Public Health Internet Applications in Promoting Health Behavior
by
Rik Crutzen, Stan Vluggen
  • LAST MODIFIED: 27 April 2017
  • DOI: 10.1093/obo/9780199756797-0162

Introduction

Internet applications fall under the umbrella of eHealth, which is an emerging field in the intersection of medical informatics, public health, and business, referring to health services and information delivered or enhanced through the Internet and related technologies. A subfield of eHealth, mHealth refers to the use of mobile computing and communication technologies for the same purpose. This article focuses on Internet applications for promoting health behavior. Essential elements of health promotion include educational, environmental, and organizational support to enable people to gain greater control over the determinants of their health. Internet applications are tools that can be used within health promotion for people to gain greater control over their health behavior. This can be relevant in primary prevention (e.g., to promote smoking cessation), secondary prevention (e.g., to promote STI screening among high risk groups), and tertiary prevention (e.g., to facilitate self-management of type-2 diabetes). Internet applications can also be used as decision aid to help people to make choices (e.g., to choose between treatment options) or to facilitate communication with health professionals.

General Overviews

Interventions aimed at behavior change are increasingly being delivered over the Internet. Kohl, et al. 2013 provides an overview on Internet applications aimed at lifestyle behavior and identify research gaps regarding reach, effectiveness, and use. Fiordelli, et al. 2013 focuses on mHealth and provides a comprehensive view of the field of mHealth research to date and aim to foster understanding of whether and how the new generation of smartphones has provided new opportunities. Despite promising potential, however, limited research is available (Dute, et al. 2016). Piette, et al. 2012 has conducted a review to identify unanswered questions for future research regarding eHealth in general, particularly on topics relevant to low- and middle-income countries.

  • Dute, Denise Jantine, Wanda Jose Erika Bemelmans, and João Breda. 2016. Using mobile apps to promote a healthy lifestyle among adolescents and students: A review of the theoretical basis and lessons learned. JMIR mHealth uHealth 4:e39.

    DOI: 10.2196/mhealth.3559Save Citation »Export Citation »E-mail Citation »

    An exploration on how mobile apps can contribute to health promotion. For the apps identified, the review describes the content, the theoretical mechanisms applied, and lessons learned.

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    • Fiordelli, Maddalena, Nicola Diviani, and Peter J Schulz. 2013. Mapping mHealth research: A decade of evolution. Journal of Medical Internet Research 15:e95.

      DOI: 10.2196/jmir.2430Save Citation »Export Citation »E-mail Citation »

      An overview on the impact of mobile phones and smartphones in health care.

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      • Kohl, Leonie F. M., Rik Crutzen, and Nanne K. de Vries. 2013. Online prevention aimed at lifestyle behaviors: A systematic review of reviews. Journal of Medical Internet Research 15:e146.

        DOI: 10.2196/jmir.2665Save Citation »Export Citation »E-mail Citation »

        An overview of forty-one reviews, which were analyzed in terms of reach, effectiveness, and use according to the RE-AIM framework.

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        • Piette, John D., K. C. Lun, Lincoln A. Moura Jr., et al. 2012. Impacts of e-health on the outcomes of care in low- and middle-income countries: Where do we go from here? Bulletin of the World Health Organization 90:365–372.

          DOI: 10.2471/BLT.11.099069Save Citation »Export Citation »E-mail Citation »

          A scoping review on the effects of eHealth on health outcomes and costs in in low- and middle-income countries.

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          Journals

          The Journal of Medical Internet Research, founded in 1999, was the first journal that published extensively on Internet applications aimed at promoting health behavior. The rise of Internet applications in general led to additional journals such as Internet Interventions. Also in journals from the field of medical informatics, such as International Journal of Medical Informatics and Telemedicine and e-Health, more attention is being paid to health promotion supported by Internet applications.

          • International Journal of Medical Informatics.

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            Official journal of the European Federation for Medical Informatics (EFMI) and the International Medical Informatics Association (IMIA), which covers information systems, computer-aided medical decision support systems, educational computer-based programs, and the impact of applications in health care from various perspectives.

