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Preact to Lower the Risk of Falling by Customized Rehabilitation Across Europe: the PRECISE Study In Italy

Preact to Lower the Risk of Falling by Customized Rehabilitation Across Europe: the PRECISE Study In Italy

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT05846776
Acronym
PRECISE
Enrollment
43
Registered
2023-05-06
Start date
2022-10-26
Completion date
2024-02-02
Last updated
2025-09-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Elderly Subjects, Risk of Fall

Keywords

ageing, rehabilitation, technological system, artificial intelligence, risk of fall

Brief summary

The PRECISE study is a 12-week pilot intervention study to evaluate the usability of the new DigiPrehab technology application in elderly subjects. The DigiPrehab system will enable the early identification of seniors with significant risk factors for falling and will propose an individualized physical training plan at home.

Detailed description

The PRECISE project takes the positive results achieved with the DigiRehab application (https://digirehab.dk/en) in home rehabilitation and takes a further step in this direction by combining the personalized training delivered through the application with an artificial intelligence-based predictive model (Artificial intelligence - Decision Support Systems platform, AI-DSS platform) for fall risk assessment in the elderly. In particular, 20 senior participants will test the DSS beta prototype. This new system, called DigiPrehab, will enable early identification of the elderly with significant risk factors for falling and propose an individualized physical training plan to attend to the identified critical areas. The PRECISE study will be a 12-week pilot intervention study to evaluate the usability of the new DigiPrehab technology application in elderly subjects. The DigiPrehab system will enable the early identification of seniors with significant risk factors for falling and will propose an individualized physical training plan at home.

Interventions

DEVICEDigiPrehab system

Using an Artificial Intelligence-Machine Learning (AI-ML) DSS platform, which analyzes a large collection of data (screening and local data) from different sources, the DigiPrehab system will allow to predict the risk of falling in the elderly subjects. Once the screening will be completed, to carry out the prevention of falls, the system will assign to the participants a personalised exercise program that the patient will carry out at home for 12 weeks. The exercises will be chosen from the following: squat at door, squat on chair, squat with knee-lift, squat with heel-raise, stand-no support, toe-raise with support, toe-raise, one leg balance, weight-shift with support, weight-shift without support, lunge with support, lunge, step on book, step over book, step onto box or stair, step forward-sideways, step forward-sideways-backwards, knee to elbow, timed up and go.

Sponsors

European Union
CollaboratorOTHER
Istituto Nazionale di Ricovero e Cura per Anziani
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
65 Months to No maximum
Healthy volunteers
No

Inclusion criteria

* Independent ambulation * Fall risk assessed by Tinetti test * Mini Mental State Examination ≥ 24 * Residents at home * Familiarity with web applications * Ability and willingness to sign informed consent

Exclusion criteria

* Unstable clinical condition by judgment of the physician * Severe visual and/or hearing impairment * Severe impairment (Activities of Daily Living) in medical record * Absence of primary caregiver

Design outcomes

Primary

MeasureTime frameDescription
Change in Usabilitybaseline and 12 weeks laterThis outcome will be measured through the System Usability Scale (SUS). It consists of a 10-item questionnaire with five response options for respondents from 'Strongly agree' to 'Strongly disagree'.

Secondary

MeasureTime frameDescription
Cognitive impairmentbaseline and 12 weeks laterThis outcome will be measured by Mini-Mental State Examination (MMSE). It is a neuropsychological test for the evaluation of disorders of intellectual efficiency and the presence of cognitive impairment. The total score is between a minimum of 0 and a maximum of 30 points. A score of 26 to 30 is an indication of cognitive normality. The score will be adjusted with the coefficient for age and schooling.
Falling riskbaseline and 12 weeks laterfalling risk will be evaluated by the Tinetti performance oriented mobility assessment (POMA). POMA test has two subscales, Balance and Gait sections. Total score is obtained by adding the scores of the two subscales (balance + gait) . Total score \< 19 high fall risk, total score 19-24 medium fall risk, total score 25-28 low fall risk
Health Questionnaire (EQ-5D-5L)baseline and 12 weeks laterThe EuroQol-5 dimensions five level index questionnaire (EQ-5D-5L) covers five dimensions of health: mobility, self-care, usual activities, pain or discomfort, and anxiety or depression. The levels of severity for each dimension ranges from no problems (1) to extreme problems/unable to perform. The raw scores are also converted to an EQ-5D index value using a scoring algorithm (British tariff) ranging from -0.594 (worst perceived health state) to 1.00 (best perceived health state)
Time Up and Go test (TUG)baseline and 12 weeks laterTime up and go test (TUG) is a successful screening method to evaluate the chance of falling. Walking pace, muscle strength and balance, sit-to-walk transition time, turning, walking and walk-to-sit transition are expressed in TUG. Participants take greater than 12 seconds to complete TUG will be at greater risk of fall.
Physical performancebaseline and 12 weeks laterChange in physical performance will be ascertained using the Short Physical Performance Battery (SPPB). Summary scores range from 0-12 and higher scores denote higher physical performance

Countries

Italy

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 6, 2026