DisabilityNCT07508813
A Digitally-Enabled System for Precision Assessment and Intervention of Disability Risk in Older Adults
A study testing Risk Assessment of Disability and Precision Intervention in people with Disability, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 1 site in China.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 65 years and olderHealthy volunteers can take part
- Conditions the study is for
- Disability
- What is being tested?
- Risk Assessment of Disability and Precision Intervention
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 238 participants
- Where
- 1 siteChina
- Study dates
- 31 Oct 2025to 30 Dec 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 65 years and older
- All sexes
- Healthy volunteers accepted
- Older adult
Who may be able to join
- 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.
Who cannot join
- Those who are unable to perform basic activities of daily living independently, or who have significant cognitive or communication impairments.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The Second Affiliated Hospital of Medical College of Zhejiang University
Hangzhou, Zhejiang, 310009
Jingfen Jin, Master, +86 13757118239, zrjzkhl@zju.edu.cn
About this study
Official title: 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
- Identify the primary risk factors for disability in older adults through multi-dimensional risk factor screening.
- 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.
- 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.
- 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.
Read the detailed description
- 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.
- 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.
- 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.
- 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.
- Conditions
- Disability
- Being tested
- Risk Assessment of Disability and Precision Intervention
- Keywords
- older adults, disability, prevention, risk
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Risk Assessment of Disability and Precision InterventionOther
Implement a digital platform for elderly disability risk intervention to conduct comprehensive, multi-scenario intelligent assessments, dynamic monitoring, and targeted interventions for disability risks.
| Study group | Role | What they receive |
|---|---|---|
| Experimental group | Experimental | Implement a digital platform for elderly disability risk intervention to conduct comprehensive, multi-scenario intelligent assessments, dynamic monitoring, and targeted interventions for disability risks. Receives: Other: Risk Assessment of Disability and Precision Intervention |
| Control group | No intervention | Conduct routine community-based geriatric health management, such as health education, exercise guidance, and dietary counseling. |
What is being measured
The primary outcome is the main question the study is designed to answer.
SPPB(SHORT PHYSICAL PERFORMANCE BATTERY PROTOCOL)
the range of score is 0-12, and the higher scores mean a better outcome.
Measured: At baseline, 3 months after intervention, and 6 months after intervention
Secondary outcomes (3)
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.
Measured: 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.
Measured: 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.
Measured: 3 months after intervention, and 6 months after intervention
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Apr 2026
- Study start31 Oct 2025
- Primary completion31 Oct 2026 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for DisabilityStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 238 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jin Jingfen, Second Affiliated Hospital, Zhejiang University, School of Medicine
- Investigators
- Jingfen Jin, Master, Principal investigator, Second Affiliated Hospital, Zhejiang University, School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07508813
- Sponsor's protocol ID
- 2023-1268
- Status last verified
- Sep 2025
- First submitted
- 24 Sep 2025
- Last updated
- 2 Apr 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Zhang Y, Mao Y, Guo Z, Gao W, Wang J, Jin J. A digital platform-based stratified intervention for preventing disability risk in community-dwelling older adults: study protocol for a randomized multicenter trial. Front Med (Lausanne). 2026 Jul 30;13:1890107. doi: 10.3389/fmed.2026.1890107. eCollection 2026.
PubMed 42597295
Study at a glance
From the registry record, last updated by the sponsor on 2 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07508813
- Acronym / sponsor ID
- 2023-1268
- Conditions
- Disability
- Keywords
- older adults, disability, prevention, risk
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 238 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Study start
- 31 Oct 2025 (actual)
- Primary completion
- 31 Oct 2026 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 2 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
- Identify the primary risk factors for disability in older adults through multi-dimensional risk factor screening.
- 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.
- 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.
- 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.
Detailed description
- 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.
- 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.
- 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.
- 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 design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Experimental group | Experimental | Other: Risk Assessment of Disability and Precision InterventionImplement a digital platform for elderly disability risk intervention to conduct comprehensive, multi-scenario intelligent assessments, dynamic monitoring, and targeted interventions for disability risks. |
| Control group | No intervention | –Conduct routine community-based geriatric health management, such as health education, exercise guidance, and dietary counseling. |
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Older adult
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (1 listed)
- Those who are unable to perform basic activities of daily living independently, or who have significant cognitive or communication impairments.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Risk Assessment of Disability and Precision InterventionImplement a digital platform for elderly disability risk intervention to conduct comprehensive, multi-scenario intelligent assessments, dynamic monitoring, and targeted interventions for disability risks. | Other | Experimental group |
Primary outcome
SPPB(SHORT PHYSICAL PERFORMANCE BATTERY PROTOCOL)
Time frame: At baseline, 3 months after intervention, and 6 months after intervention
the range of score is 0-12, and the higher scores mean a better outcome.
3 secondary outcomes
Incidence of disability
Time frame: 6 months after intervention
Disability Risk Score
Time frame: At baseline, 3 months after intervention, and 6 months after intervention
Intervention adherence
Time frame: 3 months after intervention, and 6 months after intervention
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Apr 2026.
- Derived from this study: PubMed 42597295Zhang Y, Mao Y, Guo Z, Gao W, Wang J, Jin J. A digital platform-based stratified intervention for preventing disability risk in community-dwelling older adults: study protocol for a randomized multicenter trial. Front Med (Lausanne). 2026 Jul 30;13:1890107. doi: 10.3389/fmed.2026.1890107. eCollection 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Jingfen Jin, Master principal investigator
Second Affiliated Hospital, Zhejiang University, School of Medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Risk Assessment of Disability and Precision Intervention in people with Disability, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 2 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jingfen Jin, Master (+86 15888841161, zrjzkhl@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
SPPB(SHORT PHYSICAL PERFORMANCE BATTERY PROTOCOL) (time frame: At baseline, 3 months after intervention, and 6 months after intervention). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07508813, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Apr 2026.