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Record W4401627469 · doi:10.2196/55473

Impact of a Text Messaging Intervention as an In-Between Support to Diabetes Group Visits in Federally Qualified Health Centers: Cluster Randomized Controlled Study

2024· article· en· W4401627469 on OpenAlexvenueno aff
Allie Yan, Erin M. Staab, Daisy Nuñez, Mengqi Zhu, Wen Wan, Cynthia T. Schaefer, Amanda Campbell, Michael S. Quinn, Arshiya A. Baig

Bibliographic record

VenueJMIR Diabetes · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesOffice of Diversity and InclusionPritzker School of MedicineU.S. Department of Health and Human ServicesNational Institutes of HealthChicago Center for Diabetes Translation ResearchGeorgia Clinical and Translational Science AllianceDirectorate for Biological Sciences
KeywordsPreprintText messagingCluster (spacecraft)Intervention (counseling)Randomized controlled trialControl (management)MedicineFamily medicineGerontologyPsychologyNursingComputer scienceInternet privacyWorld Wide Web

Abstract

fetched live from OpenAlex

Background: In the United States, 1 in 11 people receive primary care from a federally qualified health center (FQHC). Text messaging interventions (TMIs) are accessible ways to deliver health information, engage patients, and improve health outcomes in the health center setting. Objective: We aimed to evaluate the impact of a TMI implemented with a group visit (GV) intervention among patients with type 2 diabetes mellitus (T2DM) at FQHCs on patient-reported outcomes and clinical outcomes based on patient TMI engagement. Methods: A TMI was implemented for 11 health centers participating in a cluster randomized study of diabetes GVs in Midwestern FQHCs targeting adults with T2DM. FQHC patients participated in 6 monthly GVs either in person or online and a concurrent 25-week TMI. Outcome measures included clinical markers such as glycated hemoglobin A1c and patient-reported diabetes distress, diabetes self-care, diabetes self-efficacy, diabetes care knowledge, diabetes quality of life, diabetes social support, and TMI use and satisfaction. TMI response rate was calculated as responses to an SMS text message requesting a response divided by total messages requesting a response sent. Patients were grouped as high responders if their response rate was greater than or equal to the median response rate and low responders if their response rate was below the median. We conducted linear mixed models to compare high and low responders and within a group, adjusting for age, gender, GV attendance, and depression/anxiety at baseline. Results: In total, 101 of 124 GV patients (81.5%) enrolled in the TMI. The average age of the population in the TMI was 53 years. Of the 101 respondents, 61 (60%) were racial or ethnic minorities, while 42 of 82 respondents (51%) had a high school diploma/General Education Development or less, and 56 of 80 respondents (71%) reported an annual income less than US $30,000. In addition, 70 of 81 respondents (86%) owned a smartphone and 74 of 80 respondents (93%) had an unlimited texting plan. The median response rate was 41% and the mean response rate was 41.6%. Adjusted models showed significantly improved diabetes knowledge (P<.001), foot care (P<.001), and exercise (P=.002) in high responders (n=34) compared to low responders (n=23) at 6 months. No group difference was found in glycated hemoglobin A1c. Within high responders, diabetes distress (P=.001), social support (P<.001), quality of life (P<.001), diabetes knowledge (P<.001), foot care (P<.001), and diet (P=.003) improved from baseline to 6 months. Low responders only improved in diabetes quality of life (P=.003) from baseline to 6 months. Conclusions: In a FQHC safety net population participating in a combined TMI and GV intervention, our study showed improved diabetes distress, social support, knowledge, self-care, self-efficacy, and quality of life among patients highly engaged in the SMS text messaging program.

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.468
Teacher spread0.439 · 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 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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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