scholarly journals Trajectories of Depressive Symptoms among Multicultural Adolescents in Korea: Longitudinal Analysis Using Latent Class Growth Model

2020 ◽  
Vol 17 (21) ◽  
pp. 8217
Author(s):  
Min Kyung Song ◽  
Ju Young Yoon ◽  
Eunjoo Kim
Keyword(s):  
Depressive Symptoms ◽  
Latent Class Analysis ◽  
Predictive Factors ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Panel Study ◽  
Class Analysis ◽  
Early Screening ◽  
Care Plans ◽  
Latent Class Growth

The purpose of this study was to investigate the trajectory of depressive symptoms in multicultural adolescents using longitudinal data, and to identify predictive factors related to depressive symptoms of multicultural adolescents using latent class analysis. We used six time-point data derived from the 2012 to 2017 Multicultural Adolescents Panel Study (MAPS). Latent growth curve modeling was used to assess the overall features of depressive symptom trajectories in multicultural adolescents, and latent class growth modeling was used to determine the number and shape of trajectories. We applied multinomial logistic regression analysis to each class to explore predictive factors. We found that the overall slope of depressive symptoms in multicultural adolescents increased. Latent class analysis demonstrated three classes: (1) high-increasing class (i.e., high intercept, significantly increasing slope), (2) moderate-increasing class (i.e., moderate intercept, significantly increasing slope), and (3) low-stable class (i.e., low intercept, no significant slope). In particular, we found that the difference in the initial intercept of depressive symptoms determined the subsequent trajectory. There is a need for early screening for depressive symptoms in multicultural adolescents and preparing individual mental health care plans.

scholarly journals Physical Activity and Sedentary Activity Patterns Among Children and Adolescents: A Latent Class Analysis Approach

2011 ◽  
Vol 8 (4) ◽  
pp. 457-467 ◽  
Author(s):  
Carrie D. Patnode ◽  
Leslie A. Lytle ◽  
Darin J. Erickson ◽  
John R. Sirard ◽  
Daheia J. Barr-Anderson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Latent Class Analysis ◽  
Children And Adolescents ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Activity Patterns ◽  
Moderate Activity ◽  
Class Analysis ◽  
Sedentary Behaviors ◽  
Sedentary Activity

Background:While much is known about the overall levels of physical activity and sedentary activity among youth, few studies have attempted to define clusters of such behaviors. The purpose of this study was to identify and describe unique classes of youth based on their participation in a variety of physical activity and sedentary behaviors.Methods:Latent class analysis was used to characterize segments of youth based on patterns of self-reported and accelerometer-measured participation in 12 behaviors. Children and adolescents (N = 720) from 6th-11th grade were included in the analysis. Differences in class membership were examined using multinomial logistic regression.Results:Three distinct classes emerged for boys and girls. Among boys, the 3 classes were characterized as “Active” (42.1%), “Sedentary” (24.9%), and “Low Media/Moderate Activity” (33.0%). For girls, classes were “Active” (18.7%), “Sedentary” (47.6%), and “Low Media/Functional Activity” (33.7%). Significant differences were found between the classes for a number of demographic indicators including the proportion in each class who were classified as overweight or obese.Conclusions:The behavioral profiles of the classes identified in this study can be used to suggest possible audience segments for intervention and to tailor strategies appropriately.

Prevalence, risk factors and multi-group latent class analysis of lifetime anxiety disorders comorbid depressive symptoms

2019 ◽  
Vol 243 ◽  
pp. 360-365 ◽  
Author(s):  
Hongguang Chen ◽  
Xiao Wang ◽  
Yueqin Huang ◽  
Guohua Li ◽  
Zhaorui Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Depressive Symptoms ◽  
Anxiety Disorders ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis

Application of latent class analysis in assessing the mental health of medical students during the COVID-19 Epidemic (Preprint)

2020 ◽  
Author(s):  
Fei Wang
Keyword(s):  
Mental Health ◽  
Medical Students ◽  
High Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Risk Group ◽  
Multinomial Logistic Regression ◽  
High Risk Group ◽  
Class Analysis ◽  
Related Factors

BACKGROUND The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. OBJECTIVE This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. METHODS A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. RESULTS In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. CONCLUSIONS Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.

