SynthesisBMC medicine2020
Use of wearable biometric monitoring devices to measure outcomes in randomized clinical trials: a methodological systematic review.
Synthesis in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 40 citations in OpenAlex.
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- Digital Health Tools Embedded in a Cancer Genetics Clinic: Observational Study.JMIR formative research · 2026Observational
- What Is a Biosensor?-A Terminological Guide From Biomolecular Recognition to Bioindicators.Engineering in life sciences · 2026Review
- Measuring Treatment Adherence in Myopia Control: Current Practices and Future Directions.Ophthalmology science · 2026Article
- Predicting Engagement Patterns With Connected Wearable Devices in a Health System: Survival Analysis.Journal of medical Internet research · 2025Article
- Unleashing the full potential of digital outcome measures in clinical trials: eight questions that need attention.BMC medicine · 2024Review
- Digital endpoints in clinical trials: emerging themes from a multi-stakeholder Knowledge Exchange event.Trials · 2024Article
- Assessment of ownership of smart devices and the acceptability of digital health data sharing.NPJ digital medicine · 2024Article
- Technology and Human Support Systems in Decentralized Studies: A Participant-Centered Case Study in Cystic Fibrosis.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Wearable Devices and Nurses' Health: Protocol for an Integrative Review.JMIR research protocols · 2023Article
- Optimized progression of Full-Body In-Bed Gym workout: an educational case report.European journal of translational myology · 2023Article
- Digital Biomarker-Based Studies: Scoping Review of Systematic Reviews.JMIR mHealth and uHealth · 2022Article
- Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data.Annual review of biomedical engineering · 2022Review
- Skeletal muscle weakness in older adults home-restricted due to COVID-19 pandemic: a role for full-body in-bed gym and functional electrical stimulation.Aging clinical and experimental research · 2021Article
- To contrast and reverse skeletal muscle weakness by Full-Body In-Bed Gym in chronic COVID-19 pandemic syndrome.European journal of translational myology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWearable biometric monitoring devices (BMDs) have the potential to transform the conduct of randomized controlled trials (RCTs) by shifting the collection of outcome data from single measurements at predefined time points to dense continuous measurements.
methodsMethodological systematic review to understand how recent RCTs used BMDs to measure outcomes and to describe the reporting of these RCTs. Electronic search was performed in the Cochrane Central Register of Controlled Trials, PubMed, and EMBASE and completed a page-by-page hand search in five leading medical journals between January 1, 2018, and December 31, 2018. Three reviewers independently extracted all primary and secondary outcomes collected using BMDs, and assessed (1) the definitions used to summarize BMD outcome data; (2) whether the validity, reliability, and responsiveness of sensors was reported; (3) the discrepancy with outcomes prespecified in public clinical trial registries; and (4) the methods used to manage missing and incomplete BMD outcome data.
resultsOf the 4562 records screened, 75 RCTs were eligible. Among them, 24% tested a pharmacological intervention and 57% used an inertial measurement sensor to measure physical activity. Included trials involved 464 outcomes (average of 6 [SD = 8] outcomes per trial). In total, 35 trials used a BMD to measure a primary outcome. Several issues affected the value and transparency of trials using BMDs to measure outcomes. First, the definition of outcomes used in the trials was highly heterogeneous (e.g., 21 diabetes trials had 266 outcomes and 153 had different unique definitions to measure diabetes control), which limited the combination and comparison of results. Second, information on the validity, reliability, and responsiveness of sensors used was lacking in 74% of trials. Third, half (53%) of the outcomes measured with BMDs had not been prespecified, with a high risk of outcome reporting bias. Finally, reporting on the management of incomplete outcome data (e.g., due to suboptimal compliance with the BMD) was absent in 68% of RCTs.
conclusionsUse of BMDs to measure outcomes is becoming the norm rather than the exception in many fields. Yet, trialists need to account for several methodological issues when specifying and conducting RCTs using these novel tools.
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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.