Understanding adherence to web-based interventions

Although eHealth technologies and especially web-based interventions for the promotion of health and health related behavior have been shown to be effective, the impact is hindered by non-adherence: while many eHealth interventions reach a large group of participants, not all of these participants complete the intervention and may therefore not benefit as much from the intervention as they could. In this thesis, non-adherence is investigated by means of empirical studies into two web-based interventions: the Healthy Weight Assistant, an intervention aimed at healthy dietary and physical activity behavior; and Living to the Full, an intervention for the prevention of depression. Additionally, a systematic review was conducted to investigate the influence of intervention characteristics and persuasive design on adherence to web-based interventions. The comparison of adherers and non-adherers in the empirical studies has shown that adherers and non-adherers are different groups, but there are no universal predictors for adherence; rather it is important to create a match between participants and the intervention to improve adherence. Second, the human centered development process of Living to the Full and the investigation of the reasons for use of the Healthy Weight Assistant showed that participants expect a web-based intervention to support them in achieving their goals and to have the advantages that the Internet has to offer. Not fulfilling these expectations may well be a reason for non-adherence. Third, the systematic review showed that a persuasive design of web-based interventions, especially more use of dialogue support, increases the adherence to the intervention. The investigation of the impact of different persuasive design elements in Living to the Full showed that these persuasive design elements not always result in better adherence. Lastly, the empirical studies showed that participants do not use all features of a web-based intervention, but adherers use more features and seem to be more involved with the intervention than non-adherers. With these insights, the opportunity arises to not only hope for adherence, but to plan for adherence

Persuasive system design does matter: a systematic review of adherence to web-based interventions

Background: Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence. Objective: This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention. Methods: We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence. Results: We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p = .004), setup (p < .001), updates (p < .001), frequency of interaction with a counselor (p < .001), the system (p = .003) and peers (p = .017), duration (F = 6.068, p = .004), adherence (F = 4.833, p = .010) and the number of primary task support elements (F = 5.631, p = .005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence. Conclusions: Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adher

Development of a web-based intervention for the indicated prevention of depression

BACKGROUND: To reduce the large public health burden of the high prevalence of depression, preventive interventions targeted at people at risk are essential and can be cost-effective. Web-based interventions are able to provide this care, but there is no agreement on how to best develop these applications and often the technology is seen as a given. This seems to be one of the main reasons that web-based interventions do not reach their full potential. The current study describes the development of a web-based intervention for the indicated prevention of depression, employing the CeHRes (Center for eHealth Research and Disease Management) roadmap. The goals are to create a user-friendly application which fits the values of the stakeholders and to evaluate the process of development. METHODS: The employed methods are a literature scan and discussion in the contextual inquiry; interviews, rapid prototyping and a requirement session in the value specification stage; and user-based usability evaluation, expert-based usability inspection and a requirement session in the design stage. RESULTS: The contextual inquiry indicated that there is a need for easily accessible interventions for the indicated prevention of depression and web-based interventions are seen as potentially meeting this need. The value specification stage yielded expected needs of potential participants, comments on the usefulness of the proposed features and comments on two proposed designs of the web-based intervention. The design stage yielded valuable comments on the system, content and service of the web-based intervention. CONCLUSIONS: Overall, we found that by developing the technology, we successfully (re)designed the system, content and service of the web-based intervention to match the values of stakeholders. This study has shown the importance of a structured development process of a web-based intervention for the indicated prevention of depression because: (1) it allows the development team to clarify the needs that have to be met for the intervention to be of use to the target audience; and (2) it yields feedback on the design of the application that is broader than color and buttons, but encompasses comments on the quality of the service that the application offers

Evaluation of the use of an "ask-the-expert" e-consultation service for support on health-related requests

E-consultation in health care can be used to respond to an increasing demand for care by offering support on health-related requests. In this study we evaluated the use of an Ask-the-expert e-consultation service in order to assess whether the service is efficient and useful. A content analysis of e-mail exchange between clients and online health professionals was performed to gain insight in the purposes of use of the service. Our findings show that the service was used for e-mails requests on not urgent, minor ailments. Clients asked for health information to increase knowledge on the cause of their injury or disease, its consequences, self-care solutions and treatment options. Decision support on assessing the necessity to visit a doctor for a certain health problem was another important reason to use the service. We believe that web-based triage systems could be used to more easily assess whether certain symptoms need to be investigated

Evaluation of perceived persuasiveness constructs by combining user tests and expert assessments

To develop effective behaviour change support systems, persuasive technology can be used. The persuasive systems design model offers a framework to identify and operationalize such elements. In this pilot study, we evaluate the questionnaire developed to measure perceived persuasiveness of information technology. We analyzed verbatim user-test transcripts, and performed expert-assessments of the Nurse Antibiotic Information App (NAIA). These data were compared to questionnaire results on this app. Expert-assessment identified task support, perceived persuasiveness, unobtrusiveness, credibility, perceived effort and perceived effectiveness (as defined in the Persuasive Systems Design model) as being present within the NAIA. These constructs also scored satisfactory in the questionnaire. User-test transcripts are in line with questionnaire results. Given the consistent results in this pilot study, our approach seems promising for evaluating the questionnaire and will be applied to other settings and websites/application

