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Background: Perioperative telemonitoring of patients undergoing major surgery might lead to improved postoperative outcomes. The aim of this systematic review is to evaluate the effectiveness of current perioperative telemonitoring interventions on postoperative clinical, patient-reported, and financial outcome measures in patients undergoing major surgery. Methods: For this systematic review, PubMed, CINAHL, and Embase databases were searched for eligible articles published between January 1, 2009 and March 15, 2021. Studies were eligible as they described: (P) patients aged 18 years or older who underwent major abdominal surgery, (I) perioperative telemonitoring as intervention, (C) a control group receiving usual care, (O) any type of postoperative clinical, patient-reported, or financial outcome measures, and (S) an interventional study design. Results: The search identified 2958 articles of which 10 were eligible for analysis, describing nine controlled trials of 2438 patients. Perioperative telemonitoring comprised wearable biosensors (n = 3), websites (n = 3), e-mail (n = 1), and mobile applications (n = 2). Outcome measures were clinical (n = 8), patient-reported (n = 5), and financial (n = 2). Results show significant improvement of recovery time, stoma self-efficacy and pain in the early postoperative phase in patients receiving telemonitoring. Other outcome measures were not significantly different between the groups. Conclusion: Evidence for the effectiveness of perioperative telemonitoring in major surgery is scarce. There is a need for good quality studies with sufficient patients while ensuring that the quality and usability of the technology and the adoption in care processes are optimal. [ABSTRACT FROM AUTHOR]

Conversational agents (CAs) are often included as virtual coaches in eHealth applications. Tailoring conversations with these coaches to the individual user can increase the effectiveness of the coaching. An improvement for this tailoring process could be to (automatically) tailor the conversation at the topic level. In this article, we describe the design and evaluation of a blueprint topic model for use in the implementation of such topic selection. First, we constructed a topic model by extracting actions from the literature that a CA as coach could perform. We divided these actions in groups and labeled them with topics. We included literature from the behavioral psychology, relational agents and persuasive technology domains. Second, we evaluated this topic model through an online closed card sort study with health coaching experts. The constructed topic model contains 30 topics and 115 actions. Overall, the sorting of actions into topics was validated by the 11 experts participating in the card sort. Cards with actions that were sorted incorrectly mostly missed an immediacy indicator in their description (e.g., the difference between "you could plan regular walks" as opposed to "let's plan a walk") and/or were based on behavior change techniques that were difficult to translate to a conversation. The blueprint topic model presented in this article is an important step towards more intelligent virtual coaches. Future research should focus on the implementation of automatic topic selection. Furthermore, tailoring of coaching dialogues with CAs in multiple steps could be further investigated, for example, from the technical or user interaction perspective. [ABSTRACT FROM AUTHOR]

To identify demographics and personal motivation types that predict dropping out of eHealth interventions among older adults. We conducted an observational cohort study. Participants completed a pre-test questionnaire and got access to an eHealth intervention, called Stranded, for 4 weeks. With survival and Cox-regression analyses, demographics and types of personal motivation were identified that affect drop-out. Ninety older adults started using Stranded. 45.6% participants continued their use for 4 weeks. 32.2% dropped out in the first week and 22.2% dropped out in the second or third week. The final multivariate Cox-regression model which predicts drop-out, consisted of the variables: perceived computer skills and level of external regulation. Predicting the chance of dropping out of an eHealth intervention is possible by using level of self-perceived computer skills and level of external regulation (externally controlled rewards or punishments direct behaviour). Anticipating to these factors can improve eHealth adoption. [ABSTRACT FROM AUTHOR]

Background: Gamification within eHealth services can increase eHealth adoption. However, little is known about factors affecting adoption of gamified eHealth among older adults. In this study, we sought to explain the (continued) use of a gamified eHealth service among older adults (55+)., Methods: Participants used a gamified eHealth service, focusing on falls prevention, for 4 weeks and completed a post-test questionnaire based on the Technology Acceptance Model. We used Partial Least Squares Structural Equation Modeling to analyse our data., Results: Seventy-two older adults participated with a mean age of 65.1 years (SD = 7.0). Our results show that first, perceived ease of use affected use of the service (use duration: β = 0.303, R 2 = 0.130, and use frequency: β = 0.304, R 2 = 0.107). Second, perceived usefulness affected the intention to continue using the service (β = 0.754, R 2 = 0.640). Third, use of the service did not predict the intention to continue using it. Furthermore, enjoyment affected perceived usefulness (β = 0.783, R 2 = 0.563) and aesthetics affected perceived ease of use (β = 0.634, R 2 = 0.652)., Conclusions: This study refutes the expected relation between use and intention to continue use a gamified eHealth service. Additionally, we learned that using theoretical approaches focusing on technology acceptance, are not suitable for explaining (continued) use of gamified eHealth services.