Testing the Impact of Intensive, Longitudinal Sampling on Assessments of Statistical Power and Effect Size Within a Heterogeneous Human Population: Natural Experiment Using Change in Heart Rate on Weekends as a Surrogate Intervention
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Abstract
Background: The recent emergence of wearable devices has made feasible the passive gathering of intensive, longitudinal data from large groups of individuals. This form of data is effective at capturing physiological changes between participants (interindividual variability) and changes within participants over time (intraindividual variability). The emergence of longitudinal datasets provides an opportunity to quantify the contribution of such longitudinal data to the control of these sources of variability for applications such as responder analysis, where traditional, sparser sampling methods may hinder the categorization of individuals into these phenotypes. Objective: This study aimed to quantify the gains made in statistical power and effect size among statistical comparisons when controlling for interindividual variability and intraindividual variability compared with controlling for neither. Methods: Here, we test the gains in statistical power from controlling for interindividual and intraindividual variability of resting heart rate, collected in 2020 for over 40,000 individuals as part of the TemPredict study on COVID-19 detection. We compared heart rate on weekends with that on weekdays because weekends predictably change the behavior of most individuals, though not all, and in different ways. Weekends also repeat consistently, making their effects on heart rate feasible to assess with confidence over large populations. We therefore used weekends as a model system to test the impact of different statistical controls on detecting a recurring event with a clear ground truth. We randomly and iteratively sampled heart rate from weekday and weekend nights, controlling for interindividual variability, intraindividual variability, both, or neither. Results: Between-participant variability appeared to be a greater source of structured variability than within-participant fluctuations. Accounting for interindividual variability through within-individual sampling required 40× fewer pairs of samples to achieve statistical significance with 4× to 5× greater effect size at significance. Within-individual sampling revealed differential effects of weekends on heart rate, which were obscured by aggregated sampling methods. Conclusions: This work highlights the leverage provided by longitudinal, within-individual sampling to increase statistical power among populations with heterogeneous effects.
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The TemPredict study provided physiological, sleep, and self-report data from 33,374 individuals, of whom 389 self-reported a diabetes mellitus diagnosis (1.16%), 10,820 self-reported never receiving a diabetes mellitus diagnosis (32.4%), and 21,402 did not self-report this information (64.1%).
“We obtained physiological, sleep, and self-report data from 33,374 individuals through the TemPredict study54, of whom 389 self-reported having received a DM DX from a healthcare professional (1.16%), 10,820 self-reported having never received a DM DX from a healthcare professional (32.4%), and 21,402 individuals did not self-report this information (64.1%; see Fig. 1a).”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [54]
The Introduction and Methods > Data and Device identify TemPredict as the source of sleep-time physiological measurements and report participant self-reported demographics. The paper analyzes a different cohort (46,217 participants in Results > Participant Characteristics) and does not report the claimed 33,374-person cohort or its diabetes-diagnosis counts and percentages. (LLM, conf=0.86)
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Viswanath, V. K., Navaneethan, S., Burks, J. H., Soltani, S., Kasl, P., Hartogensis, W., Dilchert, S., Hecht, F. M., Mason, A. E., Wang, E. J., et al. (2026) · Communications Medicine · doi:10.1038/s43856-026-01501-0
Self-citation
- Authors
- Soltani, S., Viswanath, V. K., Kasl, P., Hartogensis, W., Dilchert, S., Hecht, F. M., Mason, A. E., & Smarr, B. L.
- Affiliations
- University of California San Diego (US)
- University of California, San Francisco (US)
- Baruch College (US)
- Corresponding author
- Smarr, Benjamin L
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2025-05-21
- Volume
- 27
- Pages
- e60284
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC12138295
- OpenAlex
- W4410580311
- Cited by
- 3
- References
- 39
- Citations by year
- 2026: 3
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Defense (7029991, K24AT007827, MTEC-20-12-COVID19-D.-023, 134650)
- National Metal and Materials Technology Center
- National Institutes of Health (5t32gm139790-03)
- National Institute of General Medical Sciences (T32-GM139790, 5T32GM139790-02)
- National Center for Complementary and Integrative Health (K24AT007827)
- U.S. Army Medical Research and Development Command (MTEC-20-12-COVID19-D.-023)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- intensive longitudinal data, interindividual variability, within-person variability, statistical power, effect size, resting heart rate, weekend effect, responder analysis, wearable devices, COVID-19 detection, heterogeneous populations
- MeSH terms
- Wearable Electronic Devices, COVID-19, SARS-CoV-2, Adult, Female, Heart Rate, Humans, Longitudinal Studies, Male
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