Clinical trial NCT06993389: AI-based Model for Rehabilitation Engagement and Motor Performance Evaluation in Pediatric Patients
Official title: AI-based Model for Rehabilitation Engagement and Motor Performance Evaluation in Pediatric Patients
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Neuromotor Impairments
Keywords: Engagement
Motor rehabilitation
Neuromotor impairments
Gait
Psychophysiology
Lead sponsor: IRCCS Eugenio Medea
Sponsor class: OTHER
Collaborators: name: Politecnico di Milano
First posted: 2025-05-28
Enrollment: 40
Sex: ALL
Minimum age (years): 5
Maximum age (years): 17
Standard ages: CHILD
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Potentially eligible participants will be selected from clinical databases of IRCCS Medea.

Brief summary: What is the purpose of this study? This study wants to learn how the physiological signals and movement data can help researchers understand and predict how children with neuromotor impairments engage with rehabilitation.

Who can take part? 40 participants between 5 and 17 years old will take part. Half of them will be children with neurotypical development (control). The other half will be children with neuromotor impairments.

What will happen in the study? Children within the control group will take part in 1 rehabilitation session, while those with neuromotor impairments will take part in 2 to 3 rehabilitation sessions as part of the treatment plan already provided by their referring physicians.

During each session, researchers will collect: (i) physiological signals like heart activity (ECG), heart rate variability (HRV), and electrodermal activity (EDA); (ii) movement data; (iii) exergames scores and motor performance data; (iv) questionnaires filled out by therapists to understand how engaged the participant is.

Children will use one of two technology-assisted rehabilitation systems during their sessions: either the Lokomat (robotic exoskeleton that supports children with impairments in walking) or the GRAIL system (a treadmill system with motion tracking and extended reality).

Why is this study important? Researchers will use the data to understand and predict how each child responds to therapy and engage with it. These analyses will help therapists adjust rehabilitation settings in real-time, offering more personalized and effective care for children with neuromotor impairments.


Eligibility criteria: Inclusion Criteria:

* Subjects aged between 5 and 17 with typical development.
* 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.

Exclusion Criteria:

* Uncooperative subjects.

Primary outcomes:
measure: Pediatric Rehabilitation Intervention Measure of Engagement - Observation (PRIME-O)
timeFrame: Baseline
Secondary outcomes:
measure: Service Provider-Rated Measure of Client Engagement (PRIME-SP)
timeFrame: Baseline
measure: Self-Assessment Manikin (SAM)
timeFrame: Baseline
measure: Positive Affect and Negative Affect Scale (PANAS)
timeFrame: Baseline
measure: Heart Rate (HR)
timeFrame: Baseline
measure: Root Mean Square of Successive Differences (RMSSD)
timeFrame: Baseline
measure: Low Frequency power of Heart Rate Variability (HRV LF)
timeFrame: Baseline
measure: High Frequency power of Heart Rate Variability (HRV HF)
timeFrame: Baseline
measure: Average Skin Conductance Level (AvSCL)
timeFrame: Baseline
measure: Non-Specific Skin Conductance Responses (NSSCR)
timeFrame: Baseline
measure: Low Frequency power of Skin Conductance Response (SCR LF)
timeFrame: Baseline
measure: Walk Cadence
timeFrame: Baseline
measure: Step Time
timeFrame: Baseline
measure: Exergame score
timeFrame: Baseline
Other outcomes:
Locations:
city: Bosisio Parini
country: Italy
facility: Scientific Institute IRCCS E.Medea
state: LC
Study officials:
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.