AI-based Model for Rehabilitation Engagement and Motor Performance Evaluation in Pediatric Patients: A Pilot Study

Официальное название

AI-based Model for Rehabilitation Engagement and Motor Performance Evaluation in Pediatric Patients

Обзор

What is the purpose of this study? This study aims to evaluate the usability and feasibility of an artificial intelligence-based model designed to monitor in real-time the engagement and motor performance of pediatric patients during technology-assisted rehabilitation. Who can take part? 15 participants between 5 and 17 years old with neuromotor impairments will take part, along with at least 5 of their referring physiotherapists. What will happen in the study? Each pediatric patient will take part in a single, 1-hour rehabilitation session using either the Lokomat or GRAIL system, according to their standard clinical prescription. During the session, the physiotherapist will have access to a display showing real-time data from the AI model, including the patient's heart rate, engagement level, pleasantness, activation, and motor performance. At the end of the session, the physiotherapist will complete a System Usability Scale (SUS) questionnaire and provide direct feedback on how to improve the model. Why is this study important? Assessing the usability of this real-time monitoring tool is a necessary step to understand if it is practical for clinical use. Providing therapists with objective, real-time insights into a child's psychological and physical state can ultimately help tailor therapy to the specific needs of each patient, improving the overall rehabilitation experience.

Вмешательства

• Устройство Artificial Intelligence Model for Rehabilitation Engagement Monitoring
  The intervention consists of the deployment of a real-time AI-based monitoring system during a standard technology-assisted rehabilitation session. The physiotherapist is provided with a display showing continuous feedback on the patient's engagement levels, emotional state (pleasantness and activation), motor performance, and heart rate. The model processes physiological and inertial data collected via wearable sensors, acting purely as an observational support tool without altering the standar

Первичные конечные точки

• System Usability Scale (SUS) Score [Срок оценки: Baseline]

Вторичные конечные точки (2)

• Service Provider-Rated Measure of Client Engagement (PRIME-SP) [Срок оценки: Baseline]
• AI Model-Inferred Engagement Level [Срок оценки: Baseline]

Критерии участия

Критерии включения

• Subjects aged between 5 and 17 years with neuromotor impairments who are undergoing rehabilitation therapy using the Lokomat and GRAIL devices, according to the existing clinical plan.

Критерии исключения

• Uncooperative subjects.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Не применимо

Модель

Одна группа

Маскирование

Открытое

Основная цель

Оценка устройства

Центры проведения

Италия · 1 центр

• Scientific Institute IRCCS E.Medea — Bosisio Parini

Публикации

• Bray L, Appleton V, Sharpe A. The information needs of children having clinical procedures in hospital: Will it hurt? Will I feel scared? What can I do to stay calm? Child Care Health Dev. 2019 Sep;45(5):737-743. doi: 10.1111/cch.12692. Epub 2019 Jul 18. PMID 31163093
• Flynn R, Walton S, Scott SD. Engaging children and families in pediatric Health Research: a scoping review. Res Involv Engagem. 2019 Nov 4;5:32. doi: 10.1186/s40900-019-0168-9. eCollection 2019. PMID 31700676
• Graffigna G, Barello S, Riva G, Castelnuovo G, Corbo M, Coppola L, Daverio G, Fauci A, Iannone P, Ricciardi W, Bosio AC; CCIPE Working Group. [Recommandation for patient engagement promotion in care and cure for chronic conditions.]. Recenti Prog Med. 2017 Nov;108(11):455-475. doi: 10.1701/2812.28441. Italian. PMID 29149163
• Koenig A, Omlin X, Zimmerli L, Sapa M, Krewer C, Bolliger M, Muller F, Riener R. Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation. J Rehabil Res Dev. 2011;48(4):367-85. doi: 10.1682/jrrd.2010.03.0044. PMID 21674389
• Costantini S, Falivene A, Chiappini M, Malerba G, Dei C, Bellazzecca S, Storm FA, Andreoni G, Ambrosini E, Biffi E. Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation. J Neuroeng Rehabil. 2024 Dec 19;21(1):215. doi: 10.1186/s12984-024-01519-2. PMID 39702317

Идентификаторы

NCT: NCT07664033 · L2-246-F2 · RC 2024-2026 to E. Biffi

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