Single-Arm Trial of a Flexible Multicomponent Commercial Digital Weight Management Program
Bibliographic record
Abstract
Background Hunger and food cravings predict poor outcomes in lifestyle interventions for weight management. For this reason, flexible weight management programs, as opposed to restrictive weight management programs, are needed. WW (formerly Weight Watchers)—a widely available, commercial weight management and wellness program—includes an approach that allows participants to obtain a personalized zero-point food (ZPF) list, which includes foods that do not need to be weighed, measured, or tracked. With over 300 potential options, ZPFs can include fruits, vegetables, legumes, whole grains, nonfat dairy, and lean sources of protein. Participants are assigned an individualized daily and weekly point target and can use ZPFs to help budget their points throughout the day, which can nudge participants toward a healthier overall dietary pattern. Objective In a 6-month, single-arm trial, we examined the efficacy of WW when delivered via multimodal digital tools, including a mobile app for assisting with point tracking, weekly virtual workshops, weekly 5-minute wellness check-ins, and a Facebook group in which participants could socialize and support each other. Methods The outcomes included weight change from baseline, as measured by the Bluetooth scales provided to each participant; hunger (visual analogue scale); food cravings (Food Craving Inventory); the intake of fruits and vegetables (The Five Factor Screener); physical activity (Global Physical Activity Questionnaire); and overall well-being (WHO-5 Well-Being Index). Results Of the 153 participants, 70% were female, and 66% were White. Participants’ mean age was 41.09 (SD 13.78) years, and they had a mean BMI of 31.8 (SD 5.0) kg/m2. Retention was high, as 91.5% provided 6-month follow-up data. Participants lost an average of 5.1% of body weight from baseline to 6 months (mean −4.4, SD 4.87 kg; P<.01), with 51% losing clinically significant weight (≥5%). Hunger significantly declined over 6 months (mean percent change −14.74%, SD 64.28%; P<.01), as did food cravings (mean percent change −16.99%, SD 19.98%). The intake of fruits (mean percent change 65.95%, SD 188.78%; P<.01), vegetables (mean percent change 68.29%, SD 172.61%; P<.05), and salad (mean percent change 127.43%, SD 250.82%; P<.001) significantly increased. Engagement in moderate physical activity increased by an average of 32 (SD 133) minutes per day (P<.01), and sedentary time decreased by 90 (SD 24.5) minutes per day (P<.001). Finally, well-being significantly increased (mean change 17.77%, SD 46.21%; P<.01). Conclusions This program, which used a less restrictive method of food tracking and provided personalized ZPFs, resulted in significant weight loss and an increase in fruits, vegetables, and exercise, while also reducing hunger and food cravings. Future research should compare the effectiveness of these approaches to traditional programs that require the self-monitoring of all foods and beverages. Trial Registration ClinicalTrials.gov NCT04302389; https://clinicaltrials.gov/ct2/show/NCT04302389 Conflicts of Interest None declared.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".