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Record W4284958255 · doi:10.2196/39309

Effectiveness and Implementation of a Text Messaging Intervention to Reduce Depression and Anxiety Symptoms Among Latinx and White Adults During the COVID-19 Pandemic

2022· article· en· W4284958255 on OpenAlexvenueno aff
Alein Y. Haro‐Ramos, Hector P. Rodríguez, Adrián Aguilera

Bibliographic record

VenueIproceedings · 2022
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionMoodAnxietyMental healthmHealthThematic analysisIntervention (counseling)Patient Health QuestionnaireClinical psychologyPsychologyMedicinePsychiatryQualitative research

Abstract

fetched live from OpenAlex

Background SMS text messaging interventions are increasingly being used to help people manage mental health symptoms due to the COVID-19 pandemic. Despite the widespread adoption of SMS text messaging interventions, little is known about racial and ethnic differences in effectiveness, feasibility, and implementation factors. Objective A hybrid type 1 mixed-methods study was conducted to compare the effectiveness and implementation of the StayWell intervention for Latinx and non-Latinx White (White) adults using elements of the RE-AIM framework. Methods Adults using the StayWell intervention received daily mood inquiries and skills–based text messages for 60 days and reported symptoms via online surveys and the HealthySMS portal. Reach was assessed by the share of adults reached via distinct recruitment methods, and effectiveness was evaluated using the Personal Health Questionnaire (PHQ-8) depression scale and General Anxiety Disorder (GAD-7) scale. Adoption was assessed with user engagement, defined as the share of responses to the (60) daily mood inquiries. Implementation was evaluated based on user feedback on the number/timing of messages, the difficulty of using the program, and the System Usability Scale. Maintenance was assessed with user reports of the likelihood of continuing and recommending the program. Quantitative RE-AIM indicators were assessed using a t test for continuous outcomes and a chi-square test for categorical outcomes. Mixed-effects linear regressions examined heterogeneity among Latinx and White users in the outcomes’ (ie, PHQ-8 and GAD-7) changes over time. A thematic text analysis of responses to an open-ended question about participant experiences of the program was conducted to help contextualize differences in the effectiveness and implementation of StayWell between Latinx and White users. Results Among 398 users, 262 (65.8%) responded to the postintervention assessment. Upon completion, depressive (–1.48; P=.001) and anxiety (–1.38; P=.001) symptoms decreased among all users. Compared to White adults, Latinx adults reported an additional –1.45 (P<.05) decline in PHQ-8 scores, adjusting for demographics, recruitment sample, and engagement. Among Latinx adults, StayWell resulted in a greater improvement of depression symptoms (25.8% vs 13.8% reduction; P=.02) but not in anxiety symptoms (21.2% vs 15.9% reduction; P=.23). Despite Latinx adults reporting lower usability (76.8 vs 83.9; P=.001) than White adults, they were more likely to report interest in continuing the program (7.5 vs 6.2; P=.001) and recommend StayWell to a family member or friend (7.8 vs 7.0; P=.013). Repetitive content was a critical barrier to engaging Latinx adults in StayWell; among Latinx adults, support groups and bidirectional messages were considered important program modifications. Conclusions Although anxiety and depressive symptoms decreased among all StayWell users after receiving 2 messages daily for 60 days, Latinx users experienced greater reductions in depression symptoms compared to White users. Bidirectional text messaging using contextual information and ecological momentary analysis of mood data are promising adaptations to enable the tailoring of messages and improved usability of StayWell, which may be especially effective in improving Latinx user engagement. Trial Registration ClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599 Conflicts of Interest None declared.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.372
Teacher spread0.356 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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