1. Identify the primary risk factors for disability in older adults through multi-dimensional risk factor screening.
2. Develop a risk stratification model for disability in older adults by integrating outcome indicators and temporal characteristics, and construct an intelligent early warning model to enable automated assessment and monitoring of disability risk.
3. Establish key digital technologies for early warning and prevention of disability risk in older adults, and develop a whole-process digital intervention platform incorporating a decision support system for disability prevention management.
4. Create a digitally empowered hospital-community-household collaborative system for precise assessment and intervention of disability risk in older adults, achieving data-driven whole-process active disability management. The system will be demonstrated and evaluated in communities with diverse characteristics across urban, county, and rural settings.

1. Multidimensional Risk Factor Screening and Identification of Key Risk Factors for Disability in Older Adults: Health records from a cohort of 100,000 urban and rural older adults in Zhejiang Province between 2018 and 2022 were reviewed. Data indicators including demographics, disease characteristics, cognitive psychological assessments, and family-social factors were extracted. Disability in older adults was used as the outcome variable to preliminarily screen risk factors, forming a multidimensional indicator set for disability risk. Principal component analysis was applied to identify disability risk syndromes. The Elastic Net model was employed to further extract major risk factors, and full-cycle key risk factors for disability were determined based on five-year risk exposure characteristics.
2. Construction of a Time Series-Based Risk Stratification and Early Warning Model for Disability in Older Adults:Integrating full-cycle disability characteristics (such as disability severity, features, and time points) as primary outcome indicators. Apply weighting and clustering decisions based on key risk factors to achieve risk stratification and identification. Define key risk factor abnormalities, risk syndromes, disability risk, and actual disability occurrence as monitoring nodes, corresponding to zero-level, level-one, level-two, and level-three warning tiers respectively. By integrating time-series features, a Long Short-Term Memory (LSTM) neural network model is constructed to develop an incapacitation risk early warning system.
3. Development of Digital Diagnosis and Treatment Technologies for Early Warning and Prevention of Disability Risk and Construction of an Intervention Decision Support System:Based on evidence-based medicine, multi-scenario disability prevention requirements, and expert consensus, a big data knowledge database and technology library for disability prevention and control wiil be established. The logical framework, technical architecture, and functional design of the disability risk intervention decision support system will be systematically planned, forming a knowledge graph network related to disability. Integrating modules for elderly disability risk assessment and dynamic monitoring, an internet-based technical module for elderly disability management will be established. This covers the entire process from information collection, assessment and monitoring, to early warning decision-making and targeted interventions. The system will be deeply integrated and optimised with the 'Internet Plus Nursing' platform to construct a digitalised elderly disability risk intervention platform.
4. Application of the Precision Assessment and Intervention System for Disability Risks in the Elderly: Conducting standardized demonstration research on disability prevention and control bases using this system. Establish demonstration bases in cities, counties, and townships across Zhejiang Province to develop standardized protocols for preventing disability among the elderly through coordinated efforts between hospitals, communities, and households.Based on data-driven to optimize staffing, service processes, resource integration and technical support, we will continue to improve the overall technical implementation plan for intelligent assessment, monitoring and precise intervention of disability risk in the elderly.This will build a multi-level demonstration model for the prevention and control of elderly disability risk in cities, counties and townships in Zhejiang Province.The application effect of the elderly disability risk accurate assessment and intervention system was evaluated by indicators such as disability risk score, disability incidence, disability intervention compliance.

Inclusion Criteria:

• Community-dwelling permanent residents aged ≥65 years;
• Able to operate a smartphone independently or with assistance from a primary caregiver;
• Clear consciousness with basic comprehension and communication abilities;
• Voluntarily agree to participate in the study and provide informed consent.

Exclusion Criteria:

• Those who are unable to perform basic activities of daily living independently, or who have significant cognitive or communication impairments.