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
Bibliographic record
Abstract
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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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".