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

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Abstract

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.

Related collections

Most cited references 40

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Systematic review of smartphone-based passive sensing for health and wellbeing

Victor Cornet, Richard Holden (2018)

Objective To review published empirical literature on the use of smartphone-based passive sensing for health and wellbeing. Material and Methods A systematic review of the English language literature was performed following PRISMA guidelines. Papers indexed in computing, technology, and medical databases were included if they were empirical, focused on health and/or wellbeing, involved the collection of data via smartphones, and described the utilized technology as passive or requiring minimal user interaction. Results Thirty-five papers were included in the review. Studies were performed around the world, with samples of up to 171 (median n=15) representing individuals with bipolar disorder, schizophrenia, depression, older adults, and the general population. The majority of studies used Android operating system and an array of smartphone sensors, most frequently capturing accelerometry, location, audio, and usage data. Captured data were usually sent to a remote server for processing but were shared with participants in only 40% of studies. Reported benefits of passive sensing included accurately detecting changes in status, behavior change through feedback, and increased accountability in participants. Studies reported facing technical, methodological, and privacy challenges. Discussion Studies in the nascent area of smartphone-based passive sensing for health and wellbeing demonstrate promise and invite continued research and investment. Existing studies suffer from weaknesses in research design, lack of feedback and clinical integration, and inadequate attention to privacy issues. Key recommendations relate to develop passive sensing strategies matching the problem at hand, using personalized interventions, and addressing methodological and privacy challenges. Conclusion As evolving passive sensing technology presents new possibilities for health and wellbeing, additional research must address methodological, clinical integration, and privacy issues. Doing so depends on interdisciplinary collaboration between informatics and clinical experts.

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Consumer Mobile Health Apps: Current State, Barriers, and Future Directions.

Cheng-Kai Kao, David Liebovitz (2017)

This paper discusses the current state, barriers, and future directions of consumer-facing applications (apps). There are currently more than 165,000 mobile health apps publicly available in major app stores, the vast majority of which are designed for patients. The top 2 categories are wellness management and disease management apps, whereas other categories include self-diagnosis, medication reminder, and electronic patient portal apps. Apps specific to physical medicine and rehabilitation also are reviewed. These apps have the potential to provide low-cost, around-the-clock access to high-quality, evidence-based health information to end users on a global scale. However, they have not yet lived up to their potential due to multiple barriers, including lack of regulatory oversight, limited evidence-based literature, and concerns of privacy and security. The future directions may consist of improving data integration into the health care system, an interoperable app platform allowing access to electronic health record data, cloud-based personal health record across health care networks, and increasing app prescription by health care providers. For consumer mobile health apps to fully contribute value to health care delivery and chronic disease management, all stakeholders within the ecosystem must collaborate to overcome the significant barriers.

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Quantifying App Store Dynamics: Longitudinal Tracking of Mental Health Apps

Mark Erik Larsen, Jennifer M. Nicholas, Helen Christensen (2016)

Background For many mental health conditions, mobile health apps offer the ability to deliver information, support, and intervention outside the clinical setting. However, there are difficulties with the use of a commercial app store to distribute health care resources, including turnover of apps, irrelevance of apps, and discordance with evidence-based practice. Objective The primary aim of this study was to quantify the longevity and rate of turnover of mental health apps within the official Android and iOS app stores. The secondary aim was to quantify the proportion of apps that were clinically relevant and assess whether the longevity of these apps differed from clinically nonrelevant apps. The tertiary aim was to establish the proportion of clinically relevant apps that included claims of clinical effectiveness. We performed additional subgroup analyses using additional data from the app stores, including search result ranking, user ratings, and number of downloads. Methods We searched iTunes (iOS) and the Google Play (Android) app stores each day over a 9-month period for apps related to depression, bipolar disorder, and suicide. We performed additional app-specific searches if an app no longer appeared within the main search Results On the Android platform, 50% of the search results changed after 130 days (depression), 195 days (bipolar disorder), and 115 days (suicide). Search results were more stable on the iOS platform, with 50% of the search results remaining at the end of the study period. Approximately 75% of Android and 90% of iOS apps were still available to download at the end of the study. We identified only 35.3% (347/982) of apps as being clinically relevant for depression, of which 9 (2.6%) claimed clinical effectiveness. Only 3 included a full citation to a published study. Conclusions The mental health app environment is volatile, with a clinically relevant app for depression becoming unavailable to download every 2.9 days. This poses challenges for consumers and clinicians seeking relevant and long-term apps, as well as for researchers seeking to evaluate the evidence base for publicly available apps.

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Author and article information

Contributors

Clarence Baxter:

ORCID: https://orcid.org/0000-0001-8258-4836

School of Public Health and Social WorkFaculty of HealthQueensland University of TechnologyVictoria Park RoadKelvin Grove, Queensland, 4059Australia61 488539311c.baxter@hdr.qut.edu.au

Journal

Journal ID (nlm-ta): JMIR Mhealth Uhealth

Journal ID (iso-abbrev): JMIR Mhealth Uhealth

Journal ID (publisher-id): JMU

Title: JMIR mHealth and uHealth

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-5222

Publication date Collection: February 2020

Publication date (Electronic): 3 February 2020

Volume: 8

Issue: 2

Electronic Location Identifier: e16741

Affiliations

[1 ] School of Public Health and Social Work Faculty of Health Queensland University of Technology Kelvin Grove, Queensland Australia

[2 ] Institute of Health and Biomedical Innovation Queensland University of Technology Kelvin Grove, Queensland Australia

[3 ] Digital Media Research Centre Creative Industries Faculty Queensland University of Technology Kelvin Grove, Queensland Australia

[4 ] School of Health, Medical and Applied Sciences Central Queensland University Rockhampton, Queensland Australia

Author notes

Corresponding Author: Clarence Baxter c.baxter@ 123456hdr.qut.edu.au

Author information

Clarence Baxter https://orcid.org/0000-0001-8258-4836

Julie-Anne Carroll https://orcid.org/0000-0003-0770-6527

Brendan Keogh https://orcid.org/0000-0003-4565-4641

Corneel Vandelanotte https://orcid.org/0000-0002-4445-8094

Article

Publisher ID: v8i2e16741

DOI: 10.2196/16741

PMC ID: 7055743

PubMed ID: 32012102

SO-VID: 96f9e3c8-bce3-4f60-a7da-7b86b54c55de

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 20 October 2019

Date revision requested : 8 November 2019

Date revision received : 4 December 2019

Date accepted : 16 December 2019

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