New Smartphone App May Help You Quit Smoking: Study

Researchers say they have created a smartphone app that offers real-time monitoring of smoking-induced ageing, and may help smokers quit the habit.

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Researchers say they have created a smartphone app that offers real-time monitoring of smoking-induced ageing, and may help smokers quit the habit.

Smoking is one of the major life-shortening factors that leads to accelerated ageing and premature death, said scientists from Roswell Park Cancer Institute in the US.

Quitting smoking increases lifespan and decreases biological age, as measured by DNA methylation, they said.

The researchers created the mobile app, Gero Healthspan, that offers real-time monitoring of bioage changes in response to lifestyle interventions.

People can also use it to explore how lifestyle changes such as diets, activities and supplements affect your predicted healthy life expectancy, researchers said.

The study, published in the journal Aging, offers a way to track rejuvenating effect of smoking cessation in real time through the analysis of wearable data.

The bioage acceleration caused by smoking can be detected through the analysis of physical activity signals collected from wearable devices.

A new AI algorithm trained to find certain patterns in intra-day changes of activity level to estimate the biological age of a person has been developed.

The study demonstrates that the smoking-induced ageing acceleration reverts back to normal after smoking cessation: the process can be tracked by wearable device.

"It is fascinating that the profound positive effect of lifestyle changes such as smoking cessation could be observed by analysing physical activity of a person," said Peter Fedichev, founder and Chief Science Officer of Gero.

"A biomarker of age derived from physical activity is a cheap and convenient way to track how biological age reverts back to normal after quitting," Fedichev said.

"We hope that our research and our research-based app will help people to stop deliberately shortening their lives and help to develop healthy lifestyles, he said.

The scientists applied machine learning tools to analyse 108,112 health profiles made available by the National Health and Nutrition Examination Survey and the UK Biobank.

These large databases contain activity records provided by wearable devices as well as health and lifestyle information, combined with death records up to nine years following the activity monitoring.

"The patterns of locomotion are directly related to multiple aspects of health," said Arnold Mitnitski, a professor at Dalhousie University in Canada.

The researchers applied a set of sophisticated mathematical methods to human locomotion data from large databases and found signatures of the ageing process.

By mining the locomotor activity in individuals they extracted a measure of biological age and demonstrated its strong association with remaining lifespan, health span of and the risks of mortality.