Baseline Stage, Severity, and Effort Effects Differentiate Stable Smokers from Maintainers and Relapsers

Redding, Colleen A.; Prochaska, James O.; Paiva, Andrea L.; Rossi, Joseph S.; Velicer, Wayne F.; Blissmer, Bryan; Greene, Geoffrey; Robbins, Mark L.; Sun, Xiaowu

doi:10.3109/10826084.2011.565853

Cited by 17 publications

(24 citation statements)

References 31 publications

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“…Similar to what has been found in previous studies (Redding et al, 2011; Sun et al, 2007), Self-Reevaluation (SR) was a key process that differentiated relapsers from maintainers. Those studies have indicated that once individuals quit, they benefit from decreasing their reliance on SR and increasing their utilization of Behavioral Processes such as Helping Relationships for potential stress management and support, and Stimulus Control for alteration of environmental cues to maintain the cessation process.…”

Section: Discussionsupporting

confidence: 78%

“…Significant predictors of successful cessation/abstinence have been found to include smoking addiction severity, age, education (Velicer, Redding, Sun, & Prochaska, 2007), and Stage of Change and TTM effort variables (e.g. Redding, Prochaska, Paiva, Rossi et al, 2011; Blissmer, Prochaska, Velicer, Redding et al, 2010).…”

mentioning

confidence: 99%

“…Redding, Prochaska, Paiva, Rossi et al, 2011; Blissmer, Prochaska, Velicer, Redding et al, 2010). In order to better serve the full population of smokers, a more comprehensive understanding of static and dynamic variables that could differentiate those who relapse from those who stay quit could provide insights for improving tailored cessation interventions.…”

mentioning

confidence: 99%

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Predictors of relapse among smokers: Transtheoretical effort variables, demographics, and smoking severity

Gökbayrak

Paiva

Blissmer

et al. 2015

Addictive Behaviors

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The present longitudinal study investigates baseline assessments of static and dynamic variables, including demographic characteristics, smoking severity, and Transtheoretical Model of Behavior Change (TTM) effort variables (Decisional Balance (e.g. Pros and Cons), Situational Temptations, and Processes of Change) of relapse among individuals who were abstinent at 12-months. The study sample (N=521) was derived from an integrated dataset of four population-based smoking cessation interventions. Several key findings included: Participants who were aged 25–44 and 45–64 (OR = .43, p = .01 and OR = .40, p = .01, respectively) compared to being aged 18–24 were less likely to relapse at follow-up. Participants in the control group were more than twice as likely to relapse (OR = 2.17, p = .00) at follow-up compared to participants in the treatment group. Participants who reported higher Habit Strength scores were more likely to relapse (OR = 1.05, p = .02). Participants who had higher scores of Reinforcement Management (OR = 1.05, p = .04) and Self-Reevaluation (OR = 1.08, p = .01) were more likely to relapse Findings add to one assumption that relapsers tend to relapse not solely due to smoking addiction severity, but due to immediate precursor factors such as emotional distress. One approach would be to provide additional expert guidance on how smokers can manage stress effectively when they enroll in treatment at any stage of change.

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Section: Discussionsupporting

confidence: 78%

mentioning

confidence: 99%

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Predictors of relapse among smokers: Transtheoretical effort variables, demographics, and smoking severity

Gökbayrak

Paiva

Blissmer

et al. 2015

Addictive Behaviors

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“…This protocol has been found to be effective in simultaneously changing multiple behaviors in both adults 28 and adolescents. 29,30 Exercise coaching intervention. The exercise coaching group received up to 3 proactive telephone sessions at 0, 3, and 6 months via outreach by a trained health coach.…”

Section: Interventionsmentioning

confidence: 99%

Enhancing Multiple Domains of Well-Being by Decreasing Multiple Health Risk Behaviors: A Randomized Clinical Trial

Prochaska

Evers

Castle

et al. 2012

Population Health Management

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Tailored behavior change programs have proven effective at decreasing health risk factors, but the impact of such programs on participant well-being has not been tested. This randomized trial evaluated the impact of tailored telephone coaching and Internet interventions on health risk behaviors and individual well-being. Exercise and stress management were the primary health risks of interest; improvements in other health risk behaviors were secondary outcomes. A sample of 3391 individuals who reported health risk in the areas of exercise and stress management were randomly assigned to 3 groups: telephonic coaching that applied Transtheoretical Model (TTM) tailoring for exercise and minimal tailoring (stage of change) for stress management; an Internet program that applied TTM tailoring for stress management and minimal tailoring for exercise; or a control group that received an assessment only. Participants were administered the Well-Being Assessment and, at baseline, had relatively low well-being scores (mean, 60.9 out of 100 across all groups). At 6 months, a significantly higher percentage of both treatment groups progressed to the Action stage for exercise, stress management, healthy diet, and total number of health risks, compared to the control group. Both treatment groups also demonstrated significantly greater improvements on overall well-being and the domains of emotional health, physical health, life evaluation, and healthy behaviors. There were no differences between the groups for 2 well-being domains: basic access to needs and work environment. These results indicate that scalable, tailored behavior change programs can effectively reduce health risk and accrue to improved well-being for participants.

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“…while this Survivor expresses optimism, tempered with moderately valenced realism that acknowledges challenges: "I quit smoking on Sunday evening. Day Additionally, higher relapse rates are known to correspond to the strength of addiction and severity of withdrawal symptoms [37,41]. We thus hypothesize that Relapsers' negative affect after cessation is also partly due to their more intense struggles with such negative physical and psychological feelings, which include anger, depression, and anxiety [21], as exemplified in the following tweets by …”

Section: Emotion Variablesmentioning

confidence: 97%

Unraveling abstinence and relapse

Murnane

Counts

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Analysis of smokers' posts and behaviors on Twitter reveals factors impacting abstinence and relapse during cessation attempts. Combining automatic and crowdsourced techniques, we detect users trying to quit smoking and analyze tweet and network data from a sample of 653 individuals over a two-year window of quitting. Guided by theory and practice, we derive behavioral, social, and emotional measures to compare users who abstain and relapse. We also examine the cessation process, demonstrating that Twitter can help chronicle how some people go about quitting. Among other results, we show that those who fail in their smoking cessation are far heavier posters and use relatively less positive language, while those who succeed are more social in both network ties and in directed communication. We conclude with insights on how intelligent intervention systems can harness these signals to provide tailored behavior change support.

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Baseline Stage, Severity, and Effort Effects Differentiate Stable Smokers from Maintainers and Relapsers

Cited by 17 publications

References 31 publications

Predictors of relapse among smokers: Transtheoretical effort variables, demographics, and smoking severity

Predictors of relapse among smokers: Transtheoretical effort variables, demographics, and smoking severity

Enhancing Multiple Domains of Well-Being by Decreasing Multiple Health Risk Behaviors: A Randomized Clinical Trial

Unraveling abstinence and relapse

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