Builder, and Kompyte are just a few examples of how AI is being leveraged to revolutionize mobile app market research. These tools not only enhance the efficiency of data collection and analysis but also provide more accurate and actionable insights, paving the way for more informed decision-making in the mobile app market.

Social Listening Platforms

Social listening platforms are potent tools that monitor and analyze conversations and mentions related to specific topics on social media platforms. They aggregate social data and scrutinize it based on distinct attributes or metrics. These tools are a boon for mobile app market research, providing real-time feedback and insights from users.

The social listening landscape is constantly evolving, with new tools and vendors emerging to address specific needs and challenges. Looking ahead to 2024, nearly half of social intelligence professionals plan to focus on new social listening technology . While established players like Brandwatch, Sprinklr, or Talkwalker continue to dominate, the future holds exciting possibilities for even more sophisticated and targeted solutions.

Big Data Analytics

Big data signifies the enormous volume of both structured and unstructured data generated by various sources in our digital world, including social media, e-commerce transactions, and mobile devices. In 2021, it was reported that 46% of organizations utilized big data analytics as a research method.

The advent of big data is revolutionizing the approach to market research in several ways. Particularly, big data analytics , coupled with such advanced analytical techniques as AI/ML, provides access to a significantly larger and more diverse dataset. So you can generate a more accurate and in-depth perspective of consumer behavior and preferences on a scale that was previously unattainable. 

Further, it enables you to conduct research in real-time, equipping brands with insights into consumer behavior and preferences as they occur. 

With these outstanding benefits in market research, the potential of big data analytics continues to be harnessed further in the future.

Market research isn’t just for big companies, but also startups and SMEs! With this guide, you’ve unlocked the secrets to conducting effective market research for your mobile app project. Remember, the best apps solve real problems for real people. So, grab your phone, chat with your friends, and see what makes their lives tick. Use the tools we covered, from surveys to competitor analysis, to build a picture of your perfect user. Then, design an app that makes their day easier, brighter, or simply more fun. 

Mobile app research is your superpower – use it to create something amazing, and remember, Designveloper is always here to help your app dreams take flight!

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Development of mobile application through design-based research

Asian Association of Open Universities Journal

ISSN : 2414-6994

Article publication date: 14 September 2018

Issue publication date: 12 April 2019

The purpose of this paper is to illustrate the development and testing of an innovative mobile application using design-based research.

Design/methodology/approach

This paper reports on the process of transformation of existing printed course material into digitized content through design-based research where design, research and practice were concurrently applied through several iterations of the mobile application. For this transformation, one session each from BSc in Nursing, Bachelor of Pharmacy and Bachelor of Medical Laboratory Sciences was selected. In the first phase of the design-based research, the main research question was formulated. In the second phase, a mobile learning application (OUSL MLearn) was designed and developed to address the research question. In the third phase, this application was evaluated by five groups of stakeholders: content experts to validate the content; educational technologists to check the alignment of technical and pedagogical features; novice users to check the overall effectiveness of the application; developer to develop the application, to check the ease of usage; and researchers to identify the impact of this innovation. These stakeholders were closely involved throughout the whole process which lasted over a period of four months. At the end of this development phase, the results were reflected upon and used for further enrichment.

It was observed that the developed mobile application was accessible, appealing and pedagogically constructive for users. However, optimization, development time, technical and organizational issues, workload of academics and production costs were identified as major challenges.

Research limitations/implications

This study was based on the findings of a small sample of potential users.

Practical implications

The findings have implications for designing culturally adaptive interactive mobile applications.

Originality/value

This study will benefit practitioners to design culturally sensitive mobile learning courses and researchers to conduct design-based research.

• Instructional design
• Design-based research
• Mobile learning
• Open and distance learning

Jayatilleke, B.G. , Ranawaka, G.R. , Wijesekera, C. and Kumarasinha, M.C.B. (2018), "Development of mobile application through design-based research", Asian Association of Open Universities Journal , Vol. 13 No. 2, pp. 145-168. https://doi.org/10.1108/AAOUJ-02-2018-0013

Emerald Publishing Limited

Copyright © 2018, Buddhini Gayathri Jayatilleke, Gaya R. Ranawaka, Chamali Wijesekera and Malinda C.B. Kumarasinha

Published in Asian Association of Open Universities Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Mobile technology is an exceptionally fast-growing field that is closely connected with our work and day-to-day lives. There are new developments added to its growth every day with emerging new patterns of usage, having both positive and negative implications.

In the twenty-first century, higher education institutions had to be reconstructed to adapt to changes with the increasing global competition, the growing need for higher education, the changing nature of information, rapid developments in Information and Communication Technologies (ICT) and the varying expectations and demographic features of learners ( Kukulska-Hulme, 2005a ). The changes in the dynamics of ICT, institutions and learners influence the academics working in higher education institutions to change their teaching approaches and strategies.

However, we have not seen a noteworthy adoption of these technologies in the education sector even though they are available everywhere (ubiquitous) and have tremendous potential in addressing needs of the individual learner through their unique capabilities. Furthermore, owing to the rapid changes of mobile technologies, including devices and communication technologies have opened up new research opportunities and even change the focus of research ( Parsons, 2014 ). Krull and Duart (2017) reported that in higher education, mobile learning is a growing field of research as evidenced by reviewing journal publications between 2011 and 2015. The major results of their study were that the most researched theme was on m-learning applications and systems, used both quantitative and qualitative studies and were targeted at students. As both faculty and student adoption play a crucial part in the success of mobile learning initiatives, they recommend future studies to look into the implications for both faculty and students.

However, there is a scarcity of research articles related to mobile learning and methodological frameworks for designing sustainable mobile learning activities ( Nouri et al. , 2016 ). The purpose of this research study was to address this gap by applying design-based research in designing a mobile learning solution for the undergraduates of the Faculty of Health Sciences of the Open University of Sri Lanka (OUSL). It reports on the findings of the testing phase of the mobile solution by five groups of stakeholders: content experts, educational technologists, developer, novice users and researchers prior to the delivery of the first cycle.

The first section of the paper defines briefly the mobile learning, design-based research and employing design-based research in mobile applications, and stresses the importance of conducting design-based research for future technological innovations. The second section briefly describes the context. The third section examines the methodology adopted for the design-based research for new technological innovations in teaching learning using mobile applications. The fourth section is dedicated to the findings which were collected from all the stakeholders illustrating the potential for innovative teaching practices through mobile learning. The final section is a critical examination of the viewpoints expressed by all stakeholders and formulating guiding principles for both designing mobile learning solutions and on how to conduct design-based research in mobile applications.

2. Theoretical framework

2.1 mobile learning.

Mobile devices are portable, lightweight devices such as mobile phones (cellphones, or handphones), smartphones, palmtops and handheld computers (Personal Digital Assistants or PDAs), tablet PCs, laptop computers and personal media players. These devices can be carried around easily and used for communication and collaboration, and for teaching-learning activities that are different from what is possible with other media.

Traxler (2009) has pointed out that mobile devices together with mobile communication technologies have influenced all fields including education and currently undergoing a transformation. In fact, he called this transformation period as mobile era . He further stressed that most of these mobile devices are not designed specifically for education or training but designed for personal or individual usage which mainly used for one-to-one social interaction.

In the context of education, these mobile devices offer diverse learning opportunities such as portability, social interactivity, context sensitivity, connectivity, individuality and affordance to people in academic settings or non-academic settings ( Crompton, 2013 ). Therefore, these mobile technologies are very useful for learners where they could engage in educational activities and learn by themselves without the constraints of having to come into the institution.

Since the introduction of the term mobile learning in 2005 ( Crompton, 2013 ), many scholars and practitioners have attempted to define it and initial definitions were focused solely on devices and technologies or techno-centric ( Crompton, 2013 ; Keskin and Kuzu, 2015 ). Most widely accepted mobile learning definition is “ learning across multiple contexts, through social and content interactions, using personal electronic devices ” ( Crompton, 2013 , p. 4). This definition encompasses four central constructs associated in mobile learning: pedagogy, technology, context and social interactions . Table I illustrates the categorization of the attributes of mobile learning into these central constructs.

With these attributes, it has much in common with other types of e-learning on desktop computers but allows more diverse and changing locations, more immediate (anytime) interaction, and connect through smaller, often wireless devices ( Kukulska-Hulme, 2005a ) enabling both advantages and drawbacks.

Learners can choose their own learning path to achieve their learning goal by using their own private mobile device. Hence, mobile learning can take place when the learner is not at a fixed, predetermined location or learning that happens when the learner takes advantage of the learning opportunities offered by mobile technologies ( O’Malley et al. , 2005 , p. 7). As a result, they have spontaneous personal accesses to the large number of learning resources via the internet.

However, in order to create this kind of ambient technology, providers need to design “learning enhanced” buildings and public spaces, by providing devices or establishing systems to respond to on-the-spot interactions.

Several researchers have studied the development of the theoretical frameworks of mobile learning: mobile education (FRAME) based on activity theory ( Koole, 2009 ), construction of knowledge through the exchange of knowledge via pervasive mobile devices ( Sharples et al. , 2007 ) based on the conversation theory, developed by Pask (1975) , modification of transactional distance education theory for mobile learning ( Park, 2011 ) and educational research on mobile communities ( Frohberg, 2003 ).

Still there is a lack of transferable design frameworks on mobile learning ( Cochrane, 2013 ) irrespective of those developed earlier. There is a greater need by the practitioners, instructional designers and trainers on design theories, so that they may in a better position to integrate mobile technologies into learning environments in an effective manner and to make these technologies more beneficial to users ( Koszalka and Ntloedibe-Kuswani, 2010 ; Park, 2011 ; Rajasingham, 2011 ).

When designing instruction, generally learning content is regarded as the most important factor. Thomas et al. (2002) stressed that culture has to be considered as a “dimension” of instructional design capturing, three layers: purposeful intention, interaction with learners to involve them in the design process and for introspection to identify one’s own cultural values and biases. The importance of language, culture and context is also highlighted in teaching and learning ( Gunawardena et al. , 2009 ). Chen et al. (2006) emphasized the importance of paying attention to provide support (technical, learning and social) and resources (language, culture and context, and learning content), so that learners can make effective connections between resources and the support. Therefore, designing effective learning for global audiences requires not cultural neutrality but cultural “inclusivity” ( Frechette et al. , 2014 ; Henderson, 1996 ; Powel, 1997 ) as the online medium (internet) itself is a culturally derived phenomenon ( Bowers, 2000 ) and functions as an incubator for a shared cultural experience ( Selvin, 2000 ). Therefore, it is crucial to explore the “cultural understanding” of learners ( Rogers et al. , 2007 ). Furthermore, online tutors and mentors should be more sensitive to culture when facilitating knowledge construction to global audience via online where both learners and tutors bring their own cultural identities and they have to use face-saving and negotiation strategies to build trust to develop online communities ( Gunawardena and Jayatilleke, 2014 ; Gunawardena et al. , 2009 ).

abbreviations (e.g. lol: “laugh out loud,” yolo: “you only live once”);

hieroglyphics (e.g. xoxo: “hug kiss hug kiss,” T-T: “tears,”:-: “smiling eyes”), add a new bullet emotions; and

special characters, which are unique visual representations and difficult to classify as wither text or image.

It also is necessary to get the views from learners on the designed product to shape the design itself ( McLoughlin and Oliver, 2000 ) through evaluation studies.

2.2 Design-based research

The aim of the design-based research is to improve educational practices through systematic but flexible methodology through iterative analysis throughout the design, development and implementation of the product ( Wang and Hannafin, 2005 , pp. 6-7). These educational practices are based on the views gathered between researchers and practitioners in a normal setup and these practices would lead to formulate contextually sensitive design principles and theories ( Wang and Hannafin, 2005 ). Thus, fulfilling the ultimate goal of design-based research by building stronger connection between educational research and real-world problems. In this scenario, researchers and practitioners are integral part of the research process and they are closely involved from the initial phase of design and development of the product to the final phase of implementation. However, research validity of design-based research is criticized by some due to the involvement of researchers where they felt that researchers may not be reliable and faithful in their judgments ( Barab and Squire, 2004 ). Further, the intervention may not be replicated in other settings as design-based research is contextually dependent ( Design-Based Research Collective, 2003 ).

Review conducted by Zheng (2015) showed that design-based research studies were conducted with diverse sample groups but mainly with students in higher education (29 percent), under various learning environments including distance learning (14 percent) and blended learning settings (12 percent). Natural science (38 percent) was selected as the most researched learning domain while medical science was the least selected learning domain (2 percent). The technological intervention was the major type of intervention used in the design-based research (53 percent) followed by the integrated teaching models (16 percent), other models (16 percent) and instructional methods (14 percent).

uncertainty about how it differs from other forms of research;

uncertainty about how it differs from design, or why design is not research; and

uncertainty about what might make it effective (if it is).

In view of these uncertainties, they described the design process consisted of six iterative phases: focus the problem, understand the problem, define goals, conceive the outline of a solution, build the solution and test the solution ( Easterday et al. , 2014 , p. 320) which are recursively nested within each other. These phases could be compared with the well-researched four-step framework of Reeves (2006) , where he explains design-based research as a process that consists of four steps ( Table II ).

It is clearly evident from the table that the fourth step, that is documentation and reflection to produce design principles of Reeves’s framework, was not identified in Easteday et al. ’s six-phase/step framework.

Ma and Harmon (2009) pointed out that the guidelines and the process presented by Reeves’s (2006) four-step framework provided valuable guidance on how to conduct design-based research from the long-term perspective; however, they felt the inadequacy of the framework to conduct design-based research at the individual level as it was not clear about the conduct of research activities in each step. They further enriched the Reeves’s framework by providing a more detailed and comprehensive development process incorporating research elements to the framework with specific guidance ( Ma and Harmon, 2009 , pp. 77-78). The main steps in the framework are connected linearly from steps 1 to 4 with connecting loops to each step. They mentioned that “by no means as clean and liner as it might appear” and “researchers may examine their own context to make appropriate modifications” (p. 80). These guidelines enable researchers who are new to design-based research to conduct design-based research systematically and logically.

Both design-based research and action research share many epistemological, ontological and methodological foundations thus sharing a common “meta-paradigm” pragmatism ( Cole et al. , 2005 ) and many find it difficult to distinguish the two ( Easterday et al. , 2014 ). Generally, design-based research is conducted by a research and design team whereas action research is carried out by a single teacher (practitioner), guided by a theory ( Anderson and Shattuk, 2012 ), focused primarily on already designed product/process and its application into an everyday context ( Ørngreen, 2015 ) and contributes toward theory building ( Cole et al. , 2005 ). However, collaboration between practitioners and researchers is not clearly described in the design-based research literature ( Kolmos, 2015 ) and needs further investigation.

However, some researchers have combined design-based research with action research ( Keskin and Kuzu, 2015 ). They feel that the research methodology most appropriate to the third step of the Reeves’s framework is through action research. It can make the product highly effective, efficient and useful by allowing repeated development of the product until all the identified errors of the product are resolved during the testing phase ( Susman and Evered, 1978 ). In view of this notion, Keskin and Kuzu (2015) developed the model by combining the design-based research model put forward by Ma and Harmon (2009) , and the action research cycle suggested by Susman and Evered (1978) regarding information systems. The model also has the same four steps proposed in the Reeve’s framework; however, all the steps in the Keskin and Kuzu’s model are interactive with each other. The design principles and the theory can be developed in the fourth step, based on the analyses of the data collected in each step ( Ma and Harmon, 2009 ). By following iterative research process in the design-based research, it attempts to refine the innovation systematically while also proposing design principles unlike in evaluating innovative product or intervention at the end of the development phase.

2.3 Mobile applications employing design-based research

Many researchers have conducted reviews related to the application of design-based research as a research methodology in conducting various research studies ( Anderson and Shattuk, 2012 ; Krull and Duart, 2017 ; Zheng, 2015 ). Major findings of these studies indicated that the majority used design-based research for technological interventions and applications. Anderson and Shattuk (2012) reported that the majority of interventions (68 percent) involved the use of online and mobile technologies. According to Zheng’s study reviewing research articles and publication from 2004 to 2013, technological interventions applications were the most researched area (53 percent) and were to test the effectiveness of the learning environment or a particular tool. However, the nature or the type of the tool was not specifically mentioned in his study. In reviewing journal publications between 2011 and 2015, Krull and Duart (2017) revealed that mobile learning applications and systems were the most researched area conducted using design-based research in higher education. Keskin and Kuzu (2015) combined design-based research and action research to conduct professional development program for academics using M-learning system.

The OUSL is unique in its teaching methodology as it is the only national university in Sri Lanka which is dedicated to open and distance learning. Unlike in conventional universities, the OUSL mediates instructions mainly through print course materials. With the advent of various technologies, the OUSL has gone through generations of technology integrating audio-visual, multimedia and online learning into the core print course materials ( Jayatilleke et al. , 2009 ).

Having faced with many challenges with respect to distributing printed course material on time and to reduce production and delivery costs of the course materials, there have been many suggestions from time to time to use other technologies. However, print has remained as the core medium of instruction even though many such initiatives have been taken to promote offering courses entirely online.

Aligned with this notion and also considering the immense potential of using mobile technologies for learning, the OUSL has recently proposed an alternative option to address these challenges. Providing course materials in PDF format loaded on a tablet computer would be a viable option as tablet computers are becoming cheaper by the day, harnessing the potential of improving the learning experience and thereby effect institutional change.

Hence, Faculty of Health Sciences of the OUSL took the initiative to investigate the viability of transforming the existing print course material, and offer them through mobile learning for the undergraduates of the Faculty. This project was carried out from a research grant of the OUSL which enabled to experiment with novel mediating mobile technologies.

Three Bachelor’s degree programs are offered by the Faculty of Health Sciences; Nursing; Medical Laboratory Sciences and Pharmacy. One session each from a degree program was transformed retaining the already existing content and the original framework as these courses are still being offered by the OUSL.

4. Methodology

In this study, design-based research model put forward by Ma and Harmon (2009) was used as the framework as it provided the processes clearly ( Figure 1 ). The “analysis of practical problems” is the first phase as in the Reeves’s model. In this phase, a practical problem is identified and the related literature about the practical problem is reviewed. The second phase is “development of solutions” for the practical problem identified in the first phase by conceptualizing a solution within theoretical framework, identifying research purpose and development method, developing a prototype that serves to address the research problem. The third phase is “evaluation and testing of solutions in practice.” The final phase is “documentation and reflection” where design principles are generated and documented in order to provide guidance for practitioners and researchers who are interested in conducting design-based research.

This study was also influenced by the design-based action research model put forward by Keskin and Kuzu (2015) where phase 3 is an iterative cycle rather than a linear process. In this phase, problems related to the prototype are recognized and action plans are developed. At the implementation, these plans are implemented and the consequences of the action are evaluated and reflected. This process continues until all problems are solved.

In this study, development and testing of the mobile application (phase 3) was carried out concurrently with the phase 2 through formative evaluation. Phase 2 and phase 3 were closely linked and phase 3 was incorporated in the phase 2 of the cycle ( Figure 1 ). These two phases were not separated cycles as in Keskin and Kuzu’s model. Since phase 4 (documentation and reflection) was also closely connected with these two phases through reflection, the connection between phase 3 and 4 was illustrated in a two way arrow.

Since design-based research is a multi-phase study, the present study involved five groups of stakeholders. In this study, researchers took the initiative and were involved from the beginning of the design process together with the developer, content experts/practitioners and educational technologists. All these stakeholders were closely involved throughout the whole process which lasted over a period of four months. The formative evaluation was carried out with four content experts, four educational technologists, six novice users, four researchers and one developer.

4.1 First phase – analysis of a practical problem

4.1.1 identifying a practical problem.

The analysis of a practical problem by researchers and practitioners is the first phase of this design-based research. Researchers in this study are also practitioners: two are teaching zoology/health courses while other two are training academic staff on online learning/educational technology. They have experienced the practical problem faced by the OUSL for many years; that is the difficulty in producing timely printed course materials to OUSL students with increasing student numbers. This practical problem is equally important to both OUSL students and the institution (OUSL). Thus, researchers took the initiative to conduct this research study through design-based research, where they felt the problem was significant to the learning community of the OUSL. They also believe that the findings will provide evidence to inform decision makers on the viability of providing a tablet computer loaded with the content to students so that decision makers may be in a position to take data-driven decisions rather than taking ad hoc decisions.

4.1.2 Reviewing the literature to determine the significance of the problem

An earlier study carried out with the students of the British Open University showed that the majority preferred e-books as a complementary technology and still would like to receive print course materials ( Kukulska-Hulme, 2005b , p. 130). Researcher further reported that learners faced difficulties in downloading e-books, getting satisfactory page and font size, navigation and cursor control, etc. With many technological advancements over the years, still students perceive printed texts are easier to read, understand and navigate, and have long-term access even though the digital texts are becoming cheaper ( Baglione and Sullivan, 2016 ). Comparative studies have been conducted to investigate the effects of digital reading (e.g. reading a word or PDF file on screen) with print reading; however, not much research has been carried out into examining learners reading behaviors and the educational benefits of recent, more flexible visually presented texts ( Rha, 2014 , p. 51). Rogers-Estable (2018 , p. 48) reported that many faculty members commented that if electronic texts (eTexts) are purely PDF files (or glorified PDFs) then there is no advantage in using them with students.

Based on the literature review, the decision was taken by the research team, not to provide a digitized text as a PDF (or as a glorified PDF) to learners (as an e-book) but to provide a mobile learning application with enhanced version of the already existing print material with additional pedagogical, technological (interactive), contextual and social interactive attributes associated with mobile learning with innovative strategies and tools. Social interactive attributes inherent to mobile learning was used; however, less priority was given to design peer/tutor interactions in this mobile application as it was designed as a stand-alone package to study offline considering the specific requirements of the target group, that is health professionals with demanding work pressure. However, learners have the opportunity to use the mobile application either online or offline. They can also use other channels such as e-mail and social media to discuss the content if they wish to collaborate socially.

4.1.3 Identifying the purpose and research questions for a development iteration

According to Ma and Harmon (2009) , identifying the purpose and research questions for a development iteration was discussed as the third step in the second phase (p. 77) even though they have highlighted the importance of it before commencing the development (p. 80).

In this study, research questions were formulated in the first phase as it was felt necessary to identify the purpose and the research questions before starting the development as they will direct and guide the development process through research.

