Effects of an Explicit Value Clarification Method With Computer-Tailored Advice on the Effectiveness of a Web-Based Smoking Cessation Decision Aid: Findings From a Randomized Controlled Trial.

BACKGROUND: Smoking continues to be a driver of mortality. Various forms of evidence-based cessation assistance exist; however, their use is limited. The choice between them may also induce decisional conflict. Offering decision aids (DAs) may be beneficial; however, insights into their effective elements are lacking. OBJECTIVE: This study tested the added value of an effective element (ie, an "explicit value clarification method" paired with computer-tailored advice indicating the most fitting cessation assistance) of a web-based smoking cessation DA. METHODS: A web-based randomized controlled trial was conducted among smokers motivated to stop smoking within 6 months. The intervention group received a DA with the aforementioned elements, and the control group received the same DA without these elements. The primary outcome measure was 7-day point prevalence abstinence 6 months after baseline (time point 3 [t=3]). Secondary outcome measures were 7-day point prevalence of abstinence 1 month after baseline (time point 2 [t=2]), evidence-based cessation assistance use (t=2 and t=3), and decisional conflict (immediately after DA; time point 1). Logistic and linear regression analyses were performed to assess the outcomes. Analyses were conducted following 2 (decisional conflict) and 3 (smoking cessation) outcome scenarios: complete cases, worst-case scenario (assuming that dropouts still smoked), and multiple imputations. A priori sample size calculation indicated that 796 participants were needed. The participants were mainly recruited on the web (eg, social media). All the data were self-reported. RESULTS: Overall, 2375 participants were randomized (intervention n=1164, 49.01%), of whom 599 (25.22%; intervention n=275, 45.91%) completed the DAs, and 276 (11.62%; intervention n=143, 51.81%), 97 (4.08%; intervention n=54, 55.67%), and 103 (4.34%; intervention n=56, 54.37%) completed time point 1, t=2, and t=3, respectively. More participants stopped smoking in the intervention group (23/63, 37%) than in the control group (14/52, 27%) after 6 months; however, this was only statistically significant in the worst-case scenario (crude P=.02; adjusted P=.04). Effects on the secondary outcomes were only observed for smoking abstinence after 1 month (15/55, 27%, compared with 7/46, 15%, in the crude and adjusted models, respectively; P=.02) and for cessation assistance uptake after 1 month (26/56, 46% compared with 18/47, 38% only in the crude model; P=.04) and 6 months (38/61, 62% compared with 26/50, 52%; crude P=.01; adjusted P=.02) but only in the worst-case scenario. Nonuse attrition was 34.19% higher in the intervention group than in the control group (P<.001). CONCLUSIONS: Currently, we cannot confidently recommend the inclusion of explicit value clarification methods and computer-tailored advice. However, they might result in higher nonuse attrition rates, thereby limiting their potential. As a lack of statistical power may have influenced the outcomes, we recommend replicating this study with some adaptations based on the lessons learned. TRIAL REGISTRATION: Netherlands Trial Register NL8270; https://www.trialregister.nl/trial/8270. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21772.

Integrating behaviour change interventions and patient decision aids: How to accomplish synergistic effects?

People make numerous health-related choices each day: For example, deciding to brush one's teeth or to eat well and healthy - or not to do these activities. To support complex decisions and subsequent behaviour change, both Behaviour Change Interventions (BCIs) and Patient Decision Aids (PtDAs) have been developed and evolved independently to support people in health-related decision making. In this paper, we critically review BCIs and PtDAs, examine their similarities and differences, and identify potential for integration of expertise to increase the benefits for people engaging with healthcare and health behaviours. The two approaches appear to mainly differ in terms of their (1) goals and foci, (2) theoretical basis, (3) development frameworks, (4) active ingredients and (5) effect evaluation. To facilitate the integration of scientific insights from these two fields, we recommend to (1) bring both fields together and promote interprofessional discussions, (2) train (health) professionals to recognise strengths of both approaches, (3) investigate the synergy of the two fields, (4) be prepared for and try to mitigate a culture shock when the fields start to interact. Knowledge generated by researching PtDAs could be used to facilitate decisional processes that enable patients to choose goals that are in line with their values and preferences, while insights from researching BCIs could be used to facilitate engagement with, and implementation of those goals. This integration could allow researchers and intervention providers to increase the benefits for people engaging with healthcare and health behaviours.

