Physical Therapists' Use of Functional Electrical Stimulation for Clients With Stroke: Frequency, Barriers, and Facilitators
Bibliographic record
Abstract
BACKGROUND: Best practice guidelines for stroke rehabilitation recommend functional electrical stimulation (FES) to improve gait and upper extremity function. Whether these guidelines have been implemented in practice is unknown. OBJECTIVE: The purposes of this study were: (1) to determine the frequency with which physical therapists use FES to address common therapeutic goals poststroke and (2) to identify the barriers to and facilitators of FES use. DESIGN: This was a cross-sectional, survey study. METHODS: A valid and reliable online survey was sent to Canadian physical therapists. Questions about demographic characteristics, FES use, knowledge of FES literature, and barriers and facilitators were posed. Closed-ended questions were analyzed with descriptive statistics and index scoring to produce summary scores. Pearson or point-biserial correlation coefficients correlated FES use with demographic variables. Open-ended questions about barriers and facilitators were analyzed by 3 researchers using a conventional content analysis. RESULTS: Two hundred ninety-eight physical therapists responded. Use of FES for clients with stroke was low for all therapeutic goals queried (improve walking, arm function, muscle strength and endurance, and sensation; prevent shoulder subluxation; and decrease spasticity). However, 52.6% of the respondents stated that they would like to increase their use of FES. More than 40% of the respondents were unsure of the strength of the evidence supporting FES for stroke care. Physical therapists with postgraduate FES training were more likely to use FES (r=.471, P<.001). A lack of access to resources, such as time, equipment, and training, was the most frequently cited barrier to FES use. LIMITATIONS: As an observational study, cause-and-effect relationships for FES use cannot be identified. CONCLUSIONS: Functional electrical stimulation is not widely used by physical therapists in stroke rehabilitation. Improving access to resources-in particular, continuing education-may facilitate the implementation of FES into clinical practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".