Enhancing Randomized Controlled Trials Through Smartwatch-Guided Participant Matching

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Perez, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8366773/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract 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. Health sciences/Cardiology Health sciences/Health care Health sciences/Medical research Biological sciences/Physiology Wearable devices Randomized controlled trials Participant matching Smartwatches Trial design optimization Figures Figure 1 Figure 2 INTRODUCTION Randomized controlled trials (RCTs) are widely regarded as the gold standard for assessing the safety and efficacy of medical interventions. 1 A key challenge in designing RCTs is maximizing statistical power to detect treatment efficacy and identify rare adverse events while minimizing resource use. 2 , 3 Larger sample sizes enhance statistical power but require greater participant recruitment and incentive resources. Participants are often the most costly component of RCTs, with recruitment, retention, and compensation accounting for a significant portion of trial budgets. In 2020, a COVID-19-driven vaccine revolution occurred. Just 11 months after development, the Pfizer-BioNTech vaccine received regulatory approval, supported by large-scale clinical trials involving over 43,000 participants demonstrating its safety and efficacy. 4 However, post-approval vaccine safety monitoring systems identified a causal link between mRNA COVID-19 vaccines and myocarditis 5 – an undetected association during the clinical trials. This emphasizes the critical need for enhancing clinical trial designs to capture rare but significant adverse events. Contemporary clinical trials face two significant challenges. First, their stratified randomization processes often rely on relatively simple criteria, such as sex, age, comorbidities, and (where applicable) disease progression. 6 These factors are likely insufficient to capture the complexity and variability of patient populations. This abstraction can result in imbalances in unmeasured variables, such as lifestyle factors 7 , which may also influence the probability of infection acquisition and treatment outcomes. Second, contemporary trials predominantly depend on self-reported symptoms to assess outcomes. These self-reports are inherently subjective and susceptible to variability in individual symptom perception. 8 While traditional stratification in RCTs relies on a limited number of categorical variables, wearable sensors such as smartwatches offer a powerful and scalable tool for capturing rich, continuous physiological data. Unlike conventional approaches that depend on subjective assessments or intermittent biomarker measurements, smartwatches enable passive, objective data collection, providing a comprehensive profile of physiological, health, and lifestyle metrics. Prior research has demonstrated the potential of these devices to monitor infection, vaccination, or treatment efficacy and side effects. 9 – 17 Such devices capture subtle physiological changes linked to infection progression, severity, and recovery, including fluctuations in heart rate, heart rate variability (HRV) 18 , sleep patterns, activity levels, and skin temperature. 19 Because smartwatches capture key indicators of an individual’s health, activity and rest patterns, as well as social and lifestyle factors, these data can potentially be leveraged to identify similarities between individuals, thereby enabling a more precise and effective matching process in RCTs. Here, we present a novel framework for the design of RCTs that integrates traditional recruitment methods with smartwatch-derived data to optimize both participant matching and outcome assessment. We refer to our method as smartwatch-informed matching (SIM). Participants are matched into intervention and control groups based on baseline physiological data collected via smartwatches during a fixed pre-trial period (e.g., one month). Subsequently, individual responses—to infection, vaccination, or treatment—are assessed using a combination of standard measures (e.g., self-reported symptoms) and deviations from each participant’s smartwatch-derived baseline. To evaluate our approach, we analyzed data from a prospective cohort of 4,795 individuals in Israel, identifying those who became infected with COVID-19 and calculating pre-infection smartwatch-based metrics. We demonstrate that smartwatch-based matching can improve similarity between matched pairs and can significantly reduce the number of participants required in an RCT while maintaining high statistical power. METHODS Overview First, we introduce the Smartwatch-Informed Matching process (SIM), which integrates demographic variables (age and sex) with physiological data derived from smartwatches. Next, we evaluate the performance of this matching approach using data from a two-year prospective observational study involving 4,795 participants (Appendix A, Study Protocol), comparing its effectiveness against traditional covariate matching methods, such as those used in the Pfizer-BioNTech COVID-19 vaccine Phase 3 Trial. 4 We then perform a series of power analysis simulations to assess how SIM influences statistical power. Specifically, we quantify the reduction in required sample size relative to standard matching methods to assess the potential of SIM to improve trial efficiency. Smartwatch-Informed Matching We developed SIM as a framework for pairing individuals based on physiological and behavioral similarity, enabling the construction of well-matched intervention and control groups for clinical trials (Figure 2). Smartwatches contain various biosensors—such as accelerometers, temperature monitors, and optical heart rate sensors—that can capture a wide range of physiological and behavioral traits relevant to individual health. While many of these signals may offer value for covariate matching in clinical trials, incorporating too many variables introduces the risk of overfitting, multiple testing concerns, and reduced interpretability. To address these challenges, we focused exclusively on heart rate and HRV—core physiological measures that are widely available across devices and reflect the activity of the cardiovascular and autonomic nervous systems. Heart rate is a fundamental vital sign commonly used to monitor general physiological status, including responses to infection and inflammation. HRV, while not a traditional vital sign, is a well-established digital biomarker of autonomic regulation and has been linked to stress, recovery, immune function, and early signs of physiological disruption. HRV-based signals have been shown to reliably capture meaningful physiological responses, including reactions to vaccination and early signs of infection – often preceding self-reported symptoms. 10,20,21 Building on this evidence, and following prior work 20 , we focused on daily heart rate and HRV distributions during sedentary periods; these provide a stable, interpretable signal for comparing individuals’ physiological baselines. The SIM procedure is detailed in Appendix B. Briefly, our matching process begins by stratifying the population into subgroups based on age and sex, as done in standard clinical trial designs. Within each subgroup, we calculate the distribution of heart rate and HRV for each individual during a predefined baseline period. To assess similarity between individuals, we compare their heart rate distributions using the Earth Mover's Distance (EMD), a metric that quantifies how much one distribution would need to shift to match another. Intuitively, the lower the EMD, the more similar the heart rate patterns. We apply the same process to compare HRV distributions. We then combine these two distance measures using a simple Euclidean distance to compute an overall dissimilarity score between each pair of individuals. Using this score, we apply a nearest-neighbor greedy matching algorithm to pair individuals within each subgroup. This results in matched intervention and control groups, structured similarly to how randomization is done in clinical trials, but guided by physiological similarity. Data-Driven Evaluation of SIM PerMed study To evaluate our approach, we analyzed the data from the PerMed prospective observational study. 20,22,23 The study enrolled 4,795 participants aged 18 and older, who were recruited between November 16, 2020, and May 11, 2023, from various locations across Israel (Appendix A). Recruitment was facilitated through social media advertisements and word-of-mouth. All participants provided informed consent after receiving a thorough explanation of the study from a professional survey company. Eligibility for the PerMed study required participants to be members of Maccabi Healthcare Services (Maccabi) for at least two years prior to enrollment, be smartphone users, and capable of independently providing written informed consent. All participants received both oral and written information regarding the study, and participation was contingent upon written informed consent. This study was approved by the Institutional Review Board (IRB) of Tel Aviv University headed by Prof. Meir Lahav (protocol number 0122-20-TAU). We hereby declare that all methods were performed in accordance with the IRB guidelines and regulations. Participants completed a one-time enrollment questionnaire, providing personal and health-related information. Each participant was provided with a Garmin Vivosmart 4 smartwatch and asked to install two mobile applications: (1) the PerMed application, which collects daily self-reported symptom and sleep data, and (2) a passive data collection app that records smartwatch measurements. All participants, regardless of operating system, downloaded the same applications. They were encouraged to wear their smartwatches consistently, and a survey company monitored compliance through a dedicated dashboard. This ensured that participants completed their questionnaires at least twice a week, kept their devices charged and worn, and resolved any technical issues with the apps or smartwatches. Participants completed a daily questionnaire to report symptoms related to COVID-19, with the option to include additional symptoms in free text (Appendix A). The exact date and hour of testing for each positive diagnosis was recorded in the individual’s medical record if they sought care. If participants conducted a rapid test, they were instructed to report the testing time in the PerMed app. For participants with more than one positive record, only the earliest recorded testing time was used for each disease. The smartwatches continuously recorded heart rate approximately every 60 seconds and HRV-based stress levels every 180 seconds. HRV-based stress is a Garmin-computed measure: specifically, the device calculates the interval between heartbeats using heart rate data, where lower variability during sedentary periods corresponds to higher stress levels, and greater variability indicates lower stress. 24-27 To minimize participant attrition and ensure data quality, several strategies were employed. Participants who did not complete their daily questionnaire by 19:00 received reminders through the PerMed app. Additionally, the survey company used the dashboard to identify participants who neglected their tasks, prompting follow-up via text or phone call. To increase engagement, a weekly summary report was generated, and a monthly newsletter with study updates and smartwatch tips was sent to participants. Data from the smartphones and Garmin devices were securely collected and stored at Tel Aviv University. Baseline period We defined a baseline period – analogous to a run-in phase in clinical trials – for each participant as the five-week window between seven and two weeks prior to their COVID-19 diagnosis. This timeframe was chosen to avoid the influence of early symptoms or infection-related physiological changes, as the typical incubation period for SARS-CoV-2 is 4 to 6 days. 28 We included participants who had at least 25 days with a minimum of 16 hours of smartwatch data per day, and missing data were imputed linearly. To minimize potential confounding effects from recent vaccination, we analyzed data from individuals who had received a COVID-19 vaccine at least three months prior to infection, based on evidence that the most pronounced physiological effects, particularly the presence of neutralizing antibodies, occur within the first three months after vaccination. 