Dominique Alexandra Reinwand

Effects of a web-based tailored intervention to reduce alcohol consumption in adults: Randomized controlled trial

Background Web-based tailored interventions provide users with information that is adapted to their individual characteristics and needs. Randomized controlled trials assessing the effects of tailored alcohol self-help programs among adults are scarce. Furthermore, it is a challenge to develop programs that can hold respondents’ attention in online interventions. Objective To assess whether a 3-session, Web-based tailored intervention is effective in reducing alcohol intake in high-risk adult drinkers and to compare 2 computer-tailoring feedback strategies (alternating vs summative) on behavioral change, dropout, and appreciation of the program. Methods A single-blind randomized controlled trial was conducted with an experimental group and a control group (N=448) in Germany in 2010-2011. Follow-up took place after 6 months. Drinking behavior, health status, motivational determinants, and demographics were assessed among participants recruited via an online access panel. The experimental group was divided into 2 subgroups. In the alternating condition (n=132), the tailored feedback was split into a series of messages discussing individual topics offered while the respondent was filling out the program. Participants in the summative condition (n=181) received all advice at once after having answered all questions. The actual texts were identical for both conditions. The control group (n=135) only filled in 3 questionnaires. To identify intervention effects, logistic and linear regression analyses were conducted among complete cases (n=197) and after using multiple imputation. Results Among the complete cases (response rate: 197/448, 44.0%) who did not comply with the German national guideline for low-risk drinking at baseline, 21.1% of respondents in the experimental group complied after 6 months compared with 5.8% in the control group (effect size=0.42; OR 2.65, 95% CI 1.14-6.16, P=.02). The experimental group decreased by 3.9 drinks per week compared to 0.4 drinks per week in the control group, but this did not reach statistical significance (effect size=0.26; beta=−0.12, 95% CI −7.96 to 0.03, P=.05). Intention-to-treat analyses also indicated no statistically significant effect. Separate analyses of the 2 experimental subgroups showed no differences in intervention effects. The dropout rate during the first visit to the intervention website was significantly lower in the alternating condition than in the summative condition (OR 0.23, 95% CI 0.08-0.60, P=.003). Program appreciation was comparable for the 2 experimental groups. Conclusions Complete case analyses revealed that Web-based tailored feedback can be an effective way to reduce alcohol intake among adults. However, this effect was not confirmed when applying multiple imputations. There was no indication that one of the tailoring strategies was more effective in lowering alcohol intake. Nevertheless, the lower attrition rates we found during the first visit suggest that the version of the intervention with alternating questions and advice may be preferred. Trial Registration International Standard Randomized Controlled Trial Number (ISRCTN): 91623132; http://www.controlled-trials.com/ISRCTN91623132 (Archived by WebCite at http://www.webcitation.org/6J4QdhXeG).

Who Follows eHealth Interventions as Recommended? A Study of Participants' Personal Characteristics From the Experimental Arm of a Randomized Controlled Trial

Background Computer-tailored eHealth interventions to improve health behavior have been demonstrated to be effective and cost-effective if they are used as recommended. However, different subgroups may use the Internet differently, which might also affect intervention use and effectiveness. To date, there is little research available depicting whether adherence to intervention recommendations differs according to personal characteristics. Objective The aim was to assess which personal characteristics are associated with using an eHealth intervention as recommended. Methods A randomized controlled trial was conducted among a sample of the adult Dutch population (N=1638) testing an intervention aimed at improving 5 healthy lifestyle behaviors: increasing fruit and vegetable consumption, increasing physical activity, reducing alcohol intake, and promoting smoking cessation. Participants were asked to participate in those specific online modules for which they did not meet the national guideline(s) for the respective behavior(s). Participants who started with fewer than the recommended number of modules of the intervention were defined as users who did not follow the intervention recommendation. Results The fewer modules recommended to participants, the better participants adhered to the intervention modules. Following the intervention recommendation increased when participants were older (χ2 1=39.8, P<.001), female (χ2 1=15.8, P<.001), unemployed (χ2 1=7.9, P=.003), ill (χ2 1=4.5, P=.02), or in a relationship (χ2 1=7.8, P=.003). No significant relevant differences were found between groups with different levels of education, incomes, or quality of life. Conclusion Our findings indicate that eHealth interventions were used differently by subgroups. The more frequent as-recommended intervention use by unemployed, older, and ill participants may be an indication that these eHealth interventions are attractive to people with a greater need for health care information. Further research is necessary to make intervention use more attractive for people with unhealthy lifestyle patterns.