How to design a mobile application using an existing print course material?

What was the process carried out when transforming the existing print materials into mobile application?

What types of interactivity features were added to the mobile application?

What were the challenges faced by content experts, developer and educational technologists when designing mobile application?

4.2 Second phase – development of a solution

4.2.1 conceptualizing a solution within a theoretical framework.

In the second phase, a mobile learning application called “OUSL mobile learning” (OUSL MLearn) was designed and developed specifically for the Android mobile devices to address the principle research question within the theoretical framework.

The existing print course materials were originally designed based on teaching and learning theories such as Guided didactic conversation in distance education ( Holmberg, 1983) . However, transformation of existing printed course material into digitized content requires additional research related to mobile learning such as designing content with in-built interactive features for mobile devices. Furthermore, learning is situated and contextual. Thus, research and practice were concurrently applied through design-based research with several iterations of the mobile application.

4.2.2 Determining the role of research in developing the solution

Having conceptualized the solution, next step was to decide whether research should be conducted while developing the solution. Since the solution in this study was to develop a mobile application through several iterations, research was an integral part and relevant research studies with respect to the needs and the requirements of the stakeholders (teachers and students), learning preferences of students, cultural propensities were considered when designing user-interface, content development and system technical design.

4.2.3 Identifying development methods

This mobile application was based on the existing content of the printed material thus, restricting the design and development of the mobile application. Therefore, first step was to develop a prototype that was adequate for the purpose. Having reviewed the literature on various types of prototypes, an icon-based prototype was selected for the development of mobile application as the majority learners are visual learners ( Rha, 2014 ).

4.2.4 Developing a prototype that serves the research purpose

In order to develop the mobile application, the first meeting was conducted with the content developers, educational technologists, researchers and the developer and discussed the overall objectives of this project. Three sessions (one session per a degree program) of the existing print materials were handed over to the developer highlighting the requirements, providing necessary information and devices (tablet computers). Developer was given the freedom to select appropriate technologies and tools to develop the mobile application to use with the specified tablet computer. This decision was based on the assumption that the OUSL will provide the standard tablet computers to all OUSL learners rather than requesting them to purchase or use their own tablet devices in order to minimize the technical problems. HTML was the main tool for the development and other tools were also used to enhance the capabilities of the mobile application.

First iteration was to discuss the prototype for the development of the session structure and subsequent iterations developed the prototypes for the course structure and the system architecture. Interactive features such as self-assessments questions and embedding videos were incorporated later.

The following section will describe in detail the design of the OUSL MLearn mobile application.

System architecture and implementation

The system architecture was designed for the entire university which serves as the mobile platform (OUSL MLearn) for the OUSL. In order to make the system user friendly, the unique icon-based system was designed ( Figure 2 (a) and (b)).

The navigational structure for this system was connected to the main pages forming a semantic network as illustrated in Figure 3 . The icon-based system was connected to the home page of the system, then to the Programmes Page where students can select their own program, followed by the Course Page and the Session Page, respectively.

Course/session structure

Each course was designed in such a way to make the course as a stand-alone module which can be studied offline. This decision was made considering the baseline survey of the undergraduates of the Faculty of Health Sciences and their past experience of not accessing learning resources through internet ( Jayatilleke, Wijesekara and Ranawaka, 2017 ).

The existing OUSL print materials were designed as self-instructional materials with intermittent activities to facilitate “guided didactic conversation” with text ( Holmberg, 1983 ), incorporating advance organizers at the beginning and summaries at the end ( Melton, 1997 ; Rowntree, 1990 ). These materials are designed using pedagogical features based on learning theories, research and practice.

These pedagogical features were retained in the mobile application. For instance, advance organizer at the beginning and summary at the end of each session were designed based on the pedagogical features of the original print course materials. Research shows that an advance organizer serves as a schema for the learner to associate new concepts with the already known concepts and to connect them meaningfully ( Ausubel, 1960 ), whereas a summary (post organizer) provides a synopsis that helps the learner to get a holistic picture of the concepts learned in the session. In addition, advance organizers help diverse learners, in particular FD and FI learners, respectively. Research studies have revealed that FD learners are holistic in nature and need external guidance to solve problems while FI learners are serialistic and use their own cues to solve problems ( Witkin et al. , 1977 ). Since OUSL learners are diverse, course materials have to provide provision for these two groups to learn the content without the help of the teacher. However, content was re-designed as smaller chunks to suit the mobile screen to avoid overload of information based on Sweller’s (2011) cognitive load theory.

Additional features were also were incorporated to accommodate specific requirements necessary to learn using mobile devices. Each session was transformed considering the four pedagogical aspects of instructional design, namely information design, instruction design, interface design and interaction design. Findings of the earlier research studies on online learning were also considered in designing this mobile application ( Table III ).

Figure 4 illustrates the screen casts of the animated instructions proving study guidance on how to use mobile device.

Printed course materials use icons in front of the major pedagogical components such as learning outcomes, self-assessment activities (activities), online/video integration, etc. and use them as access devises. These icons were specially designed as authentic learning objects to maintain the OUSL identity across all OUSL materials. These icons were also used in this mobile application to maintain the OUSL identity. These features could be considered as contextual attributes of mobile learning. Certain new icons were added to represent functional specific requirements associated with the mobile application (e.g. Menu, Note, etc.). Typical icons generally represented in global community were used to represent images and settings. The layout of the program control, learner control and specific icons is illustrated in Figure 5 .

This mobile application integrated a video, enriching the existing content using the affordances of mobile technologies and designed as an activity activity, based on the video. Generally, OUSL students do not watch videos, unless they are compulsory or integrated in the course materials. Thus, research and practice were considered in the design and development of this mobile application in line with the guidelines of the design-based research ( Figure 6 ).

In addition to the instructional design features, adaptive technologies were also incorporated in the mobile application considering the needs of the heterogeneous nature of OUSL learners ( Jayatilleke, 2016 ). Table III illustrates these features. Learner has the opportunity to adjust the size of the font ( Figure 7 ) and images, taking notes, highlighting the text and copying and pasting facility were some of the adaptive technologies used in this application.

The next section provides the detail account of the evaluation and testing phase of the mobile application.

4.3 Third phase – evaluation and testing of the solution

In the third phase, this mobile application was regularly tested through formative evaluation which was an integral part of the design methodology. It helped to judge strengths and weakness of the innovation while still at its developing stage, for the purpose of revising the instruction. As mentioned earlier, second and third phases conducted concurrently and could not be separated during the research process. Certain features were added after getting the feedback from various stakeholders during the testing phase.

4.3.1 Identifying research methods

In this phase, appropriate research methods were identified, collected and analyzed data to answer the research questions. Qualitative methods were used in gathering data since design and development of the innovation need in-depth analysis of the innovation. A research diary, committee meeting records, observational sheets of the users while using the tablet interview schedule for users, and checklists for error identification were used as data collection tools.

Content developers of these three sessions (four females), educational technologists (two females and two males), researchers (three females and one male) and one developer (male) were the members of the research and development team from the inception of the research project.

Purposeful sample was used to select the subjects as novice users where they have not followed these courses before to test this innovative mobile application. All the novice users were graduates in different disciplines (BSc in Natural Science −3, BSc in Information Technology −2 and BA in Social Science −1) consisting of four females and two males representing age range of 25–35 years. All of them have smartphones and comfortable of using tablet computers.

4.3.2 Gathering and analyzing data to answer research questions

The second and third phases were carried out simultaneously with regular meetings with the developer and other stakeholders through testing of the mobile application. In these meetings, the application was evaluated by five groups of stakeholders: content experts to validate the content, educational technologists to check the alignment of technical and pedagogical features, novice users to check the overall effectiveness of the application for learning purposes, developer to develop the application, modify it with the feedback and check the ease of usage and researchers to identify the impact of this innovation.

Novice users were briefed about the purpose and asked them to go through the mobile application. The lead researcher observed and made notes using observational sheets while users were explored the mobile application. At the end of the product evaluation, researcher asked questions using a structured interview schedule about their perceptions of this mobile application, their likes, dislikes, challenges and suggestions for improvement. These viewpoints were categorized using content analysis to identify the major themes.

The entire development of the application was through eight iterations where feedback from different stakeholders at different stages was integrated to the OUSL MLearn system. First iteration was to develop the initial prototype for a session. Then three iterations were focused to develop the framework to design the entire system architecture for the entire university, considering faculty, departments and program requirements. Last four iterations were testing the developed prototype with additional requirements, adding pedagogical features along with the interactive features to the course structure and testing with novice users and content developers.

4.3.3 Drawing conclusions and determining research findings

At the end of the formative evaluation, conclusions were drawn based on the findings. All the stakeholders perceived benefits of the mobile application as an effective tool for learning. Many challenges were expressed by different stakeholders and will be discussed in the results and discussion section.

4.4 Fourth phase – documentation and reflection

This is very important phase in design-based research. Unless documentation and reflection, the generation of design principles and guidelines could not be constructed and the purpose of using design-based research is not fully achieved. Ma and Harmon (2009) recommended to provide two sets of principles based on the research study. One set of principles for the practitioners on the research findings specifically related to the instructional innovation/solution/product to improve their practices. The other set of principles for researchers who are interested in conducting design-based research on how to conduct design-based research based on the reflections on the research methodology.

In this current study, reflections were part of the whole process and not only restricted to the documentation phase. Researchers reflected the research processes in all phases from phases 2 to 4 and went back and forth while documenting the process in order to generate principles. At the end of each development phase, the results were re-examined, reflected upon and used for further enrichment, producing a continuous cycle of design-reflection-design. So, formative evaluation was integrated in the testing phase of the design-based research and the results were used to improve the system to make the instruction more effective and efficient. In this study, phases 2, 3 and 4 were all connected and could not be separated as distinct phases.

The current interface of mobile application and its functionalities are the result of revisions based on the suggestions/reflections during the formative evaluation of all three phases.

4.4.1 Synthesizing design principles for developing the proposed solution (mobile application)

Having gone through the reflections, the researchers felt the design-based research is very appropriate in designing and developing technology based innovations as user testing is part of the development process. Since both researchers and practitioners were involved from the beginning, their contributions were very useful in conceptualizing the solution within a theoretical framework. The following design principles were derived from the findings of the research study for mobile application.

Principle 1

Research team should have open discussions with all the stakeholders including the developer so that diverse strategies/solutions will emerge as a result. Team can discuss these strategies and identify best solutions in order to reduce the development time of the innovation.

Principle 2

Research team should consider the existing research and practices in the local context in order to develop cultural sensitive solutions as learning is contextual and situated.

Some of the research studies conducted in western world may not directly applicable to eastern cultures. This study was influenced by the research findings of earlier studies conducted at the OUSL with three groups of culturally diverse groups of learners (Sri Lankans, Pakistanis and Mauritians), where they interact via learning management system for seven weeks ( Jayatilleke and Gunawardena, 2016 ; Jayatilleke, Kulasekara, Kumarasinha and Gunawardena, 2017 ).

Principle 3

Instructions should be integrated in the mobile application as animated learning objects considering the user needs; especially, if the application is designed for open and distance learners.

Principle 4

When developing mobile solutions, alternative technological strategies and adaptive technologies should be designed in order to accommodate diverse learners.

Principle 5

Adaptive technologies should also be integrated in the mobile application to accommodate differently abled learners to empower them while making them more inclusive in the mainstream education.

Principle 6

Institutional leadership for direction, guidance and providing mechanism for establishing support structures are crucial in order for the sustenance and adoption of innovative mobile solution. Otherwise, diffusion of innovations will be observed only at the individual level and gradually die down.

4.4.2 Synthesizing guidance for conducting design-based research

This study adapted the model proposed by Ma and Harmon (2009) and was also influenced by the research of Keskin and Kuzu (2015) . The detailed development and research procedure in the Ma and Harmon’s (2009) model was very useful in designing the procedure to conduct design-based research. However, researchers of the current study had to modify the order of certain guidelines to suit the context and the user needs. Even Ma and Harmon (2009 , p. 90) stated that researchers may examine their own context to make appropriate modifications to their model. Hence, following guiding principles are proposed for the researchers who are interested in conducting design-based research based on the reflections of this research and development team.

Identifying the purpose and research questions for a development iteration are very crucial in the design-based research as they provide the focus for the study. Thus, they should be included in the first phase of the research study – analysis of a practical problem (refer Section 4.1.3).

Identifying the importance of research at the beginning of the development of innovation of project upfront and decision should be taken to integrate research while developing solutions at the beginning prior to the development of the solution and give fullest attention to the research methodology along with the development phases of the solution.

Educational technologist should be included as a researcher in the design-based research team to provide guidance, direction and to facilitate theory-driven research process and thereby enabling theory-building outcomes of the innovation in an effective manner.

5. Results and discussion

The views expressed by the novel users indicated that the developed mobile application was generally efficient, simple to learn, easy to navigate, appealing and engaging. It was also pedagogically constructive as the content and the tools used in the application were useful from the perspective of both the content experts and the educational technologists. Thus, accomplishing the primary goal of this research study by providing effective instruction through mobile learning. It was also found that the developed mobile learning system was appropriate to the overall purpose of the university, could be served as a mobile learning system for the entire university and also could be used as an academic support system for the OUSL from the perspective of the developer.

Having gone through the reflection process and analyzing the qualitative data obtained by all the stakeholders using various tools, the challenges in implementing the MLearn for the entire university were identified using content analysis of the data. The categorized themes are illustrated in Table IV .

6. Conclusion and future direction

Having gone through this process, it was felt that the design-based research build on the principles of stakeholder centredness was effective in developing mobile learning application. This was due to the fact that the researchers and the practitioners were actively involved throughout the whole process and supported each other to produce an effective mobile application. The framework used in this study embeded the evaluation and testing of the solution phase (Phase 3) within the development of the solution phase (Phase 2) as these two phases are interconnected and run concurrently. Owing to the iterative cycles of the design-based research enabled the development of an effective mobile solution through several refinements based on existing research and practices.

Cowling and Birt (2018) also showed how the process of incremental reflection and refinement of the design-based research enabled the development of a mixed reality simulation to improve skills for students studying paramedic science at a distance.

The findings of the evaluation of the mobile application showed challenges with respect to development time, high production costs, technical and organizational issues, workload of academics and necessity of providing technical support both to remote students and faculty. Therefore, establishing adequate support structures for both teachers and students are essential for the sustenance of these innovative practices. This finding is in line with Montreux et al. ’s (2015 , p. 10) study where they also emphasized the importance of technical and pedagogical support to “stimulate teacher and student recognition of tablet devices’ potential in education.”

This application will be further evaluated through summative evaluation with actual students to assess the effectiveness of the mobile learning system to complete the design of the system fully.

The design and development of any instructional material depend on the target audience, the subject content and the organizational culture of the institution (context). As such, the findings of this study may not have a universal value; however, these findings may throw light on some of the pedagogical, technological, social interaction and contextual attributes including cultural dimensions that have to considered when designing mobile applications. It also provides guiding principles for designing both mobile solutions and on how to conduct design-based research in mobile learning.

Design-based research model of this study

Screen casts of the mobile application

Navigational structure

Animated Instructions proving study guidance on how to use the mobile device

Program control, learner control and specific icons

Video integration in the mobile application

Flexibility of using different font sizes

Categorization of the attributes of mobile learning into the central constructs

Comparison of the processes of the design-based research of Reeves’s (2006) and Easterday et al.’s (2014) framework

Categories of instructional design and pedagogical/technological/contextual/social interaction attributes of the designed mobile application with supported research evidence

Factors identified through the reflections by all stakeholders in implementing the OUSL MLearn

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Acknowledgements

The authors are grateful to the Open University of Sri Lanka for providing a research grant, to Mr Manoj Dharmartne for developing this mobile application, to all stakeholders who participated in this research project for their valuable inputs and to all anonymous reviewers of this research paper for their insightful comments and suggestions.

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• Open access
• Published: 09 August 2021

The use of mobile applications in higher education classes: a comparative pilot study of the students’ perceptions and real usage

• David Manuel Duarte Oliveira   ORCID: orcid.org/0000-0002-8763-6997 1 ,
• Luís Pedro 1 &
• Carlos Santos 1  

Smart Learning Environments volume  8 , Article number:  14 ( 2021 ) Cite this article

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This paper was developed within the scope of a PhD thesis that intends to characterize the use of mobile applications by the students of the University of Aveiro during class time. The main purpose of this paper is to present the results of an initial pilot study that aimed to fine-tune data collection methods in order to gather data that reflected the practices of the use of mobile applications by students in a higher education institution during classes. In this paper we present the context of the pilot, its technological settings, the analysed cases and the discussion and conclusions carried out to gather mobile applications usage data logs from students of an undergraduate degree on Communication Technologies.

Our study gathered data from 77 participants, taking theoretical classes in the Department of Communication and Arts at the University of Aveiro. The research was based on the Grounded Theory method approach aiming to analyse the logs from the access points of the University. With the collected data, a profile of the use of mobile devices during classes was drawn.

The preliminary findings suggest that the use of apps during the theoretical classes of the Department of Communication and Art is quite high and that the most used apps are Social networks like Facebook and Instagram. During this pilot the accesses during theoretical classes corresponded to approximately 11,177 accesses per student. We also concluded that the students agree that accessing applications can distract them during these classes and that they have a misperception about their use of online applications during classes, as the usage time is, in fact, more intensive than what participants reported.

Introduction

The use of mobile devices by higher education students has grown in the last years (GMI, 2019 ). Technological advancements are also pushing society with consequent rapidly changing environments. Higher Education Institutions (HEIs) are not exempted from these technological changes and advancements, and it is compulsory that they follow this technological evolution so that the teaching-learning process is improved and enriched.

HEI’s are trying to integrate digital devices such as mobile phones and tablets, and informal learning situations, assuming that the use of these technologies may result in a different learning approach and increase students’ motivation and proficiency (Aagaard, 2015 ).

In a study by Magda, & Aslanian ( 2018 ), students report that they access course documents and communicate with the faculty via their mobile devices, such as smartphones. Over 40% say they perform searches for reports and access institutions E-Learning systems via mobile devices (Magda, & Aslanian, 2018 ). The EDUCAUSE Horizon Report - 2019 Higher Education Edition (Alexander et al., 2019 ) also mentions M-Learning as the main development in the use of technology in higher education. However, teachers believe students use their gadgets less than they actually do, and mobile devices also challenge teaching practices. Students use devices for off-task (Jesse, 2015 ) or parallel activities and there may be inaccurate references to their actual use of mobile devices.

Mobile device users have very different usage habits of their devices and their applications, and it is important to study and characterize these behaviours in different contexts, as explained below. The reports that usually support these studies are made with questions directed to the users themselves asking them questions about the apps they have on the devices and the reasons for using them. However, Gerpott & Thomas ( 2014 ) argue that other types of studies are needed to properly support this type of research.

Studies are usually conducted in organizations, based on the opinion of the participants, and cannot be replicated and generalized, for example, regarding the use of the internet or mobile applications by the general public, because these devices, unlike desktop devices, can be used anywhere and at any time (Gerpott & Thomas, 2014 ).

Furthermore, in mobile contexts, it becomes difficult for people to remember what they have used, because mobile applications can be used for various tasks, in various contexts, whether professional or personal, and the variety of applications, the use made, the periods of use are usually so wide and differentiated, that it can become difficult for users to refer which services or applications they have used, under which circumstances and how often. (Boase & Ling, 2013 ).

Thus, it is relevant, for several areas and especially for this research area, to have studies that cross-reference reported usage with actual usage. One of the ways to achieve this is with the use of logs of the use of mobile devices and applications, as mentioned by De Reuver & Bouwman ( 2015 ):

Using this approach this pilot study aims to create and validate a methodology:

i) to show the profile of these users,

ii) to reveal the kind of applications they use in the classroom and when they are in the institutions,

iii) and also, to compare the users’ perceptions with the real use of mobile applications.

Knowing the real usage and the usage students mention may provide valuable insights to teachers and HEIs and use this data for decision making about institutional applications to support students and teachers in their teaching and learning activities. Such information can also bring insights on the integration of M-Learning strategies, promoting interaction, communication, access to courses and the completion of assignments using students’ devices.

The central focus of this study is, therefore, to show preliminary results of the use of applications by students in class time during theoretical classes, through logs collected during class time.

The paper is divided into five parts. In the first part, relevant theoretical considerations are addressed, having in mind the current state of the art in terms of the literature and empirical work in this field. The second part outlines the study methodology. In the third part, the technological setting is highlighted. The cases and the results of the data analysis are described in the fourth part. Lastly, the results are interpreted, connected and crossed with the preliminary considerations.

Literature review

The massive use of mobile devices has created new forms of social interaction, significantly reducing the spatial difficulties that could exist, and today people can be reached and connected anytime and anywhere (Monteiro et al., 2017 ). This also applies to the school environment, where students bring small devices (smartphones, tablets and e-book readers) with them, which, thanks to easy access to an Internet connection, keep them permanently connected, even during classes.

In HEIs there is also a growing tendency among members of the academic community to use mobile devices in their daily activities (Oliveira et al., 2017 ), and students expect these devices to be an integral part of their academic tasks, too (Dobbin et al., 2011 ). A great number of users take advantage of mobile devices to search information and, since they do not always have computers available, these devices allow them an easy access to academic and institutional information (Vicente, 2013 ).

One of the challenges educational institutions face today has to do with the ubiquitous character of these devices and with finding ways in which they can be useful for learning, thus approaching a new educational paradigm: Mobile Learning (M-Learning) (Ryu & Parsons, 2008 ).

M-learning allows learning to take place in multiple places, in several ways and when the learner wants to learn. As learning does not necessarily have to occur within school buildings and schedules, M-Learning reduces the limitations of learning confined to the classroom (Sharples, M., Corlett, D. & Westmancott,  2002 ), leading UNESCO to consider that M-Learning, in fact, increases the reach of education and may promote equality in education (UNESCO, 2013 ). The EDUCAUSE Horizon Report - 2019 Higher Education Edition (Alexander et al., 2019 ) also mentions M-Learning as the main development in the use of technology in higher education and, therefore, it becomes increasingly relevant to rethink learning spaces in a more open perspective, both physically and methodologically, namely through mobile learning that places the student at the centre of the learning process.

