NCT07138495

Brief Summary

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.

Trial Health

80
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
192

participants targeted

Target at P75+ for not_applicable stroke

Timeline
8mo left

Started Oct 2025

Geographic Reach
4 countries

4 active sites

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress46%
Oct 2025Dec 2026

First Submitted

Initial submission to the registry

August 14, 2025

Completed
8 days until next milestone

First Posted

Study publicly available on registry

August 22, 2025

Completed
2 months until next milestone

Study Start

First participant enrolled

October 15, 2025

Completed
1.2 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 30, 2026

Expected
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

December 30, 2026

Last Updated

December 22, 2025

Status Verified

December 1, 2025

Enrollment Period

1.2 years

First QC Date

August 14, 2025

Last Update Submit

December 16, 2025

Conditions

Keywords

Strokehome based rehabilitationdigital healthvirtual realitypersonalized rehabilitation

Outcome Measures

Primary Outcomes (1)

  • 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

Secondary Outcomes (5)

  • Cognitive function change

    From enrollment to the end of treatment at 12 weeks, and follow-up at 20 weeks

  • Disability evaluation

    From enrollment to the end of treatment at 12 weeks, and follow-up at 20 weeks

  • Emotional change

    From enrollment to the end of treatment at 12 weeks, and follow-up at 20 weeks

  • Quality of life and Health status

    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

    At the end of the study, at 20 weeks.

Other Outcomes (1)

  • RGS intrinsic measures change

    At enrollment, 2, 4, 8 weeks, end of treatment (12 weeks), and follow-up (20 weeks)

Study Arms (3)

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.

Device: AI-personalized virtual reality rehabilitation system for unsupervised home-based stroke therapy

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.

Device: AI-personalized virtual reality rehabilitation system for unsupervised home-based stroke therapy

Control Group - Standard Care

NO INTERVENTION

Participants receive usual post-stroke rehabilitation services available at their site, without access to the RGS@home platform.

Interventions

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.

RGS with AI-based Clinical Decision SupportRGS without AI-based Decision Support

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • ≥ 6 months post-stroke
  • Patients presenting a first-ever ischemic or intracerebral hemorrhagic stroke
  • Mild to Moderate unilateral upper limb motor impairment: Medical Research Council proximal and distal upper limb MRC ≥2; Action Research Arm Test: ARAT score \< 50 (0 = no function, 57 = no functional limitation).
  • Age \> 18 years old
  • Able to sit on a chair or a wheelchair and interact with RGS during an entire session
  • Minimal experience with smartphone technology based on the clinician's opinion
  • Willing to participate in the RGS therapy
  • Sign the Informed Consent Form

You may not qualify if:

  • Diagnosis with Epilepsy
  • Severe cognitive capabilities preventing the execution of the experiment or according to clinicians' criteria.
  • Severe associated impairment such as proximal but not distal spasticity, communication disabilities (sensory, Wernicke aphasia or apraxia), major pain (VAS \> 75-100 mm), orthopedic devices that would interfere with the correct execution of the experiment (Modified Ashworth Scale \> 3)
  • Unable to use the RGS app independently according to the clinician's observations and lacking support from a caregiver to use the RGS app
  • No experience with smartphone technology or based on the clinician's opinion.
  • Refusal to sign the Informed Consent
  • Participating or planning to participate in another trial while being part of the present study.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (4)

CHU de Limoges

Limoges, France

NOT YET RECRUITING

San Camillo Hospital, IRCCS

Venice, Veneto, 30126, Italy

RECRUITING

UMF

Cluj-Napoca, Romania

RECRUITING

Parc Sanitari Sant Joan de Deu (SJDD)

Barcelona, Spain

NOT YET RECRUITING

Related Publications (9)

  • 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.

    PMID: 16813784BACKGROUND
  • 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.

    PMID: 18760153BACKGROUND
  • 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.

    PMID: 19228851BACKGROUND
  • 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.

    PMID: 34972526BACKGROUND
  • 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.

    PMID: 20860808BACKGROUND
  • 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.

    PMID: 33349012BACKGROUND
  • 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.

    PMID: 32143674BACKGROUND
  • 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.

    PMID: 20332511BACKGROUND
  • 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.

    PMID: 31920570BACKGROUND

MeSH Terms

Conditions

Stroke

Condition Hierarchy (Ancestors)

Cerebrovascular DisordersBrain DiseasesCentral Nervous System DiseasesNervous System DiseasesVascular DiseasesCardiovascular Diseases

Central Study Contacts

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
DOUBLE
Who Masked
PARTICIPANT, OUTCOMES ASSESSOR
Purpose
TREATMENT
Intervention Model
PARALLEL
Sponsor Type
INDUSTRY
Responsible Party
SPONSOR

Study Record Dates

First Submitted

August 14, 2025

First Posted

August 22, 2025

Study Start

October 15, 2025

Primary Completion (Estimated)

December 30, 2026

Study Completion (Estimated)

December 30, 2026

Last Updated

December 22, 2025

Record last verified: 2025-12

Data Sharing

IPD Sharing
Will not share

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.

Locations