Effects of Feedback From Self-Monitoring Devices on Lifestyle Changes in Workers with Diabetes: 3-Month Randomized Controlled Pilot Trial
Bibliographic record
Abstract
BACKGROUND: Although lifestyle interventions are useful in the prevention and management of diabetes, they can be expensive and time-consuming. There is some evidence on the effectiveness of automated mobile technology for health self-monitoring; however, few studies have used such devices in the occupational health field. OBJECTIVE: We aimed to examine the effectiveness of a digital self-monitoring device on glucose levels and activity of workers with diabetes in Japan. The primary outcomes were changes in blood glucose levels, and the secondary outcomes were changes in weight and BMI. METHODS: A 2-arm randomized controlled pilot trial was conducted with workers from 23 organizations. The intervention group (n=50) wore an armband activity monitor, a body composition monitor, and a blood pressure monitor for 3 months and received semiautomated weekly email messages tailored to their device data. The control group (n=53) engaged in no self-monitoring. Messages were developed by a physician and a dietician. Postintervention changes in blood glucose levels, weight, and BMI were compared between the intervention and control groups, using blood tests and questionnaires. RESULTS: : intervention group mean 6.4% (SD 0.3%) vs control group mean 6.6% (SD 0.3%); Cohen d=0.7, 95% CI 0.2-1.1; P=.009). There were no significant between-group differences in weight and BMI. CONCLUSIONS: Mobile digital self-monitoring was effective in improving blood glucose levels in workers with diabetes. The use of digital health devices is a cost-effective way of implementing health self-monitoring for large numbers of individuals in the workplace. However, due to the large volume of missing values in this study, we need to be careful in interpreting the results, and well-designed intervention studies need to be conducted. TRIAL REGISTRATION: University Hospital Medical Information Network UMIN000023651; https://upload.umin.ac.jp/cgi-open-bin/icdr/ctr_view_cb.cgi?recptno=R000027244&flwp_key=1008PYbOcXKmk7CAg4Th1FWS.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".