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Record W4409728459 · doi:10.2196/68681

The Safety of Telerehabilitation: Systematic Review

2025· review· en· W4409728459 on OpenAlexaffvenue
Hila Shnitzer, Josh Chan, T. Yau, McKyla McIntyre, Angie Andreoli, Ailene Kua, Mark Bayley, Carl Froilan D. Leochico, Meiqi Guo, Sarah Munce

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2025
Typereview
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity of TorontoUniversity Health NetworkWestern UniversityToronto Rehabilitation InstituteMcMaster University
Fundersnot available
KeywordsPreprintTelerehabilitationMedicineComputer scienceTelemedicineWorld Wide WebHealth carePolitical science

Abstract

fetched live from OpenAlex

Background: Telerehabilitation involves the delivery of rehabilitation services over a distance through communication technologies. In contrast to traditional in-person rehabilitation, telerehabilitation can help overcome barriers including geographic distance and facility use. There is evidence to suggest that telerehabilitation can lead to increased patient engagement and adherence to treatment plans. However, limited research exists on the association of telerehabilitation with adverse events, potentially hindering its broader adoption and use in health care. objectives: This systematic review of randomized controlled trials aims to summarize existing research on adverse events related to telerehabilitation delivery. Methods: This review was conducted according to the methodological framework outlined by the Joanna Briggs Institute. Studies were identified from MEDLINE ALL, Embase, APA PsycINFO, CENTRAL, and CINAHL. Included studies were randomized controlled trials published between 2013 and 2023, written in English, and had no geographic or delivery mode restrictions. Data extraction used the TIDieR (Template for Intervention Description and Replication) framework, along with authors, publication year, sample size, specific telerehabilitation modes, and the incidence, type, severity, and relatedness of reported adverse events. Methodological quality was assessed using the Cochrane risk of bias tool, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation tool. Results: Search results identified 9022 references, of which 37 randomized controlled trials met the criteria for inclusion. There were a total of 3166 participants, with a mean age of 57.4 (SD 11.3) years, and 1023 (32.3%) being female participants. Various delivery modes were used, with videoconferencing emerging as the most frequently used method. A total of 201 adverse events were recorded during 65,352 sessions (0.31% or 3.1 per 1000 sessions). These events were predominantly physical (eg, falls and palpitations), nonserious or mild, and not directly attributed to the telerehabilitation intervention. Additionally, 34 (92%) of included studies implemented various safety practices including vital sign monitoring, safety checklists, and scheduled check-ins with study personnel. Conclusions: This review demonstrates that telerehabilitation exhibits a generally safe profile as an alternative to in-person rehabilitation, with most reported adverse events being rare, nonserious or mild, and unrelated to telerehabilitation protocols. However, more extensive research with detailed reporting on adverse event characteristics is needed. Moreover, future research should evaluate the effectiveness of different safety practices and their association with adverse events. An enhanced understanding of potential risks in telerehabilitation can foster broader adoption while ensuring its safe implementation among health care providers and patients.

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)0.023
metaresearch head score (Gemma)0.128
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.128
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.009
Bibliometrics0.0100.011
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.029
GPT teacher head0.400
Teacher spread0.371 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations15
Published2025
Admission routes2
Has abstractyes

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