Virtual reality (VR) technology could vastly improve the quality of life for people with dementia by helping to recall past memories, reduce aggression and improve interactions with caregivers, new research has discovered |University of Kent | via ScienceDaily
Many people with dementia (PWD) residing in long-term care may face barriers in accessing experiences beyond their physical premises; this may be due to location, mobility constraints, legal mental health act restrictions, or offence-related restrictions.
In recent years, there have been research interests towards designing non-pharmacological interventions aiming to improve the Quality of Life (QoL) for PWD within long-term care.
The authors of this study explored the use of Virtual Reality (VR) as a tool to provide 360°-video based experiences for individuals with moderate to severe dementia residing in a locked psychiatric hospital.
The paper discusses the appeal of using VR for PWD, and the observed impact of such interaction. It also presents the design opportunities, pitfalls, and recommendations for future deployment in healthcare services. This paper demonstrates the potential of VR as a virtual alternative to experiences that may be difficult to reach for PWD residing within locked setting.
Dementia UK have produced a new leaflet with the Royal College of General Practitioners, explaining GP online services for people who care for someone with dementia. Patients can now book appointments, manage repeat prescriptions, and see test results online.
Online GP services are designed so patients can:
make, change or cancel GP appointments, without having to telephone the practice. Patients can access the online services 24 hours a day, see what appointments are free in the coming days, and choose to see a particular doctor or nurse
request repeat prescriptions. Patients can also see a list of regularly prescribed medication, the prescribed dose, and when their next prescription is due
look up test results, as well as monitor their health by comparing with historic test results
see the medical notes on file, including diagnoses, any allergies, immunisations, and past surgery.
see their medical notes wherever they are. This can be useful if medical attention is required when on holiday or traveling
Moyle, W. et al. | Using a therapeutic companion robot for dementia symptoms in long-term care: reflections from a cluster-RCT | Aging & Mental Health | Vol. 23 issue 3 | p329-336
Objectives: We undertook a cluster-randomised controlled trial exploring the effect of a therapeutic companion robot (PARO) compared to a look-alike plush toy and usual care on dementia symptoms of long-term care residents. Complementing the reported quantitative outcomes , this paper provides critical reflection and commentary on individual participant responses to PARO, observed through video recordings , with a view to informing clinical practice and research.
Method: A descriptive, qualitative design with five participants selected from the PARO intervention arm of the trial. The trial is registered with the Australian New Zealand Clinical Trials Registry
Results: The five participants and their responses to PARO are presented in terms of three issues: i.) Different pre-intervention clinical presentations and different responses; ii.) Same individual, different response – the need for continual assessment and review; and iii.) The ethics of giving and retrieving PARO. Implications for clinical practice and future research are discussed in relation to each issue.
Conclusion: The findings suggest that one approach does not fit all, and that there is considerable variation in responses to PARO. A number of recommendations are discussed to aid the delivery of psychosocial interventions with PARO in practice, as well as to guide future research.
A new machine-learning model that scans routinely collected NHS data has shown promising signs of being able to predict undiagnosed dementia in primary care. The results from the feasibility study suggest that the model could significantly reduce the number of those living with undiagnosed dementia | BJGP Open | via ScienceDaily
Improving dementia care through increased and timely diagnosis is a priority for the NHS, yet around half of those living with dementia are unaware they live with the condition. Now a new machine-learning model that scans routinely collected NHS data has shown promising signs of being able to predict undiagnosed dementia in primary care.
Led by the University of Plymouth, the study collected Read-encoded data from 18 consenting GP surgeries across Devon, UK, for 26,483 patients aged over 65. The Read codes — a thesaurus of clinical terms used to summarise clinical and administrative data for UK GPs — were assessed on whether they may contribute to dementia risk, with factors included such as weight and blood pressure. These codes were used to train a machine-learning classification model to identify patients that may have underlying dementia.
The results showed that 84% of people who had dementia were detected as having the condition (sensitivity value) while 87% of people without dementia had been correctly acknowledged as not having the condition (specificity value), according to the data.