Designing a theory- and evidence-based tailored eHealth rehabilitation aftercare program in Germany and the Netherlands: study protocol.

BackgroundCardiac rehabilitation programs aim to improve health status and to decrease the risk of further cardiac events. Persons undergoing rehabilitation often have difficulties transferring the learned health behaviors into their daily routine after returning home and maybe to work. This includes physical activity as well as fruit and vegetable consumption. Computer-based tailored interventions have been shown to be effective in increasing physical activity as well as fruit and vegetable consumption. The aim of this study is, to support people in transferring these two learned behavior changes and their antecedents into their daily life after cardiac rehabilitation.MethodsThe study will have a randomized controlled design and will be conducted among German and Dutch people who participated in cardiac rehabilitation. The study will consist of one intervention group which will be compared to a waiting list control group. During the eight week duration of the intervention, participants will be invited to participate in the online after-care program once per week. The intervention encourages participants to define individual health behavior goals as well as action, and coping plans to reach these self-determined goals. The effectiveness of the program will be compared between the intervention condition and the control group in terms of behavior change, antecedents of behavior change (e.g., self-efficacy), ability to return to work and increased well-being. Further, subgroup-differences will be assessed including differences between the two countries, socioeconomic inequalities and across age groups.DiscussionThe present study will make a contribution to understanding how such an online-based tailored interventions enables study participants to adopt and maintain a healthy lifestyle. Implications can include how such an online program could enrich cardiac rehabilitation aftercare further.Trial registrationNTR 3706, NCT01909349

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”: F 2,376=5.97, P=.003; “My Healthy Behavior”: F 2,359=5.52, P=.004; “Master Your Breath”: F 2,317=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”: F 2,37=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.

Effectiveness of a Web-Based Computer-Tailored Multiple-Lifestyle Intervention for People Interested in Reducing their Cardiovascular Risk: A Randomized Controlled Trial

Background Web-based computer-tailored interventions for multiple health behaviors can improve the strength of behavior habits in people who want to reduce their cardiovascular risk. Nonetheless, few randomized controlled trials have tested this assumption to date. Objective The study aim was to test an 8-week Web-based computer-tailored intervention designed to improve habit strength for physical activity and fruit and vegetable consumption among people who want to reduce their cardiovascular risk. In a randomized controlled design, self-reported changes in perceived habit strength, self-efficacy, and planning across different domains of physical activity as well as fruit and vegetable consumption were evaluated. Methods This study was a randomized controlled trial involving an intervention group (n=403) and a waiting control group (n=387). Web-based data collection was performed in Germany and the Netherlands during 2013-2015. The intervention content was based on the Health Action Process Approach and involved personalized feedback on lifestyle behaviors, which indicated whether participants complied with behavioral guidelines for physical activity and fruit and vegetable consumption. There were three Web-based assessments: baseline (T0, N=790), a posttest 8 weeks after the baseline (T1, n=206), and a follow-up 3 months after the baseline (T2, n=121). Data analysis was conducted by analyzing variances and structural equation analysis. Results Significant group by time interactions revealed superior treatment effects for the intervention group, with substantially higher increases in self-reported habit strength for physical activity (F1,199=7.71, P=.006, Cohen’s d=0.37) and fruit and vegetable consumption (F1,199=7.71, P=.006, Cohen’s d=0.30) at posttest T1 for the intervention group. Mediation analyses yielded behavior-specific sequential mediator effects for T1 planning and T1 self-efficacy between the intervention and habit strength at follow-up T2 (fruit and vegetable consumption: beta=0.12, 95% CI 0.09-0.16, P<.001; physical activity: beta=0.04, 95% CI 0.02-0.06, P<.001). Conclusions Our findings indicate the general effectiveness and practicality of Web-based computer-tailored interventions in terms of increasing self-reported habit strength for physical activity and fruit and vegetable consumption. Self-efficacy and planning may play major roles in the mechanisms that facilitate the habit strength of these behaviors; therefore, they should be actively promoted in Web-based interventions. Although the results need to take into account the high dropout rates and medium effect sizes, a large number of people were reached and changes in habit strength were achieved after 3 months. Trial Registration Clinicaltrials.gov NCT01909349; https://clinicaltrials.gov/ct2/show/NCT01909349 (Archived by WebCite at http://www.webcitation.org/6g5F0qoft) and Nederlands Trial Register NTR3706 http://www.trialregister.nl/ trialreg/admin/rctview.asp?TC=3706 (Archived by WebCite at http://www.webcitation.org/6g5F5HMLX)

