Mobile Phone Apps for the Prevention of Unintended Pregnancy: A Systematic Review and Content Analysis
Notice bibliographique
Résumé
BACKGROUND: Over 50% of pregnancies in the United States are unintended, meaning that the pregnancy is mistimed, unplanned, or unwanted. Unintended pregnancy increases health risks for mother and child, leads to high economic costs for society, and increases social disparities. Mobile phone ownership is rapidly increasing, providing opportunities to reach at-risk populations with reproductive health information and tailored unintended pregnancy prevention interventions through mobile phone apps. However, apps that offer support for unintended pregnancy prevention remain unevaluated. OBJECTIVE: To identify, describe, and evaluate mobile phone apps that purport to help users prevent unintended pregnancy. METHODS: We conducted an extensive search of the Apple iTunes and Android Google Play stores for apps that explicitly included or advertised pregnancy prevention or decision-making support in the context of fertility information/tracking, birth control reminders, contraceptive information, pregnancy decision-making, abortion information or counseling, sexual communication/negotiation, and pregnancy tests. We excluded apps that targeted medical professionals or that cost more than US $1.99. Eligible apps were downloaded and categorized by primary purpose. Data extraction was performed on a minimum of 143 attributes in 3 domains: (1) pregnancy prevention best practices, (2) contraceptive methods and clinical services, and (3) user interface. Apps were assigned points for their inclusion of features overall and for pregnancy prevention best practices and contraceptive information. RESULTS: Our search identified 6805 app descriptions in iTunes and Google Play. Of these, 218 unique apps met inclusion criteria and were included in the review. Apps were grouped into 9 categories: fertility trackers (n=72), centers and resources (n=38), birth control reminders (n=35), general sexual and reproductive health (SRH) information (n=17), SRH information targeted specifically to young adults (YA) (n=16), contraceptive information (n=15), service or condom locators (n=12), pregnancy tests (n=10), and games (n=3). Twelve apps scored at least 50 points (out of 94) for overall number of features and at least 15 points (out of 21) for contraceptive information and pregnancy prevention best practices. Overall, 41% of apps did not mention any modern contraceptive methods and 23% mentioned only 1 method. Of apps that did mention a modern contraceptive method, fewer than 50% of these apps provided information on how to use it. YA SRH apps had the highest percentage of pregnancy prevention best practices in each app. Demographic and interface evaluation found that most apps (72%) did not target any race and only 10% explicitly targeted youth. Communication interface features were present in fewer than 50% of apps. CONCLUSIONS: This review identified several useful, evidence-based apps that support the prevention of unintended pregnancy. However, most apps miss opportunities to provide users with valuable information, interactive decision aids, and evidence-based interventions for unintended pregnancy prevention. Further, some apps in this space may increase the likelihood of unintended pregnancy due to the low effectiveness of the contraceptive methods promoted.
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 | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,007 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,001 | 0,001 |
| 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 ».