Increased life expectancy has led to a 15% growth in the population of seniors (aged 65 and above) in Canada, in the last 5 years and this trend is expected to grow. However, the provision of personalized care is bottlenecked, due a severe shortage of formal caregivers in the healthcare industry. Technological solutions are proposed to supplement or replace human care, but have not been widely accepted due to their inability of dynamically adapting to user needs and context of respective situations. Affective data (i.e., emotions and moods of individuals), can be utilized to induce context-awareness and artificial emotional intelligence in such technological solutions, and thereby provide personalized support. Moreover, the capacity of brain to process affective phenomenon can serve as an indicator of onsetting neuro-degenerative diseases. This research thoroughly investigated what affect is, and how it can be used in computing in real-life scenarios. Particularly, evidence was obtained on which biological signals collected using a wearable sensor device were capable of capturing the arousal dimension of affective states (emotions and moods) of individuals. Furthermore, a qualitative study was conducted with older adults using semi-structured interviews, to determine the feasibility and acceptability of different self-report measures of mood, which are crucial to capture the valence dimension of affect. As the hypothesis that older adults would prefer a pictorial measure to self-report their mood failed, we proposed an adjective-based mood reporting instrument prototype, and laid down implications for future research.

The present study was conducted with the aim of constructing and validating a short form of the Profile of Mood States (POMS). The POMS is a widely-applied measure for the assessment of an individual’s mood. Thus, it is of great relevance for many research questions in clinical and social psychology. To develop the short scale, we first examined psychometric properties and found the optimal 16-itemsolution among all valid combinations of the full POMS in an exploratory subsample (n = 1,029) of our complete representative sample of the German general population. We then validated this model in a confirmatory subsample (n = 977). Additionally, we examined its invariance across age groups and sex, as well as its reliability. Our results indicate that the POMS-16 is a valid and reliable measure of mood states with minimal losses compared to the 35-item version. Particularly where brevity and an economical assessment is desired, the POMS-16 should be considered.