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Record W3011561150 · doi:10.2196/14629

Optimization of Upper Extremity Rehabilitation by Combining Telerehabilitation With an Exergame in People With Chronic Stroke: Protocol for a Mixed Methods Study

2020· article· en· W3011561150 on OpenAlexaffvenue
Dorra Rakia Allegue, Dahlia Kairy, Johanne Higgins, Philippe Archambault, François Michaud, William C. Miller, Shane N. Sweet, Michel Tousignant

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of British ColumbiaMcGill UniversityUniversité de SherbrookeUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsTelerehabilitationRehabilitationPhysical therapyPhysical medicine and rehabilitationStroke (engine)MedicineRandomized controlled trialProtocol (science)Occupational therapyIntervention (counseling)TelemedicinePsychologyHealth careNursingAlternative medicine

Abstract

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BACKGROUND: Exergames have the potential to provide an accessible, remote approach for poststroke upper extremity (UE) rehabilitation. However, the use of exergames without any follow-up by a health professional could lead to compensatory movements during the exercises, inadequate choice of difficulty level, exercises not being completed, and lack of motivation to pursue exercise programs, thereby decreasing their benefits. Combining telerehabilitation with exergames could allow continuous adjustment of the exercises and monitoring of the participant's completion and adherence. At present, there is limited evidence regarding the feasibility or efficacy of combining telerehabilitation and exergames for stroke rehabilitation. OBJECTIVE: This study aims to (1) determine the preliminary efficacy of using telerehabilitation combined with exergames on UE motor recovery, function, quality of life, and motivation in participants with chronic stroke, compared with conventional therapy (the graded repetitive arm supplementary program; GRASP); (2) examine the feasibility of using the technology with participants diagnosed with stroke at home; and (3) identify the obstacles and facilitators for its use by participants diagnosed with stroke and stroke therapists and understand the shared decision-making process. METHODS: A mixed methods study protocol is proposed, including a randomized, blinded feasibility trial with an embedded multiple case study. The intervention consists of the provision of a remote rehabilitation program, during which participants will use the Jintronix exergame for UE training and the Reacts Application to conduct videoconferenced sessions with the therapists (physical or occupational therapists). We plan to recruit 52 participants diagnosed with stroke, randomly assigned to a control group (n=26; 2-month on-paper home exercise program: the GRASP with no supervision) and an experimental group (n=26; 2-month home program using the technology). The primary outcome is the Fugl-Meyer UE Assessment, a performance-based measure of UE impairment. The secondary outcomes are self-reported questionnaires and include the Motor Activity Log-28 (quality and frequency of use of the UE), Stroke Impact Scale-16 (the quality of life), and Treatment Self-Regulation Questionnaire (motivation). Feasibility data include process, resources, management, and scientific outcomes. Qualitative data will be collected by interviews with both participants and therapists. RESULTS: At present, data collection was ongoing with one participant who had completed the exergame- telerehabilitation based intervention. We expect to collect preliminary efficacy data of this technology on the functional and motor recovery of the UE, following a stroke; collect feasibility data with users at home (adherence, safety, and technical difficulties); and identify the obstacles and facilitators for the technology use and understand the shared decision-making process. CONCLUSIONS: This paper describes the protocol underlying the study of a telerehabilitation-exergame technology to contribute to understanding its feasibility and preliminary efficacy for UE stroke rehabilitation. TRIAL REGISTRATION: ClinicalTrials.gov NCT03759106; http://clinicaltrials.gov/show/NCT03759106. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14629.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.348
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.115
GPT teacher head0.530
Teacher spread0.415 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations25
Published2020
Admission routes2
Has abstractyes

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