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Introduction, methodology.

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Child labor and health: a systematic literature review of the impacts of child labor on child’s health in low- and middle-income countries

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Abdalla Ibrahim, Salma M Abdalla, Mohammed Jafer, Jihad Abdelgadir, Nanne de Vries, Child labor and health: a systematic literature review of the impacts of child labor on child’s health in low- and middle-income countries, Journal of Public Health , Volume 41, Issue 1, March 2019, Pages 18–26, https://doi.org/10.1093/pubmed/fdy018

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To summarize current evidence on the impacts of child labor on physical and mental health.

We searched PubMed and ScienceDirect for studies that included participants aged 18 years or less, conducted in low- and middle-income countries (LMICs), and reported quantitative data. Two independent reviewers conducted data extraction and assessment of study quality.

A total of 25 studies were identified, the majority of which were cross-sectional. Child labor was found to be associated with a number of adverse health outcomes, including but not limited to poor growth, malnutrition, higher incidence of infectious and system-specific diseases, behavioral and emotional disorders, and decreased coping efficacy. Quality of included studies was rated as fair to good.

Child labor remains a major public health concern in LMICs, being associated with adverse physical and mental health outcomes. Current efforts against child labor need to be revisited, at least in LMICs. Further studies following a longitudinal design, and using common methods to assess the health impact of child labor in different country contexts would inform policy making.

For decades, child labor has been an important global issue associated with inadequate educational opportunities, poverty and gender inequality. 1 Not all types of work carried out by children are considered child labor. Engagement of children or adolescents in work with no influence on their health and schooling is usually regarded positive. The International Labor Organization (ILO) describes child labor as ‘work that deprives children of their childhood, potential and dignity, and that is harmful to physical and mental development’. 2 This definition includes types of work that are mentally, physically, socially or morally harmful to children; or disrupts schooling.

The topic gained scientific attention with the industrial revolution. Research conducted in the UK, because of adverse outcomes in children, resulted in acts for child labor in 18 02. 3 Many countries followed the UK, in recognition of the associated health risks. The ILO took its first stance in 1973 by setting the minimum age for work. 4 Nevertheless, the ILO and other international organizations that target the issue failed to achieve goals. Child labor was part of the Millennium Development Goals, adopted by 191 nations in 20 00 5 to be achieved by 2015. Subsequently, child labor was included in the Sustainable Development Goals, 6 which explicitly calls for eradication of child labor by 2030.

Despite the reported decline in child labor from 1995 to 2000, it remains a major concern. In 2016, it was estimated that ~150 million children under the age of 14 are engaged in labor worldwide, with most of them working under circumstances that denies them a playful childhood and jeopardize their health. 7 Most working children are 11–14 years, but around 60 million are 5–11 years old. 7 There are no exact numbers of the distribution of child labor globally; however, available statistics show that 96% of child workers are in Africa, Asia and Latin America. 1

Research into the impacts of child labor suggests several associations between child labor and adverse health outcomes. Parker 1 reported that child labor is associated with certain exposures like silica in industries, and HIV infection in prostitution. Additionally, as child labor is associated with maternal illiteracy and poverty, children who work are more susceptible to malnutrition, 1 which predisposes them to various diseases.

A meta-analysis on the topic was published in 20 07. 8 However, authors reported only an association of child labor with higher mortality and morbidity than in the general population, without reporting individual outcome specific effects. 8 Another meta-analysis investigated the effects of adverse childhood experiences (ACEs), including child labor, on health. They reported that ACEs are risk factors for many adverse health outcomes. 9

To our knowledge, this is the first systematic review that attempts to summarize current evidence on the impacts of child labor on both physical and mental health, based on specific outcomes. We review the most recent evidence on the health impacts of child labor in low- and middle-income countries (LMICs) according to the World Bank classification. We provide an informative summary of current studies of the impacts of child labor, and reflect upon the progress of anti-child labor policies and laws.

Search strategy

We searched PubMed and ScienceDirect databases. Search was restricted to publications from year 1997 onwards. Only studies written in English were considered. Our search algorithm was [(‘child labor’ OR ‘child labor’ OR ‘working children’ OR ‘occupational health’ OR ‘Adolescent work’ OR ‘working adolescents’) AND (Health OR medical)]. The first third of the algorithm was assigned to titles/abstracts to ensure relevance of the studies retrieved, while the rest of the terms were not. On PubMed, we added […AND (poverty OR ‘low income’ OR ‘developing countries’)] to increase the specificity of results; otherwise, the search results were ~60 times more, with the majority of studies being irrelevant.

Study selection

Studies that met the following criteria were considered eligible: sample age 18 years or less; study was conducted in LMICs; and quantitative data was reported.

Two authors reviewed the titles obtained, a.o. to exclude studies related to ‘medical child labor’ as in childbirth. Abstracts of papers retained were reviewed, and subsequently full studies were assessed for inclusion criteria. Two authors assessed the quality of studies using Downs and Black tool for quality assessment. 10 The tool includes 27 items, yet not all items fit every study. In such cases, we used only relevant items. Total score was the number of items positively evaluated. Studies were ranked accordingly (poor, fair, good) (Table 1 ).

Characteristics of studies included

* The quality is based on the percentage of Downs and Black 10 tool, < 50% = poor, 50–75% = fair, > 75% = good.

** BMI, body mass index.

*** HIV, human immunodeficiency virus; HBV, hepatitis B virus; HCV, hepatitis C virus.

Data extraction and management

Two authors extracted the data using a standardized data extraction form. It included focus of study (i.e. physical and/or mental health), exposure (type of child labor), country of study, age group, gender, study design, reported measures (independent variables) and outcome measures (Table 1 ). The extraction form was piloted to ensure standardization of data collection. A third author then reviewed extracted data. Disagreements were solved by discussion.

Search results

A flow diagram (Fig. 1 ) shows the studies selection process. We retrieved 1050 studies on PubMed and 833 studies on Science Direct, with no duplicates in the search results. We also retrieved 23 studies through screening of the references, following the screening by title of retrieved studies. By reviewing title and abstract, 1879 studies were excluded. After full assessment of the remaining studies, 25 were included.

Study selection process.

Study selection process.

Characteristics of included studies

Among the included studies ten documented only prevalence estimates of physical diseases, six documented mental and psychosocial health including abuse, and nine reported the prevalence of both mental and physical health impacts (Table 1 ). In total, 24 studies were conducted in one country; one study included data from the Living Standard Measurement Study of 83 LMIC. 8

In total, 12 studies compared outcomes between working children and a control group (Table 1 ). Concerning physical health, many studies reported the prevalence of general symptoms (fever, cough and stunting) or diseases (malnutrition, anemia and infectious diseases). Alternatively, some studies documented prevalence of illnesses or symptoms hypothesized to be associated with child labor (Table 1 ). The majority of studies focusing on physical health conducted clinical examination or collected blood samples.

Concerning mental and psychosocial health, the outcomes documented included abuse with its different forms, coping efficacy, emotional disturbances, mood and anxiety disorders. The outcomes were measured based on self-reporting and using validated measures, for example, the Strengths and Difficulties Questionnaire (SDQ), in local languages.

The majority of studies were ranked as of ‘good quality’, with seven ranked ‘fair’ and one ranked ‘poor’ (Table 1 ). The majority of them also had mixed-gender samples, with only one study restricted to females. 24 In addition, valid measures were used in most studies (Table 1 ). Most studies did not examine the differences between genders.

Child labor and physical health

Fifteen studies examined physical health effects of child labor, including nutritional status, physical growth, work-related illnesses/symptoms, musculoskeletal pain, HIV infection, systematic symptoms, infectious diseases, tuberculosis and eyestrain. Eight studies measured physical health effects through clinical examination or blood samples, in addition to self-reported questionnaires. All studies in which a comparison group was used reported higher prevalence of physical diseases in the working children group.

Two studies were concerned with physical growth and development. A study conducted in Pakistan, 11 reported that child labor is associated with wasting, stunting and chronic malnutrition. A similar study conducted in India compared physical growth and genital development between working and non-working children and reported that child labor is associated with lower BMI, shorter stature and delayed genital development in working boys, while no significant differences were found among females. 12

Concerning work-related illnesses and injuries, a study conducted in Bangladesh reported that there is a statistically significant positive association between child labor and the probability to report any injury or illness, tiredness/exhaustion, body injury and other health problems. Number of hours worked and the probability of reporting injury and illness were positively correlated. Younger children were more likely to suffer from backaches and other health problems (infection, burns and lung diseases), while probability of reporting tiredness/exhaustion was greater in the oldest age group. Furthermore, the frequency of reporting any injury or illness increases with the number of hours worked, with significant variation across employment sectors. 13 A study in Iran reported that industrial workrooms were the most common place for injury (58.2%). Falling from heights or in horizontal surface was the most common mechanism of injury (44%). None of the patients was using a preventive device at the time of injury. Cuts (49.6%) were the most commonly reported injuries. 14

