Benefits and challenges of conversational agents in older adults : A scoping review.

BACKGROUND: Commercial conversational agents (CAs) bear the promise of low threshold accessibility for individuals with limited digital competencies. This applies not only for healthy aging older adults but also for specific subgroups such as those with life-long intellectual disabilities (ID). OBJECTIVE: This scoping review aims to synthesize the current evidence on benefits and challenges of CAs for older adults with and without ID. In doing so, we hope to inform future research as well as practical decision-making in the context of CAs as potential quality of life enhancers for older adults with various competence levels. MATERIAL AND METHODS: A literature search was conducted in form of a scoping review. A total of 841 publications were screened for benefits and challenges of CAs, resulting in an extraction of 18 articles targeting healthy aging older adults (60 years+) and 5 articles targeting older adults with ID (50 years+) for synthesis. RESULTS: The existing evidence suggests that CAs come with more benefits than challenges, e.g., general ease of use, easier information access, and feelings of companionship. Higher perceived agency due to using a CA seems to be a specific issue for older adults with ID. Challenges concern mostly learning how to use a CA and privacy concerns. CONCLUSION: The results indicate that CAs can serve as quality of life enhancers both in healthy aging adults and in older adults with ID; nevertheless, thoughtful preparation is necessary, especially in relation to learning needs, capabilities present and privacy concerns.

The missing piece. Physiological data as a factor for identifying emotions of people with profound intellectual and multiple disabilities.

Introduction: The preferences of people with profound intellectual and multiple disabilities (PIMD) often remain unfulfilled since it stays challenging to decode their idiosyncratic behavior resulting in a negative impact on their quality of life (QoL). Physiological data (i.e. heart rate (variability) and motion data) might be the missing piece for identifying emotions of people with PIMD, which positively affects their QoL. Method: Machine learning (ML) processes and statistical analyses are integrated to discern and predict the potential relationship between physiological data and emotional states (i.e. numerical emotional states, descriptive emotional states and emotional arousal) in everyday interactions and activities of two participants with PIMD. Results: Emotional profiles were created enabling a differentiation of the individual emotional behavior. Using ML classifiers and statistical analyses, the results regarding the phases partially confirm previous research, and the findings for the descriptive emotional states were good and even better for the emotional arousal. Conclusion: The results show the potential of the emotional profiles especially for practitioners and the possibility to get a better insight into the emotional experience of people with PIMD including physiological data. This seems to be the missing piece to better recognize emotions of people with PIMD with a positive impact on their QoL.