Evaluating the impact of movement tracking feedback on engagement with home exercise programmes of children with cerebral palsy using a new therapy app: a protocol for a mixed-methods single-case experimental design with alternating treatments
Bibliographic record
Abstract
INTRODUCTION: Children with cerebral palsy (CP) are prescribed home exercise programmes (HEPs) to increase the frequency of movement practice, yet adherence to HEPs can be low. This paper outlines the protocol for a single-case experimental design (SCED) with alternating treatments, using a new home therapy exercise application, Bootle Boot Camp (BBCamp), offered with and without movement tracking feedback. This study will explore the impact of feedback on engagement, movement quality, lower limb function and family experiences to help understand how technology-supported HEPs should be translated and the added value, if any, of movement tracking technology. METHODS AND ANALYSIS: In this explanatory sequential mixed-methods study using a SCED, 16 children with CP (aged 6-12 years, Gross Motor Function Classification System levels I-II) will set lower limb goals and be prescribed an individualised HEP by their physiotherapist to complete using BBCamp on their home television equipped with a three-dimensional camera-computer system. Children will complete four weekly exercise sessions over 6 weeks. Children will be randomised to 1 of 16 alternating treatment schedules where BBCamp will provide or withhold feedback during the first 4 weeks. The version of BBCamp that results in the most therapeutic benefit will be continued for 2 final weeks. Goals will be re-evaluated and families interviewed. The primary outcome is adherence (proportion of prescribed exercise repetitions attempted) as a measure of behavioural engagement. Secondary outcomes are affective and cognitive engagement (smiley face ratings), exercise fidelity, lower limb function, goal achievement and participant experiences. SCED data will be analysed using visual and statistical methods. Quantitative and qualitative data will be integrated using joint displays. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Research Ethics Boards at Bloorview Research Institute and the University of Toronto. Results will be distributed through peer-reviewed journals and scientific conferences. TRIAL REGISTRATION NUMBER: NCT05998239; pre-results.
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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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".