Quite often studies that intend to determine the use of mobile applications focus on general questions, but the most common ones are related to the frequency and duration of the use of these devices, for example, questions such as “how many SMS or calls are made?” or “how often do you use the device?”

In fact, instruments like questionnaires are widely used in this type of studies. However, since mobile devices are completely integrated in our daily life and we use them quite extensively, it is difficult to retain and define with plausible accuracy the actual use that we make of them.

It is therefore relevant to effectively understand how these students use these devices, more specifically the applications installed on them. To this end, most studies have been based on designs that are focused on the users’ perceptions and based are on these reports.

Thus, it was important to understand if what users report using corresponds to what they actually use, and if this use does not occur for distraction or entertainment, for example.

Considering the above, some studies have focused on the validity of the use of these instruments. One of these first studies, carried out by Parslow et al. ( 2003 ), aimed at determining the number of calls made and received in the days, weeks or months preceding the date of the questionnaire, and their duration. The answers were compared with the logs of the operators and it was concluded that self-report questionnaires do not always represent the actual pattern of use.

Finally, in self-report instruments, which refer to questions of daily activity on mobile devices, this activity may not represent a general pattern of activity, since from individual to individual the patterns of daily use may vary considerably and thus reflect a very irregular use.

In a study by Boase & Ling ( 2013 ), the authors mentioned that about 40% of studies on mobile device use, based on articles published in journals (41 articles between 2003 and 2010), are based on questionnaires.

The questions asked aim to estimate how long or what type of use they have made of their devices on a daily basis, and sometimes aim to know about time periods of several days. In most of these studies, 40% of papers use at least one measure of frequency of use and 27% a measure of duration of use that users make. Another factor that is mentioned is that users do not always report their usage completely accurately. On the other hand, the same study mentions that users may over or under report the use they make for reasons of sociability (Boase & Ling, 2013 ).

Given the moderate correlation between self-report instruments and data from records or logs (Boase & Ling, 2013 ), the author considers that researchers can significantly improve the results if they use, together with other instruments, data from logs to make their studies more accurate and rigorous. Another suggestion would be the use of mobile applications that record these usage behaviours (Raento et al., 2009 ).

Indeed, this kind of instrument is widely used in this type of studies. However, given that mobile devices are fully integrated into our daily lives and we use them quite extensively, it becomes difficult to retain and define with plausible accuracy the use we make of them. In addition to the factors mentioned in the previous paragraph, it is important that these types of studies are validated with other methods, such as the use of logs, as presented in this study. The logs in this study refer to the capture records of the mobile device traffic made by the students.

This article therefore aims to present preliminary results with an approach that uses cross-checking of log data with questionnaire results.

Methodology

This article intends to present and discuss preliminary results of a study that aims to characterize the use of mobile applications at the University of Aveiro through collected logs, crossing its results with questionnaires answered by students during the classes, and also with an observation grid with data from the analysed class and questions to teachers related to what teachers recommend regarding the use of mobile phones during class time.

The research question that motivated this article is: which digital applications/services are most frequently used on mobile devices by the students of the University of Aveiro during their classes?

The study was composed of 40 students, that answered the questionnaires.

The research was based on the Grounded Theory method aiming to analyse the logs from the access points of the University. With the collected data, a usage profile of mobile devices during classes was drawn.

Figure  1 presents a diagrammatic representation of the created methodological process.

General diagram of the study

Therefore 3 instruments were used for the data collection: a questionnaire, an observation grid and logs collected through mobile traffic in the wi-fi network of the university.

The questionnaire allowed for a quantitative assessment of the profile of the participants and collected data on the use that participants claimed to make of their mobile devices. The observation grid served as a guide for the implementation of the study, allowing to record data on the classes where the collections took place and to verify whether certain items were present, such as permission to use mobile devices or planning to use them by teachers. The observation grid would also serve to make the link between use and content in class, but in this pilot, it was not possible to make this link between the class content and the usage of mobile applications, because the author could not observe the applications used by students.

The database containing the usage records enabled the analysis of the logs, resulting in the quantification and verification of the type of activity that each (anonymous) participant made of their device.

The 3 instruments used aimed to i) determine which application(s) students were really using during the classes, through the analysis of the data logs collected from the Wi-Fi network of the University; ii) identify the participants’ representations of their activities by means of several questions regarding mobile usage during class time; iii) observe students’ behaviour and focus via an observation grid that was used by the researcher/observer when he was attending the classes.

The group who participated in this pilot study was selected in accordance with the professors and classes available, so it is considered a convenience sample. The group was constituted by students of undergraduate classes from the Communication and Arts Department of the University of Aveiro.

Table  1 summarizes the schedule of the pilots carried out, the curricular units where they took place, their duration and the instruments used. For ease of management, all the pilots took place in the same department of the University.

The Table  2 summarizes the collected data from questionnaires and logs.

This pilot aimed to build an approach to data analysis, close to the Grounded Theory methodology, in which a provisional theory is built based on the observed and analysed data (Alves et al., 2017 ; Long et al., 1993 ). The data collected in this pilot will serve to define a more complete methodology to be used in a larger study.

This chapter is divided into three parts: context, technological setting and cases analysed. In the context part, the classes which are part of the study will be described, relating the answers from the questionnaires with the teachers’ recommendations about the use of mobile devices. In the technological scenario section, it is intended to describe the technological background underlying the collection process of the logs and in the last part, analysed cases, the objective was to validate if the data to be collected matched the outlined objectives.

In the questionnaire, the questions were divided into two main groups: aspects related to the participant’s profile and aspects directly related to the use of the applications. Aspects related to participants were intended to characterize them. Regarding the use of applications, we aimed to find out the students’ perception of the applications they use in their daily routine, inside and outside of the classroom, and how they do it. Data were collected using a Google Forms form and processed using Microsoft Excel.

In this subchapter, through the data collected from the students’ answers to the questionnaires, and by crossing this information with the data collected from the teachers in the observation grid, we try to describe the context of the pilot.

All of the teachers stated that they allowed their students to use mobile phones during class time, but that they did not plan that use. They also stated that in most part of the classes several students use their mobile phones and apps to search for class related materials. The teachers also showed curiosity about knowing, with more detail, the mobile phone use their students actually have.

In the three classes analysed (Aesthetics, Scriptwriting and Music in History and Culture), when asked about the possibility of using mobile applications as a pedagogical complementary resource 43%, 47% and 55% of students fully agreed that these should be used. In these three classes, 31%, 44%, and 67% of students showed a more moderate opinion: they agreed (but not in such an assertive way) that these should be used.

Another conclusion is that most of the students used a smartphone (88,9%, 75%, 52%) during class time, but many of them also used a computer (66,7%, 100%, 84%). The percentage use of tablets is much lower (11,1%, 0%, 15%).

In the analysed scenario, the majority of the students used the android operating system and 94% also agreed that mobile applications could help to manage the academic tasks, except in the case of the “Aesthetics Curricular Unit”.

When it comes to the time of use, per week, in classes, 53%, 58%, and 22% of the students answered they used these devices between 4 to 5 days a week and 15%, 40% and 70% said they used them between 1 to 3 days a week.

Students were also asked about how frequently they accessed mobile applications during class time and, in all, 77% of the respondents reported accessing apps at least between 1 to 5 times per class. About 20% referred they accessed apps from 6 to 10 times per class.

As for the purposes of accessing apps during classes, most students mentioned categories related i) to support the class / to research (70%, 100%, 77,8%), ii) to access institutional platforms (47.4%, 66.7%, 89, 9%), iii) to access to information (47.4%, 50%, 66.7%) and iv) to work (36.8%, 50%, 44.4%).

Interestingly, the categories communication (52.6%, 41.7%, 22.3%), collaboration (10.5%, 16.7%, 0%), access to institutional services (5.3%, 0% 0%) and “I do not use them” (10.5%, 0%, 0%) presented very low percentages, namely the last one.

When questioned about the use of mobile devices that did not include academic reasons, many students referred to the categories “to be linked/connected” or “to be updated” (42.1%, 66.7%, 33.3%), “to communicate” (57.7% 75.7%, 66.7%), “to share and access content” (31.6%, 58.3%, 33.3%), but few mentioned “for entertainment” (26.3%, 16.7%, 22.2%), “as a habit or routine” (10.5%, 41.7%, 11.1%) and “I do not use them” (10.5%, 0%, 11.1%).

When asked about which mobile applications are most used in an academic context, the most relevant category was “to research / to study” (73.7%, 58.3%, 89.9%), “to check the calendar” (31.6%, 25%, 66.7% %) and “to surf the web” (47.4%, 50%, 55.6%). Again, categories such as “to work” (36.8%, 33.3%, 33.3%), “to take notes” (26,2%, 33.3%, 55.6%) and “to create content” (31.6%, 25%, 11.1%) presented relatively low percentages. It should also be noted that the respondents presented answers that created categories which were not expected such as “to watch films” (10.5%, 8.3%, 0%), “to listen to music” (31.6%, 33.3%, 33.3%), “to take photos” (10.5%, 0%, 0%) and “to play games” (5.3%, 0%, 0%) All the students said that they used applications during classes in at least one of the categories. In fact, in the three courses no one stated “not to use them” (0% in all).

When asked about the teachers’ permission to use the mobile devices in the classroom, most of the students said that teachers allowed free use (52.6%, 100%, 77.8%). Only a few stated that teachers allowed using them specifically when planned (41, 1%, 0%, 22.2%). The respondents of one course stated that teachers did not allow the use of devices (Aesthetics - 5.3%). Finally, when asked about the usefulness of integrating mobile applications in class, there was an overwhelming majority of respondents (100%, 78,9%, 100%) saying they believed that such integration could be enriching and useful.

Below is presented a table describing the most used mobile apps during class activities. It should be noted that only the two answers with the greatest relevance for each category were considered.

Table  3 systematizes what the results have been showing until now: there is an important part of students that use mobile phones during their classes and, even when teachers advise them not to use them, they ignore the recommendations and use them anyway. The main purposes stated were: to be in contact with others through social networking but also to access different kinds of information in browsers. Moreover, the classes where the use of devices is not recommended by the teachers seems to be the one where some applications are most used.

Technological setting

In this section we intend to describe the technological background underlying the process of collecting the logs. The first goal was to register and capture logs from the wi-fi network of the university, which consists of a wireless network that users can access using their universal user credentials.

In order to do that a meeting was scheduled with the university’s technology services, as our main concern was the anonymization of the data collected in order (i) to confer more neutrality to the data treatment, and (ii) to comply with European data protection legislation. Another issue for discussion was the need of powerful machines so that they could process the large amount of data collected.

In this meeting the necessary steps were agreed in order to guarantee the users’ privacy, the authorization of the university’s central services to do the study and the registration method of the logs. The overall procedure demanded several experiences of data collection to fine-tune the final pilot, which works as the basis capture setting for all the main study.

The Wi-Fi traffic capture software (Wireshark) was selected to work both with Android and IOS devices and it was possible to understand the functionalities of the software.

The pilot also helped to understand and solve additional problems that appeared during the previous tests, related to the anonymization of the users’ data. It was necessary to ensure that the users’ personal data were not identifiable, which was a commitment: in fact, only HTTPS Footnote 1 traffic was captured, being all the other information encrypted.

After the first tests, an initial data collection pilot took place in a classroom context. A specific capture system was created to allow the capture of mobile application logs used only by a certain group of students, from a designated Curricular Unit. A specific scenario was set up to ensure that only those students communicating through the IP Footnote 2 defined for the scenario and during that class time were considered and treated under the scope of this study:

If the traffic of the concerned student is communicating through one of the APs (Access Points) covering the room, then the device will be assigned a “Room network” IP;

If the student’s traffic is not communicating through one of the APs covering the room, then the device will be assigned a “Non Room network” IP;

If the student traffic does not belong to the group to be analysed and the device in question is communicating through one of the APs covering the room, then the device will be assigned an IP from a “normal eduroam network”;

In the final steps we resolved the IP’s in Wireshark (software used for the capture) and the unsolved IP’s where filtered in a PHP Footnote 3 script, through the gethostbyaddr method where the unsolved ones are incrementally added.

Finally, using an IP list, we performed a comparison to resolve any unresolved names;

This step allowed to fine tune the process and to make the final test.

Analysed cases

After performing these tests, a scenario for this final pilot was set up to validate if the data to be collected matched the outlined objectives. In this final pilot, logs were collected in a classroom so that the scenario was as close to the desired collection as possible. In this pilot, it was possible to verify that the collected data fulfilled the requirements. At this point, in addition to the HTTPS traffic packets, the packets referring to DNS Footnote 4 traffic were also included. This option made the HTTPS traffic more easily understandable. Furthermore, the researcher could conclude that all authenticated devices belonged to separate accounts.

The results show that the pre-tests/pilots and the final pilot turned out very well and in a very reliable way since they allowed to verify the main problems that could occur and helped to certify that the traffic anonymity condition was respected. In fact, only the HTTPS was considered, and all other communication was encrypted with no risk of corruption of private data. Moreover, this option had an important justification: the fact that HTTPS traffic could be more easily understandable and the fact that it allowed certifying that all the authenticated devices of the wireless network belonged to separate accounts.

To process and create output visualization of the data, the choice was an integrated solution, both for the processing stage and for creating visualisations. Given the variety of tools available, several were tried out and Tableau Software® (Tableau Prep® and Tableau Desktop®) was chosen. Tableau Software is an interactive data processing and visualisation tool that belongs to the Salesforce company and, although it is paid software, it allows for an academic licence that was used in this project.

This solution, besides allowing working with a large amount of data, also allows for a very interactive data treatment and visualisation. This software also allows the importation of data from various sources, which in the case of this study was also an advantage.

This solution allowed us to work with large amounts of data but it also allowed for a very interactive data treatment and visualization. In the case of Tableau Prep, the file with the logs was imported in a CSV format Footnote 5 and treated iteratively in a dynamic way, being refined to the desired data in a second stage. As an example, we can mention the separation of the field “time duration” in hours, minutes and seconds fields; all the IPs were converted to a generic name “student”; all the destinations visited by the students were grouped in main categories, as for instance “Facebook”, as each application had numerous distinct destinations.

About 30 changes in data treatment and in data flow “cleaning” were performed, which were, later, exported to Tableau Desktop. Each file imported to Tableau Prep, in addition to the changes applied to the previous file, was refined with more changes, in an iterative process.

After treating the data on Tableau prep the generated data flow was imported to Tableau Desktop so that dynamic data visualizations were created. At this stage, dimensions, measurements, and filters were created according to the desired data visualization. The software has the big advantage of creating dynamic visualizations of the logs’ data which allows for a different and richer perspective on the data obtained, in order to deepen further studies about the same topic.

Discussion and conclusions

This paper aimed to describe the process of a pilot to carry out a larger study where we wanted to cross-reference actual usage data (logs) of mobile applications in the classroom with data from student questionnaires. In this article we also present the main results of this pilot, both from the point of view of the process of the pilot and from the point of view of the data of use of mobile applications by students in the classroom.

From the preliminary data analysis of this pilot, we can infer that the most used apps are Facebook, Google and Instagram, as we can see in Fig.  2 and Fig.  3 , although some variations between the attendees of the courses were registered when it comes to other apps. For example, in the case of the Design course, there are alternative apps being used such as YouTube or Vimeo.

General use of applications in Scriptwriting class

General use of applications in Aesthetics classe

Another noticeable preliminary result is that students use Facebook more at the beginning of classes and Instagram is used more at the end, as we can see in Fig.  4 and Fig.  5 .

Use of Facebook per hour in Scriptwriting class

Use of Instagram per hour in Scriptwriting class

In addition, the developed model was used in the main study with a bigger convenience sampling approach, which may provide a more accurate representation of the population of mobile-phone-users in the study field.

The visualizations created in a dynamic way during this study showed that the use of logs as a complementary data provider to other instruments, such as questionnaires, can be an added value for this research field.

On the other hand, this pilot contradicts (sometimes slightly, others considerably) the results of the questionnaires answered by the students and whose logs were collected and analysed. Logs show that:

there is a common use of mobile applications during the classes;

the purpose of the access is different: participants report that they use mobile applications mostly for academic reasons, but it can be noted that there is a general use of other mobile applications such as social networks and Youtube;

the usage time is much longer than what participants reported;

the frequency is also different: students stated that they use mobile applications in classes only 1–3 days a week, but we found that, in the analysed classes, there is an almost constant use of them, and finally

students report that they do not use social networks much in class, but the use is, in fact, massive.

The students’ perception of the “use of mobile devices and applications during lessons”, and as already mentioned, during a teaching activity - 70% of the students refer using the applications between 1 to 5 times, 22% between 6 to 10 times and 4% more than 10 times. It should also be noted, as previously mentioned, that only 4% mention not using them. With regards to the use during the week, 56% of the students refer using them between 4 to 5 days per week and 39% between 1 to 3 days per week. There is also a relatively low percentage of students mentioning that they use the devices during class more than ten times (4%).

However, analysis of the logs shows that this use appears to be much more intensive. We performed a calculation based on the average number of accesses, from which we removed 40% of potential automatic accesses and divided by the average number of accesses each application had in the initial test. The results present 6.6 accesses to the device per class/student in the class with the fewest accesses, and for the highest case, 313 accesses to the device per class/student.

This result is reinforced by results from other studies, such as the Mobile Survey Report, which states that students make regular use of laptops and smartphones during lessons (Seilhamer et al., 2018 ).

These conclusions lead us to some very serious insights on this subject. Apparently, even older students have a misperception of their use of online applications during classes. There is a serious discrepancy and incongruency between the behaviours that they claim to adopt and those they actually engage in during the classes. There are authors, who argue for the need for other types of studies that support this type of approach (Gerpott & Thomas, 2014 ), because the perception reported by users may not correspond to the actual use. It means that this gap deserves a deeper reflection. Why does it happen? Are students not motivated in higher education? Is the world offered online more interesting than the one in the physical campus? We will try to answer these questions in the main study.

Availability of data and materials

Some of the visualizations created are publicly available at https://public.tableau.com/profile/davidoliveiraua

HTTPS It is a protocol used for secure communication over a computer network, and is widely used on the Internet

IP is the s a numerical label assigned to each device connected to a computer network that uses the Internet Protocol for communication

PHP is a general-purpose scripting language especially suited to web development

DNS is naming system for computers, services, or other resources connected to the Internet

Unformatted file where values are separated by commas

Abbreviations

Higher Education Institutions

Access Points

Hypertext Transfer Protocol Secure

Internet Protocol

Hypertext Preprocessor

Domain Name System

Comma-separated values

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Oliveira, D.M.D., Pedro, L. & Santos, C. The use of mobile applications in higher education classes: a comparative pilot study of the students’ perceptions and real usage. Smart Learn. Environ. 8 , 14 (2021). https://doi.org/10.1186/s40561-021-00159-6

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• Mobile application
• Mobile usage
• Higher education

Development of a Mobile App for Clinical Research: Challenges and Implications for Investigators

Affiliations.

• 1 College of Public Health, The Ohio State University, Columbus, OH, United States.
• 2 College of Medicine, Central Michigan University, Mount Pleasant, MI, United States.
• 3 Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, United States.
• 4 Department of Plastic and Reconstructive Surgery, College of Medicine, The Ohio State University, Columbus, OH, United States.
• 5 Division of Health Services Management and Policy, College of Public Health, The Ohio State University, Columbus, OH, United States.
• 6 The James Comprehensive Cancer Center-Solove Research Institute, The Ohio State University, Columbus, OH, United States.
• PMID: 35363154
• PMCID: PMC9015757
• DOI: 10.2196/32244

Advances in mobile app technologies offer opportunities for researchers to feasibly collect a large amount of patient data that were previously inaccessible through traditional clinical research methods. Collection of data via mobile devices allows for several advantages, such as the ability to continuously gather data outside of research facilities and produce a greater quantity of data, making these data much more valuable to researchers. Health services research is increasingly incorporating mobile health (mHealth), but collecting these data in current research institutions is not without its challenges. Our paper uses a specific example to depict specific challenges of mHealth research and provides recommendations for investigators looking to incorporate digital app technologies and patient-collected digital data into their studies. Our experience describes how clinical researchers should be prepared to work with variable software and mobile app development timelines; research institutions that are interested in participating in mHealth research need to invest in supporting information technology infrastructures in order to be a part of the growing field of mHealth and gain access to valuable patient-collected data.

Keywords: clinical research; data security; mHealth; mobile app; mobile health; patient data; patient-collected data; research facilities.

©Shibani Chettri, Vivian Wang, Eli Asher Balkin, Michael F Rayo, Clara N Lee. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 01.04.2022.

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How to Conduct Mobile App Research in 2023 [Market Research Step-by-Step Guide]

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Mobile app research is essential to the success of your app, and we’ll tell you why. Understanding your target audience, competitors, industry, and current market trends lie at the base of conducting mobile app research. Every attempt to design a mobile app begins with market research because it assists in validating your app concept and ensuring your app is meeting demand in the industry.

Data gathering and analysis are necessary for conducting mobile app research. You can sometimes get data from sources that already exist. Other times, you’ll need to carry out original research alone. The majority of the time, market research combines these two methods. Let’s talk about this analysis’s goals, perspectives, and types in detail.

Why Is the Mobile App Research Before Development So Important?

A thorough mobile app market research helps you determine what your customers desire from the product. You’ll be able to think of viable solutions for your business and customers after understanding your customers’ expectations. Gathering crucial information and insights enables you to design and sell your ideas. The research gives you a complete picture of your target market and rivals. 

You can identify new opportunities for your company by comprehensively understanding the market. Regardless of what occurs to the overall economy, it is essential to discover and comprehend approaches to develop your product based on shifting client needs or market trends.

How a Comprehensive Mobile App Development Research Helps to Build Successful Mobile App

Marketing analysis provides important and relevant information about the market situation, helps assess how effectively the company promotes its products, creates the right promotion strategy, and chooses possible directions for business development. 

Mobile app development research is needed whenever a company launches new large-scale projects, upgrades an existing one, or starts from scratch. Entering a new market (geographic or product) also requires details preparation based on analytical data. With solid investments, owners and managers must be fully confident in the effectiveness of investments. Market assessment, competitor analysis , and the study of channels and methods of promotion are carried out in order to guarantee the app’s future success. 

