%0 Journal Article
%@ 2291-5222
%I JMIR Publications
%V 8
%N 2
%P e16741
%T Assessment of Mobile Health Apps Using Built-In Smartphone Sensors for Diagnosis and Treatment: Systematic Survey of Apps Listed in International Curated Health App Libraries
%A Baxter,Clarence
%A Carroll,Julie-Anne
%A Keogh,Brendan
%A Vandelanotte,Corneel
%+ School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Queensland, 4059, Australia, 61 488539311, c.baxter@hdr.qut.edu.au
%K telehealth
%K mHealth
%K smartphone
%K mobile apps
%K instrumentation
%K health care quality
%K health care access
%K and health care evaluation
%K medical informatics
%K consumer health informatics
%K physician-patient relations
%K prescriptions
%K patient participation
%K patient-generated health data
%K diagnostic self evaluation
%K self-care
%K self-management
%K medical device legislation
%D 2020
%7 3.2.2020
%9 Original Paper
%J JMIR Mhealth Uhealth
%G English
%X Background: More than a million health and well-being apps are available from the Apple and Google app stores. Some apps use built-in mobile phone sensors to generate health data. Clinicians and patients can find information regarding safe and effective mobile health (mHealth) apps in third party–curated mHealth app libraries. Objective: These independent Web-based repositories guide app selection from trusted lists, but do they offer apps using ubiquitous, low-cost smartphone sensors to improve health? This study aimed to identify the types of built-in mobile phone sensors used in apps listed on curated health app libraries, the range of health conditions these apps address, and the cross-platform availability of the apps. Methods: This systematic survey reviewed three such repositories (National Health Service Apps Library, AppScript, and MyHealthApps), assessing the availability of apps using built-in mobile phone sensors for the diagnosis or treatment of health conditions. Results: A total of 18 such apps were identified and included in this survey, representing 1.1% (8/699) to 3% (2/76) of all apps offered by the respective libraries examined. About one-third (7/18, 39%) of the identified apps offered cross-platform Apple and Android versions, with a further 50% (9/18) only dedicated to Apple and 11% (2/18) to Android. About one-fourth (4/18, 22%) of the identified apps offered dedicated diagnostic functions, with a majority featuring therapeutic (9/18, 50%) or combined functionality (5/18, 28%). Cameras, touch screens, and microphones were the most frequently used built-in sensors. Health concerns addressed by these apps included respiratory, dermatological, neurological, and anxiety conditions. Conclusions: Diligent mHealth app library curation, medical device regulation constraints, and cross-platform differences in mobile phone sensor architectures may all contribute to the observed limited availability of mHealth apps using built-in phone sensors in curated mHealth app libraries. However, more efforts are needed to increase the number of such apps on curated lists, as they offer easily accessible low-cost options to assist people in managing clinical conditions. 
%M 32012102
%R 10.2196/16741
%U https://mhealth.jmir.org/2020/2/e16741
%U https://doi.org/10.2196/16741
%U http://www.ncbi.nlm.nih.gov/pubmed/32012102