636-P: Promoting Engagement in Type 2 Diabetes Self-Management—Development of the Impact of Glucose Monitoring Scale
Bibliographic record
Abstract
Introduction: Type 2 diabetes (T2D) management requires behavioural engagement and continuous glucose monitoring (CGM) technologies may increase self-management motivation. This study developed a patient reported outcome (PRO) measure assessing the impact of CGM on capability, motivation and opportunity to engage in self-management. Items were generated by those with T2D reporting CGM as a “game changer” in self-management, expert review and behaviour change theory. Method: Forty-two patient generated items described CGM as promoting Personalized Knowledge and Improving Health (Capability), Improving Relationships and having positive Device Characteristics (Opportunity) and improving Self-Management (Motivation). English speaking Canadians (N = 514) completed items along with; Glucose Monitoring Satisfaction scale (GMSS, construct validity), Diabetes Self-Management Questionnaire (DSMQ) and Diabetes Distress Scale (predictive validity). Test-retest reliability was determined on 130 participants. Final items (N = 22) were selected based on item response distribution, internal consistency and factor analysis. Results: Internal consistency was high for all scales: Capability (0.91; Personalized Knowledge = 0.85, Improved Health = 0.79), Opportunity (0.72; Improved HCP Relationships = 0.78, Improved Social Relationships = 0.73, Device Characteristics = 0.82), Motivation (0.85). Test-retest reliability ranged from 0.44 - 0.65. Capability, Opportunity and Motivation scales correlated significantly with GMSS total (0.38, 0.69, 0.47), DMSQ Total (0.18, 0.35, 0.22), and Opportunity and Motivation correlated significantly with reduced diabetes distress (−0.23, −0.42). Conclusions: This scale has strong psychometric characteristics and has the potential to screen those with T2D for engagement in diabetes self-management through CGM vis a vis its impact on capability, opportunity and motivation. Disclosure M.Vallis: Advisory Panel; Abbott Diabetes, Bausch Health, Canada, Boehringer-Ingelheim, Consultant; Novo Nordisk, Research Support; Abbott Diabetes, Bausch Health, Canada, Speaker's Bureau; Abbott Diabetes, AbbVie Inc., AstraZeneca, Bausch Health, Canada. L.Berard: Consultant; Abbott Diabetes, Bayer Inc., Medtronic, Roche Diabetes Care, Speaker's Bureau; Dexcom, Inc. E.Cosson: Board Member; Abbott, Medtronic. F.B.Kristensen: Advisory Panel; Abbott Diabetes. F.Levrat-guillen: Employee; Abbott Diabetes. N.Naiditch: None. R.Rabasa-lhoret: Consultant; Dexcom, Inc., Abbott, Janssen Pharmaceuticals, Inc., Novo Nordisk Canada Inc., Sanofi, Lilly, Tandem Diabetes Care, Inc., Insulet Corporation. W.H.Polonsky: Consultant; Abbott Diabetes, Boehringer Ingelheim and Eli Lilly Alliance, Eli Lilly and Company, Insulet Corporation, Intuity Medical, Inc., MannKind Corporation, Provention Bio, Inc., Sanofi-Aventis U.S., Vertex Pharmaceuticals Incorporated. Funding Abbott Diabetes Care
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".