“So positive his energy lifted me, even over Zoom”: Participant perspectives of a randomized pilot trial comparing physical activity prescription with activity coaching
Bibliographic record
Abstract
Physical activity (PA) can prevent and treat chronic disease, improve mental health, and enhance quality of life. Yet, inactivity remains a serious public health issue. Prescription To Get Active (RxTGA) is a community-based organization that partners with healthcare providers and the fitness industry to bridge this gap through tailored PA prescriptions. A pandemic-related enrollment decline prompted RxTGA to explore adjunct strategies to boost prescription uptake. Motivational Interviewing via Co-Active Life Coaching (MI) is a validated approach for eliciting health behaviour change; it was hypothesized that integrating MI with RxTGA would prove beneficial for enhancing PA engagement. The study purpose was to examine the effectiveness of RxTGA versus RxTGA plus activity coaching using MI on PA adherence and health indices among adult prescription recipients over a 12-week program. Study involvement experiences were captured qualitatively through open survey questions administered online. Activity coaches were volunteer health and fitness professionals trained for the study. MI participants received 6 weekly, then 3 bi-weekly 30–45-minute calls via phone or online. Overall, 269 adults expressed interest; 77 were randomized to the traditional or activity coaching group. Following study completion, traditional RxTGA participants reported heightened awareness regarding PA importance but were disappointed with the absence of personal support. MI participants expressed appreciation for the connection, motivation, and non-judgement provided by the activity coaches. Considering Canada’s low PA rates and need for wide-reaching supportive interventions, this remote RxTGA coaching-based approach is promising. Findings will be useful for those interested in PA prescription and engagement strategies.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".