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Record W3197453681 · doi:10.2196/31689

Disability-Inclusive Diabetes Self-management Telehealth Program: Protocol for a Pilot and Feasibility Study

2021· article· en· W3197453681 on OpenAlexvenueno aff
Eric J. Evans, Ayse Zengul, Allyson G. Hall, Haiyan Qu, Amanda L. Willig, Andrea Cherrington, Mohanraj Thirumalai

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
FundersNational Institute on Disability, Independent Living, and Rehabilitation ResearchNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsTelehealthCoachingMedicinePsychosocialSelf-managementDiabetes managementGlycemicIntervention (counseling)Glycated hemoglobinRandomized controlled trialPhysical therapyType 2 diabetesTelemedicineHealth carePsychologyNursingDiabetes mellitusComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals with disabilities and type 2 diabetes require self-management programs that are accessible, sustainable, inclusive, and adaptable. Health coaching has been shown to be an effective approach for improving behavioral changes in self-management. Health coaching combined with telehealth technology has the potential to improve the overall quality of and access to health services. OBJECTIVE: This protocol outlines the study design for implementing the Artificial Intelligence for Diabetes Management (AI4DM) intervention. The protocol will assess the feasibility, acceptability, and preliminary efficacy of the AI4DM telehealth platform for people with disabilities. METHODS: The AI4DM study is a 2-arm randomized controlled trial for evaluating the delivery of a 12-month intervention, which will involve telecoaching, diabetes educational content, and technology access, to 90 individuals with diabetes and physical disabilities. The hypothesis is that this pilot project is feasible and acceptable for adults with permanently impaired mobility and type 2 diabetes. We also hypothesize that adults in the AI4DM intervention groups will have significantly better glycemic control (glycated hemoglobin) and psychosocial and psychological measures than the attention control group at the 3-, 6-, and 12-month follow-ups. RESULTS: The AI4DM study was approved by the university's institutional review board, and recruitment and enrollment will begin in October 2021. CONCLUSIONS: The AI4DM study will improve our understanding of the feasibility and efficacy of a web-based diabetes self-management program for people with disabilities. The AI4DM intervention has the potential to become a scalable and novel method for successfully managing type 2 diabetes in people with disabilities. TRIAL REGISTRATION: ClinicalTrials.gov NCT04927377; https://clinicaltrials.gov/ct2/show/NCT04927377. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31689.

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)0.037
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.082
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.020
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0820.016

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 head0.291
GPT teacher head0.599
Teacher spread0.308 · 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 designNon-randomized trial
Domainnot available
GenreProtocol

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

Citations7
Published2021
Admission routes1
Has abstractyes

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