These results indicate that the model can detect those with underlying dementia with an accuracy of 84%. This suggests that the machine-learning model could, in future, significantly reduce the number of those living with undiagnosed dementia — from around 50% (current estimated figure) to 8%.
Study finds assistive technologies can improve safety for people with Dementia through reducing falls risk, accidents and other risky behaviour | Aging & Mental Health
Objectives: Assistive technology (AT) may enable people with dementia to live safely at home for longer, preventing care home admission. This systematic review assesses the effectiveness of AT in improving the safety of people with dementia living in the domestic setting, by searching for randomised controlled trials, non-randomised controlled trials and controlled before-after studies which compared safety AT with treatment as usual. Measures of safety include care home admission; risky behaviours, accidents and falls at home; and numbers of deaths. The review updates the safety aspect of Fleming and Sum’s 2014 systematic review.
Method: Seven bibliographic databases, the Social Care Institute for Excellence website and the Alzheimer’s Society website were searched for published and unpublished literature between 2011–2016. Search terms related to AT, dementia and older people. Common outcomes were meta-analysed.
Results: Three randomised controlled trials were identified, including 245 people with dementia. No significant differences were found between intervention and control groups in care home admission (risk ratio 0.85 95% CI [0.37, 1.97]; Z = 0.37; p = 0.71). The probability of a fall occurring was 50% lower in the intervention group (risk ratio 0.50 95% CI [0.32, 0.78]; Z = 3.03; p = 0.002). One included study found that a home safety package containing AT significantly reduced risky behaviour and accidents (F(45) = 4.504, p < 0.001). Limitations include the few studies found and the inclusion of studies in English only.
Conclusion: AT’s effectiveness in decreasing care home admission is inconclusive. However, the AT items and packages tested improved safety through reducing falls risk, accidents and other risky behaviour.
Review finds some promising results for identifying MCI and early dementia, but notes shortcomings within available evidence | International Journal of Geriatric Psychiatry
The aim of this review is to determine whether automated computerised tests accurately identify patients with progressive cognitive impairment and, if so, to investigate their role in monitoring disease progression and/or response to treatment.
Six electronic databases (Medline, Embase, Cochrane, Institute for Scientific Information, PsycINFO, and ProQuest) were searched from January 2005 to August 2015 to identify papers for inclusion. Studies assessing the diagnostic accuracy of automated computerised tests for mild cognitive impairment (MCI) and early dementia against a reference standard were included. Where possible, sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratios were calculated. The Quality Assessment of Diagnostic Accuracy Studies tool was used to assess risk of bias.
Sixteen studies assessing 11 diagnostic tools for MCI and early dementia were included. No studies were eligible for inclusion in the review of tools for monitoring progressive disease and response to treatment. The overall quality of the studies was good. However, the wide range of tests assessed and the non‐standardised reporting of diagnostic accuracy outcomes meant that statistical analysis was not possible.
Some tests have shown promising results for identifying MCI and early dementia. However, concerns over small sample sizes, lack of replicability of studies, and lack of evidence available make it difficult to make recommendations on the clinical use of the computerised tests for diagnosing, monitoring progression, and treatment response for MCI and early dementia. Research is required to establish stable cut‐off points for automated computerised tests used to diagnose patients with MCI or early dementia.
Research findings point to a need for early support focusing on the use of everyday technology for persons with MCI
Objectives: The aims were to describe longitudinal patterns in terms of perceived ability to use everyday technology (ET) and involvement in everyday activities over five years in older adults with mild cognitive impairment (MCI), and to examine the predictive value of these patterns regarding diagnostic outcomes.
Method: Thirty older adults diagnosed with MCI at inclusion, reported their perceived ability in using ET and involvement in everyday activities on seven occasions over five years. Individual longitudinal case plots and a pattern-oriented analysis were used to compare the participants’ distribution in earlier identified stable/ascending, fluctuating and descending patterns of functioning (year 0–2). Fisher’s exact test was used for testing the relation between pattern and diagnostic outcomes.