29 Infection period We defined the infection period as the four-week period spanning from one week before to three weeks after the testing date (days -7 to +21 relative to testing). For each participant, we identified the first new symptom reported during this phase. Only symptoms reported during this period and absent one week prior (days -14 to -7 relative to testing) were included in the analysis. For example, if a participant reported a headache one day before testing and also nine days prior, it was not included in the analysis. Participants who did not complete the questionnaire during days -13 to -7 were excluded from the analysis due to the inability to determine whether their symptoms existed before diagnosis. Outcome measures We included two primary outcome measures representing participants’ responses to COVID-19 infection: a self-reported symptom score and a smartwatch-derived heart rate reaction score. We collected daily symptom reports through the structured questionnaires. Using data from the US Centers for Disease Control and Prevention and the Pfizer clinical trial, we categorized participant-reported symptoms by severity as follows: No symptoms Mild symptoms: abdominal pain, back or neck pain, feeling cold, muscle pain, weakness, headache, dizziness, vomiting, sore throat, diarrhea, cough, leg pain, ear pain, loss of taste and smell, swelling of the lymph nodes Moderate-to-severe symptoms: a measured temperature over 37.5°C, corresponding to at least a low-grade fever or reported fever (“fever”), fast heartbeat, hypertension, chest pain, dyspnea (shortness of breath), confusion, and chills We categorized symptom severity on an ordinal scale: 0 = no symptoms, 1 = mild symptoms, and 2 = moderate-to-severe symptoms. This score captured the subjective experience of illness and was used to assess similarity between matched participants, as well as to simulate vaccine effectiveness in reducing symptoms. We used continuous heart rate data from smartwatches to compute an objective physiological response to infection. For each participant, we calculated the squared differences between hourly heart rate values during the post-infection period and the final week of baseline. We smoothed these values using a 72-hour rolling average and defined the maximum value of this curve as the participant’s HR reaction score. This measure served as a continuous outcome for evaluating physiological similarity between matched individuals and modeling the impact of vaccination on reducing physiological burden. We selected the 72-hour window based on both statistical and clinical reasoning. This window is long enough to smooth out momentary fluctuations due to stress, activity, or measurement noise, while still capturing meaningful physiological deviations. This choice is also consistent with findings from a previous study, which reported that the most severe reactions typically last between 24 to 72 hours. 20 From the smoothed curve, we extracted the maximum value to represent each participant’s peak physiological reaction to infection. This peak reflects the most intense deviation from the individual’s baseline, analogous to clinical markers such as peak fever or peak inflammatory response. Reducing each time series to a single interpretable metric enables direct comparison of physiological responses across individuals and between matched pairs in both our similarity evaluation and power analysis simulations. Statistical analysis To evaluate the effectiveness of SIM, we performed a series of analyses using baseline and infection-period data from the PerMed study. Our goal was to assess whether SIM improved covariate balance and better paired individuals with similar characteristics and responses compared to standard age- and sex-based matching. We first stratified participants into four demographic strata based on sex (male, female) and age (18–55, 56–85), following categories used in previous clinical trials. 30 Within each stratum, we applied nearest-neighbor matching using either (a) physiological similarity derived from heart rate and HRV distributions during the baseline period (SIM) or (b) random matching based on age and sex. To assess covariate balance, we calculated the correlation between matched pairs for a set of individual-level characteristics known to influence infection risk. These attributes included lifestyle and social context (e.g., household composition, smoking and alcohol habits), general health (e.g., body mass index (BMI), medical history), and activity/sleep patterns (e.g., step count, activity time, sleep duration). We derived these measures from three sources: enrollment questionnaires, daily symptom and behavior questionnaires, and baseline smartwatch data. For each matching method, we computed the correlation across matched pairs for each covariate. To quantify the uncertainty around correlation estimates for the standard matching approach, we used bootstrapping (1,000 repetitions) and reported the 2.5 th and 97.5 th percentiles as confidence bounds. To assess predictive validity, we examined the extent to which matched participants exhibited similar responses to COVID-19 infection. For this analysis, we used two outcome types: (1) self-reported symptom severity, recorded through daily questionnaires and categorized on an ordinal scale (no symptoms, mild, or moderate-to-severe), and (2) objective cardiovascular response, calculated from smartwatch data as the mean absolute change in heart rate and HRV-derived stress between baseline and infection periods. For each of these outcomes, we calculated the correlation between matched intervention and control individuals. To reflect the dependency structure observed in real data, we used Spearman’s rank correlation (ρ) for the ordinal symptom score and Pearson’s correlation (r) for the continuous cardiovascular response score. To examine potential differences in symptom severity across demographic groups, we performed a chi-square test of independence and conducted post-hoc pairwise comparisons with Bonferroni correction to control for multiple comparisons. Power Analysis We conducted a series of power analysis simulations to evaluate how SIM influences the required sample size in vaccine trials (Appendix D). Our aim was to quantify the change in required sample size achievable with SIM – compared to standard age- and sex-based matching – while maintaining a given level of statistical power. We simulated 100,000 hypothetical clinical trials, each designed to estimate the effectiveness of a hypothetical vaccine under varying trial conditions. We varied (a) statistical power targets: 0.80 and 0.95; (b) vaccine efficacy (VE): 40%, 50%, 60%, and 90%; and (c) probability of symptomatic infection (π): 0.003, 0.030, and 0.100 (π reflects the joint probability of becoming infected and developing symptoms). We assumed a fixed significance level (α) of 0.05. Each simulation followed these steps: 1. Simulating matched intervention and control outcomes We generated a set of matched participant pairs, consisting of one intervention group member (simulated vaccine recipient) and one control group member, using either SIM or the standard matching strategy. For each pair, we simulated the presence or absence of symptomatic infection based on the sampled probability π. In the intervention group, the probability of symptoms was reduced according to the specified vaccine efficacy (VE). To reflect similarity between matched individuals, we incorporated dependencies in outcomes between each pair. These dependencies were sampled using the correlation coefficients observed in our analysis of the PerMed dataset: Spearman’s ρ for self-reported symptom severity and Pearson’s r for smartwatch-derived physiological responses. This ensured that outcomes in matched pairs realistically reflected the strength of similarity achieved under each matching strategy. 2. Power analysis and sample size estimation For each simulated trial, we estimated the minimum sample size required to detect a vaccine effect with the given power level (0.80 or 0.95) at significance level α = 0.05. We performed this analysis separately for each combination of VE, π, and matching method (SIM vs. standard), and for each outcome type (self-reported symptoms vs. smartwatch-derived physiological response). We systematically repeated this simulation process across 100,000 trials. RESULTS Validation of SIM Before COVID-19 Infection A total of 1,015 participants from the PerMed study were included in the analysis (Figure 1). Of these, 533 (52.5%) were female and 482 (47.5%) were male. Participants ranged in age from 19 to 85 years, with a median age of 46 years. We first validated that Smartwatch-Informed Matching (SIM) captures individual-level factors known to influence infection risk—such as general health, physical activity, and social and lifestyle characteristics. Using baseline (pre-infection) data, we found that SIM-matched individuals demonstrated stronger correlations across these factors compared to individuals matched only by age and sex (Table 1). For instance, the correlation in daily step count between SIM-matched pairs was 16.9% (95% CI: 10.9%–29.1%), substantially higher than the 2.4% correlation (95% CI: –6.4%–11.1%) observed with age- and sex-based matching. Similarly, perceived general health showed a correlation of 9.5% (95% CI: 0.2%–18.3%) with SIM, compared to 2.8% (95% CI: –6.6%–11.3%) with traditional matching. The number of children in the household correlated at 20.0% (95% CI: 11.1%–28.7%) for SIM-matched pairs, versus 13.4% (95% CI: 5.5%–20.9%) for age- and sex-based pairs. Predictive Validity of SIM During COVID-19 Infection During COVID-19 infection, 834 participants completed sufficient questionnaires for our analysis. Among them, 222 (26.6%) reported no symptoms, 295 (35.4%) reported mild symptoms, and 317 (38.0%) reported moderate-to-severe symptoms (Table 2). We evaluated the ability of SIM matching to identify similarities between individuals' severity of COVID-19 infection based on reported symptoms. Correlation of self-reported infection severity between matched pairs was r = 0.176 (95% CI: 0.075–0.282) for SIM, versus r = 0.012 (95% CI: 0.010–0.015) for age- and sex-based matching. We also compared SIM to a one-nearest-neighbor matching based on sex and exact age. This age-based matching yielded a correlation of r = 0.011 for self-reported infection severity between matched pairs, similar to that for age- and sex-based covariate matching. We assessed the ability of SIM to identify similarities between individuals based on objective physiological responses recorded by the smartwatch rather than self-reported symptoms. Specifically, for each participant, we calculated the mean absolute change in heart rate and HRV-derived stress between the baseline and the COVID-19 infection period. Using the magnitude of these changes as an outcome measure, we found that SIM yielded a correlation of r = 0.245 (95% CI: 0.137–0.345) between matched individuals, compared to r = 0.112 (95% CI: 0.101–0.116) using age- and sex-based matching. A one-nearest-neighbor approach based on exact age and sex produced a lower correlation of r = 0.104 for the same smartwatch-measured cardiovascular response. We conducted a post-hoc analysis to validate that differences in vaccination timing between matched individuals did not bias our findings. This analysis showed that approximately 80% of matched pairs were vaccinated within 20 days of each other, confirming that vaccination timing was well-balanced and unlikely to influence our results (Appendix C). Power Analysis of Clinical Trials Using SIM We simulated clinical trial scenarios to evaluate the impact of SIM on the required sample size when using either self-reported symptoms or smartwatch-based cardiovascular responses as the primary outcome. Across all combinations of vaccine efficacy (VE) and the probability of becoming infected and developing symptoms (π), SIM consistently led to reductions in sample size compared to standard age- and sex-based matching (Table 3). When using self-reported symptoms, SIM improved matching efficiency and lowered the number of participants needed to achieve the desired statistical power. For example, under trial conditions with π = 0.003 and VE = 0.50, a trial targeting 95% power required 33,374 participants with age- and sex-based matching, compared to 30,231 with SIM, a reduction of 3,142 participants (9.4%). For 80% power under the same conditions, the required sample size