Using Virtual Reality to Stimulate Healthy and Environmentally Friendly Food Consumption among Children: An Interview Study.

Since habits formed during childhood are predictive of adult behaviour, children form an important target group when it comes to improving healthy and environmentally friendly food consumption. To explore the potential of immersive virtual reality (VR) in this respect, we conducted a semi-structured interview study (N = 22) among children aged 6-13 years. This study consisted of two parts: (1) a VR experience and (2) a semi-structured interview to investigate (1) to what extent children are able to recall and understand information about the impact of food products on their health and the environment when provided to them as pop-ups in a VR supermarket; (2) what rational and emotional processes are triggered by this information; and (3) what children's expectations about the real-life application and impact of the pop-ups are, and why. Interview data were analysed using the framework method. Results showed that although all participants were able to recall the information, only children from an average age of ten years old also understood the information. When participants understood the information, they were often aware of and felt sorry for their negative behavioural impact. Most participants expected their behaviour to positively change when imagining real-life application of the pop-ups.

An Autonomy-Supportive Online Decision Aid to Assist Smokers in Choosing Evidence-Based Cessation Assistance: Development Process and Protocol of a Randomized Controlled Trial.

BACKGROUND: Decision aids (DAs) may be used to facilitate an autonomous, informed decision to cease smoking and promote the uptake of evidence-based cessation assistance (ie, behavioral support, nicotine replacement therapy, or prescription medication). However, knowledge is lacking regarding their effective elements and (cost-)effectiveness. OBJECTIVE: We describe the development process of an online DA (called "VISOR") that helps smokers to choose evidence-based cessation assistance. Additionally, we provide a description of the protocol of an ongoing randomized controlled trial in which the DA containing an explicit value clarification method (VCM) and tailored advice is compared with a DA without an explicit VCM and tailored advice. METHODS: The development of "VISOR" was based on the International Patient Decision Aid Standards guidelines. Viewpoints of end users (collected through 20 interviews with smokers) and clinical and scientific experts (assessed using 2 Delphi studies with 24 scientists and 38 clinicians) were assessed regarding cessation tool decision making and preferred DA content. These findings, together with principles from the Self-Determination Theory, served as input for the development of the online DA. A first DA prototype was alpha-tested in September 2019 and beta-tested for usability in December 2019; feedback was incorporated and resulted in a final version. The final DA contains (1) an information section, (2) an optional knowledge quiz, (3) a brief smoking assessment, (4) intuitive decision, (5) intermediate advice, (6) an explicit VCM, (7) tailored advice, and (8) access information. A randomized controlled trial is currently being conducted to assess the DA's (cost-)effectiveness compared to a DA that does not include the explicit VCM and the tailored advice; specifically, the DA's effect on smoking abstinence, uptake of evidence-based cessation assistance, smoking abstinence mediated through uptake of evidence-based cessation assistance, and decisional conflict are investigated. Participants are randomly allocated to receive access to 1 of the 2 DAs and are asked to complete 5 questionnaires (including the baseline questionnaire) over a period of 12 months. To evaluate the effects of the DA on the outcome measures, logistic and linear regression analyses as well as mediation analyses will be carried out. An economic evaluation will be performed to assess the cost-effectiveness. RESULTS: Data regarding the effect of the VISOR DA are currently being collected, and data collection is expected to be concluded in 2021. CONCLUSIONS: By making use of an iterative process that integrated different stakeholders' perspectives (including end users), we were able to systematically design an evidence-based DA. The study will contribute to the current knowledge regarding smoking cessation DA application, the added value of explicit VCMs, and the effect of behavioral and informed decision-making outcomes. TRIAL REGISTRATION: Netherlands Trial Register NL8270; https://www.trialregister.nl/trial/8270. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21772.

