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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.
These independent Web-based repositories guide app selection from
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.
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.
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.
With origins in the early 1990s and the inception of devices such as the IBM
Innovation is a hallmark of developments in medical technology, with a rich pedigree that long precedes contemporary
Self-management of health conditions without adequate medical guidance (colloquially termed the
Several taxonomies exist for describing mHealth apps; one simple method categorizes them as either passive or active [
The utility of such a trove of sensors has not gone unnoticed by clinicians and app developers alike [
App stores such as those offered by Apple and Google present literally millions of results in response to searches on health topics [
Distinct from app stores such as the Apple App Store and Google Play Store, a number of independent third-party mHealth app repositories have emerged, with the intent of providing curated
Given the potential for health improvement arising from the availability and utility of built-in sensors in billions of smartphones worldwide, the purpose of this systematic survey was to identify smartphone mHealth apps using built-in sensors, offered by three popular contemporary international curated mHealth app repositories, and to assess which health conditions these apps address and whether they are available across different platforms [
This survey, conducted in October 2019, considered all mHealth app listings in the NHS Apps Library, AppScript, and MyHealthApps–curated mHealth app repositories (
Preferred Reporting Items for Systematic Reviews and Meta-Analyses diagram for survey.
All apps addressing health conditions using built-in mobile phone sensors to generate health data were identified using the publicly accessible search interfaces offered by each repository. As no search criteria were offered by these sites for filtering and identifying sensor-based apps, manual screening of the descriptions of all individual apps listed by each website was conducted by the lead researcher to screen for the use of built-in mobile phone sensors. Curated library descriptions for identified apps were inspected to categorize the purpose of the app as solely diagnostic, therapeutic, or a combination of both. Diagnostic apps were defined as those that identify the nature of a health condition, in contrast to treatment apps, which offered features for health condition management. Health conditions addressed by included apps were classified based on the description provided by each repository.
Apps using external or add-on sensors and nonsmartphone wearable device apps were excluded from this survey, as external components may impose additional cost, complexity, or excessive battery consumption, potentially reducing availability or accessibility to smartphone mHealth users [
Mobile phone operating systems supported by included apps were noted, assessing the availability of these apps by users of the Apple iOS and Google Android phone types. The availability of included apps on advertised platforms was confirmed by following links advertised by each repository to inspect the Apple and Google app store app listings for apps. Apps were not downloaded or tested. App listings included as in scope by the lead researcher were then reviewed by the research team.
Low numbers across result groups precluded rigorous statistical analysis. Descriptive statistics were used where appropriate to illustrate results and to allow comparison between different libraries proportionate to respective library size.
A total of 1200 apps listed in the three selected curated mHealth app repositories were identified (
Built-in sensor smartphone apps found in surveyed mobile health app libraries.
Curated mobile health app library | Total apps identified (n=1200), n | Built-in sensor apps included (n=18), n (%) |
NHSa Apps Library | 76 | 2 (3) |
AppScript | 699 | 8 (1.1) |
MyHealthApps | 425 | 8 (1.9) |
aNHS: National Health Service.
Details of smartphone mHealth apps using built-in sensors included from each respective curated mHealth library in this survey are presented in
Half (9/18, 50%) of all apps inspected were offered solely for use on the Apple iOS platform, with a further 11% (2/18) dedicated to the Android operating system (
Operating systems for apps using built-in mobile phone sensors.
Operating system | Curated mobile health app library | Total, n (%) | ||
NHSa Apps Library | AppScript | MyHealthApps | ||
Apple iOS only | 1 | 6 | 2 | 9 (50) |
Android only | 0 | 1 | 1 | 2 (11) |
Both | 1 | 1 | 5 | 7 (39) |
Total | 2 | 8 | 8 | 18 (100) |
aNHS: National Health Service.
Almost one-fourth (4/18, 22%) of all included apps were dedicated entirely to the diagnosis of health conditions (predominantly available in MyHealthApps), whereas half were solely treatment oriented (
Purpose for mobile health apps identified using built-in mobile phone sensors.
