Quitting smoking, one text at a time (podcast)

Researcher says smoking-cessation data collected via texting study subjects every two hours is far more accurate than data captured using other methods.

University of Oregon professor Elliot Berkman utilized texting to conduct a smoking-cessation study while at the University of California at Los Angeles. University of Oregon

University of Oregon Assistant Professor of Psychology Elliot Berkman recently completed a study for his University of California at Los Angeles doctoral dissertation on smoking cessation.

Like a lot of researchers before him, Berkman asked respondents whether they had smoked--and what mood they were in, when they lit up--in an attempt to better understand compliance with smoking-cessation programs. But the difference between Berkman's study and previous ones is that he was able to repeat the question every two hours by interacting with subjects via text messaging, rather than talking with them on the phone or in person, or having them fill out a questionnaire.

In an interview (scroll down to listen to podcast), Berkman explained that his goal was to "get better, more fine-grained assessments of smoking and relapse among a sample of heavy cigarette smokers who were trying to quit." The problem with previous research was, "they just didn't do it often enough...researchers were finding that the reports they were getting were not accurate. They weren't mapping to smaller-scale studies, where they actually tracked people more carefully."

In other words, people have a way of forgetting details about a habit, if you ask them once a month, or even once a day, but by asking every two hours via text message, you're likely to get a far more accurate report not only on whether they smoked during the past two hours but how they felt just before they may have lit up.

Berkman worked with Red Oxygen, a bulk SMS text-messaging company to deliver the messages. His sample included only 33 subjects--which made it more of a pilot test than a real study, but based on his results, he feels that it can scale to much larger samples.

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