dropped from 18,193 to 16,276 (10.5% reduction). The magnitude of reduction varied with VE and π but remained consistent across settings. When using objective cardiovascular responses from smartwatches as an outcome measure, SIM produced even greater reductions in required sample size. In the same scenario (π = 0.003, VE = 0.50), the sample size required for 95% power dropped from 33,374 to 28,653, representing a reduction of 4,720 participants (14.1%) compared to age- and sex-based matching. At 80% power, the sample size decreased from 18,193 to 15,945 (12.4% reduction). The reductions in required sample size were particularly notable in settings where the probability of becoming infected and developing symptoms (π) was low and vaccine efficacy (VE) was modest,conditions that typically require the largest trials. DISCUSSION Our findings demonstrate that wearable sensors can enhance clinical trial design by enabling more precise matching of participants in intervention and control groups. By incorporating smartwatch-based physiological data into the matching process, our framework enhances the statistical sensitivity to detect intervention-related changes and increases the precision of outcome assessments. These improvements can enhance the timeliness of clinical trials by accelerating the accumulation of meaningful data, thereby supporting faster evaluation and delivery of effective medical interventions. Additionally, by reducing noise and physiological heterogeneity, our approach may improve safety assessment by decreasing the likelihood of missing adverse events during the trial. In simulations across a range of realistic scenarios, we found that physiological matching consistently reduced the required sample size compared to standard stratification by age and sex. Using data from participants infected with COVID-19, the reduction ranged from 9–18% in settings where vaccine efficacy is moderate (40–60%). Given that the cost of enrolling a participant in a clinical trial ranges from USD31,000–82,000 31 , and that participant costs constitute more than one-third of total trial costs 32 , these reductions in required sample size represent significant potential savings. Smartwatch data provides an objective measure of physiological responses, reducing the bias of self-reported symptoms and offering valuable insights into treatment effects. Notably, we found that younger individuals, particularly males aged 18–55, had more pronounced physiological responses during infection than older individuals, despite older individuals being at higher risk for severe COVID-19 complications. This may reflect heightened immune activation and autonomic reactivity in younger participants, leading to more detectable changes in metrics like heart rate and heart rate variability. These findings highlight the value of wearable technology in clinical trials for precise, continuous monitoring and a deeper understanding of demographic differences, enabling more tailored treatments. We used participants' daily heart rate distributions and HRV-derived stress metrics for our matching process, as they correlate with key physical and mental health factors, and lifestyle metrics such as activity, steps, and sleep patterns. Indeed, previous studies have found that heart rate metrics derived from smartwatches are associated with a wide range of health outcomes, extending well beyond cardiovascular conditions. These include mood regulation 33 , 34 , sleep quality 35 , 36 and disorders 37 , 38 , chronic pain 39 , cognitive decline 40 , and even subtle health indicators such as low-grade fever and changes in resting heart rate 41 and HRV-derived stress. 42 , 43 Previous studies have also shown that variations in heart rate, HRV, and resting heart rate can predict and detect responses to infections such as influenza, group A streptococcus, and COVID-19. 20,44–46 By accounting for individuals' overall health and their physiological responses during infectious diseases, our approach enables a more efficient trial design while reducing the number of participants needed to achieve robust outcomes. As an immediate practical application, our framework can be implemented into retrospective analyses of vaccination and healthcare technology assessments that rely on matching approaches. Current matching methods in retrospective research typically rely on demographics and static health measures, overlooking dynamic physiological variation at the individual level 23 , 47 – 49 . This limitation can leave meaningful confounding unaddressed. SIM introduces physiologic matching that captures individual-level variation. When continuous physiological data are available, SIM can substantially improve covariate balance in retrospective cohorts, reduce bias, and strengthen causal inference, particularly in comparative effectiveness research, where physiologic state may influence both treatment selection and outcomes. Due to the limited number of participants in our study, we focused on heart measures. However, given the rich, diverse, and continuous data generated by various biosensors in smartwatches, we believe our study represents just the tip of the iceberg in what can be achieved for more accurate matching of individuals in clinical trials. Specifically, in clinical trials focused on infectious diseases, smartwatch biosensors can provide insights into lifestyle factors such as step counts, proximity to other Bluetooth devices, and social interaction patterns, which may help predict individuals who are more prone to becoming infected. Other smartwatch biosensors, such as those that measure respiratory rate, oxygen level, blood pressure, and sleeping patterns, could be valuable in identifying individuals at greater risk for complications if infected. 50 , 51 Such information could be used to create even more accurate participant matching than we have considered. Our study has several limitations. Participants were recruited via social media and word-of-mouth, resulting in a convenience sample that may not fully represent the general population. The demanding requirements – wearing a smartwatch and completing daily questionnaires for two years – could have discouraged certain individuals from participating. Additionally, COVID-19 is known to be associated with heart-related issues, which may have contributed to the effectiveness of our smartwatch matching approach based on heart rate and HRV. Furthermore, the Garmin smartwatches worn by trial participants are not medical-grade devices and may not fully capture all relevant physiological changes. In conclusion, our study demonstrates the potential of smartwatch-derived physiological data to improve clinical trial design by shifting from traditional static matching methods to a dynamic, data-driven approach. By integrating continuous metrics such as heart rate and HRV, our method can reduce sample size requirements while maintaining statistical power, compared to traditional age- and sex-based matching methods. This marks a potentially transformative step in clinical research, where wearable technology can enable improved participant matching in clinical trials, as well as real-time, objective monitoring of health and disease progression, enhancing the efficiency, accuracy, and personalization of clinical trials. Declarations Funding This work was supported by the European Research Council, project #949850, and the Israel Science Foundation (ISF), grant No. 3409/19, within the Israel Precision Medicine Partnership program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Contributors Conception and design: DY and ES. Collection and assembly of data: DY, MY, SS, and ES had access to the raw data and were responsible for verifying the data. Analysis and interpretation of the data: ES, SS, MY, DY, MP and MLB. Statistical analysis: ES and DY. Drafting the article: ES, MY, SS, DY, and MLB. Critical revision of the article for important intellectual content: all authors. Final approval of the article: All authors. Obtaining funding: DY. Declaration of Interests All authors declare no competing interests. Data sharing statements According to this study's Tel Aviv University IRB guidelines, no patient-level data is to be shared outside the permitted researchers. The statistical analysis code along with an aggregated version of the data sufficient to reproduce the results reported in this paper will be available upon publication. References Hariton E, Locascio JJ. Randomised controlled trials - the gold standard for effectiveness research. BJOG 2018;125(13):1716. Gul RB, Ali PA. 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Santa-Rosa FA, Shimojo GL, Dias DS, et al. Impact of an active lifestyle on heart rate variability and oxidative stress markers in offspring of hypertensives. Sci Rep 2020;10(1):12439. Mishra T, Wang M, Metwally AA, et al. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat Biomed Eng 2020;4(12):1208-1220. Radin JM, Vogel JM, Delgado F, et al. Long-term changes in wearable sensor data in people with and without Long Covid. NPJ Digit Med 2024;7(1):246. Levi Y, Gande V, Brandeau ML, Yamin D. Self-reported recovery versus smartwatch-detected recovery for COVID-19, influenza and group A streptococcus: A two-year prospective cohort study. Working paper 2025. Dagan N, Barda N, Kepten E, et al. BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting. New England Journal of Medicine [Internet] 2021 [cited 2021 Mar 30];NEJMoa2101765. Available from: http://www.nejm.org/doi/10.1056/NEJMoa2101765 Yaron S, Yechezkel M, Yamin D, et al. Incremental benefit of high dose compared to standard dose influenza vaccine in reducing hospitalizations. NPJ Vaccines [Internet] 2025 [cited 2025 Jan 26];10(1):3. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11718019/ Tsanani SE, Yorav S, Yaron S, et al. Effectiveness of influenza vaccination in preventing severe COPD exacerbations and pneumonia before, during, and after the COVID-19 pandemic: a retrospective cohort study. The Lancet Regional Health - Europe [Internet] 2025 [cited 2025 May 23];53. Available from: https://www.thelancet.com/action/showFullText?pii=S2666776225000997 Angelucci A, Greco M, Cecconi M, Aliverti A. Wearable devices for patient monitoring in the intensive care unit. Intensive Care Med Exp 2025;13(1):26. Hanly PJ, Millar TW, Steljes DG, Baert R, Frais MA, Kryger MH. The effect of oxygen on respiration and sleep in patients with congestive heart failure. Ann Intern Med 1989;111(10):777-82. Tables Table 1. Activity and rest, general health, and social and lifestyle correlations among matched pairs. Measure N (%) or [IQR] Total N Age- and sex-based covariate matching Mean Correlation %, (95% CI) Age-, sex- and smartwatch-based covariate matching Mean Correlation %, (95% CI) Activity and Rest Patterns, Median [IQR] Daily step count* 6965 [5259, 8957] 1015 2.4 (-6.4–11.1) † 16.9 (10.9–29.1) † Active time per day* 8833 [6721, 11018] 1015 1.8 (-6.7–11.0) † 12.6 (3.7–22.1) † Sleep duration* 6.847 [6.277, 7.440] 960 4.0 (-5.0–13.1) † 7.6 (-0.7–18.9) † Sleep quality (1-5 scale) 0.630 [0.083, 1.0] 961 0.2 (-9.0–9.5) † 4.9 (-5.7–14.0) † General Health BMI > 30 203 (20.0%) 1015 0.4 (-8.8–9.5) 11.0 (0.8–21.5) Hypertension 95 (9.5%) 1002 12.1 (1.2–24.0) 22.2 (7.4–36.3) With background diseases 389 (38.3%) 1015 5.6 (-2.2–14.3) 10.3 (1.0–19.6) With cardiovascular disease history 182 (18.2%) 1002 16.3 (6.0–26.7) 20.6 (8.3–33.5) Perceived general health (1-5 scale), Median [IQR] 4 [4, 5] 1012 2.8 (-6.6–11.3) 9.5 (0.2–18.3) Social and Lifestyle Factors Alcohol Less than 4 times per month 601 (59.2%) 1015 2.6 (-6.0–11.4) 8.1 (-1.3–17.1) 4 or more times per month 414 (40.8%) Smoking Yes 97 (9.6%) 1013 0.0 (-8.3–9.7) –0.1 (-9.0–9.9) No 918 (90.4%) Number of children in household None 264 (26.0%) 1013 13.4 (5.5–20.9) 20.0 (11.1–28.7) 1 83 (8.2%) 2 262 (25.8%) 3 277 (27.3%) 4+ 127 (12.5%) Unknown 2 (0.2%) Number of residents in household 1 78 (7.7%) 1005 9.1 (1.5–16.7) 5.2 (-3.7–13.9) 2 289 (28.5%) 3 187 (18.4%) 4 206 (20.3%) 5+ 245 (24.1%) Unknown 10 (1%) Self-reported stress, Median [IQR] -0.7 [-1.0, -0.2] 961 1.0 (-8.6–11.0) † 5.6 (-5.6–14.2) † * Measure recorded by smartwatch; other measures are self-reported † Pearson correlation coefficient; all other correlation coefficients are Spearman’s rank correlation Table 2. Distribution of self-reported symptom severity levels and smartwatch-based reaction severity levels within each stratum. Strata Self-Reported Symptoms Smartwatch-Based Reaction Severity None Mild Moderate to severe