We evaluated the seal-like robot PARO in the context of multi-sensory behavioral therapy in a local nursing home. Participants were 10 elderly nursing home residents with varying levels of dementia. We report three principle findings from our observations of interactions between the residents, PARO, and a therapist during seven weekly therapy sessions. Firstly, we show PARO provides indirect benefits for users by increasing their activity in particular modalities of social interaction, including visual, verbal, and physical interaction, which vary between primary and non-primary interactors. Secondly, PARO's positive effects on older adults' activity levels show steady growth over the duration of our study, suggesting they are not due to short-term “novelty effects.” Finally, we show a variety of ways in which individual participants interacted with PARO and relate this to the “interpretive flexibility” of its design.

This paper presents the results of research aimed at developing a methodology for the participatory design of social robots, which are meant to be incorporated into various social contexts (e.g. home, work) and establish social relations with people. In contrast to the dominant technologically driven robot development process, we aim to develop a socially robust and responsible approach to robot design using Participatory Design (PD) methods. The PD process builds on participants' self-identified issues and concerns, and develops robot concepts according to participants' interpretations of the capabilities and potential applications of robotic technologies. We present methodological insights from an ongoing PD project aimed at designing socially assistive robots with older adults diagnosed with depression and their therapists, and identify remaining challenges in this project. We particularly focus on supporting mutual learning between researchers and participants and on promoting active participation of older adults as "designers" (rather than consumers) as foundational aspects of PD. We conclude with reflections regarding how this work can contribute to the further development of social robots and relevant PD methodologies.

Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood. BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard. User demographics and self-reports for depression severity (Patient Health Questionnaire-8) were also collected. Using >14 million keypresses from 250 users who reported demographic information and a subset of 147 users who additionally completed at least 1 Patient Health Questionnaire, we employed hierarchical growth curve mixed-effects models to capture the effects of mood, demographics, and time of day on keyboard metadata. We analyzed 86 541 typing sessions associated with a total of 543 Patient Health Questionnaires. Results showed that more severe depression relates to more variable typing speed (P < .001), shorter session duration (P < .001), and lower accuracy (P < .05). Additionally, typing speed and variability exhibit a diurnal pattern, being fastest and least variable at midday. Older users exhibit slower and more variable typing, as well as more pronounced slowing in the evening. The effects of aging and time of day did not impact the relationship of mood to typing variables and were recapitulated in the 250-user group. Keystroke dynamics, unobtrusively collected in the real world, are significantly associated with mood despite diurnal patterns and effects of age, and thus could serve as a foundation for constructing digital biomarkers.