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<p>Randomized controlled trials (RCTs) aim to maximize statistical power while minimizing cost and recruitment burden. In practice, randomization is often stratified or restricted using demographic variables such as age and sex, while physiological heterogeneity that may influence treatment response is rarely incorporated. Consumer smartwatches are now widely used and provide continuous, real-world measurements of cardiovascular physiology and daily activity patterns, including resting heart rate, heart rate variability, sleep timing and regularity, and physical activity, capturing stable individual-level characteristics outside clinical settings. Leveraging these data, we developed Smartwatch-Informed Matching (SIM), a pre-randomization framework that groups physiologically similar participants and applies constrained randomization to assign participants to intervention and control arms. Using a prospective cohort of 4,795 individuals, we compared SIM with conventional age- and sex-based stratification. SIM improved covariate balance and increased similarity in symptom severity (Spearman <i>ρ</i> = 0.176 vs. 0.012) and physiological response profiles (Pearson <i>r</i> = 0.245 vs. 0.112). Power analyses showed that SIM reduced the sample size required to maintain statistical power by 9–18% across a range of effect sizes. These findings demonstrate that incorporating smartwatch-derived physiological similarity into pre-randomization design can enhance the efficiency and precision of randomized clinical trials. The SIM framework is also readily applicable to retrospective matched analyses that aim to reduce confounding.</p>

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Enhancing randomized controlled trials through smartwatch-guided participant matching for infectious disease outcomes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月12日

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  • Edan Shahmoon,
  • Matan Yechezkel,
  • Shachar Snir,
  • Marco V. Perez,
  • Margaret L. Brandeau,
  • Dan Yamin

摘要

Randomized controlled trials (RCTs) aim to maximize statistical power while minimizing cost and recruitment burden. In practice, randomization is often stratified or restricted using demographic variables such as age and sex, while physiological heterogeneity that may influence treatment response is rarely incorporated. Consumer smartwatches are now widely used and provide continuous, real-world measurements of cardiovascular physiology and daily activity patterns, including resting heart rate, heart rate variability, sleep timing and regularity, and physical activity, capturing stable individual-level characteristics outside clinical settings. Leveraging these data, we developed Smartwatch-Informed Matching (SIM), a pre-randomization framework that groups physiologically similar participants and applies constrained randomization to assign participants to intervention and control arms. Using a prospective cohort of 4,795 individuals, we compared SIM with conventional age- and sex-based stratification. SIM improved covariate balance and 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 showed that SIM reduced the sample size required to maintain statistical power by 9–18% across a range of effect sizes. These findings demonstrate that incorporating smartwatch-derived physiological similarity into pre-randomization design can enhance the efficiency and precision of randomized clinical trials. The SIM framework is also readily applicable to retrospective matched analyses that aim to reduce confounding.