Content, Behavior Change Techniques, and Quality of Pregnancy Apps in Spain: Systematic Search on App Stores
Bibliographic record
Abstract
BACKGROUND: Women consult information in mobile apps (apps) during pregnancy, and even obstetrics specialists highlight that pregnancy is the ideal moment for the use of apps as consultation sources. However, the high number of apps designed for pregnancy requires a careful assessment to determine their suitability before recommendation. OBJECTIVE: The aim of this study is to identify the apps available in Spanish that can be recommended based on their content, behavior change techniques (BCTs), and quality as a complementary tool during pregnancy. METHODS: A systematic search on app stores to identify apps was performed in the Apple App Store and Google Play with the subject term "pregnancy." The apps meeting the following criteria were chosen: pregnancy-related content, free, and available in Spanish. An app was excluded if it was classified as a game or entertainment and thus lacking an educational or health aim and if it did not target the population under study. The selected apps were downloaded, and their quality was assessed using the Mobile Application Rating Scale (MARS), with the BCTs included evaluated using the BCT taxonomy version 1 and its content. RESULTS: A total of 457 apps were identified, 25 of which were downloaded for assessment (5.6%). The median for objective and subjective quality was 2.94 (IQR 2.71-3.46) and 1.75 (IQR 1.25-2.25), respectively. Regarding content, the median of topics included in the apps was 23 (IQR 16-23), with weight gain, nutrition, fetal development, and physical activity being the most common. The median number of BCTs was 12 (IQR 0.5-3.5). The most frequently identified BCTs in the apps were "Self-Monitoring of Outcomes," followed by "Goal Behavior" and "Instructions." Statistically significant correlations were observed between objective quality and content (ρ=0.624; P=.001), subjective quality and content (ρ=0.638; P=.001), objective quality and BCTs (ρ=0.672; P<.001), subjective quality and BCTs (ρ=0.623; P<.001), and BCTs and content (ρ=0.580; P=.002). CONCLUSIONS: The results of this study suggest that only a small percentage of free pregnancy apps available in Spanish should be recommended. The apps with the best MARS scores were those that addressed a higher number of topics and included a higher number of BCTs. Those with the best content and quality, and a higher number of BCTs included could be recommended by health professionals.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".