HMN 2026: How Smartphone data predict smoking cravings and lapses, with potential to treat addiction and other conditions

Minuscule movement patterns collected from smartphones and often undetectable to humans have been used to predict cravings and compulsive behaviors with groundbreaking accuracy—potentially offering timely and bespoke treatment for smoking and other addictions and conditions.

How the smartphone study worked

The technology was tested with a group of 17 smokers whose movement data was passively collected on smartphones while they went about their daily routines. The smokers were asked to press a button on their screen when they smoked a cigarette over a two-week period. Their movement data was processed through an algorithm that predicted when they were likely to crave or smoke a cigarette, with 85% accuracy, within a five-minute window.

The model was even found to predict high craving and potential relapse situations with similarly high accuracy after the user has declared and committed to quit their addictive behavior, and when it was trained with data of other smokers. This suggests that these micro-movement signatures are sufficiently similar to other humans.

Authored by researchers from Manchester Metropolitan University and the University of Lancashire, the study, “Smartphone movement data can reliably predict smoking lapses and cravings to enable timely smoking cessation support,” is published in the journal Scientific Reports.

From predictions to real-world support

Researchers say the technology can be used to develop smarter smoking cessation apps, for example, using smartphones to alert the user to a “motivational image” prior to their predicted high craving or smoking event based on their personal reason for quitting.

This could be a picture of a race finish line if the impetus is cardiovascular health, or a family photo if the motivating factor is seeing children grow up. Timely reminders like these can disincentivize the user from acting on their craving, just as the person is likely to relapse.

It is the first time that movement data has been used to accurately predict behavior patterns that are not typically associated with movement, with much wider implications beyond simply smoking cessation. Potential for the technology to provide early detection and intervention across various addictive and compulsive health conditions will be looked at next—for example, for mental health conditions or eating disorders.

Researchers see wider health potential

Dr. Yael Benn, Manchester Met’s Senior Lecturer in Psychology and co-author of the study, said, “Our study demonstrates that there is huge untapped potential in digitally recording micro-adjustments in our everyday movement. The results move us a step closer to developing sophisticated detection, prediction, and intervention models for other health conditions.

“This system tells us with high accuracy that someone is about to smoke, which is a breakthrough in itself—but the really exciting prospect is what it might be able to tell us about behavior patterns associated with other conditions and disorders, for example, binge eating, insomnia and others. The potential is huge.”

Previously, smoking behavior research has sought to identify actions based on environmental, social and internal mental states, for example, the availability of tobacco, the presence of other smokers and places the smoker associated with smoking.

This is the first time that micro-movement collected from smartphone sensor data has been used to predict compulsive behavior and cravings, potentially providing just-in-time interventions to those who wish to quit smoking as well as those engaging with other addictive activities.

Dr. Maryam Abo-Tabik, from the School of Engineering and Computing at the University of Lancashire, added, “Predicting smoking activity using data collected from smartphone sensors in uncontrolled environments is very difficult. Previous health applications tended to rely on wearable sensors in a lab environment. Using real-life data without any restrictions on participants’ behavior offers invaluable insights into behavior change and health monitoring in the real world.”

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