Real‐world data of a digitally enabled, time‐restricted eating weight management program in public sector workers living with overweight and obesity in the United Kingdom: A service evaluation of the Roczen program
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Introduction The health of the United Kingdom workforce is key; approximately 186 million days are lost to sickness each year. Obesity and type 2 diabetes (T2D) remain major global health challenges. The aim of this retrospective service evaluation was to assess the impact of a digitally enabled, time‐restricted eating (TRE) intervention (Roczen Program, Reset Health Ltd) on weight and other health‐related outcomes. Methods This service evaluation was conducted in people living with overweight/obesity, with 89% referred from public sector employers. Participants were placed on a TRE, low‐carbohydrate, moderate protein plan delivered by clinicians and mentors with regular follow up, dietary guidance, goal setting, feedback, and social support. Results A total of 660 members enrolled and retention was 41% at 12 months. The majority were female (73.2%), 58.9% were of White ethnicity, with a mean (SD) age of 47.5 years (10.1), and a body mass index of 35.0 kg/m 2 (5.7). Data were available for 82 members at 12‐month. At 12‐month, members mean actual and percentage weight loss was −9.0 kg (7.0; p < 0.001) and −9.2% (6.7, p < 0.001) respectively and waist circumference reduced by −10.3 cm (10.7 p < 0.001), with 45.1% of members achieving ≥10% weight loss. Glycated hemoglobin was significantly improved at 6 months in people living with T2D (−11 mmol/mol [5.7] p = 0.012). Binge eating score significantly reduced (−4.4 [7.0] p = 0.006), despite cognitive restraint increasing (0.37 [0.6] p = 0.006). Conclusion Our service evaluation showed that the Roczen program led to clinically meaningful improvements in body weight, health‐related outcomes and eating behaviors that were sustained at 12‐month.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it