A Digitally-Enabled System for Precision Assessment and Intervention of Disability Risk in Older Adults

The Construction of a Digitally-Enabled Precision Assessment and Intervention System for Disability Risk in Older Adults and a Full-Cycle, Multi-Scenario Demonstration Study

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.

Study Overview

• Status: Recruiting
• Conditions: Disability
• Intervention / Treatment: Other: Risk Assessment of Disability and Precision Intervention

Detailed Description

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.

• Study Type: Interventional
• Enrollment (Estimated): 238
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Jingfen Jin, Master; Phone Number: +86 15888841161; Email: zrjzkhl@zju.edu.cn

Study Locations

• China
  • Zhejiang
    • Hangzhou, Zhejiang, China, 310009
      • Recruiting
      • The Second Affiliated Hospital of Medical College of Zhejiang University
      • Contact: Jingfen Jin, Master; Phone Number: +86 13757118239; Email: zrjzkhl@zju.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Older Adult
• Accepts Healthy Volunteers: Yes

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Experimental group; Implement a digital platform for elderly disability risk intervention to conduct comprehensive, multi-scenario intelligent assessments, dynamic monitoring, and targeted interventions for disability risks.	Other: Risk Assessment of Disability and Precision Intervention; Implement a digital platform for elderly disability risk intervention to conduct comprehensive, multi-scenario intelligent assessments, dynamic monitoring, and targeted interventions for disability risks.
No Intervention: Control group; Conduct routine community-based geriatric health management, such as health education, exercise guidance, and dietary counseling.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
SPPB(SHORT PHYSICAL PERFORMANCE BATTERY PROTOCOL)	the range of score is 0-12, and the higher scores mean a better outcome.	At baseline, 3 months after intervention, and 6 months after intervention

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Incidence of disability	The incidence of disability will be assessed using validated scales for Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). Disability is defined as a new-onset, significant limitation in performing basic or complex everyday activities. The occurrence (i.e., the number of new cases) of disability will be determined at predefined study timepoints. The final outcome will be calculated as the proportion or rate of participants who develop disability during the study period, relative to the total number of participants.	6 months after intervention
Disability Risk Score	The Disability Risk Score is a continuous variable ranging from 0 to 1, calculated using a pre-validated prediction model. A higher score indicates a greater predicted risk of developing disability.	At baseline, 3 months after intervention, and 6 months after intervention
Intervention adherence	Adherence to the intervention protocol (e.g., exercise, dietary tasks) will be objectively monitored and recorded via a dedicated smartphone application. Participants are required to log or "check-in" within the app upon completion of each prescribed task. Adherence rates will be calculated as the percentage of completed tasks logged, relative to the total number of tasks assigned during the study period.	3 months after intervention, and 6 months after intervention

Collaborators and Investigators

• Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
• Investigators: Principal Investigator: Jingfen Jin, Master, The Second Affiliated Hospital of Medical College of Zhejiang University

Study record dates

Study Major Dates

• Study Start (Actual): October 31, 2025
• Primary Completion (Estimated): October 31, 2026
• Study Completion (Estimated): December 30, 2026

Study Registration Dates

• First Submitted: September 24, 2025
• First Submitted That Met QC Criteria: March 30, 2026
• First Posted (Actual): April 2, 2026

Study Record Updates

• Last Update Posted (Actual): April 2, 2026
• Last Update Submitted That Met QC Criteria: March 30, 2026
• Last Verified: September 1, 2025

More Information

Terms related to this study

• Keywords: older adults; prevention; risk; disability
• Other Study ID Numbers: 2023-1268

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No