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Record W4365483708 · doi:10.2196/44503

An Avatar-Led Web-Based and SMS Text Message Smoking Cessation Program for Socioeconomically Disadvantaged Veterans: Pilot Randomized Controlled Trial

2023· article· en· W4365483708 on OpenAlexvenueno aff
Jaimee L. Heffner, Megan M. Kelly, Erin D. Reilly, Scott G Reece, Tracy Claudio, Edit Serfozo, Kelsey K. Baker, Noreen L Watson, Maria Karekla

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsMedicineVeterans AffairsSmoking cessationRandomized controlled trialPsychological interventionAbstinenceLogistic regressionFamily medicineGerontologyNursingPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the declining prevalence of cigarette smoking in the United States, socioeconomically disadvantaged veterans receiving care from the Veterans Health Administration have a high prevalence of smoking. Currently, available treatment options for these veterans focus on tobacco users who are ready to quit and have limited reach. Consequently, there is a great need for accessible, effective smoking cessation interventions for veterans at all levels of readiness to quit smoking. OBJECTIVE: To address these needs, we developed Vet Flexiquit, a web-based Acceptance and Commitment Therapy program for veterans, and evaluated its acceptability (primary aim), efficacy, and impact on theory-based change processes relative to the National Cancer Institute's SmokefreeVET program in a pilot randomized controlled trial. METHODS: Participants (N=49) were randomized 1:1 to receive either the Vet Flexiquit (n=25) or SmokefreeVET (n=24) web program. Both groups received SMS text messages as part of the intervention for 6 weeks. Both interventions are fully automated and self-guided. Primary outcome data were collected at 3 months after the randomization. Self-reported smoking abstinence was biochemically verified using saliva cotinine. Multivariable logistic regression, negative binomial regression, and linear regression models were used to evaluate the association between the treatment arm and outcomes of interest. RESULTS: Acceptability, as measured by overall treatment satisfaction, was high and similar across treatment arms: 100% (17/17) for Vet Flexiquit and 95% (18/19) for SmokefreeVET. Acceptability, as measured by utilization, was more modest (log-ins: M=3.7 for Vet Flexiquit and M=3.2 for SmokefreeVET). There were no statistically significant differences between treatment arms for any acceptability measures. Similarly, there were no statistically significant differences between treatment arms in the secondary outcomes of smoking cessation or change in Acceptance and Commitment Therapy's theory-based processes. In open-ended survey responses, some veterans in both treatment arms expressed interest in having support from a professional or peer to enhance their experience, as well as an expanded SMS text messaging program. CONCLUSIONS: Both programs had high ratings of acceptability, limited utilization, and a similar impact on cessation and cessation processes. Taken together with the qualitative data suggesting that additional support may enhance participants' experience of both programs, these preliminary findings suggest that the programs may have similar outcomes among veterans who are looking for a digital cessation treatment option and that integrating provider or peer support and enhancing the SMS text messaging program holds promise as a means of boosting engagement and outcomes for both programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04502524; https://clinicaltrials.gov/ct2/show/NCT04502524.

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 score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.062
GPT teacher head0.439
Teacher spread0.378 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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