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Predicting Which Smokers Won't Quit Successfully


Knowing who is more likely to opt out could help tailor better interventions for smokers.


Is a young female more likely to opt out of a text message smoking cessation intervention program? And can certain characteristics predict retention in such interventions?

Younger age, female sex, higher levels of nicotine dependence, lower intention to be smoke free and enrolling in a smoking cessation intervention program a week or less before initiating a quit attempt were associated with an increased risk of opting out, according to research published in JMIR mHealth and uHealth.

Researchers found that those who smoked within five minutes of waking up were 1.17 times more likely to opt out of the smoking cessation intervention program, called SmokefreeTXT, than those who smoked more than five minutes after waking up. Logistic regression modeling indicated that smokers who were sometimes around other smokers were 1.96 times more likely to opt out in the first three days of the quit attempt, compared to those who were never or rarely around other smokers.

“Providing additional support to users with these characteristics may increase retention in text messaging programs and ultimately lead to smoking cessation,” the study authors wrote.

Participants in the study consisted of the first quit attempt of all users who signed up for SmokefreeTXT using the web enrollment form between March and June 2016.

How the SmokefreeTXT Digital Health Program Works

SmokefreeTXT is a fully automated text messaging smoking cessation program run by the National Cancer Institute. The program has up to two weeks of preparation messages and six weeks of post-quit date messages. Messages vary in content and frequency based on the quit date the user sets.

Text messages include motivation support, tips on preparing to quit, advice on managing cravings, suggestions for smoke-free activities, smoking facts and recognition of cessation milestones.

Users can interact with the messages by texting keywords, such as “crave,” “slip,” or “mood,” any time to receive on-demand support. Program assessment questions measure smoking status, mood and craving levels. Participants can reset their quit date at any time to restart the program and can opt out at any time.

Measuring the Smoker’s Characteristics

The digital health program routinely collected data on age, sex and smoking frequency.

Researchers examined smoking context and motivational characteristics associated with opting out.

Smoking characteristics measured:

  • The level of nicotine dependence. “How soon after you wake up do you smoke your first cigarette.”
  • Frequency of reminders to smoke. “My life is full of reminders to smoke.”
  • Time of the day for cigarette cravings. “When do you crave cigarettes the most?”
  • Frequency of being around other smokers. “How often are you around people who are smoking?

Motivational characteristics measured:

  • Extrinsic motivation for quitting smoking. “I would try to quit smoking because quitting smoking is an important thing for me to do.”
  • Confidence in quitting. “I feel able to meet the challenge of quitting smoking.”
  • Long-term cessation intention. “I intend to be smoke-free one year from now.”

Understanding the Results

A majority of those enrolled in the program (69.4%) were women and smoked every day (92.4%). Nearly 69% had frequent reminders to smoke and 67.1% craved cigarettes at all times of the day. Participants reported high extrinsic and intrinsic motivation for quitting smoking.

More specifically, women, users younger than 50 years old and those who signed up for the program on their quit date or within one week of their quit data were more likely to opt out.

Those with less than high long-term intention to be smoke-free were 1.29 times more likely to opt out than those with the highest levels of long-term intention.

“Tailoring to specific influential characteristics may increase the relevance of the program to each user and provide more salient tips and resources, thereby increasing retention,” the authors wrote.

And if physicians understand which patients are more likely to opt out of such interventions, they could guide them on a different path with an alternative treatment better suited to their needs and motivations.

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