Exploring Breast Cancer Survivors’ Preferences for Messaging-Based Mobile Health Interventions Targeting Sleep and Physical Activity: Insights from Focus Groups and Individual Interviews (Preprint)
Bibliographic record
Abstract
Background: Sleep disturbances and low physical activity are common among breast cancer (BC) survivors and are associated with increased morbidity and mortality. Given the increased access to technological devices and the growing popularity of SMS text messaging-based mobile health interventions, these tools have the potential to both address sleep disturbances and promote physical activity in a scalable and cost-effective way. To understand and make effective use of these tools, it is important to consider the preferences of BC survivors with sleep disturbances, including how SMS text messaging-based mobile health interventions could deliver interventions involving physical activity and sleep hygiene. Objective: The objective of this study was to explore the perspectives and preferences of BC survivors regarding text messaging-based mobile interventions targeting sleep and physical activity. Methods: Three focus groups (n=13 participants) and 3 individual interviews (n=3) were conducted from May 2020 to March 2021 with 16 BC survivors (mean age=59.3, SD 8.9 y) currently experiencing sleep disturbances. The interview questions focused on their experiences with poor sleep and preferences for text messaging-based mobile health interventions. Thematic analysis was applied to the deidentified transcriptions of audio recordings. Results: Three themes were identified: (1) attitudes toward health interventions delivered through text messaging, (2) specific user needs, and (3) technology usage habits and preferences. Most participants reported a positive attitude toward the possibility of using technology to help improve their sleep and increase their physical activity. Most expressed a high level of acceptance toward some technologies, such as text messaging and mobile apps, but not others, such as voice interactions. In terms of desired features, reminders and accountability features, such as meeting physical activity goals, were mentioned most frequently. In addition, incorporating bedtime and relaxation exercise reminders was thought to be helpful. Regarding time and frequency, a daily reminder scheduled for 1 hour before bedtime was found to be acceptable. Conclusions: The insights have been used to guide the development of a messaging-based mobile health intervention for improving sleep and physical activity in BC survivors. Future research will focus on delivering an intervention addressing these health behaviors and assessing its acceptability and effectiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".