ArticlePDF AvailableLiterature Review

Technology and Dementia: The Future is Now

  • University of Toronto & KITE Research Institute University Health Network


Background: Technology has multiple potential applications to dementia from diagnosis and assessment to care delivery and supporting ageing in place. Objectives: To summarise key areas of technology development in dementia and identify future directions and implications. Method: Members of the US Alzheimer's Association Technology Professional Interest Area involved in delivering the annual pre-conference summarised existing knowledge on current and future technology developments in dementia. Results: The main domains of technology development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of functioning, (iii) leisure and activity, (iv) caregiving and management. Conclusions: The pace of technology development requires urgent policy, funding and practice change, away from a narrow medical approach, to a holistic model that facilitates future risk reduction and prevention strategies, enables earlier detection and supports implementation at scale for a meaningful and fulfilling life with dementia.
© 2019 The Author(s)
Published by S. Karger AG, Basel
Original Research Article
Dement Geriatr Cogn Disord
Technology and Dementia: The Future
is Now
Arlene J. Astell a–d Nicole Bouranis e Jesse Hoey f Allison Lindauer g
Alex Mihailidis a Chris Nugent h Julie M. Robillard i Technology and
Dementia Professional Interest Area ...
a Department of Occupational Sciences and Occupational Therapy, University of Toronto,
Toronto, ON, Canada; b Department of Psychiatry, University of Toronto, Toronto, ON,
Canada; c Toronto Rehabilitation Institute, Toronto, Toronto, ON, Canada; d School of
Psychology and Clinical Language Sciences, University of Reading, Reading, UK; e Layton
Aging and Alzheimer’s Disease Center, Oregon Health and Science University, Portland,
OR, USA; f David R. Cheriton School of Computer Science, University of Waterloo, Waterloo,
ON, Canada; g Oregon Roybal Center for Aging and Technology (ORCATECH), Oregon
Health and Science University, Portland, OR, USA; h School of Computing, Ulster University,
Northern Ireland, UK; i Faculty of Medicine, University of British Columbia, Vancou ver, BC,
Technology · Dementia · Aging · Big data · Policy
Background: Technology has multiple potential applications to dementia from diagnosis and
assessment to care delivery and supporting ageing in place. Objectives: To summarise key
areas of technology development in dementia and identify future directions and implications.
Method: Members of the US Alzheimer’s Association Technology Professional Interest Area
involved in delivering the annual pre-conference summarised existing knowledge on current
and future technology developments in dementia. Results: The main domains of technology
development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of
functioning, (iii) leisure and activity, (iv) caregiving and management. Conclusions: The pace
of technology development requires urgent policy, funding and practice change, away from
a narrow medical approach, to a holistic model that facilitates future risk reduction and pre-
vention strategies, enables earlier detection and supports implementation at scale for a
meaningful and fulfilling life with dementia. © 2019 The Author s
Published by S. Karger AG, Basel
Published online: ■■■
Arlene J. Astell
School of Psychology & Clinical Lan guage Sciences
University of Reading
Reading (UK)
E-Mail a.astell @
DOI: 10.1159/000497800
This article is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Interna-
tional License (CC BY-NC-ND) ( Usage and distributi-
on for commercial purposes as well as any distribution of modified material requires written permission.
Dement Geriatr Cogn Disord
Astell et al.: Technology and Dementia
© 2019 The Author(s). Published by S. Karger AG, BaselDOI: 10.1159/000497800
Smartphones and tablets have opened up personal computing to many new audiences
and have created increasing interest in how such devices can be used across all aspects of our
lives and society. Similarly, wearables, smart home systems (e.g., Amazon Alexa, Google Home
Hub), robots, virtual reality (VR), artificial intelligence (AI) and autonomous (i.e., driverless)
vehicles are triggering questions about how we can deliver better services, create efficiencies
and improve wellbeing, including for those living with dementia. Although research applying
technology to dementia has only recently gained mainstream attention, work has been taking
place alongside biomedical and health informatics research for many decades. This article
produced by members of the Alzheimer’s Association Technology Professional Interest Area
Executive Committee introduces the main areas of technological focus in dementia and impli-
cations for the future. Our views of where technology is going are informed by our direct
experience of working with people with dementia with past and present technology.
Diagnosis, Assessment and Monitoring
The longest-standing use of technology in dementia is in assessment. Two touchscreen-
based cognitive assessment batteries – Cambridge Neuropsychological Test Automated Battery
[1] and Examen Cognitif par Ordinateur [2] were developed in Europe in the 1980s and 1990s.
More recently multiple web-based and app-based cognitive assessments have emerged [3]
including digitized versions of pen and paper tasks, such as clock drawing [4] including a digital
clock-drawing pen [5]. In addition, the manner in which people use technology and changes in
their patterns of use has been identified as an early indicator of emerging cognitive impairment
[6, 7]. Technological assessment of functional activities extends to tasks such as making tea and
toast [8], coffee-making [9], as well as instrumental activities of daily living [10].
In terms of dementia diagnosis, there have been increasing applications of various
machine learning approaches, most commonly with imaging data for diagnosis and disease
progression [11]. This includes amyloid PET imaging [12], MR imaging [13] and combined
PET and MR imaging [14]. Non-imaging studies have generally focused on demographic data
and cognitive measures [15], although more recent studies have utilized novel linguistic
analysis [16] and unobtrusive monitoring of gait patterns over time [17].
The use of big data in dementia research has also recently emerged. The Big Data for
Advancing Dementia Research project initiated by the G8 Health Ministers to explore the use
of big data in dementia research, concluded that big data could potentially accelerate dementia
research and technology development by helping recognize factors that contribute to
dementia, identifying individuals with dementia earlier, promoting better support for
dementia care and creating new analysis methods (e.g., data mining) that could result in new
research opportunities [18]. Examples of data mining in dementia include working with large,
pre-existing data registries and health records to investigate co-morbidities with dementia
and other diseases [19], combining different data sources including fluid biomarkers and MRI
[20] and Alzheimer’s disease drug repositioning [21].
Maintenance of Function
Ongoing monitoring using technology has the potential to both detect progression of
dementia over time [22] and to provide prompts and support to individuals to maintain their
activities [23]. In 2007, Carrillo et al. [24] identified a gap in knowledge around the delivery of
digital prompts, specifically, how best to prompt, what to prompt or when to prompt. A body
of work has emerged addressing prompting with Cognitive Assistive Technologies [23]. For
example, the Cognitive Orthosis for Assisting aCtivities at Home (COACH) [25] system was
created to prompt people with dementia to take them through the hand washing procedures.
Dement Geriatr Cogn Disord
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DOI: 10.1159/000497800
COACH combines computer vision for tracking the current stage of activity with AI techniques
to decide what prompt is required to deliver verbal and visual prompts when people wash
their hands. This highly influential work has informed many subsequent systems and led to
the notion of “zero effort technologies” [26], which require little or no effort from the people
who use them. Zero effort technologies perform the collection, analysis and application of data
about the user and/or his/her context using advanced computing techniques, such as computer
vision, sensor fusion, decision-making and planning, machine learning and the Internet of
things (IoT). Recent work focusses on integrating models of identity and emotion into
prompting systems to make them easier to integrate seamlessly into people’s lives [27].