The Goals of Mobile App Research

Each marketing research has its own specific goal and solves a specific business problem. Without a clear goal for the research, you can get empty information and spend extra resources and time. We’ll consider the main goals of marketing research:

• Having a description of the market

Market description research aims to determine the size (capacity) and growth rates of the market, evaluate the main drivers of the market; identify key market players, describe their positioning and their share (place) in the industry; determine the main market segments and their share in the total market. The information obtained in the course of such research helps the company to form a common understanding of the significance and prospects of the mobile app within the industry.

• Carrying out market segmentation

The purpose of marketing research on market segmentation is to identify all significant consumer segments in the industry; uncover each segment of special preferences, habits of behavior, and requirements for the product; and assess segments’ size and growth rates. These findings should help the company discover free low-competitive market niches, start developing the mobile app for certain groups of segments, and correctly form the feature set.

• Assessing customer attitudes, expectations, and loyalty

The mobile app development research also aims to assess the level of customer satisfaction with a particular app or company. In the course of the study, the product’s degree of compliance with users’ expectations in terms of the main characteristics is studied. As a result, the company can understand the key disadvantages of the app (affecting the decrease in satisfaction) and form corrective measures to improve advertising, product, and service.

In general, the research on mobile applications helps you answer the following inquiries:

• Is there a need for your application in the market?
• What are the preferences of your target market?
• What opponents and challenges do you face?
• Does your mobile app stand out from the competitors in a special way?
• Do you have a successful business plan for mobile apps?
• How can you develop a marketing plan that is optimized?

Where to Use the Results of the Mobile App Initial Research

Market research is the basis for further analysis and building a future product development strategy. After completing the assessments, take note of everything you learned, compare it to where you started, and add it to your app. There may be a discrepancy between what you believed to be true and what you learned via study. It’s always preferable to detect errors in the app discovery stage. Then, the goal is to incorporate the results into your app’s creation easily. How do you do that?

Sort your findings into groups based on the topics you’ve looked into. Open a spreadsheet, for instance, and make a tab for each topic you looked at. Be careful to incorporate the following:

• your target market;
• competitors;
• a description of the sector and its developments.

Make sure to incorporate all the information you’ve gathered from outside sources and your initial findings. Comparing these to the notes you made before starting your investigation will help. You may, for instance, contrast the results regarding clients with your own idea of the ideal client. Do you notice any gaps? Remember that anything is subject to change since you’re just starting out with your app development journey. 

It’s all in App Playbook. Our tried-and-true sequence of 75 tasks has already driven 35M installs, and now it’s your turn to experience the same level of success!

What Are the Mobile App Research Types and Kinds?

Any mobile app idea must pass the market research stage to succeed over the long term. The foundation of the entire process of developing an app is analytic preparation. Without conducting app market research, it will be challenging to comprehend the pain points influencing consumer behavior and current market trends.

When it comes to practice, your company can use various research techniques to verify the viability of its mobile app concept. Here are the most vital of them:

The Primary Mobile App Research

Primary research involves actively collecting relevant data. Typically, primary research will involve focus groups, surveys, interviews with potential customers, and competitive analysis of the current market. Primary research entails proactively gathering pertinent information. For instance, doing thorough demographic surveys to identify your app’s target population and their pain areas is a component of primary research for mobile apps. 

Primary research should also emphasize the most recent technological advances and how current industry trends affect your business model. However, before you develop a mobile app, you need to clearly understand your target market and how your app will answer their problems while still fitting into your business strategy.

The Secondary Mobile App Research

Secondary research is frequently employed by businesses to polish and refine their strategies. These involve studies and reports generated by other sources. For example, you might rely on studies conducted by industry research organizations to understand industry trends and the target audience better. 

The Global Industry Research

Industry research enables businesses and entrepreneurs to evaluate specific products, processes, and services in a given industry in general. Additionally, they employ this research to comprehend a company’s surroundings better. Conducting industry research is also done to analyze the competition, make strategic decisions, and spot market trends. Industry research frequently concentrates on a specific product or product range by analyzing the next criteria:

• Key players — searching for competing elements like bigger companies in the market. The research examines significant players’ primary products, solutions, and commercial strategies.
• Growth — examining whether an industry is expanding or contracting. As it demonstrates the amount of money the sector generates and speculates how this can rise, this information is helpful when developing a business plan.
• Trends — the mobile app development research assesses trends and how they impact companies. 
• Competitors — identifying the main rivals. Analyzing the tactics of other businesses and figuring out what makes them different enables companies to benchmark their products.

When to Do Mobile App Research?

Generally, the mobile app market research is carried out after idea validation , before the discovery phase. Paul N. Hauge and Peter Jackson, in their book, Do Your Own Market Research , point out three specific situations where market research is really useful:

• Goal setting. Knowing things like the size of the market or the description of potential customers can help you set sales goals.
• Solution of problems. Market research will help you understand whether your problems are internal, such as a poor-quality product, or external, such as aggressive competition.
• Support for company growth. Understanding how and why consumers choose products will help you decide which mobile app to bring to market.

Market research does not always have to be a large and complex project. The relatively new trend of agile market research allows you to research the market regularly and economically. With this approach, you use small, iterative, and evolutionary methods to respond to rapidly changing circumstances and adapt to unknown market territories.

Also, if you are in a startup environment, especially if you are developing an innovative product, you may be interested in customer development. In this methodology, mobile app research is the most “flexible” and closely related to product development.

App Playbook is the ultimate solution. With a bulletproof sequence of 75 App Building Tasks and real-life cases that have already driven 35M app installs, your app’s success is guaranteed!

How to Conduct Mobile App Research 

Researching the volume of the market is an integral part of market research. In this context, we should mention a few abbreviations: PAM, TAM, SAM, and SOM. They present a way to estimate the market size, which is popular in startups and growing businesses. The method is used by companies who want to understand the prospects for growth — and whether it is worth investing in a project. Here are their meanings:

• PAM (Potential Available Market) — the entire market volume, considering how it will change over the time you are interested in.
• TAM (Total Addressable Market) — the indicator includes all potential customers, even those who are already buying from your competitors.
• SAM (Served/Serviceable Available Market) — share of TAM. SAM shows you how much money is already being spent on solutions like yours. SAM is a market of direct competitors and analogs.
• SOM (Serviceable & Obtainable Market) — share of SAM. This is the number of sales a company can generate using its available tools.

There are two ways to calculate metrics: top-down and bottom-up. The top-down method uses analytical data, and the calculation starts with the total market volume — TAM. In the bottom-up approach, we use known indicators of the project, and the calculation starts with a realistically achievable market size — SOM. It is better to calculate both the first way and the second. The results will likely vary, but it will help to understand the real state of affairs. Let’s look at an example – a mobile CRM application.

TAM – The global CRM industry is worth an estimated $148.49 billion. SAM – The global mobile CRM industry is estimated to be worth around $26.64 billion. SOM – Let’s imagine this particular mobile CRM application is aimed at companies with around 40-50 field sales reps. The number of companies that employ between 40-50 field reps represents about 5% of the overall SAM. So SOM – $1,33 billion.

1. Assess the Market

One of the most crucial things to think about when creating a mobile app is whether or not there will be adequate demand for it. To accurately assess the likelihood that your app is successful, you must first look at your area’s supply and demand conditions. This mobile app research stage will be quite straightforward if you’re interested in the app business. Search the Google Play and the App Store.

In Google Play, you can view how many downloads various apps have received. Analyze the information critically. 

Consider, assess, and fully grasp the reality of the situation before you. If you’re surprised and dissatisfied by the number of downloads for your topic, keep looking. Are there any competitor programs that perform this task more effectively? Maybe your app can do it better. However, mobile app development research gives life to your ideas.

2. Define the Target Audience

After verifying your mobile app concept, you must focus on locating the people who will benefit from it the most. Being as descriptive as you can when describing your target audience is essential. It is far too general and sweeping to say things like, “My app is fantastic for everyone,” or “My app is for all males.”

You should define the probable users of your application and include crucial demographic details like:

• Occupation;
• Marital status;
• Lifestyle ;
• Education level;
• Key traits.

You also need to be aware of the types of mobile devices your target market wants to utilize. You cannot write a single code line before you know which mobile devices your audience will most likely use. For instance, if your mobile app market research shows that most of your audience uses Android smartphones, you may want to consider developing Android apps and changing your app store optimization strategy to the Google Play Store.

You should also consider your target audience as inclusive rather than exclusive. If those outside your defined audience also enjoy your app, this is great and may result in more pertinent information.

The social media sites your target audience uses should also be a key factor in your study at this time. With this knowledge, your business can make social media accounts more effective for connecting with people who spend most of their time online. Social media profiles offer a useful source of candid client feedback that your company may use to develop a strong and effective marketing plan.

3. Define Your Users’ Problems

Understanding your users is vital before releasing mobile apps. Would you change to a business that offers subpar service? Yes. The same applies to mobile applications and related services. Contacting your potential customers and getting the required information is the easiest way to understand their difficulties.  

Additionally, there is much room for improvement, which is quite helpful. Users may be discussing the issues and theories with competitor apps right now on forums and social media. It is essential to use additional resources to analyze.

4. Know Your Competitors

Conducting a detailed analysis of your competitors is one of the core tasks of mobile app development research. Analyzing rivals is beneficial. You may observe what they do well and what users like, as well as areas where they fall short of the competition. You may improve your app idea , business plan, and app marketing methods by understanding where there is a market opportunity.

Your company can enhance its own app and income-generating predictions by fixing the flaws of your competitors’ goods and services. Even though you may be entering the market later than some big players, rigorous research will help you avoid their mistakes and hasten the development process.

5. Summarize Your Findings

Examine your idea’s strengths, weaknesses, unique possibilities, niches, and any potential threats from competitors or outside forces. Your company should do ongoing mobile market research as you progress through the development process to update learnings and adjust efforts to meet user and market demands.

The ultimate founder’s checklist of 75 tasks to build, launch & scale your app 3-5x faster systematically. Proven by 35M of app installs!

Mobile App Research: What Can Go Wrong?

Whether they have received professional training or not, almost everyone thinks they have what it takes to be a market researcher. Curiosity, interest in people, and having a reactive mind are the three main characteristics of a researcher. However, this is not always enough. In light of this, we mention some specific mistakes founders/marketers make when gathering and analyzing market research data:

The Most Common Mobile App Research Mistakes

• Inadequate Sampling 

Your entire analysis could be inaccurate if you choose the incorrect respondents to your queries. While a poor question pops out, poor sampling is less immediately apparent. Because of this, avoiding this error is of the utmost importance. Make careful to define your sample right away. Make a list of the people you wish to talk to and why. Establish the rules so they can specify who belongs in the audience. Decide if additional analysis or cross-comparison may be necessary by segmenting your audience into smaller groups.

Be specific about the level of quality you require from your survey. This is becoming more crucial as access panels have become more popular, and their use of qualified respondents is frequently assumed. This is true for qualitative and quantitative mobile app research.

• Vague Questions

Your analysis will be unsusceptible to interpretation if your query is not specific, suggests prejudice, or has an unclear meaning. At best, your conclusions may encounter resistance inside the company. The worst-case scenario is that the wrong decisions are made using this information.

There is frequently ambiguity in the answer blocks. It is relatively simple to write answer choices from your own point of view while omitting certain important ones that responders might consider. Spend time making sure that the phrasing of the answer choices doesn’t cause overlap between some of them, confusing your respondents.

• Incentives for cost-cutting

The best way to think of incentives is as a form of compensation for time sacrifice. Due to their hectic schedules, people are asked to contribute some of their time to mobile app development research to help you do your job. Yes, they are driven by their interest in the subject matter or by a desire to see us succeed, but these reasons shouldn’t be used as justifications for not respecting people’s time. 

You don’t want the incentives to be severely low or excessively high since we aren’t attempting to sway or skew the research; rather, we want them to be fair and reciprocal. They offer us their time, and we return the favor. Its unjust evaluation of people’s time is one of the main causes of studies getting poor response rates. If you compensate appropriately, you will get greater quality research and higher response rates.

• Poor reports

Be careful when reporting. Make sure convincing data support all conclusions. Avoid words like “some” and “may” and try to make your points as briefly as possible. Describe your discovery in detail, including who it concerns, what it means, and what may be done. If action is to be taken, accuracy is required. Likewise, don’t exaggerate your conclusions. If you do, the resulting report can be ambiguous.

For instance, avoid generalizing about a particular customer demographic or behavior when describing clients. Don’t equate behaviors with people of the same age or location. Most essential, don’t generalize your conclusions to the entire company or offer broad recommendations outside your purview.

• Graph-pocalypse

Lastly, avoid stuffing your report with information that makes it difficult to read or comprehend. People frequently mean this when discussing telling stories or developing a narrative. Use evidence, but avoid ruining the project by chronicling every little detail.

Mobile App Development Research: Summary

Regardless of your company plan, attracting customers to your mobile app will be difficult. The mobile app market research helps you study the target audience and their specific needs to develop a reliable strategy. Companies can use market research to understand the demand for, viability, and potential performance of their product. 

Primary or secondary information, which gives distinctive insights into a company’s offering, is used in market research. A crucial part of a company’s research is used in the project discovery phase since it determines the app’s success and future scaling. 

Market Research Step-by-Step Guide: FAQ

What is the main goal of market research.

Mobile app market research gives you vital knowledge about your industry and competitive environment. It can inform you of how the target clients and customers you want to reach view your business.

How to Do a Mobile App Research?

Finding out how to differentiate your app in the market is the fundamental objective of market research. Having a strong brand narrative and Unique Value Proposition (UVP) will be beneficial. Generally, market research gives you insight into the next development aspects:

• Is there a demand for your goods on the market?
• What should be your business strategy?
• How should the marketing plan be structured?
• What are the core strengths of the product?

To determine whether your product is viable, you must conduct a study on each of these subjects.

Why Doing Research Is Important Before App Development?

You may better understand the user’s expectations and what they are specifically searching for in the application by conducting research. Offer a better user experience than any other application after you understand the issues you want to solve and your target audience.

What Is the Difference Between Market Research and User Research?

User research looks at people’s behavior to identify their genuine motives and pain areas. On the other hand, mobile app development research tries to determine consumer sentiments about a product and calculate the size of the prospective market.

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Mobile learning: research context, methodologies and future works towards middle-aged adults – a systematic literature review

• Track 5: Multimedia and Education
• Published: 20 August 2022

Cite this article

• Syahida Mohtar   ORCID: orcid.org/0000-0002-4462-8890 1 ,
• Nazean Jomhari 1 ,
• Mumtaz Begum Mustafa 1 &
• Zulkifli Mohd Yusoff 2  

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3 Citations

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Over the past several years, mobile learning concepts have changed the way people perceived on mobile devices and technology in the learning environment. In earlier days, mobile devices were used mainly for communication purposes. Later, with many new advanced features of mobile devices, they have opened the opportunity for individuals to use them as mediated technology in learning. The traditional way of teaching and learning has shifted into a new learning dimension, where an individual can execute learning and teaching everywhere and anytime. Mobile learning has encouraged lifelong learning, in which everyone can have the opportunity to use mobile learning applications to gain knowledge. However, many of the previous studies on mobile learning have focused on the young and older adults, and less intention on middle-aged adults. In this research, it is targeted for the middle-aged adults which are described as those who are between the ages of 40 to 60. Middle-aged adults typically lead very active lives while at the same time are also very engaged in self-development programs aimed at enhancing their spiritual, emotional, and physical well-being. In this paper, we investigate the methodology used by researchers based on the research context namely, acceptance, adoption, effectiveness, impact, intention of use, readiness, and usability of mobile learning. The research context was coded to the identified methodologies found in the literature. This will help one to understand how mobile learning can be effectively implemented for middle-aged adults in future work. A systematic review was performed using EBSCO Discovery Service, Science Direct, Google Scholar, Scopus, IEEE and ACM databases to identify articles related to mobile learning adoption. A total of 65 journal articles were selected from the years 2016 to 2021 based on Kitchenham systematic review methodology. The result shows there is a need to strengthen research in the field of mobile learning with middle-aged adults.

Avoid common mistakes on your manuscript.

1 Introduction

Adulthood can be categorized into early, middle and late adulthood. Middle-aged adults come between the ages of 40 to 60, in other words is when one is in between the younger and older generations [ 42 , 62 ]. This stage of age, notably is the age period that Hall [ 31 ] referred to as aging, where the signs of cognitive and physical ageing start to be noticeable, from the age of 40 and rapidly increase after the age of 65 [ 6 , 54 ]. According to Yaffe and Stewart [ 94 ], a large part of adult life is made up of the mid-life period. This has been associated with many descriptive terms: mid-life syndrome, mid-life crisis, middlescence, empty nest syndrome, second adolescence, second honeymoon, age of fulfillment, and menopause. Aging population contributes to healthcare issues, not only amongst the older adults but towards middle-aged adults too. As mentioned earlier, the healthcare issues amongst the middle-aged adults are related to the decline in physical abilities, relational, and psychological capacities. For example, women in their middle age experience menopause and perceived personality change, which lead to severe depression, physical, and emotional problems [ 80 ]. According to Yaffe and Stewart [ 94 ], the most frequently identified events or concerns among middle-aged adults were: increased personal concern for health, death of a friend or relative, change in wage/salary, and concern for change in physical appearance.

When middle-aged adults enter their 60s, their reaction time starts to slow down further, and they experience a significant declination in their performance. The brain may also no longer function at its optimal level, leading to problems like memory loss, dementia, and may have issues with other cognitive functions such as language, attention, and visuospatial abilities [ 35 , 61 ]. It has been widely assumed that the midlife period is a critical period, thinking about death and mortality, as well as experiencing decline in physical abilities, relational, and psychological capacities [ 80 ]. Therefore, early prevention should therefore be looked upon at the middle age stage to help with memory impairment, as well as emotional control.

Middle-aged adults typically lead very active lives while also engaging in self-development programs aimed at enhancing their spiritual, emotional, and physical well-being. Muslim adult, for instance, will prefer to go to the mosque, surau, or Islamic center to seek for Islamic education [ 42 ] to enrich their knowledge and gain serenity through the command of Islam. This indicates that an individual in the middle-aged is inclined to reflect and improve the quality of one’s daily practice. Unfortunately, during the Covid-19 pandemic outbreak, many lectures at the mosques and other institutions could not be held, resulting in many people having to work from the home. As a result, many have taken the initiative to hold religious lectures online through video conferences such as via Zoom, WebEx, Jitsi Meet, Google Meet applications, and many more [ 1 ]. There are also those who watch religious lectures that have been prerecorded on certain channels, such as YouTube or podcasts. However, the enthusiasm and motivation for online and prerecorded learning is not the same and less encouraging as compared to face-to-face lectures.

Health management apps have shown to be useful for treating a variety of illnesses such as chronic illnesses caused by obesity, high blood pressure, diabetes, and so on [ 32 ]. As middle-aged adults are smartphone and tablet active users, they can use these portable devices to track their healthy lifestyle habits, maintain social communication, prevent accidents, and seek information [ 91 ]. In addition to chronic illness management using mobile applications, there is also a concern on how middle-aged adults can utilize mobile technology in fulfilling their spiritual journey towards a quality lifestyle. For example, they can learn how to acquire a literal understanding of the Quran through a spiritual mobile application. This will help a Muslim to elevate their understanding, motivation, and devotion towards Islam, which eventually leads them to become a better person emotionally and psychologically. All of these exhibit many important experiences associated with middle-age adults, most involving work and family, and self-development [ 53 ].

Mobile devices such as smartphones have gained popularity because they allow people to stay in touch and provide easy access to information anywhere and anytime [ 89 ]. Therefore, investigating the acceptance and adoption of mobile learning by the middle-aged adults through a systematic literature is important in highlighting the gap for any future work.

This review paper presents the fundamentals of mobile learning and the utilization of mobile technology in the learning environments. Mobile learning theories are also highlighted to show the significance of mobile learning towards middle-aged adults. Based on the research context found in the selected literature, the researchers here provide a systematic mapping of the employed methodologies in the area of mobile learning research. The purpose of the systematic mapping is to determine the most appropriate methodology for future research on middle-aged adults in areas of mobile learning.

2 Mobile learning

M-learning is a subset of ‘e-learning’ while ‘e-learning’ is the subset of distance learning that focuses on learning across context and learning with mobile devices, which can take place anytime, anywhere [ 43 , 62 ]. For example, learning may happen at the workplace, at home, and at places of leisure. The learning may be related to work demands, self-improvement, or leisure; and it is mobile with respect to time where it happens at different times during the day, on working days, or on weekends [ 68 ].

According to Ozdamli and Cavus [ 70 ], learners, teacher, environment, content, and assessment are the basic elements of mobile learning. The core characteristics of mobile learning are ubiquitous, portable size of mobile tools, blended, private, interactive, collaborative, and instant information. They enable learners to be in the right place at the right time, that is, to be where they can experience the authentic joy of learning.

Since learning can be performed anywhere and anytime using electronic devices, Traxler [ 85 ] defines that mobile learning is a learning process that is delivered through the support of mobile devices such as personal digital assistants, smartphones, wireless laptops, and tablets. This understanding is supported by Keegan [ 45 ] who suggested that m-learning should be restricted to learning on small and portable devices as mobile devices that could be carried everywhere.

According to Nordin, et al. [ 69 ], the requirements for mobile learning environment include technology, that is, (1) highly portable (to support learning whenever and wherever), (2) individual(the design should be able to support individual learning, cater for individual learning styles and be adaptable to learners’ abilities), (3) unobtrusive(where learners should be able to retrieve knowledge without the technology becoming a deterrent), (4) available(enabling communication with friends, experts and/or teachers), (5) adaptable(the context of learning should be adaptable to situations and the individual’s skills and knowledge development), (6) persistent( able to manage the learner’s learning despite the changes in the technology itself), (7) useful(useful to learners for everyday chores), and (8) user-friendly(easy for people to use and must not create technophobia among new users).

3 Mobile technology

Today, it is fortunate that mobile technology’s on-demand capability puts learning back into the learner’s hands by allowing users to take the initiative in diagnosing their learning needs, formulating learning goals, identifying human, and material resources for learning, choosing and implementing appropriate learning strategies, and evaluating those learning outcomes [ 50 ].