Purpose | Curated mobile health app library | Total (n=18), n (%) | ||
NHSa Apps Library (n=2) | AppScript (n=8) | MyHealthApps (n=8) | ||
Diagnostic (Dx) | 0 | 1 | 3 | 4 (22) |
Therapeutic (Rx) | 2 | 5 | 2 | 9 (50) |
Both | 0 | 2 | 3 | 5 (28) |
aNHS: National Health Service.
Camera (7/18, 39%) and touch screens (6/18, 33%) were the most frequently identified smartphone sensors used (
Smartphone cameras assessed pulse rate using photoplethysmography in an anxiety treatment app (Beat Panic), a respiratory therapy app (HeartRate+ Coherence), and a cardiac app (Instant Heart Rate). Beat Panic and Heart Rate+ Coherence are examples where smartphone pulse rate sensing is a secondary function to support a main therapy, namely, anxiety management and breathing exercise, respectively. Camera images were also used for automated skin cancer diagnosis (SpotMole) and in capturing images for dermatological diagnosis (UMSkinCheck, iDoc24, and MyPso).
In addition to capturing responses to speaker-generated tones in audiology testing, touch screens were used in vision training (Vision training 1 and Visual Attention Therapy Lite), neurological tremor assessment (pdFIT and Dexteria), and anxiety management (Chill Panda and Antistress Chromotherapy). Microphone sensors were used in several respiratory therapy apps (Breathing Zone, SnoreLab, and SnoreMonitor SleepLab). The use of a mobile phone accelerometer sensor was identified in a single app for neurological tremor assessment (LiftPulse).
Sensor types found in curated mobile health app libraries.
Sensor | Curated mobile health app library | Total (n=18), n (%) | ||
NHSa Apps Library (n=2) | AppScript (n=8) | MyHealthApps (n=8) | ||
Camera | 1 | 2 | 4 | 7 (39) |
Touch screen | 1 | 3 | 2 | 6 (33) |
Microphone | 0 | 3 | 0 | 3 (17) |
Accelerometer | 0 | 0 | 1 | 1 (6) |
Speaker | 0 | 0 | 1 | 1 (6) |
aNHS: National Health Service.
Respiratory (4/18, 22%), dermatological (4/18, 22%), neurological (3/18, 17%), anxiety (3/18, 17%), and visual health (2/18, 11%) were the predominant health concerns addressed by the identified apps (
Summary of health conditions where built-in mobile phone sensors were used.
Health condition | Curated mobile health app library | Total (n=18), n (%) | |||
NHSa Apps Library (n=2) | AppScript (n=8) | MyHealthApps (n=8) | |||
Respiratory | 0 | 4 | 0 | 4 (22) | |
Dermatology and skin cancer | 0 | 1 | 3 | 4 (22) | |
Anxiety | 2 | 0 | 1 | 3 (17) | |
Neurology | 0 | 1 | 2 | 3 (17) | |
Visual acuity | 0 | 2 | 0 | 2 (11) | |
Audiology | 0 | 0 | 1 | 1 (6) | |
Cardiology | 0 | 0 | 1 | 1 (6) |
aNHS: National Health Service.
Mobile phone cameras are employed in addressing the broadest range of health issues (
Sensors, health conditions, and methodologies identified.