Other studies that investigated the prevalence of general symptoms in working children in Pakistan, Egypt, Lebanon, Jordan and Indonesia reported that child labor is negatively associated with health. 15 – 19 Watery eyes, chronic cough and diarrhea were common findings, in addition to history of a major injury (permanent loss of an organ, hearing loss, bone fractures, permanent disability). 20 One study, conducted in India reported that working children suffered from anemia, gastrointestinal tract infections, vitamin deficiencies, respiratory tract infections, skin diseases and high prevalence of malnutrition. 21 Another study—of poor quality—in India reported that child labor was associated with higher incidence of infectious diseases compared to non-working children. 22

Only a few studies focused on specific diseases. A study in Brazil compared the prevalence of musculoskeletal pain between working and non-working children. Authors reported that the prevalence of pain in the neck, knee, wrist or hands, and upper back exceeded 15%. Workers in manufacturing had a significantly increased risk for musculoskeletal pain and back pain, while child workers in domestic services had 17% more musculoskeletal pain and 23% more back pain than non-workers. Awkward posture and heavy physical work were associated with musculoskeletal pain, while monotonous work, awkward posture and noise were associated with back pain. 23 A study in Nicaragua, which focused on children working in agriculture, reported that child labor in agriculture poses a serious threat to children’s health; specifically, acute pesticides poisoning. 24

A study conducted in India reported that the prevalence of eyestrain in child laborers was 25.9%, which was significantly more than the 12.4% prevalence in a comparison group. Prevalence was higher in boys and those who work more than 4 h daily. 25 Another study conducted in India documented that the difference between working and non-working children in the same area in respiratory morbidities (TB, hilar gland enlargement/calcification) was statistically significant. 26

A study in Iran explored the prevalence of viral infections (HIV, HCV and HBV) in working children. 27 The study reported that the prevalence among working street children was much higher than in general population. The 4.5% of children were HIV positive, 1.7% were hepatitis B positive and 2.6% hepatitis C positive. The likelihood of being HIV positive among working children of Tehran was increased by factors like having experience in trading sex, having parents who used drugs or parents infected with HCV.

Lastly, one study was a meta-analysis conducted on data of working children in 83 LMIC documented that child labor is significantly and positively related to adolescent mortality, to a population’s nutrition level, and to the presence of infectious diseases. 8

Child labor and mental health

Overall, all studies included, except one, 28 reported that child labor is associated with higher prevalence of mental and/or behavioral disorders. In addition, all studies concluded that child labor is associated with one or more forms of abuse.

A study conducted in Jordan reported a significant difference in the level of coping efficacy and psychosocial health between working non-schooled children, working school children and non-working school children. Non-working school children had a better performance on the SDQ scale. Coping efficacy of working non-schooled children was lower than that of the other groups. 29

A study conducted in Pakistan reported that the prevalence of behavioral problems among working children was 9.8%. Peer problems were most prevalent, followed by problems of conduct. 30 A study from Ethiopia 31 reported that emotional and behavioral disorders are more common among working children. However, another study in Ethiopia 28 reported a lower prevalence of mental/behavioral disorders in child laborers compared to non-working children. The stark difference between these two studies could be due to the explanation provided by Alem et al. , i.e. that their findings could have been tampered by selection bias or healthy worker effect.

A study concerned with child abuse in Bangladesh reported that the prevalence of abuse and child exploitation was widespread. Boys were more exposed. Physical assault was higher towards younger children while other types were higher towards older ones. 32 A similar study conducted in Turkey documented that 62.5% of the child laborers were subjected to abuse at their workplaces; 21.8% physical, 53.6% emotional and 25.2% sexual, 100% were subjected to physical neglect and 28.7% were subjected to emotional neglect. 33

One study focused on sexual assault among working females in Nigeria. They reported that the sexual assault rate was 77.7%. In 38.6% of assault cases, the assailant was a customer. Girls who were younger than 12 years, had no formal education, worked for more than 8 h/day, or had two or more jobs were more likely to experience sexual assault. 34

Main findings of this study

Through a comprehensive systematic review, we conclude that child labor continues to be a major public health challenge. Child labor continues to be negatively associated with the physical and psychological health of children involved. Although no cause–effect relation can be established, as all studies included are cross-sectional, studies documented higher prevalence of different health issues in working children compared to control groups or general population.

This reflects a failure of policies not only to eliminate child labor, but also to make it safer. Although there is a decline in the number of working children, the quality of life of those still engaged in child labor seems to remain low.

Children engaged in labor have poor health status, which could be precipitated or aggravated by labor. Malnutrition and poor growth were reported to be highly prevalent among working children. On top of malnutrition, the nature of labor has its effects on child’s health. Most of the studies adjusted for the daily working hours. Long working hours have been associated with poorer physical outcomes. 18 , 19 , 25 , 26 , 35 It was also reported that the likelihood of being sexually abused increased with increasing working hours. 34 The different types and sectors of labor were found to be associated with different health outcomes as well. 13 , 18 , 24 However, comparing between the different types of labor was not possible due to lack of data.

The majority of studies concluded that child labor is associated with higher prevalence of mental and behavioral disorders, as shown in the results. School attendance, family income and status, daily working hours and likelihood of abuse, in its different forms, were found to be associated with the mental health outcomes in working children. These findings are consistent with previous studies and research frameworks. 36

Child labor subjects children to abuse, whether verbally, physically or sexually which ultimately results in psychological disturbances and behavioral disorders. Moreover, peers and colleagues at work can affect the behavior of children, for example, smoking or drugs. The effects of child labor on psychological health can be long lasting and devastating to the future of children involved.

What is already known on this topic

Previous reviews have described different adverse health impacts of child labor. However, there were no previous attempts to review the collective health impacts of child labor. Working children are subjected to different risk factors, and the impacts of child labor are usually not limited to one illness. Initial evidence of these impacts was published in the 1920s. Since then, an increasing number of studies have used similar methods to assess the health impacts of child labor. Additionally, most of the studies are confined to a single country.

What this study adds

To our knowledge, this is the first review that provides a comprehensive summary of both the physical and mental health impacts of child labor. Working children are subjected to higher levels of physical and mental stress compared to non-working children and adults performing the same type of work. Unfortunately, the results show that these children are at risk of developing short and long-term health complications, physically or mentally.

Though previous systematic reviews conducted on the topic in 19 97 1 and 20 07 8 reported outcomes in different measures, our findings reflect similar severity of the health impacts of child labor. This should be alarming to organizations that set child labor as a target. We have not reviewed the policies targeting child labor here, yet our findings show that regardless of policies in place, further action is needed.

Most of the current literature about child labor follow a cross-sectional design, which although can reflect the health status of working children, it cannot establish cause–effect associations. This in turn affects strategies and policies that target child labor.

In addition, comparing the impacts of different labor types in different countries will provide useful information on how to proceed. Further research following a common approach in assessing child labor impacts in different countries is needed.

Limitations of this study

First, we acknowledge that all systematic reviews are subject to publication bias. Moreover, the databases used might introduce bias as most of the studies indexed by them are from industrialized countries. However, these databases were used for their known quality and to allow reproduction of the data. Finally, despite our recognition of the added value of meta-analytic methods, it was not possible to conduct one due to lack of a common definition for child labor, differences in inclusion and exclusion criteria, different measurements and different outcome measures. Nevertheless, to minimize bias, we employed rigorous search methods including an extensive and comprehensive search, and data extraction by two independent reviewers.

Compliance with ethical standards

The authors declare that they have no conflict of interest.

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Home /  Trafficking & Forced Labour  

Child labor situation in nepal: challenges and ways forward.

Year of Publication: 27 June 2023 | My Republica

Published by: CESLAM

Child labor continues to remain a pervasive problem even after three decades of collaborative efforts for its prohibition and regulation in Nepal. According to the Nepal Child Labor Report 2021 prepared by the International Labor Organisation (ILO), 1.1 million children aged 5 to 17 years are engaged in child labor (in 2018) compared to 2.6 million (in 1998). Whilst national statistics show some improvement, other studies have shown grave concern for children engaged in the hidden and exploitative forms of labor in Nepal. There is no denying that child labor compounded by social, cultural, economic, and political factors remains a grave concern for all stakeholders in the country.

On the occasion of the ‘World Day Against Child Labor’ on 12th June, the government and its development partners are hosting several events throughout the month. This article discusses some pertinent issues and offers pragmatic suggestions to make this year’s slogan ‘Social Justice for All: End Child Labor’ a reality.

Why is eliminating child labor a priority?

Children forced to work in exploitative labor conditions are not only deprived of their fundamental rights to education, health, childhood development, sports, safety, and protection (as enshrined in Nepal’s Constitution) but it also directly impacts their physical, social, and emotional development. Several legislations and policies: Child Labor Prohibition and Regulation Act (2000), The Labor Act (2017), the Children’s Act (2018), and Muluki Civil Code (2017) among others recognize child labor as a human rights violation. As a signatory to various international treaties, Nepal is committed to achieving the targets of sustainable development goals (particularly SDG target 8.7) and even developed a roadmap for eliminating the worst forms of child labor by 2030. Despite all these efforts, a significant number of children are still working as child laborers in various sectors, enterprises, and informally. To achieve this goal, all levels of the government, CSOs and the private sector need to work collaboratively to push forward the child protection agenda and support vulnerable children and their families.

