Change in Waist Circumference With Continuous Use of a Smart Belt: An Observational Study
Bibliographic record
Abstract
BACKGROUND: Health insurers and policymakers are trying to prevent and reduce cardiovascular diseases due to obesity. A smart belt that monitors activity and waist circumference is a new concept for conquering obesity and may be a promising new strategy for health insurers and policymakers. OBJECTIVE: This preliminary study evaluated whether the use of a smart belt was associated with a decrease in waist circumference. METHODS: In the manufacturer's database, there were data on a total of 427 men at baseline. A total of 223, 81, and 27 users kept using the smart belt for 4, 8, and 12 weeks, respectively. Paired t tests and repeated measures analysis of variance (ANOVA) were used to identify the change in waist circumference at specified time intervals (at 4, 8, and 12 weeks). In addition, a linear mixed model was used to incorporate all users' waist circumference data at each time point. Preexisting data on waist circumference and self-reported demographics were obtained from the manufacturer of the smart belt (WELT Corporation, South Korea). RESULTS: Compared with baseline, the waist circumference (cm) decreased significantly at all time points: -0.270 for week 4, -0.761 for week 8, and -1.972 for week 12 (all P<.01). Although each paired t test had a different sample size because of loss to follow-up, the differences between baseline and each subsequent week increased. Equal continuous reduction in waist circumference was observed with the ANOVA and mixed model analysis (beta=-0.158 every week). CONCLUSIONS: The smart belt is a newly developed, wearable device that measures real-time steps, sedentary time, and waist circumference. In this study, we showed that wearing the smart belt was associated with reducing waist circumference over 12 weeks. This direct-to-consumer smart health device may contribute toward reducing the risk of obesity and related conditions and controlling increasing health costs for health insurers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".