Bibliographic record
Abstract
A scarcity of apps for high-risk pregnancy detection motivated researchers thoroughly to evaluate the various Android mobile applications to unveil the gaps in accuracy, functionality, and usability.This review provides crucial recommendations for future investigations in pregnancy monitoring app development.The current study systematically assess Android mobile appplications deploying Mobile Application Rating Scale (MARS) for engagement, functionality, aesthetics, and information evaluation.Additionally, app characteristics, pregnancy monitoring features, guidance, and minor pregnancy disorder monitoring were assessed.A total of 1,176 mobile apps are reviewed, with a mere 18 (1.53%)meeting inclusion criteria.17 (94.44%) of these focus on assessing pregnancyrelated concerns, and exceptional MARS grade of 4.29 out of 5. Notably, the app functionality evaluation received the maximum score of 4.72.Furthermore, 7 (27%) track particular pregnancy issues, while 16 (88.88%)apps track tiredness.In addition, 14 applications (77.78%) are dedicated to monitoring minor pregnancy problems.Pregnancy Tracker, Pregnancy App, and Baby Tracker have a commendable 4.7-star rating and provide thorough pregnancy information.Minor disorders are tracked by Mylo Pregnancy, Pregnancy Track, Maternal Care, Pregnancy Calculator, and Ovia Pregnancy.What to Expect, Healofy, Amma Pregnancy, and My Pregnancy outshine with MARS scores above 4.75 to 5, confirming best user experiences.Due to certain security and privacy considerations, the study was limited to evaluate selective applications.This review findings guide future mobile health research in a moving context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".