Other examples of Cognitive Assistive Technologies combine sensors with AI and machine
learning embedded in living environments to create “smart homes,” which can detect and analyse
health [28] and other events. These first emerged in the late 1990s and have evolved to address
the needs of people with dementia, informal (family) caregivers, formal (clinical) caregivers and
social (i.e., nonmedical) caregivers [29]. The Gloucester Smart Home [30], an early prototype for
individuals with dementia coupled bath and cooker monitors with an automated nightlight, item
locator and digital message board to provide visual and verbal prompts such as “time for your
medication.” More recent examples leverage advances in sensor technology, AI and machine
learning to support cooking [31], dressing [32] and reduce demands for caregiving [29]. In
addition, smart home technologies are becoming available for individuals to install themselves
[33], creating huge potential for conducting scalable clinical trials at home [34, 35].
The availability of cloud computing allows dementia researchers and practitioners to
utilize powerful computing resources without the need for specialized computing back-
grounds. The result is that research is less expensive and faster to produce results. For these
reasons, many novel research projects investigating cloud-based solutions to dementia care,
treatment and diagnosis have recently emerged. Examples include supporting aging-in-place
and activity monitoring [36], providing location tracking [37], and supporting analysis and
interpretation of neuroimaging data [38].
Leisure and Activities
Supporting social and leisure activities is another area of technological focus in dementia.
Musical memory is relatively spared in dementia [39] and multiple technology projects have
leveraged this including the one-button radio [40], simple music-making interface [41] and
collaborative music making [42]. Enjoying art has also led to programmes such as House of
Memories1, an interactive art installation for care homes [43], making art as an enjoyable
pastime [44] and a more focused art therapy programme [45, 46].
Participating in activities outside the home remains important for persons with dementia.
Navigation and route-finding can, however, become challenging and numerous technological
solutions have been developed to support these activities [47] and enable “safe walking” [48,
49]. Smart phones with GPS and map functions, offer functionality to support wayfinding
[50], including identifying indicators of disorientation to support navigation prompting [51].
For those who have difficulty getting out or participating in regular exercise, virtual cycling
[52] and navigating virtual environments [53] can offer enjoyable alternatives.
Socialising is another key activity that people want to do that is important for their well-
being and maintenance of cognitive function. CIRCA (Computer Interactive Reminiscence and
Conversation Aid) [54], a multimedia touchscreen application promotes social interaction
which benefits caregiving relationships [55] and improves cognition and quality of life of
people with dementia [56]. Socialising through storytelling has been demonstrated with
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“StoryTable” (de Waag Society) [57], and remote socialising with family and friends using
videoconferencing technologies (e.g., Skype, FaceTime) increases positive emotions [58] and
reduces agitation [59].
Games and other digital pastimes have also been widely explored [60], with both bespoke,
for example, Eldergames [61], Living in the Moment [62] and consumer products (e.g., gaming
consoles, tablets) utilised for people with dementia. The potential of digital activities for brain
training either to reduce dementia risk has attracted much popular attention. However, while
there is consistent evidence of improvement in the aspect of cognitive function that is trained
(e.g., processing speed), to date there is no substantial evidence for dementia prevention or
delaying decline [63]. However, the FINGER trial has demonstrated that computerised
cognitive training can be beneficial in reducing dementia risk as one part of a multidimen-
sional lifestyle intervention [64].
From a leisure and pleasure perspective, mainstream mobile applications can be acces-
sible for people with dementia [65] and motion-based gaming systems (e.g., Nintendo Wii and
Xbox Kinect) can provide enjoyable group activities [66]. VR for dementia has also attracted
recent attention [67], not just as a leisure activity, but also an assessment tool [68] and for
delivering cognitive training [69]. The potential of VR and augmented reality, so-called mixed
reality technologies, to support people with dementia in everyday activities, is currently
being explored [70].
Caregiving and Management
Caregiving is another key target of dementia technology. While the potential of robots to
complement or even replace human caregivers has attracted much recent attention, research
has been underway for many years (e.g., [71, 72]). Recent projects have coupled social
presence robots with remote monitoring using sensors and videoconferencing (via tablets)
to deliver “virtual visits” [73]. Other robotics-based applications that have been explored for
persons with dementia include supporting food preparation [25], eating [74] and partici-
pation in recreational activities [75] in care homes.
Supporting informal caregiving (e.g., [76–78]), and delivering care remotely (e.g., [79,
80]) are major challenges that can be addressed at least in part through dementia technology.
Across the world, the majority of people with dementia live at home cared for by family
members [81]. This caregiving activity represents approximately USD 1 trillion per annum
[81] and takes a greater toll on caregiver mental and physical health than caring for other
conditions [82]. Unsurprisingly, a large part of caregiver Internet use relates to the demands
of caregiving and managing their own health [83]. Despite a large amount of Internet-based
interventions being developed and launched for caregivers, there is lack of systematic, large-
scale studies examining their efficacy [84]. Further, research that unobtrusively assesses
caregiver burden is in the early, but promising, stages (Thomas et al., this issue [95]).
In terms of care management, the IoT facilitates the connection between items in the
real world and computer systems, providing data more easily, efficiently and economi-
cally. IoT in dementia research [85] includes early detection through in-home sensors
[86], wearable monitoring [87] and integration of devices for healthcare management
Future Scoping
Future directions in dementia therapy are predicted to include nanotechnology either to
“repair” brain damage [89] or for drug delivery [90]. While there are still many obstacles to
resolve, work on nanoparticle and implantable therapeutics to locally deliver chemotherapy
for treating brain tumours [91], opens up huge possibilities for delivering new types of inter-
ventions for people either with dementia or ideally, before dementia develops. New means of
Dement Geriatr Cogn Disord
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DOI: 10.1159/000497800
data collection, ubiquitous monitoring technologies, population surveillance, data mining
and modelling may all play a key role in accelerating the development pipeline of preventive
therapeutics for dementia.
Alongside the rapid pace of innovation, policy and practice move much more slowly.
To maximise the current and future benefits of technology for dementia, urgent change is
needed in services and policies. This includes radically rethinking what people with
dementia need in order to live with this condition now and in the future, and how this is
going to be provided. Continuing to view dementia as purely a clinical matter addressed
with medication and, to a limited extent direct healthcare, is no longer an option as it fails
to address the majority of concerns, needs and wishes of people with dementia in their
everyday lives. Existing technology can collect prospective data, model risk and provide
supportive monitoring. Mainstream devices have the potential to empower people with
dementia in many activities and pursuits; however, adoption rates remain low, in part due
to lack of awareness or challenges in accessibility (including financial) and support.
Harnessing the power of everyday (i.e., off the shelf) technologies raises major questions
about funding and finance, not only in insurance-based healthcare systems, but also in
publicly funded systems, as many consumer devices (e.g., smart phones) are not classified
as medical devices and therefore cannot be “prescribed” by clinicians. While specific
software, such as cognitive assessments could be so classified, the use of standard func-
tions of a smart device for maintaining social interaction (through calls, texts, videocon-
ferencing) or cognitive stimulation (through playing games) may not be classified as a
medical intervention.
