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Record W1789758336 · doi:10.2196/mhealth.4344

Feasibility and Acceptability of Smartphone-Based Ecological Momentary Assessment of Alcohol Use Among African American Men Who Have Sex With Men in Baltimore

2015· article· en· W1789758336 on OpenAlexvenueno aff
Peng Yang, Beth Linas, Gregory D. Kirk, Robert C. Bollinger, Larry Chang, Geetanjali Chander, Daniel Siconolfi, Sharif A. Braxton, Abby E. Rudolph, Carl A. Latkin

Bibliographic record

VenueJMIR mhealth and uhealth · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute on Drug AbuseCenter for AIDS Research, University of WashingtonNational Institute on Alcohol Abuse and AlcoholismNational Institute of Allergy and Infectious DiseasesCenter for AIDS Research, Johns Hopkins UniversityJohns Hopkins University
KeywordsmHealthContext (archaeology)PopulationDemographyMen who have sex with menPsychologyMoodGerontologyMedicinePsychological interventionEnvironmental healthHuman immunodeficiency virus (HIV)Clinical psychologyPsychiatryFamily medicineGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Alcohol use is a risk factor for the acquisition of human immunodeficiency virus (HIV) among African American men who have sex with men (MSM). Mobile phone-based ecological momentary assessments (EMA) could minimize bias due to retrospective recall and thus provide a better understanding of the social and structural context of alcohol use and its relationship with HIV-related risk behaviors in this population as well as other highly stigmatized populations. OBJECTIVE: We describe the study design and the implementation, feasibility, reactivity, and acceptability of an EMA study of alcohol use and HIV-related behaviors among African American MSM in Baltimore. METHODS: Participants were recruited through flyers and word-of-mouth in Baltimore from September 2013 to November 2014. Each participant was loaned an Android smartphone and instructed to respond to multiple prompts from the mobile app for 4 weeks. Data were collected through (1) random prompts delivered three times daily assessing participants' location, activity, mood, and social context, (2) daily prompts capturing drinking and sex events occurring in the past 24 hours, and (3) event-contingent responses collecting participants' self-reported episodes of drinking. RESULTS: A total of 16 participants enrolled in the study. The current analyses focused on 15 participants who completed at least 24 days of follow-up (mean follow-up time 29 days; range 24-35 days). Study participants (N=15) were a median 38 years of age (range 27-62 years) with low levels of income and educational attainment. Ten individuals self-reported living with HIV/AIDS, over half reported drinking alcohol at least 2-3 times a week, and a third reported binge drinking (ie, 6 or more drinks on one occasion) on a weekly basis. Based on the Alcohol Use Disorders Identification Test (AUDIT) score, nearly half were classified as hazardous drinkers (score 8-15) and a fifth were likely dependent (score ≥16). A total of 140 participant-initiated events were reported, and 75% of 1308 random prompts and 81% of 436 daily prompts delivered were answered. Of seven devices used during the study, five were reported lost by participants. We did not observe strong reactivity effects, and self-reported acceptability to study procedures was uniformly favorable. CONCLUSIONS: This study provides evidence to support the feasibility and acceptability of using EMA methods for collecting data on alcohol use among African American men who have sex with men living in urban settings. These data provide the basis for future studies of EMA-informed mHealth interventions to promote the reduction of substance use and HIV risk-taking behaviors among African American MSM living in urban settings.

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)0.000
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 score0.017
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.099
GPT teacher head0.440
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.

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".

Quick stats

Citations87
Published2015
Admission routes1
Has abstractyes

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