Total Bottom third Middle third Top third Total Female, 18-55 60 (20%) 104 (35%) 131 (45%) 295 52 (26%) 70 (35%) 77 (39%) 199 Male, 18-55 73 (26%) 104 (38%) 99 (36%) 276 39 (19%) 67 (33%) 97 (48%) 203 Female, 56-85 45 (30%) 52 (35%) 53 (35%) 150 85 (59%) 45 (31%) 15 (10%) 145 Male, 56-85 44 (40%) 35 (30%) 34 (30%) 113 41 (39%) 35 (34%) 28 (27%) 104 Total 222 (27%) 295 (35%) 317 (38%) 834 217 (33%) 217 (33%) 217 (33%) 651 Table 3. Required sample sizes with standard stratified matching (n 0 ) and smartwatch-based matching (n) for different levels of statistical power (0.95, 0.80) and different outcome metrics (smartwatch-based reaction score, self-reported symptom severity). Metric π VE Power = 0.95 Power = 0.80 n 0 n n 0 - n % Reduction in Sample Size n 0 n n 0 - n % Reduction in Sample Size Smartwatch-Based Reaction Score .003 0.40 57,454 48,155 6,373 16.2 31,983 26,238 5,745 18.0 0.50 33,374 28,653 4,720 14.1 18,193 15,945 2,247 12.4 0.60 22,080 18,894 3,185 14.4 11,919 10,527 1,391 11.7 0.90 7,712 7,490 221 2.9 4,209 4,011 198 4.7 .030 0.40 5,496 4,458 1,038 18.9 3,080 2,520 560 18.2 0.50 3,220 2,755 465 14.4 1,692 1,426 266 15.8 0.60 2,081 1,744 336 16.2 1,171 1,042 128 11.0 0.90 772 747 25 3.3 429 413 16 3.8 .100 0.40 1,398 1,182 216 15.4 944 778 165 17.6 0.50 981 823 157 16.1 545 453 91 16.7 0.60 611 534 77 12.6 346 301 45 13.0 0.90 203 195 8 3.9 120 116 4 3.3 Self-Reported Symptom Severity .003 0.40 57,454 51,081 6,373 11.1 31,983 26,775 5,208 16.3 0.50 33,374 30,231 3,142 9.4 18,193 16,276 1916 10.5 0.60 22,080 20,093 1,987 9.0 11,919 10,614 1305 11.0 0.90 7,712 7,548 164 2.1 4,209 4,078 130 3.0 .030 0.40 5,496 5,144 352 6.4 3,080 2,817 263 8.6 0.50 3,220 2,882 337 10.5 1,692 1,495 197 11.6 0.60 2,081 1,826 254 12.2 1,171 1,076 94 8.1 0.90 772 743 29 3.8 429 419 10 2.5 .100 0.40 1,398 1,244 154 11.0 944 846 98 10.4 0.50 981 907 73 7.5 545 482 62 11.5 0.60 611 559 52 8.5 346 312 34 9.9 0.90 203 200 3 1.5 120 118 2 1.7 Additional Declarations No competing interests reported. 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00:07:55","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153018,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8366773/v1/5a553f85eb4a96a52c707b36.html"},{"id":100379466,"identity":"1119bb60-2732-40a9-88c7-fee5e2a32984","added_by":"auto","created_at":"2026-01-16 09:13:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164954,"visible":true,"origin":"","legend":"\u003cp\u003eTrial profile for the study cohort\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8366773/v1/da3eab0ef63acfe3f1acc4c4.png"},{"id":100378540,"identity":"8dbffce4-8390-4756-a0e9-08b3ca69c0e5","added_by":"auto","created_at":"2026-01-16 08:54:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":181725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the Smartwatch-Informed Matching (SIM) framework. \u003c/strong\u003eSmartwatch-derived data (top panel) are used to compute physiological similarity between individuals (middle panel). These similarity metrics guide a matching algorithm (bottom panel) that pairs participants into intervention and control groups based on their baseline characteristics.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8366773/v1/d59a1047452c87f45bd19eb9.png"},{"id":100390895,"identity":"07fcf8b8-a3bb-4ae3-b977-af42732e23eb","added_by":"auto","created_at":"2026-01-16 11:23:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1479651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8366773/v1/08502ce7-22fe-4970-96a5-7c1ae866ff1a.pdf"},{"id":100378199,"identity":"6fae7df6-9b33-43e5-a98c-37ce4e8aa704","added_by":"auto","created_at":"2026-01-16 08:50:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1523026,"visible":true,"origin":"","legend":"","description":"","filename":"SIDecember.docx","url":"https://assets-eu.researchsquare.com/files/rs-8366773/v1/182856a13db462d2a83e5302.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Randomized Controlled Trials Through Smartwatch-Guided Participant Matching","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eRandomized controlled trials (RCTs) are widely regarded as the gold standard for assessing the safety and efficacy of medical interventions.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e A key challenge in designing RCTs is maximizing statistical power to detect treatment efficacy and identify rare adverse events while minimizing resource use.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Larger sample sizes enhance statistical power but require greater participant recruitment and incentive resources. Participants are often the most costly component of RCTs, with recruitment, retention, and compensation accounting for a significant portion of trial budgets.\u003c/p\u003e \u003cp\u003eIn 2020, a COVID-19-driven vaccine revolution occurred. Just 11 months after development, the Pfizer-BioNTech vaccine received regulatory approval, supported by large-scale clinical trials involving over 43,000 participants demonstrating its safety and efficacy.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e However, post-approval vaccine safety monitoring systems identified a causal link between mRNA COVID-19 vaccines and myocarditis\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e \u0026ndash; an undetected association during the clinical trials. This emphasizes the critical need for enhancing clinical trial designs to capture rare but significant adverse events.\u003c/p\u003e \u003cp\u003eContemporary clinical trials face two significant challenges. First, their stratified randomization processes often rely on relatively simple criteria, such as sex, age, comorbidities, and (where applicable) disease progression.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e These factors are likely insufficient to capture the complexity and variability of patient populations. This abstraction can result in imbalances in unmeasured variables, such as lifestyle factors\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, which may also influence the probability of infection acquisition and treatment outcomes. Second, contemporary trials predominantly depend on self-reported symptoms to assess outcomes. These self-reports are inherently subjective and susceptible to variability in individual symptom perception.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhile traditional stratification in RCTs relies on a limited number of categorical variables, wearable sensors such as smartwatches offer a powerful and scalable tool for capturing rich, continuous physiological data. Unlike conventional approaches that depend on subjective assessments or intermittent biomarker measurements, smartwatches enable passive, objective data collection, providing a comprehensive profile of physiological, health, and lifestyle metrics. Prior research has demonstrated the potential of these devices to monitor infection, vaccination, or treatment efficacy and side effects.\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Such devices capture subtle physiological changes linked to infection progression, severity, and recovery, including fluctuations in heart rate, heart rate variability (HRV)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, sleep patterns, activity levels, and skin temperature.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Because smartwatches capture key indicators of an individual\u0026rsquo;s health, activity and rest patterns, as well as social and lifestyle factors, these data can potentially be leveraged to identify similarities between individuals, thereby enabling a more precise and effective matching process in RCTs.\u003c/p\u003e \u003cp\u003eHere, we present a novel framework for the design of RCTs that integrates traditional recruitment methods with smartwatch-derived data to optimize both participant matching and outcome assessment. We refer to our method as smartwatch-informed matching (SIM). Participants are matched into intervention and control groups based on baseline physiological data collected via smartwatches during a fixed pre-trial period (e.g., one month). Subsequently, individual responses\u0026mdash;to infection, vaccination, or treatment\u0026mdash;are assessed using a combination of standard measures (e.g., self-reported symptoms) and deviations from each participant\u0026rsquo;s smartwatch-derived baseline. To evaluate our approach, we analyzed data from a prospective cohort of 4,795 individuals in Israel, identifying those who became infected with COVID-19 and calculating pre-infection smartwatch-based metrics. We demonstrate that smartwatch-based matching can improve similarity between matched pairs and can significantly reduce the number of participants required in an RCT while maintaining high statistical power.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eOverview\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we introduce the Smartwatch-Informed Matching process (SIM), which integrates demographic variables (age and sex) with physiological data derived from smartwatches. Next, we evaluate the performance of this matching approach using data from a two-year prospective observational study involving 4,795 participants (Appendix A, Study Protocol), comparing its effectiveness against traditional covariate matching methods, such as those used in the Pfizer-BioNTech COVID-19 vaccine Phase 3 Trial.\u003csup\u003e4\u003c/sup\u003e We then perform a series of power analysis simulations to assess how SIM influences statistical power. Specifically, we quantify the reduction in required sample size relative to standard matching methods to assess the potential of SIM to improve trial efficiency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmartwatch-Informed Matching\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed SIM as a framework for pairing individuals based on physiological and behavioral similarity, enabling the construction of well-matched intervention and control groups for clinical trials (Figure 2).\u003c/p\u003e\n\u003cp\u003eSmartwatches contain various biosensors\u0026mdash;such as accelerometers, temperature monitors, and optical heart rate sensors\u0026mdash;that can capture a wide range of physiological and behavioral traits relevant to individual health. While many of these signals may offer value for covariate matching in clinical trials, incorporating too many variables introduces the risk of overfitting, multiple testing concerns, and reduced interpretability. To address these challenges, we focused exclusively on heart rate and HRV\u0026mdash;core physiological measures that are widely available across devices and reflect the activity of the cardiovascular and autonomic nervous systems. Heart rate is a fundamental vital sign commonly used to monitor general physiological status, including responses to infection and inflammation. HRV, while not a traditional vital sign, is a well-established digital biomarker of autonomic regulation and has been linked to stress, recovery, immune function, and early signs of physiological disruption. HRV-based signals have been shown to reliably capture meaningful physiological responses, including reactions to vaccination and early signs of infection \u0026ndash; often preceding self-reported symptoms.\u003csup\u003e10,20,21\u003c/sup\u003e Building on this evidence, and following prior work\u003csup\u003e20\u003c/sup\u003e, we focused on daily heart rate and HRV distributions during sedentary periods; these provide a stable, interpretable signal for comparing individuals\u0026rsquo; physiological baselines.\u003c/p\u003e\n\u003cp\u003eThe SIM procedure is detailed in Appendix B. Briefly, our matching process begins by stratifying the population into subgroups based on age and sex, as done in standard clinical trial designs. Within each subgroup, we calculate the distribution of heart rate and HRV for each individual during a predefined baseline period. To assess similarity between individuals, we compare their heart rate distributions using the Earth Mover\u0026apos;s Distance (EMD), a metric that quantifies how much one distribution would need to shift to match another. Intuitively, the lower the EMD, the more similar the heart rate patterns. We apply the same process to compare HRV distributions. We then combine these two distance measures using a simple Euclidean distance to compute an overall dissimilarity score between each pair of individuals. Using this score, we apply a nearest-neighbor greedy matching algorithm to pair individuals within each subgroup. This results in matched intervention and control groups, structured similarly to how randomization is done in clinical trials, but guided by physiological similarity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData-Driven Evaluation of SIM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerMed study\u003c/strong\u003e To evaluate our approach, we analyzed the data from the PerMed prospective observational study.