Health behaviors influencing depressive symptoms in older Koreans living alone: secondary data analysis of the 2014 Korean longitudinal study of aging

Kim, Heejung; Hong, Seong Uk; Lee, Sangeun

doi:10.1186/s12877-018-0882-4

Cited by 30 publications

(23 citation statements)

References 45 publications

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“…Similar to previous studies, we confirmed that low SES (Socioeconomic status) especially lower education levels were more likely to have depressive symptoms [41]. And the health conditions including ADL disability, self-rate health, and the number of chronic diseases were strongly associated with depressive symptoms, similar to previous reports [10, 18, 22, 41].…”

Section: Discussionsupporting

confidence: 91%

Association of living arrangements with depressive symptoms among older adults in China: a cross-sectional study

et al. 2019

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Background Depression is regarded as a major public health concern in our society. While living arrangements as a structural factor of social support may contribute to older adults’ depression. Our study aims to investigate the association between living arrangements and depressive symptoms among older adults in the whole China, and to explore whether such influences differ by genders. Methods Data were obtained from the 2015 China Health and Retirement Longitudinal Study. The sample was comprised of 6001 individuals aged ≥60 years. Depressive symptoms were measured by the 10-item Short-Form Center for Epidemiological Studies Depression. Independent variables were divided into 4 groups, considering living with/without a spouse and living with/without a child. The multivariate logistic regression was used to estimate the relationship between living arrangements and depressive symptoms in four models. Results Compared with living only with a spouse, people living with a spouse and child, or living alone were more likely to have depressive symptoms (odds ratio = 1.23 95% CI 1.06–1.42 and 1.40 95% CI 1.03–1.92, respectively). Women were more associated with depressive symptoms (odds ratio = 2.13), but there were no significant associations between living arrangements and depressive symptoms among women. Men living with a spouse and a child had stronger positively depressive symptoms (odds ratio = 1.37). Conclusions Older adults living alone, or living with both a child and spouse were more likely to have depressive symptoms. It is important to provide more social services for those older adult, particularly for men living with a spouse and child. Electronic supplementary material The online version of this article (10.1186/s12889-019-7350-8) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 91%

Association of living arrangements with depressive symptoms among older adults in China: a cross-sectional study

et al. 2019

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“…Globally, it was the third leading cause of years lived with disability in 2015, increasing by 18.4% between 2005 and 2015 [1]. In particular, older adults living alone are most vulnerable to depression compared with those living with others [2,3] because 30.2% of Koreans living alone are reported to have significant symptoms of depression [4]. In Korea, the economic burden of individuals with depression increased by about 27.1% from 2009 to 2013, with an estimated total cost of Korean $27.1 billion [5].…”

Section: Introductionmentioning

confidence: 99%

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Kim¹,

Lee²,

Lee³

et al. 2019

JMIR Mhealth Uhealth

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BackgroundAlthough geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devices could be used to collect data to classify older adults into depression groups.ObjectiveThe objective of this study was to develop a machine learning algorithm to predict the classification of depression groups among older adults living alone. We focused on utilizing diverse data collected through a survey, an Actiwatch, and an EMA report related to depression.MethodsThe prediction model using machine learning was developed in 4 steps: (1) data collection, (2) data processing and representation, (3) data modeling (feature engineering and selection), and (4) training and validation to test the prediction model. Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models).ResultsOn the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960).ConclusionsThis study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor.

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“…Psychological and psychosocial factors such as depression and stress can also cause metabolic disease [ 8 , 14 ]. The relationship between socioeconomic background and metabolic disease has been reported in previous studies [ 10 , 12 , 14 ]. Efforts to proactively identify and respond to modifiable risk factors for metabolic disease may serve as factors that prevent metabolic disorders and improve quality of life.…”

Section: Discussionmentioning

confidence: 61%

“…A reduction in physical function can be a causal factor of stress, depression, and suicide through a reduction in activity and psychological withdrawal due to difficulty with interpersonal relationships [ 11 ]. Physical illness can be a factor that induces depression and a factor that worsens physical problems when appropriate intervention for depression has not taken place [ 12 , 13 ]. Physical health problems, such as metabolic diseases and CVDs, can lower self-esteem as well as cause psychological or psychopathological problems such as depression, anxiety, stress, and suicidal impulses [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Mental Health Status of Adults with Cardiovascular or Metabolic Diseases by Gender

Kwak

Kim

Kwon

et al. 2021

IJERPH

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This study aimed to compare mental health in people with cardiovascular or metabolic diseases and the general adult population in each gender. Cardiovascular and metabolic diseases affect mental health, and the prognosis and incidence rates of these diseases differ by age. To date, studies comparing mental health in adults with cardiovascular or metabolic diseases by gender and with the general population have been insufficient. This work is a cross-sectional study. Data from 9828 men and 13,389 women aged 19 years or older from the Korea National Health and Nutrition Examination Survey V and VI (2010–2013) were used. Men and women with cardiovascular diseases showed high risks of stress, depression, and suicidal ideation. Men with metabolic diseases had worse mental health in relation to depression and suicidal ideation, and women with metabolic diseases in relation to stress and depression, indicating a need for intervention and management of mental health by gender for these individuals. There is a need to develop a clear perception and understanding, both among practitioners and the general population, of mental health issues associated with having cardiovascular or metabolic diseases. Active, specific health improvement and training programmes considering gender differences are necessary.

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Health behaviors influencing depressive symptoms in older Koreans living alone: secondary data analysis of the 2014 Korean longitudinal study of aging

Cited by 30 publications

References 45 publications

Association of living arrangements with depressive symptoms among older adults in China: a cross-sectional study

Association of living arrangements with depressive symptoms among older adults in China: a cross-sectional study

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Mental Health Status of Adults with Cardiovascular or Metabolic Diseases by Gender

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