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Record W4220875739 · doi:10.2196/35687

Continuous Glucose Monitoring Data Sharing in Older Adults With Type 1 Diabetes: Pilot Intervention Study

2022· article· en· W4220875739 on OpenAlex
Nancy A. Allen, Michelle L. Litchman, James W. Chamberlain, Ernest Grigorian, Eli Iacob, Cynthia A. Berg

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Diabetes · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesCenter on Aging, University of UtahDexcomUniversity of Utah
KeywordsContinuous glucose monitoringType 1 diabetesMedicineIntervention (counseling)Diabetes mellitusType 2 diabetesData sharingGerontologyNursingEndocrinologyAlternative medicine

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.338
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it