Continuous Glucose Monitoring Data Sharing in Older Adults With Type 1 Diabetes: Pilot Intervention Study
Bibliographic record
Abstract
BACKGROUND: Family members or friends (care partners [CPs]) of older adults with type 1 diabetes (T1DM) regularly become part of the diabetes care team, but they often lack knowledge about how to become involved to prevent hypo- and hyperglycemia. Continuous glucose monitoring (CGM) allows a person with diabetes to see their glucose levels continuously and to receive predictive alerts. A smartphone data-sharing app called the Follow app allows the person with diabetes to share continuous glucose numbers with others and to receive predictive alerts of impending hypo- and hyperglycemia. However, there are barriers to sharing this continuous glucose level data with CPs. OBJECTIVE: This study aimed to address the barriers to sharing CGM data. Our objective was to examine the feasibility of using CGM with the Follow app and a data-sharing intervention called SHARE plus in older adults with T1DM and their CPs. SHARE plus includes dyadic communication strategies, problem-solving strategies, and action planning to facilitate CGM data sharing. METHODS: Older adults with T1DM (n=20) and their CPs (n=20) received the SHARE plus intervention at baseline. People with diabetes wore the CGM for 12 weeks while sharing their glucose data using the Follow app with CPs. Feasibility data were analyzed using descriptive statistics. RESULTS: The SHARE plus intervention was feasible and was associated with high self-reported satisfaction for people with diabetes and their CPs as well as high adherence to CGM (mean 96%, SD 6.8%). Broad improvements were shown in the diabetes-related quality of life through the use of CGM in people with diabetes and their CPs. Although the majority of people with diabetes (11/20, 55%) were willing to share hyperglycemia data, several chose not to. The majority of people with diabetes (14/20, 70%) were willing to talk about glucose numbers with a CP. CONCLUSIONS: Older adults with T1DM and their CPs identified having someone else aware of glucose levels and working together with a partner on diabetes self-management as positive aspects of the use of the SHARE plus intervention. Clinicians can use these results to provide data sharing coaching in older adults and their CPs.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".