Dataset_Kelders et al_Psychometric Evaluation of the TWente Engagement with Ehealth Technologies Scale (TWEETS): Evaluation Study_JMIR2020

This data was collected for the study: Psychometric Evaluation of the TWente Engagement with Ehealth Technologies Scale (TWEETS): Evaluation Study. Background: Engagement surfaces as a predictor for effectiveness of Digital Health Interventions. However, a shared understanding of engagement is missing. Therefore, a new scale has been developed that proposes a clear definition and creates a tool to measure it. The TWente Engagement with Ehealth Technologies Scale (TWEETS) is- based on a systematic review and interviews with engaged health app users. It defines engagement as a combination of behavior, cognition and affect. Objective: First, this paper is aimed at evaluating the psychometric properties of the TWEETS. Second, a comparison is made with the experiential part of the Digital Behavior Change Intervention Engagement Scale (DBCI-ES-Ex), a scale that showed some issues in previous psychometric analyses. Methods: Participants (n = 288) were asked to use any step-counter app on their smartphone for two weeks. They completed online questionnaires at four time points (T0 = baseline, T1 = after 1 day, T2 = 1 week and T3 = 2 weeks). At T0, demographics and personality (conscientiousness and intellect/imagination) were assessed, while at T1 - T3, engagement, involvement, enjoyment, subjective usage and perceived behavior change were included as measures that are theoretically related to our definition of engagement. Analyses focused on internal consistency, reliability, convergent, divergent and predictive validity of both engagement scales. Convergent validity was assessed by correlating the engagement scales with involvement, enjoyment and subjective usage; divergent validity by correlating the engagement scales with personality; and predictive validity by regression analyses using engagement to predict perceived behavior change at later time points. Results: Cronbach's alpha of the TWEETS was 0.86, 0.86 and 0.87 on T1 - T3. Exploratory factor analyses indicated that a one-factor structure best fitted the data. The TWEETS is moderately to strongly correlated with involvement and enjoyment (theoretically related to cognitive and affective engagement respectively) (P<.001). Correlations between the TWEETS and frequency of use were non-significant or small and differences between adherers and non-adherers on the TWEETS were significant (P<.001). Correlations between personality and the TWEETS were non-significant. The TWEETS at T1 was predictive of perceived behavior change at T3, with an explained variance of 16%.The psychometric properties of the TWEETS and the DBCI-ES-Ex seemed comparable on some aspects (e.g. internal consistency) and on other aspects the TWEETS seemed somewhat superior (divergent and predictive validity). Conclusion: The TWEETS performs quite well as an engagement measure with high internal consistency, reasonable test-retest reliability and convergent validity, good divergent validity, and reasonable predictive validity. As the psychometric quality of a scale is a reflection of how closely a scale matches the conceptualization of the concept, this paper is also an attempt to conceptualize and define engagement as a unique concept, providing a first step towards an acceptable standard of defining and measuring engagement

Understanding the Usage of Content in a Mental Health Intervention for Depression: An Analysis of Log Data

Background: Web-based interventions for the early treatment of depressive symptoms can be considered effective in reducing mental complaints. However, there is a limited understanding of which elements in an intervention contribute to effectiveness. For efficiency and effectiveness of interventions, insight is needed into the use of content and persuasive features. Objective: The aims of this study were (1) to illustrate how log data can be used to understand the uptake of the content of a Web-based intervention that is based on the acceptance and commitment therapy (ACT) and (2) to discover how log data can be of value for improving the incorporation of content in Web-based interventions. Methods: Data from 206 participants (out of the 239) who started the first nine lessons of the Web-based intervention, Living to the Full, were used for a secondary analysis of a subset of the log data of the parent study about adherence to the intervention. The log files used in this study were per lesson: login, start mindfulness, download mindfulness, view success story, view feedback message, start multimedia, turn on text-message coach, turn off text-message coach, and view text message. Differences in usage between lessons were explored with repeated measures ANOVAs (analysis of variance). Differences between groups were explored with one-way ANOVAs. To explore the possible predictive value of the login per lesson quartiles on the outcome measures, four linear regressions were used with login quartiles as predictor and with the outcome measures (Center for Epidemiologic Studies—Depression [CES-D] and the Hospital Anxiety and Depression Scale—Anxiety [HADS-A] on post-intervention and follow-up) as dependent variables. Results: A significant decrease in logins and in the use of content and persuasive features over time was observed. The usage of features varied significantly during the treatment process. The usage of persuasive features increased during the third part of the ACT (commitment to value-based living), which might indicate that at that stage motivational support was relevant. Higher logins over time (9 weeks) corresponded with a higher usage of features (in most cases significant); when predicting depressive symptoms at post-intervention, the linear regression yielded a significant model with login quartile as a significant predictor (explained variance is 2.7%). Conclusions: A better integration of content and persuasive features in the design of the intervention and a better intra-usability of features within the system are needed to identify which combination of features works best for whom. Pattern recognition can be used to tailor the intervention based on usage patterns from the earlier lessons and to support the uptake of content essential for therapy. An adaptable interface for a modular composition of therapy features supposes a dynamic approach for Web-based treatment; not a predefined path for all, but a flexible way to go through all features that have to be used