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The duration of incoming quitline calls may serve as a crude proxy for the potential amount of reactive counseling provided.
To explore whether call duration may be useful for monitoring quitline capacity and service delivery.
Using data on the duration of incoming quitline calls to 1-800-QUIT-NOW from 2012 through 2015, we examined national trends and state-level variation in average call duration. We estimated a regression model of average call duration as a function of total incoming calls, nationally and by state, controlling for confounders.
From 2012 through 2015, average call duration was 11.4 min, nationally, and was 10 min or longer in 33 states. Average call duration was significantly correlated with quitline service provider. Higher incoming call volume was significantly associated with lower average call duration in 32 states and higher average call duration in five states (P-value <0.05). The relationship between call volume and call duration was not correlated with quitline service provider.
Variation in average call duration across states likely reflects different service delivery models. Average call duration was associated with call volume in many states. Significant changes in call duration may highlight potential quitline capacity issues that warrant further investigation.
Quitlines are standard care for smoking cessation; however, retaining clients in services is a problem. Little is known about factors that may predict dropout.
To examine predictors of retention while in-program and at follow-up for clients enrolling in a state quitline.
This was a retrospective analysis of quitline enrolled clients from 2011 to 2017 (N = 49,347). Client retention in-program was categorized as (a) low adherence to treatment (receiving zero coaching calls), moderate (1–2 calls), and high adherence (3+ calls). Dropout at follow-up included participants who were not reached for the 7-month follow-up.
More than half the sample dropped out during treatment; 61% were not reached for follow-up. Women (odds ratio (OR) = 1.21; 95% confidence interval (CI) = [1.16, 127]) and those with high levels of nicotine dependence (OR = 1.03; 95% CI = [1.02, 1.04]) were more likely to have moderate adherence to treatment (1–2 coaching calls). Dropout at follow-up was more likely among clients who used nicotine replacement therapy (OR = 1.14; 95% CI = [1.09, 1.19]) and less likely among those who had high treatment adherence (OR = 0.41; 95% CI = [0.39, 0.42]).
Given the relapsing nature of tobacco use and the harms related to tobacco use, quitlines can improve their impact by offering tailored services to enhance client engagement and retention in-treatment and at follow-up.
This study aims to assess the impact of a behavioural intervention, in the form of a self-monitoring record-keeping logbook, in reducing smoking tobacco expenditure amongst adult male household heads in rural Bangladesh.
The experiment was designed as a single-blind clustered randomised controlled trial utilising two-stage random sampling. A total of 650 adult male household heads were sampled from 16 chars (riverine islands) from Gaibandha, Northern Bangladesh, with eight chars in treatment and control groups each, between November 2018 and January 2019. The intervention consisted of a logbook to record daily smoking tobacco intake for 4 weeks provided only to participants in treatment chars (n = 332) while households in control chars received nothing (n = 318).
Final analysis was conducted using 222 and 210 households in the treatment and control chars respectively. The logbook intervention had a significant impact (P-value = 0.040) on reducing daily tobacco expenditure by 14% (α = 95%; CI: −0.273, −0.008) for the treatment group relative to the control group based on a difference-in-difference estimator. This is equivalent to a reduction of 20 cigarettes or 140 bidis smoked in a month.
Our minimal contact intervention successfully induced a reduction in smoking tobacco expenditure, which could effectively be incorporated with existing programs in the char regions of Bangladesh.
Several effective evidence-based tobacco treatment approaches can optimize cessation attempts; however, little is known about the utilization of such strategies by people with mental illnesses (MI) during their cessation attempts.
To examine methods used during and factors associated with tobacco cessation attempts among people with MI.
Self-administered cross-sectional survey data were obtained from 132 tobacco using inpatients from a psychiatric facility in Kentucky, USA.
Our study found ‘cold turkey’ as the most reported method by inpatient tobacco users with MI in their prior cessation attempts regardless of the psychiatric diagnosis category. Multivariate logistic regression found ethnicity (OR 26.1; 95% CI 2.9–237.1), age at 1st smoke (OR 1.1; 95% CI 1.0–1.1), importance to quit (OR 1.2; 95% CI 1.0–1.4), and receipt of brief tobacco treatment interventions (OR 1.1; 95% CI 1.0–1.3) significantly associated with quit attempt in the past year.
