ObjectiveWe focused on identifying the requirements and needs of people suffering from Alzheimer disease and early dementia stages with relation to robotic assistants.MethodsBased on focus groups performed in two centers (Poland and Spain), we created surveys for medical staff, patients, and caregivers, including: functional requirements; human–robot interaction, the design of the robotic assistant and user acceptance aspects. Using Likert scale and analysis made on the basis of the frequency of survey responses, we identified users’ needs as high, medium, and low priority.ResultsWe gathered 264 completed surveys (100 from medical staff, 81 from caregivers, and 83 from potential users). Most of the respondents, almost at the same level in each of the three groups, accept robotic assistants and their support in everyday life. High level priority functional requirements were related to reacting in emergency situations (calling for help, detecting/removing obstacles) and to reminding about medication intake, about boiling water, turning off the gas and lights (almost 60% of answers). With reference to human–robot interaction, high priority was given to voice operated system and the capability of robotic assistants to reply to simple questions.ConclusionOur results help in achieving better understanding of the needs of patients with cognitive impairments during home tasks in everyday life. This way of conducting the research, with considerations for the interests of three stakeholder groups in two autonomic centers with proven experience regarding the needs of our patient groups, highlights the importance of obtained results.

The aim of the present study is to present the results of the assessment of clinical application of the robotic assistant for patients suffering from mild cognitive impairments (MCI) and Alzheimer Disease (AD). The human-robot interaction (HRI) evaluation approach taken within the study is a novelty in the field of social robotics. The proposed assessment of the robotic functionalities are based on end-user perception of attractiveness, usability and potential societal impact of the device. The methods of evaluation applied consist of User Experience Questionnaire (UEQ), AttrakDiff and the societal impact inventory tailored for the project purposes. The prototype version of the Robotic Assistant for MCI patients at Home (RAMCIP) was tested in a semi-controlled environment at the Department of Neurology (Lublin, Poland). Eighteen elderly participants, 10 healthy and 8 MCI, performed everyday tasks and functions facilitated by RAMCIP. The tasks consisted of semi-structuralized scenarios like: medication intake, hazardous events prevention, and social interaction. No differences between the groups of subjects were observed in terms of perceived attractiveness, usability nor-societal impact of the device. The robotic assistant societal impact and attractiveness were highly assessed. The usability of the device was reported as neutral due to the short time of interaction.

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ObjectiveThe purpose of this study was to identify prognostic factors and build the predictive model based on poor-grade subarachnoid haemorrhage (SAH) population received only supportive symptomatic treatment.DesignProspective observational cohort study.SettingIntensive care unit at the Clinical Department of Neurology.ParticipantsA total of 101 patients with spontaneous SAH disqualified from neurosurgical operative treatment due to poor clinical condition. Data were collected over a 9-year period.Outcome measuresUnfavourable outcome was defined as a modified Rankin Score ≥5 at 30 days of observation.ResultsMultivariable logistic regression analysis indicated the World Federation of Neurosurgical Societies Scale score, increasing age, Fisher grade and admission leucocytosis as independent predictive factors. The proposed scale subdivides the study population into four prognostic groups with significantly different outcomes: grade I: probability of favourable outcome 89.9%; grade II: 47.5%; grade III: 4.2%; grade IV: 0%. The receiver operating characteristic (ROC) curve for the prediction of outcome performed by the new scale had an area under the curve (AUC)=0.910 (excellent accuracy).ConclusionsUnfavourable outcome in non-operated patients with poor-grade SAH is strongly predicted by traditional unmodifiable factors such as age, amount of bleeding in CT, level of consciousness as well as leucocytosis. A new predictive scale based on the above parameters seems to reliably predict the outcome and may contribute to more effective planning of therapeutic management in patients with poor-grade SAH.

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