Expanding our view of dementia beyond healthcare can be understood as akin to physical
and intellectual disabilities that influence all aspects of a person’s life. Taking this view as a
starting point, policies are needed that provide people with dementia and their caregivers
access to devices, services and other tools to live as well as possible with their condition. This
includes creating direct funding streams for technology, providing maintenance and updating
support services, delivering new types of digitally enabled services with tech-savvy staff for
people with dementia, harnessing big data to predict patterns of need and proactively identify
people at risk, and supporting rapid development, testing and deployment of next-generation
technological interventions. Supporting the technology-enabled provision of preventive and
care interventions also requires a number of ethical issues to be addressed, including privacy,
data ownership, sharing and usage, risk, rights, responsibilities and relationships (including
data sharing) between private corporations and statutory bodies [92, 93].
With the current research and commercial technology, we have the tools to tackle
many of the perceived problems currently associated with dementia. First, technology can
be used to target lifestyle factors that are associated with the risk of developing dementia
[94]. For those who are diagnosed with dementia, providing them with technology (e.g.,
apps, wearables, smart home systems), from the point of diagnosis we can monitor the
progression of their condition, identify problems emerging, deliver interventions and
avoid unnecessary emergency admissions and hospital stays. Such technology can also
provide prompts and reminders both within and outside the home to support mainte-
nance of cognitive, social and physical functioning as well as continued completion of daily
activities. Technology can also contribute both directly and indirectly to caregiving,
reducing demands on families and formal services, which are major contributors to the
economic costs of dementia.
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© 2019 The Author(s). Published by S. Karger AG, BaselDOI: 10.1159/000497800
Statement of Ethics
The authors have no ethical conflicts to disclose.
Disclosure Statement
The authors declare that they have no conflicts of interest to disclose.
Author Contributions
A.J.A. drafted the article, N.B., J.H., A.L., C.N., A.M., and J.M.R. all contributed to the development of the
concepts and revised the manuscript.
 1 RobbinsTW,JamesM,OwenAM,SahakianBJ,McInnesL,RabbittP.CambridgeNeuropsychologicalTestAuto-
mated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. Dementia.
1994 Sep-Oct; 5(5): 266–81.
 2 RitchieKA, HuppertFA,Nargeot C,PinekB, LedesertB.Computerized cognitiveexaminationof theelderly
(ECO): the development of a neuropsychological examination for clinic and population use. Int J Geriatr
Psychiatry. 1993; 8(11): 899–914.
 3 ChinnerA, Blane J, Lancaster C, Hinds C, Koychev I. Digital technologies for theassessmentofcognition:a
clinical review. Evid Based Ment Health. 2018 May; 21(2): 67–71.
 4 CohenJ,Penney DL, Davis R, Libon DJ, SwensonRA, Ajilore O, et al. Digital ClockDrawing:differentiating
“thinking” versus “doing” in younger and older adults with depression. J Int Neuropsychol Soc. 2014 Oct;
20(9): 920–8.
 5 Souillard-MandarW,Davis R, Rudin C, Au R, Libon DJ,SwensonR, et al. Learning Classification Models of
Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test. Mach Learn. 2016 Mar; 102(3):
 6 JimisonH,PavelM,McKannaJ,PavelJ.Unobtrusivemonitoringofcomputerinteractionstodetectcognitive
status in elders. IEEE Trans Inf Technol Biomed. 2004 Sep; 8(3): 248–52.
 7 KayeJ,MattekN,DodgeHH,CampbellI,HayesT,AustinD,etal.Unobtrusivemeasurementofdailycomputer
use to detect mild cognitive impairment. Alzheimers Dement. 2014 Jan; 10(1): 10–7.
 8 WhertonJP,MonkAF.Problemspeoplewithdementia havewith kitchentasks:thechallengeforpervasive
computing. Interact Comput. 2010; 22(4): 253–66.
 9 AllainP, FoloppeDA,Besnard J, YamaguchiT,Etcharry-Bouyx F, LeGallD, etal.Detectingeveryday action
deficits in Alzheimer’s disease using a nonimmersive virtual reality kitchen. J Int Neuropsychol Soc. 2014 May;
20(5): 468–77.
10 König A, Crispim-Junior CF, Covella AG, Bremond F, Derreumaux A, Bensadoun G, et al. Ecological Assessment
of Autonomy in Instrumental Activities of Daily Living in Dementia Patients by the Means of an Automatic
Video Monitoring System. Front Aging Neurosci. 2015 Jun; 7: 98.
11 Dallora AL, Eivazzadeh S, Mendes E, Berglund J, Anderberg P. Machine learning and microsimulation tech-
niques on the prognosis of dementia: A systematic literature review. PLoS One. 2017 Jun; 12(6):e0179804.
12 Mathotaarachchi S, Pascoal TA, Shin M, Benedet AL, Kang MS, Beaudry T, et al.; Alzheimer’s Disease Neuroim-
aging Initiative. Identifying incipient dementia individuals using machine learning and amyloid imaging.
Neurobiol Aging. 2017 Nov; 59: 80–90.
13 Cheng B, Liu M, Zhang D, Munsell BC, Shen D. Domain transfer learning for MCI conversion prediction. IEEE
Trans Biomed Eng. 2015 Jul; 62(7): 1805–17.
14 Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R, et al.; ADNI. Multimodal neuroimaging feature learning for multi-
class diagnosis of Alzheimer’s disease. IEEE Trans Biomed Eng. 2015 Apr; 62(4): 1132–40.
15 Bhagyashree SI, Nagaraj K, Prince M, Fall CH, Krishna M. Diagnosis of Dementia by Machine learning methods
in Epidemiological studies: a pilot exploratory study from south India. Soc Psychiatry Psychiatr Epidemiol.
2018 Jan; 53(1): 77–86.
16 Korcovelos EA FK, Meltzer J, Hirst G, Rudzicz F. Studying neurodegeneration with automated linguistic analysis
of speech data. Alzheimer's and Dementia 2017; 13:Suppl, P164–P165.
17 Dodge HH, Mattek NC, Austin D, Hayes TL, Kaye JA. In-home walking speeds and variability trajectories asso-
ciated with mild cognitive impairment. Neurology. 2012 Jun; 78(24): 1946–52.
Dement Geriatr Cogn Disord
Astell et al.: Technology and Dementia
© 2019 The Author(s). Published by S. Karger AG, Basel
DOI: 10.1159/000497800
18 Deetjen UM, Schroeder R. BigDataforAdvancingDementiaResearch :Anevaluationofdatasharingpractices
in research. OECD Digit Econ Pap; 2015.
19 ChenPH,LeeDD,Yang MH.Data miningthecomorbidassociations betweendementia andvarious kindsof
illnesses using a medicine database. Comput Electr Eng. 2018; 70: 12–20.
20 Alipour AP, Khademi M. Alzheimer’s disease detection using data mining techniques, MRI imaging, Blood-
based biomarkeres and neuropsychological tests. Res J Recent Sci. 2015; 4: 1–5.
