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| Name | Class |
|---|---|
| Universidad Miguel Hernandez de Elche | OTHER |
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The AISN multicenter randomized controlled trial will assess the effectiveness of a novel artificial intelligence (AI)-based clinical decision-support system integrated into the Rehabilitation Gaming System (RGS) for home-based post-stroke rehabilitation. Approximately 192 participants ≥6 months post-stroke will be recruited across several European centers and assigned to one of three groups: RGS with AI decision support, RGS without AI, or standard care. The primary outcome is upper limb motor improvement for stroke patients, with secondary measures including cognitive function, independence, quality of life, usability, cost-effectiveness, and AI-based support performance.
The AISN study addresses the gap in long-term, personalized stroke rehabilitation after hospital discharge by evaluating an enhanced digital therapy platform that combines the clinically validated Rehabilitation Gaming System (RGS) with a newly developed AI-based decision-support module. This AI component analyzes patient performance data to provide clinicians with diagnostic and prognostic insights, along with tailored exercise prescriptions.
The trial's key innovation is the formal validation of the AI module in real-world clinical settings, assessing its concordance with clinician decisions, predictive accuracy, and contribution to patient outcomes.
Participants will be randomized into three groups:
RGS+AI: Home-based RGS therapy with AI-driven recommendations for clinicians. RGS-AI: Home-based RGS therapy without AI support. Control: Standard rehabilitation care. The intervention phase will last 12 weeks, with daily home training for experimental groups, and follow-up at 20 weeks. In addition to standard clinical endpoints, the study will include predefined AI validation metrics, focusing on its potential as a certified medical device tool for scalable, personalized rehabilitation delivery.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| RGS with AI-based Clinical Decision Support | Experimental | Participants receive home-based virtual reality rehabilitation using the Rehabilitation Gaming System (RGS@home), with exercise prescriptions personalized by an AI-driven clinical decision support system. Clinicians can review and adjust these prescriptions remotely. |
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| RGS without AI-based Decision Support | Active Comparator | Participants receive the same home-based RGS virtual reality rehabilitation, but exercise prescriptions are set and adjusted manually by clinicians without AI assistance. |
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| Control Group - Standard Care | No Intervention | Participants receive usual post-stroke rehabilitation services available at their site, without access to the RGS@home platform. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-personalized virtual reality rehabilitation system for unsupervised home-based stroke therapy | Device | The personalized RGS app rehabilitation is a home-based, virtual reality therapy platform for motor and cognitive stroke recovery. Therapy tasks are gamified, task-specific, and adapt in difficulty based on real-time performance. An AI-driven clinical decision support system personalizes and updates exercise prescriptions after each session, with optional clinician adjustments. Integrated wearable sensors (RGSwear) track real-world activity and adherence. Data are securely uploaded to a cloud-based platform for remote monitoring. This is the first multicenter, international RCT to test AI-personalized VR rehabilitation at home with up to 12-month follow-up, combined with cost-effectiveness and usability evaluation. |
| Measure | Description | Time Frame |
|---|---|---|
| Upper limb motor change | Evaluation with the ARAT scale | From enrollment to the end of treatment at 12 weeks, and follow-up at 20 weeks |
| Measure | Description | Time Frame |
|---|---|---|
| Cognitive function change | Cognitive evaluation with TAP (alertness, sustained and divided attention, selective attention/flexibility, spatial attention, and working memory). | From enrollment to the end of treatment at 12 weeks, and follow-up at 20 weeks |
| Disability evaluation |
| Measure | Description | Time Frame |
|---|---|---|
| RGS intrinsic measures change | RGS intrinsic measures, i.e., exercise difficulty settings and kinematics RGS protocols for motor and cognitive assessment | At enrollment, 2, 4, 8 weeks, end of treatment (12 weeks), and follow-up (20 weeks) |
Inclusion Criteria
Exclusion Criteria
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Sponsor | Contact | +34 931389642 | contact@eodyne.com | |
| Anna Mura | Contact | anna3.mura@gmail.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| CHU de Limoges | Not yet recruiting | Limoges | France |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 16813784 | Background | Rabadi MH, Rabadi FM. Comparison of the action research arm test and the Fugl-Meyer assessment as measures of upper-extremity motor weakness after stroke. Arch Phys Med Rehabil. 2006 Jul;87(7):962-6. doi: 10.1016/j.apmr.2006.02.036. | |
| 18760153 | Background | Lang CE, Edwards DF, Birkenmeier RL, Dromerick AW. Estimating minimal clinically important differences of upper-extremity measures early after stroke. Arch Phys Med Rehabil. 2008 Sep;89(9):1693-700. doi: 10.1016/j.apmr.2008.02.022. |
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No Individual participant data (IPD) will be shared. Only aggregated results or fully de-identified datasets may be provided to external researchers to ensure transparency while protecting confidentiality.
