Experiences of African American Breast Cancer Survivors Using Digital Scales and Activity Trackers in a Weight Gain Prevention Intervention: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The use of digital tools to promote daily self-weighing and daily activity tracking may be a promising strategy for weight control among African American breast cancer survivors (AABCS). There have been no studies exploring the acceptability and feasibility of using digital tools for weight control or qualitative studies characterizing perceptions of daily self-weighing and daily activity tracking among AABCS. OBJECTIVE: This study aimed to explore the subjective experiences of daily self-weighing and daily activity tracking using digital tools, including wireless scales and activity trackers, in a sample of AABCS participating in two technology-based weight gain prevention interventions over 6 months. METHODS: Semistructured interviews (N=21) were conducted in person or over the phone, were audio recorded, and then transcribed verbatim. Each transcript was read to identify key themes and develop a codebook. Each transcript was coded using Atlas.ti software, and code outputs were used to identify overarching themes and patterns in the data. RESULTS: , SD 5.9), and weighed on 123.4 (SD 48.0) days out of the 168 days (73.5%) in the study period. Women tended to attribute their weight gain to cancer treatment and framed program benefits in terms of improved quality of life and perceptions of prolonging their survival following treatment. Using the smart scale for daily self-weighing was viewed as the tool by which participants could control their weight and improve their health and well-being posttreatment. The activity tracker increased awareness of physical activity and motivated participants to be more active. CONCLUSIONS: Participants reported positive experiences and benefits from daily self-weighing and daily activity tracking. Findings suggest that daily self-weighing and daily activity tracking using digital tools are well-received, acceptable, and feasible intervention strategies for AABCS in the context of posttreatment weight management.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".