  Background Despite the benefits offered by an abundance of health
applications promoted on app marketplaces (e.g., Google Play Store), the wide
adoption of mobile health and e-health apps is yet to come. Objective This
study aims to investigate the current landscape of smartphone apps that focus
on improving and sustaining health and wellbeing. Understanding the categories
that popular apps focus on and the relevant features provided to users, which
lead to higher user scores and downloads will offer insights to enable higher
adoption in the general populace. This study on 1,000 mobile health
applications aims to shed light on the reasons why particular apps are liked
and adopted while many are not. Methods User-generated data (i.e. review
scores) and company-generated data (i.e. app descriptions) were collected from
app marketplaces and manually coded and categorized by two researchers. For
analysis, Artificial Neural Networks, Random Forest and Na\"ive Bayes
Artificial Intelligence algorithms were used. Results The analysis led to
features that attracted more download behavior and higher user scores. The
findings suggest that apps that mention a privacy policy or provide videos in
description lead to higher user scores, whereas free apps with in-app purchase
possibilities, social networking and sharing features and feedback mechanisms
lead to higher number of downloads. Moreover, differences in user scores and
the total number of downloads are detected in distinct subcategories of mobile
health apps. Conclusion This study contributes to the current knowledge of
m-health application use by reviewing mobile health applications using content
analysis and machine learning algorithms. The content analysis adds significant
value by providing classification, keywords and factors that influence download
behavior and user scores in a m-health context.
