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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.
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.
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).
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 (
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.
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 [
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 [
Obesity is the fastest-growing global public health issue and media campaigns can increase public awareness of 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 [
The PRECEDE-PROCEED Model (PPM) is a widely accepted health education framework for planning and evaluating health behavior change programs [
This study involved a qualitative content analysis of the available obesity-related mHealth apps in the Google Play Store and Apple App Store.
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 [
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) [
Framework of PRECEDE-PROCEED Model factors influencing health behaviour change [
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 [
Descriptive statistics were calculated for all items under the MARS and PPM. The Cronbach
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”.
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) flowchart of the obesity related mHealth apps selection process.
Among the Google Play Store apps chosen for the study (
The quality of obesity-related mHealth apps based on the Mobile Application Rating Scale.
App Name | Engagement | Functionality | Aesthetics | Information | Subjective quality | Overall score | |
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Weight Loss Protocols | 3.2 | 4.2 | 3.0 | 3.8 | 3.5 | 3.5 |
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Fat to Fit – lose weight at home female workout | 4.4 | 4.5 | 3.0 | 3.2 | 2.5 | 3.5 |
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Fitpaa – Your fitness dad | 4.6 | 3.7 | 3.0 | 4.2 | 3.25 | 3.7 |
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Lose Belly Fat Guide | 2.0 | 3.5 | 2.0 | 1.5 | 1.0 | 2.0 |
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Help for Kids Health and Diet | 3.2 | 3.7 | 3.0 | 2.7 | 3.0 | 3.1 |
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Obesity Treatment | 3.4 | 4.0 | 4.0 | 4.0 | 3.2 | 3.7 |
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Obesity Guide | 1.6 | 3.7 | 1.6 | 1.4 | 1.0 | 1.8 |
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Indian Diet Plans | 3.6 | 4.0 | 3.6 | 2.8 | 4.0 | 3.6 |
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Obesity Treatments | 2.8 | 3.0 | 2.0 | 2.0 | 1.5 | 2.2 |
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Weight Loss Diet 7 Day Detox Cleanse | 3.0 | 4.2 | 4.3 | 2.1 | 1.7 | 3.1 |
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Child Diet Guide | 2.6 | 4.0 | 2.6 | 1.8 | 1.0 | 2.4 |
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Jeewith | 2.4 | 3.2 | 4.0 | 2.1 | 1.0 | 2.5 |
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IFSO | 2.6 | 3.0 | 3.6 | 3.1 | 2.0 | 2.8 |
The reliability of the dimensions of the MARS scores for the sample apps was found to be strongly consistent (Cronbach
The bivariate Pearson correlation was computed to test the relationship between the MARS rating and user app rating. The results (
The correlation between the Mobile Application Rating Scale (MARS) rating and user app rating (n=13).
Rating | User app rating | MARS rating | |
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1 | 0.258 |
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—b | .39 | |
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0.258 | 1 |
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.39 | — |
a
bNot applicable.
Apart from the causes for obesity listed in the coding sheet (
The presence of PRECEDE-PROCEED Model factors within the reviewed (n=13) obesity-related mHealth apps.
