Adult Disease Risk Prediction Using Wearables, Hearing, and Health Data
Summary
A prospective cohort study (NCT07541547) registered on ClinicalTrials.gov will recruit community-dwelling adults aged 18 and older in Taiwan to develop a personalized disease risk prediction model. Participants will wear a smartwatch for 2 weeks to collect continuous heart rate and physical activity data, undergo pure tone hearing testing, and provide access to personal health records and national health insurance databases for longitudinal follow-up. Machine learning methods will identify predictors and build risk prediction models combining wearable data, hearing measures, lifestyle factors, and medical records.
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What changed
This is a clinical trial registration entry on ClinicalTrials.gov, not a regulatory action. The study does not create compliance obligations for regulated entities. ClinicalTrials.gov is a public registry for research studies that tracks study status but does not impose regulatory requirements.
For healthcare providers and research institutions, this registration represents an emerging methodology for chronic disease risk prediction that integrates wearable device data, hearing assessments, and longitudinal health records. The study scope (wearables, personal health records, national insurance databases) may be relevant for institutions reviewing IRB and data protection requirements when using similar multi-source health data approaches.
Archived snapshot
Apr 22, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Adult Disease Risk Prediction Using Wearables, Hearing, and Health Data
Observational NCT07541547 Kind: OBSERVATIONAL Apr 21, 2026
Abstract
This prospective cohort study aims to develop and validate a personalized disease risk prediction model for adults by integrating multiple sources of health data. The study will recruit community-dwelling adults aged 18 years and older in Taiwan. After providing informed consent, participants will complete a structured questionnaire, undergo pure tone hearing testing, and wear a smartwatch for 2 weeks to collect continuous physiological data, including heart rate and physical activity. With participant authorization, the study will also collect data from personal health records and national health insurance databases to allow longer-term follow-up of health outcomes.
The main goals of the study are to examine the relationships among hearing, lifestyle factors, and wearable device data; to identify combinations of risk factors associated with progression from health to subclinical or chronic disease states; and to develop analytical methods for integrating heterogeneous health data from questionnaires, physiological monitoring, hearing tests, and medical databases. Machine learning methods will be used to identify important predictors and build risk prediction models.
The study hypothesis is that combining hearing measures, lifestyle information, wearable physiological data, and longitudinal medical record data will improve the ability to identify individuals at higher risk of future disease compared with using a single source of information alone. The long-term objective...
Conditions: Chronic Disease, Risk Assessment
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