Non-responders in a quitline evaluation are more likely to be smokers - a drop-out and long-term follow-up study of the Swedish National Tobacco Quitline

Impact of quitline services on tobacco cessation: an application of modern epidemiologic methods

This study investigated the effectiveness of quitline service intensity (high vs low) on past 30-day tobacco abstinence at 7-months' follow-up, using observational data from the Oklahoma Tobacco Helpline (OTH) between April 2020 and December 2021. To assess the impact of loss to follow-up and nonrandom treatment assignment, we fit the parameters of a marginal structural model to estimate inverse probability weights for censoring (IPCW), treatment (IPTW), and combined (IPCTW). The risk ratio (RR) was estimated using modified Poisson regression with robust variance estimator. Of the 4695 individuals included in the study, 64% received high-intensity cessation services, and 53% were lost to follow-up. Using the conventional complete case analysis (responders only), high-intensity cessation services were associated with abstinence (RR = 1.18; 95% CI, 1.04-1.34). The effect estimate was attenuated after accounting for censoring (RR = 1.14; 95% CI, 1.00-1.30). After adjusting for both baseline confounding and selection bias via IPTCW, high-intensity cessation services were associated with 1.23 times (95% CI, 1.08-1.41) the probability of abstinence compared to low-intensity services. Despite relatively high loss to follow-up, accounting for selection bias and confounding did not notably impact quit rates or the relationship between intensity of quitline services and tobacco cessation among OTH participants.

Smoker motivations and predictors of smoking cessation: lessons from an inpatient smoking cessation programme

INTRODUCTION

Our study aimed to review the quit rates of smokers from our inpatient smoking cessation programme in relation to habits and sociodemographic factors, and also to explore the potential usefulness of electronic cigarettes (ECs) by reviewing smoking motivations.

METHODS

This was a retrospective study of patients recruited into our inpatient smoking cessation programme from June 2008 to June 2015. Sociodemographic factors and information on smoking habits were collected using a counsellor-administered questionnaire. Patients were given intensive counselling followed by a phone interview at one, three and six months to assess smoking status.

RESULTS

A total of 2,722 patients were enrolled. 27.6% of patients were abstinent at six months' follow-up. Patients who quit smoking were older, married, initiated smoking at a later age and had lower Fagerström Test for Nicotine Dependence scores. There was a trend towards successful quitting in those with higher education levels and Chinese ethnicity, but this was not statistically significant. Patients who planned to quit cold turkey and those who quit because of social pressures were more successful. Of the smoking motivations, only nicotine dependence was an independent predictor of smoking cessation.

CONCLUSION

Smoking motivations such as habitual use and psychological dependence did not influence quit rates and therefore do not support the use of ECs. Instead, a cold turkey method of quitting was shown in our study to contribute to cessation success. We recommend an increased focus on the use of pharmacologic aids as well as involvement of peer/spousal support to aid in such quit attempts.

Did psychosocial status, sociodemographics and smoking status affect non-attendance in control participants in the Danish Lung Cancer Screening Trial? A nested observational study

OBJECTIVES

We investigated if psychosocial status, sociodemographics and smoking status affected non-attendance in the control group in the randomised Danish Lung Cancer Screening Trial (DLCST).

DESIGN AND SETTING

This study was an observational study nested in the DLCST. Due to large non-attendance in the control group in the second screening round we made an additional effort to collect questionnaire data from non-attenders in this group in the third screening round. We used a condition-specific questionnaire to assess psychosocial status. We analysed the differences in psychosocial status in the third and preceding rounds between non-attenders and attenders in the control group in multivariable linear regression models adjusted for sociodemographics and smoking status reported at baseline. Differences in sociodemographics and smoking status were analysed with χ2 tests (categorical variables) and t-tests (continuous variables).

PRIMARY OUTCOME MEASURE

Primary outcome was psychosocial status.

PARTICIPANTS

All control persons participating in the third screening round in the DLCST were included.

RESULTS

Non-attenders in the third round had significantly worse psychosocial status than attenders in the scales: 'behaviour' 0.77 (99% CI 0.18 to 1.36), 'self-blame' 0.59 (99% CI 0.14 to 1.04), 'focus on airway symptoms' 0.22 (99% CI 0.08 to 0.36), 'stigmatisation' 0.51 (99% CI 0.16 to 0.86), 'introvert' 0.56 (99% CI 0.23 to 0.89) and 'harms of smoking' 0.35 (99% CI 0.11 to 0.59). Moreover, non-attenders had worse scores than attendees in the preceding screening rounds. Non-attenders also reported worse sociodemographics at baseline.