First and foremost, in the federalised structure of Nepal, several existing policies and mechanisms envisioned by the Children’s Act 2018 need adaptation and contextualization. As such, there is a dire need to define the worst forms of child labor and update the list of hazardous work including hidden forms of child labor and economic exploitation. The limited labor inspectors cannot monitor the entire country. Strong emphasis should be given to building capacities of local bodies including child rights committees that should actively participate in key decisions made for working children.

Second, it is imperative to strengthen the child protection mechanisms at the local, provincial, and federal levels that have specific mandates and enhanced financial, technical, and human resource capacities to address the issue of child labor properly. While it is praiseworthy to see an increasing number of child welfare authorities assigned to the local municipalities, there is a massive need for their capacity development. The provision of child funds with clear guidelines is critical that specify roles and mechanisms to support vulnerable children. Only after having all these mandatory provisions, the government’s vision to enforce child labour-free declaration campaigns can sustain.

Third, the need for a proper data management system and the use of data and evidence for combating child labor is significant.There is a plethora of studies done on child labor. However, they are seldom used as tools for advocacy and guide the development of plans and programmes. One of the ground-breaking participatory action research programmes - Child Labor Action Research Innovation in South and South-Eastern Asia (CLARISSA) collected and analyzed the life stories of 400 Nepali children working in ‘dohori’ restaurants, dance bars, spa-massage parlors, eateries and guest houses. The research highlighted several factors besides the poor economic condition such as family conflict, alcoholic parents, sickness and death of family members, extramarital affairs of parents and peer influence as the key drivers that pushed them into child labor. The findings of such studies will be instrumental in developing specific interventions sensitizing parents and guardians, teachers, and employers, on the risks of child labor as well as encouraging them to value children’s participation and protection of children.

Fourth, it is notable to support the initiative led by Nepali children and youths engaged in the worst forms of child labor in building their agencies. On 16-20 January 2023, representatives of committees/associations of working children from 16 countries (including Nepal) gathered in Kigali Rwanda demanding better policymaking and practice from the local to the global level. The development actors should promote such events and foster the exchange of learning and sharing of best practices, and innovative approaches to address the issues of child labor within and amongst all concerned line departments and agencies.

Last and most important, there is a great scope to strengthen inter-governmental coordination with the National Child Rights Council and specify the role and responsibilities of concerned departments within the Ministry of Labor, Employment and Social Security, Ministry of Home Affairs, Ministry of Women, Children and Senior Citizens and Ministry of Law, Justice and Parliamentary Affairs in the monitoring of the labor situation and mobilize resources to support the rescue and rehabilitation efforts. National and international NGOs need to support system strengthening based on their global and national expertise and work with agencies at all levels to develop strategic plans to address child labor. Private businesses also have a critical role to safeguard their business and showcase ethical work and ensure a decent working environment for all.

Published on: 27 June 2023 | My Republica

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The Situation of Child Labour in Nepal: An Analysis (With Reference to Karnali Province)

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2020, Government of Karnali Province Ministry of Social Development

There are still some 152 million children in the world who are involved in some form of child labour. Of these, a large number are employed in the informal sector, while 72.5 million children are involved in worst form of child labour. Usually, the number of child labour is higher in economically poor countries. In the context of Nepal, about 47.8 percent of children are still involved in some form of work. Even though the latest data are not available, the figures for 2014 show that 27.4 percent of children are employed as child labour. Of the children involved in work, 45.45 percent did not even go to school. The figures show that the rate of child labour in Nepal is high and alarming. This is even worse in Karnali province lagging behind in all indicators. The number of children working in hazardous areas such as transportation, construction, tourism is also significant. The use of available means and resources for ending child labour, implementation of existing policies have not been effective due to lack of commitment and will power. According to the sectoral data, policies and plans have been formulated to tackle the child labour, but there has been no substantial effort by government agencies against child labour. In fact, the government formulates the plans, but the implementation does not seem to be effective. Particularly child labour resulted from economic poverty, lacking access to quality education, social acceptance, weak implementation of laws, conflict and changing family environment, modern information technology and misuse of social media, lacking attention by stakeholders and political commitment etc. Despite the government's international commitment against child labour and the formulation of various policies, plans and laws, no significant achievement has been realized in this area.

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National master plan on child labour (nepali version).

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Ten year National Master Plan on Child Labour (Nepali version), endorsed by Cabinet on July 08, 2018.

National Master Plan on Child Labour Cover page

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This comprehensive literature review of child labour summarises the literature on child time allocation, the types of work children participate in, the impact of work on schooling, health, and externalities associated with child work. It also considers the literature on the determinants of child time allocation (child labour) such as the influence of local labour markets, family interactions, the net return of schooling and poverty. Additionally, the paper discusses evidence on policy options aimed at influencing child labour.

Literature review of child labour

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Factors Fuelling the Persistence of Child Labour: Evidence from Pakistan

  • Published: 16 May 2024

Cite this article

literature review of child labour in nepal

  • Shahla Akram   ORCID: orcid.org/0000-0002-3857-3950 1 ,
  • Mehboob Ul Hassan   ORCID: orcid.org/0000-0002-3453-695X 2 &
  • Muhammad Farrukh Shahzad   ORCID: orcid.org/0000-0002-6578-4139 3  

The persistence of child labour globally can be attributed to a complex interplay of multifaceted factors. This study examines the relationship between these diverse factors of child labour, such as economic activities, working hours, hazardous conditions and overall prevalence. Logistic regression analysis was conducted using data from Pakistan’s Multiple Indicator Cluster Survey (MICS sixth wave). According to the data, poor quality education worsens child labour, while parental education and wealth protect against it. Gender differences, child disabilities, regional differences and non-violent behaviour all have significant impacts on labour force participation. This study highlights the complex interactions between socioeconomic and regional factors in determining child labour. It fills gaps in the existing literature by focusing on previously overlooked elements such as nonviolent behaviour and comprehensive disability interactions, as well as conducting a comprehensive examination of socioeconomic determinants. Understanding these dynamics is critical to targeted initiatives to eliminate child labour and ensure the well-being of children.

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The authors appreciate the support from the Deanship of Scientific Research under the Researchers Supporting Project number (RSPD2024R997), King Saud University, Riyadh, Saudi Arabia.

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Department of Economics, National College of Business Administration & Economics, Lahore, Pakistan

Shahla Akram

Department of Economics, College of Business Administration, King Saud University, Kingdom of Saudi Arabia, P.O. Box 7115, Riyadh, Saudi Arabia

Mehboob Ul Hassan

College of Economics and Management, Beijing University of Technology, Beijing, 100124, People’s Republic of China

Muhammad Farrukh Shahzad

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Akram, S., Hassan, M.U. & Shahzad, M.F. Factors Fuelling the Persistence of Child Labour: Evidence from Pakistan. Child Ind Res (2024). https://doi.org/10.1007/s12187-024-10141-6

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Impact of mHealth interventions on maternal, newborn, and child health from conception to 24 months postpartum in low- and middle-income countries: a systematic review

  • Marianne Ravn Knop 1   na1 ,
  • Michiko Nagashima-Hayashi 1   na1 ,
  • Ruixi Lin 1 ,
  • Chan Hang Saing 1 ,
  • Mengieng Ung 1 ,
  • Sreymom Oy 1 ,
  • Esabelle Lo Yan Yam 1 ,
  • Marina Zahari 1 &
  • Siyan Yi   ORCID: orcid.org/0000-0002-3045-5386 1 , 2 , 3  

BMC Medicine volume  22 , Article number:  196 ( 2024 ) Cite this article

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Mobile health (mHealth) technologies have been harnessed in low- and middle-income countries (LMICs) to address the intricate challenges confronting maternal, newborn, and child health (MNCH). This review aspires to scrutinize the effectiveness of mHealth interventions on MNCH outcomes during the pivotal first 1000 days of life, encompassing the period from conception through pregnancy, childbirth, and post-delivery, up to the age of 2 years.

A comprehensive search was systematically conducted in May 2022 across databases, including PubMed, Cochrane Library, Embase, Cumulative Index to Nursing & Allied Health (CINAHL), Web of Science, Scopus, PsycINFO, and Trip Pro, to unearth peer-reviewed articles published between 2000 and 2022. The inclusion criteria consisted of (i) mHealth interventions directed at MNCH; (ii) study designs, including randomized controlled trials (RCTs), RCT variations, quasi-experimental designs, controlled before-and-after studies, or interrupted time series studies); (iii) reports of outcomes pertinent to the first 1000 days concept; and (iv) inclusion of participants from LMICs. Each study was screened for quality in alignment with the Cochrane Handbook for Systematic Reviews of Interventions and the Joanne Briggs Institute Critical Appraisal tools. The included articles were then analyzed and categorized into 12 mHealth functions and outcome domain categories (antenatal, delivery, and postnatal care), followed by forest plot comparisons of effect measures.

From the initial pool of 7119 articles, we included 131 in this review, comprising 56 RCTs, 38 cluster-RCTs, and 37 quasi-experimental studies. Notably, 62% of these articles exhibited a moderate or high risk of bias. Promisingly, mHealth strategies, such as dispatching text message reminders to women and equipping healthcare providers with digital planning and scheduling tools, exhibited the capacity to augment antenatal clinic attendance and enhance the punctuality of child immunization. However, findings regarding facility-based delivery, child immunization attendance, and infant feeding practices were inconclusive.