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            • Internet Interventions.

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              Official journal of the European Society for Research on Internet Interventions (ESRII) and the International Society for Research on Internet Interventions (ISRII), which publishes high-impact research on Internet interventions and related areas.

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              • Journal of Medical Internet Research.

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                The Journal of Medical Internet Research (JMIR), founded in 1999, is a leading health informatics and health services/health policy journal focusing on emerging technologies in health, medicine, and biomedical research. JMIR was the first open access journal covering health informatics, and the first international scientific peer-reviewed journal on all aspects of research, information, and communication in the health-care field using Internet and Internet-related technologies.

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                • Telemedicine and e-Health.

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                  Telemedicine and eHealth is a leading international peer-reviewed journal placing emphasis on the impact of telemedicine on quality, access, and cost-effectiveness of health care. Telemedicine plays a pivotal role in health care by offering tools for home care, patient monitoring, and disease management.

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                  Origin of Internet Applications

                  Almost 50 percent of the entire world population uses the Internet. Especially in developed countries, a world without the Internet has become unthinkable as 74 percent of Europe’s inhabitants and almost 90 percent of the United States’ inhabitants use the Internet. Also in low- and middle-income countries, Internet use is on the rise. (Internet World Stats). The introduction of the personal computer and its connection to the Internet has offered people the opportunity to make information more accessible. With regard to the health-care sector, it has contributed to a strengthening of the patient position through providing access to health and disease specific information and through offering innovative communication methods. The Internet is gaining ground as a delivery system for health promotion activities. These eHealth interventions have the advantage of being globally accessible, and they can be used anonymously and at any time; they also have the potential to reach larger audiences, can be cost-effective, and are not limited to time spans and places of medical encounters (Eysenbach 2001). The term “eHealth” was seldom used before 1999 and was first used by marketers and market leaders rather than people working in academics (Eysenbach 2001). Curtis 2007 states that eHealth was mainly described as health informatics applications/systems, which digitally aided hospitals in administrative tasks (Curtis 2007). Precursors of eHealth, referred to as telehealth, even go back to the beginning of the 20th century, where radio and Morse codes were used to pass on health instructions to health-care providers elsewhere (Curtis 2007). Until the dawn of the Internet era, the evolution of eHealth stagnated, as costs and technological inabilities did not offer the required features to optimally use eHealth in the health-care setting (Curtis 2007). With the invention of the World Wide Web, personal computers, and the increasing popularity of e-mail in the 1990s, the eHealth possibilities expanded. Currently, technology is taking Internet applications to a next level, where personalization and integration in face-to-face counseling can be of added value for both health-care users and professionals. The use of mHealth applications is an expanding field of research and practice, and an innovative way to deliver health promotion interventions (Free, et al. 2013).

                  Developing Internet Applications

                  On the one hand, developing Internet applications is comparable to developing traditional interventions in the field of health promotion (e.g., to be delivered in a classroom setting or a counseling setting). Hence, existing planning models are as relevant for Internet applications as they are for traditional interventions in the field of health promotion, as systematic planning of interventions increases the likelihood of developing effective interventions. Green and Kreuter 2005 proposed a framework that can help health program planners to analyze situations and design health programs efficiently. This framework is also used in the first of six iterative steps described by Bartholomew Eldrege, et al. 2016 to develop, implement, and evaluate an intervention: (1) develop a logic model of the problem based on a needs assessment, (2) state program outcomes and objectives, (3) develop the program plan, (4) produce the intervention, (5) plan program use, and (6) develop an evaluation plan. Buunk and Van Vugt 2013 explains how in a problem-driven context, all theories, theoretical models, and constructs are potentially useful. Glanz, et al. 2015 provides a comprehensive overview of health behavior theory and how to use this in designing and evaluating interventions. On the other hand, Internet applications can make use of the unique possibilities offered by the online context. Mohr, et al. 2014 presents an integrated conceptual and technological framework for eHealth and mHealth interventions, using existing models as their point of departure. The BIT (behavioral intervention technology) model provides tools that are useful and more specific to the context of eHealth and mHealth interventions (i.e., the BIT-Tech aspect of the model). It shows the relationship among software components developed for supporting BITs, the user, and the environment. This relates to the importance of usability for Internet applications. Tullis and Albert 2008 provides detailed insight into collecting, analyzing, and presenting usability metrics for several types of usability studies focusing on topics such as evaluating navigation, information architecture, and the impact of subtle changes.

