The application of an information support robot to reduce agitation in an older adult with Alzheimer’s disease living alone in a community dwelling: a case study

Abstract

Objective/background: Agitation is a common behaviour of Alzheimer’s Disease (AD). Although healing robots have previously been used for alleviating agitation for people with dementia in hospitals and other healthcare facilities, its effects remain unclear whether an information-support robot capable of disseminating information with user needs may have similar effects when it is applied in community dwellings. This study was to investigate the effects of information-support robots for reducing agitation in older people with AD residing alone in community-dwellings. Methods One participant with AD who lived alone in a community-dwelling was recruited. Participant’s frequencies, durations, and time courses of four patterns of agitation at home at baseline and during intervention were analysed. In this study, agitation was defined as: (a) restlessness going to the corridor (RLtoC), (b) restlessness going to the toilet (RLtoT), (c) restlessness going to the entrance (RLtoE), and (d) restlessness going to multiple places (RLtoMP). In the intervention phase, the information-support robot disseminated daily schedule or date information to the participant. Results RLtoC and RLtoMP were significantly reduced in frequency during the intervention. The durations of each episode of RLtoT and RLtoMP in the intervention were slightly reduced than those at the baseline. There were no differences in time courses of episodes of RLtoC and RLtoMP after the intervention. Conclusion This case study showed that information-support robot was useful to alleviate agitation of an older adult with AD living alone in a community-dwelling. Further study is warranted.

Impact of digital assistive technologies on the quality of life for people with dementia: a scoping review

“…The varied application of DATs can enhance cognitive abilities, as indicated by 34 studies. They demonstrated that patient monitoring,43 care support30 45 and digital therapeutics21–23 26–28 30 31 40 45 46 53–55 65 71 75 83–85 90 95 98 103 115–117 119–124 can lead to improvements in cognitive functioning.…”

“…There are 12 BPSD, which include aberrant motor behaviour, agitation, anxiety, apathy, appetite changes, delusions, depression, disinhibition, elation/euphoria, hallucinations, irritability and sleep changes 125. Different BPSD were shown to be managed by DATs in 64 studies through digital therapeutics8 23 26–28 30 40 41 48 55 60 67–72 75 77 78 81–86 88 89 91–93 95 97–102 104 105 107 109 111–113 115–117 120 122–124 126–136 or by patient monitoring devices 30 48 69 86 93 98 133. All the DATS were reported to have a positive effect on BPSD.…”

Impact of digital assistive technologies on the quality of life for people with dementia: A scoping review

“…The varied application of DATs can enhance cognitive abilities, as indicated by 34 studies. They demonstrated that patient monitoring [137], care support [53], [95], digital therapeutics [21], [23]- [26], [38], [39], [41], [44], [51], [53], [55], [58], [60], [61], [64], [65], [67], [69], [70], [72], [74], [76], [87], [93], [95], [98]- [100], [123], [124], [134], [138] can lead to improvements in cognitive functioning.…”

Activity discovery using Dirichlet multinomial mixture models from discrete sensor data in smart homes

“…Rather, we focus on evaluating the performance of clustering primitive patterns in sequences of binary sensor signals under as few assumptions as possible, because the basic clustering performance is an important first step in a complex activity analysis. Also, clustering can be directly used to evaluate the quantity and quality of primitive activities, such as the restlessness behavior of patients with Alzheimer's disease [22]. To this end, we evaluated the performance of clustering activities corresponding to short time periods using various generative models for sensor signals as well as the k-means method, a typical clustering method, and frequent sequence mining [8] previously used in activity discovery other than generative models.…”