Experiences of a Digital Behavior Change Intervention to Prevent Weight Gain and Promote Risk-Reducing Health Behaviors for Women Aged 18 to 35 Years at Increased Risk of Breast Cancer: Qualitative Interview Study
Notice bibliographique
Résumé
BACKGROUND: Breast cancer is the most common form of cancer in women. Adult weight gain and modifiable health behaviors, including smoking, alcohol intake, and lack of physical activity, are well-known risk factors. Most weight gain in women occurs between the ages of 18 and 35 years. Digital interventions have the potential to address logistical challenges that arise in reaching women in this age range. We designed a digital intervention targeting weight gain prevention and other modifiable health behaviors for young women at increased risk of breast cancer. Women aged 18 to 35 years were recruited to this single-arm intervention study over 2 months to test the acceptability and usability of the intervention, which comprised a group welcome event held via videoconferencing, app, and private Facebook group. OBJECTIVE: This nested qualitative substudy explored women's views and experiences of being part of the digital health intervention to inform future intervention development for a feasibility study. METHODS: A total of 20 women aged 23 to 35 years who were at increased risk of breast cancer were interviewed via telephone within 1 month after completing the intervention, between February 2023 and March 2023. The women were asked about their experiences of the digital intervention and the extent to which it may have influenced their health behaviors. Data were analyzed thematically and organized using the framework approach. RESULTS: The interviews lasted for a median of 37 (IQR 30-46) minutes. Overall, the women perceived the digital health intervention comprising education, tracking, and support to be acceptable for weight gain prevention. In total, 4 themes were generated. A "missed opportunity" in breast cancer prevention services encompasses the lack of services that currently exist for young women at increased risk of breast cancer. The pros and cons of being part of a community encompasses the divergent views that the women had regarding engaging with other women at increased risk. The importance of an interactive app focuses on features that the women would want from the app to promote engagement with the intervention. The different wants and needs of different age groups highlights that an intervention such as this one would need to be customizable to suit the needs of women at different life stages. CONCLUSIONS: There is an unmet need in prevention services for young women aged 18 to 35 years at increased risk of breast cancer. The women perceived the app to be an acceptable intervention for weight gain prevention but emphasized that the intervention would need to be customizable to meet the needs of different age groups within the group of women aged 18 to 35 years. The digital intervention could be a scalable behavior change strategy for UK family history clinics.
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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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 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.
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