Mobile technology covers a wide range of mobile devices such as portable electronic devices used to perform a wide variety of communication, business, productivity, and lifestyle tasks such as parenting [ 26 , 66 ]. It is also connected through a cellular communication network or a wireless connection. The common mobile technologies that allow these tasks are cellular phones, PDAs, handheld computers, tablets, laptops, and wearable devices. A standard mobile technology device, such as a cellular phone, may have one or more features such as a GPS, a web browser, an instant messenger system, an audio recorder, an audio player, a video recorder, and gaming systems [ 4 ].

In the area of healthcare, numerous studies have been conducted on the use of mobile devices with wearable devices [ 21 , 39 , 87 ] to monitor the health of the elderly and individuals with disabilities. By using mobile apps, the health of the elderly and young adults can also be tracked [ 12 , 20 , 40 ] and diagnosed using mobile game-based screening tools [ 34 ], especially when facing challenges and stressful time during the Covid-19 pandemic [ 79 ] , or during post-college life transition [ 27 ]. Not all older people are proficient in using mobile devices. Therefore, there are researchers who make studies related to how older and young adults (university students) manages their mobile device security and privacy settings of their mobile devices in the context of social interaction and motivation [ 64 , 67 , 90 ].

Besides the usage of mobile devices in the healthcare area, the growth of mobile devices is significant and impactful in the education area such as in teaching history using 3D [ 57 ] and safety education [ 13 ], personal learning and workplace learning [ 29 ]. The use of mobile devices such as smartphones and tablets has become truly ubiquitous and has a potential for improving student learning, which can happen in collaborative, authentic settings, i.e., real life contexts and use active learning approaches [ 18 ]. As smartphones have become popular devices among youth nowadays [ 36 , 65 ], these devices can be utilized and embraced in the classroom teaching environment. By having a smartphone with wi-fi connectivity, Bluetooth, camera, color display, audio/video recording capability, it is already suitable for a person to adopt m-learning [ 36 ]. Majority of students spend most of their time (6 to 24 hours) on the Internet using their smartphones [ 8 ].

Smartphones also have become essential communication tools for older adults to stay connected with their family and peers [ 93 ]. Compared with younger adults, older adults tend to be more likely to use mobile phones for their original design purpose—that is, making calls for instrumental reasons such as arranging plans and other instrumental activities rather than playing games, surfing the internet, or using auxiliary applications [ 91 ]. The intervention of mobile technology in older adults’ lifestyles can improve their well-being and keep their mind and body active as well as prevent or slow down cognitive decline. For instance, mobile games can be used to capture cognitive learning outcomes and the process of knowledge acquisition [ 92 ]. Through activities such as interacting with easy games [ 71 ], taking and managing photographs, sending messages via SMS, video or audio calls, and reading newspapers via webpages may help cognitive and noncognitive stimulation of older adults.

Mobile computing devices become more situated, personal, collaborative, and lifelong and these innovations will become embedded, ubiquitous, and equipped with enhanced features for rich social interaction, contextual awareness, and access to the Internet. Hence, extending learning outside the classroom and into the learner's environment, mobile learning can have a significant impact on middle-aged adults. However, based on the research context in the areas of mobile learning, existing studies have concentrated exclusively on aspects of the mobile device use, such as accessibility, usability, and adoption, among young and older adults, while middle-aged adults have received less attention. Thus, the use of mobile devices among middle-aged adults should be further investigated to determine how mobile devices can assist them in acquiring knowledge and developing themselves while leading hectic lifestyles and having to deal with the Covid-19 pandemic, towards their long-life wellbeing.

4 Multimedia in Mobile learning application

Using mobile device as a learning tool is a new way for learners to learn as they like, anywhere and anytime. Moreover, an application that contains multimedia elements such as text, animation, graphic and video will engage and attract the attention of the student. Mobile learning application used in mobile learning environments varies, such as Learning Management System (LMS), Short Messaging services (SMS), Podcasting, Social Networking, Instant Messaging, Blogging, Facebook, Microblogging, Wiki, QR, 3D and Augmented Reality [ 81 ].

SMS and videos have long been used as language learning tool through the use of mobile phones and personal digital assistants (PDAs) [ 68 ], and today, many have benefits from using WhatsApp, flashcards and mind maps, on-line videos, and social networks in learning. Recently, Duolingo is said to be a popular application for new language learning where learners can interact with intelligent chatbots that give corrective feedback and awards at the same time [ 49 ].

In the fast-aging population countries like China, senior users have become a significant new growth point that cannot be ignored in social network sites to keep continuous competitiveness. In China, WeChat is the most popular social software for senior citizens. This is due to the good user experience and operability, where some senior users manage to operate the application although they have no computer skills or they know little about the network [ 11 ].

On the other hand, instant messaging apps such as WhatsApp and Line have become a popular mobile app amongst students. In a classroom environment, the student may use these apps to interact with teachers outside the class and using smartphones to manage their group assignments. The use of instant messaging applications promotes collaborative learning [ 7 ] and flexible learning, improves student participation, increase communication and interaction between lecturers and students, as well as improve the performance of teaching and learning [ 10 ].

Text editors such as the Mobile MS office, content management systems such as Learning Management Systems (LMSs), and audio-video recording of lectures did not get much attention by the students in terms of its usage via the smartphone. The reason for the low usage of these functions and features could be due to the limited screens space, which makes it difficult to read large documents, and the small sized keypad makes data entry cumbersome [ 36 ]. To make mobile learning more interesting, game-based elements have been used to improve the students’ engagement and enjoyment in learning. For instance, Kahoot is a game-based technological platform that can be accessed from, for instance, smart devices or a laptop. The game-based learning application (app) can benefit working adults who are adult learners with diverse learning abilities. Chunking method was used to break down complex concepts into smaller parts in the form of multiple-choice questions. The students’ learning process is tested and corrected, in real time, through the statistics which are generated from this chunking process. Kahoot creates a safe environment for students to make mistakes through multiple choice questions, and yet relearn it without being judged by their peers. However, the drawback of Kahoot is, it does not adequately support the learning experience of adult learners [ 74 ].

To achieve a successful ageing life, positive spirituality indeed has a close relationship with physical and mental abilities. There have been studies that develop an Empathic-Virtual Coach (VC) to involve senior users in enjoying a healthy lifestyle with respect to diet, physical activity, and social interactions, while in turn supporting their carers [ 41 ]. Furthermore, in addition to physical support, adults also require emotional and spiritual help for a balanced lifestyle. For example, Sevkli, et al. [ 75 ] in their study had designed and developed mobile Hadith Learning Systems (HLS) that were able to encourage and promote hadith learning for young and middle-aged Muslims. Hence, mobile apps appear to be one of the tools that can be used to promote a balanced well-being lifestyle for the older people such as their social status, independence in their everyday activities, health status, standard of living, or leisure activities of the aging population.

5 Mobile learning theory

According to Lee, et al. [ 56 ], there is an increasing number of adult learners entering or returning to university. Despite the growing number of nontraditional adult students in online higher education, little is known about the dynamic processes of adult distance learning, through which adult students struggle to develop their learning ability to balance their life and study, and to become self-regulated learners, and ultimately as competent selves and lifelong learners. The implementations of mobile learning are supported and guided by theories such as Behaviorism, Cognitivism, Constructivism, Situated Learning, Problem-Based Learning, Context Awareness Learning, Socio-Cultural Theory, Collaborative Learning, Conversational Learning, Lifelong Learning, Informal Learning as well as Activity Theory, Connectivism, Navigationism, Location-based learning [ 46 , 68 ]. The classification of activities around the main theories and areas of learning relevant to learning with mobile technologies are shown in Table 1 .

Lifelong learning happens not only in learning institutions such as community colleges or higher learning institutions, but can also happen anytime and anywhere according to the needs of the individual [ 69 ]. Informal and lifelong learning are often referred to adult education or continuing education, which means a learning process that occurs as blended learning with everyday life unobtrusively and seamlessly [ 73 ]. The unique characteristic of lifelong learning is the fact that it is centered on the learner. Because of that, the use of technology in offering a flexible learning framework is often favored by adult learners [ 69 ]. In addition, when compared to conventional methods such as textbooks, mobile learning tools, especially learning through mobile apps, are intrinsically inspiring, provide greater satisfaction, increase student well-being, and have positive implications for long-term student persistence [ 78 ].

Lifelong adult learners are different from young learners (school or university students) who may devote significant amounts of time to study each day, as their learning time is scattered due to family responsibilities, work obligations, and other social obligations [ 44 ]. However, the keys to unlocking the secrets to successful adult learning online are embedded in the basic principles that guide adult learners. The subsequent six principles upon which Knowles [ 51 ] constructed his formal and andragogical concept are shown in Table 2 .

6 Methodology

This study carried out an extensive literature review to identify the research gap, focusing on the related literature published within the period of 2016 to 2021. The aim of this systematic review is to investigate the trend of previous research on the acceptance and adoption of mobile learning by middle-aged adults. In order to justify the research gap based on the previous studies, this article will also provide views on the existing mobile learning usage targeted at solving user’s adoption of mobile learning towards young and older adults.

To conduct the systematic review, the researchers followed the procedure defined by Kitchenham [ 48 ], which is one of the most complete and suitable methods for reviewing studies in computer science. We carried out this review in three main phases: 1) planning of systematic mapping; 2) conducting the review; and 3) reporting the review. The phases of this systematic review and the related activities are shown in Fig. 1 .

Phases of conducting this systematic review

Planning of the Systematic Mapping

Activities involved in this stage were aimed to identify the objectives of the review. These activities are as follows:

Discovering the gap of the existing systematic reviews

In this step, a comprehensive search was performed in the cyberspace to locate the related review studies in mobile learning. Some of the bibliographic databases accessed included EBSCO Discovery Service, Science Direct, Google Scholar, Scopus, and IEEE.

Specifying the research questions

The research questions we have formulated for this review attempt to acquire the understanding and to determine the research gap on mobile learning usage in assisting lifelong learning in the context of spiritual among middle-aged adults. These questions are related to the acceptance and adoption of mobile learning towards middle-aged adults. The research questions are:

What are the fundamentals and background of mobile learning in the learning environment, including its adoption, acceptance, and available applications?

What are the research methodologies employed in the current studies carried out in mobile learning field?

What are the core research gaps should be further investigated by researchers in mobile learning towards middle-aged adults?

Identifying the relevant bibliographic databases

To answer the research questions and find relevant studies, bibliographic databases that cover majority of journals and conference papers associated with the field of human-computer interaction and mobile learning were selected. Related literatures published within the period of 2016 to 2021 were chosen in this research and the relevant bibliographic databases are ACM ( https://www.acm.org/ ), Emerald ( https://www.emerald.com/insight/ ), EBSCO Discovery Service ( http://search.ebscohost.com ), Science Direct ( http://sciencedirect.com ), Google Scholar ( http://scholar.google.com ), Scopus ( http://scopus.com ), and IEEE ( http://ieee.com ).

Conducting the Review

Activities involved in this stage were aimed to selecting related studies. These activities are as follows:

Identifying the Relevant Studies

In identifying the relevant studies, a search using key words such as “human-computer interaction”, “mobile learning”, “middle-aged adults”, “us- ability” was conducted. Accordingly, Boolean OR was used for alternative spellings, synonyms, or alternative terms, and Boolean AND was applied to connect the main terms. The complete list of search keywords of the review is provided in Table 3 .

Two additional search strategies were applied to retrieve the maximum number of relevant papers. The first strategy was reviewing the reference list of selected papers to find more related papers. The second strategy was googling the authors of selected studies to find potential related research.

Defining Selection Criteria

For selecting the primary papers, the following criteria based on the purpose of this study are defined.

Inclusion Criteria:

Studies containing mobile learning, acceptance, and adoption among mobile devices users.

Studies dealing with factors that contribute to the adoption and acceptance of mobile learning in the educational environments or working environments.

Studies utilizing mobile learning applications related to education, health care, data collection, and engineering that motivate users to use mobile learning.

Studied involving mobile learning users in category young adults, middle-aged adults, and older adults.

Exclusion Criteria:

Studies in learning environments that do not relate to the mobile learning context.

Studies of mobile learning that involve children such as kindergarten students or users with special needs.

Studies that are reluctant to serious mobile learning.

Papers that are only available in the form of abstracts or PowerPoint presentations.

Papers that are not written in English.

Selecting Primary Studies

The titles and abstracts of searched papers were reviewed based on the inclusion and exclusion criteria. Every paper that met at least one of the criteria and without any of the exclusion criteria was included in the review. For papers that could not be excluded based on reading of the titles and abstracts, the full texts of the papers were reviewed. Through this process, 65 articles were selected from the 531 papers initially found. 292 papers were excluded only by reading the topics, 105 papers by reading the abstracts, and 65 papers by reading the full text.

Validation control of the Primary Studies

In order to maintain the quality of the selected studies, the primary studies chosen by the first reviewer were double-checked by a second author. The evaluation of the selected paper was based on the evaluation questions as follows:

Whether a proposed mobile learning solution is implemented in the research context?

Whether the methodology of mobile learning solution is suitable for middle-aged adult?

To what extent the proposed solution effects the middle-aged adult in mobile learning?

The procedure of selecting the primary papers is illustrated in Fig.  2 .

Selecting the primary papers

Data Extraction and Synthesis

In order to extract and synthesize the data to answer the research questions, the selected studies are classified into five categories as follows:

Mobile learning and their research context: This categorization answer the first research question and helps to find the fundamentals and background in mobile research based on research context such as acceptance, adoption, effectiveness, impact, intention of use, usability, and readiness.

Methodology in the mobile learning research area : In order to answer the second research question and find the methodologies employed in the related context, the research context with the methods employed by the researchers was mapped as shown in Table 7 . Based on this mapping, the instruments that have been used in mobile learning research involving middle-aged adults can be identified.

Instruments used in Mobile learning research context: This category answers the second research question. From the systematic mapping done, it was found that the common research instruments used were Questionnaire, Interview, Experiments and Task Analysis. Here, the most preferable instruments used in mobile learning research were highlighted.

Mobile learning solutions in general: This category answers the third research question in order to find the gap in mobile learning research. Articles found in this study include mobile learning articles for young and older adults to show the trend of research towards adulthood. Since the focus of this systematic mapping is on identifying mobile learning technology applied to the middle-aged adults, thus those works focusing on the application of mobile learning not on adult learners or studies on users with special needs were excluded.

Solution for middle-aged adult in mobile learning: This category also answers the third research question in presenting the future works related to mobile learning involving middle-aged adults. This article begins by explaining the use of mobile technology in a learning environment, and the mobile learning theories that form the basis for the comparison of the existing mobile learning solutions for middle-aged adults.

Effects of mobile learning on middle-aged adult: This category answer the importance of the mobile learning towards middle-aged adults for a healthy well-being by assessing the number of studies related to middle-aged adults.

Reporting the Review

In the following section, the outcomes of reviewing the selected studies were reported and the results were discussed in detail, to respond to the defined research questions.

7 Results of the systematic mapping

From the search procedure and criteria, a total number of 65 articles are extracted. The distribution of the primary studies according to the publishing year is shown in Table 4 and Fig. 3 . The articles searched for this systematic review study are from 2016 to 2021. The reason is that this study aims to identify the latest research trends in the field of mobile learning with middle-aged adults. Finding shows that there are several studies from 2016 to 2018 that focus on this topic. The number of articles on mobile learning increased significantly from 2019 to 2020, which may be due to the outbreak of the Covid 19 pandemic. In education, for example, many institutions and organizations have drastically shifted from the traditional teaching and learning approach to online platforms. As a result, there is a considerable amount of research on mobile learning focusing on students in schools, universities, and academic staff. Meanwhile, a lot of study has been done in the field of healthcare with the elderly and middle-aged individuals, because their health begins to decline at this age.

Distribution of reviewed studies by year

It would also be interesting to find out the distribution of studies by countries, as shown in Table 5 . This shows that China had contributed the most research articles in this area of mobile learning. In article [ 13 , 21 , 72 , 77 , 81 , 88 ], the country where the study was conducted was not specified.

8 Participants

The categories of participants in the selected studies consist of young adults, middle-aged adults, and older adults. The number of studies based on age category is illustrated in Table 6 and Fig. 4 . It is found that the number of studies involving young adults is higher compared to studies involving older adults and middle-aged adults. This is due to the fact that young adults are frequent users of smartphones and are more adept at using mobile apps. Furthermore, since they are unable to attend college or universities due to the Covid-19 outbreak, many students are required to study online from home using mobile devices.

Number of studies based on participants’ age category

The details of the reference pertaining to the articles based on participants’ categories (older adults (OA), middle-aged adults (MA), and young adults (YA)) are listed in Table 8 .

9 Research context in Mobile learning

The articles obtained for this study were categorized by research area, as shown in Table 7 . Based on the results, mobile learning was studied in the following areas: Education, Healthcare, Usability, Transactional Services, and Social and Communication. Figure 5 illustrates the number of articles published on each research topic. The finding shows that many researchers prefer to conduct research in the field of education. This is because computers and mobile devices are widely used in educational institutions among young adults. On the other hand, studies that focus on middle-aged and older adults are usually concerned with language or vocabulary learning. The healthcare field is also receiving a lot of attention from researchers, and studies on mobile learning in this field are usually related to elderly and middle-aged people because older people and middle-aged people tend to be more vulnerable to health problems. The number of articles from other fields is low because studies on middle-aged adults and mobile learning did not match the scope and range of years defined for this study.

Number of articles in the research domain

Because the study related to mobile learning is very broad, therefore the article obtained has been classified into research context. Research context was determined based on the previous and current research in the field of mobile learning. It was found that many researchers in the field of mobile learning have studied the acceptance, adoption, effectiveness, impact, intention of use, readiness, and usability of mobile learning. The categorized articles are listed in Table 9 in section 11, with additional information on the methodology used in each study. Figure 6 shows the number of articles obtained by research context.

Number of papers by research context

10 Mobile learning towards the middle-aged adults

From these articles, not many researchers have examined the adoption of mobile learning by middle-aged adults. As mentioned earlier, a person in his or her forties is already inclined to focus on and enhance the standard of daily practice while also finding serenity. At this stage, many people have developed an inclination and willingness to gain more religious knowledge. Adult Muslims who work during the day, would rather choose to visit a mosque or surau to learn about Islam through religious lectures in the evening or at night. During the Covid-19 pandemic outbreak, many people were forced to work from home, and many lectures at the mosque were cancelled. As a result, many have taken the initiative to hold religious lectures via video conferences over the internet (e.g.: Zoom, WebEx). Others tend to watch religious lectures that have been posted on YouTube or other related platforms. However, as opposed to face-to-face seminars, the excitement and encouragement to attend online and prerecorded learning is lacking. Midlife brings with it a multitude of significant life experiences, the majority of which revolve around work, family, especially parenting, and self-development. Tablets are being used more commonly by middle-aged adults to monitor healthy lifestyle behaviors, maintain social contact, avoid injuries, and search information.

Many middle-aged and older adults are using the Internet to obtain information about health conditions and treatments, to get social support and advice from others with similar health-related experiences, and to access apps to help them manage their health [ 28 ]. For instance, Huang, et al. [ 32 ], studied on the attitude of middle-aged adults towards health app usage. From the study, they discovered that middle-aged adults who have no habits in health management tend to consider health applications as valuable tools and have a positive impact on them, while those who already have the habit, do not tend to consider health applications as valuable tool to be used in their daily routines. There are also some middle-aged adults who decide not to use health apps due to some sentimental reasons and the confidence of middle-aged adults in using a smartphone influences their cognitive assessment of health apps.

Table 8 shows the list of studies that are related to middle-aged adults. The age range of the middle-aged adults by each researcher varies. In this study, the age range of the adult is between 40 to 60 years old, which means the selected articles involve participants in this age range. A total of 22 articles were selected that involved middle-aged adults. In the field of language learning, two papers were identified. From these articles, it is found that the study of mobile learning with middle-aged adults is widely conducted in education area. The use of mobile apps in healthcare is also considered important, as this area is also the focus of researchers. The remaining articles are related to the study of user requirements, usability, and the design and development of mobile apps for middle-aged adults.

11 Research methodology

Research methodology is the main key to perform academic research and the strength of a research. The research methodology found used in the selected articles are Questionnaire, Interview, Systematic Literature Review, Literature Review, Reporting, Task Analysis and Experiment. Figure 7 shows the most popular research method used by a researcher in the field of mobile learning is questionnaire (n=24). This methodology has been used in studies that require a large amount of data from many respondents. The second most popular research method used in mobile learning research area is the Interview (n=9). There are also studies that require the use of multiple research methods to answer research questions.

Number of articles by research methodology

Table 9 shows the methodologies employed in the selected articles. However, articles [ 23 , 49 , 63 , 72 , 81 , 83 , 88 ], and [ 77 ] are not included because these articles are review articles.

In the literature, the questionnaire was found to be the most common method used by researchers for data collection involving many participants among young adults and middle-aged adults. On the other hand, the interview method only involved small groups of participants and was carried out in a short time period. Task analysis with interview method was used in three research studies to evaluate the usability, acceptance, and adoption. The studies were done towards young adults and older adults.

In the quantitative research method, the questionnaire instrument was used by the researchers to understand users’ motivation to use e-learning as a medium of learning [ 60 ]; the use of mobile technology and means of internet access [ 24 ]; awareness in using mobile devices towards mobile learning [ 14 ]; investigate the perception of students related to educational use of mobile phones [ 36 , 55 , 76 ]; investigate students’ behavioral intentions [ 3 ] and knowledge transfer among adult workers [ 52 ]; identify factors that affect the intention to use m-learning by learning the experience of the m-learning system by the participants [ 84 ], measure usability [ 19 , 75 ]; use and engagement with m-learning [ 2 ]; collaborative learning experience in social media environment [ 7 ], students’ immersion in the game and their perceived learning outcomes [ 33 ], and the use of mobile application [ 11 , 65 ].

Almost all researchers have formally collected demographic data such as gender, age, degree program, year of study, and race of the participants. There is only one study that collects data on working background because the participants in the study involved working adults. Amongst the selected articles , Al-Adwan, et al. [ 3 ] and Lazar, et al. [ 55 ], validated the content of the survey using experts before the questionnaire was distributed to participants. Dhanapal, et al. [ 19 ] and Huizenga, et al. [ 33 ] carried out a pilot test to identify the flaws and improves the questionnaire. All but one of the researchers used Point Likert scale, while MICAN [ 65 ] uses short answer questions, multiple choices with 1 or n answers, single or two-dimensional questions. The duration of data collection was less than 40 weeks depending on the targeted number of participants.