                  • Bartholomew Eldrege, L. Kay, Christine M. Markham, et al. 2016. Planning health promotion programs: An intervention mapping approach. San Francisco: Jossey-Bass.

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                    The fourth edition of a seminal work describing intervention mapping: a protocol for developing theory-based and evidence-based health promotion programs.

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                    • Buunk, Abraham P., and Mark van Vugt. 2013. Applying social psychology: From problems to solutions. London: SAGE.

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                      Offers a simple, systematic, step-by-step, easy-to-use methodology for applying primarily social psychological theory to a wide range of social problems.

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                      • Glanz, Karen, Barbara K. Rimer, and K. Viswanath. 2015. Health behavior: Theory, research, and practice. San Francisco: Jossey-Bass.

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                        Provides comprehensive coverage of health behavior theory as well as how to use theory in research and practice, including intervention design and evaluation.

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                        • Green, Lawrence W., and Marshall W. Kreuter. 2005. Health program planning: An education and ecological approach. New York: McGraw-Hill.

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                          Describes an education and ecological approach to broader public health and population health planning.

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                          • Mohr, David C., Stephen M. Schueller, Enid Montague, Michelle Nicole Burns, and Parisa Rashidi. 2014. The Behavioral Intervention Technology Model: An integrated conceptual and technological framework for eHealth and mHealth interventions. Journal of Medical Internet Research 16:e146.

                            DOI: 10.2196/jmir.3077Save Citation »Export Citation »E-mail Citation »

                            Describes a model that conceptually defines behavioral intervention technologies, from the clinical aim to the technological delivery framework.

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                            • Tullis, Tom, and Bill Albert. 2008. Measuring the user experience: Collecting, analyzing, and presenting usability metrics. Burlington, MA: Morgan Kaufmann.

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                              Provides practical information to enable usability professionals and product developers to choose the right metric, apply it, and effectively use the information it reveals.

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                              Effectiveness of Internet Applications

                              As the Internet is an expanding field for intervening in health promotion, the question arises if on the one hand these Internet applications are effective, and if on the other hand they are just as or more effective when compared to traditional interventions (i.e., those delivered offline). The advantages of delivering health promotion activities through the Internet are well known, just as the development of Internet interventions follows similar pathways to traditional interventions. However, this does not presume similar effectiveness. Meta-analyses exist that focus on the effects of Internet applications in promoting and changing health behavior. Webb, et al. 2010 provides an overview of the effectiveness of using the Internet in promoting health behavior change. Extended use of theory and the incorporation of additional behavior-changing techniques were associated with higher effect sizes. Kohl, et al. 2013 showed small to moderate effect sizes for Internet applications targeting dietary behaviors, physical activity, alcohol abuse, smoking cessation, condom use, weight management, and substance abuse. Attention is especially paid to energy-balance-related behaviors such as dietary behaviors and physical activity, as nearly half of the included articles focus on these behaviors when using the Internet as delivery system. Tailored feedback, interactivity, a theoretical foundation, goal setting, and blended care (i.e., a combination of online and face-to-face care) are stated as promising constructs. An earlier meta-analysis of Portnoy, et al. 2008 included seventy-five randomized, controlled trials (RCTs), comparing the computer-delivered interventions (not necessarily delivered via the Internet) to promote health behavior to various control conditions. In line with the other reviews, small to medium effects of computer-delivered interventions were found for behavior change. Moreover, these effects were caused by changes in the underlying determinants of behavior, such as knowledge, attitude, and intention. Interventions addressing a single health behavior and interventions addressing multiple health behaviors were found to have similar effects. Webb, et al. 2010 and Noar, et al. 2010 found that interventions addressing single or multiple health behaviors both show small but significant effects.

