Promoting Self-Management in Adults With Type 2 Diabetes: Development of the Impact of Glucose Monitoring on Self-Management Scale
Bibliographic record
Abstract
OBJECTIVES: Type 2 diabetes (T2D) management requires behavioural engagement to achieve optimal outcomes and continuous glucose monitoring (CGM) technologies may facilitate self-management. In this study, we describe the development and validation of a self-report instrument, the Impact of Glucose Monitoring on Self-Management Scale (IGMSS), assessing the impact of device use (primarily CGM but also self-monitored blood glucose [SMBG]) on the capability, motivation, and opportunity to engage in self-management. METHODS: Potential items were generated from 3 sources: themes and quotes from 13 adults with T2D motivated by CGM use who participated in a qualitative study; behaviour change theory identifying capability, opportunity, and motivation to self-manage; and expert committee review of items. An initial pool of 42 items were generated describing CGM as promoting personalized knowledge, improved health (Capability), improved relationships, having positive device characteristics (Opportunity), and improved engagement in self-management (Motivation). Based on expert committee consensus, items were written so as to be completed by those using any glucose-sensing device (SMBG and CGM). Psychometric evaluation was conducted with 514 English-speaking Canadians. Scale reduction (22 final items) was completed using item-response distribution, internal consistency, factor analysis, and expert opinion. Construct and convergent validity were evaluated using the Impact of Glucose Monitoring Satisfaction Scale, the Diabetes Self-Management Questionnaire, the Diabetes Distress Scale, the 5-item World Health Organization Well-Being Index, and the Centre for Epidemiology Depression Scale. Test-retest reliability was determined for 130 participants. RESULTS: Internal consistency was high for all scales, ranging from 0.73 to 0.91. Test-retest reliability ranged from 0.58 to 0.79, except for Device Characteristics. Construct and convergent validity indices were acceptable. There was substantial overlap between the IGMSS and established measures of CGM satisfaction. IGMSS findings were also predictive of self-management behaviour and emotional functioning. CONCLUSIONS: The IGMSS has positive psychometric characteristics and has the potential to screen people with T2D for engagement in diabetes self-management using CGM or any sensing device. Scores can be determined for various aspects of Capability (Personalized Knowledge, Improved Health), Opportunity (Relationships and Device Characteristics), and Motivation.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".