The CHANGE program: Comparing an interactive coaching approach with a prescriptive lifestyle treatment for obesity on self-efficacy and motivation for health behaviour change
Bibliographic record
Abstract
Obesity is an escalating epidemic and modifiable risk factor for a myriad of chronic conditions. Studies incorporating Motivational Interviewing via Co-Active Life Coaching (MI-via-CALC) have elicited positive results among adults with obesity; however there is a paucity of MI-obesity research that includes sufficient statistical power and a validated comparison group. The purpose of the CHANGE (Coaching towards Healthy Actions Naturally through Goal-related Empowerment) Program was to compare MI-via-CALC to a gold-standard obesity intervention. Undergraduate students were randomized to a 12-week: (a) personalized MI-via-CALC program whereby a certified coach worked with participants to achieve goals through dialogue; or (b) prescriptive education-based lifestyle treatment following the LEARN Program for Weight Management. A parallel group randomized trial design was used to compare task self-efficacy for health behaviour change (i.e., physical activity and nutrition), and relative autonomy/competence for behaving in a healthy way, between the two conditions during the intervention and after a six-month follow-up period. Seventy-eight students with a BMI > 30kg/m2 were enrolled. Those who completed their 12-week intervention and one follow-up assessment were included in the analyses (n = 45). Separate repeated-measures ANOVAs were conducted for each variable. No significant differences were found between groups; however, a significant time effect was observed for task self-efficacy for overcoming barriers to physical activity (p < .05) and healthy eating (p = .01), as well as perceived competence to maintain a healthy body weight (p < .001), with changes occurring primarily during the intervention period (p < .01). It appears that MI-via-CALC compares favourably with LEARN as an obesity treatment in relation to improving task self-efficacy and competence for maintaining a healthy weight. Given the integral role that these variables play regarding the initiation and maintenance of health behaviour change, the unique contributions of each approach should be considered when working with this population.Acknowledgments: This study was funded through the Social Sciences and Humanities Research Council of Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".