Long-Term Impact of Delirium on the Risk of Dementia in Hospitalized Older Patients: A Real-World Multicenter Study.

BACKGROUND: The association between delirium and dementia has been suggested, but mostly in the postoperative setting. This study aims to explore this relationship in a broader inpatient population, leveraging extensive real-world data to provide a more generalized understanding. METHODS: In this retrospective cohort study, electronic health records of 11,970,475 hospitalized patients aged over 60 from nine institutions in South Korea were analyzed. Patients with and without delirium were identified, and propensity score matching (PSM) was used to create comparable groups. A 10-year longitudinal analysis was conducted using the Cox proportional hazards model, which calculated the hazard ratio (HR) and 95% confidence interval (CI). Additionally, a meta-analysis was performed, aggregating results from all nine medical institutions. Lastly, we conducted various subgroup and sensitivity analyses to demonstrate the consistency of our study results across diverse conditions. RESULTS: After 1:1 PSM, a total of 47,306 patients were matched in both the delirium and nondelirium groups. Both groups had a median age group of 75-79 years, with 43.1% being female. The delirium group showed a significantly higher risk of all dementia compared with the nondelirium group (HR: 2.70 [95% CI: 2.27-3.20]). The incidence risk for different types of dementia was also notably higher in the delirium group (all dementia or mild cognitive impairment, HR: 2.46 [95% CI: 2.10-2.88]; Alzheimer's disease, HR: 2.74 [95% CI: 2.40-3.13]; vascular dementia, HR: 2.55 [95% CI: 2.07-3.13]). This pattern was consistent across all subgroup and sensitivity analyses. CONCLUSIONS: Delirium significantly increases the risk of onset for all types of dementia. These findings highlight the importance of early detection of delirium and prompt intervention. Further research studies are warranted to investigate the mechanisms linking delirium and dementia.

Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network.

Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to enhance accuracy of the biomarker discovery. However, previous ML-based studies often overlook interactions between proteins, which are crucial in complex disorders like dementia. While protein-protein interactions (PPIs) have been used in network models, these models often fail to fully capture the diverse properties of PPIs due to their local awareness. This drawback increases the chance of neglecting critical components and magnifying the impact of noisy interactions. In this study, we propose a novel graph-based ML model for dementia subtype diagnosis, the graph propagational network (GPN). By propagating the independent effect of plasma proteins on PPI network, the GPN extracts the globally interactive effects between proteins. Experimental results showed that the interactive effect between proteins yielded to further clarify the differences between dementia subtype groups and contributed to the performance improvement where the GPN outperformed existing methods by 10.4% on average.

Ethnic differences in the prevalence of amyloid positivity and cognitive trajectories.

INTRODUCTION: We investigated the prevalence of amyloid beta (Aß) positivity (+) and cognitive trajectories in Koreans and non-Hispanic Whites (NHWs). METHODS: We included 5121 Koreans from multiple centers across South Korea and 929 NHWs from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants underwent Aß positron emission tomography and were categorized into cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia stages. Age, sex, education, and apolipoprotein E. genotype were adjusted using multivariable logistic regression and stabilized inverse probability of treatment weights based on the propensity scores to mitigate imbalances in these variables. RESULTS: The prevalence of Aß+ was lower in CU Koreans than in CU NHWs (adjusted odds ratio 0.60). Aß+ Koreans showed a faster cognitive decline than Aß+ NHWs in the CU (B = -0.314, p = .004) and MCI stages (B = -0.385, p < .001). DISCUSSION: Ethnic characteristics of Aß biomarkers should be considered in research and clinical application of Aß-targeted therapies in diverse populations. HIGHLIGHTS: Koreans have a lower prevalence of Aß positivity compared to NHWs in the CU stage. The effects of Alzheimer's risk factors on Aß positivity differ between Koreans and NHWs. Aß-positive (Aß+) Koreans show faster cognitive decline than Aß+ NHWs in the CU and MCI stages.

Assessing cognitive impairment and disability in older adults through the lens of whole brain white matter patterns.

