CoChat
Sign Up
Cited by 15Smoking Behavior and CessationMobile Health and mHealth ApplicationsDigital Mental Health Interventions

Feature-level analysis of a smoking cessation smartphone app based on a positive psychology approach: prospective observational study

Open access
Hoepper, Bettina B, Siegel, Kaitlyn R, Carlon, Hannah A, Kahler, Christopher W, Park, Elyse R, Taylor, Steven Trevor, Simpson, Hazel V, Hoeppner, Susanne S·2022·JMIR Formative Research
DOI 10.2196/38234PMC PMC9377446
Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: Smoking cessation smartphone apps have emerged as highly accessible tools to support smoking cessation efforts. It is unknown how specific app features contribute to user engagement over time and relate to smoking outcomes. OBJECTIVE: To provide a feature-level analysis of the Smiling Instead of Smoking app (version 2) and to link feature use to subsequent smoking cessation. METHODS: Nondaily smokers (N=100) used the app for a period of 49 days (1 week before quitting and 6 weeks after quitting). Participants self-reported 30-day point-prevalence abstinence at the end of this period and at a 6-month follow up (the survey response rate was 94% and 89% at these points, respectively). Self-reported 30-day point prevalence abstinence rates were 40% at the end of treatment and 56% at the 6-month follow up. The app engaged users in both positive psychology content and traditional behavioral smoking cessation content. The app sent push notifications to prompt participants to complete prescribed content (ie, a "happiness exercise" every day and a "behavioral challenge" to use the app's smoking cessation tools on 15 out of 49 days). Actions that participants took within the app were timestamped and recorded. RESULTS: =9.28 [2-tailed]; P<.001). The prescribed content was frequently completed (45% of happiness exercises; 57% of behavioral challenges) and ad libitum tools were used on ≤7 days. Most participants used each ad libitum smoking cessation tool at least once, with higher use of personalized content (≥92% used "strategies," "cigarette log," "smoke alarms," and "personal reasons") than purely didactic content (79% viewed "benefits of quitting smoking"). The number of days participants used the app significantly predicted 30-day point-prevalence abstinence at the end of treatment (odds ratio [OR] 1.05, 95% CI 1.02-1.09; P=.002) and at the 6-month follow up (OR 1.04, 95% CI 1.008-1.07; P=.01). The number of days participants engaged with the happiness content significantly predicted smoking abstinence at the end of treatment (OR 1.05, 95% CI 1.02-1.08; P=.002) and at the 6-month follow up (OR 1.04, 95% CI 1.007-1.07; P=.02). This effect was not significant for the number of days participants engaged with the smoking cessation content of the app, either at the end of treatment (OR 1.04, 95% CI 0.996-1.08, P=.08) or at the 6-month follow up (OR 1.02, 95% CI 0.98-1.06; P=.29). CONCLUSIONS: Greater app usage predicted greater odds of self-reported 30-day point-prevalence abstinence at both the end of treatment and over the long term, suggesting that the app had a therapeutic benefit. Positive psychology content and prescriptive clarity may promote sustained app engagement over time. TRIAL REGISTRATION: ClinicalTrials.gov NCT03951766; https://clinicaltrials.gov/ct2/show/NCT03951766.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/38234

Cites

79 of 79 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Hoepper, B. B., Siegel, K. R., Carlon, H. A., Kahler, C. W., Park, E. R., Taylor, S. T., Simpson, H. V., & Hoeppner, S. S.
Affiliations
  • Massachusetts General Hospital (US)
  • University of New Mexico (US)
  • Brown University (US)
Corresponding author
Hoeppner, Susanne S
ORCID
Venue
JMIR Formative Research
Publisher
JMIR Publications Inc.
Published
2022-07-28
Volume
6
Issue
7
Pages
e38234
ISSN
2561-326X (ISSN-L)
DOI
10.2196/38234
PMC
PMC9377446
OpenAlex
W4288367731
Cited by
15
References
79
Citations by year
2026: 1 · 2025: 3 · 2024: 5 · 2023: 5 · 2022: 1
FWCI
2.17
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Formative Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
American Cancer Society (130323)
Topics
  • Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Keywords
smoking cessation, mobile application, positive psychology, app engagement, abstinence, behavioral problems, content personalization, push notifications, smoking cessation outcomes, smoke detectors

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review