Effectiveness of Message Frame-Tailoring in a Web-Based Smoking Cessation Program: Randomized Controlled Trial.

BACKGROUND: The content of online computer-tailored interventions is often determined to match an individual's characteristics, beliefs, and behavioral factors. These content-tailored interventions lead to better message processing and a higher likelihood of behavior change such as smoking cessation. However, a meta-analysis of online computer-tailored interventions showed that effect sizes, albeit positive, remain small, suggesting room for improvement. A promising strategy to enhance the effectiveness of online computer-tailored interventions is to tailor the message frame (ie, how a message is communicated) based on the preferred communication style of the user in addition to content-tailoring. One factor that determines an individual's communication style preference is the need for autonomy; some individuals prefer an autonomy-supportive communication style (offering choice and use of suggestive language), whereas others might prefer a directive communication style, which is replete with imperatives and does not provide choice. Tailoring how messages are presented (eg, based on the need for autonomy) is called message frame-tailoring. OBJECTIVE: The aim of the present study was to test the effectiveness of message frame-tailoring based on the need for autonomy, in isolation and in combination with content-tailoring, within the context of an online computer-tailored smoking cessation intervention. The primary outcome measure was the 7-day point-prevalence of smoking abstinence. Secondary outcomes were perceived message relevance, self-determined motivation to quit smoking, and sociocognitive beliefs. METHODS: A randomized controlled trial with a 2 (message frame-tailoring vs no message frame-tailoring) by 2 (content-tailoring vs no content-tailoring) design was conducted among adult smokers intending to quit smoking (N=273). RESULTS: Structural equation modeling revealed that the content-tailored condition increased smoking abstinence rates 1 month after the start of the intervention (beta=.57, P=.02). However, neither message frame-tailoring nor its interaction with content-tailoring significantly predicted smoking abstinence. In our model, message frame-tailoring, content-tailoring, as well as their interaction significantly predicted perceived relevance of the smoking cessation messages, which consequently predicted self-determined motivation. In turn, self-determined motivation positively affected attitudes and self-efficacy for smoking cessation, but only self-efficacy consequently predicted smoking abstinence. Participants in the control condition perceived the highest level of message relevance (mean 4.78, SD 1.27). However, messages that were frame-tailored for individuals with a high need for autonomy in combination with content-tailored messages led to significantly higher levels of perceived message relevance (mean 4.83, SD 1.03) compared to those receiving content-tailored messages only (mean 4.24, SD 1.05, P=.003). CONCLUSIONS: Message frame-tailoring based on the need for autonomy seems to be an effective addition to conventional content-tailoring techniques in online smoking cessation interventions for people with a high need for autonomy; however, this is not effective in its current form for people with a low need for autonomy. TRIAL REGISTRATION: Dutch Trial Register (NL6512/NRT-6700); https://www.trialregister.nl/trial/6512.

Smoker profiles and their influence on smokers' intention to use a digital decision aid aimed at the uptake of evidence-based smoking cessation tools: An explorative study.

OBJECTIVES: Evidence-based smoking cessation support tools (EBSTs) can double the quitting chances, but uptake among smokers is low. A digital decision aid (DA) could help smokers choose an EBST in concordance with their values and preferences, but it is unclear which type of smokers are interested in a digital DA. We hypothesized that smokers' general decision-making style (GDMS) could be used to identify early adopters. This study therefore aimed to identify smoker profiles based on smokers' GDMS and investigate these profiles' association with intention to use a digital DA. DESIGN: A cross-sectional dataset (N = 200 smokers intending to quit) was used to perform a hierarchical cluster analysis based on smokers' GDMS scores. METHODS: Clusters were compared on demographic and socio-cognitive variables. Mediation analyses were conducted to see if the relationship between cluster membership and intention was mediated through socio-cognitive variables (e.g., attitude). RESULTS: Two clusters were identified; " Avoidant Regretters " (n = 134) were more avoidant, more regretful and tended to depend more on others in their decision making, while " Intuitive Non-regretters " (n = 66) were more spontaneous and intuitive in their decision making. Cluster membership was significantly related to intention to use a DA, with " Avoidant Regretters " being more interested. Yet, this association ceased to be significant when corrected for socio-cognitive variables (e.g., attitude). This indicates that cluster membership affected intention via socio-cognitive variables. CONCLUSIONS: The GDMS can be used to identify smokers who are interested in a digital DA early on. As such, the GDMS can be used to tailor recruitment and DA content.