21 ZhangM,Schmitt-UlmsG,SatoC,XiZ,ZhangY,ZhouY,etal.DrugrepositioningforAlzheimer’sdiseasebased
on systematic “omics” data mining. PLoS One. 2016 Dec; 11(12):e0168812.
22 Kaye JA, Maxwell SA, Mattek N, Hayes TL, Dodge H, Pavel M, et al. Intelligent Systems For Assessing Aging
Changes: home-based, unobtrusive, and continuous assessment of aging. J Gerontol B Psychol Sci Soc Sci. 2011
Jul; 66 Suppl 1:i180–90.
23 Mihailidis A, Boger JN, Craig T, Hoey J. The COACH prompting system to assist older adults with dementia
through handwashing: an efficacy study. BMC Geriatr. 2008 Nov; 8(1): 28.
24 Carrillo MC, Dishman E, Plowman T. Everyday technologies for Alzheimer’s disease care: research findings,
directions, and challenges. Alzheimers Dement. 2009 Nov; 5(6): 479–88.
25 Begum M, Wang R, Huq R, Mihailidis A. Performance of daily activities by older adults with dementia: the role
of an assistive robot. IEEE Int Conf Rehabil Robot. 2013 Jun; 2013: 6650405.
26 BogerJ,YoungV,HoeyJ,JiancaroT,MihailidisA.Zero-effort technologies: Considerations, Challenges, and Use
in Health, Wellness and Rehabilitation. Williston (VT): Morgan & Claypool; 2018.
27 Robillard JM, Hoey J. Emotion and Motivation in Cognitive Assistive Technologies for Dementia. Computer.
2018; 51(3): 24–34.
28 Sprint GC, Fritz R, Schmitter-Edgecombe M. Using Smart Homes to Detect and Analyze Health Events.
Computer. 2016; 49(11): 29–37.
29 Amiribesheli MB. A Tailored Smart Home for Dementia Care. J Ambient Intell Humaniz Comput. 2017.
30 Orpwood R GC, Adlam T, Faulkner R, Meegahawatte D. The Gloucester Smart House for People with Dementia
— User-Interface Aspects. 2004,
31 Pigot HM, Giroux S. The intelligent habitat and everyday life activity support: 5th Int Conf Simulations. Biomed
Slov April 2003 [Internet] 2003; 2–4,
32 Burleson W, Lozano C, Ravishankar V, Lee J, Mahoney D. An assistive technology system that provides person-
alized dressing support for people living with dementia: capability study. JMIR Med Inform. 2018 May; 6(2):e21.
33 HuY,TilkeD,AdamsT,CrandallAS,CookDJ,Schmitter-EdgecombeM.Smarthomeinabox:usabilitystudy
for a large scale self-installation of smart home technologies. J Reliab Intell Environ. 2016 Jul; 2(2): 93–106.
34 Kaye J. Home-based technologies: a new paradigm for conducting dementia prevention trials. Alzheimers
Dement. 2008 Jan; 4(1 Suppl 1):S60–6.
35 Teipel S, König A, Hoey J, Kaye J, Krüger F, Robillard JM, et al. Use of nonintrusive sensor-based information
and communication technology for real-world evidence for clinical trials in dementia. Alzheimers Dement.
2018 Sep; 14(9): 1216–31.
36 Li DP, Piao M, Ryu KH, editors. The design and partial implementation of the dementia-aid monitoring system
based on sensor network and cloud computing platform. Volume 619. Springer Cham; 2016.
37 Lin Y, Chen H, Su M. Eighth International Conference on Mobile Computing and Ubiquitous Networking
(ICMU). Hakodate, Japan, 2015
38 Shen LK, Preuss N. The three nitrcs: Software, data and cloud computing for brain science and dementia
research. Alzheimers Dement. 2013; 94 supp: 78.
39 Jacobsen JH, Stelzer J, Fritz TH, Chételat G, La Joie R, Turner R. Why musical memory can be preserved in
advanced Alzheimer’s disease. Brain. 2015 Aug; 138(Pt 8): 2438–50.
40 Orpwood RC, Howcroft D, Sixsmith A, Torrington J, Gibson G, et al. Designing technology to improve quality of
life for people with dementia: user-led approaches. Univers Access Inf Soc. 2010; 9(3): 249–59.
41 Riley P, Alm N, Newell A. An interactive tool to promote musical creativity in people with dementia. Comput
Human Behav. 2009; 25(3): 599–608.
42 Favela S, Pedell S. Touch screen ensemble music: Collaborative interaction for older people with dementia.
25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collabo-
43 Luyten T, Braun S, Jamin G, van Hooren S, de Witte L. How nursing home residents with dementia respond to
the interactive art installation ‘VENSTER’: a pilot study. Disabil Rehabil Assist Technol. 2018 Jan; 13(1): 87–94.
44 LazarAC,EdasisC,PiperAM.DesigningfortheThirdHand :EmpoweringOlderAdultswithCognitiveImpair-
ments through Creating and Sharing.: 2016 ACM Conference on Designing Interactive Systems. Brisbane,
45 MihailidisAB, BogerJ, RichardsB,Zutis K,YoungL, etal.Towards thedevelopmentof atechnologyfor art
therapy and dementia: definition of needs and design constraints. Arts Psychother. 2010; 37(4): 293–300.
46 LeutyV,BogerJ,YoungL,HoeyJ, MihailidisA.Engagingolderadultswithdementia increativeoccupations
using artificially intelligent assistive technology. Assist Technol. 2013; 25(2): 72–9.
47 Liao LP, Fox D, Kautz H. Learning and inferring transportation routines. Artif Intell. 2007; 171: 311–331.
48 Wood EW, Woolham J. The development of safer walking technology: A review. J Assist Technol. 2015; 9(2):
Dement Geriatr Cogn Disord
Astell et al.: Technology and Dementia
© 2019 The Author(s). Published by S. Karger AG, BaselDOI: 10.1159/000497800
49 Teipel S, Babiloni C, Hoey J, Kaye J, Kirste T, Burmeister OK. Information and communication technology solu-
tions for outdoor navigation in dementia. Alzheimers Dement. 2016 Jun; 12(6): 695–707.
50 KwanRYC,CheungDSK,korPP-K,Theuseofsmartphonesforwayfindingbypeoplewithdementia.Dementia
51 Koldrack P, Teipel S, Kirste T. Sensing disorientation of persons with dementia in outdoor wayfinding tasks
using wearable sensors to enable situation-aware navigation assistance. Alzheimer's & Dementia 2016;
12(suppl): 160.
52 Schikhof YW. Two types of stimuli in virtual cycling for people with dementia. Gerontechnology (Valken-
swaard). 2016; 15 Suppl: 163S.
53 BlackmanT,VanSchaikP,MartyrA.VSP,MartyrA.:Outdoorenvironmentsforpeoplewithdementia :an
exploratory study using virtual reality. Ageing Soc. 2007; 27(06): 811–25.
54 Alm NA, Ellis M, Dye R, Gowans G, Campbell J. A cognitive prosthesis and communication support for people
with dementia [Internet]. Neuropsychol Rehabil. 2004; 14(1-2): 117–34.