Despite the existence of various evidence-based approaches to enhance tobacco cessation among people with MI, ‘cold-turkey’ was the most preferred method in this sample. In addition, this study highlighted ethnicity, importance to quit, age at 1st smoke, and receipt of brief interventions as important factors to consider when tailoring tobacco cessation in this population. Though ethnicity is a non-modifiable factor, an informed provider may intervene skillfully by addressing socio-cultural barriers specific to an ethnic group. Lower ratings on the motivation ruler and early age of smoking initiation could also inform providers when using motivational interviewing and other evidence-based tobacco-cessation approaches.
To date, there has been no review of the research evidence examining smoking cessation among homeless adults. The current review aimed to: (i) estimate smoking prevalence in homeless populations; (ii) explore the efficacy of smoking cessation and smoking reduction interventions for homeless individuals; and (iii) describe the barriers and facilitators to smoking cessation and smoking reduction.
Systematic review of peer-reviewed research. Data sources included electronic academic databases. Search terms: ‘smoking’ AND ‘homeless’ AND ‘tobacco’, including adult (18+ years) smokers accessing homeless support services.
Fifty-three studies met the inclusion criteria (n = 46 USA). Data could not be meta-analysed due to large methodological inconsistencies and the lack of randomised controlled trials. Smoking prevalence ranged from 57% to 82%. Although there was no clear evidence on which cessation methods work best, layered approaches with additions to usual care seemed to offer modest enhancements in quit rates. Key barriers to cessation exist around the priority of smoking, beliefs around negative impact on mental health and substance use, and environmental influences.
Homeless smokers will benefit from layered interventions which support many of their competing needs. To best understand what works, future recommendations include the need for consensus on the reporting of cessation outcomes.
Cognitive-behavioral therapy (CBT) for tobacco cessation is an evidence-based, yet underutilized intervention. More research is needed to understand why some treatment-seekers are ‘no-shows’ for the initial visit.
Examine factors associated with participant no-shows among smokers scheduled for group CBT.
Tobacco smokers (N = 115) were recruited from the community, screened, and if eligible, scheduled to begin group-based CBT plus nicotine replacement therapy. At the screening, participants reported their recruitment source, demographics, smoking history, and contact information. We computed the distance to the study site using the address provided. Regression analyses tested predictors of participant no-shows for the initial visit.
Eligible participants were mostly recruited via flyers (56%), female (58%), African American (61%), middle-aged (Mage = 49 years), averaged 16 cigarettes per day, and resided 8 miles away from the study site. The overall initial visit no-show rate was 56%. Bivariate analyses indicated that respondents who were recruited online, younger, and lived further away from the site were more likely to be no-shows. Younger age significantly predicted failure to attend in the multivariable model.
Findings highlight potential barriers to participation in a group-based intervention, and have implications for pre-intervention engagement strategies and modifications that may increase reach and uptake.
We tested if an adjunctive sleep health (SH) intervention improved smoking cessation treatment response by increasing quit rates. We also examined if baseline sleep, and improvements in sleep in the first weeks of quitting, were associated with quitting at the end of treatment.
Treatment-seeking smokers (N = 29) aged 21–65 years were randomized to a SH intervention (n = 16), or general health (GH) control (n = 13) condition. Participants received six counseling sessions across 15-weeks: SH received smoking cessation + SH counseling; GH received smoking cessation + GH counseling. Counseling began 4-weeks before the target quit date (TQD), and varenicline treatment began 1-week prior to TQD. Smoking status and SH were assessed at baseline (week 1), TQD (week 4), 3 weeks after cessation (week 7), week 12, and at the end of treatment (EOT; week 15).
SH versus GH participants had higher Carbon Monoxide (CO) -verified, 7-day point prevalence abstinence at EOT (69% vs. 54%, respectively; adjusted odds ratio (aOR) = 2.10, 95% confidence interval (CI) = 0.40–10.69, P = 0.77). Higher baseline sleep efficiency (aOR = 1.42, 95% CI = 1.03–1.96, P = 0.03), predicted higher EOT cessation. Models were adjusted for age, sex, education, and baseline nicotine dependence.
Improving SH in treatment-seeking smokers prior to cessation warrants further examination as a viable strategy to promote cessation.