This review suggests that mHealth interventions can improve antenatal care attendance and child immunization timeliness in LMICs. However, their impact on facility-based delivery and infant feeding practices varies. Nevertheless, the potential of mHealth to enhance MNCH services in resource-limited settings is promising. More context-specific implementation studies with rigorous evaluations are essential.

Peer Review reports

Despite the significant progress in maternal and child mortality globally, large inequities persist between and within countries [ 1 , 2 ]. Over 4.5 million women and babies die annually during pregnancy, childbirth, or the first weeks after birth. Most of these preventable deaths are concentrated in low- and middle-income countries (LMICs), especially among some geographical regions and populations, such as socio-economically vulnerable women in Sub-Saharan Africa and South Asia [ 1 , 2 , 3 ]. To address the challenge, strategies to integrate the programs across the maternal, newborn, and child health (MNCH) continuum have been adopted to lower costs while promoting greater efficiencies and reducing duplication of resources. The continuum of care strengthens healthcare quality, coverage, and affordability [ 4 , 5 ], as represented in the “first 1000 days” concept [ 6 , 7 ]. In LMICs, however, the degree of availability and quality of MNCH services varies considerably, and barriers, such as limited resources and poor information and communication infrastructures, compromise access to services [ 8 ].

With rapidly growing digital connectivity, the roles of mobile health technologies (mHealth) in addressing MNCH outcomes in LMICs have been recognized [ 9 , 10 , 11 ]. Expectations towards mHealth, in general, include its potential to improve the quality and coverage of healthcare, increase access to health information, services and skills, and promote positive changes in health behaviors to prevent the onset of acute and chronic diseases and improve treatment adherence and outcomes [ 10 , 11 , 12 , 13 , 14 ]. In LMICs, mHealth systems can potentially fill the critical gaps in human resources and information and communication infrastructures, reaching remote and marginalized populations and enhancing a range of low-cost life-saving interventions at the community level [ 11 , 12 , 15 , 16 ].

Studies of the efficacy of mHealth interventions vary in their design and focus, such as types of health outcomes and domains and mHealth functions. In their systematic review of systematic reviews on mHealth interventions, Marcolino et al. revealed that the most popular and successful mHealth interventions were behavior change approaches using text messaging due to the low cost and low broadband requirements [ 15 ]. However, the authors suggested further studies be conducted with more robust designs to confirm the efficacy of mHealth interventions [ 15 ]. Studies in LMICs involving mHealth technologies have often needed more representativeness, as populations most likely to benefit from the interventions (i.e., lower-income groups, women, older people, and rural populations) were excluded, owing to the lack of access to digital technologies [ 11 , 17 , 18 ]. Other systematic reviews have assessed the effectiveness of diverse mHealth interventions in LMICs targeting maternal, neonatal, and infant care individually or a combination thereof [ 8 , 19 , 20 , 21 , 22 , 23 ]. However, to the best of our knowledge, no systematic reviews have covered the MNCH spectrum, which covers a period of 1000 days from the time of conception to 2 years postpartum.

A qualitative content analysis of users’ perspectives of 75 applications for pregnant mothers and new parents revealed that women increasingly used mobile technology to improve their health literacy, monitoring, self-management, decision-making, and searching for credible information, such as how to establish breastfeeding and common infant health issues [ 24 , 25 ]. Women reported using the applications for multiple pregnancies [ 24 ], implying that such interventions offer a high potential for improving MNCH outcomes. Given the crucial need for such an integrated approach in LMICs, this systematic review will provide a comprehensive overview of available evidence and understanding of research gaps in mHealth for improving the continuum of MNCH care in LMICs by synthesizing the mHealth evidence encompassing the 1000 days. This study’s findings will support the policy decision and resource allocation for future interventions and research planning in resource-constrained settings.

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 26 ]. A detailed protocol has been registered with the International Prospective Register for Systematic Reviews (PROSPERO registration number: CRD42022354586).

Eligibility criteria

Articles were included in this review if they (i) primarily evaluated an mHealth intervention targeting MNCH outcomes; (ii) were designed as a randomized controlled trial (RCT), variations of RCT, quasi-experimental study, controlled before-and-after study, and interrupted time series study; (iii) reported outcomes relevant to the first 1000 days concept; (iv) involved participants from LMICs, according to the World Bank index [ 27 ] as of May 2022; and (v) were published in a peer-reviewed journal between 01 January 2000 and 31 May 2022. We excluded studies published before the year 2000 as we focused on more contemporary forms of mHealth that employed mobile technologies to ensure the relevance of this review. Outcomes were not pre-specified, given our interest in all outcomes related to MNCH from conception to 2 years postpartum. Therefore, we reported outcomes related to pregnant women, mothers and newborns, and children under the age of 2 years. Considering the extensive literature we identified, we included only articles published in peer-reviewed journals. Peer-reviewed articles are generally regarded as providing more trusted and reliable scientific information due to their adherence to rigorous methodological standards, as opposed to non-peer-reviewed sources.

We excluded studies (i) that did not have a control group, (ii) without accessible full-texts, and (iii) that were observational, such as cohort, case–control, cross-sectional and qualitative studies, expert opinions, reviews, project/program reports, discussion papers, or case reports. Initially, we did not restrict the publication language; however, we eventually excluded one article where translation from Thai to English was unavailable. We excluded studies that evaluated the willingness of participants to receive a mHealth intervention or the mHealth tool itself, as those outcomes are not directly relevant to MNCH outcomes.

Search strategy and information sources

We developed a systematic search strategy and quality assessment of the literature. We searched PubMed, Cochrane Library, Embase, Cumulative Index to Nursing & Allied Health (CINAHL), Web of Science, Scopus, PsycINFO, and Trip Pro in May 2022. Search terms included Medical Subject Headings (MeSH), title, abstract, and text words. The detailed search syntax can be found in Additional file 1 : Table A1. We used an online Polyglot Search Translator for database platforms [ 28 ]. Trip Pro required a different search approach, as specified in Table A1. We further searched literature via the snowballing effect by (i) reviewing relevant study protocols to identify publications reporting relevant intervention outcomes, (ii) reviewing previously published systematic reviews, and (iii) screening the reference lists of all articles included in this review.

We removed duplicate articles using Endnote (version 20.3). Two primary reviewers (MRK and RL) independently screened titles, abstracts, and full-text articles of potentially eligible articles against the inclusion and exclusion criteria. MRK extracted the data, and RL reviewed them to identify the following information: study design, research methods, location and settings, target population and size, mHealth function and forms, and research findings. We resolved discrepancies in the data selection and extraction by consensus or consulting a third reviewer within the study team.

Risk of bias assessment

MRK performed the quality assessment independently, while other team members (RL, MU, SC, SO) performed the second assessment. A third team member conducted an additional check to resolve discrepancies. We assessed intervention studies using the criteria of the Cochrane Handbook for Systematic Reviews of Interventions [ 29 ] and quasi-experimental studies using the Joanne Briggs Institute Critical Appraisal (JBI) tools [ 30 , 31 ]. We assessed the quality of studies using baseline-online-comparison designs with a control group using the JBI tool for quasi-experimental studies regardless of whether a randomization process was described.

We graded the risk of bias for RCTs into three levels (low, moderate, or high). Quasi-experimental studies received a grade according to the scale they were evaluated against. We considered the risk of bias in determining the strength of the conclusion [ 29 ].

Analysis and synthesis

We conducted systematic narrative and descriptive analyses of the 131 included articles [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 ] to capture the main characteristics of each study by mapping out the study designs, settings, population groups and sizes, intervention and control groups, outcome measures and results, outcome domains, and mHealth forms and functions (Additional file 2 : Table A2). For each study, at least two other authors further reviewed the analyzed characteristics and assigned categories to ensure consistency and rigor.

mHealth functions

We categorized the mHealth strategies adopted in each study into 12 mHealth functions described by Labrique et al. [ 163 ]. The 12 functions are (1) client education and behavior change communication (BCC), (2) sensors and point-of-care diagnostics, (3) registries and vital events tracking, (4) data collection and reporting, (5) electronic health records, (6) electronic decision support, (7) provider-to-provider communication, (8) provider work planning and scheduling, (9) provider training and education, (10) human resource management, (11) supply chain management, and (12) financial transactions and incentives. We further categorized the studies according to the outcomes measured under each health domain (antenatal care [ANC], delivery care, and postnatal care [PNC]).

Outcome domain categories

We categorized the intervention outcomes into three categories according to the relevant care period within the 1000-day timeframe (i.e., ANC, delivery care, and PNC). ANC included the outcomes such as the number of ANC visits, maternal micronutrient supplementation, medical treatment encompassing tetanus toxoid injection, and compliance to any prescribed procedures and tests (e.g., ultrasound examination, oral glucose tolerance test, urine tests, blood pressure measurement, and anemia assessment). The category “other ANC” encompassed outcomes such as depression, anxiety and stress, physical activity, and general health knowledge.

The “delivery care” category covered outcomes such as child delivery at health facilities and emergency obstetric care. The category “other delivery care” covered pregnancy outcomes, such as miscarriage, stillbirth, neonatal mortality, birth weight, birth preparation, childbirth complications, maternal and neonatal malnutrition screening, and neonatal asphyxia.

