A Mobile App, KhunLook, to Support Thai Parents and Caregivers With Child Health Supervision: Development, Validation, and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: In Thailand, children born in government hospitals receive a maternal and child health handbook (MCHH). However, when a new MCHH edition is released, those with the previous editions do not have access to the updated information. A mobile app is an appealing platform to fill this gap. We developed a mobile app called "KhunLook" as an interactive electronic MCHH intended to assist parents in child health supervision. OBJECTIVE: This study describes the user requirements and development of the KhunLook mobile app, validity of parents' growth assessments, and parents' evaluation of feasibility and acceptability of the app. METHODS: Phase 1 was a qualitative study using individual interviews. The interview data were used to revise the prototype. In phase 2, parents were randomly assigned to assess their children's growth with the app or the MCHH. The outcomes were compared to those of the physician's assessment, and congruence was determined. In phase 3, parents evaluated the feasibility and acceptability of the app in comparison to the MCHH through a web-based survey. RESULTS: Four health care providers and 8 parents participated in phase 1. Two themes were identified: (1) the mobile app potentially counters parents' infrequent use of the MCHH with accuracy, attractiveness, convenience, and simplicity, and (2) health supervision needs to be standard, up-to-date, and understandable. KhunLook was publicly launched with a family page and 7 key features: growth and nutrition, development, immunizations, oral health, reminders for the next appointment, memories, and health advice. In phase 2, 56 parents participated in the growth parameter assessments; 34 were in the App group and 22 in the MCHH group. The outcomes of the growth parameter assessments between parents and physicians in both the App and MCHH groups were not significantly different. The congruence proportions were higher in the App group for weight and head circumference, but the differences were not statistically significant. In phase 3, 356 parents from all over Thailand participated in a web-based survey. Parents rated the app feasibility as "very easy to easy" to use at higher proportions than the MCHH in all health assessment domains (growth, development, and immunizations) and ease-of-use domains with statistical significance (P<.001). The KhunLook app received a significantly higher mean score (8.59/10) than the MCHH (7.6/10) (P<.001). Most parents (317/356, 89.0%) preferred the app over MCHH. Further, 93.5% (333/356) of the parents stated that they would continue to use the app and 96.9% (345/356) would recommend others to use it. CONCLUSIONS: KhunLook, a Thai mobile app for child health supervision, was developed, validated for growth assessments, and was well accepted for ease-of-use by parents. Further studies should be conducted with a large scale of users, and the impact of this app on health behaviors and health outcomes must be evaluated.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".