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Record W4408380866 · doi:10.2196/66668

Adapting a Text Messaging Intervention to Improve Diabetes Medication Adherence in a Spanish-Speaking Population: Qualitative Study

2025· article· en· W4408380866 on OpenAlexvenueno aff
Jacqueline A. Seiglie, Seth Tobolsky, Ricaurte Crespo Trevino, Sara J. Cromer, A. Enrique Caballero, Margarita Alegrı́a, J. Jaime Miranda, Deborah J. Wexler, Lindsay S. Mayberry

Bibliographic record

VenueJMIR Human Factors · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsPreprintIntervention (counseling)Text messagingQualitative researchMedication adherenceMedicinePsychologyPopulationComputer scienceInternet privacyWorld Wide WebNursingSociologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Background: Latino adults with type 2 diabetes (T2D) have higher rates of diabetes medication nonadherence than non-Hispanic White adults. REACH (Rapid Encouragement/Education And Communications for Health) is a text message platform based on the information-motivation-behavioral skills model that addresses barriers to adherence and was shown to improve adherence and glycated hemoglobin (HbA1c) levels, but it is only available in English. Objective: This study aimed to report the multiphase, stakeholder-driven adaptation of the REACH barriers to diabetes medication adherence content to a Latino population (REACH-Español). Methods: This was a qualitative study using focus groups. We identified potentially eligible patients (≥18 y old, Latino ethnicity, Spanish-language preference, and T2D diagnosis) using a Mass General Brigham Hospital query. Eligible patients were invited to participate in a focus group conducted in Spanish between April 13 and November 9, 2023. A total of 5 focus groups were conducted. Focus groups 1-3 centered on ranking 40 barriers to diabetes medication adherence (derived from REACH and the extant literature), whereas focus groups 4-5 centered on translation and cultural modifications of the original SMS text message content associated with each of the REACH barriers. Barriers were mapped onto information-motivation-behavioral constructs. We used descriptive statistics to summarize participant characteristics. Focus groups were audio-recorded, professionally transcribed, and analyzed with thematic content analysis using NVivo (Lumivero). Results: In total, 22 participants attended the focus groups. The mean (SD) age was 63.2 (11) years, 55% (n=10/22) were female, and the mean HbA1c level was 8.5%. All participants were born in Latin America or the Caribbean and spoke Spanish as their preferred language, and 54.5% (12/22) had completed middle-school education or less. Among the top 10 ranked barriers, 50% (n=5) corresponded to information, 20% (n=2) to social motivation, 20% (n=2) to behavioral skills, and 10% (n=1) to personal motivation. Personal motivation barriers (medication burden and fear of side effects) and behavioral skills (forgetting to take medication) emerged as important themes in the focus groups. Conclusions: A stakeholder-driven approach to intervention adaptation identified and prioritized relevant barriers to diabetes medication adherence among Latino adults with T2D and facilitated the adaptation of the REACH platform to a Spanish-speaking population.

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)0.011
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.077
GPT teacher head0.519
Teacher spread0.442 · 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 designQualitative
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
Published2025
Admission routes1
Has abstractyes

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