Staying in Motion: Using Technology to Support Physical Activity Maintenance in Exercise Oncology
Bibliographic record
Abstract
Given the broad range of physical and psychosocial health benefits of physical activity (PA) for individuals living with cancer, experts recommend regular PA as well as structured exercise (aerobic, strength, flexibility, and balance) to improve overall well-being among this population. However, most individuals living with and beyond cancer remain insufficiently active, struggling to maintain consistent PA habits post-diagnosis. Exercise oncology behavior change interventions have been shown to increase PA post-intervention, yet challenges remain to ensure that participants stay physically active long-term (i.e. PA maintenance: continued PA up to and beyond 6 months after initial PA behavior change), and thereby continue to reap the benefits of PA. Individuals living with and beyond cancer face significant challenges to PA maintenance, including cost, lack of time, lack of equipment or access to facilities, lack of motivation, and lack of support. Those living in rural and remote locations may experience a greater impact of these PA maintenance barriers, and usually lack access to in-person exercise oncology programs, which are primarily delivered in urban settings. Some of these barriers may be addressed via PA behavior change interventions delivered using electronic health technology (eHealth). Despite increased research, few studies have explored the potential of eHealth to support PA maintenance, especially among rural cancer populations who may need greater PA support given their lower PA levels and greater PA barriers. The present PhD project addressed this knowledge gap, developing novel insights to better understand the potential of eHealth to support PA maintenance among individuals living with and beyond cancer. First, the effectiveness of eHealth to support PA behaviors in exercise oncology was systematically reviewed. Next, a survey of exercise oncology program participants explored technology use, literacy, and perceptions on the value of technology to support PA habits. The review and survey were then followed by a participant-oriented tailoring process to customize an existing self-monitoring app for use in a PA maintenance intervention. Finally, the effectiveness of the self-monitoring app to support PA maintenance was tested in a randomized controlled trial, which was evaluated using quantitative (i.e. self-report and objective PA levels) and qualitative (i.e. semi-structured 1-1 interviews) methods. The project contributed new knowledge to better understand the potential value of eHealth to support PA maintenance among individuals living with cancer, especially those in rural and remote locations, and highlighted important next steps to optimize and comprehensively evaluate its positive impact on PA behavior change.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".