PNC outcomes included the number of postnatal visits, childhood immunization, breastfeeding practices, and prevention of mother-to-child HIV transmission (PMTCT). The category “other postnatal care” encompassed service utilization during the postnatal period for infectious diseases, neonatal and infant death, postnatal depression, contraception use, diet, physical activity, nutritional status monitoring, and family planning. Types of outcomes assessed by each study are listed in Additional file 3 : Table A3. In this article, we report the results of selected outcomes most frequently measured and reported in the reviewed studies, i.e., the number of ANC visits, the delivery rate at health facilities, child immunization rates, and child feeding practices.

Effect measures

The included studies varied on essential aspects, such as study design, quality, duration, and settings, as well as mHealth function and outcome specifications, such as the number and place of ANC/PNC visits and the number and type of vaccinations. We used forest plots to compare the effects across articles. After attempting multiple meta-analyses and sensitivity analyses, we found the heterogeneity too high ( I 2  > 90) for a meaningful meta-analysis. We, therefore, refrained from synthesizing any pooled effect measures from these studies.

Most articles reflected an odds ratio (OR) as the primary effect, and others reported risk ratios (RR). We calculated a crude risk ratio (cRR) when the primary effect size was not reported, while data on the outcomes in the intervention and control groups were available. We calculated those studies’ crude OR (cOR) for comparison and found less than a 7% difference between OR and RR. Only cRR was included in the review, which has an advantage, especially in the cases of small numbers, that our final estimate would tend to be more conservative. RCTs or cluster RCTs reporting pre- and post-effect measures for intervention and control groups were assumed to be balanced at baseline, given that all the reviewed publications were peer-reviewed. Hence, only post-intervention effect methods were taken into account. When a difference coefficient was reported, we converted it to an OR using an exponential function.

Included studies

We identified 7119 articles—6999 through database searches and 120 through published systematic reviews [ 8 , 19 , 20 , 21 , 22 , 23 , 164 ] and reference lists. Figure  1 illustrates the screening and complete study assessment processes, indicating the number of articles excluded for a given criterion. We included 131 articles based on 121 studies (55 RCTs, 39 cluster RCTs, and 37 quasi-experimental study articles). Geographically, 33 articles were from studies in East Africa (Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Tanzania, Uganda, and Zimbabwe), 16 from North and West Africa (Côte d`Ivoire, Egypt, Ghana, Guinea, Mali, and Nigeria), seven from Central and Southern Africa (Botswana, Cameroon, and South Africa), 25 from South Asia (Bangladesh, India, Nepal, and Pakistan), 15 articles from East Asia (China and Hong Kong), 11 from Southeast Asia (Cambodia, Indonesia, Malaysia, the Philippines, Thailand, and Vietnam), 16 from the Middle East (Iran, Palestine, and Turkey), and seven from South America and South Pacific (Brazil, Ecuador, Guatemala, and Samoa). One multi-country study reported combined findings from India, Mozambique, and Pakistan [ 153 ]. The study population comprised pregnant women and children between 0 and 2 years of age and their mothers. For cases of potential data overlap when studies were carried out in the exact geographical location or when publications were derived from the same interventions, all available articles were included as long as the outcomes of interest were relevant to our study objectives.

figure 1

Flow diagram of the study selection process

Synthesis of results

Additional file 4 : Table A4 summarizes the study characteristics, outcomes, mHealth functions and forms, and quality assessment results. Further details of the study intervention designs and resulting outcome effects can be found in Additional file 1 : Table A1.

Risk of bias

The detailed quality assessment results are available in Additional file 5 : Table A5a for RCTs and cluster RCTs and Additional file 5 : Table A5b for quasi-experimental studies. Of the 94 articles on RCTs and cluster RCTs, 43 were at low, 39 at moderate, and 12 at high risk of bias. The high risk of bias was primarily due to inappropriate randomization and incomplete data. As for the articles from quasi-experimental studies, out of the nine questions stipulated in the JBI checklist [ 31 ], nine scored 9/9, one scored 8/9, and the remaining 27 scored 7/9 or below. We used these scores to categorize the level of risk into three levels: high (9/9), moderate (8/9), and low (7/9 or below). RCT and cluster-RCT articles generally performed well, with 75 (80%) exhibiting a low risk of bias in randomization, 78 (83%) low risk in performance, 71 (76%) low risk of data completeness, 81 (86%) low risk in outcome measurements, and 90 (96%) low risk in reporting. Twenty-six RCT (47%) and 15 cluster-RCT (38%) articles displayed an overall low risk of bias, while eight (15%) RCT and four (10%) cluster-RCT articles displayed an overall high risk of bias. The quality of non-randomized experimental studies was generally compromised due to dissimilarities between comparison groups and the magnitude of missing data.

mHealth form and functions

Figure  2 shows the number of studies by mHealth functions. Out of 121 studies reviewed, 105 (86.8%) used mHealth Function 1 (client education and BCC), 17 (14.0%) used mHealth Function 4 (data collection and reporting), 13 (10.7%) used mHealth Function 6 (electronic decision support), 11 (9.1%) used mHealth Function 5 (electronic health records), and 10 (8.3%) used mHealth Function 3 (registries and vital events tracking). There was a high expectation of mHealth Function 1, typically used to deliver reminders or information (BCC) for pregnant women and mothers.

figure 2

Number of included studies by 12 mHealth functions

Studies used various delivery modes (voice calls, text messaging, transfer of still-moving images, multimedia message services, videos, or audio) of mHealth. Hence, we categorized mHealth forms as either unidirectional, bidirectional, or multi-directional communication between senders and receivers. Most mHealth innovations were designed as unidirectional communication using “push” technology to deliver information or reminders to subscribers’ phones. Messages were often tailored to personal needs, such as information according to gestational age or censored according to HIV status disclosure. Bidirectional communication occurred as short message chats or phone calls between senders and receivers (e.g., nurses and clients) and was commonly employed with unidirectional communication. Data collection and reporting through tablets, phones, and other devices were done using unidirectional or bidirectional communication systems. For example, the two-way communication approach using RapidSMS [ 130 ] provided community health workers (CHWs) with a dynamic tool for field data collection and clients’ access to supportive healthcare workers, leading to decentralized decision-making.

We identified three types of interventions with presumably different origins and objectives. The first and most frequent type includes interventions investigating the effectiveness of a single mHealth function, most commonly mHealth Function 1, used as unidirectional communication (e.g., appointment reminders and educational information delivered via text messages to clients). The second type of intervention applied multiple mHealth functions layered on existing parts of a healthcare system, attempting to fill a gap or expand its effectiveness via mHealth interventions. An example of this type is a study conducted in Ethiopia where health extension workers (HEWs) registered women in the intervention groups for their children’s immunization (mHealth Functions 3 and 4). Appointment reminders were sent to the HEWs (mHealth Function 8), who could call health centers for emergency referrals (mHealth Function 7) [ 40 ]. The third type of intervention used mHealth components simultaneously at several levels within the health system, combined with other inter-sectoral improvements, such as infrastructure and capacity of human resources. A study by Modi et al. is an example of the latter, where Accredited Social Health Activists (ASHAs) were trained to use Innovative Mobile-phone Technology for Community Health Operations (ImTeCHO), a mobile phone application, to improve the case management of pregnant women within their communities [ 104 ]. The latter intervention used nine of the 12 mHealth functions.

Effects on antenatal care (ANC)

Anc attendance.

The effect of mHealth interventions on ANC attendance was assessed in 26 studies, including nine RCTs, eight cluster RCTs, and nine quasi-experimental studies. Table 1 shows the individual effect estimates obtained in respective articles or calculated as cRR based on available data for binary outcomes (≥ 3 or < 3, ≥ 4 or < 4, and ≥ 6 or < 6 ANC visits). We did not include studies that did not allow us to calculate effect estimates. Of 26 articles, mHealth Function 1 (client education and BCC) was the most commonly used function among these studies, followed by mHealth 6 (electronic decision support) and Functions 8 (provider work planning and scheduling).

Regarding effectiveness, seven studies [ 40 , 42 , 50 , 54 , 65 , 120 , 152 ] showed robust effect estimates, providing evidence that mHealth interventions could increase the percentage of women receiving at least four ANC visits as recommended by the World Health Organization (WHO) for low-income countries [ 165 ]. In a study in South Africa, women in the intervention group who received text messages (mHealth Function 1) were more likely to attend at least four ANC visits than the routine care group [ 54 ]. In rural Ethiopia, healthcare workers serving the intervention groups had access to provider work planning and scheduling tools (mHealth Function 8) and received text message reminders to conduct ANC home visits. The results showed a 15%-point increase in ANC attendance from baseline to post-intervention, significantly higher than the control group [ 40 ].

