A Study to Build a Disease Risk Prediction Model for Adults by Integrating Data From Wearable Devices, Hearing Tests, and Multiple Health Databases

What this Trial Is About

Researchers are conducting a prospective cohort study to create and validate personalized disease risk prediction models for adults living in the community in Taiwan. This study focuses on combining health information from multiple sources, including hearing tests, lifestyle questionnaires, wearable devices, and health records, to better understand how people progress from good health to early or chronic disease. The study aims to improve early risk identification and support personalized health management using machine learning techniques. Participants aged 18 years and older will complete a structured questionnaire about their demographics, family and personal disease history, and lifestyle habits like exercise, sleep, smoking, and alcohol use. They will undergo pure tone hearing tests and wear a smartwatch for two weeks to collect continuous data such as heart rate and physical activity. Study staff will help with device setup and app installation. With participant consent, their personal health records and national health insurance data will be linked for long-term follow-up. During the study, participants will be monitored through questionnaires, hearing assessments, and smartwatch data collection. Researchers will analyze the combined data to identify important predictors of disease progression and build risk models. Data privacy is strictly maintained by de-identifying information after linkage. The main outcome is how well the personalized risk models perform, assessed after data collection and validation. Participation involves initial assessments and two weeks of wearable monitoring, with extended health outcome tracking through linked databases.

Adult Disease Risk Prediction Using Wearables, Hearing, and Health Data