Gesundheit (MDH)
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BACKGROUND Interest in digital technologies in the health care sector is growing and can be a way to reduce the burden on professional caregivers while helping people to become more independent. Social robots are regarded as a special form of technology that can be usefully applied in professional caregiving with the potential to focus on interpersonal contact. While implementation is progressing slowly, a debate on the concepts and applications of social robots in future care is necessary. OBJECTIVE In addition to existing studies with a focus on societal attitudes toward social robots, there is a need to understand the views of professional caregivers and patients. This study used desired future scenarios to collate the perspectives of experts and analyze the significance for developing the place of social robots in care. METHODS In February 2020, an expert workshop was held with 88 participants (health professionals and educators; [PhD] students of medicine, health care, professional care, and technology; patient advocates; software developers; government representatives; and research fellows) from Austria, Germany, and Switzerland. Using the scenario methodology, the possibilities of analog professional care (Analog Care), fully robotic professional care (Robotic Care), teams of robots and professional caregivers (Deep Care), and professional caregivers supported by robots (Smart Care) were discussed. The scenarios were used as a stimulus for the development of ideas about future professional caregiving. The discussion was evaluated using qualitative content analysis. RESULTS The majority of the experts were in favor of care in which people are supported by technology (Deep Care) and developed similar scenarios with a focus on dignity-centeredness. The discussions then focused on the steps necessary for its implementation, highlighting a strong need for the development of eHealth competence in society, a change in the training of professional caregivers, and cross-sectoral concepts. The experts also saw user acceptance as crucial to the use of robotics. This involves the acceptance of both professional caregivers and care recipients. CONCLUSIONS The literature review and subsequent workshop revealed how decision-making about the value of social robots depends on personal characteristics related to experience and values. There is therefore a strong need to recognize individual perspectives of care before social robots become an integrated part of care in the future.
Approaches to Improvement of Digital Health Literacy (eHL) in the Context of Person-Centered Care
(2022)
The skills, knowledge and resources to search for, find, understand, evaluate and apply health information is defined as health literacy (HL). If individuals want to use health information from the Internet, they need Digital Health Literacy (eHL), which in addition to HL also includes, for example, media literacy. If information cannot be found or understood by patients due to low (e)HL, patients will not have the opportunity to make informed decisions. In addition, many health apps for self-management or prevention also require (e)HL. Thus, it follows that active participation in healthcare, in terms of Person-Centered Care (PCC) is only possible through (e)HL. Currently, there is a great need to strengthen these competencies in society to achieve increased empowerment of patients and their health. However, at the same time, there is a need to train and improve competencies in the field of healthcare professionals so that they can counsel and guide patients. This article provides an overview with a focus on HL and eHL in healthcare, shows the opportunities to adapt services and describes the possible handling of patients with low (e)HL. In addition, the opportunities for patients and healthcare professionals to improve (e)HL are highlighted.
Sensor‐based assessment of challenging behaviors in dementia may be useful to support caregivers. Here, we investigated accelerometry as tool for identification and prediction of challenging behaviors. We set up a complex data recording study in two nursing homes with 17 persons in advanced stages of dementia. Study included four‐week observation of behaviors. In parallel, subjects wore sensors 24 h/7 d. Participants underwent neuropsychological assessment including MiniMental State Examination and Cohen‐Mansfield Agitation Inventory. We calculated the accelerometric motion score (AMS) from accelerometers. The AMS was associated with several types of agitated behaviors and could predict subject's Cohen‐Mansfield Agitation Inventory values. Beyond the mechanistic association between AMS and behavior on the group level, the AMS provided an added value for prediction of behaviors on an individual level. We confirm that accelerometry can provide relevant information about challenging behaviors. We extended previous studies by differentiating various types of agitated behaviors and applying long‐term measurements in a real‐world setting.