INTRODUCTION: This study aimed to explore the potential of whole brain white matter patterns as novel neuroimaging biomarkers for assessing cognitive impairment and disability in older adults. METHODS: We conducted an in-depth analysis of magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) scans in 454 participants, focusing on white matter patterns and white matter inter-subject variability (WM-ISV). RESULTS: The white matter pattern ensemble model, combining MRI and amyloid PET, demonstrated a significantly higher classification performance for cognitive impairment and disability. Participants with Alzheimer's disease (AD) exhibited higher WM-ISV than participants with subjective cognitive decline, mild cognitive impairment, and vascular dementia. Furthermore, WM-ISV correlated significantly with blood-based biomarkers (such as glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]), and cognitive function and disability scores. DISCUSSION: Our results suggest that white matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making and determining cognitive impairment and disability. HIGHLIGHTS: The ensemble model combined both magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) and demonstrated a significantly higher classification performance for cognitive impairment and disability. Alzheimer's disease (AD) revealed a notably higher heterogeneity compared to that in subjective cognitive decline, mild cognitive impairment, or vascular dementia. White matter inter-subject variability (WM-ISV) was significantly correlated with blood-based biomarkers (glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]) and with the polygenic risk score for AD. White matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making processes and determining cognitive impairment and disability.

Sex-Specific Risk Factors and Clinical Dementia Outcomes for White Matter Hyperintensities in a large South Korean Cohort.

Objective: White matter hyperintensities (WMH) on brain MRI images are the most common feature of cerebral small vessel disease (CSVD). Studies have yielded divergent findings on the modifiable risk factors for WMH and WMH's impact on cognitive decline. Mounting evidence suggests sex differences in WMH burden and subsequent effects on cognition. Thus, we aimed to identify sex-specific modifiable risk factors for WMH. We then explored whether there were sex-specific associations of WMH to longitudinal clinical dementia outcomes. Methods: Participants aged 49-89 years were recruited at memory clinics and underwent a T2-weighted fluid-attenuated inversion recovery (FLAIR) 3T MRI scan to measure WMH volume. Participants were then recruited for two additional follow-up visits, 1-2 years apart, where clinical dementia rating sum of boxes (CDR-SB) scores were measured. We first explored which known modifiable risk factors for WMH were significant when tested for a sex-interaction effect. We additionally tested which risk factors were significant when stratified by sex. We then tested to see whether WMH is longitudinally associated with clinical dementia that is sex-specific. Results: The study utilized data from 713 participants (241 males, 472 females) with a mean age of 72.3 years and 72.8 years for males and females, respectively. 57.3% and 59.5% of participants were diagnosed with mild cognitive impairment (MCI) for males and females, respectively. 40.7% and 39.4% were diagnosed with dementia for males and females, respectively. Of the 713 participants, 181 participants had CDR-SB scores available for three longitudinal time points. Compared to males, females showed stronger association of age to WMH volume. Type 2 Diabetes was associated with greater WMH burden in females but not males. Finally, baseline WMH burden was associated with worse clinical dementia outcomes longitudinally in females but not in males. Discussion: Elderly females have an accelerated increase in cerebrovascular burden as they age, and subsequently are more vulnerable to clinical dementia decline due to CSVD. Additionally, females are more susceptible to the cerebrovascular consequences of diabetes. These findings emphasize the importance of considering sex when examining the consequences of CSVD. Future research should explore the underlying mechanisms driving these sex differences and personalized prevention and treatment strategies. Clinical trial registration: The BICWALZS is registered in the Korean National Clinical Trial Registry (Clinical Research Information Service; identifier, KCT0003391). Registration Date 2018/12/14.

Association of Geriatric Depressive Symptoms and Government-Initiated Senior Employment Program: A Population-Based Study.

OBJECTIVE: The impact of the government-initiated senior employment program (GSEP) on geriatric depressive symptoms is underexplored. Unearthing this connection could facilitate the planning of future senior employment programs and geriatric depression interventions. In the present study, we aimed to elucidate the possible association between geriatric depressive symptoms and GSEP in older adults. METHODS: This study employed data from 9,287 participants aged 65 or older, obtained from the 2020 Living Profiles of Older People Survey. We measured depressive symptoms using the Korean version of the 15-item Geriatric Depression Scale. The principal exposure of interest was employment status and GSEP involvement. Data analysis involved multiple linear regression. RESULTS: Employment, independent of income level, showed association with decreased depressive symptoms compared to unemployment (p<0.001). After adjustments for confounding variables, participation in GSEP jobs showed more significant reduction in depressive symptoms than non-GSEP jobs (ß=-0.968, 95% confidence interval [CI]=-1.197 to -0.739, p<0.001 for GSEP jobs, ß=-0.541, 95% CI=-0.681 to -0.401, p<0.001 for non-GSEP jobs). Notably, the lower income tertile in GSEP jobs showed a substantial reduction in depressive symptoms compared to all income tertiles in non-GSEP jobs. CONCLUSION: The lower-income GSEP group experienced lower depressive symptoms and life dissatisfaction compared to non-GSEP groups regardless of income. These findings may provide essential insights for the implementation of government policies and community-based interventions.

