Evidence map›Paper›PMID 37146444›Full record

ArticleBehaviour research and therapy2023

Effectiveness and implementation of a text messaging intervention to reduce depression and anxiety symptoms among Latinx and Non-Latinx white users during the COVID-19 pandemic.

Alein Y Haro-Ramos, Hector P Rodriguez, Adrian Aguilera

Registry-linked trialOpen access · hybridAbstract read
In one paragraph

Article in Behaviour research and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04473599 (Stay Well at Home), which is not on this map. Cited by 8 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 3 pooled it
2.0field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04473599 nacompletednot on this map

Stay Well at Home: A Text-messaging Study to Improve Mood and Help Cope With Social Distancing

TypeinterventionalSponsorUniversity of California, BerkeleyRan2020 to 2023Enrolled1,000ConditionsDepressive Symptoms, Anxiety, COVID-19ArmsUniform random message delivery, Reinforcement learning message delivery, Mood ratings only
3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 3 syntheses or guidelines pooled it, 10 citations in OpenAlex.

  1. Pooled it
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  3. Pooled it
  4. Trial
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Alein Y Haro-RamosSchool of Public Health, University of California, Berkeley, Berkeley, CA, USA.
Hector P RodriguezSchool of Public Health, University of California, Berkeley, Berkeley, CA, USA.
Adrian AguileraDigital Health Equity and Access Lab, School of Social Welfare, University of California, Berkeley, Berkeley, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA. Electronic address: aguila@berkeley.edu.
Berkeley Public Health Division · USUniversity of California, San Francisco · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Text messaging interventions are increasingly used to help people manage depression and anxiety. However, little is known about the effectiveness and implementation of these interventions among U.S. Latinxs, who often face barriers to using mental health tools. The StayWell at Home (StayWell) intervention, a 60-day text messaging program based on cognitive behavioral therapy (CBT), was developed to help adults cope with depressive and anxiety symptoms during the COVID-19 pandemic. StayWell users (n = 398) received daily mood inquiries and automated skills-based text messages delivering CBT-informed coping strategies from an investigator-generated message bank. We conduct a Hybrid Type 1 mixed-methods study to compare the effectiveness and implementation of StayWell for Latinx and Non-Latinx White (NLW) adults using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Effectiveness was measured using the PHQ-8 depression and GAD-7 anxiety scales, assessed before starting and after completing StayWell. Guided by RE-AIM, we conducted a thematic text analysis of responses to an open-ended question about user experiences to help contextualize quantitative findings. Approximately 65.8% (n = 262) of StayWell users completed pre-and-post surveys. On average, depressive (-1.48, p = 0.001) and anxiety (-1.38, p = 0.001) symptoms decreased from pre-to-post StayWell. Compared to NLW users (n = 192), Latinx users (n = 70) reported an additional -1.45 point (p < 0.05) decline in depressive symptoms, adjusting for demographics. Although Latinxs reported StayWell as relatively less useable (76.8 vs. 83.9, p = 0.001) than NLWs, they were more interested in continuing the program (7.5 vs. 6.2 out of 10, p = 0.001) and recommending it to a family member/friend (7.8 vs. 7.0 out of 10, p = 0.01). Based on the thematic analysis, both Latinx and NLW users enjoyed responding to mood inquiries and sought bi-directional, personalized text messages and texts with links to more information to resources. Only NLW users stated that StayWell provided no new information than they already knew from therapy or other sources. In contrast, Latinx users suggested that engagement with a behavioral provider through text or support groups would be beneficial, highlighting this group's unmet need for behavioral health care. mHealth interventions like StayWell are well-positioned to address population-level disparities by serving those with the greatest unmet needs if they are culturally adapted and actively disseminated to marginalized groups.

trial registrationClinicalTrials.gov Identifier: NCT04473599.

Indexed as

COVID-19Text MessagingAdultAnxietyDepressionHumansPandemicsAnxietyDepressionHybrid designMental healthText-messaging intervention

Identifiers

PMID37146444
PMCPMC10105646
OpenAlexW4366000279

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.