This paper is in the following e-collection/theme issue:

Quality Evaluation and Descriptive Analysis/Reviews of Multiple Existing Mobile Apps (374) mHealth for Wellness, Behavior Change and Prevention (2871) Mobile Health (mhealth) (2939) Obesity (75)

Published on in Vol 10, No 5 (2022): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15719, first published .
The Quality of Indian Obesity-Related mHealth Apps: PRECEDE-PROCEED Model–Based Content Analysis

The Quality of Indian Obesity-Related mHealth Apps: PRECEDE-PROCEED Model–Based Content Analysis

The Quality of Indian Obesity-Related mHealth Apps: PRECEDE-PROCEED Model–Based Content Analysis

Authors of this article:

Shanmuga Nathan Selvaraj1 Author Orcid Image ;   Arulchelvan Sriram1 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

    Original Paper

    Department of Media Sciences, College of Engineering, Anna University, Chennai, India

    Corresponding Author:

    Shanmuga Nathan Selvaraj, BSc, MSc

    Department of Media Sciences

    College of Engineering

    Anna University

    No 12 Sardar Patel Rd

    Guindy

    Chennai, 600025

    India

    Phone: 91 99946 63036

    Email: shunmu.nathu@gmail.com


    Background: The prevalence of obesity in India is increasing at an alarming rate. Obesity-related mHealth apps have proffered an exciting opportunity to remotely deliver obesity-related information. This opportunity raises the question of whether such apps are truly effective.

    Objective: The aim of this study was to identify existing obesity-related mHealth apps in India and evaluate the potential of the apps’ contents to promote health behavior change. This study also aimed to discover the general quality of obesity-related mHealth apps.

    Methods: A systematic search for obesity-related mHealth apps was conducted in both the Google Play Store and the Apple App Store. The features and quality of the sample apps were assessed using the Mobile Application Rating Scale (MARS) and the potential of the sample apps’ contents to promote health behavior change was assessed using the PRECEDE-PROCEED Model (PPM).

    Results: A total of 13 apps (11 from the Google Play Store and 2 from the Apple App Store) were considered eligible for the study. The general quality of the 13 apps assessed using MARS resulted in mean scores ranging from 1.8 to 3.7. The bivariate Pearson correlation between the MARS rating and app user rating failed to establish statistically significant results. The multivariate regression analysis result indicated that the PPM factors are significant determinants of health behavior change (F3,9=63.186; P<.001) and 95.5% of the variance (R2=0.955; P<.001) in the dependent variable (health behavior change) can be explained by the independent variables (PPM factors).

    Conclusions: In general, mHealth apps are found to be more effective when they are based on theory. The presence of PPM factors in an mHealth app can greatly influence the likelihood of health behavior change among users. So, we suggest mHealth app developers consider this to develop efficient apps. Also, mHealth app developers should consider providing health information from credible sources and indicating the sources of the information, which will increase the perceived credibility of the apps among the users. We strongly recommend health professionals and health organizations be involved in the development of mHealth apps. Future research should include mHealth app users to understand better the apps’ effectiveness in bringing about health behavior change.

    JMIR Mhealth Uhealth 2022;10(5):e15719

    doi:10.2196/15719

    Keywords

    obesity; mHealth apps; PRECEDE-PROCEED Model; Mobile App Rating Scale; health communication; health behavior change techniques; health information 


    Background

    Obesity is an alarming health issue that leads to significant health and social difficulties for people globally. Generally, obesity is defined by the measurement of the BMI [1]. Per clinical guidelines, a BMI of 25 kg/m2 to 29.9 kg/m2 indicates overweight or preobesity and a BMI of 30 kg/m2 or greater indicates obesity [2]. Obesity is associated with all-cause mortality. The health consequences of obesity are vast, including cardiovascular diseases, diabetes, musculoskeletal disorders, and some cancers, such as endometrial, breast, and colon cancer. The next generations are in a more dangerous position since the health consequences of childhood obesity are extensive, including premature death and disability in adulthood [3].

    Obesity in India

    An increase in the consumption of junk food and the adoption of sedentary lifestyles are the major reasons for the increase in the prevalence of obesity in India. According to the India National Family Health Survey-4, the number of people with obesity in India doubled between 2006 and 2016. The prevalence of obesity among women ages 5 to 49 years in India is 20.7%, which is a 60% increase from 2005 to 2006. The prevalence of obesity among men ages 5 to 49 years in India doubled to 18.6% from 9.3% in the year 2005 to 2006 [4,5]. A study involving 14.4 million children in India revealed that the country has the second-highest prevalence of childhood obesity in the world after China [3]. The prevalence of obesity in India is increasing at an alarming rate.