For the qualitative research method, data was collected via task analysis and interviews. Data were captured through multiple channels including video data analysis and interview content analysis. From the selected articles, it is found that task analysis and interview method were employed in the mobile learning domain to understand participants’ actions, performance, and usability towards mobile apps. The task activities that have been examined by researchers are navigation tasks (with task activity duration of 1.5 hours for older adults to complete searching and navigating using several mobile applications) [ 58 ], quiz activities using Kahoot application (held within 13 weeks for working adult and the task activities were perform in a classroom environment) [ 74 ], mobile devices usage training ( duration of 9 months of training intervention involving older people), and the task activities (e.g.: sending messages, video and audio calls ) was performed in a hospital [ 15 ]; Vocabulary learning [ 86 , 97 ]; games application with task duration of 5 to 20 minutes [ 71 ]; and usability testing [ 30 ]. Open-ended questions were used in the interview sessions [ 71 ] and all the audio recordings of the interviews were transcribed verbatim for analysis purposes [ 58 ].

In the experimental research design, two groups were created with specific condition applied. The treatment group and the control group involved in the experiment and questionnaire research approach can be seen in articles [ 9 , 16 , 38 , 92 ] as listed in Table 9 . For instance, in Bensalem [ 9 ], aims at investigating students' perceptions about the use of WhatsApp in learning vocabulary and in the study, twenty-one participants were randomly assigned to the experimental group. Participants from the experimental group are required to complete and submit their vocabulary assignments via WhatsApp. In the assignment, students are required to search the meaning of new words in a dictionary and build sentences using each word. On the other hand, participants from a control group need to submit the same homework assignment using the traditional paper and pencil method. Later, a questionnaire was distributed to the participants and the collected data was used to measure the participants’ perception of the use of WhatsApp in vocabulary learning.

12 Discussion

In this article, a systematic review was conducted to provide a thorough analysis on the methodologies adopted by researchers in mobile learning. The number of research papers in the year 2020 exceeds the number of research papers in the previous year. This could be due to the outbreak of the Covid-19 pandemic that triggered higher number of papers. During the pandemic, everyone had to work from home, and many organizations, including public and private higher learning institutions, were unable to carry out traditional teaching and learning activities. As a result, many studies or meetings were required to be conducted online.

The country with the highest number of research papers in the field of mobile learning is China with 11 articles. There is a lack of study in mobile learning that focuses on middle-aged adults. Out of 65 research papers, a total of 22 research papers are related to middle-aged adults whereby the distribution of research can be seen in countries such as in Czech Republic (n=1), United States (n=5), China (n=3), Germany (n=1). Singapore (n=2), Turkey (n=1), Brazil (n=1), Poland (n=1), Bangladesh (n=1), United Kingdom (n=2) and 2 articles did not mention the country in which the research was carried out. Studies related to middle-aged adults in Malaysia are not very encouraging, therefore the study of middle-aged adults in the field of mobile learning needs to be given more attention.

The articles selected in this systematic review were classified by research context to identify the focus of previous researchers on the use of mobile learning by middle-aged individuals. Overall, it was found that studies related to the adoption of mobile learning, mobile applications and mobile devices have gained significant attention among researchers, followed by studies related to the acceptance and mobile learning usage. However, studies on examining the adoption and effectiveness of mobile learning usage towards middle-aged adults are still lacking. Examining the effectiveness of mobile learning usage is crucial to provide guidance towards decision making and development work in the future.

The field of education is a popular field for researchers as it involves teachers and young adults who are mainly engaged in the learning environments. Research on middle-aged adults in the educational field is found in seven articles, where two of the articles focused on vocabulary learning. One study on Hadith learning for middle-aged adults, which has been classified as a study on spiritual learning under the educational research domain was also identified. The remaining four articles are respectively related to the use of game applications in teaching adults, the use of mobile devices in sharing information among adult workers, and the readiness of the teachers in adopting mobile learning in a classroom. Besides that, there is also a lack of research towards middle-aged adults in the area of mobile usability and user requirements. Research in the healthcare domain mostly involves older adults where most researchers extensively investigate the use of mobile devices and mobile applications towards healthy ageing and wellbeing.

The coding of the research methods was based on the methods reported by the researchers in their methodology section. Questionnaire is a popular instrument used across quantitative and mixed research approaches for data collection. The questionnaire developed by the researcher will be validated by the experts and tested before it was distributed accordingly to the targeted participants. Task analysis and interview approach can be used to observe the behavior of the users and to evaluate users’ feedback in the learning environment. Even though the method was not extensively used by the researchers from the selected literature focusing on middle-aged adults, this method to be employed in the mobile learning research to gain more insight on the effectiveness of mobile technologies in the learning environment of middle-aged adults was suggested.

Nowadays, almost everyone owns a smartphone, as smartphone prices have dropped significantly, making them affordable for more users. All smartphone users are capable to use most of the basic features of the mobile device, such as downloading applications from the Apple Store or Google Play. Given that middle-aged individuals are heavy smartphone users, it is critical to understand how users utilize mobile technology such as smartphones not just for work, leisure, and entertainment, but also for knowledge acquisition.

Middle-aged adults are self-directed, able to take responsibility for their learning, have a variety of experiences and backgrounds, and are motivated and willing to learn while effectively managing real-world situations. Hence, middle-aged adults can benefit from webinars and short courses delivered online. Therefore, more research should be conducted on mobile learning for middle-aged adults.

13 Conclusion and future work

The novelty of this study is that it contributes to the understanding of the research trends based on research context and methods used in research related to middle-aged adults in mobile learning. It is noted that there are still few studies that address the adoption and effectiveness of mobile apps in the area of religious orientation, especially among middle-aged adults. For instance, before the Covid-19 outbreak, middle-aged Muslims in Malaysia preferred to attend religious courses and trainings to improve their spiritual and religious orientation [ 96 ] based on face-to-face with teachers in a classroom. Therefore, it is critical to determine whether middle-aged adults intend and consent to religious and spiritual learning, such as learning the Quran to be conducted via mobile devices. It is hoped that the use of mobile learning will enable adults' lifelong learning to be improved and done continuously under any situation in the future. This study suggests further studies on middle-aged in the field of mobile learning as follows:

Skills and Knowledge Development

The use of mobile learning among middle-aged adults begins with an awareness and intention to use mobile devices. Generally, middle-aged adults who own smartphones, they already have skills to download apps from the Google Store or App Store and set security preferences. Hence, they must intend to use mobile learning to develop their skills and knowledge. This is because between the ages of 40 and 60, they are usually busy with their work while facing problems such as increasing concerns about health, death of a friend or relative, changes in wages/salaries, and concerns about changes in physical appearance. Therefore, middle-aged adults need to seek knowledge that will make them be satisfied and enable them to lead a better and healthier lifestyle. For example, middle-aged Muslims can learn to understand the Quran through mobile learning to achieve a better quality of life because the Quran is the final revelation and book from Allah s.w.t to humankind as guidance and direction to the right path.

Mobile Learning Application with Multimedia

Mobile learning Application with multimedia plays a great role in motivating learners in learning via digital devices such as smartphones. It is crucial to design and develop mobile learning apps with appropriate multimedia elements such as texts, images, icons, and animations that meet the needs of middle-aged adult learners. In addition, middle-aged adults need to be helped to increase their motivation to learn and improve their memory performance in vocabulary memorization. Therefore, for future work, mobile app development needs to be carefully developed based on user needs especially for the multimedia elements such as the text, graphic, video and animation.

Mobile Learning Application and Quick Assessment

Assessment is a critical component of learning since it demonstrates progress. Because most of the learning occurs online and involves many students, a teacher develops easy assessment tools and procedures that enable them to rapidly assess their students’ learning progress. Numerous game-based apps have aided in the facilitation of teaching and may be used to measure a student learning progress. Additionally, to make mobile learning more interesting, game-based elements have been used to improve the students’ engagement and enjoyment in learning. For instance, Kahoot is a game-based technological platform that can be accessed using, for instance, a smart device or a laptop. The game-based learning application (app) can benefit working adults who are adult learners with diverse learning abilities. Chunking method was used to break down complex concepts into smaller parts in the form of multiple-choice questions. The students’ learning process is tested and corrected, in real time, through the statistics which are generated from this chunking process. Kahoot creates a safe environment for students to make mistakes through multiple choice questions, and yet relearn it without being judged by their peers. However, the drawback of Kahoot is, it does not adequately support the learning experience of adult learners Seah [ 74 ]. Therefore, in the future, the development of mobile learning apps for middle-aged adults might include a gamification aspect that allows easy assessment for self-monitoring of learning progress.

Research Methodology

The finding of this study shows that questionnaire is a popular instrument used across quantitative and mixed research approaches for data collection. The questionnaire developed by the researcher will be validated by the experts and tested before it was distributed accordingly to the targeted participants. However, based on the research context and methodologies found in the literature, the study on middle-aged adults was not getting the enough intention among researchers. Furthermore, as Covid-19 pandemic has impacted people’s life, many are reluctant to participate in answering questionnaires as they may be unmotivated due to job loss, adaptation to new norms or due to the death of their family members. Therefore, in the future, it is hereby recommended that a contribution back to society such as given some tokens to the participants [ 66 , 90 ] can be practiced in the research methodology. Besides that, a researcher also can conduct a free intensive course of related field to a group of respondents to upgrade the lifestyle and well-being among respondents. Hence, this can increase public participation in research, especially when involving busy and elderly respondents and at the same time the respondents can learn new knowledge while also contributing to the research study.

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Mohtar, S., Jomhari, N., Mustafa, M.B. et al. Mobile learning: research context, methodologies and future works towards middle-aged adults – a systematic literature review. Multimed Tools Appl (2022). https://doi.org/10.1007/s11042-022-13698-y

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Published on 17.4.2024 in Vol 26 (2024)

Mobile Apps to Support Mental Health Response in Natural Disasters: Scoping Review

Authors of this article:

• Nwamaka Alexandra Ezeonu 1 , MBBS, MSc, MBA   ; 
• Attila J Hertelendy 2, 3 , BSc, MHS, MSc, PhD   ; 
• Medard Kofi Adu 4 , BSc, MSc   ; 
• Janice Y Kung 5 , BCom, LMIS   ; 
• Ijeoma Uchenna Itanyi 1, 6, 7 , MBBS, MPH   ; 
• Raquel da Luz Dias 4 , BSc, MSc, PhD   ; 
• Belinda Agyapong 8 , HDip, BSc, MEd   ; 
• Petra Hertelendy 9 , BS   ; 
• Francis Ohanyido 10 , MBBS, MBA, MPH   ; 
• Vincent Israel Opoku Agyapong 4 , BSc, PGD, MBChB, MSc, MD, PhD   ; 
• Ejemai Eboreime 4 , MBBS, MSc, PhD  

1 Center for Translation and Implementation Research, College of Medicine, University of Nigeria, Nsukka, Nigeria

2 Department of Information Systems and Business Analytics, College of Business, Florida International University, Miami, FL, United States

3 Department of Emergency Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States

4 Department of Psychiatry, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada

5 Geoffrey and Robyn Sperber Health Sciences Library, University of Alberta, Edmonton, AB, Canada

6 Department of Community Medicine, University of Nigeria, Enugu, Nigeria

7 Department of Public Health Sciences, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

8 Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada

9 Department of Psychology, Florida State University, Tallahassee, FL, United States

10 West African Institute of Public Health, Abuja, Nigeria

Corresponding Author:

Ejemai Eboreime, MBBS, MSc, PhD

Department of Psychiatry

Faculty of Medicine

Dalhousie University

5909 Veterans' Memorial Lane

8th Floor Abbie J Lane Memorial Building, QEII Health Sciences Centre

Halifax, NS, B3H 2E2

Phone: 1 9024732479

Email: [email protected]

Background: Disasters are becoming more frequent due to the impact of extreme weather events attributed to climate change, causing loss of lives, property, and psychological trauma. Mental health response to disasters emphasizes prevention and mitigation, and mobile health (mHealth) apps have been used for mental health promotion and treatment. However, little is known about their use in the mental health components of disaster management.

Objective: This scoping review was conducted to explore the use of mobile phone apps for mental health responses to natural disasters and to identify gaps in the literature.

Methods: We identified relevant keywords and subject headings and conducted comprehensive searches in 6 electronic databases. Studies in which participants were exposed to a man-made disaster were included if the sample also included some participants exposed to a natural hazard. Only full-text studies published in English were included. The initial titles and abstracts of the unique papers were screened by 2 independent review authors. Full texts of the selected papers that met the inclusion criteria were reviewed by the 2 independent reviewers. Data were extracted from each selected full-text paper and synthesized using a narrative approach based on the outcome measures, duration, frequency of use of the mobile phone apps, and the outcomes. This scoping review was reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews).

Results: Of the 1398 papers retrieved, 5 were included in this review. A total of 3 studies were conducted on participants exposed to psychological stress following a disaster while 2 were for disaster relief workers. The mobile phone apps for the interventions included Training for Life Skills, Sonoma Rises, Headspace, Psychological First Aid, and Substance Abuse and Mental Health Services Administration (SAMHSA) Behavioural Health Disaster Response Apps. The different studies assessed the effectiveness or efficacy of the mobile app, feasibility, acceptability, and characteristics of app use or predictors of use. Different measures were used to assess the effectiveness of the apps’ use as either the primary or secondary outcome.

Conclusions: A limited number of studies are exploring the use of mobile phone apps for mental health responses to disasters. The 5 studies included in this review showed promising results. Mobile apps have the potential to provide effective mental health support before, during, and after disasters. However, further research is needed to explore the potential of mobile phone apps in mental health responses to all hazards.

Introduction

Rising global average temperatures and associated changes in weather patterns result in extreme weather events that include hazards such as heatwaves, wildfires, hurricanes, floods, and droughts [ 1 ]. These extreme events linked to climate change are resulting in overlapping and so-called cascading disasters leading to record numbers of “billion dollar” disasters with significant losses of lives and property [ 2 , 3 ]. In 2021 alone, approximately 10,000 fatalities caused by disasters were reported globally, while the economic loss was estimated at approximately US $343 billion [ 4 ]. Disasters are predicted to become more recurring as a result of the impact of human activities such as burning fossil fuels and deforestation, which release greenhouse gases into the atmosphere that trap heat and cause global temperatures to rise [ 5 ].

These catastrophes can adversely affect physical health, mental health, and well-being in both the short and long term as a result of changes due to the political and socioeconomic content, evacuations, social disruption, damage to health care facilities, and financial losses [ 6 - 10 ]. It is estimated that about 33% of people directly exposed to natural disasters will experience mental health sequelae such as posttraumatic stress disorders (PTSDs), anxiety, and depression, among others [ 11 , 12 ].

There is growing recognition of the importance of incorporating mental health into medical and emergency aspects of disaster response [ 12 , 13 ]. However, in contrast to most medical response strategies that are largely curative, mental health response to disasters is predicated on the principles of preventive medicine, thus, emphasizing health promotion, disaster prevention, preparedness, and mitigation [ 14 ]. The strategies of mental health response span across primary prevention (mitigating the risk of ill health before it develops), secondary prevention (early detection and intervention), and tertiary prevention (managing established ailment and averting further complications) [ 15 ].

Mobile health (mHealth) technology has shown great promise in mental health and has been applied across the 3 levels of prevention [ 16 - 20 ]. For example, SMS text messaging and mobile apps have been developed to promote mental health awareness among young people and older adults (primary prevention) [ 21 ]. Additionally, during the COVID-19 pandemic, mHealth was deployed at the population level in Canada to screen for symptoms of anxiety and depression (secondary prevention) [ 22 ]. In addition, mHealth interventions were deployed to support first responders and essential workers during the pandemic [ 23 , 24 ]. Further, the technology has been deployed for therapeutic purposes in patients diagnosed with mental health conditions while simultaneously providing support against complications such as suicidal ideation (tertiary prevention) [ 25 ].

Although videoconferencing and phone calls can be used for mental health conditions, mobile apps provide more mobility and accessibility, are interactive, more adaptable to users’ routines, and can be used repeatedly [ 26 , 27 ]. While numerous academic studies have been conducted on the app of mHealth in the preventive and curative management of mental health conditions in clinical, community, and public health settings, including epidemic response and control, little is known about the use of mobile apps in the mental health components of natural disaster management. This scoping review aims to fill this gap in the literature by mapping where and how mobile apps have been used as part of natural disaster mental health response strategies.

This scoping review was reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) [ 28 ]. The PRISMA-ScR checklist is available in Multimedia Appendix 1 . The protocol was not registered.

Search Strategy

A medical librarian (JYK) collaborated with the research team to identify relevant keywords and subject headings for the review, such as mHealth or m-health; mobile health or mobile applications; public health emergency, disaster, or catastrophe; and flood, earthquake, or hurricane. Equipped with this knowledge, the librarian developed and executed comprehensive searches in 6 electronic databases, including Ovid MEDLINE, Ovid Embase, APA PsycInfo, CINAHL, Scopus, and Web of Science Core Collection. The search was conducted on June 30, 2022, and was limited to the English language. The full search strategies are available in Multimedia Appendix 2 .

Inclusion and Exclusion Criteria

We included papers that applied mobile apps for mental health responses to disasters. Papers were included if the study participants were persons affected by a natural disaster (setting), the intervention included using a mobile phone app, and the outcome included the assessment of a mental health problem. Studies in which participants were exposed to a man-made disaster were included if the sample also included some participants exposed to a natural disaster. The mental health conditions included were stress, anxiety, depression, and PTSD. Only full-text studies published in English were included. Studies that did not include any intervention with a mobile app for mental health, those focused on videoconferencing or phone calls, and papers on protocols, trial registration, or review were excluded.

Selection of Studies

The search identified papers that were retrieved from the databases. After removing duplicates, the initial titles and abstracts of the unique papers were screened by 2 independent review authors based on the inclusion criteria in a web-based tool called Covidence (Veritas Health Innovation Ltd) [ 29 ]. Full texts of the selected papers that met the inclusion criteria were reviewed by the 2 independent reviewers. The research team resolved disagreements through discussion. The bibliographies from the included studies were also reviewed to identify additional studies for inclusion.

Data Extraction and Synthesis

Data from each selected full-text paper were extracted into a data extraction form developed by the research team. The data included the author and year of publication, country of study, study design, number of participants, type of natural disaster, name of the mobile app, duration of use of the app, outcome measures, and the study’s findings. These data were synthesized using a narrative approach based on the outcome measures, the duration, frequency of use of the mobile apps, and the outcomes.

Search Results

Of the 1532 papers retrieved from the searches, 976 unique papers had their titles and abstracts screened after deduplication. A total of 38 papers were moved to full-text screening, and data were extracted from 5 papers [ 30 - 34 ] ( Figure 1 ). Table 1 shows the summary of the details of the papers.

a TLS: Training for Life Skills.

b PTSD: posttraumatic stress disorder.

c MBSR: Mindfulness-Based Stress Reduction.

d PFA: Psychological First Aid.

e SAMHSA: Substance Abuse and Mental Health Services Administration.

Characteristics of Included Studies

Of the 5 studies included in this review, 3 (60%) were conducted in the United States [ 30 , 31 , 34 ], while 2 (40%) were conducted in South Korea [ 32 , 33 ]. All studies used different study designs. A total of 3 studies used a quasi-experimental design—the first, a single group postexperiment with 22 participants [ 32 ]; the second, a multiple-baseline single case experimental design with 7 participants [ 30 ], while the third study used a 1-group pre- and posttest design with 318 participants [ 31 ]. The Training for Life Skills (TLS) app study had only a posttest following the use of the app [ 32 ]; the other 2 had baseline and follow-up measurements with the Sonoma Rises app study having, in addition, preintervention and postintervention measurements. The Psychological First Aid (PFA) study was designed as a qualitative study, while the Substance Abuse and Mental Health Services Administration (SAMHSA) study used a mixed methods descriptive design.

Characteristics of the Population

The TLS, Sonoma, and Headspace apps were designed for disaster survivors, while the PFA and SAMHA apps were designed to support disaster relief workers. The TLS app study was administered to adults with a median age of 32 years. Participants of the Sonoma Rises app study had a mean age of 16 (SD 0.98) years, while participants of the Headspace app study had a mean age of 46.1 (SD 10) years. The TLS app study focused on all types of disasters; the Sonoma Rises study focused on adolescents exposed to wildfires, while the Headspace app focused on women who experienced hurricanes and deep-water oil spillage. The PFA study involved 19 disaster health care workers who first underwent disaster simulation training using the mobile app.

Characteristics of the Mobile App Interventions

The included studies revealed several mobile phone apps used as interventions. The first, the TLS app, was used as a psychological first aid program for disaster survivors with content on information, psychological healing, and mood change [ 32 ]. The second was the Sonoma Rises app, a Health Insurance Portability and Accountability Act (HIPAA)–compliant, cloud-based mobile app with daily push notifications as reminders designed to help survivors of wildfires or other disasters to find their new routines, build resilience, and increase well-being. The app included 6 self-paced content sections, psychoeducation, and direct connections to free and local mental health care services. The third was the Headspace app for a mindfulness-based stress reduction program that included a series consisting of 10 sessions designed to be used for about 10 minutes per day. The SAMHSA Disaster App equips behavioral health providers to respond to all kinds of traumatic incidents by enabling them to readily access disaster-specific information and other important materials directly on their mobile devices [ 34 ]. The PFA mobile app provided evidence-based information and tools for disaster workers to prepare for, execute, and recover from providing psychological first aid during disasters. Accessibility via smartphones and the inclusion of multimedia interventions and assessments tailored for disaster contexts were key features enabling its use integrated with the simulation training [ 33 ].

Frequency and Duration of App Use

The 3 survivor-based apps had variations in the duration of the intervention (app use), which were 8 weeks, at least 5 times a week, frequency of use per day not specified [ 32 ]; 4 weeks for 10 minutes per day [ 30 ]; and 6 weeks for 5-10 minutes per day [ 31 ]. Both the TLS app and the Sonoma Rises app studies had weekly follow-up assessments. The different interventions were applied at least a year following the disasters. Participants in the Sonoma Rises app study used the app on an average of 17 (SD 8.92) days and visited the app an average of 43.50 (SD 30.56) times, with an average session lasting 56.85 (SD 27.87) seconds. The mean time spent on the app was 35.77 (SD 30.03) minutes, while for the TLS app study, the median time spent on the app over the 8 weeks of use was 200-399 minutes. Participants used the Headspace app an average of 24 (SD 36) days and logged in an average of 36 (SD 80) times. There was no description of the frequency and duration of use for the relief worker apps.

