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Record W3033302379 · doi:10.2196/17534

User Engagement Among Diverse Adults in a 12-Month Text Message–Delivered Diabetes Support Intervention: Results from a Randomized Controlled Trial

2020· article· en· W3033302379 on OpenAlexvenueno aff
Lyndsay A. Nelson, Andrew J. Spieker, Robert A. Greevy, Lauren M. LeStourgeon, Kenneth A. Wallston, Lindsay S. Mayberry

Bibliographic record

VenueJMIR mhealth and uhealth · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institutes of HealthVanderbilt University
KeywordsRandomized controlled trialText messageText messagingIntervention (counseling)MedicineDiabetes mellitusPsychologyComputer scienceWorld Wide WebNursingInternal medicineComputer network

Abstract

fetched live from OpenAlex

BACKGROUND: Text message-delivered interventions are a feasible and scalable approach for improving chronic disease self-care and reducing health disparities; however, information on long-term user engagement with these interventions is limited. OBJECTIVE: The aim of this study is to examine user engagement in a 12-month text message-delivered intervention supporting diabetes self-care, called REACH (Rapid Education/Encouragement And Communications for Health), among racially and socioeconomically diverse patients with type 2 diabetes (T2D). We explored time trends in engagement, associations between patient characteristics and engagement, and whether the addition of a human component or allowing patients to change their text frequency affected engagement. Qualitative data informed patients' subjective experience of their engagement. METHODS: We recruited patients with T2D for a randomized trial evaluating mobile phone support relative to enhanced treatment as usual. This analysis was limited to participants assigned to the intervention. Participants completed a survey and hemoglobin A1c (HbA1c) test and received REACH text messages, including self-care promotion texts, interactive texts asking about medication adherence, and adherence feedback texts. For the first 6 months, texts were sent daily, and half of the participants also received monthly phone coaching. After 6 months, coaching stopped, and participants had the option to receive fewer texts for the subsequent 6 months. We defined engagement via responses to the interactive texts and responses to a follow-up interview. We used regression models to analyze associations with response rate and thematic and structural analysis to understand participants' reasons for responding to the texts and their preferred text frequency. RESULTS: The participants were, on average, aged 55.8 (SD 9.8) years, 55.2% (137/248) female, and 52.0% (129/248) non-White; 40.7% (101/248) had ≤ a high school education, and 40.7% (101/248) had an annual household income <US $25,000. The median response rate to interactive texts was 91% (IQR 75%-97%) over 12 months. Engagement gradually declined throughout the intervention but remained high. Engagement did not differ by age, gender, education, income, diabetes duration, insulin status, health literacy, or numeracy. Black race and worse baseline medication adherence and HbA1c were each associated with lower engagement, although the effects were small. Nearly half of the participants chose to continue receiving daily texts for the last 6 months of the intervention. Participants who continued daily text messages said they wanted to continue experiencing benefits to their health, whereas those who chose fewer texts said that the daily texts had helped them create routines and they no longer needed them as often. Engagement was not impacted by receiving coaching or by participants' chosen text frequency. CONCLUSIONS: Well-designed interactive text messages can engage diverse patients in a self-care intervention for at least 1 year. Variation in and reasons for frequency preference suggest that offering a frequency choice may be important to users' engagement.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient 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 score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.405
Teacher spread0.341 · 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

Citations47
Published2020
Admission routes1
Has abstractyes

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