Remote health monitoring (RHM) provides various benefits to older adults, but its use is still limited. Remote monitoring may help in avoiding emergencies and prolong users’ independence. To understand how to design systems which support older adults, we studied a large-scale remote health monitoring system. The system used fitness-grade smartwatches to monitor the vital signs of more than 2000 users constantly. To probe the lived experience of using RHM, we conducted an explorative interview study (N = 41) with operators, carers, and users of the RHM system. Our thematic analysis reveals that personalisation of care ecology is crucial for developing users’ confidence and trust in the system. We found that participation in RHM may catalyse positive changes in older adults’ lifestyles. Based on our findings, we formulate five recommendations for designing future health monitoring systems. Our work contributes to insights into the lived experience and stakeholder ecology of health monitoring systems.

Learning outcomes are measurable statements that articulate educational aims in terms of what knowledge, skills, and other competences students possess after successfully completing a given learning experience. This paper presents an analysis of the disparity between the claimed and formulated learning outcomes categorized in knowledge, skills, and social responsibility competency classes as it is postulated in the European Qualification Framework. We employed machine learning classification algorithms to detect and reveal main errors in their formulation that result in incorrect classification using generally available syllabus data from 22 universities. The proposed method was employed in two stages: preprocessing (creating a Python dataframe structure) and classification (by performing tokenization with the term frequency–inverse document frequency method). The obtained results demonstrated high effectiveness in correct classification for a number of machine learning algorithms. The obtained sensitivity and specificity reached 0.8 for most cases with acceptable positive predictive values for social responsibility competency classes and relatively high negative predictive values greater than 0.8 for all classes. Hence, the presented methodology and results may be a prelude to conducting further studies associated with identifying learning outcomes.