Monitoring physical behavior in pediatric physical therapy: A mixed methods feasibility study to evaluate a newly developed toolkit and training
Bibliographic record
Abstract
INTRODUCTION: Pediatric physical therapists (PPTs) aim to enhance active physical behavior but lack feasible accelerometry devices to assess and evaluate physical activity (PA). We developed an activity monitoring prototype toolkit (AM-p Toolkit) consisting of a wearable, a docking station, a digital tool for data analysis, and physical tools for communication with children and parents. A training for PPTs was also created. We aim to explore the feasibility of the AM-p Toolkit from the perspectives of PPTs, children, and parents and to assess if training improved PPTs' knowledge, skills, and confidence in using the Toolkit. PARTICIPANTS AND METHODS: Using an explanatory sequential mixed methods design, we collected data through questionnaires, individual interviews, and focus groups, guided by Bowen's dimensions of 'acceptability,' 'demand,' and 'practicality.' We included children with the ability to walk, their parents, and their PPTs. The training was evaluated by analyzing PPTs' knowledge, skills, and confidence using the AM-p Toolkit. Quantitative results were analyzed descriptively (mean [SD] and median [interquartile range] when appropriate and qualitative data were analyzed thematically. RESULTS: Fifteen PPTs, 17 parents, and 20 children completed the study. PPTs rated overall satisfaction on a 10-point scale with the AM-p Toolkit at 6.3 (SD 1.2), and parents rated it 7.3 (SD 1.6). The following themes emerged for acceptability, demand, and practicality respectively: for acceptability: 1) expected added value, 2) quality and usability, and 3) design; for demand: 1) use and non-use, 2) further development, and 3) willingness for future use; and for practicality: 1) time constraints and 2) integration. CONCLUSION: The AM-p Toolkit shows promise in PPT, with generally positive acceptability among all end-users. PPTs see potential for certain groups of children who can benefit from the AM-p Toolkit. Practicality requires improvements in the web application and refinement of the strap. Training is important and can be strengthened by emphasizing the analysis of assessment results, clinical reasoning, and functional goal-setting.
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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 | 0.034 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".