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.