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Blended Smoking Cessation Treatment: Exploring Measurement, Levels, and Predictors of Adherence.

ABSTRACT: BACKGROUND:Blended face-to-face and Web-based treatment is a promising way to deliver cognitive behavioral therapy. Since adherence has been shown to be a measure for treatment's acceptability and a determinant for treatment's effectiveness, in this study, we explored adherence to a new blended smoking cessation treatment (BSCT). OBJECTIVE:The objective of our study was to (1) develop an adequate method to measure adherence to BSCT; (2) define an adequate degree of adherence to be used as a threshold for being adherent; (3) estimate adherence to BSCT; and (4) explore the possible predictors of adherence to BSCT. METHODS:The data of patients (N=75) were analyzed to trace adherence to BSCT delivered at an outpatient smoking cessation clinic. In total, 18 patient activities (eg, using a Web-based smoking diary tool or responding to counselors' messages) were selected to measure adherence; the degree of adherence per patient was compared with quitting success. The minimum degree of adherence of patients who reported abstinence was examined to define a threshold for the detection of adherent patients. The number of adherent patients was calculated for each of the 18 selected activities; the degree of adherence over the course of the treatment was displayed; and the number of patients who were adherent was analyzed. The relationship between adherence and 33 person-, smoking-, and health-related characteristics was examined. RESULTS:The method for measuring adherence was found to be adequate as adherence to BSCT correlated with self-reported abstinence (P=.03). Patients reporting abstinence adhered to at least 61% of BSCT. Adherence declined over the course of the treatment; the percentage of adherent patients per treatment activity ranged from 82% at the start of the treatment to 11%-19% at the final-third of BSCT; applying a 61% threshold, 18% of the patients were classified as adherent. Marital status and social modeling were the best independent predictors of adherence. Patients having a partner had 11-times higher odds of being adherent (OR [odds ratio]=11.3; CI: 1.33-98.99; P=.03). For social modeling, graded from 0 (=partner and friends are not smoking) to 8 (=both partner and nearly all friends are smoking), each unit increase was associated with 28% lower odds of being adherent (OR=0.72; CI: 0.55-0.94; P=.02). CONCLUSIONS:The current study is the first to explore adherence to a blended face-to-face and Web-based treatment (BSCT) based on a substantial group of patients. It revealed a rather low adherence rate to BSCT. The method for measuring adherence to BSCT could be considered adequate because the expected dose-response relationship between adherence and quitting could be verified. Furthermore, this study revealed that marital status and social modeling were independent predictors of adherence. TRIAL REGISTRATION:Netherlands Trial Registry NTR5113; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5113 (Archived by WebCite at http://www.webcitation.org/71BAPwER8).

SUBMITTER: Siemer L

PROVIDER: S-EPMC6094087 | biostudies-other | 2018 Aug

REPOSITORIES: biostudies-other

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Blended Smoking Cessation Treatment: Exploring Measurement, Levels, and Predictors of Adherence.

Siemer Lutz L Brusse-Keizer Marjolein Gj MG Postel Marloes G MG Ben Allouch Somaya S Patrinopoulos Bougioukas Angelos A Sanderman Robbert R Pieterse Marcel E ME

Journal of medical Internet research 20180801 8

<h4>Background</h4>Blended face-to-face and Web-based treatment is a promising way to deliver cognitive behavioral therapy. Since adherence has been shown to be a measure for treatment's acceptability and a determinant for treatment's effectiveness, in this study, we explored adherence to a new blended smoking cessation treatment (BSCT).<h4>Objective</h4>The objective of our study was to (1) develop an adequate method to measure adherence to BSCT; (2) define an adequate degree of adherence to be ...[more]

