Search results

This study aimed to assess the potential of the Clinical Dementia Rating (CDR) Scale in the prognosis of dementia in elderly subjects.

Dementia staging severity is clinically an essential task, so the authors used machine learning (ML) on the magnetic resonance imaging (MRI) features to locate and study the impact of various MR readings onto the classification of demented and nondemented patients. The authors used cross-sectional MRI data in this study. The designed ML approach established the role of CDR in the prognosis of inflicted and normal patients. Moreover, the pattern analysis indicated CDR as a strong cohort amongst the various attributes, with CDR to have a significant value of p < 0.01. The authors employed 20 ML classifiers.

The mean prediction accuracy varied with the various ML classifier used, with the bagging classifier (random forest as a base estimator) achieving the highest (93.67%). A series of ML analyses demonstrated that the model including the CDR score had better prediction accuracy and other related performance metrics.

The results suggest that the CDR score, a simple clinical measure, can be used in real community settings. It can be used to predict dementia progression with ML modeling.

One limitation of research that assesses the effectiveness of physical activity interventions for people with dementia is that most do not describe the intervention in sufficient detail to ascertain a theoretical basis or mechanism of action that determines the effective components. This paper aims to identify studies which evaluate the mechanisms of action of physical activity interventions for people with dementia, to further inform effective intervention development.

Papers were screened for evidence of evaluation of specific forms of physical activity, using pre-defined inclusion criteria. Analysis was conducted to ascertain if mechanisms of action were corroborated by data within and between studies.

The authors identified 26 studies with a measured mechanism of action; these related to the effects of physical activity on either neurological structure or endocrinal markers, including hormones. Physical activity had potential to reduce hippocampal atrophy, increase neural recruitment, activate the noradrenergic system and improve anti-inflammatory responses. While individual studies were hampered by small sample sizes, the body of evidence indicated that physical activity may have potential to delay cognitive decline.

Mechanisms of action in relation to dementia and physical activity are likely to be multifaceted, and physical activity may be protective against progression in the early stages of cognitive decline. Physical activity may be of greatest benefit if incorporated into on-going lifestyle, rather than engaged in for short periods, and combined with social interaction.

This paper is unique in its focus on the mechanisms of action of physical activity interventions for people with dementia.

The purpose of this paper is to review studies that evaluated technology-based prompting systems for supporting participants with dementia or acquired cognitive impairment in their performance of multistep daily tasks.

A scoping review was conducted to identify eligible studies through a search of four electronic databases, that is, PubMed, PsycINFO, Web of Science and Institute of Electrical and Electronics Engineers.

The search, which covered the 2010–2020 period, led to the identification of 1,311 articles, 30 of which were included in the review. These articles evaluated six different types of prompting systems: context-aware, automatic computer prompting, context-aware, mediated computer prompting, teleoperated robot prompting, self-operated augmented reality prompting, self-operated computer or tablet prompting and time-based (preset) computer, tablet or smartphone prompting.

Technology-aided prompting to help people with dementia or acquired cognitive impairment perform relevant multistep daily tasks is considered increasingly important. This review provides a picture of the different prompting options available and of their level of readiness for application in daily contexts.

Mental health is as important as physical health and new university entrants report high prevalence of depression. In open and distance learning (ODL), students must manage both work and studies. Those who are unable to effectively balance these aspects may experience negative outcomes such as dropping out, distress and physical health problems. Therefore, the study aims to investigate psychological distress amongst distance-learning undergraduate students to gather evidence for recommending necessary interventions.

A cross-sectional study was conducted using depression, anxiety, stress scale (DASS-21). Participants were undergraduate students from the Open University of Sri Lanka (OUSL). The students' socio-demographic details, history of physical and mental illnesses were also recorded.

According to scoring, 51% of the sample was categorised as “psychologically distressed” relating to the anxiety levels they reported whilst depression (35%) and stress remained (20%) at low levels. The three-factor structure of DASS-21 was also confirmed with reliability scores of 0.8 obtained for all three sub-scales.

The limitations were low-response rate (less than 50%) and inability to provide causal explanations for psychological distress. Further research could address these.

The current research identified anxiety as a psychologically distressing factor for ODL students with the use of a reliable screening tool. Therefore, exploring reasons and interventions to help reduce anxiety could be developed.

Majority of distance learners are contributing to a country's economy whilst learning to improve their current socio-economic status. Therefore, addressing these negative impacts is important.

The study explored ODL students' psychological distress and highlighted the need to identify causes and development of support systems to enhance mental well-being.

The purpose of this paper is to assess frailty, geriatric conditions and multimorbidity in people experiencing homelessness (PEH) using holistic evaluations based on comprehensive geriatric assessment (CGA) and draw comparisons with general population survey data.

Cross-sectional observational study conducted in a London-based hostel for single PEH over 30 years old in March–April 2019. The participants and key workers completed health-related questionnaires, and geriatric conditions were identified using standardised assessments. Frailty was defined according to five criteria in Fried’s phenotype model and multimorbidity as the presence of two or more long-term conditions (LTCs). Comparisons with the general population were made using data from the English Longitudinal Study of Ageing and the Health Survey for England.

A total of 33 people participated with a mean age of 55.7 years (range 38–74). Frailty was identified in 55% and pre-frailty in 39%. Participants met an average of 2.6/5 frailty criteria, comparable to 89-year-olds in the general population. The most common geriatric conditions were: falls (in 61%), visual impairment (61%), low grip strength (61%), mobility impairment (52%) and cognitive impairment (45%). All participants had multimorbidity. The average of 7.2 LTCs (range 2–14) per study participant far exceeds the average for even the oldest people in the general population.

To the best of authors’ knowledge, this is the first UK-based study measuring frailty and geriatric conditions in PEH and the first anywhere to do so within a CGA-type evaluation. It also demonstrates the feasibility of conducting holistic evaluations in this setting, which may be used clinically to improve the health outcomes for PEH.