Your Smartphone May Help You Quit Smoking by Tracking its Rejuvenating Effect

Smoking

Studies have shown that smoking is major life-threatening factor that often results in accelerated aging and premature death. As measured by DNA methylation, quitting smoking not only improves the lifespan but also reduces biological age. However, most smokers find it difficult to quit.

In a study, a group of researchers from Roswell Park Comprehensive Cancer Center and Gero developed a method to keep a track on rejuvenating effect of quitting smoking in real-time by analyzing data from a wearable device.

Study results are detailed in an article appeared on the journal cover of Aging.

According to the researchers, analyzing the signals of physical activity collected from wearable devices could help detect the acceleration of aging caused by smoking. From this, they developed an AI algorithm trained to look for specific patterns in intraday change of activity level in order to predict an individual’s biological age.

The study also demonstrated that the smoking-induced bioage acceleration reverts to normal after quitting and the process could be tracked through wearable devices.

It is indeed fascinating that significant positive effect of lifestyle changes including smoking cessation can be observed by examining a person’s physical activity level, Gero’s founder and chief science officer Peter Fedichev said. An age biomarker derived from physical activity is easy and inexpensive way to track how biological age comes back to normal after smoking cessation, he added.

The researchers drew inspiration from their study findings and developed a free mobile application called ‘Gero Healthspan’. It offers real-time tracking of changes in bioage in response to lifestyle interventions. With the app, a user can investigate the way lifestyle changes – including diets, supplements, and activities – can affect their estimated life expectancy. The researchers hope that their work and research-based app would help people to stop deliberately shortening their lifespan and promote a healthy lifestyle.

With the application of machine learning tools, the team examined about 107,662 health profiles obtained from the UK Biobank and National Health and Nutrition Examination Survey. The massive databases include health and lifestyle information, physical activity records provided by wearable devices, along death records up to 9 years following the activity monitoring.

The locomotion patterns are directly related to several health aspects; when a set of sophisticated mathematical techniques are applied to human locomotion data from the databases, the researchers found significant factors of the aging process.  Then, by exploring the locomotive activity in individuals, they obtained a measure of biological age and evaluated its strong relation with the remaining lifespan. It is a promising research that creates the opportunity to determine one’s health status from wearable devices, according to Arnold Mitnitski, Research Professor at Dalhousie University.

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Rahul Pandita

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