TY  - JOUR
AU  - Baxter, Clarence
AU  - Carroll, Julie-Anne
AU  - Keogh, Brendan
AU  - Vandelanotte, Corneel
PY  - 2020
DA  - 2020/2/3
TI  - 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
JO  - JMIR Mhealth Uhealth
SP  - e16741
VL  - 8
IS  - 2
KW  - telehealth
KW  - mHealth
KW  - smartphone
KW  - mobile apps
KW  - instrumentation
KW  - health care quality
KW  - health care access
KW  - and health care evaluation
KW  - medical informatics
KW  - consumer health informatics
KW  - physician-patient relations
KW  - prescriptions
KW  - patient participation
KW  - patient-generated health data
KW  - diagnostic self evaluation
KW  - self-care
KW  - self-management
KW  - medical device legislation
AB  - 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. 
SN  - 2291-5222
UR  - https://mhealth.jmir.org/2020/2/e16741
UR  - https://doi.org/10.2196/16741
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32012102
DO  - 10.2196/16741
ID  - info:doi/10.2196/16741
ER  -