Five studies showed higher rates of ANC visits in the mHealth intervention groups compared to the routine care groups [ 62 , 77 , 97 , 98 , 99 , 118 , 144 ]. However, many other studies found only a borderline significance. Studies in India [ 42 ], Guinea [ 65 ], and Kenya [ 120 ] suggested the effectiveness of their interventions using mHealth Function 1 in women attending at least four ANC visits. However, the risk of bias in these studies was high. Five studies found no significant effect of mHealth interventions on ANC attendance [ 66 , 100 , 107 , 108 , 125 , 139 ]. Seven articles presented results on ANC attendance in varying outcome formats and were not included in Table  1 . Of these seven articles, four studies did not assess the number of ANC attendance in isolation [ 95 , 122 , 131 , 156 ]. Both Li et al. and Sabin et al. reported composite outcomes, including ANC attendance, while Xie et al. and Paratmanitya et al. focused on the timing of the first ANC visit. The three remaining studies did not find a significant effect of mHealth interventions on their ANC outcomes [ 84 , 130 , 154 ]. An additional article by Coleman et al. [ 53 ] underwent full review; nevertheless, it was not included in Table  1 due to potential data overlap with a more recent article published by the same authors [ 54 ].

Effects on delivery care

Facility delivery.

The effect of mHealth interventions on place of delivery was assessed in six RCTs, 11 cluster RCTs, and five quasi-experimental studies. Table 2 displays the individual effect estimates obtained in individual articles or calculated as a cRR based on available data on the number of events in each group. mHealth Function 1 (education and BCC) was most commonly used ( n  = 12, 60%) [ 66 , 74 , 77 , 83 , 98 , 99 , 108 , 125 , 131 , 151 , 42 , 50 , 62 ] either as a sole function or one of the multiple functions employed in the intervention. mHealth Function 4 (data collection and reporting) was also commonly used ( n  = 9, 45%) [ 40 , 44 , 50 , 74 , 77 , 126 , 135 , 139 , 153 ], followed by mHealth Function 6 (electronic decision support, n  = 8, 40%) [ 44 , 50 , 74 , 77 , 125 , 126 , 135 , 153 ], mHealth Function 8 (provider work planning and scheduling, n  = 6, 30%) [ 40 , 50 , 74 , 77 , 125 , 139 ], mHealth Function 5 (electronic health records, n  = 6, 30%) [ 44 , 125 , 126 , 135 , 139 , 153 ], and mHealth Function 3 (registries and vital events tracking, n  = 5, 25%) [ 40 , 44 , 126 , 135 , 153 ]. Other functions used by other studies included mHealth Function 7 (provider-to-provider communication, n  = 2, 10%) [ 40 , 100 ], mHealth Function 9 (provider training and education, n  = 2, 10%) [ 83 , 139 ], and mHealth Function 12 (financial transactions and incentives, n  = 1, 5%) [ 152 ].

Eight articles included in this review presented the effect of mHealth interventions in increasing deliveries in health facilities, though with varied effect sizes [ 40 , 62 , 74 , 77 , 97 , 98 , 99 , 100 , 139 , 62 ]. In Uganda, village health teams conducted educational sessions with families on relevant MNCH topics and could call professional health workers (mHealth Function 7) on challenging matters [ 100 ]. The study found a significant difference in the proportion of facility delivery between the intervention and routine care groups. Another study in Tanzania equipped CHWs with smartphone-based job aids for data collection, decision-making support, and home-visit scheduling functions (mHealth Functions 4, 6, and 8). The CHWs were prompted to counsel pregnant women on the importance of the delivery place (mHealth Function 1). The proportion of women giving birth in a facility was significantly higher in the intervention than in the control group [ 74 ]. In a study in India, female frontline workers received mobile phone tools for scheduling reminders for ANC home visits. The proportion of women delivering in a health facility increased significantly in the intervention group relative to the control and the quasi-control groups [ 77 ]. In Kenya, ANC appointment reminders were sent to pregnant women directly with relevant educational information (mHealth Function 1) via text messages and phone calls. The study found a significant increase in facility delivery rates in the intervention group [ 62 ].

Two other articles from Rwanda and Nigeria found improvement in facility delivery [ 119 , 130 ]. However, we did not include them in Table  2 as the outcome format did not allow us to derive a comparable effect estimate. The remaining 12 studies did not find a significant increase in facility delivery rates attributable to the respective mHealth intervention [ 42 , 44 , 50 , 66 , 83 , 125 , 126 , 131 , 135 , 151 , 152 , 153 ].

Effect on postnatal care (PNC)

For PNC outcomes, we report findings on the most frequently measured outcomes among the reviewed articles—child immunization rates, exclusive breastfeeding, and early breastfeeding initiation.

Child immunization

Twelve articles assessed childhood immunization coverage per national guidelines until approximately 12 months of age [ 49 , 54 , 55 , 58 , 50 , 66 , 67 , 68 , 71 , 84 , 107 , 108 , 149 , 152 ], a combination of vaccinations for a shorter or longer duration [ 40 , 43 , 56 , 85 , 109 ], including boosters [ 125 , 134 ]. Nine RCTs, six cluster RCTs, and six quasi-experimental studies assessed the effect of mHealth interventions on childhood immunization. Table 3 displays the individual effect estimates obtained in individual articles or calculated as a cRR based on the available data on the number of events in each group.

As with the studies assessing other outcomes, mHealth Function 1 (education and BCC) was the most commonly used ( n  = 13/15) as a sole function or one of the multiple functions employed in the interventions. Two studies used other functions, such as financial transactions and incentives (mHealth Function 12), and one study used electronic health records (mHealth Function 5), electronic decision support (mHealth Function 6), and provider work planning and scheduling (mHealth Function 8).

As for the outcome effects, seven articles found that mHealth intervention improved immunization rates [ 43 , 49 , 58 , 84 , 107 , 108 , 149 , 152 ]. For example, a study in Zimbabwe sent text message reminders (mHealth Function 1) to women in the intervention group before the 6th, 10th, and 14th week vaccination appointments resulting in a significant increase in immunization coverage among the intervention group at 6 weeks (96.7% vs. 82.2%, p  < 0.001), 10 weeks (96.1% vs. 80.3%, p  < 0.001), and 14 weeks (94.7% vs. 75.0%, p  < 0.001) compared to the control group. Furthermore, the controls had a more significant delay in vaccination [ 43 ]. Three studies in Nigeria sent reminders to women using text messages, emails, or voice recordings (mHealth Function1) and increased immunization rates in intervention groups [ 49 , 58 , 84 ]. Similar findings were observed in studies in India [ 107 , 108 ] and Bangladesh [ 149 ]. In Kenya, women received conditional cash transfers (mHealth Function 12) equivalent to US$4.5 per visit to health facilities for ANC, delivery, PNC, and childhood immunization. A modest increase in childhood immunization appointments was reported [ 152 ].

However, eight studies did not find significant effects of mHealth interventions on immunization [ 50 , 54 , 66 , 71 , 85 , 109 , 125 , 134 ]. We did not include six studies [ 40 , 55 , 56 , 67 , 68 , 116 ] in Table  3 because the outcomes reported did not allow us to extract or calculate effect estimates. Among these studies, the results were contradictory, with two studies showing significant mHealth intervention effects on immunization rates Field [ 75 , 82 ], while four had no significant impact.

Feeding practices

Table 4 shows the outcomes of exclusive breastfeeding reported in 17 papers [ 34 , 39 , 46 , 47 , 50 , 63 , 64 , 67 , 68 , 78 , 79 , 80 , 86 , 91 , 102 , 112 , 140 , 146 , 151 , 155 ]. Six of these studies additionally assessed the effect of mHealth on early breastfeeding initiation within one-hour post-delivery [ 34 , 46 , 64 , 140 , 155 , 50 ].

We reviewed seven articles on early breastfeeding initiation, as shown in Table  5 , including a study from India [ 107 , 108 ]. Some studies also assessed the effect on colostrum feeding [ 46 , 47 , 64 , 107 , 108 , 140 ], pre-lacteal feeding [ 46 , 47 , 140 , 155 ], complementary feeding, supplementary feeding, bottle feeding, formula feeding, and breastfeeding awareness [ 34 , 39 , 61 , 100 , 102 , 124 , 135 , 140 ].

In terms of mHealth functions, all 18 articles on exclusive breastfeeding and early breastfeeding initiation used mHealth Function 1 (education and BCC). A study in India additionally used mHealth Function 4 (data collection and reporting), mHealth Function 6 (electronic decision support), and mHealth Function 8 (provider work planning and scheduling) [ 50 , 107 , 108 ].

Results of the effectiveness of mHealth interventions on exclusive breastfeeding and early breastfeeding initiation were mixed. Nine studies [59, 79-86, 89-] found moderate to higher exclusive breastfeeding rates attributable to mHealth interventions, of which two [ 64 , 140 ] further demonstrated their effectiveness on early breastfeeding initiation. Examples of effective exclusive breastfeeding interventions include an RCT study in Iran in which pregnant women in the intervention group received breastfeeding self-efficacy education sessions, information booklets, and biweekly text messages (mHealth Function 1). The exclusive breastfeeding rates differed significantly between the intervention and control groups at 8 weeks postpartum [ 39 ]. In a study in Bangladesh [ 78 ], nurses underwent training on infant and young child feeding. They subsequently provided women in the intervention group with tailor-made support on breastfeeding, contacted them biweekly, and had a lactation consultant available as needed. The exclusive breastfeeding rate was significantly higher among the intervention than the control group.

