The Impact of Behavior Change Counseling Delivered via a Digital Health Tool Versus Routine Care Among Adolescents With Obesity: Pilot Randomized Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Youth overweight and obesity is a public health crisis and increases the risk of poor cardiovascular health (CVH) and chronic disease. Health care providers play a key role in weight management, yet few tools exist to support providers in delivering tailored evidence-based behavior change interventions to patients. OBJECTIVE: The goal of this pilot randomized feasibility study was to determine the feasibility of implementing the Patient-Centered Real-Time Intervention (PREVENT) tool in clinical settings, generate implementation data to inform scale-up, and gather preliminary effectiveness data. METHODS: A pilot randomized clinical trial was conducted to examine the feasibility, implementation, and preliminary impact of PREVENT on patient knowledge, motivation, behaviors, and CVH outcomes. The study took place in a multidisciplinary obesity management clinic at a children's hospital within an academic medical center. A total of 36 patients aged 12 to 18 years were randomized to use PREVENT during their routine visit (n=18, 50%) or usual care control (n=18, 50%). PREVENT is a digital health tool designed for use by providers to engage patients in behavior change education and goal setting and provides resources to support change. Patient electronic health record and self-report behavior data were collected at baseline and 3 months after the intervention. Implementation data were collected via PREVENT, direct observation, surveys, and interviews. We conducted quantitative, qualitative, and mixed methods analyses to evaluate pretest-posttest patient changes and implementation data. RESULTS: PREVENT was feasible, acceptable, easy to understand, and helpful to patients. Although not statistically significant, only PREVENT patients increased their motivation to change their behaviors as well as their knowledge of ways to improve heart health and of resources. Compared to the control group, PREVENT patients significantly improved their overall CVH and blood pressure (P<.05). CONCLUSIONS: Digital tools can support the delivery of behavior change counseling in clinical settings to increase knowledge and motivate patients to change their behaviors. An appropriately powered trial is necessary to determine the impact of PREVENT on CVH behaviors and outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT06121193; https://www.clinicaltrials.gov/study/NCT06121193.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".