Motivations for Use, User Experience and Quality of Reproductive Health Mobile Applications in a Pre-Menopausal User Base: A Scoping Review
Bibliographic record
Abstract
Background: The global mHealth market is growing at an unprecedented rate and is expected to reach an estimated value of $187.7 billion by 2033, with many apps now addressing women’s health and the menstrual cycle. This scoping review (ScR) aimed to comprehensively assess and describe the existing peer-reviewed literature on motivations for use, user experience, and reproductive health app quality. Methods: The protocol and review were conducted according to the JBI methodology and PRISMA guidelines for scoping reviews. Studies published in English since 2010 were included and searched in MEDLINE, Embase (Ovid platform), Scopus (Elsevier), ACM Digital Library, and IEEE Xplore. Studies were screened independently by two reviewers and the data explored through charting and synthesis. Results: Data were extracted from 58 papers published in English between 2014 and 2023. Several major themes related to motivations for app use, user experience, and app quality were identified and are reported on. Conclusions: Users were motivated to engage in reproductive health apps for education, contraception, and conception. This ScR identified several benefits, such as improving menstrual health literacy. We also identified limitations of current reproductive health apps that adversely affect user experience. Recommendations for future studies include increasing diversity, exploring perspectives of different user groups, and investigating the role healthcare providers may have in app development and patient education.
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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 | 0.029 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".