Exploring the potential of social robots to support resilience in dementia
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Dementia is a debilitating progressive neurological syndrome that causes stress to people with the condition and their families. Most people with dementia want to be independent and with support, many people with dementia can maintain a level of independence. Robotics devices are being developed to support the independence of people with dementia but currently, there is limited empirical evidence concerning how they should be developed and deployed. There has also been little attempt to date to underpin the research into robotic technologies for people with dementia with substantive theory. Resilience is a substantive theory and a strength-based approach to dementia care. This approach focuses on a person with dementia’s existing abilities and it employs person-centred strategies that empower and support a person’s sense of personal identity. This doctoral thesis aims to explore the potential of social robots to support the resilience of people with dementia. In doing so, it applies the theory of resilience to the study of social robots for the first time. This thesis contains four papers. The first two papers report literature reviews that identify the current evidence base. The first review was conducted to determine what robots need to be like, in terms of their form and function, for people with dementia to find them acceptable and to engage with them. This review focused on identifying the key factors that impact the acceptability of social robots in the context of dementia. Then a second literature review was undertaken to determine how social robots could potentially be used to support the resilience of people with dementia. This review focused on identifying the content, structure, and effectiveness of psychosocial interventions that have aimed to support the resilience of people with dementia. The review also aimed to identify how the concept of resilience had been defined and operationalised in previous research. The literature reviews revealed that an empirical study was needed to provide an in-depth examination of a social robot within the real-world context of a dementia care setting. It was also important that the robot facilitated a person-centred approach and that the investigation captured the perceptions and experiences of people with dementia. Case study methodology was chosen for the empirical study and the third paper included in the thesis reports a critical appraisal of the DESCARTE model (DESign of CAse Research in healThcarE). This appraisal was conducted to determine the model's suitability to guide case study investigations that involve social robots for resilience in dementia care settings. The fourth paper reports a multiple case study that was designed using the DESCARTE model. This study examined the effect of the social robot MARIO on the resilience of people with dementia (n=10) in a residential care setting. The first literature review (paper one), which included forty-four studies, found that multiple factors impact the acceptability of social robots for people with dementia. Acceptance can be increased through robots using human-like communication and if they are personalised in response to the needs of individual users. However, many studies had been conducted in a laboratory rather than in real-world settings. The exploration of the literature on psychosocial interventions for resilience (paper two) included three studies that had investigated five interventions that aimed to support resilience in dementia. This review revealed that the interventions supported people with dementia by enabling reciprocal social interactions and meaningful activities, but that further interventions were needed to support people with dementia who lack family caregivers and/or have more advanced dementia. The review also identified that resilience had been defined as a process of adapting to stress that operates at multiple interacting levels of the individual, community, and society. The critical appraisal of the DESCARTE model (paper three) found that the model could usefully guide the design and conduct of the MARIO case study investigation. DESCARTE provided a useful framework to enhance the quality of case study research by requiring the researcher to focus on philosophical cohesiveness, ethical considerations, reflexivity, and data analysis. However, recommendations were made to improve DESCARTE's utility by adding a task list and providing researchers with additional guiding questions. The empirical study (paper four) found that MARIO could provide an embodied presence and personalised stimulus that engaged people with dementia, with the support of a facilitator. Whilst using MARIO, eight out of ten people with dementia were empowered to co-create meaningful activity. This supported resilience by increasing their positive sense of self-identity because, whilst people with dementia were engaged with MARIO, they were ‘doing okay’ despite the limitations of living with dementia. Based on the literature and the empirical study, this body of work reveals that social robots have the potential to support the resilience of people with dementia. To support resilience robots need to facilitate people with dementia to maintain a positive sense of self-identity, through enhancing their ability to co-create meaningful activity and reciprocal social relationships. However, future social robots need to have a greater technical capacity to react to human emotions and communicate in a more human-like way, if they are to support the resilience of people with dementia, independent of human facilitators. These conclusions are based on a relatively small body of existing literature and an empirical study that used a small sample size and investigated only one residential care setting. Nevertheless, this thesis is underpinned by recognised theories of technology acceptance and resilience, and it is derived from comprehensive literature reviews and an empirical study that used robust methodology and provided an in-depth inquiry in a real-world clinical context.
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