Brief report: Compensatory health beliefs are negatively associated with intentions for regular fruit and vegetable consumption when self-efficacy is low

Compensatory health beliefs (the beliefs that an unhealthy behaviour can be compensated by a healthy behaviour) can interfere with adherence to fruit and vegetable consumption recommendations. Fruit and vegetable consumption, social cognitive variables and compensatory health beliefs were investigated via self-report at baseline (T0) and 8-week follow-up (T1) in N = 790 participants. Self-efficacy predicted fruit and vegetable consumption intentions. Planning mediated between intentions and T1 fruit and vegetable consumption. Compensatory health beliefs negatively predicted intentions at low self-efficacy levels only. The results propose the use of self-efficacy interventions to diminish the negative effects of compensatory health beliefs when forming fruit and vegetable consumption intentions and foster planning to translate intentions into behaviour.

Understanding the positive associations of sleep, physical activity, fruit and vegetable intake, as predictors of quality of life and subjective health across age groups: a theory based, cross-sectional web-based study

Background: Due to the increase in unhealthy lifestyles and associated health risks, the promotion of healthy lifestyles to improve the prevention of non-communicable diseases is imperative. Thus, research aiming to identify strategies to modify health behaviors has been encouraged. Little is known about addressing multiple health behaviors across age groups (i.e., young, middle-aged, and older adults) and the underlying mechanisms. The theoretical framework of this study is Compensatory Carry-Over Action Model which postulates that different health behaviors (i.e., physical activity and fruit and vegetable intake) are interrelated, and they are driven by underlying mechanisms (more details in the main text). Additionally, restful sleep as one of the main indicators of good sleep quality has been suggested as a mechanism that relates to other health behaviors and well-being, and should therefore also be investigated within this study. The present study aims to identify the interrelations of restful sleep, physical activity, fruit and vegetable intake, and their associations with sleep quality as well as overall quality of life and subjective health in different age groups. Methods: A web-based cross-sectional study was conducted in Germany and the Netherlands. 790 participants aged 20–85 years filled in the web-based baseline questionnaire about their restful sleep, physical activity, fruit and vegetable intake, sleep quality, quality of life, and subjective health. Descriptive analysis, multivariate analysis of covariance, path analysis, and multi-group analysis were conducted. Results: Restful sleep, physical activity, and fruit and vegetable intake were associated with increased sleep quality, which in turn was associated with increased overall quality of life and subjective health. The path analysis model fitted the data well, and there were age-group differences regarding multiple health behaviors and sleep quality, quality of life, and subjective health. Compared to young and older adults, middle-aged adults showed poorest sleep quality and overall quality of life and subjective health, which were associated with less engagement in multiple health behaviors. Conclusion: A better understanding of age-group differences in clustering of health behaviors may set the stage for designing effective customized age-specific interventions to improve health and well-being in general and clinical settings. Trial Registration: A clinical trial registration was conducted with ClinicalTrials.gov (NCT01909349) https://clinicaltrials.gov/ct2/show/NCT01909349.

Latent user groups of an eHealth physical activity behaviour change intervention for people interested in reducing their cardiovascular risk

ABSTRACT EHealth behaviour change interventions that help participants to adhere to professional physical activity recommendations can help to prevent future events of cardiovascular diseases (CVD). Therefore, identifying user groups of such interventions based on stages of health behaviour change is of great importance to provide tailored content to users instead of one-size-fits-all approaches. Our study used Latent Class Analysis (LCA) to identify underlying classes of users of an eHealth behaviour change intervention based on stages of change and associated variables. We compared participants’ self-allocated stage with their latent class stage membership to display the correlation and mean differences between the two approaches. This was done by analysing baseline data of N = 310 people interested in reducing their CVD risk. LCA identified a three-class solution: (non-)intenders (19.4%), non-habituated actors (43.2%) and habituated actors (37.4%). The interrelation between self-allocated and latent class stage membership was moderate (ρ(308) = .49, p < .001). Significant mean differences for (non-)intenders and non-habituated actors were found in social-cognitive variables. Results showed that self-allocated stage outcomes represent a pseudo stage model − linear trends can be reported for stage-associated social-cognitive variables. The study provides information on the validity of stage measures, which can inform future interventions.

