Lifestyle and behavioural changes in older adults during the Covid-19 pandemic are associated with subjective cognitive complaints.

Subjective cognitive complaints (SCC) is a self-reported experience of persistently impaired cognitive functions which could be the earliest red flag of neurocognitive disorders. The COVID-19 pandemic and related restriction measures changed the lifestyle and behaviour of older adults. The aim of this study was to assess the relation of these changes and SCC status in Hungary. This cross-sectional study analysed the data of 359 elderly Hungarians who filled out the WW-FINGERS-SARS-CoV2 survey. A quarter of the respondents (n:88) reported SCC in connection with the pandemic. We compared sociodemographic features, health status, lifestyle, and social life parameters between subjects with reported SCC and without. To eliminate the potential interrelation across group differences, stepwise logistic regression was applied. Participants with SCC showed the following characteristics, compared to individuals without: (1) they were older; (2) they were more likely to be women; (3) they had a higher number of chronic disorders; (4) showed more prominent impairment in physical mobility; (5) had worse sleep quality; (6) spent less time with family; and (7) used internet more frequently during the pandemic (all p's < 0.001). Logistic regression highlighted that only two parameters were related to SCC status independently, the physical mobility (ability to walk 500 m without difficulties; OR = 1.186; p < 0.001; 95%CI = 1.101, 1.270) and changes in time spent with grandchildren (OR = 1.04; p = 0.015; 95%CI = 1.008, 1.073). Our study draws attention to the importance of physical mobility and quality time with family as key factors in the cognitive well-being of elderly people.

Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment.

Mild cognitive impairment (MCI) is a potential therapeutic window in the prevention of dementia; however, automated detection of early cognitive deterioration is an unresolved issue. The aim of our study was to compare various classification approaches to differentiate MCI patients from healthy controls, based on rs-fMRI data, using machine learning (ML) algorithms. Own dataset (from two centers) and ADNI database were used during the analysis. Three fMRI parameters were applied in five feature selection algorithms: local correlation, intrinsic connectivity, and fractional amplitude of low frequency fluctuations. Support vector machine (SVM) and random forest (RF) methods were applied for classification. We achieved a relatively wide range of 78-87% accuracy for the various feature selection methods with SVM combining the three rs-fMRI parameters. In the ADNI datasets case we can also see even 90% accuracy scores. RF provided a more harmonized result among the feature selection algorithms in both datasets with 80-84% accuracy for our local and 74-82% for the ADNI database. Despite some lower performance metrics of some algorithms, most of the results were positive and could be seen in two unrelated datasets which increase the validity of our methods. Our results highlight the potential of ML-based fMRI applications for automated diagnostic techniques to recognize MCI patients.

[Care of dementia in the general practice]. / A dementia ellátása a háziorvosi gyakorlatban.

Treating and caring for people with dementia is a complex task, which can be achieved through cooperation between primary and specialist healthcare, social care and specialist care services. General practitioners are key players in the prevention, screening, treatment and care of dementia. Our aim was to present the general practitioner's aspects of modern dementia care through different levels of prevention. Educating patients to lead a healthy lifestyle and optimising their cardiovascular status reduces the risk of developing dementia. Emphasis was placed on early screening and referral to a specialist, and the importance of timely, individualised therapy for modern care. General practitioner's care of patients with dementia includes monitoring the progression of the disease as well as co-morbidities so that the quality of life of both the patients and their family can be improved by reducing complications. Family doctors also have an important role to support family members who care for the patient. In addition to presenting the current possibilities in Hungary, we reviewed the international literature and national guidelines, which must be followed continuously to ensure quality patient care. Orv Hetil. 2023; 164(32): 1263-1270.

Differentiation of patients with mild cognitive impairment and healthy controls based on computer assisted hand movement analysis: a proof-of-concept study.

Mild cognitive impairment (MCI) is the prodromal phase of dementia, and it is highly underdiagnosed in the community. We aimed to develop an automated, rapid (< 5 min), electronic screening tool for the recognition of MCI based on hand movement analysis. Sixty-eight individuals participated in our study, 46 healthy controls and 22 patients with clinically defined MCI. All participants underwent a detailed medical assessment including neuropsychology and brain MRI. Significant differences were found between controls and MCI groups in mouse movement characteristics. Patients showed higher level of entropy for both the left (F = 5.24; p = 0.001) and the right hand (F = 8.46; p < 0.001). Longer time was required in MCI to perform the fine motor task (p < 0.005). Furthermore, we also found significant correlations between mouse movement parameters and neuropsychological test scores. Correlation was the strongest between motor parameters and Clinical Dementia Rating scale (CDR) score (average r: - 0.36, all p's < 0.001). Importantly, motor parameters were not influenced by age, gender, or anxiety effect (all p's > 0.05). Our study draws attention to the utility of hand movement analysis, especially to the estimation of entropy in the early recognition of MCI. It also suggests that our system might provide a promising tool for the cognitive screening of large populations.

Impact of the third wave of the COVID-19 pandemic on the lifestyle, mental and physical health of the Hungarian population over 60 / A COVID-19-pandémia harmadik hullámának hatása a 60 év feletti magyar lakosság életmódjára, mentális és fizikai egészségére

Introduction: Elderly population is the most vulnerable group of the COVID-19 pandemic, since they often live with chronic diseases. Objective: The goal of our research is to analyze the direct and indirect effects of the pandemic on the Hungarian population over 60 years of age. Method: We collected data using the authentic Hungarian translation of the,World-Wide FINGERS SARS-CoV-2 Survey between 1st of February and 1st of June 2021. Results: Our study included 431 people with a low rate of COVID infection (6%). The most marked changes were the increase in the use of digital services in 71%, increased feeling of loneliness in 46%, decrease in subjective sleep quality in 47%, and reduced contact with friends and relatives in 80% of the respondents. Eight-six percent of participants had at least one chronic illness and 23% missed an illness-related medical visit during the pandemic. In 45%, the subjective quality of life deteriorated and 25% reported impairment of memory functions. Discussion: Participants became socially isolated during the pandemic having a significant negative impact on their way of life. The changes in physical and mental health are likely to be reflected in an increased incidence and accelerated progression of age-related diseases in the elderly. Conclusion: In order to reduce the direct and indirect harmful effects of the COVID-19 pandemic, it is of paramount importance to know how the pandemic and the following restrictions affect the behavior and lifestyle of the elderly as well as the care of patients living with chronic diseases.