Smartphone movement data can reliably predict smoking lapses and cravings to enable timely smoking cessation support
Year: 2026
Type: article
Abstract: Decades of research aiming to develop effective smoking interventions have identified triggers that contribute to failed quitting attempts including environmental (e.g. location), social (presence of other smokers), or internal (e.g. stress). Here, it is shown for the first time that passively collected movement data from smokers' smartphones' sensors (accelerometer, gyroscope and magnetometer) can be used to predict smoking-behaviour. Feeding the movement data into a Deep Learning (DL) model (1D-CNN-BiLSTM), smoking-behaviour was predicted with 85% accuracy within the subsequent 5-minute window. This compares to 63% accuracy when using traditional triggers (e.g. time of the day). Crucially, movement data can be used to predict high-craving incidents and lapses in the 3 months period following quitting smoking with similarly high accuracy, even when predictions are made without any personal data (i.e. when the model is trained using only data from other smokers). These findings can transform smoking-cessation apps, enabling the provision of just-in-time personalised support to those wishing to quit smoking. Importantly, the findings have implications beyond smoking-cessation applications, by revealing that human movements, largely overlooked to date, can be used for early detection of, and intervention for, health (and other) behaviours, including those that are not genetic or typically characterised by changes in movement.
Source: Scientific Reports
Authors: Maryam Abo-Tabik, Nicholas Costen, Yael Benn
Institutions: University of Lancashire, Manchester Metropolitan University
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7161979171