PMID: 30068503

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Project description:BackgroundBlended face-to-face and web-based treatment is a promising way to deliver smoking cessation treatment. Since adherence has been shown to be an indicator of treatment acceptability and a determinant for effectiveness, we explored and compared adherence and predictors of adherence to blended and face-to-face alone smoking cessation treatments with similar content and intensity.ObjectiveThe objectives of this study were (1) to compare adherence to a blended smoking cessation treatment with adherence to a face-to-face treatment; (2) to compare adherence within the blended treatment to its face-to-face mode and web mode; and (3) to determine baseline predictors of adherence to both treatments as well as (4) the predictors to both modes of the blended treatment.MethodsWe calculated the total duration of treatment exposure for patients (N=292) of a Dutch outpatient smoking cessation clinic who were randomly assigned either to the blended smoking cessation treatment (n=130) or to a face-to-face treatment with identical components (n=162). For both treatments (blended and face-to-face) and for the two modes of delivery within the blended treatment (face-to-face vs web mode), adherence levels (ie, treatment time) were compared and the predictors of adherence were identified within 33 demographic, smoking-related, and health-related patient characteristics.ResultsWe found no significant difference in adherence between the blended and the face-to-face treatments. Participants in the blended treatment group spent an average of 246 minutes in treatment (median 106.7% of intended treatment time, IQR 150%-355%) and participants in the face-to-face group spent 238 minutes (median 103.3% of intended treatment time, IQR 150%-330%). Within the blended group, adherence to the face-to-face mode was twice as high as that to the web mode. Participants in the blended group spent an average of 198 minutes (SD 120) in face-to-face mode (152% of the intended treatment time) and 75 minutes (SD 53) in web mode (75% of the intended treatment time). Higher age was the only characteristic consistently found to uniquely predict higher adherence in both the blended and face-to-face groups. For the face-to-face group, more social support for smoking cessation was also predictive of higher adherence. The variability in adherence explained by these predictors was rather low (blended R2=0.049; face-to-face R2=0.076). Within the blended group, living without children predicted higher adherence to the face-to-face mode (R2=0.034), independent of age. Higher adherence to the web mode of the blended treatment was predicted by a combination of an extrinsic motivation to quit, a less negative attitude toward quitting, and less health complaints (R2=0.164).ConclusionsThis study represents one of the first attempts to thoroughly compare adherence and predictors of adherence of a blended smoking cessation treatment to an equivalent face-to-face treatment. Interestingly, although the overall adherence to both treatments appeared to be high, adherence within the blended treatment was much higher for the face-to-face mode than for the web mode. This supports the idea that in blended treatment, one mode of delivery can compensate for the weaknesses of the other. Higher age was found to be a common predictor of adherence to the treatments. The low variance in adherence predicted by the characteristics examined in this study suggests that other variables such as provider-related health system factors and time-varying patient characteristics should be explored in future research.Trial registrationNetherlands Trial Register NTR5113; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5113.

Project description:BACKGROUND:Blended web-based and face-to-face (F2F) treatment is a promising electronic health service because the strengths of one mode of delivery should compensate for the weaknesses of the other. OBJECTIVE:The aim of this study was to explore this compensation by examining patients' user experience (UX) in a blended smoking cessation treatment (BSCT) in routine care. METHODS:Data on patients' UX were collected through in-depth interviews (n=10) at an outpatient smoking cessation clinic in the Netherlands. A content analysis of the semantic domains was used to analyze patients' UX. To describe the UX, the Hassenzahl UX model was applied, examining 4 of the 5 key elements of UX from a user's perspective: (1) patients' standards and expectations, (2) apparent character (pragmatic and hedonic attributes), (3) usage situation, and (4) consequences (appeal, emotions, and behavior). RESULTS:BSCT appeared to be a mostly positively experienced service. Patients had a positive-pragmatic standard and neutral-open expectation toward BSCT at the treatment start. The pragmatic attributes of the F2F sessions were mostly perceived as positive, whereas the pragmatic attributes of the web sessions were perceived as both positive and negative. For the hedonic attributes, there seemed to be a difference between the F2F and web sessions. Specifically, the hedonic attributes of the web sessions were experienced as mostly negative, whereas those of the F2F sessions were experienced as mostly positive. For the usage situation, the physical and social contexts were experienced positively, whereas the task and technical contexts were experienced negatively. Nevertheless, the consequential appeal of BSCT was positive. However, the consequential emotions and behavior varied, ultimately resulting in diverse combinations of consequential appeal, emotions, and behavior (positive, negative, and mixed). CONCLUSIONS:This study provided insights into the UX of a blended treatment, and the results support the expectation that in a blended treatment, the strengths of one mode of delivery may compensate for the weaknesses of the other. However, in this certain setting, this is mainly achieved in only one way: F2F sessions compensated for the weaknesses of the web sessions. As a practical conclusion, this may mean that the web sessions, supported by the strengths of the F2F sessions, offer an interesting approach for further improving the blended treatment. Our theoretical findings reflect the relevance of the aspects of hedonism, such as fun, joy, or happiness in the UX, which were not mentioned in relation to the web sessions and were only scarcely mentioned in relation to the F2F sessions. Future research should further investigate the role of hedonistic aspects in a blended treatment and whether increased enjoyment of a blended treatment could increase treatment adherence and, ultimately, effectiveness.