Factors in turnover intention of cardiothoracic surgery residents.

Increasing numbers of cardiothoracic surgery residents are resigning, without completing their training. This study analyzes how their turnover intention is related to the training environment, and individual psychological factors. Responses by 57 Korean cardiothoracic surgery residents were analyzed. Their levels of depression, anxiety, grit, and empathy, working conditions, the effect of someone's presence to discuss their concerns with, burnout, and turnover intention were identified as the research variables. Descriptive statistical analysis, correlation analysis, and structural equation modeling were used for data analysis. Burnout has the most significant relationship with turnover intention. It has a mediating effect on the influence of depression, grit (sustained interest), and working conditions, over turnover intention. Empathy, and the presence of someone to discuss concerns with, also affect turnover intention directly. The study also confirmed that grit and work satisfaction affect turnover intention indirectly, through burnout. The study identified both individual- and systemic-level factors for an effective training environment, to reduce cardiothoracic surgery residents' tendencies of leaving the residency program, and supporting them for greater satisfaction with their career choice. In order to resolve negative emotions such as burnout and depression, and foster empathy, a human resource development program for the residents' psychological support must be prepared. The program director should be adequately educated to take charge of the training program, oversee the residents' education and welfare, and perform the roles of role-model and mentor.

Mediating effect of depressive symptoms on the relationship between digital literacy and cognitive function in older adults.

Introduction: Although several studies have examined the individual relationships among digital literacy, cognitive function, and depressive symptoms, few have integrated all three factors into a single model. This study aimed to address this gap by investigating the mediating effect of depressive symptoms on the relationship between digital literacy and cognition. In doing so, we hoped to contribute to a more comprehensive understanding of the complex interplay among these variables and their implications for mental health and well-being. Methods: Participants were 7,988 older adults (65 years or older) who participated in the Living Profiles of Older People Survey 2020. The main type of exposure was digital literacy (communication, information, media, and online transaction literacy). The main outcomes were depressive symptoms measured using the Short Geriatric Depression Scale of Korean version and cognitive function measured using the Mini-Mental State Examination score. Multiple linear regression and mediation analyses were also performed. Results: After adjusting for covariates, our analysis found a significant association between digital literacy and both depressive symptoms and cognitive function (ß of four types of digital literacy and depressive symptoms = -0.123, -0.172, -0.702, and - 0.639, respectively; ß of four types of digital literacy and cognitive function = 2.102, 2.217, 1.711, and 1.436, respectively). Moreover, our study showed that depressive symptoms play a mediating role in the relationship between media and online transaction literacy and cognitive function (95% CI of indirect effects = 0.0647-0.1212 and 0.0639-0.1277, respectively), implying an indirect pathway (digital literacy, depressive symptoms, and cognitive function). Discussion: This study sheds light on the relationship between digital literacy, depressive symptoms, and cognitive function in older adults. We found that depressive symptoms mediated the association between specific aspects of digital literacy (online transaction and media literacy) and cognitive function. Our results indicate that community-based digital literacy programs could be effective in reducing depression and preserving or improving cognitive function in older adults.

Relationships of walking activity with depressed mood and suicidal ideation among the middle-aged Korean population: a nationwide cross-sectional study.