                              • Kohl, Leonie F. M., Rik Crutzen, and Nanne K. de Vries. 2013. Online prevention aimed at lifestyle behaviors: A systematic review of reviews. Journal of Medical Internet Research 15:e146.

                                DOI: 10.2196/jmir.2665Save Citation »Export Citation »E-mail Citation »

                                An overview of forty-one reviews, which focuses on effectiveness of Internet applications aimed at dietary behaviors, physical activity, alcohol use, smoking, and condom use.

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                                • Noar, Seth M., Nancy Grant Harrington, Stephanie K. Van Stee, and Rosalie Shemanski Aldrich. 2010. Tailored health communication to change lifestyle behaviors. American Journal of Lifestyle Medicine 5.2: 112–122.

                                  DOI: 10.1177/1559827610387255Save Citation »Export Citation »E-mail Citation »

                                  This article operationalizes the concept of tailoring and reviews the literature on existing tailored interventions that target lifestyle behavior change.

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                                  • Portnoy, D. B., L. A. Scott-Sheldon, B. T. Johnson, and M. P. Carey. 2008. Computer-delivered interventions for health promotion and behavioral risk reduction: A meta-analysis of 75 randomized controlled trials, 1988–2007. Journal of Preventive Medicine 47.1: 3–16.

                                    DOI: 10.1016/j.ypmed.2008.02.014Save Citation »Export Citation »E-mail Citation »

                                    Reviews the efficacy of Internet-delivered interventions to promote health behavior, through a meta-analysis including seventy-five RCTs.

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                                    • Webb, Thomas L., Judith Joseph, Lucy Yardley, and Susan Michie. 2010. Using the internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research 12.1: e4.

                                      DOI: 10.2196/jmir.1376Save Citation »Export Citation »E-mail Citation »

                                      This article reviews the use of theory, behavior change techniques, and delivery modes and the way these relate to effect sizes.

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                                      Cost Effectiveness

                                      It was stated earlier that Internet applications might be cost effective. This reasoning is based on the idea that fully automated systems have limited costs per user. However, Internet applications can still contain a substantial amount of human involvement, which makes assumptions on cost effectiveness less certain. This is especially relevant in the light of the 21st-century emphasis in health care on cost reduction. Health-care professionals should not only provide insight in the effectiveness of treatment they offer but should also offer insight into its cost effectiveness. An important question regarding this increased interest in cost effectiveness is how much more society wants to pay in addition, in comparison to increased effectiveness (Ponds, et al. 2010). When conducting a cost-effectiveness analysis, also referred to as an “economic evaluation,” two components are essential: (1) two or more interventions should be compared to each other, and (2) of the compared interventions, both the effectiveness and the costs should be assessed. The relevance of costs and effects that are taken into account depends on the perspective and nature of the study conducted (Ponds, et al. 2010). A study of Drost and colleagues concluded that computer-tailored feedback could be a cost-effective medium to target alcohol consumption (Drost, et al. 2016), when taking into account intersectoral costs and benefits. However, while intersectoral costs and benefits might be especially relevant in terms of prevention, relatively few cost-effectiveness studies take these into account. A systematic review of Elberg and colleagues on cost effectiveness of Internet applications in somatic diseases found promising results (Elbert, et al. 2014). In mental health, similar results were found, which indicates that Internet applications are promising in terms of cost effectiveness (Donker, et al. 2015).

                                      • Donker, T., M. Blankers, E. Hedman, B. Ljótsson, K. Petrie, et al. 2015. Economic evaluations of Internet interventions for mental health: A systematic review. Psychological Medicine 45.16: 3357–3376.

                                        DOI: 10.1017/S0033291715001427Save Citation »Export Citation »E-mail Citation »

                                        This study reviews the cost-effectiveness for online interventions in mental health.

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                                        • Drost, Ruben M., W. A., Aggie, and T. G. Paulus, et al. 2016. A web-based computer-tailored alcohol prevention program for adolescents: Cost-effectiveness and intersectoral costs and benefits. Journal of Medical Internet Research 18.4: e93.

