## 5 Conclusions and Future Work  
In this work, we evaluated the UX and usability of our health monitoring system and achieved high results in both. The ratings were the highest in the users with the least robotic experience, while they also had the most difficulties in using the system, indicating that the novelty effect may have positively influenced the UX results. The ratings were also high in the experienced and tech-savvy group of younger expert users, while the older adults with the most experience with Pepper specifically were the most critical in their responses. Based on these results, we draw the conclusion that previous robotic experience and technological expertise play an important role in the evaluation of the UX of robot-assisted health assessments. The care setting of the older adults may also play a role in this evaluation, though these needs to be further investigated. All of these factors provide important indications as to what needs to be considered when developing, but also evaluating, health monitoring systems that provide a positive UX, specifically for older adults. In the future, the system should be able to respond more individually to the user's condition. Here, the rather generic assessment we tested in this study will be used to determine current health issues and warning signs. As users are intended to undergo the assessment on a daily basis, we will implement different versions of our health questionnaire to improve the UX. If a condition can be determined based on the user's responses to our questionnaire, e.g., signs of hypertension, the robot will then conduct a condition-specific, medically validated PROM assessment. Subsequently, we will test the system in a long-term study. Thus, the influence of the novelty effect, if any, will become visible, while the continuous use of the system will contribute to the broadening of the health database available to us. While conducting this long-term study we will also continuously optimize the system based on user feedback, to guarantee a positive UX and thus promote the long-term motivation of older adults to use this system, which can then contribute to their autonomy and independence by increasing their health literacy while supporting them in managing their own health. In the short term, the system should then be able to classify a user's health status based on a combination of the subjective and objective data collected. Finally, a long-term goal of the project is the use of machine learning to predict a user's health status and provide adequate advice and recommendations to the users. Conceptualization, C.N. ; methodology, C.N. and K.L. ; software, C.N. and K.L. ; validation, C.N. and K.L. ; formal analysis, C.N. and K.L. ; investigation, C.N. and K.L. ; resources, A.R. ; data curation, C.N. and K.L. ; writing--original draft preparation, C.N. and K.L. ; writing--review and editing, C.N., K.L. and A.R. ; visualization, C.N. ; supervision, A.R. ; project administration, C.N. and A.R. All authors have read and agreed to the published version of the manuscript. This research was partially supported by the German Federal Ministry of Education and Research (BMBF) in the project GeneRobot (project number 16 GDI102A). The study was reviewed and approved by the Ethics Research Committee of the TH Koln--University of Applied Sciences (application no. THK-2022-0004). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Not applicable. The authors would like to thank [PERSON], [PERSON], [PERSON], [PERSON], and all participants for their valuable input to this work. **Conflicts of Interest:** The authors declare no conflict of interest.