Noriki Yamaya

Amid the progression of an aging society, it is essential to develop a long-term predictive model capable of distinguishing between older adults with high and low risks of requiring Long-Term Care Insurance (LTCI) services. However, previous models were limited in that they could not simultaneously examine multiple and heterogeneous relationships, despite the fact that risk factors interact in complex and multifaceted ways. In addition, the existing model was restricted to a short-term prediction span. This study aimed to develop a predictive model for LTCI certification using Kihon Checklist data and a 6-year follow-up data with a decision tree analysis. This study was designed as a retrospective cohort, longitudinal, and observational study. Data were obtained from 3263 individuals aged 65 and older in a community-dweller. The dependent variable was LTCI certification status after 6 years. Independent variables included 6 Kihon Checklist domains, age, and gender. Decision tree analysis and a confusion matrix were used to make and evaluate the model. The decision tree model identified age, gender, and depression risk as key predictive factors. The model demonstrated moderate specificity (79.4%) and high negative predictive value (97.1%) but limited sensitivity (52.2%) and positive predictive value (11.1%). These findings indicate that the model performs better in identifying individuals unlikely to require LTCI certification than in detecting future LTCI users. Although it may provide preliminary population-level risk stratification over a 6-year horizon, its predictive performance is insufficient for standalone clinical or policy decision-making, and further refinement and validation are required.

This study aimed to identify predictive risk patterns for older individuals who are likely or unlikely to reverse from frailty 3 years later using the Kihon Checklist (KCL) and a machine learning model. Data were collected from community-dwelling individuals aged 65 and over with frailty but without long-term care at baseline. A decision tree analysis revealed that the cognitive function domain of the KCL was the primary determinant of frailty reversal. Among those with low cognitive function, low physical activity predicted remaining frailty status 3 years later. The derived model achieved a specificity of 81.0% and a precision of 82.6% in predicting individuals with remaining persistent frailty. These findings suggest that the pattern combining cognitive and physical risk factors plays a key role in predicting frailty outcomes, and that the proposed model may be useful for screening individuals who require targeted interventions.

Ensuring healthy aging and extending health span is critical, particularly in aging societies. This study analyzed patterns of risk for long-term care (LTC) certification within 3 years in rapidly aging areas of Japan using the Kihon Checklist (KCL) and machine learning models. Data from adults aged 65 years or older in Iiyama City (aging rate 37.0%) were analyzed using Exhaustive Chi-squared Automatic Interaction Detector decision trees. The dependent variable was LTC certification, and independent variables included age, sex, and six KCL domains: physical strength, nutrition, oral function, isolation, memory, and mood. Three risk patterns demonstrated consistent results across training and evaluation datasets. Age was the strongest determinant of LTC certification. Among those aged 80 years or older, low cognitive function and depression were key risk factors, while younger groups showed stronger associations with physical weakness. Results suggest age-specific segmentation of populations is crucial for designing effective interventions to prevent LTC certification.

Despite the significant health consequences of anxiety, the neural basis of regulation for personal anxious events is not well understood. We examined brain activity and functional connectivity during cognitive emotion regulation strategies (reappraisal and acceptance) for personal anxious events. Functional MRI (fMRI) data were obtained while 35 college students were thinking about (the control condition), reappraising, or accepting their own anxiety-provoking situations. Although reappraisal and acceptance decreased anxiety, no statistically significant differences were observed in the brain activation levels between cognitive emotion regulation strategies and the control condition. However, acceptance decreased activation in the posterior cingulate cortex and precuneus more than reappraisal. Moreover, functional connectivity with the amygdala and ventral anterior insula distinguished the emotion regulation strategies for anxiety. Reappraisal showed stronger negative functional connectivity with the amygdala and cognitive control regions than other strategies. In addition, reappraisal had negative functional coupling between the ventral anterior insula and temporal pole compared with acceptance. In contrast, acceptance showed stronger positive functional coupling between the ventral anterior insula and precentral and postcentral gyrus compared with the control condition. Our findings contribute to the understanding of emotion regulation processes by revealing the brain activity and functional connectivity patterns in reappraisal and acceptance for personal anxious events.

Objectives: A single session of brief focused attention meditation (FAM) has a state effect, which temporarily enhances response inhibition processes. However, previous research has two unanswered questions: (i) How long does the state effect last? (ii) How does effort toward FAM relate to the resulting state effect? Method: Thirty-nine healthy participants participated in two sessions: FAM and sham meditation (SHAM). The participants conducted each meditation for 10 min. The state effect on response inhibition processes was observed as Stroop task performance immediately before and after each meditation, and 20, 40, and 60 min after each meditation. In addition, the subjective effort toward meditation was evaluated using a questionnaire immediately after each meditation. Results: An analysis of variance revealed a significant interaction between session and time. In the post-hoc analysis, FAM showed significantly better Stroop task performance than the SHAM 60 min after meditation. Furthermore, using correlational analysis, we found that at 60 min, the higher the subjective effort, the better Stroop task performance. Conclusions: In contrast to previous findings, the state effect was not found immediately after FAM but instead 60 min after. The results can be partially explained by cognitive fatigue; that is, the FAM may have the state effect of preventing cognitive fatigue. This state effect is greater when the subjective effort is greater.

Introduction: Previous studies have revealed that one‐session focused attention meditation (FAM) can improve top‐down attention control, which is one of the factors of working memory capacity (WMC). In addition, FAM shares various neural substrates, including the dorsolateral prefrontal cortex (DLPFC), with WMC. Thus, we hypothesized that one‐session FAM would improve WMC by activating the DLPFC evoked by the top‐down attention control. In this study, we examined whether FAM modified WMC in individuals with little to no meditation experience.Methods: The participants were randomly assigned to either the FAM group (N = 13) or the control group (N = 17) who engaged in random thinking (i.e., mind‐wandering). Before and after each 15‐min intervention, the participants’ WMC was measured according to the total number of correct answers in the Reading Span Test. During each intervention, functional near‐infrared spectroscopy was employed to measure the blood flow in the participants’ DLPFC and determine the top‐down attention control effect.Results: In the FAM group, WMC increased, and the bilateral DLPFC was activated during the intervention. As for the control group, WMC decreased after the intervention, and the bilateral DLPFC was not activated during the intervention. A correlation was also found among all participants between the increase in WMC and the activation of the bilateral DLPFC.Conclusion: The study findings suggest that top‐down attention control during FAM can activate the bilateral DLPFC and increase WMC among meditation novices.