Categorization and Analysis of Primary Care mHealth Apps Related to Breast Health and Breast Cancer: Systematic Search in App Stores and Content Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».