Sensor and health condition | Measure | Methodology used | |
|
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General anxiety disorder | Heart rate | Photoplethysmography |
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Cardiac | Heart rate | Photoplethysmography |
|
Dermatology (n=2) | Photography | Clinician inspection |
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Respiratory (breathing exercise) | Heart rate variability | Photoplethysmography |
|
Skin cancer | Photography | Steganographic pattern matching from photo |
|
Skin cancer | Photography | Clinician inspection |
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Panic attacks | Screen image display | Images displayed to reduce panic |
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Visual acuity (n=2) | Touch accuracy | Eye-hand coordination assessment and coaching |
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Parkinson disease | Touch accuracy | Fine motor skill assessment and coaching |
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Respiratory (sleep; n=2) | Snoring sound level and frequency | Snoring and apnea detection |
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Respiratory (breathing exercise) | Breath sound detection | Feedback to encourage slow purposeful breaths |
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Neurology | Tremor detection | Calculates tremor frequency |
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Audiology | Calibrated sound generation | Self-administered hearing test |
Curation activities offered by third-party mHealth libraries, which are underpinned by medical device regulation, contribute to informing and protecting mHealth consumers. In this study, we surveyed three popular curated libraries regarding a specific subset of mHealth apps, namely, those using built-in mobile phone sensors for diagnosis or treatment of health conditions. Key aims of this survey included determining app availability, mobile phone operating system compatibility, intended purpose (diagnosis or therapy), types of sensors employed, and the range of health conditions where built-in smartphone sensors are used. First, this survey yielded a relatively small number of apps across the libraries examined, with differences found in the number of apps available between libraries. Second, more apps were available for the users of Apple iOS smartphones than for those of Android devices; cross-platform availability differed between the libraries surveyed. Third, the majority of apps offered treatment and combined diagnosis and treatment, with a smaller proportion offering dedicated diagnostic functionality. Fourth, cameras, touch screens, and microphones were the most frequently used mobile phone sensors in these apps. Finally, the range of health conditions addressed by these apps included respiratory, dermatological, anxiety, and neurological conditions.
Searching for apps related to particular health topics or medical concerns pose challenges for health professionals and consumers alike. Search engines, such as Google and Bing, which index available apps based on keyword search algorithms, often yield large volumes of uncurated search results for a given health topic [
No studies could be found that quantify the prevalence of mHealth apps using built-in sensors in curated mHealth app libraries. A 2016 review of health care–related apps available from the Google and Apple app stores identifies 80 clinical or health care–related mHealth apps for diagnosis or health monitoring [
Critics highlight a lack of transparency in standards applied to the screening of submitted apps before inclusion and hosting in popular app stores and search engines, resulting in mHealth app offerings, which may vary in quality or safety [
In contrast to a million health and well-being apps on offer to mobile phone users from popular app stores, only 18 mHealth apps using built-in smartphone sensors are identified in this survey, representing 1.50% (18/1200) of all mHealth apps collectively offered by the three curated libraries examined here (
The respective smartphone market shares for Apple and Android devices are comparable [
Half of the identified mHealth apps (9/18, 50%) offer dedicated treatment features, with further about one-fourth (4/18, 22%) dedicated to diagnosis (
Cameras and touch screens are the most frequently used sensors in the identified apps, followed by microphones and accelerometers. Apps using camera sensors are most prominent in the MyHealthApps library, whereas AppScript lists more microphone and touch screen apps (
This survey found that mHealth apps using built-in sensors for diagnosis and treatment represented but a modicum of all apps found in the curated mHealth libraries examined. The nature and rigor of the curation process go some way to explain this observation, including the constraints of regulatory requirements for software deemed as medical devices. This may also help explain the smaller proportion of dedicated diagnostic apps observed in these libraries. Some health consumers may be disadvantaged by differences in the availability of apps on competing mobile phone platforms. Cameras, touch screens, and microphones were used most frequently in the surveyed apps. A limited range of health concerns were addressed by the surveyed apps.
Further efforts are needed to increase the availability of ubiquitous, low-cost mobile phone sensor technology in curated lists to assist with health conditions.
Details of mobile health apps using built-in mobile phone sensors in surveyed curated libraries.
Mobile Application Rating Scale
mobile health
National Health Service
CB conceptualized the survey, developed methodology, and collected data for the survey. He categorized and evaluated the results and prepared the draft manuscript. JC supervised the overall work. BK and CV contributed to discussing and revising the manuscript.
None declared.