\u003csup\u003e20,22,23\u003c/sup\u003e The study enrolled 4,795 participants aged 18 and older, who were recruited between November 16, 2020, and May 11, 2023, from various locations across Israel (Appendix A). Recruitment was facilitated through social media advertisements and word-of-mouth. All participants provided informed consent after receiving a thorough explanation of the study from a professional survey company. Eligibility for the PerMed study required participants to be members of Maccabi Healthcare Services (Maccabi) for at least two years prior to enrollment, be smartphone users, and capable of independently providing written informed consent. All participants received both oral and written information regarding the study, and participation was contingent upon written informed consent. This \u0026nbsp; study was approved by the Institutional Review Board (IRB) of Tel Aviv University headed by Prof. Meir Lahav (protocol number 0122-20-TAU). We hereby declare that all methods were performed in accordance with the IRB guidelines and regulations. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants completed a one-time enrollment questionnaire, providing personal and health-related information. Each participant was provided with a Garmin Vivosmart 4 smartwatch and asked to install two mobile applications: (1) the PerMed application, which collects daily self-reported symptom and sleep data, and (2) a passive data collection app that records smartwatch measurements. All participants, regardless of operating system, downloaded the same applications. They were encouraged to wear their smartwatches consistently, and a survey company monitored compliance through a dedicated dashboard. This ensured that participants completed their questionnaires at least twice a week, kept their devices charged and worn, and resolved any technical issues with the apps or smartwatches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants completed a daily questionnaire to report symptoms related to COVID-19, with the option to include additional symptoms in free text (Appendix A). The exact date and hour of testing for each positive diagnosis was recorded in the individual\u0026rsquo;s medical record if they sought care. If participants conducted a rapid test, they were instructed to report the testing time in the PerMed app. For participants with more than one positive record, only the earliest recorded testing time was used for each disease.\u003c/p\u003e\n\u003cp\u003eThe smartwatches continuously recorded heart rate approximately every 60 seconds and HRV-based stress levels every 180 seconds. HRV-based stress is a Garmin-computed measure: specifically, the device calculates the interval between heartbeats using heart rate data, where lower variability during sedentary periods corresponds to higher stress levels, and greater variability indicates lower stress.\u003csup\u003e24-27\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eTo minimize participant attrition and ensure data quality, several strategies were employed. Participants who did not complete their daily questionnaire by 19:00 received reminders through the PerMed app. Additionally, the survey company used the dashboard to identify participants who neglected their tasks, prompting follow-up via text or phone call. To increase engagement, a weekly summary report was generated, and a monthly newsletter with study updates and smartwatch tips was sent to participants. Data from the smartphones and Garmin devices were securely collected and stored at Tel Aviv University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline period\u0026nbsp;\u003c/strong\u003eWe defined a baseline period \u0026ndash; analogous to a run-in phase in clinical trials \u0026ndash; for each participant as the five-week window between seven and two weeks prior to their COVID-19 diagnosis. This timeframe was chosen to avoid the influence of early symptoms or infection-related physiological changes, as the typical incubation period for SARS-CoV-2 is 4 to 6 days.\u003csup\u003e28\u003c/sup\u003e We included participants who had at least 25 days with a minimum of 16 hours of smartwatch data per day, and missing data were imputed linearly. To minimize potential confounding effects from recent vaccination, we analyzed data from individuals who had received a COVID-19 vaccine at least three months prior to infection, based on evidence that the most pronounced physiological effects, particularly the presence of neutralizing antibodies, occur within the first three months after vaccination.\u003csup\u003e29\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfection period\u0026nbsp;\u003c/strong\u003eWe defined the infection period as the four-week period spanning from one week before to three weeks after the testing date (days -7 to +21 relative to testing). For each participant, we identified the first new symptom reported during this phase. Only symptoms reported during this period\u0026nbsp;and absent one week prior (days -14 to -7 relative to testing) were included in the analysis. For example, if a participant reported a headache one day before testing and also nine days prior, it was not included in the analysis. Participants who did not complete the questionnaire during days -13 to -7 were excluded from the analysis due to the inability to determine whether their symptoms existed before diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome measures\u003c/strong\u003e We included two primary outcome measures representing participants\u0026rsquo; responses to COVID-19 infection: a self-reported symptom score and a smartwatch-derived heart rate reaction score.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe collected daily symptom reports through the structured questionnaires. Using data from the US Centers for Disease Control and Prevention and the Pfizer clinical trial, we categorized participant-reported symptoms by severity as follows:\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eNo symptoms\u003c/li\u003e\n \u003cli\u003eMild symptoms: abdominal pain, back or neck pain, feeling cold, muscle pain, weakness, headache, dizziness, vomiting, sore throat, diarrhea, cough, leg pain, ear pain, loss of taste and smell, swelling of the lymph nodes\u003c/li\u003e\n \u003cli\u003eModerate-to-severe symptoms: a measured temperature over 37.5\u0026deg;C, corresponding to at least a low-grade fever or reported fever (\u0026ldquo;fever\u0026rdquo;), fast heartbeat, hypertension, chest pain, dyspnea (shortness of breath), confusion, and chills\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe categorized symptom severity on an ordinal scale: 0 = no symptoms, 1 = mild symptoms, and 2 = moderate-to-severe symptoms. This score captured the subjective experience of illness and was used to assess similarity between matched participants, as well as to simulate vaccine effectiveness in reducing symptoms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used continuous heart rate data from smartwatches to compute an objective physiological response to infection. For each participant, we calculated the squared differences between hourly heart rate values during the post-infection period and the final week of baseline. We smoothed these values using a 72-hour rolling average and defined the maximum value of this curve as the participant\u0026rsquo;s HR reaction score. This measure served as a continuous outcome for evaluating physiological similarity between matched individuals and modeling the impact of vaccination on reducing physiological burden.\u003c/p\u003e\n\u003cp\u003eWe selected the 72-hour window based on both statistical and clinical reasoning. This window is long enough to smooth out momentary fluctuations due to stress, activity, or measurement noise, while still capturing meaningful physiological deviations. This choice is also consistent with findings from a previous study, which reported that the most severe reactions typically last between 24 to 72 hours.\u003csup\u003e20\u003c/sup\u003e From the smoothed curve, we extracted the maximum value to represent each participant\u0026rsquo;s peak physiological reaction to infection. This peak reflects the most intense deviation from the individual\u0026rsquo;s baseline, analogous to clinical markers such as peak fever or peak inflammatory response. Reducing each time series to a single interpretable metric enables direct comparison of physiological responses across individuals and between matched pairs in both our similarity evaluation and power analysis simulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003eTo evaluate the effectiveness of SIM, we performed a series of analyses using baseline and infection-period data from the PerMed study. Our goal was to assess whether SIM improved covariate balance and better paired individuals with similar characteristics and responses compared to standard age- and sex-based matching. We first stratified participants into four demographic strata based on sex (male, female) and age (18\u0026ndash;55, 56\u0026ndash;85), following categories used in previous clinical trials.\u003csup\u003e30\u003c/sup\u003e Within each stratum, we applied nearest-neighbor matching using either (a) physiological similarity derived from heart rate and HRV distributions during the baseline period (SIM) or (b) random matching based on age and sex.\u003c/p\u003e\n\u003cp\u003eTo assess covariate balance, we calculated the correlation between matched pairs for a set of individual-level characteristics known to influence infection risk. These attributes included lifestyle and social context (e.g., household composition, smoking and alcohol habits), general health (e.g., body mass index (BMI), medical history), and activity/sleep patterns (e.g., step count, activity time, sleep duration). We derived these measures from three sources: enrollment questionnaires, daily symptom and behavior questionnaires, and baseline smartwatch data. For each matching method, we computed the correlation across matched pairs for each covariate. To quantify the uncertainty around correlation estimates for the standard matching approach, we used bootstrapping (1,000 repetitions) and reported the 2.5\u003csup\u003eth\u003c/sup\u003e and 97.5\u003csup\u003eth\u003c/sup\u003e percentiles as confidence bounds.\u003c/p\u003e\n\u003cp\u003eTo assess predictive validity, we examined the extent to which matched participants exhibited similar responses to COVID-19 infection. For this analysis, we used two outcome types: (1) self-reported symptom severity, recorded through daily questionnaires and categorized on an ordinal scale (no symptoms, mild, or moderate-to-severe), and (2) objective cardiovascular response, calculated from smartwatch data as the mean absolute change in heart rate and HRV-derived stress between baseline and infection periods.\u0026nbsp;For each of these outcomes, we calculated the correlation between matched intervention and control individuals. To reflect the dependency structure observed in real data, we used Spearman\u0026rsquo;s rank correlation (\u0026rho;) for the ordinal symptom score and Pearson\u0026rsquo;s correlation (r) for the continuous cardiovascular response score. To examine potential differences in symptom severity across demographic groups, we performed a chi-square test of independence and conducted post-hoc pairwise comparisons with Bonferroni correction to control for multiple comparisons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a series of power analysis simulations to evaluate how SIM influences the required sample size in vaccine trials (Appendix D). Our aim was to quantify the change in required sample size achievable with SIM \u0026ndash; compared to standard age- and sex-based matching \u0026ndash; while maintaining a given level of statistical power.