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Record W4402917868 · doi:10.2196/57376

Outcomes of a Comprehensive Mobile Vaping Cessation Program in Adults Who Vape Daily: Cohort Study

2024· article· en· W4402917868 on OpenAlexvenueno aff
Jennifer D Marler, Craig A Fujii, MacKenzie T Utley, Daniel J Balbierz, Joseph A. Galanko, David S. Utley

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintCohortMedicineCohort studyGerontologyComputer scienceWorld Wide WebInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In the United States, e-cigarettes, or vapes, are the second most commonly used tobacco product. Despite abundant smartphone app-based cigarette cessation programs, there are few such programs for vaping and even fewer supporting data. OBJECTIVE: This exploratory, prospective, single-arm, remote cohort study of the Pivot vaping cessation program assessed enrollment and questionnaire completion rates, participant engagement and retention, changes in attitudes toward quitting vaping, changes in vaping behavior, and participant feedback. We aimed to establish early data to inform program improvements and future study design. METHODS: American adults aged ≥21 years who vaped daily, reported ≥5 vape sessions per day, and planned to quit vaping within 6 months were recruited on the web. Data were self-reported via app- and web-based questionnaires. Outcomes included engagement and retention (ie, weeks in the program, number of Pivot app openings, and number of messages sent to the coach), vaping attitudes (ie, success in quitting and difficulty staying quit), vaping behavior (ie, quit attempts, Penn State Electronic Cigarette Dependence Index, 7- and 30-day point-prevalence abstinence [PPA], and continuous abstinence [defined as ≥7-day PPA at 12 weeks+30-day PPA at 26 weeks+0 vaping sessions since 12 weeks]), and participant feedback. RESULTS: In total, 73 participants onboarded (intention-to-treat sample); 68 (93%) completed the 12- and 26-week questionnaires (completer samples). On average, participants were active in Pivot for 13.8 (SD 7.3) weeks, had 87.3 (SD 99.9) app sessions, and sent 37.6 (SD 42.3) messages to their coach over 26 weeks. Mean success in quitting and difficulty staying quit (scale of 1-10) improved from baseline to 12 weeks-4.9 (SD 2.9) to 7.0 (SD 3.0) and 4.0 (SD 2.8) to 6.2 (SD 3.1), respectively (P<.001 in both cases). Most participants (64/73, 88%) made ≥1 quit attempt. At 26 weeks, intention-to-treat 7-day PPA, 30-day PPA, and continuous abstinence rates were 48% (35/73), 45% (33/73), and 30% (22/73), respectively. In total, 45% (33/73) of the participants did not achieve 7-day PPA at 26 weeks; their mean Penn State Electronic Cigarette Dependence Index score decreased from baseline (13.9, SD 3.1) to 26 weeks (10.8, SD 4.5; mean change -3.2, SD 3.9; P<.001); 48% (16/33) of these participants improved in the e-cigarette dependence category. At 2 weeks, 72% (51/71) of respondents reported that using Pivot increased their motivation to quit vaping; at 4 weeks, 79% (55/70) reported using Pivot decreased the amount they vaped per day. CONCLUSIONS: In this first evaluation of Pivot in adult daily vapers, questionnaire completion rates were >90%, average program engagement duration was approximately 14 weeks, and most participants reported increased motivation to quit vaping. These and early cessation outcomes herein suggest a role for Pivot in vaping cessation and will inform associated future study and program improvements.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.070
GPT teacher head0.466
Teacher spread0.396 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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