Adherence to Blended or Face-to-Face Smoking Cessation Treatment and Predictors of Adherence: Randomized Controlled Trial.
ABSTRACT: BACKGROUND:Blended 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. OBJECTIVE:The 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. METHODS:We 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. RESULTS:We 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). CONCLUSIONS:This 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 REGISTRATION:Netherlands Trial Register NTR5113; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5113.
Project description: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).
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:Phone counseling has become standard for behavioral smoking cessation treatment. Newer options include Web and integrated phone-Web treatment. No prior research, to our knowledge, has systematically compared the effectiveness of these three treatment modalities in a randomized trial. Understanding how utilization varies by mode, the impact of utilization on outcomes, and predictors of utilization across each mode could lead to improved treatments.One thousand two hundred and two participants were randomized to phone, Web, or combined phone-Web cessation treatment. Services varied by modality and were tracked using automated systems. All participants received 12 weeks of varenicline, printed guides, an orientation call, and access to a phone supportline. Self-report data were collected at baseline and 6-month follow-up.Overall, participants utilized phone services more often than the Web-based services. Among treatment groups with Web access, a significant proportion logged in only once (37% phone-Web, 41% Web), and those in the phone-Web group logged in less often than those in the Web group (mean = 2.4 vs. 3.7, p = .0001). Use of the phone also was correlated with increased use of the Web. In multivariate analyses, greater use of the phone- or Web-based services was associated with higher cessation rates. Finally, older age and the belief that certain treatments could improve success were consistent predictors of greater utilization across groups. Other predictors varied by treatment group.Opportunities for enhancing treatment utilization exist, particularly for Web-based programs. Increasing utilization more broadly could result in better overall treatment effectiveness for all intervention modalities.
Project description:Introduction:Efficacious pharmacological interventions for smoking cessation are available, but poor adherence to these treatments may limit these interventions overall impact. To improve adherence to smoking cessation interventions, it is first necessary to identify and understand smoker-level characteristics that drive nonadherence (ie, nonconformance with a provider's recommendation of timing, dosage, or frequency of medication-taking during the prescribed length of time). Methods:We present a literature review of studies examining correlates of, or self-reported reasons for, nonadherence to smoking cessation pharmacotherapies. Studies were identified through PubMed-using MeSH terms, Embase-using Emtree terms, and ISI Web of Science. Results and Conclusions:This literature review included 50 studies that examined nonpreventable (eg, sociodemographics) and preventable (eg, forgetfulness) factors associated with adherence to smoking cessation medication and suggestions for overcoming some of the identified barriers. Systematic study of this topic would be facilitated by consistent reporting of adherence and correlates thereof in the literature, development of consistent definitions of medication adherence across studies, utilization of more objective measures of adherence (eg, blood plasma levels vs. self-report) in addition to reliance on self-reported adherence. Implications:This article provides the most comprehensive review to date on correlates of adherence to pharmacological smoking cessation interventions. Challenges and specific gaps in the literature that should be a priority for future research are discussed. Future priorities include additional research, particularly among vulnerable populations of smokers, developing standardized definitions of adherence and methods for measuring adherence, regular assessment of cessation pharmacotherapy adherence in the context of research and clinical practice, and development of novel treatments aimed at preventable barriers to medication adherence.
