ArticleJMIR mHealth and uHealth2022
Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design.
Article 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 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
32 citing papers in PubMed.
- Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial.The Lancet. Digital health · 2024Trial
- Facilitators and Barriers to Bring Your Own Device (BYOD) Electronic Patient-Reported Outcome (ePRO) Adoption in Oncology Clinical Trials: A Qualitative Study of Clinical Research Professionals (CRPs).Therapeutic innovation & regulatory science · 2026Article
- Impact of Daylight Saving Time on Physical Activity Patterns.Nature health · 2026Article
- Should We Keep Changing the Clock? Characterizing Causal Effects of Daylight Saving Time on Behavior and Physiology.medRxiv : the preprint server for health sciences · 2026Article
- Population differences in wearable device wear time: Rescuing data to address biases and advance health equity.medRxiv : the preprint server for health sciences · 2026Article
- Big data integration for enhanced epidemiological research: insights and directions from NHLBI's workshop.Frontiers in digital health · 2026Article
- American Life in Realtime: Benchmark, publicly available person-generated health data for equity in precision health.PNAS nexus · 2025Article
- Early Warning of Infectious Disease Outbreaks Using Social Media and Digital Data: A Scoping Review.International journal of environmental research and public health · 2025Article
- Consumer Wearable Usage to Collect Health Data Among Adults Living in Germany: Nationwide Observational Survey Study.JMIR mHealth and uHealth · 2025Observational
- Validation of Ecological Momentary Assessment With Reference to Accelerometer Data: Repeated-Measures Panel Study With Multilevel Modeling.Journal of medical Internet research · 2025Article
- Participant Contributions to Person-Generated Health Data Research Using Mobile Devices: Scoping Review.Journal of medical Internet research · 2025Article
- Data from the All of Us research program reinforces existence of activity inequality.NPJ digital medicine · 2025Article
- Identification of key factors related to digital health observational study adherence and retention by data-driven approaches: an exploratory secondary analysis of two prospective longitudinal studies.The Lancet. Digital health · 2025Article
- Remote data collection of infant activity and sleep patterns via wearable sensors in the HEALthy Brain and Child Development Study (HBCD).Developmental cognitive neuroscience · 2024Article
- Study of Postacute Sequelae of COVID-19 Using Digital Wearables: Protocol for a Prospective Longitudinal Observational Study.JMIR research protocols · 2024Observational
- Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association.Circulation. Genomic and precision medicine · 2024Article
- Monitoring Adolescent and Young Adult Patients With Cancer via a Smart T-Shirt: Prospective, Single-Cohort, Mixed Methods Feasibility Study (OncoSmartShirt Study).JMIR mHealth and uHealth · 2024Article
- Experiences with Wearable Sensors in Oncology during Treatment: Lessons Learned from Feasibility Research Projects in Denmark.Diagnostics (Basel, Switzerland) · 2024Article
- Wearable Technologies for Healthy Ageing: Prospects, Challenges, and Ethical Considerations.The Journal of frailty & aging · 2024Article
- Augmented reality versus standard tests to assess cognition and function in early Alzheimer's disease.NPJ digital medicine · 2023Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Digital health technologies, such as smartphones and wearable devices, promise to revolutionize disease prevention, detection, and treatment. Recently, there has been a surge of digital health studies where data are collected through a bring-your-own-device (BYOD) approach, in which participants who already own a specific technology may voluntarily sign up for the study and provide their digital health data. BYOD study design accelerates the collection of data from a larger number of participants than cohort design; this is possible because researchers are not limited in the study population size based on the number of devices afforded by their budget or the number of people familiar with the technology. However, the BYOD study design may not support the collection of data from a representative random sample of the target population where digital health technologies are intended to be deployed. This may result in biased study results and biased downstream technology development, as has occurred in other fields. In this viewpoint paper, we describe demographic imbalances discovered in existing BYOD studies, including our own, and we propose the Demographic Improvement Guideline to address these imbalances.
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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.