Classification of Lapses in Smokers Attempting to Stop - A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App

Aug 6, 2026

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Smoking lapses after the quit date often trigger full relapse, limiting the effectiveness of standard relapse prevention. This study used routinely collected observational data from a popular smoking cessation smartphone app to build supervised machine learning algorithms that distinguish lapse from non-lapse reports. Group-level models were trained with app features including craving severity, mood, activity, social context, and lapse incidence, then evaluated for both out-of-sample observations and individuals.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Real-time lapse prevention and JITAIs

Smoking lapses and contextual risk variation

Aims and Methods

Data source and app-derived features

Supervised learning models and evaluation

Results

Group-level model performance

Individual-level and hybrid model construction

Conclusions and Implications

Feasibility and limitations for unseen individuals

Recommendations for future intervention development

Nicotine and Tobacco Research, 2023, 25, 1330–1339 [https://doi.org/10.1093/ntr/ntad051](https://doi.org/10.1093/ntr/ntad051) Advance access publication 27 March 2023 Original Investigation Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App Olga Perski PhD1,2,, Kezhi Li PhD3, Nikolas Pontikos PhD4,, David Simons MSc5,, Stephanie P. Goldstein PhD6,7, Felix Naughton PhD8,, Jamie Brown PhD1,2, 1Department of Behavioural Science and Health, University College London, London, UK 2SPECTRUM Consortium, London, UK 3 Institute of Health Informatics, University College London, London, UK 4UCL Institute of Ophthalmology, University College London, London, UK 5Centre for Emerging, Endemic and Exotic Diseases, Royal Veterinary College, London, UK 6Weight Control and Diabetes Research Center, The Miriam Hospital, Providence, RI, USA 7Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA 8 Behavioural and Implementation Science Research Group, School of Health Sciences, University of East Anglia, Norwich, UK Corresponding Author: Olga Perski, PhD, Department of Behavioural Science and Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK. Telephone: 44(0)20 7679 1258; E-mail: [olga.perski@ucl.ac.uk](olga.perski@ucl.ac.uk) Abstract Introduction: Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports. Aims and Methods: We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (eg, Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample (1) observations and (2) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested. Results: Participants (N = 791) provided 37 002 data entries (7.6% lapses) . The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% confidence interval [CI] = 0.961 to 0.978) . Its ability to classify lapses for out-ofsample individuals ranged from poor to excellent (AUC = 0.482–1.000) . Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518–1.000) . Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375–1.000) . Conclusions: Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual’s dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual’s data, had improved performance but could only be constructed for a minority of participants. Implications: This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants because of the lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between u

FAQs

Why focus on classification of smoking lapses during quit attempts?

Smoking lapses after the quit date are a key pathway to full relapse. Accurately distinguishing lapse from non-lapse events can support tailored, real-time lapse prevention.

What data and features were used to train the supervised machine learning models?

The models used observational, unprompted app entries from users with sufficient data. Features included craving severity, mood, activity, and social context, along with information related to lapse incidence.

How did the algorithms perform when classifying lapses for new individuals?

The best group-level algorithm showed strong average performance, but classification ability for out-of-sample individuals ranged widely from poor to excellent. Individual-level and hybrid approaches improved performance for some participants but could not be built for everyone due to outcome variability.

Aug 6, 2026

Upload by Jiven