Introduction: The suicide rate of middle-aged adults has increased rapidly, which is a significant public health concern. A depressed mood and suicidal ideation are significant risk factors for suicide, and non-pharmacological interventions such as exercise therapy have been suggested as potential treatments. Walking is a feasible and accessible form of exercise therapy for middle-aged adults. Methods: We conducted a study based on the Seventh Korea National Health and Nutrition Examination Survey (2016-2018) data of 6,886 general middle-aged adults in South Korea to investigate the relationships of walking exercise with depressed mood and suicidal ideation. Multiple logistic regression analysis was used to adjust for confounding variables. Sampling weights were applied to obtain estimates for the general Korean population. Results: Participants who walked ≥5 days per week had a significantly lower odds ratio (OR) for depressed mood [OR = 0.625, 95% confidence interval (CI): 0.424-0.921, p = 0.018] and suicidal ideation (OR = 0.252, 95% CI: 0.125-0.507, p < 0.001) compared to those who never walked, regardless of the duration of exercise. The same results were obtained for males after stratifying the data by sex and suicidal ideation was associated with walking in females. Conclusion: Regular walking exercise was associated with diminished mental health problems in middle-aged adults. Light walks may serve as a useful starting point for patients with serious mental health issues, such as suicidal ideation.

Moderators of the Association Between Contact Frequency With Non-Cohabitating Adult Children and Depressive Symptoms Among Community-Dwelling Older Adults.

OBJECTIVE: Contact frequency with adult children plays a critical role in late-life depression. However, evidence on possible moderators of this association remains limited. Moreover, considering alterations in contact modes after the coronavirus disease-2019 pandemic, there is a need to investigate this association post-pandemic to develop effective therapeutic interventions. METHODS: This study included 7,573 older adults who completed the Living Profiles of the Older People Survey in Korea. Participants' contact frequency and depressive symptoms were analyzed. Regression analysis was performed after adjusting for covariates. The moderating effects of variables were verified using a process macro. RESULTS: Multivariable logistic regression analysis revealed that infrequent face-to-face (odd ratio [OR]=1.86, 95% confidence interval [CI]=1.55-2.22) and non-face-to-face contact (OR=1.23, 95% CI=1.04-1.45) in the non-cohabitating adult children group was associated with a higher risk of late-life depression compared to that in the frequent contact group. Linear regression analysis indicated consistent results for face-to-face and non-face-to-face contact (estimate=0.458, standard error [SE]=0.090, p<0.001 and estimate=0.236, SE= 0.074, p=0.001, respectively). Moderation analysis revealed that the association between late-life depression and frequency of face-toface contact was moderated by age, household income quartiles, number of chronic diseases, physical activity frequency, presence of spouse, nutritional status, and whether the effect of frequency of non-face-to-face contact on late-life depression was increased by participation in social activity, frequent physical activity, and good cognitive function (p for interaction<0.05). CONCLUSION: Frequent contact with non-cohabitating children lowers the risk of depression later in life. Several variables were identified as significant moderators of contact frequency and depression symptoms.

Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K+ Cohorts Consortium study.

BACKGROUND: As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD). METHODS: The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure. RESULTS: We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD. CONCLUSIONS: As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.

Comparison of automated quantification of amyloid deposition between PMOD and Heuron.

Several programs are widely used for clinical and research purposes to automatically quantify the degree of amyloid deposition in the brain using positron emission tomography (PET) images. Given that very few studies have investigated the use of Heuron, a PET image quantification software approved for clinical use, this study aimed to compare amyloid deposition values quantified from 18F-flutemetamol PET images using PMOD and Heuron. Amyloid PET data obtained from 408 patients were analysed using each quantitative program; moreover, the standardized uptake value ratios (SUVRs) of target areas were obtained by dividing the standardized uptake value (SUV) of the target region by the SUV of cerebellar grey matter as a reference. Compared with PMOD, Heuron yielded significantly higher SUVRs for all target areas (paired sample t-test, p < 0.001), except for the PC/PCC (p = 0.986). However, the Bland-Altman plot analysis indicated that the two quantitative methods may be used interchangeably. Moreover, receiver operating characteristic curve analysis revealed no significant between-method difference in the performance of the SUVRs in evaluating the visual positivity of amyloid deposits (p = 0.948). In conclusion, Heuron and PMOD have comparable performance in quantifying the degree of amyloid deposits in PET images.

Relationships of Physical Activity, Depression, and Sleep with Cognitive Function in Community-Dwelling Older Adults.

