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Record W4400621406 · doi:10.2196/58622

Couple-Focused Smartphone Intervention to Reduce Problem Drinking: Pilot Randomized Control Trial

2024· article· en· W4400621406 on OpenAlexvenueno aff
David H. Gustafson, David H. Gustafson, Marie‐Louise Mares, Darcie C Johnston, Olivia Vjorn, John J. Curtin, Elizabeth E. Epstein, Genie L. Bailey

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsPreprintRandomized controlled trialIntervention (counseling)Smartphone applicationSmartphone appMedicinePsychologyInternet privacyComputer scienceMultimediaWorld Wide WebNursingSurgery

Abstract

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BACKGROUND: Alcohol use disorder is among the most pervasive substance use disorders in the United States, with a lifetime prevalence of 30%. Recommended treatment options include evidence-based behavioral interventions; smartphone-based interventions confer a number of benefits such as portability, continuous access, and stigma avoidance; and research suggests that interventions involving couples may outperform those for patients only. In this context, a behavioral intervention delivered to couples through smartphones may serve as an effective adjunct to alcohol use disorder treatment. OBJECTIVE: This pilot study aimed to (1) evaluate the feasibility of comparing a patient-only (Addiction version of the Comprehensive Health Enhancement Support System; A-CHESS) versus a couple-focused (Partner version of the Comprehensive Health Enhancement Support System; Partner-CHESS) eHealth app for alcohol misuse delivered by smartphone, (2) assess perceptions about and use of the 2 apps, and (3) examine initial indications of differences in primary clinical outcomes between patient groups using the 2 apps. Broadly, these aims serve to assess the feasibility of the study protocol for a larger randomized controlled trial. METHODS: A total of 33 romantic couples were randomized to 6 months of A-CHESS app use (active treatment control) or Partner-CHESS app use (experimental). Couples comprised a patient with current alcohol use disorder (25/33, 76% male) and a romantic partner (26/33, 79% female). Patients and partners in both arms completed outcome measure surveys at 0, 2, 4, and 6 months. Primary outcomes were patients' percentage of days with heavy drinking and percentage of days with any drinking, measured by timeline follow back. Secondary outcomes included app use and perceptions, and multiple psychosocial variables. RESULTS: At 6 months, 78% (14/18) of Partner-CHESS patients and 73% (11/15) of A-CHESS patients were still using the intervention. The apps were rated helpful on a 5-point scale (1=not at all helpful, 5=extremely helpful) by 89% (29/33) of both Partner-CHESS patients (mean 3.7, SD 1) and partners (mean 3.6, SD 0.9) and by 87% (13/15) of A-CHESS patients (mean 3.1, SD 0.9). At 6 months, Partner-CHESS patients had a nonsignificantly lower percentage of days with heavy drinking compared with A-CHESS patients (β=-17.4, 95% CI -36.1 to 1.4; P=.07; Hedges g=-0.53), while the percentage of drinking days was relatively equal between patient groups (β=-2.1, 95% CI -24.8 to 20.7; P=.85; Hedges g=-0.12). CONCLUSIONS: Initial results support the feasibility of evaluating patient-only and couple-focused, smartphone-based interventions for alcohol misuse. Results suggest that both interventions are perceived as helpful and indicate maintained engagement of most participants for 6 months. A future, fully powered trial is warranted to evaluate the relative effectiveness of both interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04059549; https://clinicaltrials.gov/ct2/show/NCT04059549.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.077
GPT teacher head0.423
Teacher spread0.346 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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