Telerehabilitation for functional neurological disorders: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Functional neurological disorder (FND) is a prevalent condition requiring various rehabilitation needs. Telerehabilitation may be a promising solution. This scoping review explored how telerehabilitation is used for FND and identified gaps. METHODS: A systematic search was performed from May 18 to June 3, 2024, using MEDLINE, Embase, Cochrane Database of Systematic Reviews, Cochrane Central, PsycINFO, CINAHL, and clinicaltrials.gov. Ongoing research (protocols and conference abstracts) was included. Citation screening was done for reviews and narrative articles to identify missing articles. Screening and data extraction were conducted in duplicate. Data were analyzed both quantitatively and qualitatively. RESULTS: We identified 180 records, and 26 met inclusion criteria (six were reviews or narratives). We identified 11 completed primary studies (one from citation tracing), seven protocols, and three abstracts. Telerehabilitation was predominantly delivered one-on-one, often using synchronous or hybrid formats. The most common form of therapy was cognitive behavioural therapy. Most completed studies involved a small number of participants, and all were conducted in high-income countries. Identified gaps included disparities in telerehabilitation access and limited completed studies for some subtypes (e.g., functional cognitive and speech disorders) or forms of therapy (e.g., physical therapy, etc.). CONCLUSION: Telerehabilitation as a viable approach to management for some FND patients has been primarily described in the literature in one-on-one and synchronous/hybrid formats, providing insights for future research and clinical practices. Emerging trends explore understudied subtypes, other remotely deliverable therapies, and the use of innovative technologies such as wearable sensors and a Tremor Retrainer smartphone application.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".