CONCLUSIONS

Non-attenders had a significantly worse psychosocial status and worse sociodemographics compared with attenders. The results of our study contribute with evidence of non-response and attrition driven by psychosocial status, which in turn may be influenced by the screening intervention itself. This can be used to adjust cancer screening trial results for bias due to differential non-attendance.

TRIAL REGISTRATION NUMBER

Clinicaltrials.gov Protocol Registration System (NCT00496977).

Telephone counselling for smoking cessation

BACKGROUND

Telephone services can provide information and support for smokers. Counselling may be provided proactively or offered reactively to callers to smoking cessation helplines.

OBJECTIVES

To evaluate the effect of telephone support to help smokers quit, including proactive or reactive counselling, or the provision of other information to smokers calling a helpline.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group Specialised Register, clinicaltrials.gov, and the ICTRP for studies of telephone counselling, using search terms including 'hotlines' or 'quitline' or 'helpline'. Date of the most recent search: May 2018.

SELECTION CRITERIA

Randomised or quasi-randomised controlled trials which offered proactive or reactive telephone counselling to smokers to assist smoking cessation.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures expected by Cochrane. We pooled studies using a random-effects model and assessed statistical heterogeneity amongst subgroups of clinically comparable studies using the I2 statistic. In trials including smokers who did not call a quitline, we used meta-regression to investigate moderation of the effect of telephone counselling by the planned number of calls in the intervention, trial selection of participants that were motivated to quit, and the baseline support provided together with telephone counselling (either self-help only, brief face-to-face intervention, pharmacotherapy, or financial incentives).

MAIN RESULTS

We identified 104 trials including 111,653 participants that met the inclusion criteria. Participants were mostly adult smokers from the general population, but some studies included teenagers, pregnant women, and people with long-term or mental health conditions. Most trials (58.7%) were at high risk of bias, while 30.8% were at unclear risk, and only 11.5% were at low risk of bias for all domains assessed. Most studies (100/104) assessed proactive telephone counselling, as opposed to reactive forms.Among trials including smokers who contacted helplines (32,484 participants), quit rates were higher for smokers receiving multiple sessions of proactive counselling (risk ratio (RR) 1.38, 95% confidence interval (CI) 1.19 to 1.61; 14 trials, 32,484 participants; I2 = 72%) compared with a control condition providing self-help materials or brief counselling in a single call. Due to the substantial unexplained heterogeneity between studies, we downgraded the certainty of the evidence to moderate.In studies that recruited smokers who did not call a helpline, the provision of telephone counselling increased quit rates (RR 1.25, 95% CI 1.15 to 1.35; 65 trials, 41,233 participants; I2 = 52%). Due to the substantial unexplained heterogeneity between studies, we downgraded the certainty of the evidence to moderate. In subgroup analysis, we found no evidence that the effect of telephone counselling depended upon whether or not other interventions were provided (P = 0.21), no evidence that more intensive support was more effective than less intensive (P = 0.43), or that the effect of telephone support depended upon whether or not people were actively trying to quit smoking (P = 0.32). However, in meta-regression, telephone counselling was associated with greater effectiveness when provided as an adjunct to self-help written support (P < 0.01), or to a brief intervention from a health professional (P = 0.02); telephone counselling was less effective when provided as an adjunct to more intensive counselling. Further, telephone support was more effective for people who were motivated to try to quit smoking (P = 0.02). The findings from three additional trials of smokers who had not proactively called a helpline but were offered telephone counselling, found quit rates were higher in those offered three to five telephone calls compared to those offered just one call (RR 1.27, 95% CI 1.12 to 1.44; 2602 participants; I2 = 0%).

AUTHORS' CONCLUSIONS

There is moderate-certainty evidence that proactive telephone counselling aids smokers who seek help from quitlines, and moderate-certainty evidence that proactive telephone counselling increases quit rates in smokers in other settings. There is currently insufficient evidence to assess potential variations in effect from differences in the number of contacts, type or timing of telephone counselling, or when telephone counselling is provided as an adjunct to other smoking cessation therapies. Evidence was inconclusive on the effect of reactive telephone counselling, due to a limited number studies, which reflects the difficulty of studying this intervention.

