Utilising exposure measures of Internet-delivered interventions
Evidence from efficacy trials indicates that exposure rates to Internet-delivered interventions are low (De Nooijer, Oenema, Kloek, Brug, De Vries, & De Vries, 2005), and they may be even lower when these interventions are implemented in real life rather than in a research setting (Evers, Cummins, Prochaska, & Prochaska, 2005). Exposure of individuals to the intervention content, through use of the intervention, is necessary since attention is a prerequisite to establish desired behaviour change (McGuire, 1985). Therefore, it remains important to assess exposure to Internet-delivered interventions.
There are several measures to assess exposure to Internet-delivered interventions, such as frequency and duration of visits, but there is no gold standard. Each exposure measure relates to a different aspect of exposure (Danaher, Boles, Akers, Gordon, & Severson, 2006). One can visit an intervention very frequent, for example, but only for a short period of time. Duration of visits, on the other hand, does not necessarily give a clear picture of participants’ online activity, since one does not know to how much information participants are possibly exposed. Therefore, number of visited web pages would be more appropriate to assess online activity. All such exposure measures can be tracked objectively and are, in contrast to self-reported exposure measures, independent of participants’ memory, interpretation, or social desirability.
Linking exposure measures to variables at the individual level
To fruitfully utilise exposure measures it has to be possible to link them to variables at the individual level, to be able to compare subgroups that differ on socio-demographic, psycho-social, or behavioural measures. Moreover, exposure measures on the individual level can also be linked to outcome measures of an intervention. By doing so, it is possible to study potentially mediating effects of objectively tracked exposure on interventions’ outcome measures. The latter, however, is no common practice to date (Crutzen, De Nooijer, Brouwer, Oenema, Brug, & De Vries, submitted). For example, recent studies (An et al., 2006; Chen & Yeh, 2006; Escoffery, McCormick, & Bateman, 2004) reported a limited number of exposure measures and did not relate them to the intervention’s outcome measures, making it impossible to study potentially mediating effects of exposure.
Practical implications
There are no known technical barriers to track exposure measures of Internet-delivered interventions. It is important, however, to realize this from the start of an intervention development process and to involve technical staff during this initial phase (Crutzen, De Nooijer, Brouwer, Oenema, Brug, & De Vries, e-pub ahead of print). Furthermore, dependent on the laws in certain countries or states, it may be necessary to take additional steps. In a recent Dutch project, for example, it was necessary to register the project at the Dutch Data Protection Authority, which supervises the fair and lawful use and security of personal data (Crutzen, De Nooijer, Candel, & De Vries, 2008). If these legal issues are covered, we recommend to track as many exposure measures as possible since there is no gold standard. Furthermore, having exposure measures available is also useful during process evaluation of Internet-delivered interventions, as has been shown in other studies (Barak & Fisher, 2003; Lou, Zhao, Gao, & Shah, 2006; Patten et al., 2007; Roberto, Zimmerman, Carlyle, & Abner, 2007). These exposure measures provide detailed insight into where participants either leave the intervention website or have come to a standstill. This information can be used to adapt interventions to users’ needs and therewith increase exposure rates and probability of behaviour change.
References:
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