J. Med. Internet Res. - Persuasive system design does matter: a systematic review of adherence to web-based interventions.


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CKGROUND: 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.

Resumo Limpo

ckground although webbas intervent promot health healthrel behavior can effect poor adher common issu need address technolog mean communic content webbas intervent neglect research inde technolog often seen blackbox mere tool effect valu serv vehicl deliv intervent content paper examin technolog holist perspect see vital insepar aspect webbas intervent help explain understand adherenceobject studi aim review literatur webbas health intervent investig whether intervent characterist persuas design affect adher webbas interventionmethod conduct systemat review studi webbas health intervent per intervent intervent characterist persuas technolog element adher code perform multipl regress analysi investig whether variabl predict adherenceresult includ articl intervent typic webbas intervent meant use week modular setup updat week last week includ interact system counselor peer web includ persuas technolog element particip adher intervent regard persuas technolog see primari task support element common employ mean possibl dialogu support social support less common employ mean possibl respect compar intervent differ health care area find signific differ intend usag p setup p updat p frequenc interact counselor p system p peer p durat f p adher f p number primari task support element f p final regress model explain varianc adher model rct studi oppos observ studi increas interact counselor frequent intend usag frequent updat extens employ dialogu support signific predict better adherenceconclus use intervent characterist persuas technolog element substanti amount varianc adher can explain although differ health care area intervent characterist health care area per se predict adher rather differ technolog interact predict adher result studi can use make inform decis design webbas intervent patient like adher

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