Self-perceived ability to cope with stress and depressive mood without smoking predicts successful smoking cessation 12 months later in a quitline setting: a secondary analysis of a randomized trial

Background

Telephone-based smoking cessation services (‘quitlines’) are both effective and cost-effective. Knowledge of modifiable baseline factors in real-life settings with heterogeneous participants is essential for the development and improvement of treatment protocols to assist in telephone-based smoking cessation. The aim was to assess if self-perceived abilities to cope measured at baseline, would predict abstinence at the 12-month follow-up at the Swedish National Tobacco Quitline (SNTQ).

Methods

The data were retrieved from a previous randomized controlled trial comparing the effectiveness of proactive and reactive service at the SNTQ. Included were 612 clients calling the SNTQ between February 2009 and September 2010. Outcome measures were self-reported point prevalence and 6-month continuous abstinence at the 12-month follow-up. Plausible predictors of smoking cessation were assessed at the first call and in a baseline questionnaire. Self-perceived abilities at baseline were measured by two questions: (1) How likely is it that you will be smoke-free in one year? and (2) How likely are you to be able to handle stress and depressive mood without smoking? The associations between potential predictors and outcome (smoke-free at 12-month follow-up) were assessed by logistic regression analysis.

Results

Of the two potential predictors for abstinence at 12-month follow-up, only the perceived ability to handle stress and depressive mood without smoking remained significant in the adjusted analyses (Odds Ratio, OR 1.13, 95% CI 1.00–1.27 for point prevalence and OR 1.16, 95% CI 1.01–1.33 for 6-month continuous abstinence according to intention-to-treat). The overall strongest predictor in the adjusted analyses was smoking status in the week before baseline (OR 3.30, 95% CI 1.79–6.09 for point prevalence and OR 3.97, 95% CI 2.01–7.83 for 6-month continuous abstinence).

Conclusions

The perceived ability to handle stress and depressive mood without smoking at baseline predicted the subjects’ abstinence at the 12-month follow-up. An assessment of/adjustment for stress and depressive mood coping skills may be appropriate in future smoking cessation treatment and research. The treatment protocol can be tailored to individual differences and needs for optimal support.

Trial registration

ClinicalTrials.gov: NCT02085616. Registered March 10, 2014, ‘retrospectively registered’.

Cigarette, waterpipe and e-cigarette use among an international sample of medical students. Cross-sectional multicenter study in Germany and Hungary

Background

Tobacco use is the leading preventable cause of death worldwide. Besides cigarette smoking, waterpipe and e-cigarettes are gaining popularity among young adults. Medical students’ smoking behavior is of particular interest because of their impending role in health promotion as future physicians. Aim of our study is to examine the prevalence and predictors of cigarette, waterpipe and e-cigarette use and the association of tobacco use with self-reported health status in an international sample of medical students.

Methods

In a multicenter cross-sectional study data on different aspects of health behavior were collected from medical students of 65 nationalities using a self-administered questionnaire in Germany (Dresden, Munich) and Hungary (Budapest, Pécs). The survey was conducted among 1st, 3rd and 5th year students. To explore associations between smoking behavior and socio-cultural factors Pearson’s chi²-tests and multivariate binary logistic regression analyses were performed.

Results

The largest subpopulations were formed by German (n = 1289), Hungarian (n = 1055) and Norwegian (n = 147) students. Mean age was 22.5 ± 3.3 years. Females represented 61.6% of the sample. In the whole sample prevalence of cigarette smoking was 18.0% (95% CI 16.6–19.4%), prevalence of waterpipe use was 4.8% (95% CI 4.0–5.7%), that of e-cigarette 0.9% (95% CI 0.5–1.2%). More males (22.0%) than females (15.5%) reported cigarette smoking. The lowest prevalence of cigarette smoking was found among Norwegian students (6.2%). Cigarette smokers were older, waterpipe users were younger than non-users. E-cigarette use was not associated with age of the students. Religious involvement was protective only against cigarette smoking. Financial situation showed no association with any kind of tobacco consumption. Cigarette smokers and e-cigarette users were less likely to report very good or excellent health status.

Conclusions

Cigarette smoking is still the most popular way of consuming tobacco, although alternative tobacco use is also prevalent among medical students. To further health consciousness, medical schools should pay more attention to students’ health behavior, especially their smoking habits. Tobacco prevention and cessation programs for medical students should consider not only the health risks of cigarette smoking but the need to discourage other forms of tobacco use, such as waterpipe.

