Prostate cancer patients' and caregivers' use of behaviour change techniques during a web-based physical activity and self-management program
Bibliographic record
Abstract
Prostate cancer patients and their caregivers can face physical and psychological challenges navigating the diagnosis, treatment, and survivorship stages. To address the needs of the patient-caregiver dyad, TEMPO, a web-based, dyadic, physical activity and psychosocial self-management program was created, with a primary focus on improving quality of life. Behaviour change techniques (BCTs) were implemented as a main strategy to facilitate behaviour changes. Identifying the BCTs that successfully modify behaviour and understanding how BCTs are used may be beneficial for assessing the feasibility of TEMPO and future interventions. The goal of this study was to explore which BCTs were used and the dyads' experiences using BCTs in TEMPO. Seventeen dyads enrolled in TEMPO completed three interviews throughout the program. Interviews were transcribed verbatim and analyzed using deductive and inductive thematic analyses. The deductive analysis was guided by Michie's BCT Taxonomy to identify the BCTs discussed and the inductive analysis identified common experiences using the BCTs. The dyads described learning how to use BCTs from the step-by-step descriptions and examples provided in TEMPO. The dyads commonly referred to BCTs like goal setting, self-monitoring, and reviewing goals to enhance their behaviour changes. The dyads also described the positive outcomes as a result of using BCTs to change behaviour, like increased physical activity and improved communication. The results highlight how the dyads engage with BCTs in a behaviour change intervention. These findings can help enhance the development, design, and delivery of programs by gaining a deeper understanding of how BCTs are used by participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".