Peer-reviewedCC BY 4.0JMIR Formative Research27 Oct 2026

Determinants of Successful Mobile Smoking Cessation Interventions: Cross-Sectional Study

Bikash K Pradhan^1,2
Katerina Lukavska³
Roman Gabrhelík³
Daniel Novak¹

¹Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, Praha 6, 16627, Czech Republic, 420 22435 ext 7314, 420 224357314
²Department of Analytics, School of Business, Woxsen University, Hyderabad, India
³Department of Addictology, Charles University, Prague, Czech Republic

Abstract

Background: In recent years, digital interventions for smoking cessation have seen increasing use, but program completion rates vary widely. Therefore, understanding the factors associated with program completion in cessation programs is critical for evaluating the feasibility and acceptability of the digital intervention program.

Objective: This study aims to identify the predictors (sex, age group, and health) of program completion in a cognitive behavioral therapy–based mobile smoking cessation program.

Methods: Participants enrolled in Adiquit, a mobile app–based smoking cessation program, were classified as completers if they had entered the Withdrawal-Relapse phase and had reported no lapse for 15 consecutive days or had finished all program sessions. User data for this study covered the period from launch through July 2024. Users who did not meet the completion criteria by the end of the study were classified as noncompleters. Predictors were assessed during the Entry and Equilibrium phases, including demographic characteristics, psychosocial factors, and early engagement metrics. Logistic regression was used to identify factors associated with completion.

Results: The three strongest predictors of program completion, as identified by the multivariate model, are higher early engagement (session attendance and screen time), stronger perceived social support, and positive prequitting feelings.

Conclusions: The results of this study will help clarify the variables that lead to successful completion of such a digital intervention program, as well as their importance by sex, age group, health status, and employment status.

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Cite

BibTeX RIS

BibTeX

@article{pradhan2026determinants,
  title = {{Determinants of Successful Mobile Smoking Cessation Interventions: Cross-Sectional Study}},
  author = {Bikash K Pradhan and Katerina Lukavska and Roman Gabrhelík and Daniel Novak},
  journal = {JMIR Formative Research},
  year = {2026},
  month = oct,
  doi = {10.2196/80428},
  url = {https://doi.org/10.2196/80428},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - Determinants of Successful Mobile Smoking Cessation Interventions: Cross-Sectional Study
AU  - Bikash K Pradhan
AU  - Katerina Lukavska
AU  - Roman Gabrhelík
AU  - Daniel Novak
PY  - 2026
DA  - 2026/10/27
JO  - JMIR Formative Research
DO  - 10.2196/80428
UR  - https://doi.org/10.2196/80428
AB  - Background: In recent years, digital interventions for smoking cessation have seen increasing use, but program completion rates vary widely. Therefore, understanding the factors associated with program completion in cessation programs is critical for evaluating the feasibility and acceptability of the digital intervention program.  Objective: This study aims to identify the predictors (sex, age group, and health) of program completion in a cognitive behavioral therapy–based mobile smoking cessation program.  Methods: Participants enrolled in Adiquit, a mobile app–based smoking cessation program, were classified as completers if they had entered the Withdrawal-Relapse phase and had reported no lapse for 15 consecutive days or had finished all program sessions. User data for this study covered the period from launch through July 2024. Users who did not meet the completion criteria by the end of the study were classified as noncompleters. Predictors were assessed during the Entry and Equilibrium phases, including demographic characteristics, psychosocial factors, and early engagement metrics. Logistic regression was used to identify factors associated with completion.  Results: The three strongest predictors of program completion, as identified by the multivariate model, are higher early engagement (session attendance and screen time), stronger perceived social support, and positive prequitting feelings.  Conclusions: The results of this study will help clarify the variables that lead to successful completion of such a digital intervention program, as well as their importance by sex, age group, health status, and employment status.
SN  - 2561-326X
ER  -

Abstract

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