Effectiveness Outcomes

Effectiveness outcomes refer to the effects or impact of an intervention or program on the intended outcomes or goals. Different measures were used to assess the effectiveness of the apps’ use as either the primary or secondary outcome. Emotional quotients (emotional stability), basic rhythm quotients (brain stability), alpha-blocking rates (increased positive mood), and brain quotients assessed using electroencephalogram (EEG)–measured brainwave activities adjusted for self-reported app use time were used in the TLS app study [ 32 ]. The Headspace app study assessed effectiveness using a combination of measures such as trait mindfulness using a 15-item Mindful Attention Awareness Scale (MAAS)—trait version; depressive symptoms using the Center for Epidemiologic Studies Depression Scale-10 (CESD-10); perceived stress with the Perceived Stress Scale, 4-item version (PSS-4); and sleep quality using the Pittsburgh Sleep Quality Index (PSQI) [ 31 ]. The Sonoma Rises app study measured efficacy using daily ratings of anxiety and fear, weekly measures of post-traumatic stress symptoms using the Child PTSD Symptom Scale (CPSS-5) for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition ( DSM-5 ), internalizing and externalizing symptoms using the Behaviour and Feelings Survey (BFS), psychosocial functioning using the Ohio Scale for Youth—Functioning subscale (OSY), and measures of anxiety (Generalized Anxiety Disorder-7 [GAD-7]), depression (Patient Health Questionnaire-9 [PHQ-9]), well-being—Warwick-Edinburgh Mental Well-being Scale (WEMWBS), sleep (Insomnia—Severity Index [ISI]), academic engagement (Student Engagement Instrument [SEI]), and perceived social support (Wills’ Social Support Scale [WSSS]) [ 30 ].

All 3 survivor-based apps were found to have positive benefits in addressing mental health issues among persons exposed to natural disasters. The TLS mobile app was shown to be effective in increasing positive and decreasing negative psychological factors according to app use time. The TLS mobile apps’ use had a significant effect on the emotional quotients (β=.550; P <.008), explanatory power (EP) was 30%, had a significant positive effect on the basic rhythm quotient (left brain: β=.598; P <.003; EP 35; right brain: β=.451; P <.035; EP 20%). Additionally, it had a significant positive effect on the alpha-blocking rate (left brain: β=.510; P <.015; EP 26%; right brain: β=.463; P <.035, EP 21%); and a significant positive effect on the brain quotient (β=.451; P <.035; EP 20%) [ 16 ]. The Headspace app had a positive effect on depression (odds ratio [OR] 0.3, 95% CI 0.11-0.81), physical activity (OR 2.8, 95% CI 1.0-7.8), sleep latency (OR 0.3, 95% CI 0.11-0.81), sleep duration (OR 0.3, 95% CI 0.07-0.86), and sleep quality (OR 0.1, 95% CI 0.02-0.96); however, there was no change in mindfulness scores from baseline to follow-up. For the Sonoma Rises app, no significant effects were observed for the clinical and functional outcomes because the longitudinal part of the study was affected by limited statistical power as a result of small sample size and historical confounds that made the participants miss data submission. However, visual inspection of individual data following the intervention showed downward trends across the study phases for daily levels of anxiety, fearfulness, and individual posttraumatic stress symptom severity.

For the PFA app, the qualitative study explored disaster health workers’ experiences with simulation training using focus group discussions. A total of 19 participants engaged in disaster scenarios with standardized patients, using a PFA app for guidance. Workers valued the practical educational approach, felt increased self-efficacy to support survivors, and identified areas for enhancing simulations and app tools to optimize effectiveness.

Implementation Outcomes

Implementation outcomes refer to the effects of an intervention or program implementation on various aspects of the implementation process, such as the fidelity of implementation, acceptability, adoption, feasibility, and maintainability. In the papers reviewed, feasibility was assessed using enrollment, program participation, and retention. Acceptability was measured using how well participants liked the app using a rating scale, how much of the app program was completed, the biggest barriers, and whether the app would be recommended to others. Data on characteristics of app use (engagement) were measured using the total number of log ins, average log ins per program completer, platform used (iOS, Android, or web-based), day of week of use (weekday vs weekend), and time of day of use (in 4-hour blocks) [ 30 , 31 ].

The Headspace app was reported to be cost-effective to implement and easy to use [ 31 ]. For engagement, only 14% (43/318) of the enrolled women used the app. The level of engagement with the app was high, with 72% (31/43) of participants completing some or all the sessions. Retention was also high with 74% (32/43) of the participants completing the follow-up survey. Lack of time was cited as the main barrier to using the app for 37% (16/43) of users and 49% (94/193) of nonusers. The majority of the users (32/43, 74%) reported high levels of satisfaction with the app. Acceptability was also high, with most participants (32/43, 74%) reporting that they liked the app and 86% (37/43) reporting that they would recommend it to others. Characteristics of app use showed that of the 1530 log ins, most participants (n=1191, 78%) used the iOS platform, mainly on weekdays (n=1147, 75%) and at different times of day mostly from noon to 4 PM (n=375, 25%).

Sonoma Rises was found to be feasible in terms of engagement and satisfaction among teens with high levels of disaster-related posttraumatic stress symptoms [ 30 ]. The self-assessment and data visualization features of the Sonoma Rises app strongly appealed to all the participants, and they were willing to recommend the app to their friends. Self-satisfaction with the mobile app was rated as extremely high (mean 8.50, SD 0.58, on a scale of 0 to 10, with 10 as totally satisfied). The participants agreed or strongly agreed to recommend this intervention to a friend. The participants found the intervention helpful (mean 2, SD 0.82); had the content, functions, and capabilities they needed (mean 3, SD 1.12); and were satisfied with how easy it was to use the app (mean 2, SD 0), on a scale of 1 to 5 with 1 as strongly agree and 5 as strongly disagree. In the qualitative feedback, to make the use of the app better, the participants suggested more notifications to return to the app and the use of the app immediately after a disaster. Implementation outcome was not an objective of the TLS app, hence, none was reported.

Other Mobile Apps With Potential Use in Disasters

Some mobile apps not meeting the inclusion criteria showed promise for supporting mental health in disasters. PTSD Coach provides tools for managing PTSD symptoms [ 35 ]. Though not disaster-specific, its psychoeducation, symptom tracking, and coping strategies could aid survivors. Similarly, COVID Coach was designed to help manage pandemic-related stress and anxiety [ 36 ]. These apps are summarized in Table 2 .

a PTSD: posttraumatic stress disorder.

Principal Findings

This review sought to identify and map the use of mobile apps for the mental health component of natural disaster management. We found only 5 studies meeting the inclusion criteria. The scarcity of published literature in this area suggests that mobile apps have not been extensively used in mental health responses to natural disasters. Academic studies on the public’s use of mobile technologies in disaster management are still nascent [ 37 ], but there has been increased interest in developing and deploying digital technology and mobile apps by governments and nonstate actors as part of disaster preparedness and response [ 38 , 39 ]. A recent systematic review found that there is a lack of mental health preparedness in most countries when it comes to disasters [ 40 ]. The 5 studies included in our scoping review confirmed this gap and further demonstrated that mobile apps can provide mental health support to disaster-affected individuals and communities. The studies found that the use of mobile apps was associated with improvements in mental health outcomes, such as decreased anxiety and depression symptoms and increased resilience. The reviewed studies also suggest that mobile apps can be effective in delivering psychoeducation and coping skills training to disaster-affected individuals. A 2017 scoping review found that mobile apps have been largely used for communication purposes in disaster management [ 37 ]. The scope of use was classified into 5 categories which are not mutually exclusive. These categories are (1) crowdsourcing (organize and collect disaster-related data from the crowd), (2) collaborating platforms (serve as a platform for collaboration during disasters), (3) alerting and information (disseminate authorized information before and during disasters), (4) collating (gather, filter, and analyze data to build situation awareness), and (5) notifying (for users to notify others during disasters) [ 37 ].

Some authors classify disaster response into 3 phases: preparedness, response, and mitigation [ 41 ]. The studies included in this review exclusively examined the use of mobile apps during the recovery phase of disaster management. However, none of the studies explored the potential of mobile apps during the preparedness or response phases of disaster management. By addressing this gap, future research could help to provide more comprehensive and effective strategies for the use of mobile apps throughout all phases of disaster management. Examples of potential opportunities are demonstrated in Figure 2 .

Preparedness Phase

Mobile apps can play a critical role as primary prevention interventions by raising awareness and promoting mental health literacy in the community in preparation for natural disasters. These apps can provide information on common mental health problems that may arise during and after disasters and offer tips on staying mentally healthy. For example, apps can include psychoeducation modules on coping skills, stress reduction, and self-care techniques, as well as information on how to prepare for a disaster and what steps to take to protect one’s mental health during and after a disaster. The use and effectiveness of mobile apps in health literacy have been demonstrated in the literature [ 19 ], thus providing a foundation for adaptation in disaster management.

Response Phase

Mobile apps can be used to connect people in need of mental health support with mental health professionals or other resources. For example, apps can provide information on emergency hotlines, crisis intervention services, and support groups. This was demonstrated as effective during the COVID-19 pandemic [ 42 ]. Mobile apps can also provide coping strategies and techniques to manage stress and anxiety in response to other natural disasters [ 34 ]. In this scoping review, we found that 3 apps had positive benefits in addressing mental health issues among persons exposed to natural disasters.

Recovery Phase

As part of secondary and tertiary prevention strategies, mobile apps can provide valuable ongoing support to those affected by disasters. For secondary prevention, mobile apps can be designed to support early detection and intervention for mental health problems after a natural disaster. These apps can include screening tools to identify common mental health issues such as anxiety, depression, and PTSD and offer appropriate referral pathways [ 43 ]. Additionally, apps can provide symptom-tracking tools to help individuals monitor their mental health over time [ 43 ]. For tertiary prevention, mobile apps can support the ongoing management of established mental health problems after a natural disaster. For example, apps can provide evidence-based psychotherapy interventions, such as cognitive-behavioral therapy, to help individuals manage their symptoms [ 44 ]. They can also connect individuals with support groups and peer-to-peer networks to provide additional emotional support and help individuals connect with others who have experienced similar challenges. Furthermore, mobile apps can offer self-help tools, such as meditation exercises and mood tracking, to help people cope with the ongoing mental health effects of the disaster. They can also provide information on local mental health services and support groups, helping individuals access the resources they need to manage their mental health.

General Mental Health Apps Show Promise for Disaster Response

While not specifically designed for disaster contexts, some mobile apps demonstrate strategies to support mental health that could aid disaster survivors. PTSD Coach delivers PTSD psychoeducation, symptom tracking tools, coping skills training, and crisis resource access—elements that could help survivors experiencing common postdisaster issues like trauma or loss [ 35 ]. Though it was tailored for veterans and civilians with PTSD, 1 study found it improved users’ depression and functioning. Similarly, COVID Coach offered pandemic-related stress management through symptom tracking, healthy coping recommendations, and crisis line referrals [ 36 ]. By leveraging the scalability of mobile apps, COVID Coach reached many struggling during a global crisis. These examples illustrate that apps may provide accessible, far-reaching mediums for disseminating disaster mental health resources—even without disaster-specific tailoring. Research should further explore adapting evidence-based, general mental health apps for disaster contexts or incorporate elements of them into future disaster response tools. With mental health needs magnified during disasters, mobile apps with thoughtful design show promise in expanding access to psychosocial support.

There are several potential limitations when using mobile apps for mental health responses to disasters. One of the main concerns is the accessibility of these apps, as not all members of the affected communities may have access to smartphones or internet connectivity. Furthermore, language and cultural barriers may prevent effective use. Another potential limitation is the quality and accuracy of the information provided. Without proper oversight, some apps may provide misinformation or inaccurate advice, which could exacerbate mental health issues. In addition, privacy concerns around collecting and storing sensitive data must be addressed.

Barriers like lack of mobile devices and internet access can impede adoption, especially in marginalized areas. Apps not designed for low literacy users or that are only available in certain languages could also limit accessibility. Concerns around privacy and security may deter some individuals. However, smartphone ubiquity globally enables use by vulnerable groups. Government agencies and nongovernmental organizations (NGOs) can promote adoption by integrating vetted apps into disaster protocols and funding dissemination. Developing apps with stakeholders and prelaunch user testing also facilitate uptake. Monitoring user feedback allows for ongoing optimization and troubleshooting of barriers. Cultural tailoring to address stigma and use local beliefs further enables implementation success. Finally, limited evidence-based research into app effectiveness highlights the need for more rigorous evaluation and testing of mobile apps for disaster mental health response.

This scoping review has certain methodological limitations that should be considered while interpreting its results. First, the search was restricted to 6 electronic databases and only English-language papers were considered. We also searched MEDLINE and not PubMed, and these may have led to the omission of some relevant studies. Second, the study focused on mobile phone apps for mental health response to disasters, disregarding other types of technology that could also be used in disaster management such as telehealth, SMS text messaging, and emails. Moreover, since the study included only 5 papers, it may not offer a comprehensive overview of the use of mobile phone apps in disaster response strategies. There is the possibility of the existence of apps not yet published in academic literature. Fourth, the nonuse of a control group in the design of the studies makes it difficult to determine whether the observed effects were entirely due to the use of the apps or other characteristics of the participants that predisposed them to use the apps. Fifth, the small sample sizes for the studies mean they require caution with generalization. Despite these limitations, the review provides valuable insights into the use of mobile apps in disaster response and serves as a useful resource for developing contextually appropriate mobile apps for disaster management. Last, our study focused on natural disasters, further research should examine the role of apps in supporting mental health in conflict and complex emergencies such as wars, outbreaks of violence, and complex political conflict situations [ 45 ].

Conclusions

This scoping review found that mobile apps have not been extensively used in mental health responses to natural disasters, with only 5 studies meeting the inclusion criteria. However, the studies included in this review demonstrate that mobile apps can be useful in providing mental health support to disaster-affected individuals, as well as equip disaster responders. There is a critical gap identified in this study, as none of the studies investigated the use of mobile apps for potential victims in the preparedness or response phases of disaster management. We, therefore, recommend that mobile apps be integrated into the various phases of disaster management as part of mental health response. Additionally, it is important to ensure that these apps are accessible to all members of the community, taking into account cultural, linguistic, and other factors that may impact their effectiveness. Mobile apps have great potential to provide valuable ongoing support to those affected by disasters, and they can be a valuable resource in disaster management, helping people cope with the mental health effects of disasters and connecting with the necessary support services.

The findings from this scoping review have important implications for policy makers, disaster management professionals, and mental health practitioners. There is a clear need for policies and protocols that integrate evidence-based mobile apps into mental health disaster planning and response. Disaster agencies should invest in developing, evaluating, and widely disseminating mobile apps specifically designed to mitigate psychological trauma before, during, and after catastrophic events. Mental health professionals can incorporate vetted mobile apps into their standard of care for at-risk disaster survivors. Going forward, a collaborative approach across these groups will be essential to leverage mobile technology in building community resilience and addressing the rising mental health burdens in an era defined by climate change–fueled natural disasters.

Acknowledgments

This work was funded by the Department of Psychiatry, Dalhousie University, Halifax, Canada. The funder was not involved in the conceptualization or implementation of the study, nor the decision to publish the findings.

Conflicts of Interest

None declared.

The PRISMA-SCR checklist. PRISMA-SCR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

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Abbreviations

Edited by G Eysenbach; submitted 13.06.23; peer-reviewed by T Benham, K Goniewicz, R Konu, J Ranse, P Moreno-Peral; comments to author 10.01.24; revised version received 25.02.24; accepted 23.03.24; published 17.04.24.

©Nwamaka Alexandra Ezeonu, Attila J Hertelendy, Medard Kofi Adu, Janice Y Kung, Ijeoma Uchenna Itanyi, Raquel da Luz Dias, Belinda Agyapong, Petra Hertelendy, Francis Ohanyido, Vincent Israel Opoku Agyapong, Ejemai Eboreime. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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A new way to detect radiation involving cheap ceramics

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The radiation detectors used today for applications like inspecting cargo ships for smuggled nuclear materials are expensive and cannot operate in harsh environments, among other disadvantages. Now, in work funded largely by the U.S. Department of Homeland Security with early support from the U.S. Department of Energy, MIT engineers have demonstrated a fundamentally new way to detect radiation that could allow much cheaper detectors and a plethora of new applications.

They are working with Radiation Monitoring Devices , a company in Watertown, Massachusetts, to transfer the research as quickly as possible into detector products.

In a 2022 paper in Nature Materials , many of the same engineers reported for the first time how ultraviolet light can significantly improve the performance of fuel cells and other devices based on the movement of charged atoms, rather than those atoms’ constituent electrons.

In the current work, published recently in Advanced Materials , the team shows that the same concept can be extended to a new application: the detection of gamma rays emitted by the radioactive decay of nuclear materials.

“Our approach involves materials and mechanisms very different than those in presently used detectors, with potentially enormous benefits in terms of reduced cost, ability to operate under harsh conditions, and simplified processing,” says Harry L. Tuller, the R.P. Simmons Professor of Ceramics and Electronic Materials in MIT’s Department of Materials Science and Engineering (DMSE).

Tuller leads the work with key collaborators Jennifer L. M. Rupp, a former associate professor of materials science and engineering at MIT who is now a professor of electrochemical materials at Technical University Munich in Germany, and Ju Li, the Battelle Energy Alliance Professor in Nuclear Engineering and a professor of materials science and engineering. All are also affiliated with MIT’s Materials Research Laboratory

“After learning the Nature Materials work, I realized the same underlying principle should work for gamma-ray detection — in fact, may work even better than [UV] light because gamma rays are more penetrating — and proposed some experiments to Harry and Jennifer,” says Li.

Says Rupp, “Employing shorter-range gamma rays enable [us] to extend the opto-ionic to a radio-ionic effect by modulating ionic carriers and defects at material interfaces by photogenerated electronic ones.”

Other authors of the Advanced Materials paper are first author Thomas Defferriere, a DMSE postdoc, and Ahmed Sami Helal, a postdoc in MIT’s Department of Nuclear Science and Engineering.

Modifying barriers

Charge can be carried through a material in different ways. We are most familiar with the charge that is carried by the electrons that help make up an atom. Common applications include solar cells. But there are many devices — like fuel cells and lithium batteries — that depend on the motion of the charged atoms, or ions, themselves rather than just their electrons.

The materials behind applications based on the movement of ions, known as solid electrolytes, are ceramics. Ceramics, in turn, are composed of tiny crystallite grains that are compacted and fired at high temperatures to form a dense structure. The problem is that ions traveling through the material are often stymied at the boundaries between the grains.

In their 2022 paper, the MIT team showed that ultraviolet (UV) light shone on a solid electrolyte essentially causes electronic perturbations at the grain boundaries that ultimately lower the barrier that ions encounter at those boundaries. The result: “We were able to enhance the flow of the ions by a factor of three,” says Tuller, making for a much more efficient system.

Vast potential

At the time, the team was excited about the potential of applying what they’d found to different systems. In the 2022 work, the team used UV light, which is quickly absorbed very near the surface of a material. As a result, that specific technique is only effective in thin films of materials. (Fortunately, many applications of solid electrolytes involve thin films.)

Light can be thought of as particles — photons — with different wavelengths and energies. These range from very low-energy radio waves to the very high-energy gamma rays emitted by the radioactive decay of nuclear materials. Visible light — and UV light — are of intermediate energies, and fit between the two extremes.

The MIT technique reported in 2022 worked with UV light. Would it work with other wavelengths of light, potentially opening up new applications? Yes, the team found. In the current paper they show that gamma rays also modify the grain boundaries resulting in a faster flow of ions that, in turn, can be easily detected. And because the high-energy gamma rays penetrate much more deeply than UV light, “this extends the work to inexpensive bulk ceramics in addition to thin films,” says Tuller. It also allows a new application: an alternative approach to detecting nuclear materials.

Today’s state-of-the-art radiation detectors depend on a completely different mechanism than the one identified in the MIT work. They rely on signals derived from electrons and their counterparts, holes, rather than ions. But these electronic charge carriers must move comparatively great distances to the electrodes that “capture” them to create a signal. And along the way, they can be easily lost as they, for example, hit imperfections in a material. That’s why today’s detectors are made with extremely pure single crystals of material that allow an unimpeded path. They can be made with only certain materials and are difficult to process, making them expensive and hard to scale into large devices.

Using imperfections

In contrast, the new technique works because of the imperfections — grains — in the material. “The difference is that we rely on ionic currents being modulated at grain boundaries versus the state-of-the-art that relies on collecting electronic carriers from long distances,” Defferriere says.

Says Rupp, “It is remarkable that the bulk ‘grains’ of the ceramic materials tested revealed high stabilities of the chemistry and structure towards gamma rays, and solely the grain boundary regions reacted in charge redistribution of majority and minority carriers and defects.”

Comments Li, “This radiation-ionic effect is distinct from the conventional mechanisms for radiation detection where electrons or photons are collected. Here, the ionic current is being collected.”

Igor Lubomirsky, a professor in the Department of Materials and Interfaces at the Weizmann Institute of Science, Israel, who was not involved in the current work, says, “I found the approach followed by the MIT group in utilizing polycrystalline oxygen ion conductors very fruitful given the [materials’] promise for providing reliable operation under irradiation under the harsh conditions expected in nuclear reactors where such detectors often suffer from fatigue and aging. [They also] benefit from much-reduced fabrication costs.”

As a result, the MIT engineers are hopeful that their work could result in new, less expensive detectors. For example, they envision trucks loaded with cargo from container ships driving through a structure that has detectors on both sides as they leave a port. “Ideally, you’d have either an array of detectors or a very large detector, and that’s where [today’s detectors] really don’t scale very well,” Tuller says.