Customization in mobile health apps: explaining effects on physical activity intentions by the need for autonomy.

Given the widespread adoption and technical possibilities of mobile technology, mobile health apps could be potentially effective tools to intervene in people's daily routines and stimulate physical activity. Self-determination theory and the motivational technology model both suggest that mobile technology can promote health behaviour change by allowing users to customize their online experience when using mobile health apps. However, we know very little about why and for whom customization is most effective. Using a between-subjects experimental design, we tested the effects of customization in mobile health apps among a convenience sample (N = 203). We assessed the effects of customization on perceived active control over mobile health apps, autonomous motivation to use mobile health apps, and intention to engage in physical activity, and tested the moderating role of need for autonomy. Structural equation modelling showed that customization in mobile health apps does not increase perceived active control, autonomous motivation, or the intention to engage in physical activity. However, an interaction effect between customization and need for autonomy showed that customization in mobile health apps leads to higher intentions to engage in physical activity for those with a greater need for autonomy, but not for those with a lesser need for autonomy. The implications for theory and practice are discussed.

Identifying the Most Autonomy-Supportive Message Frame in Digital Health Communication: A 2x2 Between-Subjects Experiment.

BACKGROUND: The effectiveness of digital health communication may be increased by enhancing autonomy supportiveness. OBJECTIVE: This study aimed to identify the most autonomy-supportive message frame within an intervention for increasing vegetable intake by testing the effect of the following 2 strategies: (1) using autonomy-supportive language and (2) providing choice. METHODS: A Web-based 2 (autonomy-supportive vs controlling language)×2 (choice vs no choice) experiment was conducted among 526 participants, recruited via a research panel. The main outcome measures were perceived autonomy support (measured using the Virtual Care Climate Questionnaire, answered with scores 1 to 5), perceived relevance (measured with one question, answered with scores 1 to 5), and overall evaluation of the intervention (measured with 1 open-ended question, answered with scores 1 to 10). RESULTS: Choice had a significant positive effect on the overall evaluation of the intervention (b=.12; P=.003), whereas for participants with a high need for autonomy, there was a significant positive effect on perceived relevance (b=.13; P=.02). The positive effect of choice on perceived autonomy support approached significance (b=.07; P=.07). No significant effects on any of the three outcomes were observed for language. CONCLUSIONS: Results suggest that provision of choice rather than the use of autonomy-supportive language can be an easy-to-implement strategy to increase the effectiveness of digital forms of health communication, especially for people with a high need for autonomy.

The Effectiveness of a Computer-Tailored E-Learning Program for Practice Nurses to Improve Their Adherence to Smoking Cessation Counseling Guidelines: Randomized Controlled Trial.

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).

Subgroups Among Smokers in Preparation: A Cluster Analysis Using the I-Change Model.

