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Record W4412621376 · doi:10.2196/71861

Culturally Tailored Diabetes Self-Management Education and Support Programs in Black African and Caribbean Adults With Type 2 Diabetes (HEAL-D): Protocol for a Multicenter, Pragmatic Randomized Controlled Trial

2025· article· en· W4412621376 on OpenAlexvenueno aff
Louise M. Goff, Vicky Bell, Susan A. Blyden, Peter Bower, Jeremy Dale, Tess Harris, Andrew Healey, Eleanor Hoverd, Huajie Jin, Tony Kelly, Carol Rivas, Paul H. Robinson, Jayne Thorpe, S. Tomlinson, Michael Ussher, Charlotte Wahlich, Barbara McGowan

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsDiabetes mellitusMedicineRandomized controlled trialSelf-managementProtocol (science)GerontologyPsychologyAlternative medicinePhysical therapySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: People of Black African and Black Caribbean ethnicity experience higher rates and poorer outcomes of type 2 diabetes (T2D) than people of White European ethnicity; these inequalities are compounded by poor healthcare access. Cultural tailoring of diabetes self-management education and support (DSMES) programs has the potential to improve healthcare engagement and clinical outcomes for ethnic minority groups. Healthy Eating & Active Lifestyles for Diabetes (HEAL-D) is a co-designed, culturally tailored group-based DSMES program for adults of Black African and Black Caribbean ethnicity. OBJECTIVE: This trial aims to evaluate the clinical and cost effectiveness of the HEAL-D intervention, compared to standard DSMES programs, in Black African and Black Caribbean adults living with T2D. METHODS: , blood lipids, anthropometric outcomes, blood pressure, physical activity, and patient-reported outcome measures relating to psychological well-being and self-management support, lifestyle behaviors, and health economics will be collected at baseline and follow-up visits (6, 12, and 24 months). Cost-effectiveness will be assessed through a cost-utility analysis conducted from a health and social care perspective. A mixed methods process evaluation will provide a formative evaluation of delivery, intervention fidelity, and implementation of HEAL-D, and an embedded study within a project will assess the impact of multiple long-term conditions on uptake of, and engagement with HEAL-D, and the impact of HEAL-D on multiple long-term conditions. The trial received Research Authority and Research Ethics Council approval on April 22, 2024. RESULTS: Funding began in August 2023. Site "green light" was received on August 15, 2024, for London; November 29, 2024, for Manchester; and January 31, 2025, for the West Midlands. Recruitment commenced in August 2024 and is due to run for 11 months. As of March 26, 2025, a total of 76 participants have consented. Last patient, last visit is expected in June 2027; primary data analysis is expected to begin in July 2027. Final results are anticipated to be available in September 2027, and publication is expected by the end of 2027. CONCLUSIONS: The HEAL-D trial will address whether a culturally tailored DSMES program, provided in-person or via videoconferencing, is clinically and cost-effective compared to standard DSMES at improving diabetes management in Black African and Black Caribbean adults. If effective, this would provide an evidence-based model of equitable DSMES services and improve the implementation of healthcare programs for ethnic minority groups. TRIAL REGISTRATION: ISRCTN 1434448; https://www.isrctn.com/ISRCTN14344948. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/71861.

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)0.039
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.076
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.028
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0120.007
Bibliometrics0.0030.004
Science and technology studies0.0050.004
Scholarly communication0.0050.004
Open science0.0040.003
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0760.011

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 head0.042
GPT teacher head0.434
Teacher spread0.392 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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