This cross-sectional, observational study aimed to integrate the analyses of relationships of physical activity, depression, and sleep with cognitive function in community-dwelling older adults using a single model. To this end, physical activity, sleep, depression, and cognitive function in 864 community-dwelling older adults from the Suwon Geriatric Mental Health Center were assessed using the International Physical Activity Questionnaire, Montgomery-Asberg Depression Rating Scale, Pittsburgh Sleep Quality Index, and Mini-Mental State Examination for Dementia Screening, respectively. Their sociodemographic characteristics were also recorded. After adjusting for confounders, multiple linear regression analysis was performed to investigate the effects of physical activity, sleep, and depression on cognitive function. Models 4, 5, 7, and 14 of PROCESS were applied to verify the mediating and moderating effects of all variables. Physical activity had a direct effect on cognitive function (effect = 0.97, p < 0.01) and indirect effect (effect = 0.36; confidence interval: 0.18, 0.57) through depression. Moreover, mediated moderation effects of sleep were confirmed in the pathways where physical activity affects cognitive function through depression (F-coeff = 13.37, p < 0.001). Furthermore, these relationships differed with age. Thus, the associations among physical activity, depression, and sleep are important in interventions for the cognitive function of community-dwelling older adults. Such interventions should focus on different factors depending on age.

The moderating effect of cognitive function on the association between social support and depressive symptoms among community-dwelling older adults: Cross-sectional and longitudinal analyses.

BACKGROUND: The moderating effect of cognitive function on the association between social support and late-life depressive symptoms has not been thoroughly investigated. Identifying cognitive function as a possible moderator of this association might help plan community-based interventions for late-life depressive symptoms. METHODS: Participants were community-dwelling older adults who visited a community-based mental health center. The ENRICHD Social Support Instrument and the Montgomery-Asberg Depression Rating Scale were used to evaluate social support and depressive symptoms, respectively. Cognitive function was assessed using the Korean version of the Mini-Mental State Examination. Data from 1088 and 506 participants were included in the cross-sectional and longitudinal analyses, respectively. Multiple linear regression analysis was performed to assess the effects of social support on depressive symptoms and the possible moderating effect of cognition. RESULTS: After adjusting for possible confounders, greater social support at baseline was associated with fewer depressive symptoms in both cross-sectional (estimate = -0.25 standard error [SE] = 0.03, P < 0.001) and longitudinal analyses (estimate = -0.11, SE = 0.05, P = 0.014). Moreover, the association between social support and depressive symptoms was significantly moderated by cognitive function (P for interaction < 0.001 for cross-sectional analysis, and P for interaction = 0.011 for longitudinal analysis). LIMITATIONS: The tool for assessing social support was self-reported. There may have been a selection bias in the study sample. CONCLUSIONS: Greater social support was associated with fewer late-life depressive symptoms in both analyses. However, social support may have less benefits for alleviating depressive symptoms in older adults with cognitive decline.

Independent replication of advanced brain age in mild cognitive impairment and dementia: detection of future cognitive dysfunction.

We previously developed a novel machine-learning-based brain age model that was sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility using independent clinical data. We recruited 650 participants from South Korean memory clinics to undergo magnetic resonance imaging and clinical assessments. We employed a pretrained brain age model that used data from an independent set of largely Caucasian individuals (n = 757) who had no or relatively low levels of amyloid as confirmed by positron emission tomography (PET). We investigated the association between brain age residual and cognitive decline. We found that our pretrained brain age model was able to reliably estimate brain age (mean absolute error = 5.68 years, r(650) = 0.47, age range = 49-89 year) in the sample with 71 participants with subjective cognitive decline (SCD), 375 with mild cognitive impairment (MCI), and 204 with dementia. Greater brain age was associated with greater amyloid and worse cognitive function [Odds Ratio, (95% Confidence Interval {CI}): 1.28 (1.06-1.55), p = 0.030 for amyloid PET positivity; 2.52 (1.76-3.61), p < 0.001 for dementia]. Baseline brain age residual was predictive of future cognitive worsening even after adjusting for apolipoprotein E e4 and amyloid status [Hazard Ratio, (95% CI): 1.94 (1.33-2.81), p = 0.001 for total 336 follow-up sample; 2.31 (1.44-3.71), p = 0.001 for 284 subsample with baseline Clinical Dementia Rating ≤ 0.5; 2.40 (1.43-4.03), p = 0.001 for 240 subsample with baseline SCD or MCI]. In independent data set, these results replicate our previous findings using this model, which was able to delineate significant differences in brain age according to the diagnostic stages of dementia as well as amyloid deposition status. Brain age models may offer benefits in discriminating and tracking cognitive impairment in older adults.

