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. 2010 Nov 23:10:73.
doi: 10.1186/1472-6947-10-73.

Stages of use: consideration, initiation, utilization, and outcomes of an internet-mediated intervention

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Stages of use: consideration, initiation, utilization, and outcomes of an internet-mediated intervention

Teresa M L Chiu et al. BMC Med Inform Decis Mak. .

Abstract

Background: Attrition, or nonuse of the intervention, is a significant problem in e-health. However, the reasons for this phenomenon are poorly understood. Building on Eysenbach's "Law of Attrition", this study aimed to explore the usage behavior of users of e-health services. We used two theoretical models, Andersen's Behavioral Model of Health Service Utilization and Venkatesh's Unified Theory of Acceptance and Use of Technology, to explore the factors associated with uptake and use of an internet-mediated intervention for caregivers taking care of a family member with dementia.

Methods: A multiphase, longitudinal design was used to follow a convenience sample of 46 family caregivers who received an e-health intervention. Applying the two theories, usage behavior was conceptualized to form four stages: consideration, initiation, utilization (attrition or continuation), and outcome. The variables and measurement scales were selected based on these theories to measure the sociodemographic context, technology aptitudes, and clinical needs of the caregivers.

Results: In the Consideration Stage, caregivers who felt that the information communication technology (ICT)-mediated service was easy to use were more likely to consider participating in the study (p = 0.04). In the Initiation Stage, caregivers who showed greater technology acceptance were more likely to initiate service earlier (p = 0.02). In the Utilization Stage, the frequent users were those who had a more positive attitude toward technology (p = 0.04) and a lower perceived caregiver competence (p = 0.04) compared with nonusers. In the Outcome Stage, frequent users experienced a decline in perceived burden compared with an escalation of perceived burden by nonusers (p = 0.02).

Conclusions: We illustrate a methodological framework describing how to develop and expand a theory on attrition. The proposed framework highlighted the importance of conceptualizing e-health "use" and "adoption" as dynamic, continuous, longitudinal processes occurring in different stages, influenced by different factors to predict advancement to the next stage. Although usage behavior was influenced mainly by technological factors in the initial stages, both clinical and technological factors were equally important in the later stages. Frequency of use was associated with positive clinical outcomes. A plausible explanation was that intervention benefits motivated the caregivers to continue the service and regular use led to more positive clinical outcome.

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Figures

Figure 1
Figure 1
Stages of use
Figure 2
Figure 2
Sample selection and data collection flow diagram
Figure 3
Figure 3
Factors affecting usage behavior. 1 Significant in multivariate analysis 2 Significant in univariate analysis but excluded in multivariate analysis

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References

    1. Eysenbach G. The law of attrition. Journal of medical Internet research. 2005;7:e11. doi: 10.2196/jmir.7.1.e11. - DOI - PMC - PubMed
    1. Brouwer W, Oenema A, Crutzen R, de Nooijer J, de Vries NK, Brug J. An exploration of factors related to dissemination of and exposure to internet-delivered behavior change interventions aimed at adults: a Delphi study approach. J Med Internet Res. 2008;10:e10. doi: 10.2196/jmir.956. - DOI - PMC - PubMed
    1. Christensen H, Griffiths K, Farrer L. Adherence in Internet Interventions for Anxiety and Depression. Journal of Medical Internet Research. 2009;11:e13. doi: 10.2196/jmir.1194. - DOI - PMC - PubMed
    1. Couper M, Alexander G, Maddy N, Zhang N, M N, McClure J, Calvi J, Rolnick S, Stopponi M, Little R, Johnson CC. Engagement and retention in an online intervention. J Med Internet Res. 2010. forthcoming. - PMC - PubMed
    1. Danaher BG, Boles SM, Akers L, Gordon JS, Severson HH. Defining participant exposure measures in Web-based health behavior change programs. J Med Internet Res. 2006;8:e15. doi: 10.2196/jmir.8.3.e15. - DOI - PMC - PubMed

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