Adverse events associated with the delivery of telerehabilitation across rehabilitation populations: A scoping review
Bibliographic record
Abstract
OBJECTIVE: This scoping review aimed to map existing research on adverse events encountered during telerehabilitation delivery, across rehabilitation populations. This includes identifying characteristics of adverse events (frequency/physical/non-physical, relatedness, severity) and examining adverse events by different modes of telerehabilitation delivery and disease states. INTRODUCTION: Telerehabilitation, a subset of telemedicine, has gained traction during the COVID-19 pandemic for remote service delivery. However, no prior scoping review, systematic review, or meta-analysis has identified and summarized the current primary research on adverse events in telerehabilitation. Understanding adverse events, such as falls during physiotherapy or aspiration pneumonia during speech therapy, is crucial for identifying limitations and optimizing delivery through risk mitigation and quality indicators. This understanding could also help to improve the uptake of telerehabilitation among clinicians and patients. This review addresses this gap by summarizing published literature on adverse events during telerehabilitation. METHODS: The review followed the Joanna Briggs Institute framework and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. The review protocol was registered and published on Open Science Framework. A comprehensive search across multiple databases (MEDLINE ALL/EMBASE/APA PsycINFO/CENTRAL/CINAHL) was conducted. Screening, extraction, and synthesis were performed in duplicate and independently. Data extraction followed the Template for Intervention Description and Replication framework and also involved extraction on authors, publication year (pre- or post-COVID), population, sample size, and modes of telerehabilitation delivery (asynchronous, synchronous, hybrid). For synthesis, data were summarized quantitatively using numerical counts and qualitatively via content analysis. The data were grouped by intervention type and by type of adverse event. INCLUSION CRITERIA: This scoping review included qualitative and quantitative studies published between 2013-2023, written in English, and conducted in any geographic area. All modes of telerehabilitation delivery were included. Systematic reviews, meta-analyses, commentaries, protocols, opinion pieces, conference abstracts, and case series with fewer than five participants were excluded. RESULTS: The search identified 11,863 references, and 81 studies were included in this review with a total of 3,057 participants (mean age:59.3 years; females:44.6%). Modes of telerehabilitation delivery (whether asynchronous, synchronous or hybrid) used in the studies included videoconferencing (52), phone calls (25), text messaging (4), email (6), mobile apps (10), and internet-based virtual reality systems (3). A total of 295 adverse events occurred during 84,534 sessions (0.3%), with the majority being physical (e.g., falls or musculoskeletal pain), non-serious/non-severe/mild, and unrelated to (i.e., not caused by) to the telerehabilitation provided. CONCLUSIONS: From the 81 included studies, telerehabilitation was delivered with related adverse events being rare, and mostly characterized as mild/non-severe. A comparable occurrence of adverse events (~30%) was found between asynchronous and synchronous telerehabilitation studies. When categorized by disease type, cardiac telerehabilitation studies had the most frequent adverse events. Detailed reporting of telerehabilitation interventions and adverse event characteristics is recommended for future studies (i.e., use of TIDieR reporting guidelines). Telerehabilitation has the potential to make rehabilitation services more accessible to patients; however, more evidence on the safety of telerehabilitation is needed.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".