Vibrotactile Feedback Strategies for Trunk-Stabilizing Exercises in a Home-Based Scenario: Qualitative Interview Study Among Physiotherapists
Bibliographic record
Abstract
Background: Physiotherapy treatments frequently incorporate a combination of hands-on, therapist-led exercises and hands-off, home-based exercises, whereby patients perform exercises independently. A key challenge in home-based rehabilitation is providing patients with effective feedback to guide their movements. A range of strategies is being reviewed to address this issue and a starting point for developing effective and implementable strategies to gather feedback requirements from the end users is also being considered. Objective: This study aims to derive requirements for designing a vibrotactile feedback strategy for a wearable device for physiotherapy home exercises. To achieve this, it is essential to consider the principles of feedback design as set forth by the field of instructional psychology and to involve physiotherapists in the process. Methods: The feedback behavior of 9 physical therapists was observed during a staged training scenario. Guided interviews were conducted to explore their recommendations for a vibrotactile feedback system. Observational data and interviews were analyzed using a combined deductive and inductive category system. For each exercise-specific motion pattern, it was recorded whether feedback was provided. Instances of feedback were systematically coded according to feedback modality, timing, and content; haptic feedback was additionally categorized by localization. Interview statements referring to the use of vibrotactile feedback were categorized by modality, task requirements, localization, time, content, frequency, function, and individualization. Quantitative data from observations were evaluated using frequency distributions, and qualitative interview data were analyzed using content structuring content analysis. Results: On average, 50.2% (SD 13.4%) of the observed feedback content provided by each therapist consisted of the presentation of correct exercise execution. A smaller proportion was exclusively confirmatory feedback or the additional provision of elaborated information. Therapists provided feedback in 57.3% (SD 16.3%) of the movement repetitions, on average, while no feedback was given in 42.7% (SD 16.3%) of the repetitions. The interview data supported a reduction in feedback. Regarding feedback timing, 70.8% (SD 13.1%) of the feedback observations were given concurrently with the task across therapists; the proportion of feedback given after the exercise execution was smaller. On average, across therapists, 51.9% (SD 12.1%) of the feedback was auditory, and a further proportion was multimodal with an auditory component. Regarding feedback localization, haptic feedback was often given proximal to the trunk, in the knee and foot region. Frequent combinations of the categories were auditory, simple confirmatory feedback, often given when terminal, or the presentation of the correct exercise execution given auditorily or multimodally concurrent to exercise execution. Conclusions: This study offers insights into the design of a vibrotactile feedback strategy. We identified key feedback characteristics recommended by physical therapists: reduced frequency, differentiated content, multimodal feedback, and trunk-proximal vibrotactile cues. These approaches should be tested in hypotheses-driven research and further patient studies should be conducted.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".