    Obesity and Media

    Obesity is the fastest-growing global public health issue and media campaigns can increase public awareness of obesity [6]. Media campaigns are found to be more effective in raising awareness about the causes of obesity, health problems associated with obesity, and healthy habits to prevent and manage obesity [7,8]. Public attention to a particular issue correlates with the degree of salience of the issues covered in the media. Media can be used to provide information as simply as possible and to update the information constantly [9]. Though media can have an impact on knowledge and attitudes about obesity among the public, evidence is still limited as to whether media can influence health behavior change [10].

    mHealth Apps for Obesity

    Television was the dominant form of media for increasing obesity awareness, but with the rapid advance of digital media, the evaluation of other media, such as internet-based media, is increasingly important [10]. The most recent and fastest evolving internet-based media is mobile media [11]. Substantially, mobile media are used for the delivery of health information [12]. The World Health Organization defined mHealth as medical and public health practices supported by mobile devices [13]. Smartphones have gained popularity and are being adopted for mHealth practices. There are different types of mHealth apps developed and available for general use in obesity management [14]. The benefits of mHealth apps include cost-effectiveness, the potential for real-time data collection, feedback capability, minimized participant burden, relevance to multiple populations, and increased dissemination capability [15]. Obesity-related mHealth apps have proffered an exciting opportunity to remotely deliver obesity-related information. This opportunity raises the question of whether such apps are truly effective. Therefore, the purpose of this study was to identify existing obesity-related mHealth apps in India and evaluate the potential of the app contents in promoting health behavior change.

    The PRECEDE-PROCEED Model

    The PRECEDE-PROCEED Model (PPM) is a widely accepted health education framework for planning and evaluating health behavior change programs [16,17]. The anticipated influence on health behavior change can be evaluated by the presence of 3 factors in health interventions, predisposing factors, enabling factors, and reinforcing factors. Predisposing factors include the following variables, which act as antecedents to health behavior change: knowledge, attitudes, beliefs, values, and motivation. Enabling factors include the following variables, which act as antecedents that facilitate health behavior change: teaching skills, providing resources, providing a service, and tracking progress. Reinforcing factors include the following variables, which provide rewards or feedback for health behavior change: interacting with health professionals to obtain support and interfacing with social media sites for encouragement [18]. This study attempts to identify the presence of PPM variables in Indian obesity-related mHealth apps for promoting health behavior change. This study also aimed to examine the overall quality of obesity-related mHealth apps.


    This study involved a qualitative content analysis of the available obesity-related mHealth apps in the Google Play Store and Apple App Store.

    Study Sample

    There are studies showing that mHealth app users are more likely to use free apps, which is why most previous studies on mHealth apps focused only on free apps [19] (R Subramanian, PhD, unpublished data, August 2015). Likewise, this study will focus only on free obesity-related mHealth apps. Free obesity-related apps were identified using the following search terms in the Google Play Store and Apple App Store during June 2021: “obesity”, “obese”, “obesity calculator”, “obesity diet”, and “obesity exercise”. An app was considered for inclusion if the app content had obesity related-information and the app was rated above 3 out of 5 stars.

    Measurement

    Each sample app was coded for basic descriptive information, such as the app name, user rating, and the number of downloads. The features and quality of the sample apps were assessed using the Mobile Application Rating Scale (MARS) [20-22] and the potential of the app contents to promote health behavior change was assessed using the PPM [18]. MARS is a measure for classifying and assessing the quality of mHealth apps. The MARS uses a Likert scale ranging from 1 (inadequate) to 5 (excellent) to score apps on the following criteria: engagement, functionality, aesthetics, information quality, and subjective quality [22]. The PPM (Figure 1) was used to measure each app according to its level of anticipated influence on health behavior change.

    ‎
    Figure 1. Framework of PRECEDE-PROCEED Model factors influencing health behaviour change [23].
    View this figure

    Data Collection

    The MARS and PPM were explained to 2 coders, who were researchers studying mHealth apps with several years of experience and a good knowledge of mHealth apps [24,25]. The coding sheet is presented in Multimedia Appendix 1. The coders were instructed on each measure and its definition to ensure clear differentiation between the items used to assess the sample apps [20]. Both coders assessed the content of the sample apps independently. Finally, the researchers and the coders discussed disagreements until a consensus was reached [18].

