Do Somatic Symptoms Predict the Severity of Depression? A Validation Study of the Korean Version of the Depression and Somatic Symptoms Scale

Jeon, Sang Won; Yoon, Seo Young; Ko, Young Hoon; Joe, Sook Haeng; Kim, Yong Ku; Han, Changsu; Yoon, Ho Kyoung; Liu, Chia Yih

doi:10.3346/jkms.2016.31.12.2002

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…The data for a probable diagnosis of depression were based on the second measure at 2 weeks, during which the correlation between SGDS-K and K-HDRS was the highest (r=0.753; P =.01). To develop a diagnostic model, participants were classified into 2 groups: depression (SGDS-K≥7 and K-HDRS≥8) and nondepression (SGDS-K≤6 and K-HDRS≤7) groups [39-41]. Finally, 18 older adults were classified into the depression group and 29 were classified into the nondepression group (Figure 2).…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…These physical symptoms are strongly correlated with the onset and outcome of depression and anxiety (Preis, Golm, Kröner‐Herwig, & Barke, ). Clinical observations have shown that the severity of gastrointestinal symptoms and neurotrophic symptoms in patients with depression is significantly associated with suicidal tendency in patients with depression, with mild physical symptoms increasing the suicidal intention of depressed patients by 1.136 times and severe physical symptoms increasing the risk by 2.028 times (Jeon et al, ; Jeong et al, ). The hypothalamus controls the release of endocrine hormones through the pituitary gland and connects nerves through the autonomic nervous system and other pathways to regulate sleep, body temperature and feeding (Caron & Richard, ; Markakis, ).…”

Section: Introductionmentioning

confidence: 99%

Xiaoyaosan regulates depression‐related behaviors with physical symptoms by modulating Orexin A/OxR1 in the hypothalamus

Hou

Liu

et al. 2020

The Anatomical Record

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The functional regulation of the orexin system in the central nervous system is closely related to the occurrence and development of psychotic disorders. Abnormal changes in the lateral region of the hypothalamus are associated with the comorbidity of depression and physical symptoms, and how the traditional Chinese formula Xiaoyaosan regulates these changes may reveal aspects of the pathogenesis of depression. This study aimed to establish a rat model of depression in order to examine changes in Orexin A/OxR1 expression in the lateral region of the hypothalamus and the effects of Xiaoyaosan. Sixty specific pathogen‐free (SPF) male healthy Sprague–Dawley (SD) rats were used in the experiment and randomly divided into the control group, the model group, the Xiaoyaosan group and the fluoxetine group. The depression model was established by 21‐day chronic immobilization stress (CIS). Food intake and body weight were recorded, and the sucrose preference test (SPT) and open field test (OFT) were used to evaluate the model. Then, the expression of Orexin A/OxR1 in the hypothalamus was measured by ELISA, Western blot and quantitative real‐time polymerase chain reaction (qRT‐PCR) analysis. The expression of Orexin A and OxR1 in the lateral hypothalamic area was significantly down regulated in the model group, compared with the control group. Xiaoyaosan significantly reversed these changes with obvious curative effects. Abnormal changes in Orexin A/OxR1 in the lateral hypothalamic area of rats with depression caused by chronic stress are closely related to the pathogenesis of depression accompanied by physical symptoms. Xiaoyaosan can improve depression accompanied by physical symptoms by regulating Orexin A/OxR1.

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“…The reliability and validity of the Chinese, Korean, and English versions of the DSSS have been established (Hung et al, 2006a;Jeon et al, 2016;Tse et al, 2018). Several validities of the DSSS among patients with depression have been tested, as follows.…”

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confidence: 99%

“…Several validities of the DSSS among patients with depression have been tested, as follows. (1) The DS score is significantly correlated with the scores of the Hamilton Depression Rating Scale (HAMD), Montgomery-Asberg Depression Rating Scale, Center for Epidemiologic Studies Depression Scale, and mental subscales of the Short-Form 36 (Hung et al, 2006a;Jeon et al, 2016;Tse et al, 2018). ( 2) The DS is sensitive to pharmacotherapy-the improvement percentage of the DS score after four weeks of pharmacotherapy is correlated with that of the HAMD score (Hung et al, 2006a).…”

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confidence: 99%

“…(3) The cut-off points of the DS score of ≥9 and ≥19 for non-full remission and a MDE, respectively, among patients with major depressive disorder (MDD) are of good sensitivity and specificity (Hung et al, 2012). ( 4) The SS score is significantly correlated with the scores of the somatization subscale of the Symptom Checklist-90-Revised, the somatic component of the HAMD, and the physical subscales of the Short-Form 36 (Hung, Liu, Cheng, & Wang, 2009;Jeon et al, 2016). ( 5) The SS score at baseline predicts the outcome of depression at the 2-year follow-up point (Hung et al, 2010).…”

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confidence: 99%

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Comparing the associations of three psychometric scales at baseline with long‐term prognosis of depression over a 10‐year period

Hung¹,

Liu²,

Hsu³

et al. 2021

Int J Methods Psych Res

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Objective: The Depression and Somatic Symptoms Scale (DSSS), a free scale, includes depression (DS) and somatic (SS) subscales. This study aimed to compare the associations of the baseline DSSS, Hamilton Depression Rating Scale (HAMD) and Hospital Anxiety and Depression Scale (HADS) scores with the outcome of depression over a 10-year follow-up period.Methods: Two hundred ninety outpatients with major depressive disorder (MDD) were enrolled and were followed-up at the 6-month, 2-year, and 10-year points.The three scales were administered at each follow-up. Multiple linear regressions were used to compare the associations. Results:In a comparison of the HAMD, DS, and HADS-depression, the HAMD and DS scores at baseline were most strongly associated with the HAMD score at two (6-month and 2-year) and one (10-year) follow-up points, respectively. In a comparison of the HAMD, DS, SS, HADS-depression, and HADS-anxiety, the SS and HAMD scores at baseline were most strongly associated with the HAMD score at two (6-month and 10-year) and one (2-year) follow-up points, respectively. Conclusions:The DS, SS, and HAMD scores at baseline were significantly associated with the long-term outcome of depression. Scales or subscales assessing somatic symptoms might be more strongly associated with the outcome of depression.

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Do Somatic Symptoms Predict the Severity of Depression? A Validation Study of the Korean Version of the Depression and Somatic Symptoms Scale

Cited by 12 publications

References 21 publications

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

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

Xiaoyaosan regulates depression‐related behaviors with physical symptoms by modulating Orexin A/OxR1 in the hypothalamus

Comparing the associations of three psychometric scales at baseline with long‐term prognosis of depression over a 10‐year period

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