Studies reporting effectiveness in exclusive breastfeeding and early breastfeeding initiation include a cluster RCT in Nigeria, where pregnant women were provided with breastfeeding learning sessions and educational text messages (mHealth Function 1), together with songs and dramas conveying the information and messages. The study found significantly higher rates of exclusive breastfeeding at six months and early breastfeeding initiation in the intervention group than in the routine care group [ 64 ]. A similar study in India demonstrated strong effects of the mHealth intervention on prolonging exclusive breastfeeding and early breastfeeding initiation compared to a control group receiving routine care [ 140 ]. However, a study in India [ 50 ] in which ASHAs were equipped with a mobile application to provide health information, guidelines, and checklists (mHealth Function 6), patient tracking and data collection (mHealth Function 4), and automated scheduling tools (mHealth Function 8) found no evidence of improved exclusive breastfeeding six months postpartum. However, the effect on early breastfeeding initiation was statistically significant. Seven studies found no significant impact of mHealth interventions on exclusive breastfeeding rates [ 34 , 50 , 63 , 86 , 91 , 146 , 151 , 155 ]. We did not include a study in Malawi [ 67 , 68 ] in Table  4 because the reported outcome did not allow us to extract an effect estimate.

Overall, this systematic review suggests that mHealth interventions targeting MNCH may increase attendance in ANC. However, the high heterogeneity between studies and the limited reporting quality prohibited calculating a pooled estimate. mHealth interventions can be considered adequate for improving vaccination timeliness for those who attend their appointments. However, the effects of mHealth on facility-based deliveries or child immunization attendance were inconsistent. The synthesized evidence suggests the positive impact of mHealth reminders and information provision on ANC and PNC attendance, although the effects were moderate [ 22 , 166 , 167 , 168 , 169 ]. A review by Colaci et al. found that text messages enhanced the acceptability of maternal care among pregnant women, including skilled birth attendance [ 168 ]. Another meta-analysis of studies from 11 LMICs by Eze et al. suggests that SMS reminders can contribute to achieving high and timely childhood immunization coverage [ 170 ]. Concerning the feeding practice, the effects of mHealth were inconsistent, which may reflect a complex interplay of barriers in promoting exclusive breastfeeding [ 171 ]. However, improving awareness among pregnant women and mothers and performing regular follow-ups are crucial to addressing low breastfeeding rates [ 172 , 173 , 174 ], and the significant role the mHealth may play is envisaged.

Besides the study quality, the inconsistent results in this review may be due to the complex interaction of a plethora of determinants that mHealth cannot fully address. The factors may include sociocultural beliefs, economic and physical accessibility, knowledge and perception of benefits and needs, and service quality [ 175 ]. The mHealth behavior change interventions must be designed based on theoretically validated mechanisms and guided by formative research of the specific target populations and their behavioral determinants [ 176 , 177 ]. In the LMIC context, the gap mHealth can fill is often not the only missing link to improve the MNCH [ 178 ]. For example, nudging women with information and reminders may not necessarily result in women delivering at facilities or improving feeding practices, as these behaviors are highly affected by socio-economic, environmental, cultural, and health system factors [ 175 , 179 ]. In this context, evaluating mHealth interventions implemented with high fidelity may provide an opportunity to identify further gaps in health programming.

In terms of mHealth functions, we observed that all 12 functions of mHealth described by Labrique et al. [ 163 ] was used in the reviewed articles. The most frequently used function among the reviewed studies by far was “client education and BCC” (mHealth Function 1), as seen in past reviews [ 22 , 164 , 167 ], providing relevant information and reminders for ANC/PNC appointments, childbirth, immunization, and breastfeeding, which had the advantage of simplicity, feasibility, and achievability.

mHealth functions as direct support for health workers (mHealth Functions 6–9) were employed in 7–13% of the studies. These mHealth interventions may have had an indirect impact on the health outcomes of the beneficiaries. However, these functions were often used alongside other functions that directly targeted the beneficiaries, and the effect attributable to each function was not measured independently. The potentially powerful sensors and point-of-care diagnostics, human resource management, supply chain management, and financial transaction (mHealth Functions 2, 10, 11, and 12) were not commonly used in the reviewed studies, reflecting a possible limitation of our search strategy or a genuine scarcity of such interventions in the area of MNCH in LMICs.

Concerning quality, our analysis found that several factors may account for the absence of definitive results in this review: (1) moderate or high risk of bias among the more significant proportion of studies (62%); (2) lack of power due to small sample size (a characteristic of pilot studies), high rates of loss to follow-up, and the multitude of outcomes reported by each study (especially for educational interventions); (3) data reliability of self-reported outcomes (such as with infant feeding practices); and (4) circumstantial challenges such as technological failures, staff turnover, and relocation of participants. Studies have pointed out that mHealth studies are typically under-theorized, poorly specified, and vaguely described, and as a result, lack the specific rigor required for experimental studies [ 8 , 180 , 181 ]. We found that the articles in this review commonly would have benefitted from more detailed descriptions of randomization processes, allocation concealment, and blinding, without which the validity of the methodology could not be established. Referencing the evaluation guidelines for reporting evidence of mHealth interventions, such as the Mobile Application Rating Scale (MARS) [ 182 ] and WHO’s mHealth Evidence Reporting and Assessment (mERA) checklist, in addition to standard guidance on trials such as Consolidated Standards of Reporting Trials (CONSORT) [ 183 , 184 ] is strongly recommended for future studies.

Our review further exposed the critical need to consider the digital infrastructure and technical capacity in LMIC settings, which can often be the significant barriers to flourishing mHealth implementation [ 180 , 185 ]. There persist apparent age and sex, not to mention urban–rural gaps in access to mobile communication technology, especially in LMICs [ 186 ]. In the reviewed studies, mobile phone ownership was often a prerequisite to participation in mHealth programs, and some of the participating women relied on shared devices with partners or families. When devices are shared, client confidentiality and autonomy can be compromised. Mobile phone ownership, literacy, rural and urban residency, and socio-economic status could risk further marginalizing vulnerable groups [ 21 , 187 , 188 ]. For the HCPs and CHWs, health systems and the workforce often lack the capacity to manage data and digital technology [ 189 ], and the introduction of mHealth tools could burden the users [ 190 ]. At the same time, mHealth is often considered to promote their empowerment, autonomy, and improved incentives. Implementation science research to explore usability, feasibility, and acceptability in the specific context is strongly recommended as part of RCTs to enhance the adoption and informativeness of the overall trial interventions [ 191 , 192 ]. Coupled with high-quality evidence with large-scale and more rigorous RCT designs to establish the validity and cost-effectiveness of mHealth interventions, accumulating such evidence will guide the replication and scaling-up of effective intervention models while enabling optimal allocation of limited resources in the LMICs.

This systematic review focused exclusively on experimental and quasi-experimental studies at the risk of neglecting the complete picture of the currently available evidence. This selection was to ensure the quality of the review by excluding observational studies, which lack internal (i.e., methodological strength) and external (i.e., generalizability) validity. We limited our search to English-language papers published in peer-reviewed journals, which may have resulted in the omission of informative articles on trials, including those conducted by organizations outside conventional academia. By focusing on LMICs, we excluded the studies in high-income countries, including studies investigating mHealth use in disadvantaged or marginalized populations in those countries, who may have had much in common with residents of LMICs. Finally, we acknowledge the time lapse between the initial search and the completion of the analysis. The comprehensive analyses necessitated more than 12 months to complete, involving meticulous review of a significant number of included studies. This extensive process ensured accurate comparison of effect measures across heterogeneous studies, precise categorization, thorough quality assessment, and comprehensive descriptive reporting.

Our review demonstrated that mHealth interventions could be a practical approach to increase ANC attendance and improve the timeliness of child immunization. However, their effects on facility-based deliveries, child immunization coverage, and breastfeeding practices were inconclusive. Nonetheless, mHealth’s potential to fill the longstanding gaps in BCC and data collection in resource-limited LMICs is unquestionable. However, while the number of mHealth studies in LMICs has been proliferating, weak and inconsistent evidence continues to plague the field, thus preventing us from drawing robust conclusions. Further quantitative research with high rigor to assess the effectiveness of mHealth and implementation research to explore the context-specific facilitators and intervention barriers are highly warranted.

Availability of data and materials

No additional data are available.


Antenatal care

Accredited Social Health Activist

Behavior change communication

Community health worker

Cumulative Index to Nursing & Allied Health

Crude odds ratio

Crude risk ratio

Consolidated Standards of Reporting Trials

Health care provider

Health extension worker

Innovative Mobile-phone Technology for Community Health Operations

Joanne Briggs Institute

Low- and middle-income country

Mobile Application Rating Scale

Medical subject heading

Maternal, newborn, and child health

MHealth Evidence Reporting and Assessment

Mobile health

Prevention of mother-to-child transmission

Postnatal care

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

International Prospective Register for Systematic Reviews

Randomized controlled trial

Short message service

World Health Organization

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This systematic review was conducted as part of the formative study to support the development of i-MoMCARE (Innovative Mobile Technology for Maternal and Child Health Care in Cambodia), a cluster randomized controlled trial funded by the Bill & Melinda Gates Foundation. It was also partly funded by the UHS-SSHSPH Integrated Research Programme, Saw Swee Hock School of Public Health, National University of Singapore.

