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Record W3198242429 · doi:10.2196/32134

Effects of Telerehabilitation on Patient Adherence to a Rehabilitation Plan: Protocol for a Mixed Methods Trial

2021· article· en· W3198242429 on OpenAlexaffvenue
Isabelle Gaboury, Michel Tousignant, Hélène Corriveau, Matthew Menear, Guylaine Le Dorze, Christian M. Rochefort, Brigitte Vachon, Annie Rochette, Sylvie Gosselin, François Michaud, Jessica C Bollen, Sarah Dean

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in RehabilitationUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsTelerehabilitationRehabilitationMedicineIntervention (counseling)Physical therapyStroke (engine)Physical medicine and rehabilitationTelemedicineHealth careNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Strong evidence supports beginning stroke rehabilitation as soon as the patient's medical status has stabilized and continuing following discharge from acute care. However, adherence to rehabilitation treatments over the rehabilitation phase has been shown to be suboptimal. OBJECTIVE: The aim of this study is to assess the impact of a telerehabilitation platform on stroke patients' adherence to a rehabilitation plan and on their level of reintegration into normal social activities, in comparison with usual care. The primary outcome is patient adherence to stroke rehabilitation (up to 12 weeks), which is hypothesized to influence reintegration into normal living. Secondary outcomes for patients include functional recovery and independence, depression, adverse events related to telerehabilitation, use of services (up to 6 months), perception of interprofessional shared decision making, and quality of services received. Interprofessional collaboration as well as quality of interprofessional shared decision making will be measured with clinicians. METHODS: In this interrupted time series with a convergent qualitative component, rehabilitation teams will be trained to develop rehabilitation treatment plans that engage the patient and family, while taking advantage of a telerehabilitation platform to deliver the treatment. The intervention will be comprised of 220 patients who will take part in stroke telerehabilitation with an interdisciplinary group of clinicians (telerehabilitation group) versus face-to-face standard of care (control group: n=110 patients). RESULTS: Our Research Ethics Board approved the study in June 2020. Data collection for the control group is underway, with another year planned before we begin the intervention phase. CONCLUSIONS: This study will contribute to the minimization of both knowledge and practice gaps, while producing robust, in-depth data on the factors related to the effectiveness of telerehabilitation in a stroke rehabilitation continuum. Findings will inform best practice guidelines regarding telecare services and the provision of telerehabilitation, including recommendations for effective interdisciplinary collaboration regarding stroke rehabilitation. TRIAL REGISTRATION: ClinicalTrials.gov NCT04440215; https://clinicaltrials.gov/ct2/show/NCT04440215. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32134.

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)0.044
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.084
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.039
Meta-epidemiology (narrow)0.0080.003
Meta-epidemiology (broad)0.0120.007
Bibliometrics0.0030.005
Science and technology studies0.0040.003
Scholarly communication0.0050.004
Open science0.0040.002
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0840.014

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 head0.199
GPT teacher head0.604
Teacher spread0.405 · 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 designNon-randomized trial
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".

Quick stats

Citations8
Published2021
Admission routes2
Has abstractyes

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