Project description:<h4>Background</h4>Many studies have provided evidence for the effectiveness of Internet-based stand-alone interventions for mental disorders. A newer form of intervention combines the strengths of face-to-face (f2f) and Internet approaches (blended interventions).<h4>Objective</h4>The aim of this review was to provide an overview of (1) the different formats of blended treatments for adults, (2) the stage of treatment in which these are applied, (3) their objective in combining face-to-face and Internet-based approaches, and (4) their effectiveness.<h4>Methods</h4>Studies on blended concepts were identified through systematic searches in the MEDLINE, PsycINFO, Cochrane, and PubMed databases. Keywords included terms indicating face-to-face interventions ("inpatient," "outpatient," "face-to-face," or "residential treatment"), which were combined with terms indicating Internet treatment ("internet," "online," or "web") and terms indicating mental disorders ("mental health," "depression," "anxiety," or "substance abuse"). We focused on three of the most common mental disorders (depression, anxiety, and substance abuse).<h4>Results</h4>We identified 64 publications describing 44 studies, 27 of which were randomized controlled trials (RCTs). Results suggest that, compared with stand-alone face-to-face therapy, blended therapy may save clinician time, lead to lower dropout rates and greater abstinence rates of patients with substance abuse, or help maintain initially achieved changes within psychotherapy in the long-term effects of inpatient therapy. However, there is a lack of comparative outcome studies investigating the superiority of the outcomes of blended treatments in comparison with classic face-to-face or Internet-based treatments, as well as of studies identifying the optimal ratio of face-to-face and Internet sessions.<h4>Conclusions</h4>Several studies have shown that, for common mental health disorders, blended interventions are feasible and can be more effective compared with no treatment controls. However, more RCTs on effectiveness and cost-effectiveness of blended treatments, especially compared with nonblended treatments are necessary.
Project description:INTRODUCTION:Individuals in the U.S. criminal justice system now represent over 12% of all current U.S. smokers. With smoking banned in most U.S. jails and prisons, the cessation focus for this population has shifted to individuals who are under community correction supervision (e.g., probation, parole). The aim of this study was to examine predictors of successful smoking cessation among criminal justice individuals supervised in the community. METHODS:Five hundred participants under community corrections supervision were randomized to receive either four sessions of smoking cessation counseling or no counseling in conjunction with 12weeks of bupropion treatment plus brief physician advice to quit. Logistic regression analyses examined associations of smoking variables with medication adherence and successful abstinence. Mediation analysis evaluated the indirect effects of medication adherence on smoking abstinence. RESULTS:The strongest associate of medication adherence was previous use of bupropion, while the strongest associate of smoking abstinence was medication adherence. Mediation analysis indicated that previous use of bupropion indirectly increased cessation rates through the pathway of increased medication adherence. CONCLUSIONS:These results highlight the importance of medication adherence for smoking cessation among community corrections smokers. Providing exposure to medication may be a promising intervention to increase medication adherence and subsequent cessation rates in this population.
Project description:BACKGROUND:Improving practice nurses' (PN) adherence to smoking cessation counseling guidelines will benefit the quality of smoking cessation care and will potentially lead to higher smoking abstinence rates. However, support programs to aid PNs in improving their guideline uptake and adherence do not exist yet. OBJECTIVE:The aim of this study was to assess the effects of a novel computer-tailored electronic learning (e-learning) program on PNs' smoking cessation guideline adherence. METHODS:A Web-based randomized controlled trial (RCT) was conducted in which an intervention group (N=147) with full access to the e-learning program for 6 months was compared with a control group (N=122) without access. Data collection was fully automated at baseline and 6-month follow-up via online questionnaires, assessing PNs' demographics, work-related factors, potential behavioral predictors based on the I-Change model, and guideline adherence. PNs also completed counseling checklists to retrieve self-reported counseling activities for each consultation with a smoker (N=1175). To assess the program's effectiveness in improving PNs' guideline adherence (ie, overall adherence and adherence to individual counseling guideline steps), mixed linear and logistic regression analyses were conducted, thus accommodating for the smokers being nested within PNs. Potential effect moderation by work-related factors and behavioral predictors was also examined. RESULTS:After 6 months, 121 PNs in the intervention group (82.3%, 121/147) and 103 in the control group (84.4%, 103/122) completed the follow-up questionnaire. Mixed linear regression analysis revealed that counseling experience moderated the program's effect on PNs' overall guideline adherence (beta=.589; 95% CI 0.111-1.068; PHolm-Bonferroni =.048), indicating a positive program effect on adherence for PNs with a more than average level of counseling experience. Mixed logistic regression analyses regarding adherence to individual guideline steps revealed a trend toward moderating effects of baseline levels of behavioral predictors and counseling experience. More specifically, for PNs with less favorable scores on behavioral predictors (eg, low baseline self-efficacy) and high levels of counseling experience, the program significantly increased adherence. CONCLUSIONS:Results from our RCT showed that among PNs with more than average counseling experience, the e-learning program resulted in significantly better smoking cessation guideline adherence. Experienced PNs might have been better able to translate the content of our e-learning program into practically applicable counseling strategies compared with less experienced colleagues. Less favorable baseline levels of behavioral predictors among PNs possibly contributed to this effect, as there was more room for improvement by consulting the tailored content of the e-learning program. To further substantiate the effectiveness of e-learning programs on guideline adherence by health care professionals (HCPs), it is important to assess how to support a wider range of HCPs. TRIAL REGISTRATION:Netherlands Trial Register NTR4436; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4436 (Archived by WebCite at http://www.webcitation.org/6zJQuSRq0).
