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Record W4405988013 · doi:10.12968/ijtr.2024.0043

Perceptions of physiotherapists about virtual reality using exergames in cardiovascular rehabilitation: a qualitative study

2024· article· en· W4405988013 on OpenAlexaff
Mayara Moura Alves da Cruz, Gabriela Lima de Melo Ghisi, Isis Grigoletto, Laís Manata Vanzella, Murilo Reis Alves da Cruz, Luana Almeida Gonzaga, Márcia R. Franco, Luiz Carlos Marques Vanderlei

Bibliographic record

VenueInternational Journal of Therapy and Rehabilitation · 2024
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsToronto Rehabilitation InstituteUniversity Health Network
Fundersnot available
KeywordsRehabilitationPerceptionVirtual realityQualitative researchPsychologyPhysical medicine and rehabilitationMedicinePhysical therapyHuman–computer interactionApplied psychologyComputer scienceSociologyNeuroscience

Abstract

fetched live from OpenAlex

Background/Aims Cardiovascular rehabilitation is a secondary preventative care model for outpatients. Despite its benefits, cardiac rehabilitation is greatly underused. Virtual reality‑based therapy could be a complementary treatment for traditional cardiac rehabilitation which promotes physical activity, enhancing motivation and increasing adherence to rehabilitation programmes. This qualitative study examined physiotherapists' perceptions toward using virtual reality‑based therapy based on exergames as part of cardiac rehabilitation programmes. Methods This qualitative study used focus groups to evaluate the perceptions of nine physiotherapists who have used virtual reality‑based therapy with exergames in cardiac rehabilitation. Results Participants' attitudes towards virtual reality‑based therapy were overall positive, and found it could improve patients' adherence to a cardiac rehabilitation programme. Barriers to VRBT were also identified, including technical difficulties, patients being insecure about using a new therapeutic modality and managing the level of effort during the session. Suggestions for improvements included training for clinical staff, and having a introductory session for patients to help get used to the therapy and to find out what games they would prefer to play. Conclusions Virtual reality‑based therapy can provide a change from the traditional treatment routine; however, there are some barriers, including potential safety concerns and technical issues. Feedback on haemodynamic parameters and identification of signs and symptoms of a cardiac event could be incorporated into games made specifically for patients undergoing cardiac rehabilitation. Implications for practice Finding different ways to engage patients is needed. Based on the perceptions of physiotherapists from this study, VRBT could be one of the solutions to reach these goals for patients receiving cardiac rehabilitation.

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)0.010
metaresearch head score (Gemma)0.012
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 score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.388
Teacher spread0.362 · 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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Citations0
Published2024
Admission routes1
Has abstractyes

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