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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Elderly Subjects, Risk of Fall

Intervention / Treatment
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* Device: DigiPrehab system


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 65 Months

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Other
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: elderly subjects at risk of fall<br> | Device: DigiPrehab system<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in Usability | This 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'. | baseline and 12 weeks later | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Cognitive impairment | This 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. | baseline and 12 weeks later | 
| Falling risk | falling 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 | baseline and 12 weeks later | 
| Health Questionnaire (EQ-5D-5L) | The 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) | baseline and 12 weeks later | 
| Time Up and Go test (TUG) | Time 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. | baseline and 12 weeks later | 
| Physical performance | Change 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 | baseline and 12 weeks later | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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ageing, rehabilitation, technological system, artificial intelligence, risk of fall