The mediating role of social support between physical activity habit strength and depressive symptoms: Findings from an internet-based study (Preprint)

Background Regular physical activity treatment has been advocated for the prevention and rehabilitation of patients at risk of cardiovascular diseases and depressive symptoms. How physical activity is related to depressive symptoms is widely discussed. Objective The aim of this internet-based study was to investigate the role of perceived social support in the relationship between physical activity habit strength and depressive symptoms. Methods In total, 790 participants (mean 50.9 years, SD 12.2, range 20-84 years) who were interested in reducing their cardiovascular risk were recruited in Germany and the Netherlands. Data collection was conducted via an internet-based questionnaire addressing physical activity habit strength, depressive symptoms, and perceived social support. Cross-sectional data analysis was done with SPSS version 24 using the Macro PROCESS version 2 16.3 by Hayes with bootstrapping (10,000 samples), providing 95% CIs. Results Physical activity habit strength was negatively related to depressive symptoms (r=–.13, P=.006), but this interrelation disappeared when controlling for perceived social support (beta=–.14, SE 0.09, P=.11). However, there was an indirect relationship between physical activity habit strength and depressive symptoms, which was mediated via perceived social support (beta=–.13; SE 0.04, 95% CI –0.21 to 0.06). The negative relationship between physical activity habit strength and depressive symptoms was fully mediated by perceived social support. Conclusions We suggest that physical activity treatment in people interested in reducing their cardiovascular risk should also embed social support to target depressive symptoms. Internet-based interventions and electronic health may provide a good option for doing so. Trial Registration ClinicalTrials.gov NCT01909349; https://clinicaltrials.gov/ct2/show/NCT01909349 (Archived by WebCite at http://www.webcitation.org/73Y9RfdiY)

Website Use and Effects of Online Information About Tobacco Additives Among the Dutch General Population: A Randomized Controlled Trial

Background As a legal obligation, the Dutch government publishes online information about tobacco additives to make sure that it is publicly available. Little is known about the influence this website (”tabakinfo”) has on visitors and how the website is evaluated by them. Objective This study assesses how visitors use the website and its effect on their knowledge, risk perception, attitude, and smoking behavior. The study will also assess how the website is evaluated by visitors using a sample of the Dutch general population, including smokers and nonsmokers. Methods A randomized controlled trial was conducted, recruiting participants from an online panel. At baseline, participants (N=672) were asked to fill out an online questionnaire about tobacco additives. Next, participants were randomly allocated to either one of two experimental groups and invited to visit the website providing information about tobacco additives (either with or without a database containing product-specific information) or to a control group that had no access to the website. After 3 months, follow-up measurements took place. Results At follow-up (n=492), no statistically significant differences were found for knowledge, risk perception, attitude, or smoking behavior between the intervention and control groups. Website visits were positively related to younger participants (B=–0.07, 95% CI –0.12 to –0.01; t11=–2.43, P=.02) and having a low risk perception toward tobacco additives (B=–0.32, 95% CI –0.63 to –0.02; t11=–2.07, P=.04). In comparison, having a lower education (B=–0.67, 95% CI –1.14 to –0.17; t11=–2.65, P=.01) was a significant predictor for making less use of the website. Furthermore, the website was evaluated less positively by smokers compared to nonsmokers (t324=–3.55, P<.001), and males compared to females (t324=–2.21, P=.02). Conclusions The website did not change perceptions of tobacco additives or smoking behavior. Further research is necessary to find out how online information can be used to effectively communication about the risks of tobacco additives. Trial Registration Nederlands Trial Register NTR4620; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4620 (Archived by WebCite at http://www.webcitation.org/6oW7w4Gnj)