1,721,696 research outputs found
The Psychological Impact of COVID-19 Pandemic and Lockdown on Caregivers of People With Dementia
No full textBackground: Caregivers of people with dementia (pwD) are at risk of depression, anxiety, and burden. COVID-19 pandemic and government-imposed lockdown as a preventive measure might increase psychological symptoms in caregivers. The authors performed a study to measure the change of psychological symptoms during quarantine or self-isolation for COVID-19 in a sample of Italian caregivers of pwD, and to investigate if the resilience is associated with psychological changes in the sample. Methods: Eighty-four caregivers of pwD completed an online survey including questionnaires assessing depressive symptomatology and anxiety before and during the lockdown, caregiver burden and levels of resilience. Results: The multivariate analysis of variance revealed an effect of time (before and during the lockdown) in the whole group on depression scores; a significant interaction between time and resilience was found on anxiety scores, revealing that caregivers with high resilience showed a more significant increase of anxiety levels during lockdown than caregivers with low resilience. Moreover, the regression analysis revealed that caregiver burden was associated negatively with resilience scores, and positively with higher functional dependence. Conclusion: COVID-19 pandemic and the lockdown produced psychological consequences in caregivers of pwD, with an increase of levels of depression. Moreover, high resilience had a negative effect on anxiety levels and no effect on depressive symptomatology during the lockdown; moreover, it was associated with lower levels of caregiver burden. All caregivers, even those with high resilience levels, should be addressed to psychological interventions to reduce levels of depression, anxiety and caregiver burden
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Letter by Maestrini et al Regarding Article, "classification of Covert Brain Infarct Subtype and Risk of Death and Vascular Events"
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Functional autonomy in dementia of the Alzheimer’s type, mild cognitive impairment, and healthy aging: a meta-analysis
No full textBackground: Activities of daily living (ADL) are fundamental skills required to independently care for oneself and are categorized in basic (BADLs) and instrumental (IADLs) activities of daily living. ADL evaluation is of paramount importance in clinical practice to discriminate between healthy individuals (HC) and patients with mild cognitive impairment (MCI) or Alzheimer’s disease (AD). However, it is unclear whether and to what extent BADL and IADL deficits occur in MCI, when compared with AD. Therefore, the present study aimed at comparing performance on both BADLs and IADLs in HC, MCI, and AD. Methods: Three electronic databases were consulted for studies comparing total BADLs/IADLs, and single BADLs/IADLs in AD, MCI, and HC (comparisons: AD versus MCI, AD versus HC, MCI versus HC). Ninety-six studies were included in the meta-analysis with random effect models (Hedges’ g). Meta-regression was performed to evaluate the effect of clinical variables on ESs. Results: AD group had more difficulties in BADLs and IADLs than HC and MCI groups; people with MCI showed more difficulties in both IADLs and BADLs than HC. The meta-regression analysis revealed that the percentage of males in the samples was a significant predictor of the ES in the meta-analysis comparing total BADL scores between MCI and HC; in the comparison between AD and HC, age at evaluation predicted the ES on some single IADLs: preparing food, handling medication, and finances. Conclusions: In MCI, it should be considered not only a decline of IADLs but also subtle decline of BADL abilities
GAP-LSTM: Graph-Based Autocorrelation Preserving Networks for Geo-Distributed Forecasting
Full text linkForecasting methods are important decision support tools in geo-distributed sensor networks. However, challenges such as the multivariate nature of data, the existence of multiple nodes, and the presence of spatio-temporal autocorrelation increase the complexity of the task. Existing forecasting methods are unable to address these challenges in a combined manner, resulting in a suboptimal model accuracy. In this article, we propose, a novel geo-distributed forecasting method that leverages the synergic interaction of graph convolution, attention-based long short-term memory (LSTM), 2-D-convolution, and latent memory states to effectively exploit spatio-temporal autocorrelation in multivariate data generated by multiple nodes, resulting in improved modeling capabilities. Our extensive evaluation, involving real-world datasets on traffic, energy, and pollution domains, showcases the ability of our method to outperform state-of-the-art forecasting methods. An ablation study confirms that all method components provide a positive contribution to the accuracy of the extracted forecasts. The method also provides an interpretable visualization that complements forecasts with additional insights for domain experts
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