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Record W3195908715 · doi:10.2196/29760

A Smoking Cessation App for Nondaily Smokers (Version 2 of the Smiling Instead of Smoking App): Acceptability and Feasibility Study

2021· article· en· W3195908715 on OpenAlexvenueno aff
Bettina B. Hoeppner, Kaitlyn R Siegel, Hannah A. Carlon, Christopher W. Kahler, Elyse R. Park, Susanne S. Hoeppner

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersAmerican Cancer Society
KeywordsSmoking cessationHappinessPsychologySmartphone appClinical psychologyMedicinePhysical therapyPsychotherapistWorld Wide Web

Abstract

fetched live from OpenAlex

Background Recent evidence highlights the significant detrimental impact of nondaily smoking on health and its disproportionate prevalence in underserved populations; however, little work has been done to develop treatments specifically geared toward quitting nondaily smoking. Objective This study aims to test the feasibility, acceptability, and conceptual underpinnings of version 2 of the Smiling Instead of Smoking (SiS2) smartphone app, which was developed specifically for nondaily smokers and uses a positive psychology approach. Methods In a prospective, single-group study, nondaily smokers (N=100) were prescribed use of the SiS2 app for 7 weeks while undergoing a quit attempt. The app assigned daily positive psychology exercises and behavioral tasks every 2 to 3 days, which guided smokers through using the smoking cessation tools offered in the app. Participants answered surveys at baseline and at 2, 6, 12, and 24 weeks postquit. Feasibility was evaluated based on app use and acceptability based on survey responses. The underlying conceptual framework was tested by examining whether theorized within-person changes occurred from baseline to end of treatment on scales measuring self-efficacy, desire to smoke, and processing of self-relevant health information (ie, pros and cons of smoking, importance of the pros and cons of quitting, and motivation). Results Participants used the SiS2 app on an average of 24.7 (SD 13.8) days out of the 49 prescribed days. At the end of treatment, most participants rated the functions of the app as very easy to use (eg, 70/95, 74% regarding cigarette log and 59/95, 62% regarding happiness exercises). The average score on the System Usability Scale was 79.8 (SD 17.3; A grade; A+ ≥84.1, B+ <78.8). Most participants reported that the app helped them in their quit attempt (83/95, 87%), and helped them stay positive while quitting (78/95, 82%). Large effects were found for within-person decreases in the desire to smoke (b=−1.5, 95% CI −1.9 to −1.1; P<.001; gav=1.01), the importance of the pros of smoking (b=-20.7, 95% CI −27.2 to −14.3; P<.001; gav=0.83), and perceived psychoactive benefits of smoking (b=−0.8, 95% CI −1.0 to −0.5; P<.001; gav=0.80). Medium effects were found for increases in self-efficacy for remaining abstinent when encountering internal (b=13.1, 95% CI 7.6 to 18.7; P<.001; gav=0.53) and external (b=11.2, 95% CI 6.1 to 16.1; P<.001; gav=0.49) smoking cues. Smaller effects, contrary to expectations, were found for decreases in motivation to quit smoking (P=.005) and the perceived importance of the pros of quitting (P=.009). Self-reported 30-day point prevalence abstinence rates were 40%, 56%, and 56% at 6, 12, and 24 weeks after the quit day, respectively. Conclusions The SiS2 app was feasible and acceptable, showed promising changes in constructs relevant to smoking cessation, and had high self-reported quit rates by nondaily smokers. The SiS2 app warrants testing in a randomized controlled trial.

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)0.008
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.103
GPT teacher head0.439
Teacher spread0.337 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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