An individually-tailored smoking cessation intervention for rural Veterans: a pilot randomized trial

BACKGROUND

Tobacco use remains prevalent among Veterans of military service and those residing in rural areas. Smokers frequently experience tobacco-related issues including risky alcohol use, post-cessation weight gain, and depressive symptoms that may adversely impact their likelihood of quitting and maintaining abstinence. Telephone-based interventions that simultaneously address these issues may help to increase treatment access and improve outcomes.

METHODS

This study was a two-group randomized controlled pilot trial. Participants were randomly assigned to an individually-tailored telephone tobacco intervention combining counseling for tobacco use and related issues including depressive symptoms, risky alcohol use, and weight concerns or to treatment provided through their state tobacco quitline. Selection of pharmacotherapy was based on medical history and a shared decision interview in both groups. Participants included 63 rural Veteran smokers (mean age = 56.8 years; 87 % male; mean number of cigarettes/day = 24.7). The primary outcome was self-reported 7-day point prevalence abstinence at 12 weeks and 6 months.

RESULTS

Twelve-week quit rates based on an intention-to-treat analysis did not differ significantly by group (Tailored = 39 %; Quitline Referral = 25 %; odds ratio [OR]; 95 % confidence interval [CI] = 1.90; 0.56, 5.57). Six-month quit rates for the Tailored and Quitline Referral conditions were 29 and 28 %, respectively (OR; 95 % CI = 1.05; 0.35, 3.12). Satisfaction with the Tailored tobacco intervention was high.

CONCLUSIONS

Telephone-based treatment that concomitantly addresses other health-related factors that may adversely affect quitting appears to be a promising strategy. Larger studies are needed to determine whether this approach improves cessation outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov identifier number NCT01592695 registered 11 April 2012.

The Effect of Tailored Web-Based Feedback and Optional Telephone Coaching on Health Improvements: A Randomized Intervention Among Employees in the Transport Service Industry

Background

Lifestyle-related health problems are an important health concern in the transport service industry. Web- and telephone-based interventions could be suitable for this target group requiring tailored approaches.

Objective

To evaluate the effect of tailored Web-based health feedback and optional telephone coaching to improve lifestyle factors (body mass index—BMI, dietary intake, physical activity, stress, sleep, tobacco and alcohol consumption, disease history, self-perceived health, and motivation to change health habits), in comparison to no health feedback or telephone coaching.

Methods

Overall, 3,876 employees in the Swedish transport services were emailed a Web-based questionnaire. They were randomized into: control group (group A, 498 of 1238 answered, 40.23%), or intervention Web (group B, 482 of 1305 answered, 36.93%), or intervention Web + telephone (group C, 493 of 1333 answered, 36.98%). All groups received an identical questionnaire, only the interventions differed. Group B received tailored Web-based health feedback, and group C received tailored Web-based health feedback + optional telephone coaching if the participants’ reported health habits did not meet the national guidelines, or if they expressed motivation to change health habits. The Web-based feedback was fully automated. Telephone coaching was performed by trained health counselors. Nine months later, all participants received a follow-up questionnaire and intervention Web + telephone. Descriptive statistics, the chi-square test, analysis of variance, and generalized estimating equation (GEE) models were used.

Results

Overall, 981 of 1473 (66.60%) employees participated at baseline (men: 66.7%, mean age: 44 years, mean BMI: 26.4 kg/m²) and follow-up. No significant differences were found in reported health habits between the 3 groups over time. However, significant changes were found in motivation to change. The intervention groups reported higher motivation to improve dietary habits (144 of 301 participants, 47.8%, and 165 of 324 participants, 50.9%, for groups B and C, respectively) and physical activity habits (181 of 301 participants, 60.1%, and 207 of 324 participants, 63.9%, for B and C, respectively) compared with the control group A (122 of 356 participants, 34.3%, for diet and 177 of 356 participants, 49.7%, for physical activity). At follow-up, the intervention groups had significantly decreased motivation (group B: P<.001 for change in diet; P<.001 for change in physical activity; group C: P=.007 for change in diet; P<.001 for change in physical activity), whereas the control group reported significantly increased motivation to change diet and physical activity (P<.001 for change in diet; P<.001 for change in physical activity).

Conclusion

Tailored Web-based health feedback and the offering of optional telephone coaching did not have a positive health effect on employees in the transport services. However, our findings suggest an increased short-term motivation to change health behaviors related to diet and physical activity among those receiving tailored Web-based health feedback.