A wearable two-wristband sensor system can help ex-smokers refrain from going back to smoking and provide insight on when and why former smokers give in to cravings.
Called the puffMarker, the model uses sensors to track a person's hand gestures and capture breathing patterns, which can help identify the antecedents and precipitants of a smoking lapse, according to research conducted and published at The Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K).
"A primary hurdle in achieving a higher success rate is a lack of methods that can intervene or deliver treatment at the right moment when an abstinent smoker is most vulnerable," the report's authors say. The puffMarker model, which the report claims is the first to illustrate that detection of potential relapse is possible, involved 61 newly-abstinent smokers who wore MD2K sensors for three days following a quit attempt, according to an announcement.
Helping smokers quit and ex-smokers avoid relapse has long been a focus for mHealth devices and app makers. Yet many smoking cessation apps may not sufficiently stimulate autonomous motivation, which is key for kicking the habit, according to research published last year in the Journal of Medical Internet Research.
Another study found that customized text messages are twice as effective at helping smokers quit compared to self-help initiatives.
The MD2K report notes additional research is needed regarding the puffMarker model, as are sophisticated capabilities and personalized models to boost accuracy.
"Our work opens up a very rich area of research for discovering efficacious just-in-time interventions that can be triggered from predictors detected by sensors such as GPS, smart eyeglasses, electronic and social media, and physiological sensors," the authors say.
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