\u003c/p\u003e\n\u003cp\u003eWe simulated 100,000 hypothetical clinical trials, each designed to estimate the effectiveness of a hypothetical vaccine under varying trial conditions. We varied (a) statistical power targets: 0.80 and 0.95; (b) vaccine efficacy (VE): 40%, 50%, 60%, and 90%; and (c) probability of symptomatic infection (\u0026pi;): 0.003, 0.030, and 0.100 (\u0026pi; reflects the joint probability of becoming infected and developing symptoms). We assumed a fixed significance level (\u0026alpha;) of 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach simulation followed these steps:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1. Simulating matched intervention and control outcomes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe generated a set of matched participant pairs, consisting of one intervention group member (simulated vaccine recipient) and one control group member, using either SIM or the standard matching strategy. For each pair, we simulated the presence or absence of symptomatic infection based on the sampled probability \u0026pi;. In the intervention group, the probability of symptoms was reduced according to the specified vaccine efficacy (VE).\u003c/p\u003e\n\u003cp\u003eTo reflect similarity between matched individuals, we incorporated dependencies in outcomes between each pair. These dependencies were sampled using the correlation coefficients observed in our analysis of the PerMed dataset: Spearman\u0026rsquo;s \u0026rho; for self-reported symptom severity and Pearson\u0026rsquo;s r for smartwatch-derived physiological responses. This ensured that outcomes in matched pairs realistically reflected the strength of similarity achieved under each matching strategy.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2. Power analysis and sample size estimation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor each simulated trial, we estimated the minimum sample size required to detect a vaccine effect with the given power level (0.80 or 0.95) at significance level \u0026alpha; = 0.05. We performed this analysis separately for each combination of VE, \u0026pi;, and matching method (SIM vs. standard), and for each outcome type (self-reported symptoms vs. smartwatch-derived physiological response). We systematically repeated this simulation process across 100,000 trials.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eValidation of SIM Before COVID-19 Infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1,015 participants from the PerMed study were included in the analysis (Figure 1). Of these, 533 (52.5%) were female and 482 (47.5%) were male. Participants ranged in age from 19 to 85 years, with a median age of 46 years.\u003c/p\u003e\n\u003cp\u003eWe first validated that Smartwatch-Informed Matching (SIM) captures individual-level factors known to influence infection risk\u0026mdash;such as general health, physical activity, and social and lifestyle characteristics. Using baseline (pre-infection) data, we found that SIM-matched individuals demonstrated stronger correlations across these factors compared to individuals matched only by age and sex (Table 1). For instance, the correlation in daily step count between SIM-matched pairs was 16.9% (95% CI: 10.9%\u0026ndash;29.1%), substantially higher than the 2.4% correlation (95% CI: \u0026ndash;6.4%\u0026ndash;11.1%) observed with age- and sex-based matching. Similarly, perceived general health showed a correlation of 9.5% (95% CI: 0.2%\u0026ndash;18.3%) with SIM, compared to 2.8% (95% CI: \u0026ndash;6.6%\u0026ndash;11.3%) with traditional matching. The number of children in the household correlated at 20.0% (95% CI: 11.1%\u0026ndash;28.7%) for SIM-matched pairs, versus 13.4% (95% CI: 5.5%\u0026ndash;20.9%) for age- and sex-based pairs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive Validity of SIM During COVID-19 Infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring COVID-19 infection, 834 participants completed sufficient questionnaires for our analysis. Among them, 222 (26.6%) reported no symptoms, 295 (35.4%) reported mild symptoms, and 317 (38.0%) reported moderate-to-severe symptoms (Table 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe evaluated the ability of SIM matching to identify similarities between individuals\u0026apos; severity of COVID-19 infection based on reported symptoms. Correlation of self-reported infection severity between matched pairs was \u003cem\u003er\u003c/em\u003e = 0.176 (95% CI: 0.075\u0026ndash;0.282) for SIM, versus \u003cem\u003er\u003c/em\u003e = 0.012 (95% CI: 0.010\u0026ndash;0.015) for age- and sex-based matching. We also compared SIM to a one-nearest-neighbor matching based on sex and exact age. This age-based matching yielded a correlation of \u003cem\u003er\u003c/em\u003e = 0.011 for self-reported infection severity between matched pairs, similar to that for age- and sex-based covariate matching.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe assessed the ability of SIM to identify similarities between individuals based on objective physiological responses recorded by the smartwatch rather than self-reported symptoms. Specifically, for each participant, we calculated the mean absolute change in heart rate and HRV-derived stress between the baseline and the COVID-19 infection period. Using the magnitude of these changes as an outcome measure, we found that SIM yielded a correlation of \u003cem\u003er\u003c/em\u003e = 0.245 (95% CI: 0.137\u0026ndash;0.345) between matched individuals, compared to \u003cem\u003er\u003c/em\u003e = 0.112 (95% CI: 0.101\u0026ndash;0.116) using age- and sex-based matching. A one-nearest-neighbor approach based on exact age and sex produced a lower correlation of \u003cem\u003er\u003c/em\u003e = 0.104 for the same smartwatch-measured cardiovascular response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe conducted a post-hoc analysis to validate that differences in vaccination timing between matched individuals did not bias our findings. This analysis showed that approximately 80% of matched pairs were vaccinated within 20 days of each other, confirming that vaccination timing was well-balanced and unlikely to influence our results (Appendix C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower Analysis of Clinical Trials Using SIM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe simulated clinical trial scenarios to evaluate the impact of SIM on the required sample size when using either self-reported symptoms or smartwatch-based cardiovascular responses as the primary outcome. Across all combinations of vaccine efficacy (VE) and the probability of becoming infected and developing symptoms (\u0026pi;), SIM consistently led to reductions in sample size compared to standard age- and sex-based matching (Table 3).\u003c/p\u003e\n\u003cp\u003eWhen using self-reported symptoms, SIM improved matching efficiency and lowered the number of participants needed to achieve the desired statistical power. For example, under trial conditions with \u0026pi; = 0.003 and VE = 0.50, a trial targeting 95% power required 33,374 participants with age- and sex-based matching, compared to 30,231 with SIM, a reduction of 3,142 participants (9.4%). For 80% power under the same conditions, the required sample size dropped from 18,193 to 16,276 (10.5% reduction). The magnitude of reduction varied with VE and \u0026pi; but remained consistent across settings.\u003c/p\u003e\n\u003cp\u003eWhen using objective cardiovascular responses from smartwatches as an outcome measure, SIM produced even greater reductions in required sample size. In the same scenario (\u0026pi; = 0.003, VE = 0.50), the sample size required for 95% power dropped from 33,374 to 28,653, representing a reduction of 4,720 participants (14.1%) compared to age- and sex-based matching. At 80% power, the sample size decreased from 18,193 to 15,945 (12.4% reduction). The reductions in required sample size were particularly notable in settings where the probability of becoming infected and developing symptoms (\u0026pi;) was low and vaccine efficacy (VE) was modest,conditions that typically require the largest trials.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur findings demonstrate that wearable sensors can enhance clinical trial design by enabling more precise matching of participants in intervention and control groups. By incorporating smartwatch-based physiological data into the matching process, our framework enhances the statistical sensitivity to detect intervention-related changes and increases the precision of outcome assessments. These improvements can enhance the timeliness of clinical trials by accelerating the accumulation of meaningful data, thereby supporting faster evaluation and delivery of effective medical interventions. Additionally, by reducing noise and physiological heterogeneity, our approach may improve safety assessment by decreasing the likelihood of missing adverse events during the trial.\u003c/p\u003e \u003cp\u003eIn simulations across a range of realistic scenarios, we found that physiological matching consistently reduced the required sample size compared to standard stratification by age and sex. Using data from participants infected with COVID-19, the reduction ranged from 9\u0026ndash;18% in settings where vaccine efficacy is moderate (40\u0026ndash;60%). Given that the cost of enrolling a participant in a clinical trial ranges from USD31,000\u0026ndash;82,000\u003csup\u003e31\u003c/sup\u003e, and that participant costs constitute more than one-third of total trial costs\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, these reductions in required sample size represent significant potential savings.\u003c/p\u003e \u003cp\u003eSmartwatch data provides an objective measure of physiological responses, reducing the bias of self-reported symptoms and offering valuable insights into treatment effects. Notably, we found that younger individuals, particularly males aged 18\u0026ndash;55, had more pronounced physiological responses during infection than older individuals, despite older individuals being at higher risk for severe COVID-19 complications. This may reflect heightened immune activation and autonomic reactivity in younger participants, leading to more detectable changes in metrics like heart rate and heart rate variability. These findings highlight the value of wearable technology in clinical trials for precise, continuous monitoring and a deeper understanding of demographic differences, enabling more tailored treatments.\u003c/p\u003e \u003cp\u003eWe used participants' daily heart rate distributions and HRV-derived stress metrics for our matching process, as they correlate with key physical and mental health factors, and lifestyle metrics such as activity, steps, and sleep patterns. Indeed, previous studies have found that heart rate metrics derived from smartwatches are associated with a wide range of health outcomes, extending well beyond cardiovascular conditions. These include mood regulation\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, sleep quality\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and disorders\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, chronic pain\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, cognitive decline\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, and even subtle health indicators such as low-grade fever and changes in resting heart rate\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and HRV-derived stress.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Previous studies have also shown that variations in heart rate, HRV, and resting heart rate can predict and detect responses to infections such as influenza, group A streptococcus, and COVID-19.\u003csup\u003e20,44\u0026ndash;46\u003c/sup\u003e By accounting for individuals' overall health and their physiological responses during infectious diseases, our approach enables a more efficient trial design while reducing the number of participants needed to achieve robust outcomes.\u003c/p\u003e \u003cp\u003eAs an immediate practical application, our framework can be implemented into retrospective analyses of vaccination and healthcare technology assessments that rely on matching approaches. Current matching methods in retrospective research typically rely on demographics and static health measures, overlooking dynamic physiological variation at the individual level \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. This limitation can leave meaningful confounding unaddressed. SIM introduces physiologic matching that captures individual-level variation. When continuous physiological data are available, SIM can substantially improve covariate balance in retrospective cohorts, reduce bias, and strengthen causal inference, particularly in comparative effectiveness research, where physiologic state may influence both treatment selection and outcomes.