    Data Analysis

    Descriptive statistics were calculated for all items under the MARS and PPM. The Cronbach α was used to evaluate the reliability between each item under the 5 criteria of the MARS, engagement, functionality, aesthetics, information quality, and subjective quality. The Pearson correlation coefficient was then calculated to determine the relationship between the MARS rating and app user rating. The Cronbach α was used to evaluate the reliability between each measure item under the 3 factors of the PPM (predisposing factors, enabling factors, and reinforcing factors) and items used by reviewers to assess the app’s ability to promote health behavior change. Multivariate regression analysis was then performed to test the influence of PPM factors on the app’s ability to promote health behavior change, as assessed by reviewers.


    mHealth App Sample Selection

    The initial search with the following search terms resulted in 2483 apps from the Google Play Store (n=1732) and the Apple App Store (n=751): “obesity”, “obese”, “obesity calculator”, “obesity diet”, and “obesity exercise”. Figure 2 shows a flowchart of the obesity-related mHealth app selection process. Descriptive information on the sample apps is presented in Multimedia Appendix 2.

    ‎
    Figure 2. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) flowchart of the obesity related mHealth apps selection process.
    View this figure

    General Quality: MARS

    Among the Google Play Store apps chosen for the study (Table 1), Fitpaa- Your Fitness Dad received the highest score in the engagement (4.6) and information (4.2) categories. The app Fat to Fit – lose weight at home female workout received the highest score in the functionality domain (4.5); Weight Loss Diet 7 Day Detox Cleanse received the highest score in the aesthetics domain (4.3) and Indian Diet Plans received the highest score in the subjective quality (4.0) domain. Among the Apple App Store apps chosen for the study, Jeewith received the highest score in the functionality (3.2), and aesthetics (4.0) domains and IFSO received the highest score in the engagement (2.6), information (3.1), and subjective quality (2.0) domains. Fitpaa – Your fitness dad and Obesity Treatment received the highest overall mean scores based on each dimension of the MARS (3.7).

    Table 1. The quality of obesity-related mHealth apps based on the Mobile Application Rating Scale.
    App NameEngagementFunctionalityAestheticsInformationSubjective qualityOverall score
    Google Play Store apps

    		Weight Loss Protocols3.24.23.03.83.53.5

    		Fat to Fit – lose weight at home female workout4.44.53.03.22.53.5

    		Fitpaa – Your fitness dad4.63.73.04.23.253.7

    		Lose Belly Fat Guide2.03.52.01.51.02.0

    		Help for Kids Health and Diet3.23.73.02.73.03.1

    		Obesity Treatment3.44.04.04.03.23.7

    		Obesity Guide1.63.71.61.41.01.8

    		Indian Diet Plans3.64.03.62.84.03.6

    		Obesity Treatments2.83.02.02.01.52.2

    		Weight Loss Diet 7 Day Detox Cleanse3.04.24.32.11.73.1

    		Child Diet Guide2.64.02.61.81.02.4
    Apple App Store apps

    		Jeewith2.43.24.02.11.02.5

    		IFSO2.63.03.63.12.02.8

    MARS Rating Versus User App Rating

    The reliability of the dimensions of the MARS scores for the sample apps was found to be strongly consistent (Cronbach α=.938). Internal reliability was found to be strong for the subjective quality domain (α=.947), good for the aesthetics (α=.820) and information (α=.888) domains, and fair for the engagement (α=.791) domain. Internal reliability was found to be poor for the functionality (α=.645) domain, so the performance measure item was removed and after doing so, the internal reliability was found to be good (α=.826).

    The bivariate Pearson correlation was computed to test the relationship between the MARS rating and user app rating. The results (Table 2) show that the MARS rating and user app rating are not statistically significantly correlated (R=0.258; P=.39).

    Table 2. The correlation between the Mobile Application Rating Scale (MARS) rating and user app rating (n=13).
    RatingUser app ratingMARS rating
    User app rating

    		r10.258

    		P valuea—b.39
    MARS rating

    		r0.2581

    		P valuea.39—

    aP values are derived from a 2-tailed t test.

    bNot applicable.

    The Presence of PPM Factors

    Apart from the causes for obesity listed in the coding sheet (Table 3), there were a few other causes mentioned in the sample apps, which include sleep deprivation, certain medications, a diet with high amounts of simple carbohydrates, biological causes, hormonal causes, and the frequency of eating. Apart from the effects of obesity listed in the coding sheet, there were a few more effects mentioned in the study sample apps, including gall stone formations, gout and gouty arthritis, insulin resistance, Alzheimer disease, social stigmatization, depression among youth, sleep apnea, joint problems, liver disease, infertility, and effects on sperm quality.