Factors, variables, and items | Apps, n (%) | ||||
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About obesity | 6 (46) | ||
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Geneticsa | 5 (38) | ||
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Overeatinga | 6 (46) | ||
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Physical inactivitya | 5 (38) | ||
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Social issuesa | 2 (15) | ||
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Psychological factorsa | 3 (23) | ||
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Hypothyroidisma | 2 (15) | ||
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Type 2 diabetesb | 6 (46) | ||
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High blood pressureb | 5 (38) | ||
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High cholesterolb | 3 (23) | ||
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Strokeb | 5 (38) | ||
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Heart attackb | 5 (38) | ||
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Cancerb | 6 (46) | ||
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What is BMI? | 4 (31) | ||
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Classification of BMI | 6 (46) | ||
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BMI calculator | 5 (38) | ||
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Requires log-in | 3 (23) | ||
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Mentions the sources of information | 2 (15) | ||
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Exercise tips from a physiotherapist | 2 (15) | ||
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Food recommendations from a nutritionist | 3 (23) | ||
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Color indication to create fear | 1 (8) | ||
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Testimonial | 0 (0) | ||
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Walkingc | 3 (23) | ||
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Swimmingc | 1 (8) | ||
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Cyclingc | 0 (0) | ||
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Exercise precaution | 1 (8) | ||
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Diet plan | 9 (69) | ||
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Food calorie chart | 2 (15) | ||
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Healthy recipes | 4 (31) | ||
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Nutritional breakdown of specific food items | 1 (8) | ||
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Representations of food with images | 1 (8) | ||
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In appd | 3 (23) | ||
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External linkd | 0 (0) | ||
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Image demonstration for exercise | 2 (15) | ||
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Treatment for obesity (surgery) | 4 (31) | ||
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Calorie or food tracker | 0 (0) | ||
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Exercise tracker | 3 (23) | ||
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BMI tracker | 3 (23) | ||
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Weekly or monthly report of calories consumed | 0 (0) | ||
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Weekly or monthly report of exercise progress | 0 (0) | ||
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Goal setting | 3 (23) | ||
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Reminders | 1 (8) | ||
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Sharing completion of exercises or weight reduction on social media | 2 (15) | ||
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Community | 2 (15) | ||
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Interaction with health professionals | 2 (15) | ||
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Interaction with a trainer or coach | 2 (15) | ||
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Games | 0 (0) | ||
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Rewards for goal completion | 2 (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.
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
The internal consistency of PRECEDE-PROCEED Model (PPM) variables.
PPM factors and variables | Excluded itemsa | Internal consistency of items | Internal consistency of variables | |
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Knowledge and information | None | .938 | .911 |
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Attitudes, beliefs, and values | None | .855 |
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Confidence and motivation | Testimonial | Not performed as there is only one item |
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Teaching skills | Cycling and exercise precaution | .710 | .845 |
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Providing resources | None | .830 |
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Providing services | None | Not performed as there is only one item |
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Tracking or recording Behavior |
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.756 |
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Interfacing with social media | None | Not performed as there is only one item | .960 |
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Support and encouragement | None | .899 |
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Rewards | None | Not performed as there is only one item |
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Enough information to bring about health behavior change (predisposing factors) | N/Ab | N/A | .827 |
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Enough resources to bring about health behavior change (enabling factors) | N/A | N/A |
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Enough support to bring about health behavior change (reinforcing factors) | N/A | N/A |
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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.
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.
Model |
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Adjusted |
Standard error of the estimate |
1 | 0.977 | 0.955 | 0.940 | 0.50642 |
aPredictors: constant and reinforcing, predisposing, and enabling factors.
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.
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Sum of squares | Degrees of freedom | Mean square | ||
Regression | 48.615 | 3 | 16.205 | 63.186 (3) | .001b |
Residual | 2.308 | 9 | .256 | N/Ac | N/A |
Total | 50.923 | 12 | N/A | N/A | N/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.
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.
Predictors | Unstandardized coefficients | Standardized coefficients | |||
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Standard error |
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(Constant) | 3.922 | 0.259 |
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15.165 (12) | .001 |
Predisposing factors | 0.112 | 0.024 | .339 | 4.649 (12) | .001 |
Enabling factors | 0.257 | 0.065 | .440 | 3.930 (12) | .003 |
Reinforcing factors | 0.581 | 0.123 | .530 | 4.746 (12) | .001 |
aDependent variable: reviewer’s assessment of the app’s ability to promote health behavior change.
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 [
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 [
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 [
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 [
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 [
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 [
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 [
Coding sheet.
Descriptive information of study sample apps.
Mobile Application Rating Scale
PRECEDE-PROCEED Model
None declared.