\u003c/p\u003e \u003cp\u003eDue to the limited number of participants in our study, we focused on heart measures. However, given the rich, diverse, and continuous data generated by various biosensors in smartwatches, we believe our study represents just the tip of the iceberg in what can be achieved for more accurate matching of individuals in clinical trials. Specifically, in clinical trials focused on infectious diseases, smartwatch biosensors can provide insights into lifestyle factors such as step counts, proximity to other Bluetooth devices, and social interaction patterns, which may help predict individuals who are more prone to becoming infected. Other smartwatch biosensors, such as those that measure respiratory rate, oxygen level, blood pressure, and sleeping patterns, could be valuable in identifying individuals at greater risk for complications if infected.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e Such information could be used to create even more accurate participant matching than we have considered.\u003c/p\u003e \u003cp\u003eOur study has several limitations. Participants were recruited via social media and word-of-mouth, resulting in a convenience sample that may not fully represent the general population. The demanding requirements \u0026ndash; wearing a smartwatch and completing daily questionnaires for two years \u0026ndash; could have discouraged certain individuals from participating. Additionally, COVID-19 is known to be associated with heart-related issues, which may have contributed to the effectiveness of our smartwatch matching approach based on heart rate and HRV. Furthermore, the Garmin smartwatches worn by trial participants are not medical-grade devices and may not fully capture all relevant physiological changes.\u003c/p\u003e \u003cp\u003eIn conclusion, our study demonstrates the potential of smartwatch-derived physiological data to improve clinical trial design by shifting from traditional static matching methods to a dynamic, data-driven approach. By integrating continuous metrics such as heart rate and HRV, our method can reduce sample size requirements while maintaining statistical power, compared to traditional age- and sex-based matching methods. This marks a potentially transformative step in clinical research, where wearable technology can enable improved participant matching in clinical trials, as well as real-time, objective monitoring of health and disease progression, enhancing the efficiency, accuracy, and personalization of clinical trials.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the European Research Council, project #949850, and the Israel Science Foundation (ISF), grant No. 3409/19, within the Israel Precision Medicine Partnership program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: DY and ES. Collection and assembly of data: DY, MY, SS, and ES had access to the raw data and were responsible for verifying the data. Analysis and interpretation of the data: ES, SS, MY, DY, MP and MLB. Statistical analysis: ES and DY. Drafting the article: ES, MY, SS, DY, and MLB. Critical revision of the article for important intellectual content: all authors. Final approval of the article: All authors. Obtaining funding: DY.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to this study\u0026apos;s Tel Aviv University IRB guidelines, no patient-level data is to be shared outside the permitted researchers. The statistical analysis code along with an aggregated version of the data sufficient to reproduce the results reported in this paper will be available upon publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHariton E, Locascio JJ. Randomised controlled trials - the gold standard for effectiveness research. BJOG 2018;125(13):1716.\u003c/li\u003e\n\u003cli\u003eGul RB, Ali PA. Clinical trials: the challenge of recruitment and retention of participants. J Clin Nurs 2010;19(1-2):227-33.\u003c/li\u003e\n\u003cli\u003eSpeich B, von Niederhausern B, Schur N, et al. Systematic review on costs and resource use of randomized clinical trials shows a lack of transparent and comprehensive data. J Clin Epidemiol 2018;96:1-11.\u003c/li\u003e\n\u003cli\u003ePolack FP, Thomas SJ, Kitchin N, et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl J Med 2020;383(27):2603-2615.\u003c/li\u003e\n\u003cli\u003eWise J. 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(https://patents.google.com/patent/US20050256414A1/en).\u003c/li\u003e\n\u003cli\u003eLauer SA, Grantz KH, Bi Q, et al. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Ann Intern Med 2020;172(9):577-582.\u003c/li\u003e\n\u003cli\u003eXu QY, Xue JH, Xiao Y, et al. Response and duration of serum anti-SARS-CoV-2 antibodies after inactivated vaccination within 160 days. Front Immunol 2021;12:786554.\u003c/li\u003e\n\u003cli\u003eBioNTech SE. Study to Describe the Safety, Tolerability, Immunogenicity, and Efficacy of RNA Vaccine Candidates Against COVID-19 in Healthy Individuals. (https://clinicaltrials.gov/study/NCT04368728).\u003c/li\u003e\n\u003cli\u003eMoore TJ, Zhang H, Anderson G, Alexander GC. Estimated costs of pivotal trials for novel therapeutic agents approved by the US Food and Drug Administration, 2015-2016. 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Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN). Cambridge, MA2015:1-6.\u003c/li\u003e\n\u003cli\u003eSathyanarayana A, Joty S, Fernandez-Luque L, et al. Sleep quality prediction from wearable data using deep learning. JMIR Mhealth Uhealth 2016;4(4):e125.\u003c/li\u003e\n\u003cli\u003ePenzel T, Schobel C, Fietze I. New technology to assess sleep apnea: wearables, smartphones, and accessories. F1000Res 2018;7:413.\u003c/li\u003e\n\u003cli\u003eXu S, Faust O, Seoni S, et al. A review of automated sleep disorder detection. Comput Biol Med 2022;150:106100.\u003c/li\u003e\n\u003cli\u003eZheng NS, Annis J, Master H, et al. Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program. Nat Med 2024;30(9):2648-2656.\u003c/li\u003e\n\u003cli\u003eBoletsis C, McCallum S, Landmark BF. The use of smartwatches for health monitoring in home-based dementia care. In: Zhou J, Salvendy G, eds. Human Aspects of IT for the Aged Population. Design for Everyday Life: Springer; 2015.\u003c/li\u003e\n\u003cli\u003eQuer G, Gouda P, Galarnyk M, Topol EJ, Steinhubl SR. Inter- and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI, and time of year: Retrospective, longitudinal cohort study of 92,457 adults. PLoS One 2020;15(2):e0227709.\u003c/li\u003e\n\u003cli\u003eGrosicki GJ, Culver MN, McMillan NK, et al. Self-recorded heart rate variability profiles are associated with health and lifestyle markers in young adults. Clin Auton Res 2022;32(6):507-518.\u003c/li\u003e\n\u003cli\u003eSanta-Rosa FA, Shimojo GL, Dias DS, et al. Impact of an active lifestyle on heart rate variability and oxidative stress markers in offspring of hypertensives. Sci Rep 2020;10(1):12439.\u003c/li\u003e\n\u003cli\u003eMishra T, Wang M, Metwally AA, et al. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat Biomed Eng 2020;4(12):1208-1220.\u003c/li\u003e\n\u003cli\u003eRadin JM, Vogel JM, Delgado F, et al. Long-term changes in wearable sensor data in people with and without Long Covid. NPJ Digit Med 2024;7(1):246.\u003c/li\u003e\n\u003cli\u003eLevi Y, Gande V, Brandeau ML, Yamin D. Self-reported recovery versus smartwatch-detected recovery for COVID-19, influenza and group A streptococcus: A two-year prospective cohort study. Working paper 2025.\u003c/li\u003e\n\u003cli\u003eDagan N, Barda N, Kepten E, et al. BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting. New England Journal of Medicine [Internet] 2021 [cited 2021 Mar 30];NEJMoa2101765. Available from: http://www.nejm.org/doi/10.1056/NEJMoa2101765\u003c/li\u003e\n\u003cli\u003eYaron S, Yechezkel M, Yamin D, et al. Incremental benefit of high dose compared to standard dose influenza vaccine in reducing hospitalizations. NPJ Vaccines [Internet] 2025 [cited 2025 Jan 26];10(1):3. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11718019/\u003c/li\u003e\n\u003cli\u003eTsanani SE, Yorav S, Yaron S, et al. Effectiveness of influenza vaccination in preventing severe COPD exacerbations and pneumonia before, during, and after the COVID-19 pandemic: a retrospective cohort study. The Lancet Regional Health - Europe [Internet] 2025 [cited 2025 May 23];53. Available from: https://www.thelancet.com/action/showFullText?pii=S2666776225000997\u003c/li\u003e\n\u003cli\u003eAngelucci A, Greco M, Cecconi M, Aliverti A. Wearable devices for patient monitoring in the intensive care unit. Intensive Care Med Exp 2025;13(1):26.\u003c/li\u003e\n\u003cli\u003eHanly PJ, Millar TW, Steljes DG, Baert R, Frais MA, Kryger MH. The effect of oxygen on respiration and sleep in patients with congestive heart failure. Ann Intern Med 1989;111(10):777-82.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"935\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 935px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 1. Activity and rest, general health, and social and lifestyle correlations among matched pairs.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%) or [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge- and sex-based covariate matching\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMean Correlation %, (95% CI)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge-, sex- and smartwatch-based covariate matching\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMean Correlation %, (95% CI)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActivity and Rest Patterns, Median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eDaily step count*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e6965 [5259, 8957]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e2.4 (-6.4\u0026ndash;11.1)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e16.9 (10.9\u0026ndash;29.1)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eActive time per day*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e8833 [6721, 11018]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e1.8 (-6.7\u0026ndash;11.0)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e12.6 (3.7\u0026ndash;22.1)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eSleep duration*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e6.847 [6.277, 7.440]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e4.0 (-5.0\u0026ndash;13.1)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e7.6 (-0.7\u0026ndash;18.9)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eSleep quality (1-5 scale)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0.630 [0.083, 1.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.2 (-9.0\u0026ndash;9.5)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e4.9 (-5.7\u0026ndash;14.0)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral Health\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eBMI \u003cu\u003e\u0026gt;\u003c/u\u003e 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e203 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.4 (-8.8\u0026ndash;9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e11.0 (0.8\u0026ndash;21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e95 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e12.1 (1.2\u0026ndash;24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e22.2 (7.4\u0026ndash;36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eWith background diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e389 (38.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e5.6 (-2.2\u0026ndash;14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e10.3 (1.0\u0026ndash;19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eWith cardiovascular disease history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e182 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e16.3 (6.0\u0026ndash;26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e20.6 (8.3\u0026ndash;33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003ePerceived general health (1-5 scale), Median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e4 [4, 5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e2.8 (-6.6\u0026ndash;11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e9.5 (0.2\u0026ndash;18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial and Lifestyle Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eLess than 4 times per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e601 (59.