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Updated: May 14, 2026
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Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
Published on: February 3, 2023
Enhancing randomized controlled trials through smartwatch-guided participant matching for infectious disease outcomes
Edan Shahmoon1, Matan Yechezkel2, Shachar Snir1
1School of Industrial Engineering and Intelligent Systems, Tel Aviv University, Tel Aviv, Israel.
Scientific Reports
|May 12, 2026
Summary
Smartwatch-Informed Matching (SIM) uses wearable data to group similar participants before clinical trials. This method enhances trial efficiency and precision by improving covariate balance and reducing necessary sample sizes.
Area of Science:
- Clinical Trials
- Biomedical Engineering
- Digital Health
Background:
- Randomized controlled trials (RCTs) traditionally use demographic stratification (age, sex) for participant allocation.
- Physiological heterogeneity, a key factor in treatment response, is rarely incorporated into trial design.
- Consumer smartwatches offer continuous, real-world physiological and activity data, capturing individual characteristics.
Purpose of the Study:
- To introduce and evaluate the Smartwatch-Informed Matching (SIM) framework for pre-randomization in clinical trials.
- To compare the effectiveness of SIM against conventional age- and sex-based stratification.
- To assess the impact of SIM on covariate balance, participant similarity, and statistical power.
Main Methods:
- Developed SIM, a pre-randomization framework utilizing smartwatch data to group physiologically similar participants.
- Applied constrained randomization within these groups to assign participants to intervention and control arms.
- Compared SIM with conventional age- and sex-based stratification in a prospective cohort of 4,795 individuals.
Main Results:
- SIM demonstrated improved covariate balance compared to conventional methods.
- SIM significantly increased similarity in symptom severity (Spearman ρ=0.176 vs. 0.012) and physiological response profiles (Pearson r=0.245 vs. 0.112).
- Power analyses indicated that SIM could reduce required sample sizes by 9-18% while maintaining statistical power.
Conclusions:
- Incorporating smartwatch-derived physiological similarity into pre-randomization design enhances the efficiency and precision of RCTs.
- The SIM framework offers a novel approach to optimize participant allocation and reduce confounding in clinical research.
- SIM is adaptable for retrospective matched analyses, improving the validity of observational studies.
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