55 Astell AJ, Bernardi L, Alm N, Dye R, Gowans G, et al. Using a touch screen computer to support relationships
between people with dementia and caregivers. Interact Comput. 2010; 22(4): 267–75.
56 Astell AJ, Smith SK, Potter S, Preston-Jones E. Computer Interactive Reminiscence and Conversation Aid
groups-Delivering cognitive stimulation with technology. AlzheimersDement(NY). 2018 Sep; 4: 481–7.
57 Knipscheer K, Nieuwesteeg J, Oste J. Persuasive Story Table: Promoting Exchange of Life History Stories
Among Elderly in Institutions. Int Conf Persuas Tehcnology; 2006. pp 191–194.
58 Hori M, Kubota M, Ando K, Kihara T, Takahashi R, Kinoshita A. The effect of videophone communication (with
skype and webcam)for elderly patients with dementia and their caregivers. Gan To Kagaku Ryoho. 2009 Dec;
36 Suppl 1: 36-8.
59 Van der Ploeg ES, Eppingstall B, O’Connor DW. Internet video chat (Skype) family conversations as a treatment
of agitation in nursing home residents with dementia. Int Psychogeriatr. 2016 Apr; 28(4): 697–8.
60 McCallum SB. Dementia games: A literature review of dementia-related serious games., ed Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinfor-
matics). 2013.
61 Gamberini LM, Seraglia B, Spagnolli A, Fabregat M, Ibanez F, et al. Eldergames project: An innovative mixed
reality table-top solution to preserve cognitive functions in elderly people. Proceedings - 2009 2nd Conference
on Human System Interactions, HSI ’09; 2009.
62 Astell AA, Dye R, Gowans G, Vaughan P, Ellis M. Digital video games for older adults with cognitive impairment.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics) 2014; 8547.
63 Butler M, McCreedy E, Nelson VA, Desai P, Ratner E, Fink HA, et al. Does Cognitive Training Prevent Cognitive
Decline?: A Systematic Review. Ann Intern Med. 2018 Jan; 168(1): 63–8.
64 Ngandu T, Lehtisalo J, Solomon A, Levälahti E, Ahtiluoto S, Antikainen R, et al. A 2 year multidomain inter-
vention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive
decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet. 2015 Jun; 385(9984): 2255–
65 Joddrell PH, Astell AJ. Identifying existing, accessible touchscreen games for people living with dementia. Lect
Notes Comput Sci. 2016; 9758: 509–14.
66 Dove E, Astell A. The Kinect Project: group motion-based gaming for people living with dementia. Dementia.
2017 Jan 1: 1471301217743575.
67 Garcia-Betances RI, Fico G, Cabrera-Umpierrez MF, editors. A succinct overview of virtual reality technology
use in Alzheimer’s disease. [Internet]. Universidad Politecnica de Madrid, Politecnica de Madrid, Av.
Complutense s/n, Madrid, Spain, 28040, Frontiers Media S.A.; 2015, Life Supporting
Technologies (LifeSTech), ETSI Telecomunicaciones; 2015.
68 Seo K, Kim JK, Oh DH, Ryu H, Choi H. Virtual daily living test to screen for mild cognitive impairment using
kinematic movement analysis. PLoS One. 2017 Jul; 12(7):e0181883.
69 García-Betances RI, Jiménez-Mixco V, Arredondo MT, Cabrera-Umpiérrez MF. Using virtual reality for cognitive
training of the elderly. Am J Alzheimers Dis Other Demen. 2015 Feb; 30(1): 49–54.
70 Hayhurst J, editor. How augmented reality and virtual reality is being used to support people living with
dementia: Design challenges and future directions. 2018.
71 Roy N BG, Fox D, Gemperle F, Goetz J, Hirsch T, et al. Towards Personal Service Robots for the Elderly. Work
Interact Robot Entertain (WIRE 2000). 25: 184.
72 Pineau JM, Pollack M, Roy N, Thrun S, editors. Towards robotic assistants in nursing homes: Challenges and
73 Coradeschi SC, Cortellessa G, Coraci L, Gonzalez J, Karlsson L, et al. GiraffPlus: Combining social interaction
and long term monitoring for promoting independent living. 6th International Conference on Human System
Interactions; 2013. pp 578-585.
74 Derek MC, Nejat G. A Socially Assistive Robot for Meal-Time Cognitive Interventions [Internet]. J Med Device.
2012; 6(1): 6.
75 LouieWY,MohamedC,DespondF,LeeV,NejatG.TangytheRobotBingoFacilitator:APerformanceReview
[Internet]. J Med Device. 2015; 9.
Dement Geriatr Cogn Disord
Astell et al.: Technology and Dementia
© 2019 The Author(s). Published by S. Karger AG, Basel
DOI: 10.1159/000497800
76 Chiu T, Marziali E, Colantonio A, Carswell A, Gruneir M, Tang M, et al. Internet-based caregiver support for
Chinese Canadians taking care of a family member with alzheimer disease and related dementia. Can J Aging.
2009 Dec; 28(4): 323–36.
77 Czaja SJ, Loewenstein D, Schulz R, Nair SN, Perdomo D. A videophone psychosocial intervention for dementia
caregivers. Am J Geriatr Psychiatry. 2013 Nov; 21(11): 1071–81.
78 Williams K, Arthur A, Niedens M, Moushey L, Hutfles L. In-home monitoring support for dementia caregivers:
a feasibility study. Clin Nurs Res. 2013 May; 22(2): 139–50.
79 LeeJH,KimJH,JhooJH,LeeKU,KimKW,LeeDY,etal.Atelemedicinesystemasacaremodalityfordementia
patients in Korea. Alzheimer Dis Assoc Disord. 2000 Apr-Jun; 14(2): 94–101.
80 WangLY,RobinsonMN,FredricksonKR,ThielkeSM,TsuangDW,etal.ADementiaCareSharedMedicalVisit
Model for Patients and Caregivers Using Telemedicine. J Neuropsychiatry Clin Neurosci. 2014.
81 PrinceMW,GuerchetM,Gemma-ClaireA,WuYT,PrinaM.World Alzheimer Report 2015: The Global Impact
of Dementia - An analysis of prevalence, incidence, cost and trends. Alzheimer’s Dis Int; 2015. p. 84.
82 Ory MG, Hoffman RR 3rd, Yee JL, Tennstedt S, Schulz R. Prevalence and impact of caregiving: a detailed
comparison between dementia and nondementia caregivers. Gerontologist. 1999 Apr; 39(2): 177–85.
83 Kim H. Understanding Internet Use Among Dementia Caregivers: Results of Secondary Data Analysis Using
the US Caregiver Survey Data. Interact J Med Res. 2015 Feb; 4(1):e1.
84 Boots LM, de Vugt ME, van Knippenberg RJ, Kempen GI, Verhey FR. A systematic review of Internet-based
supportive interventions for caregivers of patients with dementia. Int J Geriatr Psychiatry. 2014 Apr; 29(4):
85 Dimirioglou N, Kardaras D, Barbounaki S. Multicriteria Evaluation of the Internet of Things Potential in Health
Care: The Case of Dementia Care. IEEE 19th Conference on Business Informatics; 2017.