This review was funded by the Bill & Melinda Gates Foundation as part of the i-MoMCARE trial (INV-022514) and the UHS-SSHSPH Integrated Research Programme, Saw Swee Hock School of Public Health, National University of Singapore.

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Marianne Ravn Knop and Michiko Nagashima-Hayashi contributed equally to this work.

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Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore

Marianne Ravn Knop, Michiko Nagashima-Hayashi, Ruixi Lin, Chan Hang Saing, Mengieng Ung, Sreymom Oy, Esabelle Lo Yan Yam, Marina Zahari & Siyan Yi

KHANA Center for Population Health Research, Phnom Penh, Cambodia

Public Health Program, College of Education and Health Sciences, Touro University California, Vallejo, CA, USA

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MRK and SY conceived the study. MRK, MN-H, and RL conducted the search and retrieval. MRK, MN-H, RL, and SY conducted the analyses and drafted the manuscript. CHS, MU, SO, ELYY, MZ, and SY reviewed and provided critical inputs. All authors read and approved the final manuscript.

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Additional file 1: table a1 search strategy, additional file 2: table a2 descriptive review results, additional file 3: table a3 outcomes by health domains, additional file 4: tables a4a and a4b descriptive review and results, additional file 5: table a5a and a5b quality assessment results, rights and permissions.

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Knop, M.R., Nagashima-Hayashi, M., Lin, R. et al. Impact of mHealth interventions on maternal, newborn, and child health from conception to 24 months postpartum in low- and middle-income countries: a systematic review. BMC Med 22 , 196 (2024). https://doi.org/10.1186/s12916-024-03417-9

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  4. Breaking the child labour cycle through education: issues and impacts

    Introduction. The status of Nepalese children of families who seasonally migrate in-country for employment in brick kilns is precarious. Despite child labour being illegal in Nepal, children are conspicuously involved in paid and unpaid work in this hazardous occupation, and face specific barriers to engagement in education (Ministry of Education Citation 2015).

  5. Understanding Children's Work in Nepal : Report on child labor

    Daily Updates of the Latest Projects & Documents. The current report as part of UCW project activities in Nepal. It provides an overview of the child labor phenomenon in the Kingdom - its extent and nature, its .

  6. Legal knowledge and child labour in Nepal: Does knowing the law make a

    Drawing on data from communities in the Terai of Nepal, we use mixed methods to find correlations between legal knowledge, compliance with child labour laws and prevalence of child labour. We draw on two novel data sets: a large household survey of parents and children; and a small census of owners of brick kilns, where child labour is often seen.


    LITERATURE REVIEW 10-26 2.1 Child labor: A global overview 10 2.2 Child labor in Nepal 12 2.3 Legal provision on Child labor 16 2.3.1 National prospective 16 2.3.2 Nepal's international commitment 19 2.4 Cause and consequence of child labor 20 2.5 Education and child labor 21 2.6 Micro bus and child labor 23 2.7 Conceptual framework 26

  8. PDF 2021 Findings on the Worst Forms of Child Labor: Nepal

    Children in Nepal are subjected to the worst forms of child labor, including commercial sexual exploitation and forced begging. Children also perform dangerous tasks in producing bricks. (1,2) In 2021, the Government of Nepal, along with the ILO, published disaggregated data on child labor in Nepal based on the Nepal Labor Force Survey from ...

  9. PDF 2022 Findings on the Worst Forms of Child Labor: Nepal

    In 2022, Nepal made moderate advancement in efforts to eliminate the worst forms of child labor. The Government of Nepal announced the liberation of the Haruwa-Charuwa agricultural bonded laborers and promised to establish a recovery program and provide restitution for this group. The police also removed 27 children exploited as bonded laborers ...

  10. Nepal Child Labour Report 2021

    The statistics reveal that child labour is still significant although the overall trend is declining in Nepal (2.6 million in 1998, 1.6 million in 2008 and 1.1 million in 2018). Agriculture is found to be the sector with highest per cent of child labour (87%) and dalit children constitute the highest (19.4%) proportion based on caste and ethnicity.

  11. PDF Lucy P

    This paper presents a systematic review of the literature and case study focusing on hazardous child labor in the brick kilns of Nepal. There has been a range of suggested intervention frameworks to address hazardous labor in Nepal. Yet there is a dearth of research evidence linking current intervention models to principles of best practice in

  12. Hazardous child labor in Nepal: The case of brick kilns

    Hazardous child labor in Nepal is a serious concern, particularly in the brick kiln industry. ... This literature review revealed a significant gap in this evidence base with only one study out of the 16 reviewed considered strong methodologically, with all remaining studies having very low level empirical evidence. Academic research in this ...

  13. PDF Child Labour in Nepal

    This thesis entitled Child Labour in Nepal: A Case Study of Micro-bus conductors of Kathmandu Valley", prepared by Mr. Mithilesh Mishra for the ... REVIEW OF LITERATURE 2.1 Child Labour: An Overview 9 2.2 Causes of Increasing Child Labour 11 2.3 Child Labour and Legal and Policy Provision in Nepal 16

  14. The Effect of Remittances on Child Labor and Child Education in Nepal

    The rest of this paper is divided as follows: in Section II is a review of some of the literature pertaining to child labor, education, and remittances, particularly in Nepal. In section III is described the theoretical model to be analyzed, and the theory behind Heckman's two stage regression in particular.

  15. Child labor situation in Nepal: challenges and ways forward

    According to the Nepal Child Labor Report 2021 prepared by the International Labor Organisation (ILO), 1.1 million children aged 5 to 17 years are engaged in child labor (in 2018) compared to 2.6 million (in 1998). Whilst national statistics show some improvement, other studies have shown grave concern for children engaged in the hidden and ...

  16. Child labour rises to 160 million

    Indeed, the Nepal Child Labour Report 2021, a joint publication of the ILO and Central Bureau of Statistics shows a declining trend of overall child labour in Nepal, reaching 1.1 million in 2018 from 1.6 million in 2008. A significant decline is observed in the number of children in hazardous occupations (0.62 million in 2008 to 0.22 million in ...

  17. Findings on the Worst Forms of Child Labor

    In 2022, Nepal made moderate advancement in efforts to eliminate the worst forms of child labor. The Government of Nepal announced the liberation of the Haruwa-Charuwa agricultural bonded laborers and promised to establish a recovery program and provide restitution for this group. The police also removed 27 children exploited as bonded laborers from brick kilns in southern Nepal and the Nepal ...

  18. PDF Child labor in Nepal

    Child labor in Nepal A Case study of Hotels and Restaurants of Gangabu-4, New Bus Park-Area A Thesis submitted to The Faculty of Humanities and social sciences, ... Review of Literature 2.1. Review of Literature 8 2.2. Meaning and Definition of Children 9 CHAPTER THREE Research methodology 3.1. Source of Data and Data Collection Method 14

  19. (PDF) The Situation of Child Labour in Nepal: An Analysis (With

    Usually, the number of child labour is higher in economically poor countries. In the context of Nepal, about 47.8 percent of children are still involved in some form of work. Even though the latest data are not available, the figures for 2014 show that 27.4 percent of children are employed as child labour. Of the children involved in work, 45. ...

  20. (PDF) A Survey of Literature on Child Labour

    The Economics of Child Labour in t he Era of Globalization by Sarbajit Chaudhu ri and Jayanta Kumar. Dwibedi, Routledge 2016. 1. Chapter 1. A Survey of Literature on Child Labour. 1.1 Introduction ...

  21. National Master Plan on Child Labour (Nepali version)

    Ten year National Master Plan on Child Labour (Nepali version), endorsed by Cabinet on July 08, 2018. Author (s) Government of Nepal Ministry of Labour, Employment and Social Security. Publication date. July 2018. Languages. Nepali.

  22. PDF Child labour in Nepal :A Case study of Hotels and Resturants of Gangabu

    Review of Literature 2.1. Review of the literature Review of the literatures is an important part of any kind of study. It gives insight to the researcher and what other says on the topic She/he is going to research on .It also helps to analyze the situation more deeply to examine the gap in the past existing reviewed on literatures.

  23. Literature review of child labour

    Literature review of child labour. This comprehensive literature review of child labour summarises the literature on child time allocation, the types of work children participate in, the impact of work on schooling, health, and externalities associated with child work. It also considers the literature on the determinants of child time ...

  24. Factors Fuelling the Persistence of Child Labour: Evidence from

    The literature review uncovers gaps in understanding the dynamics of child labour and education. While existing research highlights the connection between education and child labour, comprehensive studies on educational quality, socioeconomic factors, cultural influences and gender differences are lacking.

  25. Impact of mHealth interventions on maternal, newborn, and child health

    Background Mobile health (mHealth) technologies have been harnessed in low- and middle-income countries (LMICs) to address the intricate challenges confronting maternal, newborn, and child health (MNCH). This review aspires to scrutinize the effectiveness of mHealth interventions on MNCH outcomes during the pivotal first 1000 days of life, encompassing the period from conception through ...

  26. PDF Ethical guidelines for research on child labour

    Method 1. Literature review and development of decision tree X A literature review was conducted of relevant sources, including those suggested by the ILO, and searches were conducted by the research team, with a focus on guidelines for national surveys. X A decision tree was developed to serve as guidance on when to pursue