Project description:Web-based smoking cessation interventions can deliver evidence-based treatments to a wide swath of the population, but effectiveness is often limited by insufficient adherence to proven treatment components. This study evaluated the impact of a social network (SN) intervention and free nicotine replacement therapy (NRT) on adherence to evidence-based components of smoking cessation treatment in the context of a Web-based intervention.A sample of adult U.S. smokers (N = 5290) was recruited via BecomeAnEX.org, a free smoking cessation Web site. Smokers were randomized to one of four arms: (1) an interactive, evidence-based smoking cessation Web site (WEB) alone; (2) WEB in conjunction with an SN intervention designed to integrate participants into the online community (WEB+SN); (3) WEB plus free NRT (WEB+NRT); and (4) the combination of all treatments (WEB+SN+NRT). Adherence outcomes assessed at 3-month follow-up were as follows: Web site utilization metrics, use of skills training components, intratreatment social support, and pharmacotherapy use.WEB+SN+NRT outperformed all others on Web site utilization metrics, use of practical counseling tools, intratreatment social support, and NRT use. It was the only intervention to promote the sending of private messages and the viewing of community pages over WEB alone. Both social network arms outperformed WEB on most metrics of online community engagement. Both NRT arms showed higher medication use compared to WEB alone.This study demonstrated the effectiveness of two approaches for improving adherence to evidence-based components of smoking cessation treatment. Integrated approaches to medication provision and social network engagement can enhance adherence to components known to improve cessation.This study demonstrated that an integrated approach to medication provision and social network integration, when delivered through an online program, can enhance adherence across all three recommended components of an evidence-based smoking cessation program (skills training, social support, and pharmacotherapy use). Nicotine replacement therapy-when provided as part of an integrated program-increases adherence to other program elements, which in turn augment its own therapeutic effects. An explicit focus on approaches to improve treatment adherence is an important first step to identifying leverage points for optimizing intervention effectiveness.

Project description:AimsTemperament and impulsivity are powerful predictors of addiction treatment outcomes. However, a comprehensive assessment of these features has not been examined in relation to smoking cessation outcomes.MethodsNaturalistic prospective study. Treatment-seeking smokers (n = 140) were recruited as they engaged in an occupational health clinic providing smoking cessation treatment between 2009 and 2013. Participants were assessed at baseline with measures of temperament (Temperament and Character Inventory), trait impulsivity (Barratt Impulsivity Scale), and cognitive impulsivity (Go/No Go, Delay Discounting and Iowa Gambling Task). The outcome measure was treatment status, coded as "dropout" versus "relapse" versus "abstinence" at 3, 6, and 12 months endpoints. Participants were telephonically contacted and reminded of follow-up face to face assessments at each endpoint. The participants that failed to answer the phone calls or self-reported discontinuation of treatment and failed to attend the upcoming follow-up session were coded as dropouts. The participants that self-reported continuing treatment, and successfully attended the upcoming follow-up session were coded as either "relapse" or "abstinence", based on the results of smoking behavior self-reports cross-validated with co-oximetry hemoglobin levels. Multinomial regression models were conducted to test whether temperament and impulsivity measures predicted dropout and relapse relative to abstinence outcomes.ResultsHigher scores on temperament dimensions of novelty seeking and reward dependence predicted poorer retention across endpoints, whereas only higher scores on persistence predicted greater relapse. Higher scores on the trait dimension of non-planning impulsivity but not performance on cognitive impulsivity predicted poorer retention. Higher non-planning impulsivity and poorer performance in the Iowa Gambling Task predicted greater relapse at 3 and 6 months and 6 months respectively.ConclusionTemperament measures, and specifically novelty seeking and reward dependence, predict smoking cessation treatment retention, whereas persistence, non-planning impulsivity and poor decision-making predict smoking relapse.