    Table 3. The presence of PRECEDE-PROCEED Model factors within the reviewed (n=13) obesity-related mHealth apps.
    Factors, variables, and itemsApps, n (%)
    Predisposing factors

    		Knowledge and information


    		About obesity6 (46)


    		Geneticsa5 (38)


    		Overeatinga6 (46)


    		Physical inactivitya5 (38)


    		Social issuesa2 (15)


    		Psychological factorsa3 (23)


    		Hypothyroidisma2 (15)


    		Type 2 diabetesb6 (46)


    		High blood pressureb5 (38)


    		High cholesterolb3 (23)


    		Strokeb5 (38)


    		Heart attackb5 (38)


    		Cancerb6 (46)


    		What is BMI?4 (31)


    		Classification of BMI6 (46)


    		BMI calculator5 (38)

    		Attitudes, beliefs, and values


    		Requires log-in3 (23)


    		Mentions the sources of information2 (15)


    		Exercise tips from a physiotherapist2 (15)


    		Food recommendations from a nutritionist3 (23)

    		Confidence and motivation


    		Color indication to create fear1 (8)


    		Testimonial0 (0)
    Enabling factors

    		Teach skills


    		Walkingc3 (23)


    		Swimmingc1 (8)


    		Cyclingc0 (0)


    		Exercise precaution1 (8)


    		Diet plan9 (69)

    		Provide resources


    		Food calorie chart2 (15)


    		Healthy recipes4 (31)


    		Nutritional breakdown of specific food items1 (8)


    		Representations of food with images1 (8)


    		In appd3 (23)


    		External linkd0 (0)


    		Image demonstration for exercise2 (15)

    		Provide services


    		Treatment for obesity (surgery)4 (31)

    		Track or record behavior


    		Calorie or food tracker0 (0)


    		Exercise tracker3 (23)


    		BMI tracker3 (23)


    		Weekly or monthly report of calories consumed0 (0)


    		Weekly or monthly report of exercise progress0 (0)


    		Goal setting3 (23)


    		Reminders1 (8)
    Reinforcing factors

    		Interfacing with social media sites for encouragement


    		Sharing completion of exercises or weight reduction on social media2 (15)

    		Support and encouragement


    		Community2 (15)


    		Interaction with health professionals2 (15)


    		Interaction with a trainer or coach2 (15)


    		Games0 (0)

    		Rewards


    		Rewards for goal completion2 (15)

    aThese items are classified as causes of obesity.

    bThese are effects of obesity.

    cThese are general exercise recommendations.

    dThese are video demonstrations for exercises.

    The Relationship Between PPM Factors and Health Behavior Change

    Table 4 presents the internal consistency (Cronbach α) of PPM variables and the internal consistency of the measure items under the reviewer’s assessment of the app’s ability to promote health behavior change. All the measure items of PPM factors and the app’s ability to promote health behavior change were found to be internally consistent.

    A multivariate regression analysis was performed to test the influence of PPM factors on the app’s ability to promote health behavior change, as assessed by the reviewers. The results from Table 5, Table 6, and Table 7 show that the PPM factors are significant determinants of health behavior change (F3,9=63.186; P=.001). The value of R=0.977 indicates a strong positive correlation and R2=0.955 indicates that 95.5% of the variance in the dependent variable (health behavior change) can be explained by the independent variables (PPM factors).

    Table 4. The internal consistency of PRECEDE-PROCEED Model (PPM) variables.
    PPM factors and variablesExcluded itemsaInternal consistency of itemsInternal consistency of variables
    Predisposing factors

    		Knowledge and informationNone.938.911

    		Attitudes, beliefs, and valuesNone.855

    		Confidence and motivationTestimonialNot performed as there is only one item
    Enabling factors

    		Teaching skillsCycling and exercise precaution.710.845

    		Providing resourcesNone.830

    		Providing servicesNoneNot performed as there is only one item

    		Tracking or recording Behavior
    		.756
    Reinforcing factors

    		Interfacing with social mediaNoneNot performed as there is only one item.960

    		Support and encouragementNone.899

    		RewardsNoneNot performed as there is only one item
    App’s ability to promote health behavior change

    		Enough information to bring about health behavior change (predisposing factors)N/AbN/A.827

    		Enough resources to bring about health behavior change (enabling factors)N/AN/A

    		Enough support to bring about health behavior change (reinforcing factors)N/AN/A

    aThese items were excluded from analysis as there is no variance in scores between the apps, or the items were deleted.

    bN/A: not applicable. There are no items associated with these variables.

