Combining functional electrical stimulation with visual feedback balance training: a qualitative study of end-user perspectives on designing a clinically feasible intervention
Bibliographic record
Abstract
BACKGROUND: Individuals with stroke or spinal cord injury (SCI) often have poor balance control, leading to falls and activity limitations. One intervention that targets balance control-functional electrical stimulation with visual feedback balance training (FES+VFBT)-may improve balance control but needs modifications for clinical use. OBJECTIVE: To use a participatory design approach to identify potential challenges and solutions for the clinical implementation of FES+VFBT as a balance intervention. DESIGN/METHODS: A descriptive qualitative study involving four semi-structured focus group meetings was conducted to explore the perspectives of individuals with stroke and SCI, physical therapists and a hospital administrator on the feasibility and challenges of implementing FES+VFBT into clinical settings. The interviews were transcribed and analysed using deductive and inductive content analyses. The deductive analysis was based on the social ecological model (SEM) levels, while the inductive approach was used to identify categories and codes. SETTING: Virtual. PARTICIPANTS: Two individuals with chronic SCI and one individual with chronic stroke who were able to stand but reported deficits in their balance control. Two physical therapists who had experience with FES and the rehabilitation of individuals with SCI or stroke. One hospital administrator who worked within a neurological rehabilitation setting. RESULTS: Themes were organised according to the SEM's four levels: intrapersonal, interpersonal, organisational/training environment and society/policy. Identified categories included potential challenges at the intrapersonal level (ie, lack of knowledge, safety and tolerance of user) and organisational/training environment level (ie, technical challenges, cost, physical space and time). The categories also included possible solutions at all SEM levels, such as intrapersonal (ie, reading and education), interpersonal (ie, practising together), organisational/training environment (ie, technology characteristics and creating resources) and society/policy (ie, purchasing options, guidelines and foundation grants). CONCLUSIONS: End-users identified anticipated challenges and solutions to using the FES+VFBT system clinically. The results will inform the design and clinical implementation of a revised version of the system and other FES devices.
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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 | 0.031 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".