86 Ishii H, Kimino K, Aljehani M, Ohe N, Inoue M. An Early Detection System for Dementia Using the M2 M/IoT
Platform. Procedia Comput Sci. 2016; 96: 1332–40.
87 Shin DS, Shin D, Shin D. Ubiquitous health management system with watch-type monitoring device for
dementia patients. J Appl Math. 2014; 2014: 1–8.
88 Enshaeifar S, Zoha A, Markides A, Skillman S, Acton ST, Elsaleh T, et al. Health management and pattern
analysis of daily living activities of people with dementia using in-home sensors and machine learning tech-
niques. PLoS One. 2018 May; 13(5):e0195605.
89 Saniotis A, Henneberg M, Sawalma AR. Integration of Nanobots Into Neural Circuits As a Future Therapy for
Treating Neurodegenerative Disorders. Front Neurosci. 2018 Mar; 12: 153.
90 LiX,TsibouklisJ,WengT,ZhangB,YinG,FengG,etal.Nanocarriersfordrugtransportacrosstheblood-brain
barrier. J Drug Target. 2017 Jan; 25(1): 17–28.
91 Chakroun RW, Zhang P, Lin R, Schiapparelli P, Quinones-Hinojosa A, Cui H. Nanotherapeutic systems for local
treatment of brain tumors. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2018 Jan; 10(1): 10.
92 Robillard JM, Illes J, Arcand M, Beattie BL, Hayden S, Lawrence P, et al. Scientific and ethical features of English-
language online tests for Alzheimer’s disease. Alzheimers Dement (Amst). 2015 Jul; 1(3): 281–8.
93 Robillard JM, Cleland I, Hoey J, Nugent C. Ethical adoption: A new imperative in the development of technology
for dementia. Alzheimers Dement. 2018 Sep; 14(9): 1104–13.
94 Hartin PJ, Nugent CD, McClean SI, Cleland I, Tschanz JT, Clark CJ, et al. JoAnn T. Tschanz, Christine J. Clark, and
Maria C. Norton. The empowering role of mobile apps in behavior change interventions: the Gray Matters
randomized controlled trial. JMIR Mhealth Uhealth. 2016 Aug; 4(3):e93.
95 Thomas NWD, Lindauer A, Kaye J. EVALUATE-AD and Tele-STAR: Novel Methodologies for Assessment of
Caregiver Burden in a Telehealth Caregiver Intervention – A Case Study. Dement Geriatr Cogn Disord.
... From a few years ago until now there has been an exponential interest in focusing on how technology can contribute to the maintenance of different health conditions. There have been multiple potential applications of technological approaches in dementia, including diagnosis, assessment and monitoring, entertainment and engagement, care and management (Astell et al., 2019;Husebo et al., 2020;Shu & Woo, 2021). ...
... Different projects aimed to people living with Alzheimer have benefited from advances in new technological solutions, which have shown a significant increase in recent years (Astell et al., 2019). A growing number of studies indicate that digital technologies could have a great potential in diagnostic, support, and treatment of neuropsychological symptoms in people living with dementia (Alberdi et al., 2018;Chiberska, 2018;Coelho, 2022;Costanzo et al., 2020;Husebo et al., 2020;Klimova, Valis, & Kuca, 2018;Piau, Wild, Mattek, & Kaye, 2019;Sánchez-Gutiérrez, Ortega-Bastidas, & Cano-de-la-Cuerda, 2019;Stroud, Onnela, & Manji, 2019). ...
The aging of the population is associated with an increase in the prevalence of neurodegenerative diseases such as dementia. Our research aim was to determine how technology can help to improve the support for behavioral and psychological challenges of dementia. A systematic review of the literature was performed following PRISMA guidelines. Papers meeting the inclusion criteria and reporting technological devices aimed to the management of these challenges. After screening 1085 papers, 18 studies were eligible for inclusion in the review. Technologies identified in this search (e.g., robots, mobile, apps, computer, software, GPS, wearables, assistive technology) suggest that this non-pharmacological approach may be useful in projects aimed to the management and control of behavioral and psychological manifestations in people living with Alzheimer. The manifestations that are most successfully supported by means of technological applications and devices are depression, sleep disorders, anxiety, apathy, motor activity, and agitation.
... Technology intervention can be an asset to the caregiving role [2]. Both informal and formal caregivers can benefit from the innovation [34], especially as caregiving changes during the transition from community to supported care environments [35] and throughout the dementia care pathway [36]. Equally, research illustrates that caregivers play a key role in enabling active use of technology in the lives of people living with dementia [37]. ...
... The findings highlighted very limited use of digital technology within the supported living environment overall. Opportunities for social interaction, social robots and gaming technologies can provide a more holistic approach to dementia care [34]. Digital illiteracy is a significant barrier for this population [23]. ...
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Background Technology innovation provides an opportunity to support the rising number of people living with dementia globally. The present study examines experiences of people who have dementia and live in technology enriched supported care models. Additionally, it explores caregiver’s attitudes towards technology use with the housing scheme. Methods A qualitative research design was adopted, and eight housing schemes consented to take part in the study. A technology audit was undertaken in addition to participant interviews and caregiver survey. Seven peer researchers conducted semi-structured interviews with 22 people living with dementia. Interviews were analysed using thematic analysis. Informal and formal caregivers were invited to complete a survey to capture their attitudes towards technology use. A total of 20 informal and 31 formal caregiver surveys were returned. All surveys were input into Survey Monkey and downloaded into excel for analysis. Closed questions were analysed using descriptive statistics and open-ended questions were organised into themes and described descriptively. Results The technology audit identified that technologies were in place from as early as 2002. Technology heterogeneity of, both passive and active devices, was found within the housing schemes. Technologies such as wearable devices were reportedly used according to need, and mobile phone use was widely adopted. The themes that developed out of the tenant interviews were: Attitudes and Engagement with Technology; Technology Enhancing Tenants Sense of Security; Seeking Support and Digital Literacy; and Technology Enabled Connection. A lack of awareness about living alongside technology was a major finding. Technologies enabled a sense of reassurance and facilitated connections with the wider community. The interaction with technology presented challenges, for example, remembering passwords, access to Wi-Fi and the identification of its use in an emergency. The caregiver survey reported a range of facilitators and barriers for the use of technology within care. Both types of caregivers held relatively similar views around the benefits of technology, however their views on issues such as privacy and consent varied. Safety was considered more important than right to privacy by family caregivers. Conclusions The present study provides new insight into stakeholder’s experiences of living, working and caregiving alongside technology in supported living environments. As the generation of people living with dementia become more tech savvy, harnessing everyday technologies to support care could enable holistic care and support the transition through the care continuum. Advance care planning and technology assessments are at the very core of future technology provision. It is evident that a paternalistic attitudes towards technology use could impact the multitude of benefits technology can play in both health and leisure for people living with dementia and their caregivers.