    Table 5. Model summary for the regression analysisa between PRECEDE-PROCEED Model factors and the reviewer’s assessment of the app’s ability to promote health behavior change.
    ModelRR2Adjusted R2Standard error of the estimate
    10.9770.9550.9400.50642

    aPredictors: constant and reinforcing, predisposing, and enabling factors.

    Table 6. ANOVA results for the regression analysisa between PRECEDE-PROCEED Model factors and the reviewer’s assessment of the app’s ability to promote health behavior change. All data are based on model 1 from the regression analysis.

    		Sum of squaresDegrees of freedomMean squareF test (df)P value
    Regression48.615316.20563.186 (3).001b
    Residual2.3089.256N/AcN/A
    Total50.92312N/AN/AN/A

    aDependent variable: reviewer’s assessment of the app’s ability to promote health behavior change.

    bPredictors: constant and reinforcing, predisposing, and enabling factors.

    cN/A: not applicable.

    Table 7. Coefficients from the regression analysisa between PRECEDE-PROCEED Model factors and the reviewer’s assessment of the app’s ability to promote health behavior change. All data are based on model 1 from the regression analysis.
    PredictorsUnstandardized coefficientsStandardized coefficients

    		βStandard errorβt test (df)P value
    (Constant)3.9220.259
    		15.165 (12).001
    Predisposing factors0.1120.024.3394.649 (12).001
    Enabling factors0.2570.065.4403.930 (12).003
    Reinforcing factors0.5810.123.5304.746 (12).001

    aDependent variable: reviewer’s assessment of the app’s ability to promote health behavior change.


    Principal Findings

    This study aimed to examine the features and quality of obesity-related mHealth apps using the MARS and assess the presence of factors that promote health behavior change using the PPM. We analyzed a total of 13 obesity-related mHealth apps, 11 from the Google Play Store and 2 from the Apple App Store. The Apple App Store had a much lower number of obesity-related mHealth apps compared to the Google Play Store. Regarding the overall quality of the 13 apps assessed using the MARS, the mean scores ranged from 1.8 to 3.7. This study supports the findings of previous studies that suggest when mHealth apps focus heavily on the functionality domain of the MARS, the performance, ease of use, navigation, and gestural design are compromised [20]. The subjective quality domain of the MARS depends on all 4 domains, engagement, functionality, aesthetics, and information. Among all 4 domains, the apps in this study scored the lowest in information. The information domain comprises accuracy, goals, quality of information, quantity of information, visual information, credibility, and evidence-based information. The absence of sources of information in most of the apps studied affected the credibility score and the evidence-based information score. These findings support the findings of previous studies which established that mHealth apps containing evidence-based information and information from credible sources receive high scores in the information domain of the MARS [26] and mHealth apps that do not include sources of information receive the lowest scores [27]. Among the studied apps, all received moderate mean scores for each of the 4 domains of the MARS, engagement, functionality, aesthetics, and information; this affected the mean score for the subjective quality domain of the study sample apps since the subjective quality domain depends on the other 4 domains of the MARS.

    There are many mHealth apps currently available for various health issues; finding an appropriate app among the wide selection for a particular health issue is challenging for users [9,28]. Normally, users select an mHealth app based on ratings and reviews; thus, ratings become key for any app to be downloaded by new users [28,29]. We failed to establish a statistically significant Pearson correlation coefficient between MARS scores and the ratings of study sample apps in the app store. This nonsignificant result may be due to information asymmetry between coders and app users with regard to the app quality attributes. The trustworthiness of apps with few ratings may also be compromised by fake reviews from app developers; this may partly explain the nonsignificant result [30].

    Most of the study sample apps were established upon predisposing factors to address obesity, including the following variables: knowledge and information about obesity; attitudes, beliefs, and values; and confidence and motivation. Commonly, mHealth app users will form judgements about apps’ contents by evaluating the information using web-based platforms, especially when they come across unfamiliar information about health conditions, and they use the sources of the information to judge its credibility [31]. Therefore, mentioning the sources of information and ensuring that recommendations of exercise and diet plans are provided by health professionals is important; this was found in only a small number of study sample apps. None of the sample apps had testimonials, but previous studies strongly recommended apps add testimonials or narrative messages that focus on real experiences of users, which can lead to strong emotional arousal among users and are an important factor in promoting health behavior change [32,33].

