A Companion Robot for Children With Asthma: Descriptive Development and Feasibility Pilot Study
Bibliographic record
Abstract
Background: Consistent medication use and proper inhaler technique are essential in pediatric asthma, and young children require supportive tools to maintain these practices. Objective: This study aimed to investigate the caregivers' ability to use a companion robot-assisted app for children with asthma, their attitudes toward the usage, and the characteristics that hinder or facilitate the implementation. Methods: This study employed a descriptive design. The sample group consisted of 30 children with asthma aged 3-6 years who received treatment at an asthma clinic and their caregivers. The companion robot for children with asthma called "Pukkabot," which is an innovation that is developed to teach inhalation techniques and to raise awareness about consistent medication administration through positive reinforcement, was examined. Data collection included personal information questionnaires, the System Usability Scale (SUS) for evaluating usage and overall satisfaction, and interviews to gather attitudes toward apps and characteristics that hinder or facilitate. Data were analyzed with descriptive statistics and content analysis. Results: The study revealed that the scores for usability and overall satisfaction were 80.6, which is above the standard threshold and rated at grade A or an excellent level. Additionally, caregivers stated that the companion robots were easy to use, not complicated, had appropriately sized screens, and demonstrated clear images and sounds. The detailed steps for inhalation were exhibited, and reminders included those of medication times. The children with asthma enjoyed the app and were very interested, making most caregivers want to continue their use. Conclusions: Caregivers were generally satisfied with the usability, finding it easy to use and engaging, which successfully attracted the interest of the children with asthma. Therefore, the companion robot may be used further, with the following recommendations: improving its physical design, adding content, incorporating tracking and symptom assessment systems, and creating a downloadable mobile app for greater accessibility and convenience.
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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 | 0.004 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".