Digital and technical solutions can help older people to maintain their independence and autonomy and to continue their everyday habits and routines, even if they face limitations. This opens up new possibilities and great potential with regard to the perennial wish to stay living within our own four walls for as long as possible. Frequently, different people use the same system – under certain circumstances, often for extended periods and in different stages of health. With sustainability in mind, in order to maximise the time for which these systems can be used and integrated into the daily routines of end users, the changing demands (in construction, technological and social respects) that this presents on the technical system must also be borne in mind, so they must be flexible enough to adapt to external parameters. Barriers to the use and acceptance of such systems can stem from both external factors and people’s own ideas, imprinted mind sets, etc., or a combination of both. In order to identify reasons for rejection and/or make these systems more accessible to (potential) users, it is important to improve our understanding of their motives by involving them as early on as possible in the relevant aspects of the design process. This work presents an overview of the complex range of possible reasons for not using technical assistance systems / support systems at home from the perspective of older people as the end users. The methodology involved researching and evaluating existing literature that examines the reasons for not using or accepting digital / technical solutions, in the context of older people with care and support needs in order to derive conclusions on the relevance of solution-oriented and human-centric approaches to development.
Artificial intelligence (AI) has arrived in our society as an omnipresent technology. Indeed, AI has the potential to change nursing care in private households and to positively influence established processes in care for older people in their own homes. This literature review focuses on the question as to what potential and which application scenarios can be identified for AI in at-home care arrangements to relieve the affected agents involved care settings. In addition, the challenges associated with the implementation of AI will be highlighted. To start with, the relevant terms (AI, machine learning, deep learning) will be defined. In addition, selected application scenarios for nursing care will be described (predicting fall events, route and deployment planning for outpatient nursing services), including scrutinising their potential. The specific challenges and opportunities that arise when implementing AI-based technology will be discussed. Finally, research desiderata will be identified, summarising that AI applications for care at home / in the context of ageing and care for older people that go beyond the technologies already established in everyday life are still niche technologies in this country.
Against the backdrop of the crisis in care, the prospect of using unpaid helpers and volunteers to relieve the burden on carers has been discussed in recent years. Whilst it is possible to find general key data for the full spectrum of caring activity, particularly about the tasks and roles performed, less seems to be known about people’s inroads to these activities and their motives. The role played by digitalisation in this field also seems insufficiently examined. Thus an exploratory review of literature was used as a starting point to ascertain existing findings and desirables for these aspects in order to then develop our own prospective lines of research. The care-related tasks and roles that people perform cover a vast spectrum. As in other contexts, people seem to offer care based on intrinsic motivation. There is evidence of a reflective shift towards more short-term involvement. Increasing digitalisation also seems to play a role in this respect, with digital allocation of assistants on matching platforms also taking off in the provision of care. The impacts of such digital pathways seem to cut both ways. There is need for research not only into the actual alleviatory effects anticipated from digitally aided allocation of helpers to households needing care or from incorporating willing volunteers into local care provision networks, but also into the potential downsides such as eliciting payment for effort or de-professionalising or ‘Uber’-ising professional care work.
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