Motivational Interviewing as an intervention to increase adolescent self-efficacy and promote weight loss: Methodology and design
Bibliographic record
Abstract
BACKGROUND: Childhood obesity is associated with serious physiological and psychological consequences including type 2 diabetes, higher rates of depression and low self-esteem. With the population of overweight and obese youth increasing, appropriate interventions are needed that speak to the issue of readiness to change and motivation to maintain adherence to healthy behavior changes. Motivational Interviewing (MI) is a method of therapy found to resolve ambivalence, enhance intrinsic motivation and promote confidence in a person's ability to make behavior changes. While MI has shown promise in the adult obesity literature as effecting positive lifestyle change, little is known about the effectiveness of MI with overweight and obese youth. This study aims to: 1) demonstrate that MI is an effective intervention for increasing a person's self-efficacy; 2) demonstrate that exposure to MI will facilitate healthy behavior changes; 3) explore psychological changes related to participation in MI and 4) compare physiological and anthropometric outcomes before and after intervention. METHODS/DESIGN: The current investigation is a prospective study conducted with ongoing participants who regularly attend an outpatient pediatric care center for weight-loss. Overweight youth (BMI > 85th %ile) between the ages of 10 and 18 who meet eligibility criteria will be recruited. Participants will be randomly assigned to a control group (social skills training) or a treatment group (MI). Participants will meet with the therapist for approximately 30 minutes prior to seeing the dietician, over the course of 6 months. Participants will also undergo a full day assessment at the beginning and end of psychology intervention to evaluate body fat, and metabolic risk (screening for diabetes, high cholesterol, high blood pressure and fitness level). The paper and pencil portions of the assessments as well as the clinical testing will occur at baseline and at the conclusion of the intervention (6 months) with a repeat assessment 6 months following the completion of the intervention. DISCUSSION: Results from this study are expected to enhance our understanding of the efficacy of MI with children and adolescents who are overweight or obese.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".