    With regard to enabling factors, the teaching skills variable was found in a number of study sample apps. One of the least common enabling factors among the apps was the ability to track or record behavior, which contradicted a previous study on diabetes management apps [34]. Previous studies found that the tracking facility in mHealth apps proved to be motivating and influenced health behavior change among app users, especially for weight loss [35,36]. Self-tracking of food and exercise helps users set goals and track their achievements [9]. The self-tracking, goal setting, and daily, weekly, or monthly reporting features in mHealth apps were found to be very helpful in bringing about health behavior change [9], but those features were also only found in a small number of study sample apps. One important finding from the study is that 69% (9/13) of the sample obesity-related mHealth apps specified diet plans as a measure to address obesity, but only 23% (n=3) of sample apps included exercise as a recommendation. This finding supports the findings of previous studies that the mHealth apps focus either on physical activity or dieting practices, but not equally on both for weight loss [37].

    Reinforcing factors, which include interfacing with social media sites for encouragement, support and encouragement from a community or health professionals, and rewards for goal completion, were found to be present in only 2 apps among the study sample, 1 from the Google Play store and 1 from the Apple App Store. This finding is consistent with the findings of previous studies that only a few mHealth apps allow users to connect the app to external systems or communities, such as social media platforms [18]. Sharing task completion on social media is the most welcomed feature by mHealth app users because they can obtain emotional support and motivation from others [9]. Such mobile features help or guide users to undergo health behavior change by establishing interactions with health professionals, allowing them to gain support from their peer group, and providing them with access to a virtual coach. Past studies have shown that a lack of motivation and social support among mHealth app users reduces the likelihood of health behavior change [38]. This study found that most of the sample mHealth apps did not include reinforcing factors, which are considered vital in bringing about health behavior change among app users.

    Limitations

    The findings of this study should be taken into consideration with some limitations. First, the obesity-related mHealth apps used in the analysis were free; analyses including paid apps may produce different results since paid apps are generally given extra care during the development of all aspects of the app. This study is not supported by any funding, which is the reason for the omission of paid versions of obesity-related mHealth apps. Similarly, we were also unable to download and study inaccessible apps, which required log-in credentials from an affiliated health care organization or clinic [39]. Second, the study did not collect data from actual users of the mHealth apps; doing so may result in a better understanding of the influence of the apps’ features on health behavior change. This may also open up a new dimension to this study.

    Conclusion

    There are numerous mHealth apps available in the Google Play Store and the Apple App Store to promote health behavior change. Previous studies have shown that mHealth apps are more effective when they are based on scientific theories [18]. This study found that the presence of PPM factors in an mHealth app can greatly influence users’ health behavior change. So, this study suggests that mHealth app developers consider this when developing efficient apps. Also, mHealth app developers should consider providing health information from credible sources and including the sources of the information, which will increase the perceived credibility of the apps among users. Users of mHealth apps vary in gender and age group; so, mHealth app developers should concentrate on providing general health behavior tips that can be used by all gender and age groups or tips for specific gender and age groups. Though there are numerous mHealth apps available, there is a paucity in the involvement of health professionals and health organizations in the development of these apps. Most of the available mHealth apps bypass regulations and nationally recognized health guidelines (R Subramanian, PhD, unpublished data, August 2015). So, we strongly suggest health experts be directly involved in the development of mHealth apps rather than third-party developers [37]. The findings of this study make several contributions to the current literature related to mHealth apps. Future research should include actual mHealth app users to better understand the apps’ effectiveness in bringing about health behavior change.

    Conflicts of Interest

    None declared.

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    Multimedia Appendix 1

    Coding sheet.

    DOCX File , 20 KB
    ‎
    Multimedia Appendix 2

    Descriptive information of study sample apps.

    PNG File , 2192 KB
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    ‎
    MARS: Mobile Application Rating Scale
    PPM: PRECEDE-PROCEED Model

    Edited by L Buis, A Mavragani; submitted 01.08.19; peer-reviewed by DM Hardey, T Powell-Wiley, C Carrion, J Alvarez Pitti, S Smith, T Haggerty; comments to author 21.07.20; revised version received 05.08.21; accepted 20.02.22; published 11.05.22

    Copyright

    ©Shanmuga Nathan Selvaraj, Arulchelvan Sriram. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 11.05.2022.

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