Predictors of the Number of Installs in Psychiatry Smartphone Apps: Systematic Search on App Stores and Content Analysis

Abstract

BackgroundMental health is integral to our salubrity, but mental disorders are very debilitating and common. Therefore, it is critical to provide accessible, timely, and inexpensive mental care. This can be done through mobile health (mHealth), namely, mobile medical apps, which are gaining popularity among clinicians and patients. mHealth is a fast-paced field, and there is significant variation in the number of installs among psychiatry apps. However, the factors that influence psychiatry app installs have yet to be studied.ObjectiveThis study aimed to identify predictors of the number of app installs in psychiatry.MethodsA literature review identified which factors influence app installs. Psychiatry apps available in the Google Play Store were reviewed, and publicly available data were collected. A multivariate ordinal logistic regression analysis was performed to evaluate the effect of said factors on the number of installs.ResultsOur search identified 128 psychiatry apps: 2.3% (3/128) had never been installed, approximately half (53.1%, 68/128) had less than 500 installs, and only 0.8% (1/128) had over 10,000,000 installs. A multivariate logistic regression analysis identified that apps with a lower price (P<.001), a higher rating (P<.001), optional in-app purchases (P<.001), and age restriction (P=.04) had a higher number of installs. The involvement of a psychiatrist or other health care professional (HCP) had no statistically significant influence on the number of installs. Only data from the Google Play Store and the developers’ websites were available for analysis, and the depth of involvement of HCPs was impossible to document.ConclusionsPsychiatry apps with a lower price, optional in-app purchases, age restriction, and a higher rating are expected to have a higher number of installs. Unlike other medical fields, in this study, the explicit participation of psychiatrists in app development was not a significant predictor of the number of installs. Research is needed to identify other factors that may influence the number of installs, as that can help mHealth app development.

What affects consumer behavior in mobile health professional diagnosis applications

“…The comments and rating of other colleagues that have already used the application and the quality of the app, including engagement, functionality, aesthetics, and information quality are also important factors in their decision to download the app. This finding is also discussed and empirically proved in many other related studies investigating the influential factors of mHealth users (Carare, 2012; Ghose & Han, 2014; Kübler et al., 2018; Pinheiro et al., 2019; Krishnan & Selvam, 2019; Biviji et al., 2020).…”

“…Researchers highlight the moderators of the effects of online ratings and whether they have become ubiquitous (Kübler et al., 2018) indicating that download choices are shaped by other participants preferences (Carare, 2012; Krishnan & Selvam, 2019; Pinheiro et al., 2019; Biviji et al., 2020). Based on that, we also hypothesize that: H4: Social influence (STAR) of app star rating is positively related to downloads (DL) …”

“…The dependent variable derives from the number of downloads of each app provided by the app store. We chose the particular variable following the approach of numerous studies that selected the particular measure to determine which apps are preferred for use (Ghose & Han, 2014; Pereira‐Azevedo et al., 2016; Engström & Forsell, 2018; Krishnan & Selvam, 2019; Pinheiro et al., 2019; Biviji et al., 2020). In our case, the purpose is to measure the intention to try and use the innovation intervention offered by these mHealth apps.…”

Targeting the Needs of Self-Determination Theory: An Overview of Mental Health Care Apps

“…Download rates were included for apps available from Google Play only as iTunes does not report download information. Apps were reviewed according to titles, descriptions, and imagery; a supported method of data collection in mHealth research (Pinheiro et al, 2019;Wali et al, 2019). The coding scheme was developed by using the conceptualization of the SDT variables outlined earlier.…”

Business analytics in healthcare operations and the use of mobile applications for decision making by health professionals

“…The DV is downloads and the are numerous different IVs Empirical analysis of mHealth apps specifically for Psychiatry with data collected from Google Play Store determined through a regression model that a greater number of downloads was significantly associated with apps with higher user rating, cheaper apps and with available in-app purchases but not with number of reviews, app size, number of screenshots, length of description, availability in the Apple App Store and new published versions. (Pinheiro et al 2019) The study takes a similar approach but includes all free mHealth apps for professionals, for all medical specialties, incorporating also factors of reliability and usefulness as major IV Empirical analysis of mHealth apps specifically for Maternal and Infant Health with data collected from Google Play and Apple App stores determined through a regression model that a greater number of downloads was significantly positively associated with user rating, in-app purchases and in-app advertisements and negatively associated with app price and period since last update (Biviji et al 2020) The study takes a similar approach but includes all free mHealth apps for professionals, for all medical specialties, incorporating also factors of reliability and usefulness as major IV Empirical analysis of mHealth apps specifically for diabetes with data collected from Google Play Store determined through a regression model that a greater number of downloads was significantly positively associated with apps with higher user rating, free apps, app age, developed in US but negatively with months since last update (Krishnan and Selvam 2019) The study takes a similar approach but includes all free mHealth apps for professionals, for all medical specialties, incorporating also factors of reliability and usefulness as major IV H5.AB is positively related to BI There are no identified studies that investigate the effect of app quality (measured in other ways than user rating) to app downloads.…”