Project description:Many lifelong smokers establish smoking habits during young adulthood. A university can be an effective setting for early smoking cessation. We evaluated long-term predictors of smoking cessation among smokers in a university setting.We longitudinally followed a cohort of smokers enrolled in a university smoking cessation program in Seoul, South Korea. Sociodemographic factors, smoking-related variables, and changes in smoking habits were assessed during 6-week visit sessions and follow-up telephone interviews conducted 1 year or more later.A total of 205 participants were followed up (mean follow-up duration: 27.1 months). Cessation rates were 47.3% at the end of the visit sessions and 28.8% at follow-up. The long-term persistent smoking rate was significantly higher among individuals with peers who smoked (odds ratio [OR] = 8.64; 95% confidence interval [CI] = 1.75, 42.80), with family members who smoked (OR = 3.28; 95% CI = 1.20, 9.00), and who smoked 10 to 19 cigarettes/day (OR?=?4.83; 95% CI?=?1.49, 15.69). Conversely, persistent smoking was less likely among those who attended the program regularly (OR?=?0.84 per visit; 95% CI?=?0.72, 0.99) and attempted quitting more frequently (OR?=?0.93 per attempt; 95% CI?=?0.87, 0.99). Use of smoking cessation medications (varenicline or bupropion) was not significantly associated with long-term quitting (OR?=?0.71; 95% CI?=?0.26, 1.93).Peer influences were the strongest predictors of failure in long-term cessation among smokers who attempted to quit. Similarly, the existence of smokers in the family was negatively associated with successful quitting. Regular attendance at a smoking cessation program and a high number of attempts to quit were positively associated with successful quitting. Targeting peer and family smoking groups together rather than targeting individual smokers alone, implementing active cessation programs encouraging regular attendance, and providing comprehensive antismoking environments might be effective strategies in a university setting.

Project description:INTRODUCTION The number of cessation clinics in China have been increasing ever since the Chinese government supported the establishment of smoking cessation clinics (SCCs) in each province in 2014. Many studies have examined smoking cessation behaviors among male smokers, but few of female smokers. This study aimed to understand female smokers’ quitting behaviors in SCCs and identify predictors of successful cessation. METHODS This study used data of the SCCs Platform in China from 2018 to 2020. The self-reported 7-day point prevalence of abstinence rate (PPAR) at 1 month and at 3 months follow-up and the continuous abstinence rate (CAR) at 3 months follow-up are reported based on smokers’ characteristics and intention to treat analysis. A multiple logistic regression model was used to identify predictors of continuous abstinence at 3 months follow-up. RESULTS The 7-day PPAR of female outpatients in SCCs was 29.20% at 1 month follow-up and 28.36% at 3 months follow-up. The CAR at 3 months follow-up was 19.88%. Female smokers who were prepared to quit within 7 days (AOR=2.86; 95% CI: 1.53–5.32), today (AOR=4.01; 95% CI: 2.35–6.85), had started to quit (AOR=7.11; 95% CI: 4.12–12.27), and used a combination of counseling and drugs (AOR=2.41; 95% CI: 1.73–3.35) were more likely to quit smoking. Associated with lower quitting rates were: living in the central region of China (AOR=0.47; 95% CI: 0.31–0.73) and the west region (AOR=0.48; 95% CI: 0.31–0.73); being aged 30–39 years (AOR=0.39; 95% CI: 0.23–0.64), and 40–49 years (AOR=0.41; 95% CI:0.24–0.69); being unemployed (AOR=0.64; 95% CI: 0.45–0.91); having a fair perceived health status at the first visit (AOR=0.65; 95% CI: 0.47–0.91) and a poor one (AOR=0.37; 95% CI: 0.21–0.64); having a moderate nicotine dependence (AOR=0.64; 95% CI: 0.44–0.92) and a severe one (AOR=0.50; 95% CI: 0.34–0.72). CONCLUSIONS In our study, the region of residence, age, employment, perceived health status, Fagerström test for nicotine dependence (FTND), readiness to quit, and intervention model were independent predictors of quitting for female smokers. Improving the motivation to quit, providing intensive psychological interventions and equipping SCCs with cessation medication would assist female smokers to quit.

Dataset Information

Blended Smoking Cessation Treatment: Exploring Measurement, Levels, and Predictors of Adherence.

Publications

Blended Smoking Cessation Treatment: Exploring Measurement, Levels, and Predictors of Adherence.

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