Designing a theory- and evidence-based tailored eHealth rehabilitation aftercare program in Germany and the Netherlands: study protocol

Abstract

BackgroundCardiac rehabilitation programs aim to improve health status and to decrease the risk of further cardiac events. Persons undergoing rehabilitation often have difficulties transferring the learned health behaviors into their daily routine after returning home and maybe to work. This includes physical activity as well as fruit and vegetable consumption. Computer-based tailored interventions have been shown to be effective in increasing physical activity as well as fruit and vegetable consumption. The aim of this study is, to support people in transferring these two learned behavior changes and their antecedents into their daily life after cardiac rehabilitation.MethodsThe study will have a randomized controlled design and will be conducted among German and Dutch people who participated in cardiac rehabilitation. The study will consist of one intervention group which will be compared to a waiting list control group. During the eight week duration of the intervention, participants will be invited to participate in the online after-care program once per week. The intervention encourages participants to define individual health behavior goals as well as action, and coping plans to reach these self-determined goals. The effectiveness of the program will be compared between the intervention condition and the control group in terms of behavior change, antecedents of behavior change (e.g., self-efficacy), ability to return to work and increased well-being. Further, subgroup-differences will be assessed including differences between the two countries, socioeconomic inequalities and across age groups.DiscussionThe present study will make a contribution to understanding how such an online-based tailored interventions enables study participants to adopt and maintain a healthy lifestyle. Implications can include how such an online program could enrich cardiac rehabilitation aftercare further.Trial registrationNTR 3706, NCT01909349

Persuasive system features in computer-mediated lifestyle modification interventions for physical activity

“…The tunnelling feature was identified in many of the reviewed articles which described systems that provided tailored information based on the results of an assessment conducted on an individual user (46). This feature is presented in the systems in a variety of forms such as users sending weekly self-monitoring data via their mobile phone (48) which will become the basis for the tailored messages that they receive about their lifestyle management.…”

“…Despite the culling process, some of the reviewed articles -which had indicated that an intervention had been conducted -were found to be discussions about a system that was still in the proposed stage of intervention (40) or the design and implementation stage (88) or the study details were presented but not the outcome (46,65). Tailoring also often appeared in conjunction with the dialogue support features as it can enhance the personalised experience of users.…”

Latent user groups of an eHealth physical activity behaviour change intervention for people interested in reducing their cardiovascular risk

“…The cross-sectional baseline data for the present analyses from the German study arm were analysed, due to significant differences between German and Dutch study participants (Storm et al, 2016). The complete study design was published in a study protocol (Reinwand et al, 2013).…”

“…HAPA based social-cognitive variables, physical activity behaviour and physical activity habit was measured online via self-report questionnaires (Reinwand et al, 2013). All scales were validated and tested for reliability in a previous study (Lippke, Wienert, Kuhlmann, Fink, & Hambrecht, 2015;Wienert, Kuhlmann, Fink, Hambrecht, & Lippke, 2016;Wienert et al, 2017a;Wienert, Kuhlmann, Fink, Hambrecht, & Lippke, 2017b).…”

“…This provides the opportunity to complement tailoring of eHealth interventions based on stage measures, which might help to increase their effectiveness in the long run due to more precise tailoring of intervention content. Therefore, the current study applied a latent class approach to baseline data of study participants from a tailored eHealth intervention targeting people interested in reducing their cardiovascular risk (Kuhlmann, Reips, Wienert, & Lippke, 2016;Reinwand, Kuhlmann, Wienert, de Vries, & Lippke, 2013Tan et al, 2018) and compared it to the traditional and well-validated stage measure. The theory behind stage measures assumes different patterns of socialcognitive variables between the stages (non-linear discontinuity patterns).…”