Remotely Monitored Gamification and Social Incentives to Improve Glycemic Control Among Adults With Uncontrolled Type 2 Diabetes (iDiabetes): Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes is a significant cause of morbidity and mortality in the United States. Lifestyle modifications including increasing physical activity and losing weight have been demonstrated to improve glycemic control. However, most patients struggle to make these changes. Many stakeholders are interested in using gamification and social incentives to increase engagement in healthy behaviors. However, these approaches often do not appropriately leverage insights from behavioral economics that could be used to address predictable barriers to behavior change. OBJECTIVE: This study aimed to describe the protocol for the Influencing DIabetics to Adapt Behaviors related to Exercise and weighT by Enhancing Social incentives (iDiabetes) trial, which aimed to evaluate the effectiveness of gamification interventions that leverage insights from behavioral economics to enhance supportive, competitive, or collaborative social incentives to improve glycemic control, promote weight loss, and increase physical activity among overweight and obese adults with type 2 diabetes. METHODS: level, and low-density lipoprotein cholesterol level and then repeat these measures at 6 and 12 months. The control arm receives no other interventions. Patients randomized to one of the three intervention arms are entered into a game designed using insights from behavioral economics to enhance supportive, competitive, or collaborative social incentives. To examine predictors of strong or poor performance, participants completed validated questionnaires on a range of areas including their personality, risk preferences, and social network. RESULTS: Enrollment of 361 patients was completed in January 2019. Results are expected in 2020. CONCLUSIONS: The iDiabetes trial represents a scalable model to remotely monitor the daily health behaviors of adults with type 2 diabetes. Results from this trial will help provide insights into how to improve management of patients with type 2 diabetes. TRIAL REGISTRATION: ClinicalTrials.gov NCT02961192; https://clinicaltrials.gov/ct2/show/NCT02961192. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14180.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.069 | 0.010 |
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".