Content Analysis of Apps for Growth Monitoring and Growth Hormone Treatment: Systematic Search in the Android App Store
Bibliographic record
Abstract
BACKGROUND: The use of mobile apps for health is growing. This rapid growth in the number of health apps can make it hard to assess their quality and features. The increased demand for and availability of mobile health apps highlights the importance of regular publication of reviews to identify potential areas of unmet needs and concern. The focus of this review is mobile apps for monitoring growth for health care professionals, caregivers, and patients. Monitoring growth as a part of healthy physical development is important across different periods of childhood and adolescence. OBJECTIVE: The goal of this content analysis is to map and understand the types of apps that currently exist that are related to growth monitoring and growth hormone treatment. METHODS: A semiautomated search was undertaken using the app search engine 42Matters, complemented by a manual search for growth apps using the web search tool of Google Play (Android App Store). Apps were rated on their relevance to growth monitoring and categorized by independent raters. RESULTS: In total, 76 apps were rated relevant to growth monitoring or growth hormone treatment. The level of agreement was measured for the semiautomated search and was very high (Κ=0.97). The target audience for 87% of the apps (66/76) was patients and relatives, followed by health care professionals (11%; 8/76) and both (3%; 2/76). Apps in the category "growth tracking tools for children and babies" were retrieved most often (46%; 35/76) followed by "general baby care apps" (32%; 24/76), "nonpharmacological solutions for growth" (12%; 9/76) and "growth hormone-related" (11%; 8/76). Overall, 19/76 apps (25%) tracked a precise location. CONCLUSIONS: This study mapped the type of apps currently available for growth monitoring or growth hormone treatment that can be used as a foundation for more detailed evaluations of app quality. The popularity of care apps for children and growth monitoring apps should provide a great channel for potential intervention in childhood health in the future.
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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 | 0.025 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.040 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".