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| ID | Term |
|---|---|
| D020521 | Stroke |
| ID | Term |
|---|---|
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
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|
Assessment of global disability with the Modified Ranking Scale - mRS |
| From enrollment to the end of treatment at 12 weeks, and follow-up at 20 weeks |
| Emotional change | Assessed with the Hamilton Depression Scale | From enrollment to the end of treatment at 12 weeks, and follow-up at 20 weeks |
| Quality of life and Health status | Assessed with EQ-5D-5L questionnaire | From enrollment to the end of treatment at 12 weeks, and follow-up at 20 weeks |
| Therapists' qualitative evaluation of the AI-based decision support system performance | Usability (standardized questionnaire on usage, credibility, and time consumption) Cost efficiency (between the two experimental groups RGS+AI/-AI, time spent for making the prescription, technical support, patient visits during therapy) RGS +AI/-AI performance (pre- and post-treatment values of diagnostics, prognostics, recommendations, and deviations | At the end of the study, at 20 weeks. |
| San Camillo Hospital, IRCCS | Recruiting | Venice | Veneto | 30126 | Italy |
|
| UMF | Recruiting | Cluj-Napoca | Romania |
|
| Parc Sanitari Sant Joan de Deu (SJDD) | Not yet recruiting | Barcelona | Spain |
|
| 19228851 | Background | Hsieh YW, Wu CY, Lin KC, Chang YF, Chen CL, Liu JS. Responsiveness and validity of three outcome measures of motor function after stroke rehabilitation. Stroke. 2009 Apr;40(4):1386-91. doi: 10.1161/STROKEAHA.108.530584. Epub 2009 Feb 19. |
| 34972526 | Background | Ballester BR, Antenucci F, Maier M, Coolen ACC, Verschure PFMJ. Estimating upper-extremity function from kinematics in stroke patients following goal-oriented computer-based training. J Neuroeng Rehabil. 2021 Dec 31;18(1):186. doi: 10.1186/s12984-021-00971-8. |
| 20860808 | Background | Cameirao MS, Badia SB, Oller ED, Verschure PF. Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation. J Neuroeng Rehabil. 2010 Sep 22;7:48. doi: 10.1186/1743-0003-7-48. |
| 33349012 | Background | Duncan PW, Bushnell C, Sissine M, Coleman S, Lutz BJ, Johnson AM, Radman M, Pvru Bettger J, Zorowitz RD, Stein J. Comprehensive Stroke Care and Outcomes: Time for a Paradigm Shift. Stroke. 2021 Jan;52(1):385-393. doi: 10.1161/STROKEAHA.120.029678. Epub 2020 Dec 22. |
| 32143674 | Background | Maier M, Ballester BR, Leiva Banuelos N, Duarte Oller E, Verschure PFMJ. Adaptive conjunctive cognitive training (ACCT) in virtual reality for chronic stroke patients: a randomized controlled pilot trial. J Neuroeng Rehabil. 2020 Mar 6;17(1):42. doi: 10.1186/s12984-020-0652-3. |
| 20332511 | Background | Moher D, Hopewell S, Schulz KF, Montori V, Gotzsche PC, Devereaux PJ, Elbourne D, Egger M, Altman DG. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. 2010 Mar 23;340:c869. doi: 10.1136/bmj.c869. No abstract available. |
| 31920570 | Background | Maier M, Ballester BR, Verschure PFMJ. Principles of Neurorehabilitation After Stroke Based on Motor Learning and Brain Plasticity Mechanisms. Front Syst Neurosci. 2019 Dec 17;13:74. doi: 10.3389/fnsys.2019.00074. eCollection 2019. |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |