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Record W3023951907 · doi:10.2196/16688

Social Media Interventions for Risky Drinking Among Adolescents and Emerging Adults: Protocol for a Randomized Controlled Trial

2020· article· en· W3023951907 on OpenAlexvenueno aff
Erin E. Bonar, Diane M Schneeberger, Carrie Bourque, José A. Bauermeister, Sean D. Young, Frederic C. Blow, Rebecca M. Cunningham, Amy M. Bohnert, Marc A. Zimmerman, Maureen A. Walton

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersCenters for Disease Control and PreventionNational Institute on Alcohol Abuse and AlcoholismUniversity of MichiganU.S. Department of Health and Human Services
KeywordsRandomized controlled trialPsychological interventionAlcohol Use Disorders Identification TestSocial mediaMedicineBinge drinkingIntervention (counseling)Poison controlPsychologySuicide preventionFamily medicineInjury preventionPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Despite intervention efforts to date, the prevalence of risky drinking among adolescents and emerging adults remains high, increasing the risk for health consequences and the development of alcohol use disorders. Peer influences are particularly salient among this age group, including via social media. Thus, the development of efficacious early interventions for youth, delivered with a broad reach via trained peers on social media, could have an important role in addressing risky drinking and concomitant drug use. OBJECTIVE: This paper describes the protocol of a randomized controlled trial (RCT) testing the efficacy of a social media intervention among adolescents and emerging adults who meet the criteria for risky drinking (using the Alcohol Use Disorders Identification Test-Consumption [AUDIT-C]), delivered with and without financial incentives for participation, compared with an attention placebo control condition (ie, entertaining social media content), on alcohol consumption and consequences. METHODS: This RCT involved recruiting 955 youths (aged 16-24 years) via advertisements on Facebook and Instagram to self-administer a brief web-based screening survey. Those screening positive for past 3-month risky drinking (AUDIT-C positive: ages 16-17 years: ≥3 females and ≥4 males; and ages 18-24 years: ≥4 females and ≥5 males) were eligible for the RCT. After providing consent (a waiver of parental consent was obtained for minors), participants completed a web-based baseline survey and several verification procedures, including a selfie photo matched to Facebook profile photos. Participants were then randomized to join invitation-only secret Facebook groups, which were not searchable or viewable by parents, friends, or anyone not recruited by the study. The 3 conditions were social media intervention with incentives, social media intervention without incentives (SMI), and attention placebo control. Each condition lasted 8 weeks and consisted of bachelor's-level and master's-level therapist electronic coaches posting relevant content and responding to participants' posts in a manner consistent with Motivational Interviewing. Participants in the control condition and SMI condition did not receive payments but were blind to condition assignment between these 2 conditions. Follow-ups are ongoing and occur at 3, 6, and 12 months poststart of the groups. RESULTS: We enrolled 955 participants over 10 waves of recruitment who screened positive for risky drinking into the RCT. CONCLUSIONS: The findings of this study will provide the critical next step in delivering early alcohol interventions to the youth, capitalizing on social media platforms, which could have significant public health impact by altering alcohol use trajectories of adolescents and emerging adults engaged in risky drinking. TRIAL REGISTRATION: ClinicalTrials.gov NCT02809586; https://clinicaltrials.gov/ct2/show/NCT02809586. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16688.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.017
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.219
GPT teacher head0.542
Teacher spread0.323 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations34
Published2020
Admission routes1
Has abstractyes

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