Another potential application involves accessing geothermal energy, or the extreme heat below our feet that is being explored as a carbon-free alternative to fossil fuels. Ceramic sensors at the ends of drill bits could detect pockets of heat — radiation — to drill toward. Ceramics can easily withstand extreme temperatures of more than 800 degrees Fahrenheit and the extreme pressures found deep below the Earth’s surface.

The team is excited about additional applications for their work. “This was a demonstration of principle with just one material,” says Tuller, “but there are thousands of other materials good at conducting ions.”

Concludes Defferriere: “It’s the start of a journey on the development of the technology, so there’s a lot to do and a lot to discover.”

This work is currently supported by the U.S. Department of Homeland Security, Countering Weapons of Mass Destruction Office. This support does not constitute an express or implied endorsement on the part of the government. It was also funded by the U.S. Defense Threat Reduction Agency.

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Sustainable Computing: A Systems View

Meeting Time: 09:45 AM‑11:00 AM  Instructor: Abhishek Chandra Course Description: In recent years, there has been a dramatic increase in the pervasiveness, scale, and distribution of computing infrastructure: ranging from cloud, HPC systems, and data centers to edge computing and pervasive computing in the form of micro-data centers, mobile phones, sensors, and IoT devices embedded in the environment around us. The growing amount of computing, storage, and networking demand leads to increased energy usage, carbon emissions, and natural resource consumption. To reduce their environmental impact, there is a growing need to make computing systems sustainable. In this course, we will examine sustainable computing from a systems perspective. We will examine a number of questions:   • How can we design and build sustainable computing systems?   • How can we manage resources efficiently?   • What system software and algorithms can reduce computational needs?    Topics of interest would include:   • Sustainable system design and architectures   • Sustainability-aware systems software and management   • Sustainability in large-scale distributed computing (clouds, data centers, HPC)   • Sustainability in dispersed computing (edge, mobile computing, sensors/IoT)

Registration Prerequisites: This course is targeted towards students with a strong interest in computer systems (Operating Systems, Distributed Systems, Networking, Databases, etc.). Background in Operating Systems (Equivalent of CSCI 5103) and basic understanding of Computer Networking (Equivalent of CSCI 4211) is required.

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Skyrmions move at record speeds: A step towards the computing of the future

An international research team led by scientists from the CNRS 1 has discovered that the magnetic nanobubbles 2 known as skyrmions can be moved by electrical currents, attaining record speeds up to 900 m/s.

Anticipated as future bits in computer memory, these nanobubbles offer enhanced avenues for information processing in electronic devices. Their tiny size 3 provides great computing and information storage capacity, as well as low energy consumption.

Until now, these nanobubbles moved no faster than 100 m/s, which is too slow for computing applications. However, thanks to the use of an antiferromagnetic material 4 as medium, the scientists successfully had the skyrmions move 10 times faster than previously observed.

These results, which were published in Science on 19 March, offer new prospects for developing higher-performance and less energy-intensive computing devices.

This study is part of the SPIN national research programme 5 launched on 29 January, which supports innovative research in spintronics, with a view to helping develop a more agile and enduring digital world.

1 -- The French laboratories involved are SPINTEC (CEA/CNRS/Université Grenoble Alpes), the Institut Néel (CNRS), and the Charles Coulomb Laboratory (CNRS/Université de Montpellier).

2 -- A skyrmion consists of elementary nanomagnets ("spins") that wind to form a highly stable spiral structure, like a tight knot.

3 -- The size of a skyrmion can reach a few nanometres, which is to say approximately a dozen atoms.

4 -- Antiferromagnetic stacks consist of two nano-sized ferromagnetic layers (such as cobalt) separated by a think non-magnetic layer, with opposite magnetisation.

5 -- The SPIN priority research programme and equipment (PEPR) is an exploratory programme in connection with the France 2030 investment plan.

• Spintronics Research
• Computers and Internet
• Information Technology
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• Mobile Computing
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• Computing power everywhere
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• Quantum computer
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Story Source:

Materials provided by CNRS . Note: Content may be edited for style and length.

Journal Reference :

• Van Tuong Pham, Naveen Sisodia, Ilaria Di Manici, Joseba Urrestarazu-Larrañaga, Kaushik Bairagi, Johan Pelloux-Prayer, Rodrigo Guedas, Liliana D. Buda-Prejbeanu, Stéphane Auffret, Andrea Locatelli, Tevfik Onur Menteş, Stefania Pizzini, Pawan Kumar, Aurore Finco, Vincent Jacques, Gilles Gaudin, Olivier Boulle. Fast current-induced skyrmion motion in synthetic antiferromagnets . Science , 2024; 384 (6693): 307 DOI: 10.1126/science.add5751

Cite This Page :

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• v.8(12); 2020 Dec

Mobile Health Apps for Medical Emergencies: Systematic Review

Alejandro plaza roncero.

1 Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Valladolid, Spain

Gonçalo Marques

2 Polytechnic of Coimbra, Escola Superior de Tecnologia e Gestão de Oliveira do Hospital, Oliveira do Hospital, Portugal

Beatriz Sainz-De-Abajo

Francisco martín-rodríguez.

3 Advanced Clinical Simulation Center, School of Medicine, University of Valladolid, Valladolid, Spain

Carlos del Pozo Vegas

4 Emergency Department, Hospital Clínico Universitario de Valladolid, Valladolid, Spain

Begonya Garcia-Zapirain

5 eVIDA Research Group, University of Deusto, Bilbao, Spain

Isabel de la Torre-Díez

Mobile health apps are used to improve the quality of health care. These apps are changing the current scenario in health care, and their numbers are increasing.

We wanted to perform an analysis of the current status of mobile health technologies and apps for medical emergencies. We aimed to synthesize the existing body of knowledge to provide relevant insights for this topic. Moreover, we wanted to identify common threads and gaps to support new challenging, interesting, and relevant research directions.

We reviewed the main relevant papers and apps available in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was used in this review. The search criteria were adopted using systematic methods to select papers and apps. On one hand, a bibliographic review was carried out in different search databases to collect papers related to each application in the health emergency field using defined criteria. On the other hand, a review of mobile apps in two virtual storage platforms (Google Play Store and Apple App Store) was carried out. The Google Play Store and Apple App Store are related to the Android and iOS operating systems, respectively.

In the literature review, 28 papers in the field of medical emergency were included. These studies were collected and selected according to established criteria. Moreover, we proposed a taxonomy using six groups of applications. In total, 324 mobile apps were found, with 192 identified in the Google Play Store and 132 identified in the Apple App Store.

Conclusions

We found that all apps in the Google Play Store were free, and 73 apps in the Apple App Store were paid, with the price ranging from US $0.89 to US $5.99. Moreover, 39% (11/28) of the included studies were related to warning systems for emergency services and 21% (6/28) were associated with disaster management apps.

Introduction

Internet and mobile computing technologies have changed people’s lifestyle. With regard to mobile devices in health, mobile devices, such as personal digital assistant devices (PDAs), smartphones, and tablets, have been widely adopted by medical professionals. These devices are quickly becoming some of the main instruments for accessing clinical information, especially for young health professionals and students [ 1 ]. Several medical resources are available on the digital distribution platforms of mobile apps (Google Play Store and Apple App Store) for Android and iOS operating systems [ 2 ].

According to the World Health Organization, the development of apps for the health domain is directly or indirectly intended to maintain or improve healthy behaviors, quality of life, and people’s well-being [ 3 ].

Mobile health (mHealth) refers to the practice of medicine and public health. Robert Istepanian mentioned “the emerging use of mobile communications and network technologies for health” [ 4 ]. The field of mHealth has become a subbranch of eHealth, which has to do with the use of information and communication technologies, such as computers, mobile phones, GPS, and patient monitors for health and information services. mHealth includes the use of mobile devices in the collection, delivery, and access of health information by professionals, researchers, and patients. It is an emerging and rapidly developing field, which plays a vital role in the transformation of health care to increase its quality and efficiency.

On one hand, mobile apps are specifically aimed at helping people in their own health and wellness management. On the other hand, numerous mobile apps aim to assist health care providers as tools to improve and facilitate the provision of patient care [ 5 ]. According to a recent 2019 report on global mHealth, the market can be segmented based on the following: (1) equipment/connected medical devices, (2) mHealth services, and (3) mHealth apps [ 6 ].

The main objective of this paper was to present a systematic review that addresses the study of mobile apps for health emergencies. Furthermore, this paper presents the mobile apps available for the Android and iOS operating systems [ 7 - 11 ]. The main contribution is synthesis of the existing body of knowledge to provide relevant insights and to identify common threads and gaps to support new challenging, interesting, and relevant research directions.

In summary, mobile apps in the health sector are continually growing, and soon, they will be able to change the concept of medicine [ 7 ]. These apps will allow patients to access their health information, have small consultations for specific issues without consulting a professional, and locate emergency services. They will also help monitor chronic patients, increase safety in taking medication, and help network with people in the same situation. Moreover, professionals have access to specific information and tools to create new relationships with patients. Prehospital medical care starts from the occurrence of the event, involves transfer, and ends at admission to the welfare institution. Moreover, it always has to be offered by a health care professional. Consequently, we consider these apps as medical emergency apps.

A search for the term “medical emergency apps” in mHealth does not provide results. All reports and studies are focused on mHealth and do not distinguish between the different branches into which this technology can be divided. Therefore, we found a lack of interest in this important domain that can improve prehospital care for patients with the use of new technologies.

The use of mobile apps can facilitate the exchange of information between health professionals in the case of a possible health emergency. Consequently, this analysis has two focuses. First, we review the current status in the literature. Second, we review the mobile apps available in the main virtual stores. Currently, owing to the proliferation of mobile apps in this domain, it is necessary to evaluate their importance to promote health care.

Following the selection of relevant studies, a statistical analysis was carried out. The results have been discussed to analyze the main contributions of each publication. Finally, the most important findings have been reported.

In this section, the methodology used in this study is defined. The search process for information extraction from the available apps in the field of health emergencies is also reported. Our study focused first on the available literature and second on the available apps in the field of health emergency.

Literature Review

The procedure for the selection of articles was the same as that followed in other previous work [ 8 ]. The articles are analyzed by reading the title and abstract to identify the most relevant papers. The number of scientific publications is very high. Therefore, we followed a protocol that allowed us to synthesize the most relevant information. Two relevant protocols for systematic reviews are Quality of Reporting of Meta-Analysis (QUOROM) [ 9 ] and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 10 ].

On one hand, QUOROM focuses on the presentation of a meta-analysis of randomized clinical trials and includes checklists for authors, reviewers, and editors of biomedical journals, as well as a diagram of the flow that describes the whole process. On the other hand, the PRISMA protocol is an extension of QUOROM with more pedagogical purposes accompanying the checklist with extensive documentation that justifies a series of check items. Furthermore, PRISMA is applicable to all types of systematic reviews and is not limited to a meta-analysis of clinical trials. This type of protocol was introduced with the idea that clinical trial publications follow the type of standards set by each protocol, and thus, works of this type will be standardized.

Therefore, this study adopted the PRISMA methodology. This process is divided into the following four phases:

• Identification: The title is considered in the choice.
• Selection: The summary for the choice of the paper is taken into account.
• Eligibility: The content is taken into account for the choice.
• Inclusion: We finally obtain the papers with the highest potential content.

The PRISMA protocol starts with the identification phase. In this stage, we used specified keywords to identify the relevant papers in several databases. Using these series of papers, we performed a set of steps to finally obtain the papers with which we carried out our review.

The search engines on which we obtained the different papers for the analysis were as follows: IEEE Xplore, Science Direct, PubMed, Web of Science, and Google Scholar. These databases were used since they cover the majority of papers that are within the scope of this review and include the most relevant sources. Moreover, the above-mentioned databases have been used in several systematic review papers on mHealth [ 8 , 11 , 12 ]. The papers were selected and screened by two different reviewers. Moreover, all the selected papers were included with the common agreement of all the authors.

The logic and keywords used to conduct this review were as follows: “emergency” AND “app,” “emergency” AND “mHealth,” “emergency” AND “eHealth,” and “eEmergency.”

We focused on review papers and research papers, excluding other results that these search engines offer, such as book chapters, patents, conference summaries, and news. The search was carried out starting from the year 2009, considering the papers published for 10 years until the end of 2019. The search and selection of papers were conducted during March 2020.

Review papers were included since they collect information from the most relevant sources. These papers provide clear and concise insights, which were used to carry out our analysis. Concerning the dates of publication, this systematic review considered the previous 10 years. This review only included papers in the English language because it is the universal language par excellence (Lingua Franca).

After the identification process, the papers were ordered according to relevance. Moreover, the identification process followed the PRISMA protocol that can be represented in a flow chart. Figure 1 presents the section process of a particular search engine through which the search was performed and for choosing a particular search string or logic.

Flow diagram for the identification phase.

After the identification process, the obtained papers from a different database were exanimated to find duplicates. Thereafter, we conducted the selection phase. The section state started with the exclusion of papers by reading the title and abstract. The eligibility phase included reading the full text of the remaining papers obtained in the selection phase. Finally, in the inclusion phase, the final number of papers included in the review were defined. This entire process is presented in Figure 2 . In total, we found 28 relevant papers that met the search criteria and respected the exclusion criteria.

PRISMA flow chart.

Review of the Apps Available in Mobile Market Stores

After reviewing the available literature, a review of the mobile apps and websites available was conducted.

On one hand, the Google Play Store and Apple App Store were used to search for mobile apps, since they are the two app stores that have more apps. On the other hand, Google Chrome was used to perform a webpage search on eHealth. The search process on available mobile apps was conducted during March 2020.

The methodology used for this second part was very similar to the flow chart in Figure 3 . The following keywords were defined to try to obtain all possible results for this analysis: “emergency” OR “emergencia,” “eEmergency,” “safety” OR “seguridad,” “alert” OR “alerta,” “disaster” OR “desastre,” “SOS,” “112,” and “blood donation” OR “donación de sangre.”

Flowchart process for selection of apps.

In addition, the language and country used by the different mobile apps were not taken into account. The criterion followed to choose mobile apps in terms of content was to analyze the information offered by each search engine about the app. If the app corresponded to the field of health emergency (whether designed to help health care staff or the patient), it was considered in our study.

Figure 3 presents the chart that was followed in the process of selecting the different mobile apps in our study.

This section presents first an analysis of the results obtained in the literature review and second an analysis of the available mobile apps.

The number of results obtained per year after the analysis and selection conducted by the authors is presented in Table 1 . The findings have been categorized according to the year of publication and the number of obtained results.

Distribution of publications per year before applying the selection criteria.

The distribution of the number of papers selected after the systematic review according to the year is presented in Table 2 . The studies analyzed in this systematic review were distributed from 2013 to 2019, and most of the results (n=8) involved 2017.

Distribution of publications per year after applying the methodology.

Table 3 presents the title, the date of publication, and the summary of the main contributions of each paper included in the systematic review [ 13 - 40 ].

Main contributions of each paper included.

Results for the Google Play Store

The results obtained for the Android operating system were analyzed. The categorization of apps provided by the Google Play Store is presented in Table 4 .

Categorization of apps for Android.

The results of the apps obtained for Android according to the filter used in the search conducted are presented in Table 5 .

Categorization of apps by filter for Android.

Results for the Apple App Store

The results obtained for the iOS mobile operating system were analyzed. The categorization of apps provided by the Apple App Store is presented in Table 6 .

Categorization of apps for iOS.

The results of the apps obtained for iOS according to the filter used in the search conducted are presented in Table 7 .

Categorization of apps by filter for iOS.

Comparison of the Results

Comparison of the categorization of the apps on each platform (Google Play Store and Apple App Store) was conducted, and the findings are presented in Table 8 .

Categorization of apps (Android and iOS).

The results of all apps obtained according to the filter used are presented in Table 9 .

Categorization of apps by filter (Android and iOS).

In total, 192 selected mobile apps with potential content were obtained from the Google Play Store, while 132 were obtained from the Apple App Store.

On the Android operating system, the most number of results were obtained in the Health and Wellness, and Medicine categories. Out of the 192 apps obtained, 56 apps belonged to the Health and Wellness category. Moreover, 48 apps belonged to the Medicine category. However, on the iOS operating system, in the Apple App Store, we found a more equitable distribution of the number of apps when considering the category. Out of 132 apps, 20 belonged to the category of Medicine, 19 belonged to the category of Tools, 17 belonged to the categories of Health and Wellness, and Travel and Guides, and 16 belonged to the category of Lifestyle.

In total, 73 and 51 apps were obtained from the Google Play Store using the filters “blood donation” OR “donación de sangre” and “emergency” OR “emergencia,” respectively. For the Apple App Store, 38, 32, and 23 mobile apps were obtained with the filters “emergency” OR “emergencia,” “SOS,” and “alert” OR “alerta,” respectively.

Out of 324 apps, 73 and 68 were from the Health and Wellness, and Medicine categories, respectively.

Finally, regarding the categorization according to the filter applied in the search, the three filters through which more apps with potential content were obtained included “emergency” OR “emergencia,” “blood donation” OR “donación de sangre,” and “SOS,” with 89, 79, and 56 results, respectively.

Regarding the price of apps, we found that all identified apps available in the Google Play Store were free. However, 59 mobile apps in the Apple App Store were paid, with the price ranging from US $0.89 to US $5.99. The price of apps is relevant to their use since it can be a critical limitation to their use by people with economic issues.

In total, 79% (22/28) of studies were found in the IEEE Xplore database. Moreover, 17% (5/28) of the studies were selected from Google Scholar and 4% (1/28) from ScienceDirect. Finally, an analysis of the filters used in the search process has been conducted.

According to the filters applied in the search engines, 79% (22/28) of papers resulted from the “emergency” AND “app” filter and 17% (5/28) resulted from the “emergency” AND “mHealth” filter. Moreover, 4% (1/28) of the papers resulted from the “emergency” AND “eHealth” filter. The “eEmergency” filter did not return relevant results.

Principal Findings

The 28 obtained papers were categorized by the authors into the following six different groups: (1) prehospital medical care; (2) apps for disaster management; (3) warning systems for emergency services and medical services; (4) automobile circulation control; (5) communication between medical staff; and (6) apps for blood donation. The classification of the analyzed studies is presented in Table 10 .

Warning systems for emergency services and medical services led, with 39% (11/28) of the obtained publications. These systems are usually apps in which medical services are notified with the press of a button. Followed by this, we have the group of apps for disaster management, owing to the problems that exist with different natural disasters in Eastern countries (6/28 [21%] of the obtained publications). These two groups had a higher percentage than the rest of the groups. Communication between medical staff is critical for the success of emergency care services. Effective and efficient methods of communication involving health care staff play major roles in improving global health care services. Apps for blood donation are also critical, since in emergency scenarios, the availability of the correct blood type for a patient is crucial for the recovery process.

Prehospital care should be highlighted to include prehospital medical care methods, as well as automobile circulation control for the circulation of medical vehicles since it directly influences an improvement in prehospital medical care by improving the time it takes for a medical vehicle to care for patients.

mHealth has great potential, as it can provide citizens with the necessary means to manage their health and stay healthy longer. Consequently, people can improve the quality of health care and patient comfort, and help health professionals in their work.

The search for mHealth solutions can contribute to the development of modern, efficient, and sustainable health systems. It is also expected to reduce costly visits to the hospital, help citizens to take charge of their state of health and well-being, and promote health focused on prevention rather than cure. Furthermore, it is an excellent opportunity for the flourishing app sector and entrepreneurs.

This literature review mentioned the relevant usage of mobile apps in the health emergency domain. In addition, this paper stated the need to investigate the realization of more studies on prehospital care. Recent studies are focused on mHealth technology, and they leave aside the different branches that may arise from this technology.

Wearable devices are becoming increasingly relevant not only in applications in the health sector, but also in global mobile telecommunication. The applications can be notified through a mobile device, such as a smartphone or tablet and a wearable device.

Considering the extensive inclusion of 5G technology, mobile communication technology can be assumed to be experiencing a breakthrough, since we can send more information in seconds and with less energy consumption. In addition, the network coverage offered by mobile communication technologies can help to reach remote areas such as mountains. Moreover, at present, mobile networks offer high data transfer rates, which enable remote surgery tasks. Finally, given the increase in the use of mobile devices worldwide, we found a large number of apps.

There was greater availability of apps for Android than for iOS. Furthermore, Android apps were free compared with iOS apps (73/132 [55.5%] were paid). Considering the large number of apps found in the category of Medicine in this study, we can conclude that mobile apps are mainly introduced in this area. Moreover, the category of Health and Wellness involved even more mobile apps. Even though mobile technology has increased, there is much growth in the field of health emergencies and mHealth.

After the completion of this work, three essential aspects are planned as future lines of research. Owing to the tremendous digital transformation experienced by both industry and society, they increasingly require the use of a highly organized infrastructure on the network, that is, on the internet, and the loading or unloading of large amounts of data, which, in the case of health, is of utmost importance. Therefore, the three most relevant technologies that will be increasingly relevant owing to both the demand for cloud services and large volumes of data, and the search for the solvency of vulnerabilities in data management are cybersecurity, big data, and cloud computing. In addition, wearable devices are a critical matter of study in the mHealth app space. Finally, internet of things and smart sensor communication are becoming increasingly widespread and are crucial for enhanced telemedicine.

Distribution of the studies per category.

We conducted an analysis of the current status of mHealth technologies and apps for medical emergencies. The PRISMA methodology was used in this review. First, the available literature of the previous 10 years (2009-2019) was analyzed. Second, a review of mobile apps in the two common virtual storage platforms (Google Play Store and Apple App Store) was carried out. The Google Play Store and Apple App Store are for the Android and iOS operating systems, respectively.

In total, 28 papers in the field of medical emergencies were included. These studies were categorized into six different groups. Overall, 39% (11/28) of the included studies were related to warning systems for emergency services and 21% (6/28) were associated with disaster management apps.

In total, 324 mobile apps were found, with 59.3% (n=192) identified in the Google Play Store and 40.7% (n=132) identified in the Apple App Store. All identified mobile apps in the Google Play Store were free, and in the Apple App Store, 55.5% (73/132) of the identified apps were paid, with the price ranging from US $0.89 to US $5.99.

Acknowledgments

This research has been partially supported by the European Commission and the Ministry of Industry, Energy and Tourism under the project AAL-20125036 named “Wetake Care: ICT-based Solution for (Self-) Management of Daily Living.”

Abbreviations

Conflicts of Interest: None declared.

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