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Life sciences · Observational Study
ClinicalTrials.gov · September 28, 2026
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Observational Study.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
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Registry record from ClinicalTrials.gov (NCT07650669). This is a study registration, not published results. Lead sponsor: The Cleveland Clinic. Recruitment status: NOT_YET_RECRUITING. Study type: OBSERVATIONAL. Enrollment: 500 participants (ESTIMATED). Conditions: Heart Failure. Primary outcome measures: Establish Wearables Registry , 10 years. Brief summary: The primary objective is to establish a Cleveland Clinic-led registry designed to integrate continuous physiological data from consumer wearables, captured by the HeartBit app and a Case Western Reserve University (CWRU)-based platform, with clinical data from patients' individual Electronic Health Record (EHR). This proposal is for understanding the use of personal wearable devices (such as: Fitbit, and Apple Watch) to gather physiological data (such as: heart-rate, steps taken, sleep cycles, and calories expended) and correlate those data with self-reported health status. The research team intends to collect all of the data generated by the participant's personal wearable devices and to perform an integrated analysis of the physiological data generated by the wearable devices. The investigators specifically aim to uncover patterns from the data that they can evolve models from. The investigators' goal is to better predict and diagnose the adverse effects associated with daily lifestyle, including cardiovascular risk, musculoskeletal, metabolic risks, stress, and sleep disturbances. These data may be used to better understand the daily habits and lifestyle choices to possibly predict a person's health and disease risk to provide a more comprehensive diagnosis with early recognition and prevention. As wearable devices become more accurate and cheaper to purchase for the general public, integrative analysis of wearable data from multiple devices may lead to models that can be used by clinicians and basic scientists to make inferences about the daily lifestyle and its effects on health and disease.