Needs of persons with dementia and their family caregivers in dementia cafés.

BACKGROUND AND AIMS: Several studies have shown the effectiveness and diversity of dementia cafés, whereas there are few published articles in academic research focusing on what persons with dementia and their family caregivers need and whether the services provided satisfy their needs. This study aimed to identify the needs of persons with dementia and their family caregivers participating in dementia cafés in Japan. METHODS: Interviews and participant observations were conducted in nine dementia cafés. Study participants were persons with dementia, their caregivers, and the staff in dementia cafés. Data were analysed using qualitative content analysis. RESULTS AND DISCUSSION: A total of 24 participants were recruited. Needs for persons with dementia were subdivided into five categories: to express their feelings about their current condition; to be accommodated through consideration of their physical and cognitive status; for changes in their health conditions to be noticed; to enjoy entertainment; and to keep in touch with others inside and outside of the dementia café. Needs for family caregivers were subdivided into four subcategories: to express their feelings such as anxiety and guilt and complaints regarding caregiving; to consult about difficulties in daily life; to listen to other family caregivers' experiences; and to keep in touch with others inside and outside of the dementia café. The needs of persons with dementia and family caregivers differ partly. CONCLUSIONS: Dementia cafés should create programmes and comfortable environments answering to the differences of their needs.

Quantitative Analysis of the Self-Assembly Process of Hexagonal PtII Macrocyclic Complexes: Effect of the Solvent and the Components.

The self-assembly process of three PtII -linked hexagonal macrocycles consisting of dinuclear PtII complexes and organic ditopic ligands was investigated in polar and less polar solvents by a recently developed approach: quantitative analysis of the self-assembly process (QASAP). In polar CD3 NO2 , for all the three macrocycles, an ML2 complex was the dominant intermediate during self-assembly, as a result of high positive allosteric cooperativity for the ligand exchange on the PtII centers of the dinuclear PtII complexes. On the other hand, in less polar CD2 Cl2 , the self-assembly process was affected by the components. For two of the three macrocycles, the chainlike oligomers that contain fewer metals and ligands than the corresponding macrocycles grew with time and the type of the chainlike intermediates formed correlated with the allostericity of the two binding sites in the organic ditopic ligands. In every case, no long oligomers containing more components than the macrocycles themselves were produced during the self-assembly even though free rotation around single bonds in the chainlike oligomers allows them to adopt various conformations that do not facilitate the cyclization. This result suggests that electrostatic and/or steric factors besides rigidity of the components make the cyclization advantageous not only thermodynamically but also kinetically.

Self-Assembly Process of Dodecanuclear Pt(II)-Linked Cyclic Hexagon.

The self-assembly process of a Pt(II)-linked hexagonal macrocycle consisting of six linear dinuclear Pt(II) units and six organic ditopic bent ligands was investigated. The process was monitored by (1)H NMR, and the intermediates in the self-assembly were analyzed by the n-k analysis. It was found that a 1:2 complex of a dinuclear Pt(II) unit and an organic ditopic ligand was exclusively observed as an intermediate with a certain lifetime and that the reaction of the 1:2 complex is the rate-determining step in the supramolecular macrocycle formation. The key 1:2 complex was unambiguously characterized by (1)H and DOSY NMR and ESI-TOF mass measurement.

Multifaceted well-being experienced by community dwelling older adults engaged in volunteering activities of frailty prevention in Japan.

AIM: This study aims to elucidate what volunteering activities mean for older adults in Japan by analyzing their emotions and evaluations from hedonic (e.g., happiness), eudaimonic (e.g., self-growth), and social (e.g., social coherence) well-being. METHODS: The qualitative research was conducted to describe the subjective experience of older adults' volunteering activities (frailty checkups) in the community-setting. Eight older adults were interviewed about their experiences during these activities. The interview data were analyzed from two assumption frameworks: first, three aspects of well-being, and second, timeframes of well-being, during the activity, medium-term, and long-term. Previous studies have not focused on the polysemy or the timeframe of well-being. RESULTS: Our results showed that hedonic, eudaimonic, and social well-being are not independent, but overlap. Furthermore, even if older adults experience certain emotions at a point of time, they may change in the long term. This implies that it is important to analyze older adults' feelings and experiences from not only one aspect but from different perspectives and measure their feelings not just at a particular moment but in the long term. This is the first empirical study to examine qualitatively the holistic experiences of well-being among older adults who volunteer. CONCLUSIONS: We conclude that this study is unique in that it attempted to associate empirically the experiences of older adults during volunteering with their general psychological status of well-being. These findings could help make volunteering activities more meaningful for older adults and create or promote an active community. Geriatr Gerontol Int 2024; 24: 273-278.

Prediction of Mental Health Problem Using Annual Student Health Survey: Machine Learning Approach.

BACKGROUND: One of the reasons why students go to counseling is being called on based on self-reported health survey results. However, there is no concordant standard for such calls. OBJECTIVE: This study aims to develop a machine learning (ML) model to predict students' mental health problems in 1 year and the following year using the health survey's content and answering time (response time, response time stamp, and answer date). METHODS: Data were obtained from the responses of 3561 (62.58%) of 5690 undergraduate students from University A in Japan (a national university) who completed the health survey in 2020 and 2021. We performed 2 analyses; in analysis 1, a mental health problem in 2020 was predicted from demographics, answers for the health survey, and answering time in the same year, and in analysis 2, a mental health problem in 2021 was predicted from the same input variables as in analysis 1. We compared the results from different ML models, such as logistic regression, elastic net, random forest, XGBoost, and LightGBM. The results with and without answering time conditions were compared using the adopted model. RESULTS: On the basis of the comparison of the models, we adopted the LightGBM model. In this model, both analyses and conditions achieved adequate performance (eg, Matthews correlation coefficient [MCC] of with answering time condition in analysis 1 was 0.970 and MCC of without answering time condition in analysis 1 was 0.976; MCC of with answering time condition in analysis 2 was 0.986 and that of without answering time condition in analysis 2 was 0.971). In both analyses and in both conditions, the response to the questions about campus life (eg, anxiety and future) had the highest impact (Gain 0.131-0.216; Shapley additive explanations 0.018-0.028). Shapley additive explanations of 5 to 6 input variables from questions about campus life were included in the top 10. In contrast to our expectation, the inclusion of answering time-related variables did not exhibit substantial improvement in the prediction of students' mental health problems. However, certain variables generated based on the answering time are apparently helpful in improving the prediction and affecting the prediction probability. CONCLUSIONS: These results demonstrate the possibility of predicting mental health across years using health survey data. Demographic and behavioral data, including answering time, were effective as well as self-rating items. This model demonstrates the possibility of synergistically using the characteristics of health surveys and advantages of ML. These findings can improve health survey items and calling criteria.