An Interview Study for Developing Subjective Measures to Record Self-Reported Mood in Older Adults: Implications for Assistive Technology Development

Abstract

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.