Childhood adversity and suicidal ideation in older Korean adults: unraveling the mediating mechanisms of mental health, physical health, and social relationships.

BACKGROUND: Suicide rates in older adults are much higher than those in younger age groups. Given the rapid increase in the proportion of older adults in Korea and the high suicide rate of this age group, it is worth investigating the mechanism of suicidal ideation for older adults. Generally, adverse childhood experiences are positively associated with suicidal ideation; however, it is not fully understood what mediating relationships are linked to the association between these experiences and current suicidal ideation. METHODS: The data from 685 older Korean adults were analyzed utilizing logistic regression, path analyses, and structural equation modeling. Based on our theoretical background and the empirical findings of previous research, we examined three separate models with mental health, physical health, and social relationship mediators. After that, we tested a combined model including all mediators. We also tested another combined model with mediation via mental health moderated by physical health and social relationships. RESULTS: The univariate logistic regression results indicated that childhood adversity was positively associated with suicidal ideation in older adults. However, multivariate logistic regression results demonstrated that the direct effect of childhood adversity became nonsignificant after accounting all variables. Three path models presented significant mediation by depression and social support in the association between childhood adversity and suicidal ideation. However, combined structural equation models demonstrated that only mediation by a latent variable of mental health problems was statistically significant. Social relationships moderated the path from mental health problems to suicidal ideation. CONCLUSIONS: Despite several limitations, this study has clinical implications for the development of effective strategies to mitigate suicidal ideation. In particular, effectively screening the exposure to adverse childhood experiences, early identification and treatment of depressive symptoms can play a crucial role in weakening the association between childhood adversity and suicidal ideation in older adults.

Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol.

INTRODUCTION: Despite the high prevalence of major depressive disorder (MDD) among the elderly population, the rate of treatment is low due to stigmas and barriers to medical access. Wearable devices such as smartphones and smartwatches can help to screen MDD symptoms earlier in a natural setting while forgoing these concerns. However, previous research using wearable devices has mostly targeted the younger population. By collecting longitudinal data using wearable devices from the elderly population, this research aims to produce prediction algorithms for late-life depression and to develop strategies that strengthen medical access in community care systems. METHODS AND ANALYSIS: The current cohort study recruited a subsample of 685 elderly people from the Korean Genome and Epidemiology Study-Cardiovascular Disease Association Study, a national large cohort established in 2004. The current study has been conducted over a 3-year period to explore the development patterns of late-life depression. Participants have completed three annual face-to-face interviews (baseline, the first follow-up and the second follow-up) and 2 years of app-based surveys and passive sensing data collection. All the data collection will end at the second follow-up interview. The collected self-report, observational and passive sensing data will be primarily analysed by machine learning. ETHICS AND DISSEMINATION: This study protocol has been reviewed and approved by the Yonsei University Mirae Campus Institutional Review Board (1041849-2 02 111 SB-180-06) in South Korea. All participants provided written informed consent. The findings of this research will be disseminated by academic publications and conference presentations.

Long-term effects of deep-learning digital therapeutics on pain, movement control, and preliminary cost-effectiveness in low back pain: A randomized controlled trial.

Objective: The present study aimed to compare the effects of a deep learning-based digital application with digital application physical therapy (DPT) and those of conventional physical therapy (CPT) on back pain intensity, limited functional ability, lower extremity weakness, radicular symptoms, limited range of motion (ROM), functional movement, quality of life, cost-effectiveness, and postintervention questionnaires for perceived transmission risk of COVID-19 and satisfaction results in 100 participants with low back pain (LBP). Methods: One hundred participants with LBP were randomized into either DPT or CPT groups, three times per week over four weeks. Outcome measures included the (1) Oswestry Disability Index, (2) Quebec Back Pain Disability Scale, (3) Roland-Morris Disability Questionnaire (RMDQ), (4) Numeric Pain Rating Scale, (5) functional movement screen (FMS), (6) short form-12, (7) lower extremity strength, (8) ROM of trunk flexion, extension, and bilateral side bending, (9) questionnaires for perceived transmission risk of COVID-19, (10) preliminary cost-effectiveness, and (11) postintervention satisfaction questionnaire results. The analysis of variance was conducted at p < 0.05. Results: Analysis of variance showed that DPT showed superior effects, compared to CPT on RMDQ, hip extensor strength, transmission risk of COVID-19, as well as satisfaction. Both groups showed significant improvement pre- and postintervention, suggesting that DPT is as effective as CPT, and was superior in preliminary cost-effectiveness and transmission risk of COVID-19. Conclusions: Our results provide novel, promising clinical evidence that DPT was as effective as CPT in improving structural and functional impairment, activity limitation, and participation restriction. Our results highlight the successful incorporation of DPT intervention for clinical outcome measures, lower extremity strength, trunk mobility, ADL improvement, QOL improvement, and FMS in LBP.

Childhood adversity and late-life depression: moderated mediation model of stress and social support.

Background: As life expectancy increases, understanding the mechanism for late-life depression and finding a crucial moderator becomes more important for mental health in older adults. Childhood adversity increases the risk of clinical depression even in old age. Based on the stress sensitivity theory and stress-buffering effects, stress would be a significant mediator, while social support can be a key moderator in the mediation pathways. However, few studies have tested this moderated mediation model with a sample of older adults. This study aims to reveal the association between childhood adversity and late-life depression in older adults, taking into consideration the effects of stress and social support. Methods: This study used several path models to analyze the data from 622 elderly participants who were never diagnosed with clinical depression. Results: We found that childhood adversity increases the odds ratio of depression by approximately 20% in older adults. Path model with mediation demonstrates that stress fully mediates the pathway from childhood adversity to late-life depression. Path model with moderated mediation also illustrates that social support significantly weakens the association between childhood adversity and perceived stress. Conclusion: This study provides empirical evidence to reveal a more detailed mechanism for late-life depression. Specifically, this study identifies one crucial risk factor and one protective factor, stress and social support, respectively. This brings insight into prevention of late-life depression among those who have experienced childhood adversity.