BACKGROUND: Investigating potential sub-stages of change could provide important information that could be used to improve the tailoring of smoking cessation interventions to individual smokers' profiles. Smokers in the preparation stage may be most interesting, as they are most likely to participate in smoking cessation interventions. OBJECTIVE: To examine whether Dutch adult smokers in the preparation stage of change, i.e. motivated to quit smoking within one month, can be organized into subgroups. METHODS: Data from 753 smokers who participated in an effectiveness trial of a web-based, computer-tailored smoking cessation programme were subjected to secondary analysis. Cluster analyses were based on respondents' baseline responses to items on pros and cons of quitting and quitting self-efficacy. Chi-squared tests and ANOVA were used to compare the baseline characteristics of the resulting clusters. Logistic and multinomial regression were used for longitudinal comparisons of clusters with respect to smoking abstinence and stage transition at six-week and six-month follow-ups. RESULTS: Four clusters were identified; Classic, Unprepared, Progressing and Disengaged Preparers. Cross-sectional and longitudinal analyses validated these clusters: they differed with respect to the clustering variables, gender, cigarette dependence and educational level. Disengaged Preparers were less likely than Progressing Preparers to report smoking abstinence at six months (OR = 0.28; p < .05). CONCLUSIONS: These results suggest that smoking cessation interventions tailored to the preparation stage of change, i.e. the set of cognitions usually present in preparers, are only appropriate for the subgroup we defined as Classic Preparers. The other clusters might need different interventions as they display different cognition sets.

The Virtual Care Climate Questionnaire: Development and Validation of a Questionnaire Measuring Perceived Support for Autonomy in a Virtual Care Setting.

BACKGROUND: Web-based health behavior change interventions may be more effective if they offer autonomy-supportive communication facilitating the internalization of motivation for health behavior change. Yet, at this moment no validated tools exist to assess user-perceived autonomy-support of such interventions. OBJECTIVE: The aim of this study was to develop and validate the virtual climate care questionnaire (VCCQ), a measure of perceived autonomy-support in a virtual care setting. METHODS: Items were developed based on existing questionnaires and expert consultation and were pretested among experts and target populations. The virtual climate care questionnaire was administered in relation to Web-based interventions aimed at reducing consumption of alcohol (Study 1; N=230) or cannabis (Study 2; N=228). Item properties, structural validity, and reliability were examined with item-response and classical test theory methods, and convergent and divergent validity via correlations with relevant concepts. RESULTS: In Study 1, 20 of 23 items formed a one-dimensional scale (alpha=.97; omega=.97; H=.66; mean 4.9 [SD 1.0]; range 1-7) that met the assumptions of monotonicity and invariant item ordering. In Study 2, 16 items fitted these criteria (alpha=.92; H=.45; omega=.93; mean 4.2 [SD 1.1]; range 1-7). Only 15 items remained in the questionnaire in both studies, thus we proceeded to the analyses of the questionnaire's reliability and construct validity with a 15-item version of the virtual climate care questionnaire. Convergent validity of the resulting 15-item virtual climate care questionnaire was confirmed by positive associations with autonomous motivation (Study 1: r=.66, P<.001; Study 2: r=.37, P<.001) and perceived competence for reducing alcohol intake (Study 1: r=.52, P<.001). Divergent validity could only be confirmed by the nonsignificant association with perceived competence for learning (Study 2: r=.05, P=.48). CONCLUSIONS: The virtual climate care questionnaire accurately assessed participants' perceived autonomy-support offered by two Web-based health behavior change interventions. Overall, the scale showed the expected properties and relationships with relevant concepts, and the studies presented suggest this first version of the virtual climate care questionnaire to be reasonably valid and reliable. As a result, the current version may cautiously be used in future research and practice to measure perceived support for autonomy within a virtual care climate. Future research efforts are required that focus on further investigating the virtual climate care questionnaire's divergent validity, on determining the virtual climate care questionnaire's validity and reliability when used in the context of Web-based interventions aimed at improving nonaddictive or other health behaviors, and on developing and validating a short form virtual climate care questionnaire.

The Missing=Smoking Assumption: A Fallacy in Internet-Based Smoking Cessation Trials?

