Effects of smoking cessation treatment attendance on abstinence: The moderating role of psychologically based behavioral health conditions

Contingency management for smoking cessation for individuals with overweight or obesity: A randomized controlled trial

BACKGROUND

Interventions for quitting smoking and weight control among individuals with excess weight are scarce. Our study evaluated the effectiveness of cognitive behavioral therapy (CBT) plus contingency management (CM) in this population, and examined whether CM for smoking cessation improved CBT treatment outcomes at end of treatment (EOT) and at 1-, 3-, 6-, and 12-month follow-ups (FU).

METHODS

In an 8-week randomized clinical trial, 120 adults who smoke with overweight or obesity (54.16% females; MBMI = 31.75 ± 4.31) were randomly assigned to CBT for both quitting smoking and weight control (n = 60) or the same treatment plus CM for smoking cessation (n = 60). Outcome variables were compared (i.e., treatment completion, smoking abstinence, weight change and secondary outcomes).

RESULTS

At EOT, the CBT + CM group achieved 78.33% 7-day point-prevalence abstinence rates compared to 61.67% in the CBT group (p = .073), and rates declined over time (12-month FU: 18% vs 12%). Participants who attained abstinence weighed more compared to baseline at EOT (Mkg = 1.07; SD = 1.88) and over time (12-month FU: Mkg = 4.19; SD = 4.31). No differences were found between the two groups in outcome variables.

CONCLUSIONS

Both interventions were effective in promoting abstinence and reducing tobacco use over time. Combining CBT with CM for smoking cessation did not improve treatment outcomes in individuals with overweight or obesity compared to CBT only. Future studies should evaluate whether implementing CM for weight maintenance helps control post-cessation weight gain in this population.

Smoking Cessation Apps: A Systematic Review of Format, Outcomes, and Features

Smoking cessation interventions are effective, but they are not easily accessible for all treatment-seeking smokers. Mobile health (mHealth) apps have been used in recent years to overcome some of these limitations. Smoking cessation apps can be used in combination with a face-to-face intervention (FFSC-Apps), or alone as general apps (GSC-Apps). The aims of this review were (1) to examine the effects of FFSC-Apps and GSC-Apps on abstinence, tobacco use, and relapse rates; and (2) to describe their features. A systematic review was conducted following the internationally Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Of the total 6016 studies screened, 24 were included, of which nine used GSC-Apps and 15 FFSC-Apps. Eight studies reported significant differences between conditions in smoking cessation outcomes, with three of them being in favor of the use of apps, and two between different point-assessments. Concerning Apps features, most GSC-Apps included self-tracking and setting a quit plan, whereas most of the FFSC-Apps included self-tracking and carbon monoxide (CO) measures. Smartphone apps for smoking cessation could be promising tools. However, more research with an adequate methodological quality is needed to determine its effect. Nevertheless, smartphone apps' high availability and attractiveness represent a great opportunity to reach large populations.

Opioid Misuse among Smokers with Chronic Pain: Relations with Substance Use and Mental Health

Opioid misuse is a significant public health concern with substantial medical, social, and economic costs. Cross cutting the personal and societal effects of this current crisis, opioid misuse is associated with poorer physical and mental health outcomes that impair function across numerous life domains. Importantly, opioid misuse disproportionately affects persons with chronic pain and individuals who smoke tobacco. Despite the higher risk for smokers with chronic pain to engage in opioid misuse, little work has examined how opioid misuse may be related to mental health problems, including other substance use, among this vulnerable group. The current study examined opioid misuse as a predictor of substance use and mental health problems among 187 (M_age = 39.02, SD = 9.94, 74.9% female) daily cigarette smokers with chronic pain who currently use opioids. Results indicated that opioid misuse is associated with greater tobacco (13% of variance), alcohol (27% of variance), and cannabis (22% of variance) problems, as well as anxiety (26% of variance) and depressive symptoms (26% of variance). These results highlight the potential importance of opioid misuse in terms of concurrent substance and mental health problems among smokers with chronic pain. Future work is needed to explicate directionality and temporal ordering in the observed relations.

Clinical symptoms and personality traits predict subpopulations of treatment-seeking substance users

BACKGROUND

The heterogeneity of treatment-seeking substance users represents a challenge, as most studies include participants having problems with specific substances or merge polysubstance users into the same category without considering differences between profiles. Considering the inconsistent literature on predictors of treatment outcomes, this study aimed to identify subpopulations of individuals with substance use disorders (SUDs) and analyze the association among class membership, previous relapses, and treatment retention.

METHODS

The study recruited a total of 159 participants (mean age = 40.60, SD = 8.70; 85.5% males) from two treatment facilities (outpatient daycare and inpatient residential centers). The baseline assessment gathered lifetime and current substance use, and personality and psychopathology measures. The study performed a latent class analysis to identify subpopulations of substance users and explored predictors of class membership using a multinomial regression analysis.

RESULTS

The study found six different classes of substance users based on their diagnosis and pattern of substance use: class 1 (6.92% of participants): individuals with cannabis as primary substance, alcohol/cocaine as secondary substance and additional use of stimulants or other drugs; class 2 (30.82%): cocaine as primary substance, alcohol as secondary and additional cannabis use; class 3 (20.13%): alcohol as primary substance, cocaine as secondary and additional cannabis use; class 4 (17.61%): cocaine as primary substance, cannabis as secondary and additional alcohol/other drugs use; class 5 (16.35%): alcohol as primary and cannabis as secondary substance; class 6 (8.18%): heroin as primary substance, cocaine as secondary and additional alcohol use. Several traits and clinical symptoms predicted distinct class memberships. Participants pertaining to class 6 presented the highest number of relapses (M = 2.54, SD = 1.56).

CONCLUSIONS

These results have several clinical implications. Belonging to class 6 was associated with a greater number of previous relapses. Also, specific psychopathological symptoms and personality traits may impact SUD treatment response, which may help clinicians to guide initial assessment and treatment allocation.