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Record W2977610195 · doi:10.2196/15258

Participation in a Virtual Diabetes Clinic Improves Glycemic Control in Adults with Type 2 Diabetes

2019· article· en· W2977610195 on OpenAlexvenueno aff
Ronald F. Dixon, Howard Zisser, Nathan A. Barleen, Jennifer E. Layne, Daniel Moloney, Amit R. Majithia, Josh Riff

Bibliographic record

VenueIproceedings · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGlycemicTelehealthType 2 diabetesDiabetes mellitusTelemedicineDiabetes managementmHealthCoachingFamily medicineCertificationPhysical therapyHealth careNursingPsychological interventionPsychology

Abstract

fetched live from OpenAlex

Background Telemedicine for people with type 2 diabetes (T2D) has the potential to positively impact self-management behaviors and improve health outcomes. The Onduo Virtual Diabetes Clinic (VDC) is a comprehensive telehealth program for people with T2D that combines mobile app technology, remote personalized lifestyle coaching from certified diabetes educators and health coaches, connected devices including blood glucose meters and continuous glucose monitoring systems, and clinical support from board certified endocrinologists. Objective To describe the VDC care delivery model and present preliminary data on change in glycemic control in program participants with up to 6 months of follow-up. Methods Adults ≥18 years of age with T2D and who were members of sponsoring health plans and employers throughout the US were eligible to participate. Those who elected to enroll downloaded the VDC app to their smartphone, provided demographic and clinical information, completed an onboarding survey, and were mailed a self-management kit that included a connected blood glucose meter, test strips and a home glycosylated hemoglobin (HbA1c) testing kit. Participants interacted with their care team primarily through the VDC app, with occasional phone calls, and by synchronous video consultations with endocrinologists, as clinically appropriate. Change in glycemic control in participants who completed a baseline survey from February 2018 through December 31, 2018, with an initial HbA1c measurement within 30 days of enrollment and a follow-up measurement between 90 and 180 days after baseline was analyzed. Results Participants (n=740) were (mean ± SD): 53.8 ± 8.8 years of age, 62% female, BMI 35.6 ± 8.5, initial HbA1c 7.7% ± 1.8, 31.0% were on insulin and 25.9% were on sulfonylureas at baseline, and 30.0% lived in a rural area. HbA1c decreased significantly by 2.3% ± 1.9, 0.7% ± 1.0 and 0.2% ± 0.8 across the baseline categories of >9.0%, 8.0% to 9.0% and 7.0% to <8.0%, respectively (all P<.001). Within these categories, HbA1c improved in 91.9%, 77.3% and 63.5% of participants. For the group with an initial HbA1c >9.0%, HbA1c decreased from 10.7% ± 1.4 to 8.3% ± 1.5, and when stratified by HbA1c ≥8.0% the mean decrease in HbA1c was 1.5%, from 9.5% ± 1.5 to 8.0% ± 1.3, with 84.5% of participants demonstrating improvement. Participants with an initial HbA1c <7.0% who were meeting treatment targets at baseline, HbA1c 6.3% ± 0.4, continued to maintain this level of glycemic control at follow-up, HbA1c 6.4% ± 0.6 (ns). Conclusions Participation in the VDC was associated with a significant improvement in HbA1c in adults with T2D who were not meeting treatment targets, with the greatest improvement observed in those with an initial HbA1c >9.0%. Importantly, the majority of program participants experienced an improvement in glycemic control. Our findings suggest that the VDC program is an effective approach to support individuals with T2D and their clinicians in diabetes management between office visits.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.016
GPT teacher head0.363
Teacher spread0.347 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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