Characteristics and Quality of Mobile Apps Containing Prenatal Genetic Testing Information: Systematic App Store Search and Assessment
Bibliographic record
Abstract
BACKGROUND: Prenatal genetic testing is an essential part of routine prenatal care. Yet, obstetricians often lack the time to provide comprehensive prenatal genetic testing education to their patients. Pregnant women lack prenatal genetic testing knowledge, which may hinder informed decision-making during their pregnancies. Due to the rapid growth of technology, mobile apps are a potentially valuable educational tool through which pregnant women can learn about prenatal genetic testing and improve the quality of their communication with obstetricians. The characteristics, quality, and number of available apps containing prenatal genetic testing information are, however, unknown. OBJECTIVE: This study aims to conduct a firstreview to identify, evaluate, and summarize currently available mobile apps that contain prenatal genetic testing information using a systematic approach. METHODS: We searched both the Apple App Store and Google Play for mobile apps containing prenatal genetic testing information. The quality of apps was assessed based on the criteria adopted from two commonly used and validated mobile app scoring systems, including the Mobile Application Rating Scale (MARS) and the APPLICATIONS evaluation criteria. RESULTS: A total of 64 mobile apps were identified. Of these, only 2 apps were developed for a specific prenatal genetic test. All others were either pregnancy-related (61/64, 95%) or genetics-related (1/64, 2%) apps that provided prenatal genetic testing information. The majority of the apps (49/64, 77%) were developed by commercial companies. The mean quality assessment score of the included apps was 13.5 (SD 2.9), which was equal to the average of possible theoretical score. Overall, the main weaknesses of mobile apps in this review included the limited number of prenatal genetic tests mentioned; incomprehensiveness of testing information; unreliable and missing information sources; absence of developmental testing with users (not evidence based); high level of readability; and the lack of visual information, customization, and a text search field. CONCLUSIONS: Our findings suggest that the quality of mobile apps with prenatal genetic testing information must be improved and that pregnant women should be cautious when using these apps for prenatal genetic testing information. Obstetricians should carefully examine mobile apps before referring any of them to their patients for use as an educational tool. Both improving the quality of existing mobile apps, and developing new, evidence-based, high-quality mobile apps targeting all prenatal genetic tests should be the focus of mobile app developers going forward.
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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 | 0.021 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".