Project description:INTRODUCTION:Adherence to smoking cessation treatment is generally low, especially among socio-economically disadvantaged groups including individuals experiencing homelessness and those with mental illnesses. Despite the high smoking rates in homeless populations (~70%) no study to date has systematically examined predictors of adherence to nicotine replacement therapy (NRT) in this population. OBJECTIVE:The aim of this secondary analysis was to identify predictors of adherence to NRT in a smoking cessation trial conducted among homeless smokers. METHODS:Secondary analysis of data from a randomized controlled trial enrolling 430 persons who were homeless and current cigarette smokers. Participants were assigned to one of the two study conditions to enhance smoking cessation: Motivational Interviewing (MI; 6 sessions of MI + 8 weeks of NRT) or Standard Care (Brief advice to quit+ 8 weeks of NRT). The primary outcome for the current analysis was adherence to NRT at end of treatment (8 weeks following randomization). Adherence was defined as a total score of zero on a modified Morisky adherence scale). Demographic and baseline psychosocial, tobacco-related, and substance abuse measures were compared between those who did and did not adhere to NRT. RESULTS:After adjusting for confounders, smokers who were depressed at baseline (OR=0.58, 95% CI, 0.38-0.87, p=0.01), had lower confidence to quit (OR=1.10, 95% CI, 1.01-1.19, p=0.04), were less motivated to adhere (OR=1.04, 95% CI, 1.00-1.07, p=0.04), and were less likely to be adherent to NRT. Further, age of initial smoking was positively associated with adherence status (OR= 0.83, 95% CI, 0.69-0.99, p=0.04). CONCLUSION:These results suggest that smoking cessation programs conducted in this population may target increased adherence to NRT by addressing both depression and motivation to quit. TRIAL REGISTRATION:clinicaltrials.gov: NCT00786149.
Project description:Millions of smokers use the Internet for smoking cessation assistance each year; however, most smokers engage minimally with even the best designed websites. The ubiquity of mobile devices and their effectiveness in promoting adherence in other areas of health behaviour change make them a promising tool to address adherence in Internet smoking cessation interventions. Text messaging is used by most adults, and messages can proactively encourage use of a web-based intervention. Text messaging can also be integrated with an Internet intervention to facilitate the use of core Internet intervention components.We identified four aspects of a text message intervention that may enhance its effectiveness in promoting adherence to a web-based smoking cessation programme: personalisation, integration, dynamic tailoring and message intensity. Phase I will use a two-level full factorial design to test the impact of these four experimental features on adherence to a web-based intervention. The primary outcome is a composite metric of adherence that incorporates general utilisation metrics (eg, logins, page views) and specific feature utilisation shown to predict abstinence. Participants will be N=860 adult smokers who register on an established Internet cessation programme and enrol in its text message programme. Phase II will be a two-arm randomised trial to compare the efficacy of the web-based cessation programme alone and in conjunction with the optimised text messaging intervention on 30-day point prevalence abstinence at 9 months. Phase II participants will be N=600 adult smokers who register to use an established Internet cessation programme and enrol in text messaging. Secondary analyses will explore whether adherence mediates the effect of treatment condition on outcome.This protocol was approved by Chesapeake IRB. We will disseminate study results through peer-reviewed manuscripts and conference presentations related to the methods and design, outcomes and exploratory analyses.NCT02585206.