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      The Limitations of User-and Human-Centered Design in an eHealth Context and How to Move Beyond Them

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          Abstract

          Human-centered design (HCD) is widely regarded as the best design approach for creating eHealth innovations that align with end users’ needs, wishes, and context and has the potential to impact health care. However, critical reflections on applying HCD within the context of eHealth are lacking. Applying a critical eye to the use of HCD approaches within eHealth, we present and discuss 9 limitations that the current practices of HCD in eHealth innovation often carry. The limitations identified range from limited reach and bias to narrow contextual and temporal focus. Design teams should carefully consider if, how, and when they should involve end users and other stakeholders in the design process and how they can combine their insights with existing knowledge and design skills. Finally, we discuss how a more critical perspective on using HCD in eHealth innovation can move the field forward and offer 3 directions of inspiration to improve our design practices: value-sensitive design, citizen science, and more-than-human design. Although value-sensitive design approaches offer a solution to some of the biased or limited views of traditional HCD approaches, combining a citizen science approach with design inspiration and imagining new futures could widen our view on eHealth innovation. Finally, a more-than-human design approach will allow eHealth solutions to care for both people and the environment. These directions can be seen as starting points that invite and support the field of eHealth innovation to do better and to try and develop more inclusive, fair, and valuable eHealth innovations that will have an impact on health and care.

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          The Transtheoretical Model of Health Behavior Change

          The transtheoretical model posits that health behavior change involves progress through six stages of change: precontemplation, contemplation, preparation, action, maintenance, and termination. Ten processes of change have been identified for producing progress along with decisional balance, self-efficacy, and temptations. Basic research has generated a rule of thumb for at-risk populations: 40% in precontemplation, 40% in contemplation, and 20% in preparation. Across 12 health behaviors, consistent patterns have been found between the pros and cons of changing and the stages of change. Applied research has demonstrated dramatic improvements in recruitment, retention, and progress using stage-matched interventions and proactive recruitment procedures. The most promising outcomes to data have been found with computer-based individualized and interactive interventions. The most promising enhancement to the computer-based programs are personalized counselors. One of the most striking results to date for stage-matched programs is the similarity between participants reactively recruited who reached us for help and those proactively recruited who we reached out to help. If results with stage-matched interventions continue to be replicated, health promotion programs will be able to produce unprecedented impacts on entire at-risk populations.
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            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 adhere.
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              The Person-Based Approach to Intervention Development: Application to Digital Health-Related Behavior Change Interventions

              This paper describes an approach that we have evolved for developing successful digital interventions to help people manage their health or illness. We refer to this as the “person-based” approach to highlight the focus on understanding and accommodating the perspectives of the people who will use the intervention. While all intervention designers seek to elicit and incorporate the views of target users in a variety of ways, the person-based approach offers a distinctive and systematic means of addressing the user experience of intended behavior change techniques in particular and can enhance the use of theory-based and evidence-based approaches to intervention development. There are two key elements to the person-based approach. The first is a developmental process involving qualitative research with a wide range of people from the target user populations, carried out at every stage of intervention development, from planning to feasibility testing and implementation. This process goes beyond assessing acceptability, usability, and satisfaction, allowing the intervention designers to build a deep understanding of the psychosocial context of users and their views of the behavioral elements of the intervention. Insights from this process can be used to anticipate and interpret intervention usage and outcomes, and most importantly to modify the intervention to make it more persuasive, feasible, and relevant to users. The second element of the person-based approach is to identify “guiding principles” that can inspire and inform the intervention development by highlighting the distinctive ways that the intervention will address key context-specific behavioral issues. This paper describes how to implement the person-based approach, illustrating the process with examples of the insights gained from our experience of carrying out over a thousand interviews with users, while developing public health and illness management interventions that have proven effective in trials involving tens of thousands of users.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                October 2022
                5 October 2022
                : 24
                : 10
                : e37341
                Affiliations
                [1 ] eHealth Department Roessingh Research and Development Enschede Netherlands
                [2 ] Department of Communication Science University of Twente Enschede Netherlands
                [3 ] Department of Design, Production and Management University of Twente Enschede Netherlands
                [4 ] Biomedical Systems and Signals group University of Twente Enschede Netherlands
                Author notes
                Corresponding Author: Geke Ludden g.d.s.ludden@ 123456utwente.nl
                Author information
                https://orcid.org/0000-0003-0599-8706
                https://orcid.org/0000-0003-4508-9865
                https://orcid.org/0000-0003-2319-3186
                Article
                v24i10e37341
                10.2196/37341
                9582917
                36197718
                4beb9813-9604-4606-80f3-56e82ad8535c
                ©Lex van Velsen, Geke Ludden, Christiane Grünloh. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.10.2022.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 16 February 2022
                : 3 May 2022
                : 27 July 2022
                : 19 August 2022
                Categories
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                Medicine
                user-centered design,human-centered design,ehealth, value-sensitive design,citizen science

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