A Study to Build a Disease Risk Prediction Model for Adults by Integrating Data From Wearable Devices, Hearing Tests, and Multiple Health Databases
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 1,500
- 试验地点
- 1
- 主要终点
- Performance of Personalized Disease Risk Prediction Models
研究概览
简要总结
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 is to support early risk identification, personalized health management, and prevention strategies in community adults.
详细描述
This study is a prospective cohort study designed to integrate multimodal health data for the development and validation of personalized disease risk prediction models in community-dwelling adults in Taiwan. The study focuses on combining actively collected research data, continuous wearable device data, hearing assessment results, and longitudinal health records to better understand the transition from health to subclinical states and chronic disease.
Participants aged 18 years and older will be recruited from community settings. After informed consent is obtained, study procedures will include a structured questionnaire, pure tone audiometry, and 2 weeks of smartwatch monitoring. The questionnaire will collect demographic characteristics, personal and family disease history, and lifestyle factors such as exercise, sleep, smoking, and alcohol use. Hearing function will be assessed using pure tone audiometry. Continuous physiological data collected from the wearable device will include heart rate and physical activity, such as step counts. Research staff will assist participants with device setup, application installation, and instructions for use to improve data completeness and consistency.
With participant authorization, the study will also obtain personal health record data and link study data with national health insurance databases to support longitudinal follow-up and ascertainment of disease outcomes. These linked data sources may include outpatient, inpatient, pharmacy, insurance enrollment, catastrophic illness, death registry, cancer registry, and adult preventive health examination records. The integration of these heterogeneous data sources is intended to provide a more complete picture of individual health trajectories and disease progression than can be achieved with any single data source alone.
The scientific objectives of the study are to:
- evaluate the associations among hearing status, lifestyle factors, and continuous wearable-derived physiological measures;
- identify combinations of key predictors associated with progression from health to subclinical or chronic disease states;
- establish an analytical framework for integrating heterogeneous data from questionnaires, hearing tests, wearable monitoring, and medical databases; and
- develop and validate high-accuracy personalized disease risk prediction models using statistical and machine learning methods.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Adults aged 18 years and older
- •Living in the community in Taiwan
- •Able to understand the study procedures and provide written informed consent
- •Able and willing to complete the study questionnaire, hearing assessment, and wearable device monitoring procedures
- •Has access to a smartphone and is able to install and use the study-related application, with assistance from study staff if needed
排除标准
- •Diagnosis of dementia
- •Too frail or has other health conditions that make participation in the study procedures not feasible
- •Bilateral deafness without use of any hearing assistive device
- •Does not have a smartphone or is unable to use a smartphone application required for the study procedures
研究组 & 干预措施
Community-Dwelling Adults
Adults aged 18 years and older recruited from community settings in Taiwan. Participants will complete a structured questionnaire, undergo hearing assessment, wear a smartwatch for 2 weeks, and authorize collection of personal health records and linked national health insurance data for health outcome follow-up.
结局指标
主要结局
Performance of Personalized Disease Risk Prediction Models
时间窗: At the completion of data collection and model validation, including baseline assessment and 2-week wearable monitoring
Model performance will be evaluated for personalized disease risk prediction models developed using integrated questionnaire, hearing, wearable device, personal health record, and national health insurance data. Performance metrics will include area under the receiver operating characteristic curve (AUC-ROC), accuracy, and related validation measures in training and test datasets.
次要结局
未报告次要终点