Identifying priorities for balance interventions through a participatory co-design approach with end-users
Bibliographic record
Abstract
BACKGROUND: Most individuals living with spinal cord injuries/diseases (SCI/D) or stroke experience at least one fall each year; hence, the development of interventions and technologies that target balance control is needed. The purpose of this study was to identify and explore the priorities for balance-focused interventions and technologies from the perspectives of end-users to assist with the design of an intervention that combines functional electrical stimulation (FES) with visual feedback training for standing balance. METHODS: Two individuals with SCI/D, one individual with stroke, two physical therapists (PT) and one hospital administrator were recruited. Participants attended three focus group meetings that followed a participatory co-design approach. A semi-structured interview guide, developed from the FAME (Feasibility, Appropriateness, Meaningfulness, Effectiveness, Economic Evidence) framework, was used to lead the discussion, querying participants' experiences with balance deficits and interventions, and FES. Meetings were audio-recorded and transcribed verbatim. An iterative and reflexive inductive thematic analysis was applied to the transcripts by three researchers. RESULTS: Four themes were identified: (1) Balance is meaningful for daily life and rehabilitation. Participants acknowledged various factors influencing balance control and how balance deficits interfered with participation in activities. End-users stressed the importance of continuing to work on one's balance after discharge from hospital-based rehabilitation. (2) Desired characteristics of balance interventions. Participants explained that balance interventions should be tailored to an individual's unique needs and goals, relevant to their lives, balance their safety and risk, and be engaging. (3) Prior experiences with FES to inform future therapeutic use. Participants with stroke or SCI/D described initial apprehension with FES, but experienced numerous benefits that motivated them to continue with FES. Challenges with FES were mentioned, including wires, cost, and time of set up. (4) Potential role of FES in balance interventions. Participants felt that FES would complement balance interventions; however, they had not experienced this combination of therapies previously. CONCLUSIONS: End-users described how their experiences with balance deficits, rehabilitation, and FES informed their priorities for balance interventions. The findings inform the design and implementation of future balance interventions for individuals with SCI/D or stroke, including an intervention involving FES and visual feedback training.
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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 | 0.108 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".