Improving Diet Quality of People Living With Obesity by Building Effective Dietetic Service Delivery Using Technology in a Primary Health Care Setting: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Almost a third of Australian adults are living with obesity, yet most cannot access medical nutrition therapy from dietitians, that is, the health professionals trained in dietary weight management services. Across the health system, primary care doctors readily identify people who may benefit from weight management services, but there are limited referral options in the community. Dietitians are trained to provide evidence-informed dietary treatment of overweight and obesity but are underutilized and underresourced. The chat2 (Connecting Health and Technology 2) trial will test combining new technologies for dietary assessment with behavior change techniques to improve outcomes for people living with obesity. OBJECTIVE: This study aimed to compare the effectiveness of a 1-year digital dietary intervention, with standard care on body weight reduction and improved diet quality, in adults living with obesity delivered by dietitians in a primary care setting. METHODS: ). Participants will be recruited by letters sent to individuals randomly selected from the electoral roll and supplemented by hospital site posters, newsletters, and unaddressed mailbox delivery postcards sent to residential street points. The primary outcome is change in body weight, measured face-to-face at a baseline, 6 months, and 12 months. A 4-day, image-based dietary assessment tool (mobile Food Record) will be used to measure diet quality score. Secondary outcomes include diet quality score; dual-energy absorptiometry body composition; and total cholesterol, triglyceride, low-density lipoprotein, high-density lipoprotein, glycated hemoglobin, and fasting glucose levels. The intervention group will receive 8 video counseling sessions with a trained dietitian delivered over 12 months to support dietary behavior change and relapse prevention. The trial is unblinded. Both groups will receive feedback on their clinical chemistry and dual-energy absorptiometry scans at each time point. RESULTS: Participant recruitment commenced in July 2023 and ended in August 2024. Data analysis will commence in 2025, with the anticipated publication of results in 2026. CONCLUSIONS: If found to be effective, the results of this randomized controlled trial will support the delivery of effective, evidence-based weight management advice using new technologies. Improving community access to high-quality dietetic services will ensure more effective use of the dietetic workforce to improve outcomes for people living with obesity. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12622000803796; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=383838. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64735.
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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 | 0.040 | 0.033 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.082 | 0.013 |
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".