Home-Based mHealth Platform (Active-Feet) for Children With Idiopathic Toe Walking: Design, Development, and Acceptability Study
Bibliographic record
Abstract
Background: Physical exercise and stretching programs are the best initial options to treat idiopathic toe walking (ITW). These programs are designed to improve the flexibility and strength of lower limb muscles, enhancing the ankle's range of motion and allowing for a normal gait pattern. In the pediatric population, one of the major limitations reported by therapists is low adherence to rehabilitation treatments or a lack of follow-up. In this context, children using mobile health (mHealth) tools could play an active and central role in their treatment of ITW, while mobile apps could also allow for daily monitoring by health care professionals. Objective: This study aims to design and develop a mHealth platform for individuals with ITW. In addition, a feasibility and acceptance test of a home-based exercise program was conducted using a comprehensive mobile app intended to improve walking in children with ITW. Methods: This study describes the context, content preparation, and mHealth platform design, as well as subsequent evaluation using a self-administered satisfaction questionnaire. Initially, the main features of the Active-Feet platform were discussed, focusing on its primary goal of helping children with ITW adhere to the rehabilitation program. However, this study did not evaluate the platform's effectiveness in improving adherence or ankle range of motion. A set of 3D avatars consisting of animated characters was designed. Posterior muscle chain stretching exercises were selected following the main guidelines. The Active-Feet development process was carried out in 5 stages: requirements specification, platform design, platform implementation, platform deployment, and alpha testing of the app. Results: The final version of the Active-Feet app was evaluated from both the parents' and children's perspectives. Twenty patients and 1 parent per child assessed the app over 2 weeks and answered specially designed questionnaires. Parents rated the app's impact on their child's motivation and its overall effectiveness highly, with median scores of 4 (IQR 4-4). Notably, the item related to reconciling family life with rehabilitation treatment received a median score of 5 (IQR 4-5). Children's responses also indicated positive ratings for motivation and user-friendliness, with a median score of 4 (very good; IQR 3.25-4). Questions about the app's impact, helpfulness in learning, and exercise mirroring received a median score of 3 (good; IQR 3-4). Conclusions: This study describes the development process and testing of Active-Feet, an mHealth-based platform designed to offer treatment for children with ITW. After the long process, an attractive and easy-to-use platform for ITW was developed for the first time.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".