###### Abstract  
Computational analysis and integration of smartwatch data with Electronic Medical Records (EMR) present potential uses in preventing, diagnosing, and managing chronic diseases. One of the key requirements for the successful clinical application of smartwatch data is understanding healthcare professional (HCP) perspectives on whether these devices can play a role in preventive care. Gaining insights from the vast amount of smartwatch data is a challenge for HCPs, thus tools are needed to support HCPs when integrating personalized health monitoring devices with EMR. This study aimed to develop and evaluate an application prototype, co-designed with HCPs and employing design science research methodology and diffusion of innovation frameworks to identify the potential for clinical integration. A machine learning algorithm was developed to detect possible health anomalies in smartwatch data, and these were presented visually to HCPs in a web-based platform. HCPs completed a usability questionnaire to evaluate the prototype, and over 60% of HCPs scored positively on usability. This preliminary study tested the proposed research to solve the practical challenges of HCP in interpreting smartwatch data before fully integrating smartwatches into the EMR. The findings provide design directions for future applications that use smartwatch data to improve clinical decision-making and reduce HCP workloads. Electronic Medical Record (EMR); smartwatch; machine learning; anomaly detection; health monitoring; co-design; design science; diffusion of innovation +
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an increased workload [3; 16]. Integration of wearables into EMR still has to be improved and standardized for wider acceptance into healthcare practice for preventive care [3]. One of the key requirements for the successful clinical application of smartwatch data is understanding HCP perspectives on whether these devices can play a role in preventive care [1; 3; 4]. Gaining these perspectives would shift the focus of development from designing systems for users to designing systems with users [17; 18], and healthcare providers should be directly involved in the development of the technology that will, ultimately, impact them. The co-design approach actively involves users to value their expertise and recognise them as collaborators throughout the design process [17]. Co-design is a user-centred approach that brings together domain experts, users, and researchers to design and develop systems to increase user engagement and meet the needs of multidisciplinary collaboration [18]. However, there is a dearth of research on designing smartwatch-based health monitoring applications for preventive healthcare employing a co-design approach. Practical cases demonstrating the usefulness of smartwatches in health and lifestyle support are required to guide the use of this technology in real-world health monitoring applications. Moreover, meaningful involvement of HCP in initial artefact design and evaluation should take place to assist in the development of relevant and practical applications for deployment in clinical settings [17]. Therefore, this study aimed to co-design, develop, and evaluate a health monitoring tool (a prototype) using smartwatch data analytics to assist HCP with clinical decision-making. This study has both theoretical and practical contributions. Theoretically, it contributes to the diffusion of innovation theory by operationalizing the designing for diffusion concept [19], through the involvement of end users in the prototype design. The participatory design aimed to ensure that HCPs' requirements were met and that a useful solution was created without increasing HCPs' workload burden. The study's practical contribution is through demonstrating the feasibility of smartwatch data integration for health monitoring, enabling data-driven clinical decision-making.