‘Weighing’ Losses and Gains: Evaluation of the Healthy Lifestyle Modification After Breast Cancer Pilot Program
Bibliographic record
Abstract
Objectives This pilot study sought to develop and evaluate a novel online group-based intervention (Healthy Lifestyle Modification after Breast Cancer; HLM-ABC) to help breast cancer survivors (BCSs) make healthy lifestyle changes intended to yield not only beneficial physical outcomes (i.e., weight loss, reduced body mass index) but also greater behavioral (e.g., increased physical activity, healthier eating), and psychosocial well-being (e.g., self-efficacy, motivation, body image). Methods An exploratory single-arm, mixed-method triangulation design was employed to evaluate the feasibility and preliminary effectiveness of the HLM-ABC intervention for overweight BCSs. Fourteen women participated in the 10-week intervention and completed quantitative measures of the above-mentioned outcomes at baseline, post-treatment, 6-month, and 12-month follow-up time points. Qualitative data were obtained post-treatment via semi-structured interviews and a treatment satisfaction questionnaire. Results Participants lost an average of 2.83% of their baseline weight (M = 196.65; SD = 38.59) by 1-year follow-up (M = 191.29; SD = 33.91), equal to a small effect size (d = –0.37). Despite achieving only modest weight loss, participants achieved meaningful gains in the form of increased physical activity (d = 0.2), discovery of gratifying movement, more intuitive eating habits (d = 1.12), greater bodily and emotional awareness, and positive shifts in beliefs about being able to make healthy choices regarding food (d = 0.63) and physical activity (d = 0.38). Furthermore, they demonstrated a slight improvement in body image (d = 0.36) and described feeling more self-compassionate, empowered, and acknowledging of variables beyond control (i.e., hormonal therapy, unsatisfactory surgery) that can present barriers to change. Conclusion After completing a 10-week online program, participants achieved meaningful and lasting changes on a number of healthful indicators, even when this did not correspond with a significant reduction in weight. Findings highlight the complex, multifaceted nature of “health” and lend support for promotion of healthier lifestyle following cancer treatment that encompasses not only physical weight, but also behavior, psychosocial well-being, and (often unmodifiable) circumstances such as life-preserving hormonal treatments.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".