Structural covariance changes in major cortico-basal ganglia and thalamic networks in amyloid-positive patients with white matter hyperintensities.

Synergistic effects of amyloid deposition and cerebral small vessel disease (CSVD) on the systematic disruption of large-scale brain anatomical organization are not well known. We investigated the brain structural covariance network (SCN) in 245 cognitively impaired older adults with the information of amyloid deposition and CSVD represented by white matter hyperintensities (WMH). We stratified the participants into 4 groups based on amyloid burden (A+/A -) and WMH severity (W+/W-). Using source-based morphometry analysis, we selected 13 independent components (ICs) in functional brain networks. SCNs between ICs were defined using Pearson correlations between individual weights; SCNs of the A+W+ group were compared with those of other groups using Fisher's r-to-z transformation. Our results revealed that SCN characteristics related to amyloid burden with CSVD could be represented by decreased intra- and increased cortico-subcortical inter-network connectivity in the salience (SN) and default mode networks (DMN), decreased cortico-subcortical internetwork connectivity in the central executive network (CEN), and altered internetwork connectivity among DMN-SN-CEN. Amyloid deposition and CSVD maybe associated with altered connectivity in structural networks in the brain and should be considered when assessing network disruption.

The effectiveness of a motivational enhancement smartphone application promoting lifestyle improvement for brain health: A randomized controlled trial.

Multidomain lifestyle modification is considered an effective intervention for dementia prevention due to its multifactorial nature. Recognizing that participants' activity adherence is crucial for successful lifestyle modification, our team developed a smartphone application to enhance motivation toward brain health behavior based on gamification theory, which influences behaviors by enhancing motivation. The developed smartphone application has two main functions: delivering supporting videos from family, friends, and medical staff, and self-evaluation. We assessed the effectiveness of this smartphone application with regard to brain health behavior. In this eight-week randomized controlled trial, 40 participants were randomly assigned to the smartphone application intervention group or control group. The primary outcome reflected participants' brain health behavior in three categories: physical activity, cognitive activity, and healthy diet. Each brain health behavior was measured by the Korean version of the Global Physical Activity Questionnaire, Cognitive Activity Score, and Mediterranean DASH Intervention for Neurodegenerative Delay Diet Score. Furthermore, we investigated the change in motivation, measured by the Situational Motivation Scale. Additionally, we reviewed participants' self-record diary during the first, fourth, and eighth week of intervention for evaluation of adherence. The intervention group was found to have a positive association with moderate metabolic equivalent activities (P = 0.01) and intrinsic motivation change (P = 0.01). There was a significant difference between the intervention and control groups regarding average physical activity at week 8 (P = 0.037). An eight-week intervention with the smartphone application induced physical activity of moderate intensity through intrinsic motivation enhancement. We suggest that the motivation enhancement application could be an efficient option for maintaining and promoting psychosocial health behavior. This smartphone application can be applied to any other disease that needs behavioral change. Through the application, a broader spectrum of the population, regardless of time, space, and human resources, can benefit from community health services. Trial registration: Korean National Clinical Trial Registry CRIS identifier: KCT0005231.

Differential Effects of Sleep Disturbance and Malnutrition on Late-Life Depression Among Community-Dwelling Older Adults.