                                          DOI: 10.2196/jmir.5223Save Citation »Export Citation »E-mail Citation »

                                          This study describes the cost-effectiveness of an online computer-tailored program for the prevention of alcohol use in adolescents.

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                                          • Elbert, N. J., H. Van Os-Medendorp, W. Van Renselaar, A. G. Ekeland, L. Hakkaart-van Roijen, et al. 2014. Effectiveness and cost-effectiveness of eHealth interventions in somatic diseases: A systematic review of systematic reviews and meta-analyses. Journal of Medical Internet Research 16.4: e110.

                                            DOI: 10.2196/jmir.2790Save Citation »Export Citation »E-mail Citation »

                                            This study reviewed thirty-one articles on the (cost) effectiveness of eHealth interventions in somatic diseases. It provides promising evidence for its (cost) effectiveness.

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                                            • Ponds, R., C. van Heugten, L. Fasotti, and E. Wekking. 2010. Economic evaluation of neuro psychological treatment. In Neuropsychologische behandeling. Edited by Ponds, R., C. van Heugten, L. Fasotti, and E. Wekking, 115–138. Amsterdam: Boom.

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                                              Chapter 6 of this book describes how to conduct economic evaluations and focuses on economic evaluations in treating neuropsychological conditions.

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                                              Use of Internet Applications

                                              In order for Internet applications to have a positive impact in terms of behavior change outcomes, it is essential that they are actually used by the target group. While Internet applications can potentially reach a large part of the population, intervention use is essential: otherwise the intervention content will not have a public health impact. Eysenbach 2005 considers limited use to be one of the fundamental characteristics and methodological challenges for studying Internet applications aimed at promoting health behavior changes. Donkin, et al. 2011 has shown that limited use also negatively affects behavior change. Intervention use has received limited attention so far but is a challenge on its own. Just as theory and evidence are needed to develop intervention content, Morrison 2015 argues that the same applies to intervention use. Intervention use consists of multiple behaviors (e.g., first use, sustained use). O’Brien and Toms 2008, for example, proposed a conceptual framework for defining engagement with technology. Within this framework, the engagement process is initiated by interest (i.e., point of engagement: first use), which moves people into engagement. Sustained engagement (i.e., period of engagement: sustained use) is characterized by positive affect: people have an enjoyable experience while using an Internet application. Crutzen, et al. 2008 argue that embedding Internet applications in a social context could be defined as a feasible and appropriate way to disseminate them and stimulate first use. For example, an Internet application aimed at adolescents could be embedded in a social context by linking the intervention to school activities. This is in line with the idea of blended care (i.e., a combination of online and face-to-face care). Kelder, et al. 2012 demonstrates that increased interaction with a health professional was associated with more use of Internet applications. However, this does not mean that involvement of a health professional is always cost-effective (Smit, et al. 2013). Furthermore, it is important to note that there are differences in the uses of the Internet that are not necessarily tied to having access to the Internet. Instead, this concerns differences in the way the Internet is used that are based on personal characteristics such gender, age, and socioeconomic status. These differences are also reflected in the use of Internet applications in promoting health behavior (Reinwand 2016).

                                              • Crutzen, Rik, Jascha de Nooijer, and Nanne K. de Vries. 2008. How to reach a target group with Internet-delivered interventions? European Health Psychologist 10:77–79.

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                                                Explores the possibility of disseminating Internet applications through popular online places and embedding them in a social context.

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                                                • Donkin, Liesje, Helen Christensen, Sharon L. Naismith, Bruce Neal, Ian B. Hickie, and Nick Glozier. 2011. A systematic review of the impact of adherence on the effectiveness of e-therapies. Journal of Medical Internet Research 13:e52.

                                                  DOI: 10.2196/jmir.1772Save Citation »Export Citation »E-mail Citation »

                                                  Describes the methods used to assess adherence and evaluates the association of adherence with outcomes of interventions.

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                                                  • Eysenbach, Gunther. 2005. The law of attrition. Journal of Medical Internet Research 7:e11.

