Longitudinal Analysis of Abstinence-Specific Social Support and Smoking Cessation
ABSTRACT (1) To replicate previous research finding that abstinence-specific social support during the active phase of quitting predicts short- and long-term smoking cessation treatment outcome. (2) To describe time-related changes in abstinence-specific support, including how support provided during middle and later phases of the quitting process is associated with treatment outcome.
Combined data from three randomized clinical trials of smoking cessation treatment (N = 739) were analyzed using logistic regression and analysis of variance.
Measures included the Partner Interaction Questionnaire (PIQ; Cohen & Lichtenstein, 1990), a measure of smoking-related social support, and smoking status according to 7-day point-prevalence abstinence.
Longitudinal analyses found that positive support peaked at week 12, decreasing thereafter. Positive support provided after week 12 did not differentiate between those who never quit smoking, those who quit and relapsed, and those who maintained abstinence. In contrast, negative support was monotonic and was useful at follow-up points for distinguishing between outcome groups.
These results suggest that positive and negative support are both important factors in the early phase of quitting, but it is the continued minimization of negative support that best predicts maintenance of nonsmoking.
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- "Additionally, recently relocated or isolated individuals may face particular challenges in the process, perhaps due to stress or lack of supportive ties. Research on tobacco cessation has identified the positive roles of social integration (e.g., Cobb et al., 2010) and social support (e.g., Lawhon et al., 2009; Mermelstein et al., 1986). The current study is suggestive of similar results and it may be fruitful for future work to directly examine the role of social integration and social support on marijuana cessation. "
ABSTRACT: Background: With marijuana use increasing among American adolescents, better understanding of the factors associated with decreasing use and quitting can help inform cessation efforts. This study evaluates a range of neighborhood, family, peer network, and individual factors as predictors of marijuana use, change, and non-use over one year, and cessation over six years. Methods: Data come from adolescents in Waves I and II of the National Longitudinal Study of Adolescent Health (N=. 458, one-year sample), or Waves I and III (N=. 358, six-year sample), and reported using marijuana at least four times in the past month at Wave I. Results: Eighteen percent of adolescents stopped using marijuana after six years. Results suggest neighborhood context affects overall use level, whereas neighborhood context and friends were critical to cessation vs. continuation of use. Decrease in use were more likely among adolescents in disadvantaged or less cohesive neighborhoods, or who moved between waves. Non-use after one year was more likely among adolescents who did not move, had fewer marijuana-using friends, and did not exclusively have outside-of-school friends. Cessation at six years was more likely among adolescents in less disadvantaged and more cohesive neighborhoods, and for those with within-school friends. Conclusions: Results highlight the importance of both objective and subjective neighborhood characteristics, as well as peer networks, on adolescent marijuana use. Factors associated with decreases in use appear distinct from those that predict quitting, suggesting that continuation vs. cessation is linked to peers as well as neighborhood context. Relocated and isolated individuals may face challenges with cessation.Drug and Alcohol Dependence 09/2014; 144. DOI:10.1016/j.drugalcdep.2014.08.019 · 3.28 Impact Factor
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- "None of the demographic, psychological, or psychiatric variables studied in the present study were strongly associated with further smoking cessation. This finding is possibly due to the sample size, or to the contribution of other factors to the change process, such as environmental support . Treatment group allocation was however found to predict smoking cessation at T2. "
ABSTRACT: The main objective of our study was to assess the impact of a board game on smoking status and smoking-related variables in current smokers. To accomplish this objective, we conducted a randomized controlled trial comparing the game group with a psychoeducation group and a waiting-list control group. The following measures were performed at participant inclusion, as well as after a 2-week and a 3-month follow-up period: “Attitudes Towards Smoking Scale” (ATS-18), “Smoking Self-Efficacy Questionnaire” (SEQ-12), “Attitudes Towards Nicotine Replacement Therapy” scale (ANRT-12), number of cigarettes smoked per day, stages of change, quit attempts, and smoking status. Furthermore, participants were assessed for concurrent psychiatric disorders and for the severity of nicotine dependence with the Fagerström Test for Nicotine Dependence (FTND). A time × group effect was observed for subscales of the ANRT-12, ATS-18 and SEQ-12, as well as for the number of cigarettes smoked per day. At three months follow-up, compared to the participants allocated to the waiting list group, those on Pick-Klop group were less likely to remain smoker. Outcomes at 3 months were not predicted by gender, age, FTND, stage of change, or psychiatric disorders at inclusion. The board game seems to be a good option for smokers. The game led to improvements in variables known to predict quitting in smokers. Furthermore, it increased smoking-cessation rates at 3-months follow-up. The game is also an interesting alternative for smokers in the precontemplation stage.Substance Abuse Treatment Prevention and Policy 01/2013; 8(1):3. DOI:10.1186/1747-597X-8-3 · 1.16 Impact Factor
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- "Diet is influenced by a range of environmental factors, including social support (Bandura, 1986; Booth, et al., 2005; Thornton, et al., 2006); therefore, confidence in overcoming the barriers to healthy dietary behavior could be influenced by social support and other means of affective reinforcement (Bandura, 1986; Sheeshka et al., 1993). Several studies have shown that social support is associated with healthy behaviors such as smoking cessation (Wagner et al., 2004; Turner et al., 2008; Vaananen et al., 2008; Lawhon et al., 2009), reducing alcohol consumption (Steptoe et al., 1996; Hagihara et al., 2003), increasing physical activity (Spanier et al., 2001; Okun et al., 2003; Anderson et al., 2006; Kanu et al., 2008), and cancer screening (Seow et al., 2000; Katapodi et al., 2002). These studies have found that people with a high level of social support were more likely to quit risky health behaviors and engage in health-promoting behaviors. "
ABSTRACT: Improvements in diet can decrease the cancer rates. The aim of the present study was to determine the relationships between self-perception of diet quality and personality, impulsiveness, stress, coping strategy, sense of coherence, self-efficacy, and social support. This cross-sectional study was conducted using a multiple-stratified random sampling method based on the Korea Census of 2007. In October 2009, investigators conducted 15-minute face-to-face interviews with 1,530 South Korean volunteers who ranged from 30 to 69 years of age without a history of cancer. Respondents were more likely to perceive that they consumed a healthy diet if they were older than 50 years, lived with a partner, had a monthly family income greater than $4,000 USD, had a low perceived risk of cancer, consumed less alcohol, exercised regularly, had a less agreeable or conscientious personality, had low stress levels, had a high sense of coherence or self-efficacy, and had ample social support. Psychosocial factors, such as personality, stress, sense of coherence, self-efficacy, and social support, are associated with the self- perception of diet quality. Analysis of the factors that contribute to a perceived healthy diet could assist with the design of educational campaigns.Asian Pacific journal of cancer prevention: APJCP 04/2012; 13(4):1495-504. DOI:10.7314/APJCP.2012.13.4.1495 · 2.51 Impact Factor