... Innovation in Aging, 2023, Vol. 7, No. 3 of technologies for people living with dementia (Astell et al., 2019;Moyle, 2019;Thordardottir et al., 2019;Vermeer et al., 2019). These are variously referred to as monitoring technologies, ambient assisted living, or even assistive technologies. ...
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Background and Objective Care partners of people living with dementia require support to knowledgeably navigate decision making about how and when to use monitoring technologies for care purposes. We conducted a pilot study of a novel self-administered intervention, “Let’s Talk Tech,” for people living with mild dementia and their care partners. This paper presents preliminary efficacy findings of this intervention designed to educate and facilitate dyadic communication about a range of technologies used in dementia care and to document the preferences of the person living with dementia. It is the first-of-its-kind decision-making and planning tool with a specific focus on technology use. Research Design and Methods We used a 1-group pretest–post-test design and paired t tests to assess change over 2 time periods in measures of technology comprehension, care partner knowledge of the participant living with mild Alzheimer’s disease’s (AD) preferences, care partner preparedness to make decisions about technology use, and mutual understanding. Thematic analysis was conducted on postintervention interview transcripts to elucidate mechanisms and experiences with Let’s Talk Tech. Results Twenty-nine mild AD dementia care dyads who live together completed the study. There was statistically significant improvement with medium and large effect sizes on outcome measures of care partners’ understanding of each technology, care partners’ perceptions of the person living with dementia’s understanding of each technology, knowledge of the person living with dementia’s preferences, decision-making preparedness, and care partners’ feelings of mutual understanding. Participants reported that it helped them have important and meaningful conversations about using technology. Discussion and Implications Let’s Talk Tech demonstrated promising preliminary efficacy on targeted measures that can lead to informed, shared decision making about technologies used in dementia care. Future studies should assess efficacy with larger samples and more diverse sample populations in terms of race, ethnicity, and dementia type.
... It is a very futuristic technology and has not been properly developed yet. Machine co-creativity has applications for the diagnosis of degenerative nerve diseases such as dementia to assess and understand the stage in which the patient tracks the progress of the disorder and can also be used as a leisure activity by the patients [120]. An example of this is music. ...
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Degenerative nerve diseases such as Alzheimer’s and Parkinson’s diseases have always been a global issue of concern. Approximately 1/6th of the world’s population suffers from these disorders, yet there are no definitive solutions to cure these diseases after the symptoms set in. The best way to treat these disorders is to detect them at an earlier stage. Many of these diseases are genetic; this enables machine learning algorithms to give inferences based on the patient’s medical records and history. Machine learning algorithms such as deep neural networks are also critical for the early identification of degenerative nerve diseases. The significant applications of machine learning and deep learning in early diagnosis and establishing potential therapies for degenerative nerve diseases have motivated us to work on this review paper. Through this review, we covered various machine learning and deep learning algorithms and their application in the diagnosis of degenerative nerve diseases, such as Alzheimer’s disease and Parkinson’s disease. Furthermore, we also included the recent advancements in each of these models, which improved their capabilities for classifying degenerative nerve diseases. The limitations of each of these methods are also discussed. In the conclusion, we mention open research challenges and various alternative technologies, such as virtual reality and Big data analytics, which can be useful for the diagnosis of degenerative nerve diseases.
As the global ageing trend increases, dementia pressures families and society. Mobile apps that provide interventions and independence for people with dementia (PwD) may relieve this pressure. This study reviews mobile app-based interventions designed for use with PwD, focusing on the type, design, and evaluation of mobile apps. This study searched PubMed, Web of Science, SpringerLink, Taylor & Francis, and IEEE Xplore databases for mobile applications designed for people with disabilities and reported the evaluation results. This study aimed to find out what types of mobile apps developed for people with dementia were marketed during the COVID-19 pandemic, to find out what relevant studies have been done to evaluate mobile apps, and whether users have benefited. twenty papers were eligible, covering four different intervention types and assessment methods. This review found that Serious games can improve the cognitive abilities of PwD and contribute to the mental recovery of patients. Recall therapy and musical mobile apps help PwD slow down memory loss. Personal life mobile apps are effective in assisting PwD to improve independent living.
Conference Paper
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«It is not about the mastery of tools, but rather the creation of engaging learning environments» – this is our understanding of the mission that e-learning is facing today. The vibrant environment of the ZHdK, with its many different creative and hands-on practices, now calls for digital or hybrid solutions due to the Covid-19 pandemic. To face this challenge, we have developed a fresh approach to e-learning: The «PHEW» model containing «play», «hybrid», «easy» and «walk» as distinct features.
One of the latest achievements in the field of medicine is the introduction into practice of technologies using computerized training and training in Virtual Reality (VR), which create new opportunities for prevention, as well as treatment for people suff ering from various cognitive impairments. Purpose of the study . The aim is to investigate classical (conventional), computerized and VR training using neural interfaces focused on the prevention and rehabilitation of functional changes in higher nervous activity. Methods . In March 2022, a search was made for scientifi c full-text publications using the electronic databases of the RSCI, PubMed and Google Scholar. The following keywords and their combinations were used for the query: “dementia”, “aging” and “virtual reality”. Articles of interest for this review had to be peer-reviewed, published no later than 2015, and written in English or Russian. Results . The key methods of non-drug interventions in people suff ering from various cognitive impairments, the advantages and disadvantages of the techniques used were considered. It also shows the main advantages of VR technology as a simple, safe and eff ective tool. VR has great potential for personalized cognitive trainings.
Background: Stigma often surrounds people with dementia when it comes to use of computer technology, although evidence does not always support this. More understanding is needed to investigate attitudes and experience in relation to computer technology use among those living with dementia and their readiness to use it to support self-management. Methods: An online self-report questionnaire was completed by adults living with a dementia diagnosis and those living with them. Questions explored how long the participants had been using computer technology; how regularly they used it; the popularity of common communication apps; and whether they were interested in using an app to support their independence. Results: 47 participants with dementia and 62 supporters responded to the questionnaire. There were no obvious differences between those with dementia and supporters when it came to regular technology usage and both groups showed positive attitudes to the use of it for independence in dementia. Conclusions: There was active use of computer technology among this population. Benefits were shown to include communication, increasing individuals' understanding of dementia diagnoses, and enabling independent activities for both those with dementia and supporters.
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Aims: We explored the relationship between objective and subjective measures of burden prior to and after a telehealth-based caregiver intervention. One caregiver participated in two studies, one to assess the feasibility of objective, home-based monitoring (EVALUATE-AD), the second to assess the feasibility of a caregiver education telehealth-based intervention, Tele-STAR. Methods: Subjective measures of burden and depression in Tele-STAR and objective measures related to daily activities of the caregiver in EVALUATE-AD were compared to examine trends between the different outcome measures. Results: While the caregiver reported an increase in distressing behaviors by her partner, burden levels did not significantly change during or after the Tele-STAR intervention, while objective measures of activity and sleep showed a slight decline. Conclusion: Unobtrusive home-based monitoring may provide a novel, objective method to assess the effectiveness of caregiver intervention programs.
