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Record W4285417645 · doi:10.2196/31305

Lessons Learned From Clinicians and Stroke Survivors About Using Telerehabilitation Combined With Exergames: Multiple Case Study

2022· article· en· W4285417645 on OpenAlexaffvenueabout
Dorra Rakia Allegue, Shane N. Sweet, Johanne Higgins, Philippe Archambault, François Michaud, William C. Miller, Michel Tousignant, Dahlia Kairy

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2022
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of British ColumbiaMcGill UniversityUniversité de SherbrookeUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsTelerehabilitationStroke (engine)Physical medicine and rehabilitationPhysical therapyPsychologyMedicineTelemedicineEngineeringHealth carePolitical science

Abstract

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BACKGROUND: In Canada, stroke survivors have difficulty accessing community-based rehabilitation services because of a lack of resources. VirTele, a personalized remote rehabilitation program combining virtual reality exergames and telerehabilitation, was developed to provide stroke survivors an opportunity to pursue rehabilitation of their chronic upper extremity (UE) deficits at home while receiving ongoing follow-up from a clinician. OBJECTIVE: We aimed to identify the behavioral and motivational techniques used by clinicians during the VirTele intervention, explore the indicators of empowerment among stroke survivors, and investigate the determinants of VirTele use among stroke survivors and clinicians. METHODS: This multiple case study involved 3 stroke survivors with chronic UE deficits and their respective clinicians (physiotherapists) who participated in the VirTele intervention, a 2-month remote rehabilitation intervention that uses nonimmersive virtual reality exergames and telerehabilitation aimed at improving UE deficits in stroke survivors. Study participants had autonomous access to Jintronix exergames and were asked to use them for 30 minutes, 5 times a week. The VirTele intervention included 1-hour videoconference sessions with a clinician 1 to 3 times a week, during which the clinician engaged in motivational interviewing, supervised the stroke survivors' use of the exergames, and monitored their use of the affected UE through activities of daily living. Semidirected interviews were conducted with the clinicians and stroke survivors 4 to 5 weeks after the end of the VirTele intervention. All interviews were audiorecorded and transcribed verbatim. An abductive thematic analysis was conducted to generate new ideas through a dynamic interaction between data and theory. RESULTS: Three stroke survivors (n=2, 67%, women and n=1, 33%, man), with a mean age of 58.8 (SD 19.4) years, and 2 physiotherapists participated in the study. Five major determinants of VirTele use emerged from the qualitative analyses, namely technology performance (usefulness and perception of exergames), effort (ease of use), family support (encouragement), facilitators (considerations of the stroke survivors' safety as well as trust and understanding of instructions), and challenges (miscommunication and exergame limits). During the VirTele intervention, both clinicians used motivational and behavioral techniques to support autonomy, competence, and connectivity. All these attributes were reflected as empowerment indicators in the stroke survivors. Lessons learned from using telerehabilitation combined with exergames are provided, which will be relevant to other researchers and contexts. CONCLUSIONS: This multiple case study provides a first glimpse into the impact that motivational interviewing can have on adherence to exergames and changes in behavior in the use of the affected UE in stroke survivors. Lessons learned regarding the supportive role caregivers play and the new responsibilities clinicians have when using the VirTele intervention may inform the use of exergames via telerehabilitation. These lessons will also serve as a model to guide the implementation of similar interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/14629.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0100.002
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.047
GPT teacher head0.346
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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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Citations14
Published2022
Admission routes3
Has abstractyes

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