                                                    DOI: 10.2196/jmir.7.1.e11Save Citation »Export Citation »E-mail Citation »

                                                    Argues for the need for a “science of attrition,” that is, a need to develop models for discontinuation of Internet applications and the related phenomenon of participants dropping out of intervention trials.

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                                                    • Kelder, Saskia M., Robin N. Kok, Hans C. Ossebaard, and Julia E. W. C. van Gemert-Pijnen. 2012. Persuasive system design does matter: A systematic review of adherence to web-based interventions. Journal of Medical Internet Research 14:e152.

                                                      DOI: 10.2196/jmir.2104Save Citation »Export Citation »E-mail Citation »

                                                      Reviews the literature on Internet applications to investigate whether intervention characteristics and persuasive design affect adherence to Internet applications.

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                                                      • Morrison, Leanne G. 2015. Theory-based strategies for enhancing the impact and usage of digital health behavior change interventions: A review. Digital Health: 1–10.

                                                        DOI: 10.1177/2055207615595335Save Citation »Export Citation »E-mail Citation »

                                                        Provides a critical review of psychological theories and models in order to consider their implications for the design of digital interventions.

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                                                        • O’Brien, Heather L., and Elaine G. Toms. 2008. What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology 59:938–955.

                                                          DOI: 10.1002/asi.20801Save Citation »Export Citation »E-mail Citation »

                                                          Critically deconstructs the term “engagement” as it applies to peoples’ experiences with technology.

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                                                          • Reinwand, Dominique. 2016. Online usage inequalities: Internet and web-based health intervention use among people with different personal characteristics. Maastricht, The Netherlands: Universitaire Pers Maastricht.

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                                                            Provides a broad insight into the use of the Internet in general and the use of web-based health information and interventions, focusing on differences in personal characteristics.

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                                                            • Smit, Eline S., Silvia M. Evers, Hein de Vries, and Ciska Hoving. 2013. Cost-effectiveness and cost-utility of Internet-based computer tailoring for smoking cessation. Journal of Medical Internet Research 15:e57.

                                                              DOI: 10.2196/jmir.2059Save Citation »Export Citation »E-mail Citation »

                                                              Assesses 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. This is compared with an Internet-based multiple computer-tailored program only and care as usual.

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                                                              Tailoring

                                                              Because every person is unique, one size does not fit all. More and more information, products, and services are tailored and personalized to individuals and their needs. Tailoring is defined by Kreuter, et al. 1999a, as “any combination of information or change strategies intended to reach one specific person, based on characteristics that are unique to that person, related to the outcome of interest, and have been derived from an individual assessment.” Two important characteristics of this definition are noteworthy: (1) information is intended to and unique to a specific person, and is based on individual characteristics of that person, and (2) these characteristics have been derived from an individual assessment, meaning that prior to tailoring, information is extracted from a person, leading to unique assessment-based messages—in contrast to generic information. Hence, tailoring allows for the addressing of one’s specific needs, beliefs, and interests (Kreuter, et al. 1999b). The idea of adapting information based on the characteristics of (specific individuals within) the target group has been applied in health research and in promoting health behavior. At first, health promotion materials were generic in nature, aiming to provide as much information as possible about a certain topic, not taking into account people’s potential needs differences. Targeted health promotion aims to reach subgroups of a population. Mostly targeting is based on demographic characteristics as for example health information is made specific for older women receiving content on breast cancer screening. This strategy builds on the assumption that enough homogeneity exists within these subgroups, which makes these “members” eligible for receiving the same message. Personalization focuses on getting attention by using someone’s name in delivering (generic) health messages. This strategy is often used in mass mailings or letters but can also be applied in Internet-delivered health promotion activities. To a large extent, health behavior can be explained by cognitive determinants, needs, and individual beliefs rather than by a person’s demographic characteristics (Hawkins, et al. 2008). Tailoring focuses on assessing these cognitions on an individual level and adjusting a health promotion message to the outcome of this assessment (Kreuter, et al. 1999a). The assessment of determinants and cognitions should be substantiated by theories and/or empirical evidence, as for example in providing tailored advice to reduce sitting behavior at work (de Cocker, et al. 2015).