INTRODUCTION: In this study, penalized imputation (PI), a common approach to handle missing smoking status data and sometimes referred to as "missing=smoking," is compared with other missing data approaches using data from internet-based smoking cessation trials. Two hypotheses were tested: (1) PI leads to more conservative effect estimates than complete observations analysis; and (2) PI and multiple imputation (MI) lead to similar effect estimates under balanced (equal missingness proportions among the trial arms) and unbalanced missingness. METHODS: First, the outcomes of 22 trials included in a recent Cochrane review on internet-based smoking cessation interventions were reanalyzed using only the complete observations, and after applying PI. Second, in a simulation study outcomes under PI, complete observations analysis, and two types of MI were compared. For this purpose, individual patient data from one of the Cochrane review trials were used. Results of the missing data approaches were compared with reference data without missing observations, upon which balanced and unbalanced missingness scenarios were imposed. RESULTS: In the reanalysis of 22 trials, relative risks (RR = 1.15 [1.00; 1.33]) after PI were nearly identical to those under complete observations analysis (RR = 1.14 [0.98; 1.32]). In the simulation study, PI was the only approach that led to deviations from the reference data beyond its 95% confidence interval. CONCLUSIONS: Analyses after PI led to pooled results equivalent to complete observations analyses. PI also led to significant deviations from the reference in the simulation studies. PI biases the reported effects of interventions, favoring the condition with the lowest proportion of missingness. Therefore, more sophisticated missing data approaches than PI should be applied.

Impact of Educational Level on Study Attrition and Evaluation of Web-Based Computer-Tailored Interventions: Results From Seven Randomized Controlled Trials.

BACKGROUND: Web-based computer-tailored interventions have shown to be effective in improving health behavior; however, high dropout attrition is a major issue in these interventions. OBJECTIVE: The aim of this study is to assess whether people with a lower educational level drop out from studies more frequently compared to people with a higher educational level and to what extent this depends on evaluation of these interventions. METHODS: Data from 7 randomized controlled trials of Web-based computer-tailored interventions were used to investigate dropout rates among participants with different educational levels. To be able to compare higher and lower educated participants, intervention evaluation was assessed by pooling data from these studies. Logistic regression analysis was used to assess whether intervention evaluation predicted dropout at follow-up measurements. RESULTS: In 3 studies, we found a higher study dropout attrition rate among participants with a lower educational level, whereas in 2 studies we found that middle educated participants had a higher dropout attrition rate compared to highly educated participants. In 4 studies, no such significant difference was found. Three of 7 studies showed that participants with a lower or middle educational level evaluated the interventions significantly better than highly educated participants ("Alcohol-Everything within the Limit": F2,376=5.97, P=.003; "My Healthy Behavior": F2,359=5.52, P=.004; "Master Your Breath": F2,317=3.17, P=.04). One study found lower intervention evaluation by lower educated participants compared to participants with a middle educational level ("Weight in Balance": F2,37=3.17, P=.05). Low evaluation of the interventions was not a significant predictor of dropout at a later follow-up measurement in any of the studies. CONCLUSIONS: Dropout attrition rates were higher among participants with a lower or middle educational level compared with highly educated participants. Although lower educated participants evaluated the interventions better in approximately half of the studies, evaluation did not predict dropout attrition. Further research is needed to find other explanations for high dropout rates among lower educated participants.

Metadata Correction: Impact of Educational Level on Study Attrition and Evaluation of Web-Based Computer-Tailored Interventions: Results From Seven Randomized Controlled Trials.

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Predictors of successful and unsuccessful quit attempts among smokers motivated to quit.

INTRODUCTION: Despite their positive motivation to quit, many smokers do not attempt to quit or relapse soon after their quit attempt. This study investigated the predictors of successful and unsuccessful quit attempts among smokers motivated to quit smoking. METHODS: We conducted secondary data analysis among respondents motivated to quit within 6 months, randomized to the control group (N=570) of a Web-based smoking cessation intervention study. Using chi-square tests and ANOVA with Tukey post hoc comparisons, we investigated baseline differences by smoking status (successful quitter/relapse/persistent smoker) assessed after 6 weeks (N=214). To identify independent predictors of smoking status, multivariate multinomial logistic regression analyses were conducted. RESULTS: Successful quitters at 6-week follow-up (26%) had reported significantly higher baseline levels of self-efficacy than relapsers (45%) and persistent smokers (29%). Furthermore, both successful quitters and relapsers had reported a significantly higher baseline intention to quit than persistent smokers and successful quitters had reported significantly more preparatory planning at baseline than persistent smokers. Results from regression analyses showed that smokers' baseline intention to quit positively predicted quit attempts reported after 6 weeks, while self-efficacy positively predicted quit attempt success. CONCLUSIONS: Different factors appear to play a role in predicting quit attempts and their success. Whereas intention to quit only appeared to play a role in predicting quit attempts, self-efficacy was the main factor predicting quit attempt success. More research is needed to determine the role of preparatory planning and plan enactment and to investigate whether these findings can be replicated on the long term.

