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Record W4360774748 · doi:10.2196/40359

Learning in a Virtual Environment to Improve Type 2 Diabetes Outcomes: Randomized Controlled Trial

2023· article· en· W4360774748 on OpenAlexvenueno aff
Constance Johnson, Gail D’Eramo Melkus, Louise Reagan, Wei Pan, Sathya Amarasekara, Katherine Pereira, Nancy Hassell, Sarah Nowlin, Allison Vorderstrasse

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthNational Heart, Lung, and Blood InstituteGeorgia Clinical and Translational Science Alliance
KeywordsRandomized controlled trialDescriptive statisticsMedicineReferralDemographicsIntervention (counseling)Type 2 diabetesPhysical therapyDiabetes mellitusGerontologyFamily medicineDemographyNursing

Abstract

fetched live from OpenAlex

Background Given the importance of self-management in type 2 diabetes mellitus (T2DM), a major aspect of health is providing diabetes self-management education and support. Known barriers include access, availability, and the lack of follow through on referral to education programs. Virtual education and support have increased in use over the last few years. Objective The purpose of the Diabetes Learning in a Virtual Environment (LIVE) study was to compare the effects of the LIVE intervention (educational 3D world) to a diabetes self-management education and support control website on diet and physical activity behaviors and behavioral and metabolic outcomes in adults with T2DM over 12 months. Methods The LIVE study was a 52-week multisite randomized controlled trial with longitudinal repeated measures. Participants were randomized to LIVE (n=102) or a control website (n=109). Both contained the same educational materials, but the virtual environment was synchronous and interactive, whereas the control was a flat website. Data were collected at baseline and 3, 6, and 12 months using surveys and clinical, laboratory, and Fitbit measures. Descriptive statistics included baseline characteristics and demographics. The effects of the intervention were initially examined by comparing the means and SDs of the outcomes across the 4 time points between study arms, followed by multilevel modeling on trajectories of the outcomes over the 12 months. Results This trial included 211 participants who consented. The mean age was 58.85 (SD 10.1) years, and a majority were White (127/211, 60.2%), non-Hispanic (198/211, 93.8%), married (107/190, 56.3%), and female (125/211, 59.2%). Mean hemoglobin A1c (HbA1c) level at baseline was 7.64% (SD 1.79%) and mean BMI was 33.51 (SD 7.25). We examined weight loss status versus randomized group, where data with no weight change were eliminated, and the LIVE group experienced significantly more weight loss than the control group (P=.04). There were no significant differences between groups in changes in physical activity and dietary outcomes (all P>.05), but each group showed an increase in physical activity. Both groups experienced a decrease in mean HbA1c level, systolic and diastolic blood pressure, cholesterol, and triglycerides over the course of 12 months of study participation, including those participants whose baseline HbA1c level was 8.6% or higher. Conclusions This study confirmed that there were minor positive changes on glycemic targets in both groups over the 12-month study period; however, the majority of the participants began with optimal HbA1c levels. We did find clinically relevant metabolic changes in those who began with an HbA1c level >8.6% in both groups. This study provided a variety of resources to our participants in both study groups, and we conclude that a toolkit with a variety of services would be helpful to improving self-care in the future for persons with T2DM. Trial Registration ClinicalTrials.gov NCT02040038; https://clinicaltrials.gov/ct2/show/NCT02040038

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 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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.035
GPT teacher head0.388
Teacher spread0.352 · 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 teacher head, not a consensus.

Study designRandomized trial
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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

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