Results: An initial descending pattern of functioning tended to continue; none of these participants later developed a more stable pattern. More congruent trajectories of change appeared over time. Pattern affinity years 0–2 and diagnostic outcome were significantly related (p = .05), with a dementia diagnosis being more likely for those initially displaying an early descending pattern
Conclusion: These findings point to a need for early support focusing on the use of ET for persons with MCI who early after diagnosis descend in functioning.
A ‘brain training’ game could help improve the memory of patients in the very earliest stages of dementia, suggests a new study. | International Journal of Neuropsychopharmacology. | via ScienceDaily
Researchers from the University of Cambridge have developed a memory game app, ‘Game Show’, and have tested its effects on cognition and motivation in patients with amnestic mild cognitive impairment (aMCI).
The researchers randomly assigned forty-two patients with amnestic MCI to either the cognitive training or control group. Participants in the cognitive training group played the memory game for a total of eight one-hour sessions over a four-week period; participants in the control group continued their clinic visits as usual.
The results showed that patients who played the game made around a third fewer errors, needed fewer trials and improved their memory score by around 40%, showing that they had correctly remembered the locations of more information at the first attempt on a test of episodic memory.
In addition, participants in the cognitive training group indicated that they enjoyed playing the game and were motivated to continue playing across the eight hours of cognitive training. Their confidence and subjective memory also increased with gameplay. The researchers say that this demonstrates that games can help maximise engagement with cognitive training.
This paper provides an overview of the role of technology in dementia care, treatment and support by mapping existing technologies – by function, target user and disease progression.
Technologies identified are classified into seven functions: memory support, treatment, safety and security, training, care delivery, social interaction and other. Different groups of potential users are distinguished: people with mild cognitive impairment and early stages of dementia, people with moderate to severe dementia and unpaid carers and health- and social care professionals. We also identified the care settings, in which the technologies are used (or for which the technologies are developed): at home in the community and in institutional care settings.
The evidence has been drawn from a rapid review of the literature, expert interviews and web and social media searches. The largest number of technologies identified aim to enhance the safety and security of people with dementia living in the community. These devices are often passive monitors, such as smoke detectors. Other safety interventions, such as panic buttons, require active intervention.
The second largest number of interventions aims to enhance people’s memory and includes global positioning systems devices and voice prompts. These technologies mostly target people in the early stages of dementia. A third group focusing on treatment and care delivery emerged from the literature. These interventions focus on technology-aided reminiscence or therapeutic aspects of care for people with dementia and their carers.
While the review found a range of technologies available for people with dementia and carers there is very little evidence of widespread practical application. Instead, it appears that stakeholders frequently rely on everyday technologies re-purposed to meet their needs.
Objectives: We explored whether newly developed application (Smartphone-based brain Anti-aging and memory Reinforcement Training, SMART) improved memory performance in older adults with subjective memory complaints (SMC).
Method: A total of 53 adults (range: 50-68 years; 52.8% female) were randomized into either one of two intervention groups [SMART (n = 18) vs. Fit Brains® (n = 19)] or a wait-list group (n = 16). Participants in the intervention groups underwent 15-20 minutes of training per day, five days per week for 8 weeks. We used objective cognitive measures to evaluate changes with respect to four domains: attention, memory, working memory (WM), and response inhibition. In addition, we included self-report questionnaires to assess levels of SMC, depression, and anxiety.
Results: Total WM quotient [t(17) = 6.27, p < .001] as well as auditory-verbal WM score [t(17) = 4.45, p < .001] increased significantly in the SMART group but not in the control groups. Self-reports of memory contentment, however, increased in the Fit Brains® group only [t(18) = 2.12, p < .05).
Conclusion: Use of an 8-week smartphone-based memory training program may improve WM function in older adults. However, objective improvement in performance does not necessarily lead to decreased SMC.