Determinants of practice nurses' intention to implement a new smoking cessation intervention: the importance of attitude and innovation characteristics.

AIMS: To identify determinants of practice nurses' intention to implement a new smoking cessation intervention and to investigate the independent value of attitude and Rogers' innovation characteristics. BACKGROUND: While effective smoking cessation interventions exist, implementation is often suboptimal. No previous studies have disentangled the independent value of beliefs towards implementation and innovation characteristics in explaining implementation. DESIGN: A cross-sectional descriptive study. METHODS: In 2010, 56 of 91 general practice nurses who participated in an intervention effectiveness trial completed an online questionnaire concerning demographics, patient population characteristics, attitude, innovation characteristics, self-efficacy, perceived social influence and intention to implement the intervention in the future. Recruitment success during the trial was defined as the number of patients participating. To detect differences between intending and non-intending practice nurses, independent sample t-tests and Chi-squared tests were conducted. Correlation coefficients were calculated to identify associations between potential determinants of intention. To identify significant determinants logistic hierarchical regression analyses were conducted. RESULTS: Innovation characteristics and attitude were both significantly associated with practice nurses' intention to implement. While recruitment success showed a significant positive association with intention, perceived patient support was only a significant determinant when including innovation characteristics or attitude. CONCLUSION: To increase new interventions' implementation rates, it is most important to convince health professionals of its beneficial characteristics, to generate a positive attitude towards the intervention, to aid practice nurses in recruiting smoking patients and to increase perceived patient support.

Cost-effectiveness and cost-utility of Internet-based computer tailoring for smoking cessation.

BACKGROUND: Although effective smoking cessation interventions exist, information is limited about their cost-effectiveness and cost-utility. OBJECTIVE: To assess the cost-effectiveness and cost-utility of an Internet-based multiple computer-tailored smoking cessation program and tailored counseling by practice nurses working in Dutch general practices compared with an Internet-based multiple computer-tailored program only and care as usual. METHODS: The economic evaluation was embedded in a randomized controlled trial, for which 91 practice nurses recruited 414 eligible smokers. Smokers were randomized to receive multiple tailoring and counseling (n=163), multiple tailoring only (n=132), or usual care (n=119). Self-reported cost and quality of life were assessed during a 12-month follow-up period. Prolonged abstinence and 24-hour and 7-day point prevalence abstinence were assessed at 12-month follow-up. The trial-based economic evaluation was conducted from a societal perspective. Uncertainty was accounted for by bootstrapping (1000 times) and sensitivity analyses. RESULTS: No significant differences were found between the intervention arms with regard to baseline characteristics or effects on abstinence, quality of life, and addiction level. However, participants in the multiple tailoring and counseling group reported significantly more annual health care-related costs than participants in the usual care group. Cost-effectiveness analysis, using prolonged abstinence as the outcome measure, showed that the mere multiple computer-tailored program had the highest probability of being cost-effective. Compared with usual care, in this group €5100 had to be paid for each additional abstinent participant. With regard to cost-utility analyses, using quality of life as the outcome measure, usual care was probably most efficient. CONCLUSIONS: To our knowledge, this was the first study to determine the cost-effectiveness and cost-utility of an Internet-based smoking cessation program with and without counseling by a practice nurse. Although the Internet-based multiple computer-tailored program seemed to be the most cost-effective treatment, the cost-utility was probably highest for care as usual. However, to ease the interpretation of cost-effectiveness results, future research should aim at identifying an acceptable cutoff point for the willingness to pay per abstinent participant.

