Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 23, 2023
Date Accepted: Dec 12, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Towards Comprehensive mHealth App Analysis: Metadata Analysis of Heart Disease Apps in India
ABSTRACT
Background:
Smartphone health apps (mHealth apps) have gained significant popularity recently. mHealth apps can address healthcare disparities and enhance access to healthcare services in low-resource countries. With its vast and diverse population, India presents a unique context for studying the landscape of mHealth apps. However, only a few studies explored the breadth of mHealth apps available in India.
Objective:
Our study aimed to address this critical gap and aid in comprehensive evaluations by introducing an automated method for collecting data on mHealth apps. We aimed to create a comprehensive dataset initially focusing on heart disease-related apps.
Methods:
We collected individual app data from apps in the "Medical" and "Health & Fitness" categories from the Google Play Store and the Apple App Store in December 2022 and July 2023, respectively. Using Natural Language Processing (NLP) techniques, we selected apps relevant to heart diseases (HD apps), performed statistical analysis, and applied Latent Dirichlet Allocation (LDA) for clustering and topic modelling to categorise the resulting HD apps.
Results:
We collected 118.555 apps from the Apple App Store and 108.945 apps from the Google Play Store. Within these datasets, we found that approximately 1.7 percent apps on the Apple App Store and 0.5 percent on the Google Play Store included support for Indian languages. Using monograms and bigrams related to heart disease, we identified 1.681 HD apps from the Apple App Store and 588 HD apps from the Google Play Store. HD apps make only a small fraction of the total number of health apps available in India. About 90 percent of the HD apps are free of cost. However, more than 70 percent of HD apps have no reviews and rating scores, indicating low usage. Exploratory correlation analysis revealed that apps with longer descriptions tend to have higher rating-scores. Clustering analysis of these HD apps revealed three prominent clusters in both the Apple App Store and Google Play Store datasets - Clinical apps, Fitness & Lifestyle apps, and Sleep & Wellbeing apps.
Conclusions:
Our paper contributes to mapping mHealth apps in India. It offers a user-friendly approach for researchers, potentially overcoming barriers to accessing and analysing valuable health app metadata. This approach's adaptability to other low-resource settings and diverse health issues makes it useful for further research and analysis.
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