                                                              • De Cocker, K., I. De Bourdeaudhuij, G. Cardon, and C. Vandelanotte. 2015. Theory-driven, web-based, computer-tailored advice to reduce and interrupt sitting at work: Development, feasibility and acceptability testing among employees. BMC Public Health 15.1: 1.

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                                                                Provides an example of how the theory of planned behavior is applied in a computer tailoring intervention, aimed at the reduction of sitting behavior.

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                                                                • Hawkins, R. P., M. W. Kreuter, K. Resnicow, M. Fishbein, and A. Dijkstra. 2008. Understanding tailoring in communicating about health. Health Education Research 23.3: 454–466.

                                                                  DOI: 10.1093/her/cyn004Save Citation »Export Citation »E-mail Citation »

                                                                  Describes how tailoring works in the health-care context. It also focuses on differences between targeting, segmentation, customization, personalization, and tailoring.

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                                                                  • Kreuter, M. W., D. Farrell, L. Olevitch, and L. Brennan. 1999a. Tailored health messages: Customizing communication with computer technology. Mahwah, NJ: Lawrence Erlbaum.

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                                                                    This book provides a detailed overview on how to tailor health messages. It also provides an overview of the evolution of generic health messages to tailored health communication.

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                                                                    • Kreuter, M. W., V. J. Strecher, and B. Glassman. 1999b. One size does not fit all: The case for printing tailored materials. Annals of Behavioral Medicine 21.4: 276–283.

                                                                      DOI: 10.1007/BF02895958Save Citation »Export Citation »E-mail Citation »

                                                                      Describes the rationale and practical steps for using tailored materials in health communication.

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                                                                      Dissemination and Adoption

                                                                      Internet applications can be made available relatively easy. Once made available, dissemination can partly occur naturally. Moreover, the dissemination process can be tracked by means of, for example, server registrations. However, because it is so easy to make an Internet application available, there are also many available options. This makes it difficult for users to choose which Internet applications are most appropriate for them (e.g., in terms of usefulness and effectiveness). With regard to mobile applications, Van Velsen, et al. 2013 even talks about a “mobile health app overload,” implying that both health professionals and citizens have difficulty with finding the right app and that information and features are fragmented over too many apps, thereby limiting their usefulness. Adams 2015 provides an introduction to search engine marketing (SEM), which is a form of Internet marketing that involves the promotion of websites by increasing their visibility in search engine results pages (SERPs). This form of marketing can be used to increase visibility of evidence-based Internet applications, but the same strategies can be used by everybody offering Internet applications. So, although the importance of SEM is acknowledged, this does not guarantee that health professionals and citizens adopt evidence-based Internet applications. Yang, et al. 2015 has shown that contemporary physical activity apps favored behavior change methods with a modest evidence base over others with more established evidence of efficacy. Crane, et al. 2015 demonstrated that only a minority of alcohol-related apps promoted health while the majority implicitly or explicitly promoted the use of alcohol. There are also initiatives to establish “quality seals” for Internet applications geared toward citizens. Although it is unknown to what extent citizens take such quality seals into account in their decision to adopt an Internet application, such attempts are in line with the idea that all treatment decisions made by health professionals should be evidence based. In other words, health professionals could use such “quality seals” to decide which Internet applications to recommend to their patients. In fact, guidelines for health professionals could be used to stimulate them to only recommend approved Internet applications.

                                                                      • Adams, R. L. 2015. SEO 2016: Learn search engine optimization. Seattle, WA: CreateSpace Independent Publishing Platform.

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                                                                        Explains the set of principles, tools, and techniques used to organically rank content on search engines such as Google.

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                                                                        • Crane, David, Claire Garnett, James Brown, Robert West, and Susan Michie. 2015. Behavior change techniques in popular alcohol reduction apps: Content analysis. Journal of Medical Internet Research 17:e118.

                                                                          DOI: 10.2196/jmir.4060Save Citation »Export Citation »E-mail Citation »

                                                                          An article assessing the proportion of popular alcohol-related apps available in the United Kingdom that focus on alcohol reduction and identify their content.