Effectiveness of a Web-based multiple tailored smoking cessation program: a randomized controlled trial among Dutch adult smokers.

BACKGROUND: Distributing a multiple computer-tailored smoking cessation intervention through the Internet has several advantages for both provider and receiver. Most important, a large audience of smokers can be reached while a highly individualized and personal form of feedback can be maintained. However, such a smoking cessation program has yet to be developed and implemented in The Netherlands. OBJECTIVE: To investigate the effects of a Web-based multiple computer-tailored smoking cessation program on smoking cessation outcomes in a sample of Dutch adult smokers. METHODS: Smokers were recruited from December 2009 to June 2010 by advertising our study in the mass media and on the Internet. Those interested and motivated to quit smoking within 6 months (N = 1123) were randomly assigned to either the experimental (n = 552) or control group (n = 571). Respondents in the experimental group received the fully automated Web-based smoking cessation program, while respondents in the control group received no intervention. After 6 weeks and after 6 months, we assessed the effect of the intervention on self-reported 24-hour point prevalence abstinence, 7-day point prevalence abstinence, and prolonged abstinence using logistic regression analyses. RESULTS: Of the 1123 respondents, 449 (40.0%) completed the 6-week follow-up questionnaire and 291 (25.9%) completed the 6-month follow-up questionnaire. We used a negative scenario to replace missing values. That is, we considered respondents lost to follow-up to still be smoking. The computer-tailored program appeared to have significantly increased 24-hour point prevalence abstinence (odds ratio [OR] 1.85, 95% confidence interval [CI] 1.30-2.65), 7-day point prevalence abstinence (OR 2.17, 95% CI 1.44-3.27), and prolonged abstinence (OR 1.99, 95% CI 1.28-3.09) rates reported after 6 weeks. After 6 months, however, no intervention effects could be identified. Results from complete-case analyses were similar. CONCLUSIONS: The results presented suggest that the Web-based computer-tailored smoking cessation program had a significant effect on abstinence reported after a 6-week period. At the 6-month follow-up, however, no intervention effects could be identified. This might be explained by the replacement of missing values on the primary outcome measures due to attrition using a negative scenario. While results were similar when using a less conservative scenario (ie, complete-case analyses), the results should still be interpreted with caution. Further research should aim at identifying strategies that will prevent high attrition in the first place and, subsequently, to identify the best strategies for dealing with missing data when studies have high attrition rates. TRIAL REGISTRATION: Dutch Trial Register NTR1351; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=1351 (Archived by WebCite at http://www.webcitation.org/67egSTWrz).

Factors influencing Dutch practice nurses' intention to adopt a new smoking cessation intervention.

AIMS: This article is a report of a study that aimed to identify factors influencing practice nurses' and nurse practitioners' intention to adopt a new smoking cessation intervention. BACKGROUND: Although effective smoking cessation interventions exist and practice nurses can offer a considerable resource in advertising patients to quit smoking, due to several reasons the majority of practice nurses do not implement these interventions. METHODS: A cross-sectional study was undertaken among Dutch practice nurses and nurse practitioners working in general practices (n = 139) using electronic questionnaires. Data were collected from January until March in 2009. T-tests were used to compare adopters with non-adopters about their predisposing and motivational factors. Logistic regression analyses were conducted to assess the variation in intention explained by these factors. RESULTS: The majority of practice nurses did not intend to adopt the new intervention (n = 85; 61.2%). More practice nurses than nurse practitioners intended to adopt the intervention. Attitude and perceived social norms were found to be positively correlated with the intention to adopt the intervention whereas satisfaction with current smoking cessation activities was found to be negatively correlated. CONCLUSION: Important associations were found between profession, attitude, social norms and satisfaction, and the intention to adopt the new smoking cessation intervention. Practice nurses who do not intend to adopt need to be persuaded of the advantages of adopting. Perceived social norms need to be restructured and before presenting the intervention to a general practice current smoking cessation activities should be determined to increase the intervention's compatibility with these current practices.