ReviewDigital biomarkers
Considerations for Conducting Bring Your Own "Device" (BYOD) Clinical Studies.
Review in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- 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
- Comparison of Consumer Smartwatch and Research-Grade Accelerometer-Derived Step Counts in Amyotrophic Lateral Sclerosis.Muscle & nerve · 2026Observational
- Patient Experiences of Digital Technology Use in Interstitial Lung Disease (PRODIGY-ILD Study): Qualitative Study Using Reflexive Thematic Analysis.JMIR human factors · 2026Observational
- Adolescents' experience of living with X-linked hypophosphataemia (XLH): a mixed-methods analysis of those who continued and discontinued burosumab treatment after end of skeletal growth.Orphanet journal of rare diseases · 2026Article
- Accurate quantification of steps from multiple smartphone positions.Scientific reports · 2026Article
- A Preliminary Study Introducing Electronic Patient-Reported Outcome (ePRO) Using Bring Your Own Device (BYOD) in Post-marketing Surveillance in Japan.Therapeutic innovation & regulatory science · 2026Observational
- An app-based physical activity intervention for people with hip and knee osteoarthritis: protocol for the PIANISSIMO feasibility study.Pilot and feasibility studies · 2025Article
- Operationalizing Decentralized Clinical Trials: Technology Insights from the Trials@Home RADIAL Proof-of-Concept Trial.Clinical pharmacology and therapeutics · 2025Article
- How secure are your health devices-stopping wearables becoming a personal and national security risk?NPJ digital medicine · 2025Article
- Feasibility of Long-Term Physical Activity Measurement With a Wearable Activity Tracker in Patients With Axial Spondyloarthritis: 1-Year Longitudinal Observational Study.JMIR human factors · 2025Observational
- Decentralized Clinical Trials in the Era of Real-World Evidence: A Statistical Perspective.Clinical and translational science · 2025Review
- Factors influencing the adoption of the BYOD policy in teaching hospitals: A cross-sectional study from Southeastern Iran.PloS one · 2025Article
- Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings.Sensors (Basel, Switzerland) · 2024Article
- How can regulation and reimbursement better accommodate flexible suites of digital health technologies?NPJ digital medicine · 2024Article
- Feasibility of Observing Cerebrovascular Disease Phenotypes with Smartphone Monitoring: Study Design Considerations for Real-World Studies.Sensors (Basel, Switzerland) · 2024Article
- The Next Horizon of Drug Development: External Control Arms and Innovative Tools to Enrich Clinical Trial Data.Therapeutic innovation & regulatory science · 2024Review
- TRITRIAL: The Impact of Fixed Triple Therapy with Beclometasone/Formoterol/Glycopyrronium on Health Status and Adherence in Chronic Obstructive Pulmonary Disease in an Italian Context of Real Life.International journal of chronic obstructive pulmonary disease · 2024Observational
- The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program.JMIR mHealth and uHealth · 2023Article
- Decentralized clinical trials and rare diseases: a Drug Information Association Innovative Design Scientific Working Group (DIA-IDSWG) perspective.Orphanet journal of rare diseases · 2023Review
- Time-varying model of engagement with digital self reporting: Evidence from smoking cessation longitudinal studies.Frontiers in digital health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Digital health technologies are attracting attention as novel tools for data collection in clinical research. They present alternative methods compared to in-clinic data collection, which often yields snapshots of the participants' physiology, behavior, and function that may be prone to biases and artifacts, e.g., white coat hypertension, and not representative of the data in free-living conditions. Modern digital health technologies equipped with multi-modal sensors combine different data streams to derive comprehensive endpoints that are important to study participants and are clinically meaningful. Used for data collection in clinical trials, they can be deployed as provisioned products where technology is given at study start or in a bring your own "device" (BYOD) manner where participants use their technologies to generate study data. Summary: The BYOD option has the potential to be more user-friendly, allowing participants to use technologies that they are familiar with, ensuring better participant compliance, and potentially reducing the bias that comes with introducing new technologies. However, this approach presents different technical, operational, regulatory, and ethical challenges to study teams. For example, BYOD data can be more heterogeneous, and recruiting historically underrepresented populations with limited access to technology and the internet can be challenging. Despite the rapid increase in digital health technologies for clinical and healthcare research, BYOD use in clinical trials is limited, and regulatory guidance is still evolving. Key Messages: We offer considerations for academic researchers, drug developers, and patient advocacy organizations on the design and deployment of BYOD models in clinical research. These considerations address: (1) early identification and engagement with internal and external stakeholders; (2) study design including informed consent and recruitment strategies; (3) outcome, endpoint, and technology selection; (4) data management including compliance and data monitoring; (5) statistical considerations to meet regulatory requirements. We believe that this article acts as a primer, providing insights into study design and operational requirements to ensure the successful implementation of BYOD clinical studies.
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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.