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                                                                          • Van Velsen, Lex, Desirée J. M. A. Beaujean, and Julia E. W. C. van Gemert-Pijnen. 2013. Why mobile health app overload drives us crazy, and how to restore the sanity. BMC Medical Informatics and Decision Making 13:23.

                                                                            DOI: 10.1186/1472-6947-13-23Save Citation »Export Citation »E-mail Citation »

                                                                            This article describes actions that should be taken by suppliers in order to combat the health app overload.

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                                                                            • Yang, Chih-Hsiang, Jaclyn P. Maher, and David E. Conroy. 2015. Implementation of behavior change techniques in mobile applications for physical activity. American Journal of Preventive Medicine 48:452–455.

                                                                              DOI: 10.1016/j.amepre.2014.10.010Save Citation »Export Citation »E-mail Citation »

                                                                              Characterizes the extent to which behavior change methods have been implemented in apps from a systematic user inspection of apps.

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                                                                              Technological Developments

                                                                              The traditional research process is slow. Ioannidis 1998 shows that randomized efficacy trials take approximately 5.5 years from the initiation of enrollment to publication. This is especially worrisome in the field of Internet applications aimed at promoting health behavior, because in such a time frame, technological developments will occur that may make the eventual findings less relevant or even obsolete. Hence, using efficient and innovative research designs might be warranted. For example, by replacing the traditional pilot trial with a more flexible iterative intervention testing and optimization approach, analogous to the agile software development process that places a premium on failing early to succeed later (Gary, et al. 2011). Riley, et al. 2013 argues that besides the use of efficient and innovative research designs, we need “rapid-learning research systems” that integrate researchers, funders, health systems, practitioners, and community partners asking relevant questions. These relevant research questions might pertain to why certain interventions are effective. A focus on underlying principles might result in insights that are applicable to unforeseen developments—even when technologies develop at a rapid pace. Peters, et al. 2015 argues that it is necessary to incorporate experimental tests in this evidence-building process. Controlled experiments enable manipulation of single behavior change methods and individual parameters for effectiveness.

                                                                              • Gary, Kevin, Andinet Enquobahrie, and Luis Ibanez, et al. 2011. Agile methods for open source safety-critical software. Software: Practice and Experience 2:945–962.

                                                                                DOI: 10.1002/spe.1075Save Citation »Export Citation »E-mail Citation »

                                                                                Describes agile methods including necessary activities such as formal specification and requirements management and that focus more on continuous process management and code-level quality than classic software engineering process models.

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                                                                                • Ioannidis, John P. A. 1998. Effect of the statistical significance of results on time to completion and publication of randomized efficacy trials. JAMA 2:281–286.

                                                                                  DOI: 10.1001/jama.279.4.281Save Citation »Export Citation »E-mail Citation »

                                                                                  An evaluation of whether the time to completion and the time to publication of randomized phase 2 and phase 3 trials are affected by the statistical significance of results. Also describes the natural history of such trials.

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                                                                                  • Peters, Gjalt-Jorn Ygram, Marijn de Bruin, and Rik Crutzen. 2015. Everything should be as simple as possible, but no simpler: Towards a protocol for accumulating evidence regarding the active content of health behavior change interventions. Health Psychology Review 9:1–14.

                                                                                    DOI: 10.1080/17437199.2013.848409Save Citation »Export Citation »E-mail Citation »

                                                                                    Describes an iterative protocol for evidence-based accumulation that integrates evidence derived from both experimental and applied behavior 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 behavior change can develop.

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                                                                                    • Riley, William T., Russell E. Glasgow, Lynn Etheredge, and Amy P. Abernethy. 2013. Rapid, responsive, relevant (R3) research: A call for a rapid learning health research enterprise. Clinical and Translational Medicine 2:10.

                                                                                      DOI: 10.1186/2001-1326-2-10Save Citation »Export Citation »E-mail Citation »

                                                                                      In order to produce more rapid, responsive, and relevant research, the article describes approaches that increase relevance via greater stakeholder involvement, speed research via innovative designs, streamline review processes, and create and/or better leverage research infrastructure.

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