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Introduction Group-based cognitive stimulation is the only nonpharmacologic intervention recommended by the UK National Institute for Clinical and Health Excellence for people with dementia. The potential of technology to extend the availability of group-based cognitive stimulation has not been tested. Methods One hundred sixty-one people with dementia participated in an eight-session group activity using Computer Interactive Reminiscence and Conversation Aid (CIRCA). Cognition, quality of life, and general health were assessed before intervention, postintervention, and 3 months later. Results There was a significant improvement in cognition and quality of life at the end of the CIRCA group intervention, which was further improved at 3-month follow-up. Discussion CIRCA group sessions improved cognition and quality of life similar to group-based cognitive stimulation approved by the National Institute for Clinical and Health Excellence. These benefits were maintained at 3-month follow-up. The data confirm the potential of CIRCA, which can be populated with different cultural and language contents for different user groups.
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Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials.
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The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients’ routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.
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Background: Individuals living with advancing stages of dementia (persons with dementia, PWDs) or other cognitive disorders do not have the luxury of remembering how to perform basic day-to-day activities, which in turn makes them increasingly dependent on the assistance of caregivers. Dressing is one of the most common and stressful activities provided by caregivers because of its complexity and privacy challenges posed during the process. Objective: In preparation for in-home trials with PWDs, the aim of this study was to develop and evaluate a prototype intelligent system, the DRESS prototype, to assess its ability to provide automated assistance with dressing that can afford independence and privacy to individual PWDs and potentially provide additional freedom to their caregivers (family members and professionals). Methods: This laboratory study evaluated the DRESS prototype's capacity to detect dressing events. These events were engaged in by 11 healthy participants simulating common correct and incorrect dressing scenarios. The events ranged from donning a shirt and pants inside out or backwards to partial dressing-typical issues that challenge a PWD and their caregivers. Results: A set of expected detections for correct dressing was prepared via video analysis of all participants' dressing behaviors. In the initial phases of donning either shirts or pants, the DRESS prototype missed only 4 out of 388 expected detections. The prototype's ability to recognize other missing detections varied across conditions. There were also some unexpected detections such as detection of the inside of a shirt as it was being put on. Throughout the study, detection of dressing events was adversely affected by the relatively smaller effective size of the markers at greater distances. Although the DRESS prototype incorrectly identified 10 of 22 cases for shirts, the prototype preformed significantly better for pants, incorrectly identifying only 5 of 22 cases. Further analyses identified opportunities to improve the DRESS prototype's reliability, including increasing the size of markers, minimizing garment folding or occlusions, and optimal positioning of participants with respect to the DRESS prototype. Conclusions: This study demonstrates the ability to detect clothing orientation and position and infer current state of dressing using a combination of sensors, intelligent software, and barcode tracking. With improvements identified by this study, the DRESS prototype has the potential to provide a viable option to provide automated dressing support to assist PWDs in maintaining their independence and privacy, while potentially providing their caregivers with the much-needed respite.
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Dementia is the most widespread form of neurodegenerative disorder and is associated with an immense societal and personal cost. Prevalence of this disorder is projected to triple worldwide by 2050 leading to an urgent need to make advances in the efficiency of both its care and therapy research. Digital technologies are a rapidly advancing field that provide a previously unavailable opportunity to alleviate challenges faced by clinicians and researchers working in this area. This clinical review aimed to summarise currently available evidence on digital technologies that can be used to monitor cognition. We identified a range of pervasive digital systems, such as smartphones, smartwatches and smart homes, to assess and assist elderly demented, prodromal and preclinical populations. Generally, the studies reported good level of agreement between the digital measures and the constructs they aimed to measure. However, most of the systems are still only in the initial stages of development with limited data on acceptability in patients. Although it is clear that the use of digital technology to monitor and support the cognitive domains affected by dementia is a promising area of development, additional research validating the efficacy, utility and cost-effectiveness of these systems in patient populations is needed.
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Recent neuroscientific research demonstrates that the human brain is becoming altered by technological devices. Improvements in biotechnologies and computer based technologies are now increasing the likelihood for the development of brain augmentation devices in the next 20 years. We have developed the idea of an “Endomyccorhizae like interface” (ELI) nanocognitive device as a new kind of future neuroprosthetic which aims to facilitate neuronal network properties in individuals with neurodegenerative disorders. The design of our ELI may overcome the problems of invasive neuroprosthetics, post-operative inflammation, and infection and neuroprosthetic degradation. The method in which our ELI is connected and integrated to neuronal networks is based on a mechanism similar to endomyccorhizae which is the oldest and most widespread form of plant symbiosis. We propose that the principle of Endomyccorhizae could be relevant for developing a crossing point between the ELI and neuronal networks. Similar to endomyccorhizae the ELI will be designed to form webs, each of which connects multiple neurons together. The ELI will function to sense action potentials and deliver it to the neurons it connects to. This is expected to compensate for neuronal loss in some neurodegenerative disorders, such as Alzheimer's disease and Parkinson's disease.
As people's average life expectancy has significantly improved, the prevalence of dementia among elderly people has increased. The symptoms of dementia might influence patients’ interpersonal relationships and their ability to work. This study was based on the nationwide dataset applied from the National Health Insurance program of Taiwan, and two types of medication ingredients, which are constantly prescribed for dementia patients, were used for case selection. This study included 5041 case patients and 13,902 control patients who were matched with age, gender, and index date. Using logistic regression, the current study has identified the comorbid associations between dementia and various kinds of illnesses. Advanced stratification analyses revealed that some comorbid illnesses associated with dementia only exists in subgroups of patients with specific age, gender, or prescription for dementia drugs. The mined characteristics would be helpful in managing patients with dementia.
The aim of this study was to explore the acceptability, feasibility and usability of older people with mild dementia to use smartphone for wayfinding. Thirty cognitively normal older people and 16 people with mild dementia were recruited to participate in a wayfinding trial in the free-living environment. Five feasibility and three acceptability markers were compared between the groups. Content analysis on the video-recorded trial processes and individual interviews was employed to identify the usability issues. The results found that there were no significant between-group differences on the feasibility markers, except that the people with mild dementia needed significantly more time to complete the wayfinding trial and workshop; or on the acceptability items. Sensory/cognitive impairment and GPS signal reliability affected their usability. Mild dementia does not limit the older people to use smartphones for wayfinding in the free-living environment. Future studies should examine the efficacy and safety of smartphone to promote outdoor independence of the people with mild dementia.
Introduction: Technology interventions are showing promise to assist persons with dementia and their carers. However, low adoption rates for these technologies and ethical considerations have impeded the realization of their full potential. Methods: Building on recent evidence and an iterative framework development process, we propose the concept of "ethical adoption": the deep integration of ethical principles into the design, development, deployment, and usage of technology. Results: Ethical adoption is founded on five pillars, supported by empirical evidence: (1) inclusive participatory design; (2) emotional alignment; (3) adoption modelling; (4) ethical standards assessment; and (5) education and training. To close the gap between adoption research, ethics and practice, we propose a set of 18 practical recommendations based on these ethical adoption pillars. Discussion: Through the implementation of these recommendations, researchers and technology developers alike will benefit from evidence-informed guidance to ensure their solution is adopted in a way that maximizes the benefits to people with dementia and their carers while minimizing possible harm.