2020
DOI: 10.2196/16741
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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

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 ubiquitou… Show more

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Cited by 83 publications
(56 citation statements)
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“…We also performed searches on 3 independent third-party mHealth app libraries: (1) the government-funded National Health Service Apps Library [ 41 ], (2) the privately funded AppScript Library [ 42 ], and (3) the privately funded MyHealthApps Cochrane Library [ 43 ]. These third-party mHealth app libraries are web portals that curate smartphone apps [ 44 ]. This ancillary search of smartphone apps on webpages, tinnitus forums, and third-party mHealth app libraries resulted in the identification of 5 additional apps; therefore, a total of 34 apps were identified for assessment and evaluation.…”
Section: Methodsmentioning
confidence: 99%
“…We also performed searches on 3 independent third-party mHealth app libraries: (1) the government-funded National Health Service Apps Library [ 41 ], (2) the privately funded AppScript Library [ 42 ], and (3) the privately funded MyHealthApps Cochrane Library [ 43 ]. These third-party mHealth app libraries are web portals that curate smartphone apps [ 44 ]. This ancillary search of smartphone apps on webpages, tinnitus forums, and third-party mHealth app libraries resulted in the identification of 5 additional apps; therefore, a total of 34 apps were identified for assessment and evaluation.…”
Section: Methodsmentioning
confidence: 99%
“…By relying on the experience of unhappy users, valuable comments were obtained to validate developers claimed performances, hence helping to answer Q3. This makes it possible to know the true state of the clinical and operational features, thus, highlighting the important design and development flaws, which are common with many of the apps [2,37]. Since the best way to determine the effectiveness of DMAS feature utilization will involve statistical analysis, we were able to answer Q4 using the frequency of features utilization and statistical analysis that hinged on ANOVA.…”
Section: Answering Research Questions and Hypotheses Testingmentioning
confidence: 99%
“…This era of Internet connectivity has transformed our lifestyles to be more online and reliant on mobile apps to provide us with information and support concerning wellness and self-management of chronic conditions [1]. Thus, it should be of no surprise that over 1 million mobile health apps [2] have been downloaded over 3.7 billion times [3] because most of these are advertised as vital tools for managing chronic conditions such as diabetes. Unfortunately, the ubiquitous nature of these apps has not translated into quality products [4], hence, there exists a key need to understand the challenges of such apps and identify critical enablers to facilitate better designs.…”
Section: Introductionmentioning
confidence: 99%
“…In the identified apps, microphones, mobile phone speakers and accelerometers were used more rarely. None of the included apps used GPS[ 75 ]. These data can be correlated longitudinally and continuously to forecast critical or medically relevant situations[ 76 ].…”
Section: Opportunitiesmentioning
confidence: 99%