Categorization and Analysis of Primary Care mHealth Apps Related to Breast Health and Breast Cancer: Systematic Search in App Stores and Content Analysis
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most common cause of cancer mortality among women globally. The use of mobile health tools such as apps and games is increasing rapidly, even in low- and middle-income countries, to promote early diagnosis and to manage care and support of survivors and patients. OBJECTIVE: The primary objective of this review was to categorize selected mobile health apps related to breast health and prevention of breast cancer, based on features such as breast self-examination (BSE) training and reminders, and to analyze their current dissemination. An ancillary objective was to highlight the limitations of existing tools and suggest ways to improve them. METHODS: We defined strict inclusion and exclusion criteria, which required apps to have titles or descriptions that suggest that they were designed for the general public, and not for patients with breast cancer or health workers. Apps that focused on awareness and primary care via self-check were included, while those that focused on topics such as alternative treatments and medical news were excluded. Apps that were not specifically related to breast cancer were also excluded. Apps (in any language) that appeared in the search with keywords were included. The database consisted of apps from AppAgg and Google Play Store. Only 85 apps met the inclusion criteria. Selected apps were categorized on the basis of their alleged interactive features. Descriptive statistics were obtained, and available language options, the number of downloads, and the cost of the apps were the main parameters reviewed. RESULTS: The selected apps were categorized on the basis of the following features: education, BSE training, reminders, and recording. Of the 85 selected apps, 72 (84.7%) focused on disseminating breast cancer information. BSE training was provided by only 47% (n=40) of the apps, and very few had reminder (n=26, 30.5%) and recording (n=11, 12.9%) features. The median number of downloads was the highest for apps with recording features (>1000 downloads) than those with education, BSE training, reminder, and recording features (>5000 downloads). Most of these apps (n=74, 83.5%) were monolingual, and around 80.3% (n=49) of these apps were in English. Almost all the apps on Google Play Store were free of charge. CONCLUSIONS: Although there exist several apps on Google Play Store to promote awareness about breast health and cancer, the usefulness of most of them appears debatable. To provide a complete breast health package to the users, such apps must have all of the following features: reminders or notifications and symptom recording and tracking. There is still an urgent need to scientifically evaluate existing apps in the target populations in order to make them more functional and user-friendly.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".