An Innovative, Paradigm-Shifting Lifestyle Intervention to Reduce Glucose Excursions With the Use of Continuous Glucose Monitoring to Educate, Motivate, and Activate Adults With Newly Diagnosed Type 2 Diabetes: Pilot Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes (T2D) is a growing epidemic in the United States, and metabolic control has not been improved over the last 10 years. Glycemic excursion minimization (GEM) is an alternative lifestyle treatment option focused on reducing postnutrient glucose excursions rather than reducing weight. GEM has been proven to be superior to routine care when delivered face to face, and equivalent or superior to conventional weight loss therapy, but it has not been evaluated among patients newly diagnosed with T2D or in a self-administered format. OBJECTIVE: ]) while diminishing or delaying the need for diabetes medications in adults recently diagnosed with T2D. These primary objectives were hypothesized to be achieved by reducing carbohydrate intake and increasing physical activity to diminish CGM glucose excursions, leading to the secondary benefits of an increase in diabetes empowerment and reduced diabetes distress, depressive symptoms, and BMI. METHODS: levels 8.0%, SD 1.6%; 40% female; 33.3% non-White), with the aid of a 4-chapter pocket guide and diary, automated motivational text messaging, and feedback from an activity monitor, along with CGM and supplies for the 6-week intervention and the 3-month follow-up. Treatment was initiated with one telephone call reviewing the use of the technology and 3 days later with a second call reviewing the use of the GEM pocket guide and intervention. RESULTS: levels (-1.8%; P<.001). Participants also experienced significant reductions in high-glycemic-load carbohydrates routinely consumed, CGM readings that were >140 mg/dL, diabetes distress, depressive symptoms, and BMI. Participants felt that use of the CGM was the most significant single element of the intervention. CONCLUSIONS: GEM augmented with CGM feedback may be an effective initial intervention for adults newly diagnosed with T2D. A self-administered version of GEM may provide primary care physicians and patients with a new tool to help people recently diagnosed with T2D achieve remission independent of medication and without weight loss as the primary focus. Future research is needed with a larger and more diverse sample.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".