BackgroundThe Internet has become a popular medium for the delivery of tailored healthy lifestyle promoting interventions. The actual reach of Internet-delivered interventions seems, however, lower than expected, and attrition from interventions is generally high. Characteristics of an intervention, such as personally tailored feedback and goal setting, are thought to be among the important factors related to of use of and exposure to interventions. However, there is no systematic overview of which characteristics of Internet-delivered interventions may be related to more exposure.ObjectiveThe present study aims to identify (1) which potentially exposure-promoting methods and strategies are used in existing Internet interventions, (2) which objective outcome measures are used to measure exposure to Internet interventions, and (3) which potentially exposure-promoting methods and strategies are associated with better exposure.MethodsA systematic review of the literature was conducted based on the Cochrane guidelines. Papers published between 1995 and 2009 were searched in the PubMed, PsycINFO, and Web of Science databases. In total, 64 studies were included that reported objective exposure measures such as completion of an initial visit, number of log-ins, and time spent on the website. Information about intervention-related characteristics (ie, interactive behavior change strategies, interactive elements for fun, peer or counsel support, email/phone contact, and regular updates of the website) that could potentially contribute to better exposure and objective exposure outcomes were abstracted from the studies and qualitative systematic descriptive analyses were performed.ResultsThe results showed that a large variety of behavior change techniques and other exposure-promoting elements were used in the interventions and that these methods and strategies varied for the various lifestyle behaviors. Feedback, interactive elements, and email/phone contact were used most often. In addition, there was much variety and a lack of consistency in the exposure measures that were reported. Of all the categories of intervention characteristics that may be associated with better exposure, there were indications that peer and counselor support result in a longer website visit and that email/phone contact and updates of the website result in more log-ins.ConclusionsResults of this qualitative systematic review indicate that of all intervention characteristics that could potentially enhance exposure, only peer support, counselor support, email/phone contact with visitors, and updates of the intervention website were related to better exposure. The diversity of intervention methods used and the inconsistency in the report of exposure measures prevented us from drawing firmer conclusions. More research is needed to identify whether other characteristics of Internet interventions are associated with greater exposure.

Engagement in electronic health (eHealth) and mobile health (mHealth) behavior change interventions is thought to be important for intervention effectiveness, though what constitutes engagement and how it enhances efficacy has been somewhat unclear in the literature. Recently published detailed definitions and conceptual models of engagement have helped to build consensus around a definition of engagement and improve our understanding of how engagement may influence effectiveness. This work has helped to establish a clearer research agenda. However, to test the hypotheses generated by the conceptual modules, we need to know how to measure engagement in a valid and reliable way. The aim of this viewpoint is to provide an overview of engagement measurement options that can be employed in eHealth and mHealth behavior change intervention evaluations, discuss methodological considerations, and provide direction for future research. To identify measures, we used snowball sampling, starting from systematic reviews of engagement research as well as those utilized in studies known to the authors. A wide range of methods to measure engagement were identified, including qualitative measures, self-report questionnaires, ecological momentary assessments, system usage data, sensor data, social media data, and psychophysiological measures. Each measurement method is appraised and examples are provided to illustrate possible use in eHealth and mHealth behavior change research. Recommendations for future research are provided, based on the limitations of current methods and the heavy reliance on system usage data as the sole assessment of engagement. The validation and adoption of a wider range of engagement measurements and their thoughtful application to the study of engagement are encouraged.

BackgroundInterventions aimed at behavior change are increasingly being delivered over the Internet. Although research on intervention effectiveness has been widely conducted, their true public health impact as indicated by reach, effectiveness, and use is unclear.ObjectiveThe aim of this paper is to (1) review the current literature on online prevention aimed at lifestyle behaviors, and (2) identify research gaps regarding reach, effectiveness, and use.MethodsA systematic search in PubMed revealed relevant literature published between 2005 and 2012 on Internet-delivered behavior change interventions aimed at dietary behaviors, physical activity, alcohol use, smoking, and condom use. Our search yielded 41 eligible reviews, which were analyzed in terms of reach, effectiveness, and use according to the RE-AIM framework.ResultsAccording to health priorities, interventions are largely targeted at weight-related behaviors, such as physical activity and dietary behavior. Evaluations are predominantly effect-focused and overall effects are small, variable, and not sustainable. Determinants of effectiveness are unclear; effectiveness cannot yet be unambiguously attributed to isolated elements. Actual reach of interventions is undiversified, mostly reaching participants who are female, highly educated, white, and living in high-income countries. One of the most substantial problems in online prevention is the low use of the interventions, a phenomenon seen across all behavior domains.ConclusionsMore research is needed on effective elements instead of effective interventions, with special attention to long-term effectiveness. The reach and use of interventions need more scientific input to increase the public health impact of Internet-delivered interventions.

One of the goals of a pilot study is to identify unforeseen problems, such as ambiguous inclusion or exclusion criteria or misinterpretations of questionnaire items. Although sample size calculation methods for pilot studies have been proposed, none of them are directed at the goal of problem detection. In this article, we present a simple formula to calculate the sample size needed to be able to identify, with a chosen level of confidence, problems that may arise with a given probability. If a problem exists with 5% probability in a potential study participant, the problem will almost certainly be identified (with 95% confidence) in a pilot study including 59 participants.

There is a need to consolidate the evidence base underlying our toolbox of methods of behaviour change. Recent efforts to this effect have conducted meta-regressions on evaluations of behaviour change interventions, deriving each method's effectiveness from its association to intervention effect size. However, there are a range of issues that raise concern about whether this approach is actually furthering or instead obstructing the advancement of health psychology theories and the quality of health behaviour change interventions. Using examples from theory, the literature and data from previous meta-analyses, these concerns and their implications are explained and illustrated. An iterative protocol for evidence base accumulation is proposed that integrates evidence derived from both experimental and applied behaviour change research, and combines theory development in experimental settings with theory testing in applied real-life settings. As evidence gathered in this manner accumulates, a cumulative science of behaviour change can develop.