Background: Late-life depression is a complex phenomenon that cannot be fully understood simply as depression occurring in older adults, prompting researchers to suggest that it represents a component of geriatric syndrome. Given the inherent complexity and multifactorial nature of geriatric syndrome, understanding the interactions between the comorbid conditions involved is important for establishing appropriate preventive strategies. While sleep disturbance and malnutrition are common manifestations of geriatric syndrome, they have also been regarded as indicators of late-life depression. However, the differential effects of sleep disturbance and malnutrition on late-life depression and their interrelationships remain unclear. Objective: The objective of this study was to examine the effects of sleep disturbance and malnutrition on depression and the interactions between them among community-dwelling older adults. Methods: Sleep disturbance and malnutrition in 1,029 community-dwelling older adults from Suwon Geriatric Mental Health Center were assessed using the Pittsburgh Sleep Quality Index (PSQI) and Mini Nutritional Assessment (MNA), respectively. The Korean version of the Short Form of the Geriatric Depression Scale (SGDS-K) was used to evaluate depressive symptoms. Sociodemographic parameters were recorded. A multiple linear regression analysis was conducted to examine the effects of sleep and nutrition on depressive symptoms after adjusting for covariates. The effect size and conditional effects of sleep disturbance and malnutrition on late-life depression were assessed using Cohen's f2 values and the Johnson-Neyman technique, respectively. Results: After possible confounders were adjusted, the SGDS-K score was positively associated with the PSQI score (standardized beta = 0.166, P < 0.001) and negatively associated with the MNA score (standardized beta = -0.480, P < 0.001). The local effect size of the associations was small for PSQI and medium for MNA. A significant interaction was observed between the PSQI and MNA scores. The result of the Johnson-Neyman technique indicated that the influence of PSQI on SGDS-K became weaker and insignificant as nutritional status worsened. However, the association between the MNA and SGDS-K scores was significant regardless of PSQI. Conclusion: Both sleep disturbance and malnutrition were significantly associated with late-life depression, although malnutrition may be more critically associated with depression than sleep disturbance in community-dwelling older adults.

Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia.

There is a proven correlation between the severity of dementia and reduced brain volumes. Several studies have attempted to use activity data to estimate brain volume as a means of detecting reduction early; however, raw activity data are not directly interpretable and are unstructured, making them challenging to utilize. Furthermore, in the previous research, brain volume estimates were limited to total brain volume and the investigators were unable to detect reductions in specific regions of the brain that are typically used to characterize disease progression. We aimed to evaluate volume prediction of 116 brain regions through activity data obtained combining time-frequency domain- and unsupervised deep learning-based feature extraction methods. We developed a feature extraction model based on unsupervised deep learning using activity data from the National Health and Nutrition Examination Survey (NHANES) dataset (n = 14,482). Then, we applied the model and the time-frequency domain feature extraction method to the activity data of the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer's disease Study (BICWALZS) datasets (n = 177) to extract activity features. Brain volumes were calculated from the brain magnetic resonance imaging of the BICWALZS dataset and anatomically subdivided into 116 regions. Finally, we fitted linear regression models to estimate each regional volume of the 116 brain areas based on the extracted activity features. Regression models were statistically significant for each region, with an average correlation coefficient of 0.990 ± 0.006. In all brain regions, the correlation was > 0.964. Particularly, regions of the temporal lobe that exhibit characteristic atrophy in the early stages of Alzheimer's disease showed the highest correlation (0.995). Through a combined deep learning-time-frequency domain feature extraction method, we could extract activity features based solely on the activity dataset, without including clinical variables. The findings of this study indicate the possibility of using activity data for the detection of neurological disorders such as Alzheimer's disease.

Associations of actigraphy derived rest activity patterns and circadian phase with clinical symptoms and polysomnographic parameters in chronic insomnia disorders.

We explored the associations of actigraphy-derived rest-activity patterns and circadian phase parameters with clinical symptoms and level 1 polysomnography (PSG) results in patients with chronic insomnia to evaluate the clinical implications of actigraphy-derived parameters for PSG interpretation. Seventy-five participants underwent actigraphy assessments and level 1 PSG. Exploratory correlation analyses between parameters derived from actigraphy, PSG, and clinical assessments were performed. First, participants were classified into two groups based on rest-activity pattern variables; group differences were investigated following covariate adjustment. Participants with poorer rest-activity patterns on actigraphy (low inter-day stability and high intra-daily variability) exhibited higher insomnia severity index scores than participants with better rest-activity patterns. No between-group differences in PSG parameters were observed. Second, participants were classified into two groups based on circadian phase variables. Late-phase participants (least active 5-h and most active 10-h onset times) exhibited higher insomnia severity scores, longer sleep and rapid eye movement latency, and lower apnea-hypopnea index than early-phase participants. These associations remained significant even after adjusting for potential covariates. Some actigraphy-derived rest-activity patterns and circadian phase parameters were significantly associated with clinical symptoms and PSG results, suggesting their possible adjunctive role in deriving plans for PSG lights-off time and assessing the possible insomnia pathophysiology.