ReviewJMIR mHealth and uHealth2022
Data Collection Mechanisms in Health and Wellness Apps: Review and Analysis.
Review in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 26 citations in OpenAlex.
- Hawthorne Effect in Screening Ambulatory Activity Status Using Wearable Pedometers Varies Across Age Groups.AJPM focus · 2026Article
- Young adults' perceptions and experiences in using mobile apps and wearables for health: a qualitative study with public involvement.BMC public health · 2026Article
- Participant Contributions to Person-Generated Health Data Research Using Mobile Devices: Scoping Review.Journal of medical Internet research · 2025Article
- Mobile Health Interventions: A Frontier for Mitigating the Global Burden of Cardiovascular Disease.Cureus · 2024Review
- The user-centered design and development of a childhood and adolescent obesity Electronic Health Record tool, a mixed-methods study.Frontiers in digital health · 2024Article
- Willingness of Participation in an Application-Based Digital Data Collection among Different Social Groups and Smartphone User Clusters.Sensors (Basel, Switzerland) · 2023Article
- Health-Seeking Behavior and Its Associated Technology Use: Interview Study Among Community-Dwelling Older Adults.JMIR aging · 2023Article
- Prospective acceptability of digital phenotyping among pregnant and parenting people with opioid use disorder: A multisite qualitative study.Frontiers in psychiatry · 2023Article
- There's no app for that! perspectives on engaging diverse communities to promote equitable care.Digital healthArticle
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere has been a steady rise in the availability of health wearables and built-in smartphone sensors that can be used to collect health data reliably and conveniently from end users. Given the feature overlaps and user tendency to use several apps, these are important factors impacting user experience. However, there is limited work on analyzing the data collection aspect of mobile health (mHealth) apps.
objectiveThis study aims to analyze what data mHealth apps across different categories usually collect from end users and how these data are collected. This information is important to guide the development of a common data model from current widely adopted apps. This will also inform what built-in sensors and wearables, a comprehensive mHealth platform should support.
methodsIn our empirical investigation of mHealth apps, we identified app categories listed in a curated mHealth app library, which was then used to explore the Google Play Store for health and medical apps that were then filtered using our selection criteria. We downloaded these apps from a mirror site hosting Android apps and analyzed them using a script that we developed around the popular AndroGuard tool. We analyzed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects health data.
resultsWe retrieved 3251 apps meeting our criteria, and our analysis showed that 10.74% (349/3251) of these apps requested Bluetooth access. We found that 50.9% (259/509) of the Bluetooth service universally unique identifiers to be known in these apps, with the remainder being vendor specific. The most common health-related Bluetooth Low Energy services using known universally unique identifiers were Heart Rate, Glucose, and Body Composition. App permissions showed the most used device module or sensor to be the camera (669/3251, 20.57%), closely followed by location (598/3251, 18.39%), with the highest occurrence in the staying healthy app category.
conclusionsWe found that not many health apps used built-in sensors or peripherals for collecting health data. The small number of the apps using Bluetooth, with an even smaller number of apps using standard Bluetooth Low Energy services, indicates a wider use of proprietary algorithms and custom services, which restrict the device use. The use of standard profiles could open this ecosystem further and could provide end users more options for apps. The relatively small proportion of apps using built-in sensors along with a high reliance on manual data entry suggests the need for more research into using sensors for data collection in health and fitness apps, which may be more desirable and improve end user experience.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.