Iris Kanera

1 15 INTRODUCTION encouragement to set (new) goals. Prior research demonstrated that CBT is an effective therapy for insomnia among breast cancer survivors, by improving mood, fatigue, and global and cognitive dimensions of QoL (Arico, Raggi, & Ferri, 2016).  It is possible to address a large audience personally without causing high costs. Tailoring is a proven effective method to personalize information and support within health communication by matching intervention components to previously measured characteristics of the individual (De Nooijer, Lechner, & de Vries, 2002; de Vries & Brug, 1999; de Vries, Kremers, Smeets, Brug, & Eijmael, 2008; Noar, Benac, & Harris, 2007; Oenema, Brug, Dijkstra, de Weerdt, & de Vries, 2008; Stanczyk, de Vries, Candel, Muris, & Bolman, 2016). By using an (online) questionnaire, specific characteristics of the target group are gathered, including demographic characteristics, current behavior, but also aspects such as knowledge, social influences, attitudes, and self-efficacy. Subsequently, messages can be designed and adapted to the participants’ characteristics. As a result, personalized feedback can be generated while redundant information can be avoided (de Vries & Brug, 1999). In fully automated eHealth interventions, the specific responses of the participants are automatically selected and combined with the appropriate message from a pre- programmed message library. Therefore, specific decision rules and algorithms need to be predefined. An advantage of fully automated online interventions is that tailored feedback canbe displayed immediately after completing the online assessment.The tailoring variables should be based on a behavioral theory and related to behavior change or to other relevant factors, such as socio-demographic and cancer-related factors (Kok et al., 2015; Noar et al., 2007). Effective health behavior interventions among the general population included advice that was tailored to the current behavior, personal and psychological characteristics, concepts such as risk perception, attitudes, self-efficacy, stage of change, processes of change, and social influences (Noar et al., 2007; Peels et al., 2012; Springvloet, Lechner, de Vries, Candel, & Oenema, 2015; Stanczyk et al., 2014; van Keulen et al., 2010; Walthouwer, Oenema, Soetens, Lechner, & de Vries, 2013). Moreover, by assessing the current behavior and relevant determinants at different time points, participants can receive personalized, possibly varying feedback at different moments in time. This thesis describes how relevant behavior change methods were applied in a fully automated eHealth intervention in order to change relevant determinants of cancer survivors’ lifestyle behaviors to contribute to the optimization of cancer aftercare. Cancer aftercare and the promotion of a healthy lifestyle As previously outlined, the growing number of cancer survivors living longer will be at risk of (long-term) physical and psychosocial residual problems, as well as developing new cancers. Moreover, survivors are in need of support to manage these problems, and to (re) gain a healthy lifestyle balance. Consequently, current cancer aftercare does not match the