The current study addressed the relationship between subjective memory complaints and negative affect, well-being, and demographic variables by investigating the Hungarian version of Multifactorial Memory Questionnaire. The original factor structure showed a poor fit on our data; therefore, principal component analysis was conducted on data from 577 participants, ranging in age from 18 to 92 years. Our analysis provided a six-component solution: Satisfaction, Retrospective memory mistakes, Prospective memory mistakes, External Strategies, Internal Strategies, and Frustration. To improve the reliability and internal consistency indicators we created four subscales by combining Frustration with Satisfaction, and Retrospective and Prospective memory mistakes subscales. Thus, we were able to preserve the factor structure similar to the original. Subjective memory complaints were correlated positively with anxiety and depression and were associated negatively with well-being. We found a slight positive correlation between age and memory ability, and age was associated negatively with the frequency of external strategy use. Individuals with higher education were satisfied with their memory, used more frequent external strategies. Furthermore, men were more satisfied with their memory and reported better memory ability, while women tended to use more external and internal strategies. Women also showed a higher level of anxiety and depression than men. In conclusion, self-reported memory changes are of particular importance because of their association with perceived mental health status and implications for later disease development.
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http://dx.doi.org/10.1186/s41235-023-00469-y | DOI Listing |
Neurology
February 2025
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Background And Objectives: Previous studies have shown inconsistent associations between red meat intake and cognitive health. Our objective was to examine the association between red meat intake and multiple cognitive outcomes.
Methods: In this prospective cohort study, we included participants free of dementia at baseline from 2 nationwide cohort studies in the United States: the Nurses' Health Study (NHS) and the Health Professionals Follow-Up Study (HPFS).
Rev Bras Enferm
January 2025
Universidade Federal de Minas Gerais. Belo Horizonte, Minas Gerais, Brazil.
Objective: To analyze the reach and engagement on the history of nursing on social media of the Memory Center of the School of Nursing, Federal University of Minas Gerais (CEMENF/UFMG), in light of Pierre Lévy.
Methods: Documentary study carried out on CEMENF's Instagram and on the YouTube of the School of Nursing of UFMG, from September to December 2021. The findings were analyzed according to Pierre Lévy's concepts.
Rev Bras Enferm
January 2025
Universidade Federal do Rio de Janeiro. Rio de Janeiro, Rio de Janeiro, Brazil.
Objectives: to identify how first-year nursing students use cyberspace and propose an orientation guide with criteria guiding the use of cyberspace.
Methods: qualitative and descriptive research, carried out with 24 nursing students from a federal public institution in Rio de Janeiro. Data collection was carried out through semi-structured interviews.
PLoS One
January 2025
School of Electronic Information Engineering, Inner Mongolia University, Hohhot, Inner Mongolia, China.
Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN's local feature extraction capability and LSTM's advantage in handling sequential data to optimize sensing accuracy and efficiency.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Information Systems and Cybersecurity, University of Bisha, Bisha, KSA.
Accurate energy demand forecasting is critical for efficient energy management and planning. Recent advancements in computing power and the availability of large datasets have fueled the development of machine learning models. However, selecting the most appropriate features to enhance prediction accuracy and robustness remains a key challenge.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!