Progressive Transition From Supervised to Unsupervised Robot-Assisted Therapy After Stroke: Protocol for a Single-Group, Interventional Feasibility Study.

neurocognitive rehabilitation neurorehabilitation rehabilitation technology robot-assisted therapy robot. self-directed therapy stroke technology-assisted rehabilitation unsupervised therapy

Journal

JMIR research protocols
ISSN: 1929-0748
Titre abrégé: JMIR Res Protoc
Pays: Canada
ID NLM: 101599504

Informations de publication

Date de publication:
09 Nov 2023
Historique:
received: 25 04 2023
accepted: 02 10 2023
revised: 21 09 2023
medline: 9 11 2023
pubmed: 9 11 2023
entrez: 9 11 2023
Statut: epublish

Résumé

Increasing the dose of therapy delivered to patients with stroke may improve functional outcomes and quality of life. Unsupervised technology-assisted rehabilitation is a promising way to increase the dose of therapy without dramatically increasing the burden on the health care system. Despite the many existing technologies for unsupervised rehabilitation, active rehabilitation robots have rarely been tested in a fully unsupervised way. Furthermore, the outcomes of unsupervised technology-assisted therapy (eg, feasibility, acceptance, and increase in therapy dose) vary widely. This might be due to the use of different technologies as well as to the broad range of methods applied to teach the patients how to independently train with a technology. This paper describes the study design of a clinical study investigating the feasibility of unsupervised therapy with an active robot and of a systematic approach for the progressive transition from supervised to unsupervised use of a rehabilitation technology in a clinical setting. The effect of unsupervised therapy on achievable therapy dose, user experience in this therapy setting, and the usability of the rehabilitation technology are also evaluated. Participants of the clinical study are inpatients of a rehabilitation clinic with subacute stroke undergoing a 4-week intervention where they train with a hand rehabilitation robot. The first week of the intervention is supervised by a therapist, who teaches participants how to interact and train with the device. The second week consists of minimally supervised therapy, where the therapist is present but intervenes only if needed as participants exercise with the device. If the participants properly learn how to train with the device, they proceed to the unsupervised phase and train without any supervision during the third and fourth weeks. Throughout the duration of the study, data on feasibility and therapy dose (ie, duration and repetitions) are collected. Usability and user experience are evaluated at the end of the second (ie, minimally supervised) and fourth (ie, unsupervised) weeks, allowing us to investigate the effect of therapist absence. As of April 2023, 13 patients were recruited and completed the protocol, with no reported adverse events. This study will inform on the feasibility of fully unsupervised rehabilitation with an active rehabilitation robot in a clinical setting and its effect on therapy dose. Furthermore, if successful, the proposed systematic approach for a progressive transition from supervised to unsupervised technology-assisted rehabilitation could serve as a benchmark to allow for easier comparisons between different technologies. This approach could also be extended to the application of such technologies in the home environment, as the supervised and minimally supervised sessions could be performed in the clinic, followed by unsupervised therapy at home after discharge. ClinicalTrials.gov NCT04388891; https://clinicaltrials.gov/study/NCT04388891. DERR1-10.2196/48485.

Sections du résumé

BACKGROUND BACKGROUND
Increasing the dose of therapy delivered to patients with stroke may improve functional outcomes and quality of life. Unsupervised technology-assisted rehabilitation is a promising way to increase the dose of therapy without dramatically increasing the burden on the health care system. Despite the many existing technologies for unsupervised rehabilitation, active rehabilitation robots have rarely been tested in a fully unsupervised way. Furthermore, the outcomes of unsupervised technology-assisted therapy (eg, feasibility, acceptance, and increase in therapy dose) vary widely. This might be due to the use of different technologies as well as to the broad range of methods applied to teach the patients how to independently train with a technology.
OBJECTIVE OBJECTIVE
This paper describes the study design of a clinical study investigating the feasibility of unsupervised therapy with an active robot and of a systematic approach for the progressive transition from supervised to unsupervised use of a rehabilitation technology in a clinical setting. The effect of unsupervised therapy on achievable therapy dose, user experience in this therapy setting, and the usability of the rehabilitation technology are also evaluated.
METHODS METHODS
Participants of the clinical study are inpatients of a rehabilitation clinic with subacute stroke undergoing a 4-week intervention where they train with a hand rehabilitation robot. The first week of the intervention is supervised by a therapist, who teaches participants how to interact and train with the device. The second week consists of minimally supervised therapy, where the therapist is present but intervenes only if needed as participants exercise with the device. If the participants properly learn how to train with the device, they proceed to the unsupervised phase and train without any supervision during the third and fourth weeks. Throughout the duration of the study, data on feasibility and therapy dose (ie, duration and repetitions) are collected. Usability and user experience are evaluated at the end of the second (ie, minimally supervised) and fourth (ie, unsupervised) weeks, allowing us to investigate the effect of therapist absence.
RESULTS RESULTS
As of April 2023, 13 patients were recruited and completed the protocol, with no reported adverse events.
CONCLUSIONS CONCLUSIONS
This study will inform on the feasibility of fully unsupervised rehabilitation with an active rehabilitation robot in a clinical setting and its effect on therapy dose. Furthermore, if successful, the proposed systematic approach for a progressive transition from supervised to unsupervised technology-assisted rehabilitation could serve as a benchmark to allow for easier comparisons between different technologies. This approach could also be extended to the application of such technologies in the home environment, as the supervised and minimally supervised sessions could be performed in the clinic, followed by unsupervised therapy at home after discharge.
TRIAL REGISTRATION BACKGROUND
ClinicalTrials.gov NCT04388891; https://clinicaltrials.gov/study/NCT04388891.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) UNASSIGNED
DERR1-10.2196/48485.

Identifiants

pubmed: 37943580
pii: v12i1e48485
doi: 10.2196/48485
pmc: PMC10667973
doi:

Banques de données

ClinicalTrials.gov
['NCT04388891']

Types de publication

Journal Article

Langues

eng

Pagination

e48485

Informations de copyright

©Giada Devittori, Raffaele Ranzani, Daria Dinacci, Davide Romiti, Antonella Califfi, Claudio Petrillo, Paolo Rossi, Roger Gassert, Olivier Lambercy. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 09.11.2023.

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Auteurs

Giada Devittori (G)

Rehabilitation Engineering Laboratory, Swiss Federal Institute of Technology Zürich, Zurich, Switzerland.

Raffaele Ranzani (R)

Rehabilitation Engineering Laboratory, Swiss Federal Institute of Technology Zürich, Zurich, Switzerland.

Daria Dinacci (D)

Clinica Hildebrand Centro di Riabilitazione Brissago, Brissago, Switzerland.

Davide Romiti (D)

Clinica Hildebrand Centro di Riabilitazione Brissago, Brissago, Switzerland.

Antonella Califfi (A)

Clinica Hildebrand Centro di Riabilitazione Brissago, Brissago, Switzerland.

Claudio Petrillo (C)

Clinica Hildebrand Centro di Riabilitazione Brissago, Brissago, Switzerland.

Paolo Rossi (P)

Clinica Hildebrand Centro di Riabilitazione Brissago, Brissago, Switzerland.

Roger Gassert (R)

Rehabilitation Engineering Laboratory, Swiss Federal Institute of Technology Zürich, Zurich, Switzerland.
Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.

Olivier Lambercy (O)

Rehabilitation Engineering Laboratory, Swiss Federal Institute of Technology Zürich, Zurich, Switzerland.
Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.

Classifications MeSH