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 204px;\"\u003e\n \u003cp\u003e2.6 (-6.0\u0026ndash;11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 222px;\"\u003e\n \u003cp\u003e8.1 (-1.3\u0026ndash;17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e4 or more times per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e414 (40.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e97 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 204px;\"\u003e\n \u003cp\u003e0.0 (-8.3\u0026ndash;9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026ndash;0.1 (-9.0\u0026ndash;9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e918 (90.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eNumber of children in household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e264 (26.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 204px;\"\u003e\n \u003cp\u003e13.4 (5.5\u0026ndash;20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 222px;\"\u003e\n \u003cp\u003e20.0 (11.1\u0026ndash;28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e83 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e262 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e277 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e4+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e127 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e2 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eNumber of residents in household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e78 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 204px;\"\u003e\n \u003cp\u003e9.1 (1.5\u0026ndash;16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 222px;\"\u003e\n \u003cp\u003e5.2 (-3.7\u0026ndash;13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e289 (28.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e187 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e206 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003e5+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e245 (24.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e10 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 318px;\"\u003e\n \u003cp\u003eSelf-reported stress, Median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e-0.7 [-1.0, -0.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e1.0 (-8.6\u0026ndash;11.0)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e5.6 (-5.6\u0026ndash;14.2)\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 935px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eMeasure recorded by smartwatch; other measures are self-reported\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003ePearson correlation coefficient; all other correlation coefficients are Spearman\u0026rsquo;s rank correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"bottom\" style=\"width: 631px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Distribution of self-reported symptom severity levels and smartwatch-based reaction severity levels within each stratum.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStrata\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-Reported Symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 272px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmartwatch-Based Reaction Severity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate to severe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBottom third\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle third\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTop\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ethird\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eFemale, 18-55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e60 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e104 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e131 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e52 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e70 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e77 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMale, 18-55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e73 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e104 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e99 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e39 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e67 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e97 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eFemale, 56-85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e45 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e52 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e53 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e85 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e45 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e15 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMale, 56-85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e44 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e35 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e34 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e41 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e35 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e28 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e222 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e295 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e317 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e217 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e217 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e217 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"762\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"bottom\" style=\"width: 762px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3. Required sample sizes with standard stratified matching (n\u003csub\u003e0\u003c/sub\u003e) and smartwatch-based matching (n) for different levels of statistical power (0.95, 0.80) and different outcome metrics (smartwatch-based reaction score, self-reported symptom severity).\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026pi;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 268px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePower = 0.95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 268px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePower = 0.80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003csub\u003e0\u003c/sub\u003e - n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Reduction in Sample Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003csub\u003e0\u003c/sub\u003e - n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Reduction in Sample Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" style=\"width: 138px;\"\u003e\n \u003cp\u003eSmartwatch-Based Reaction Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 46px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e57,454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e48,155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e6,373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e31,983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e26,238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e5,745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e33,374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e28,653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e4,720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e18,193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e15,945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e2,247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e22,080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e18,894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3,185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e11,919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e10,527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e1,391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e7,712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e7,490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4,209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4,011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 46px;\"\u003e\n \u003cp\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e5,496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4,458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e1,038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e3,080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2,520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e3,220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2,755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1,692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1,426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2,081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1,744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1,171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1,042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 46px;\"\u003e\n \u003cp\u003e.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1,398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1,182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" style=\"width: 138px;\"\u003e\n \u003cp\u003eSelf-Reported Symptom Severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 46px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e57,454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e51,081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e6,373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e31,983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e26,775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e5,208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e33,374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e30,231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3,142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e18,193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e16,276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e1916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 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devices, Randomized controlled trials, Participant matching, Smartwatches, Trial design optimization","lastPublishedDoi":"10.21203/rs.3.rs-8366773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8366773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRandomized 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\u0026thinsp;=\u0026thinsp;0.176 vs. 0.012) and physiological response profiles (Pearson r\u0026thinsp;=\u0026thinsp;0.245 vs. 0.112). Power analyses showed that SIM reduced the sample size required to maintain statistical power by 9\u0026ndash;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.\u003c/p\u003e","manuscriptTitle":"Enhancing Randomized Controlled Trials Through Smartwatch-Guided Participant Matching","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 00:07:50","doi":"10.21203/rs.3.rs-8366773/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-17T15:08:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T17:46:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-21T15:41:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162830722681393690600466939262561449537","date":"2026-01-10T13:59:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215499149241883719364961113942866464417","date":"2026-01-08T16:19:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136989452925965301600231461919717964293","date":"2026-01-08T14:25:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T13:37:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T11:35:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-23T22:41:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-22T07:45:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-22T07:30:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc885580-feed-4433-a022-2f593a197e49","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":60924702,"name":"Health sciences/Cardiology"},{"id":60924703,"name":"Health sciences/Health care"},{"id":60924704,"name":"Health sciences/Medical research"},{"id":60924705,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-05-06T08:23:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 00:07:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8366773","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8366773","identity":"rs-8366773","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}