Profiles and characteristics of clinical subtypes of perinatal depressive symptoms: A latent class analysis

2019 ◽  
Vol 75 (11) ◽  
pp. 2753-2765 ◽  
Author(s):  
Ji‐Wei Sun ◽  
Dan‐Feng Cao ◽  
Jia‐Huan Li ◽  
Xuan Zhang ◽  
Ying Wang ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Perinatal Depressive Symptoms

scholarly journals Coping Patterns Among Primary Informal Dementia Caregivers in Singapore and Its Impact on Caregivers—Implications of a Latent Class Analysis

2020 ◽  
Author(s):  
Qi Yuan ◽  
Peizhi Wang ◽  
Tee Hng Tan ◽  
Fiona Devi ◽  
Daniel Poremski ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Latent Class Analysis ◽  
Coping Strategies ◽  
Latent Class ◽  
Personal Characteristics ◽  
Future Research ◽  
Class Analysis ◽  
Dementia Caregivers ◽  
Design And Methods ◽  
Coping Patterns

Abstract Background and Objectives Existing studies typically explore the factor structure of coping strategies among dementia caregivers. However, this approach overlooks the fact that caregivers often use different coping strategies simultaneously. This study aims to explore the coping patterns of primary informal dementia caregivers in Singapore, examine their significant correlates, and investigate whether different patterns would affect the depressive symptoms of caregivers. Research Design and Methods Two hundred eighty-one primary informal caregivers of persons with dementia (PWD) were assessed. Coping strategies were measured by the Brief Coping Orientation to Problem Experienced inventory. A latent class analysis was performed to explore caregivers’ coping patterns, followed by logistic regressions to identify the significant correlates and the relationships between coping patterns and caregiver depression. Results The latent class analysis suggested a three-class solution that was featured by the frequency and variety of coping strategies used by caregivers—high coping (36.3%), medium coping (37.7%), and low coping (26.0%). Factors influencing the coping patterns of our sample were mainly related to caregivers’ individual resources such as personal characteristics and caregiving stressors like PWD’s problematic behaviors and caregiving burden. Compared to caregivers in the low coping group, those in the medium coping group had significantly higher risks of potential depression. Discussion and Implications The current study confirmed that there are distinct coping patterns among primary informal dementia caregivers, and caregivers with the low coping pattern had fewer depressive symptoms. Future research is needed to explore if coping patterns from our sample are generalizable to dementia caregivers elsewhere.

scholarly journals Associative stigma among mental health professionals in Singapore: a cross-sectional study

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e028179 ◽  
Author(s):  
Louisa Picco ◽  
Sherilyn Chang ◽  
Edimansyah Abdin ◽  
Boon Yiang Chua ◽  
Qi Yuan ◽  
...  
Keyword(s):  
Mental Health ◽  
Job Satisfaction ◽  
Latent Class Analysis ◽  
Mental Health Professionals ◽  
Health Professionals ◽  
Online Survey ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Class Analysis ◽  
Associative Stigma

Objectives(1) Investigate and explore whether different classes of associative stigma (the process by which a person experiences stigmatisation as a result of an association with another stigmatised person) could be identified using latent class analysis; (2) determine the sociodemographic and employment-related correlates of associative stigma and (3) examine the relationship between associative stigma and job satisfaction, among mental health professionals.DesignCross-sectional online survey.ParticipantsDoctors, nurses and allied health staff, working in Singapore.MethodsStaff (n=462) completed an online survey, which comprised 11 associative stigma items and also captured sociodemographic and job satisfaction-related information. Latent class analysis was used to classify associative stigma on patterns of observed categorical variables. Multinomial logistic regression was used to examine associations between sociodemographic and employment-related factors and the different classes, while multiple linear regression analyses were used to examine the relationship between associative stigma and job satisfaction.ResultsThe latent class analysis revealed that items formed a three-class model where the classes were classified as ‘no/low associative stigma’, ‘moderate associative stigma’ and ‘high associative stigma’. 48.7%, 40.5% and 10.8% of the population comprised no/low, moderate and high associative stigma classes, respectively. Multinomial logistic regression showed that years of service and occupation were significantly associated with moderate associative stigma, while factors associated with high associative stigma were education, ethnicity and occupation. Multiple linear regression analyses revealed that high associative stigma was significantly associated with lower job satisfaction scores.ConclusionAssociative stigma was not uncommon among mental health professionals and was associated with sociodemographic factors and poorer job satisfaction. Associative stigma has received comparatively little attention from empirical researchers and continued efforts to address this understudied yet important construct in conjunction with future efforts to dispel misconceptions related to mental illnesses are needed.

Course of Depressive Symptoms After Myocardial Infarction and Cardiac Prognosis: A Latent Class Analysis

2006 ◽  
Vol 68 (5) ◽  
pp. 662-668 ◽  
Author(s):  
Kirsten I. Kaptein ◽  
Peter de Jonge ◽  
Rob H. S. van den Brink ◽  
Jakob Korf
Keyword(s):  
Myocardial Infarction ◽  
Depressive Symptoms ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis

The course of depressive symptoms in unipolar depressive disorder during electroconvulsive therapy: A latent class analysis

2010 ◽  
Vol 124 (1-2) ◽  
pp. 141-147 ◽  
Author(s):  
S. Cinar ◽  
R.C. Oude Voshaar ◽  
J.G.E. Janzing ◽  
T.K. Birkenhäger ◽  
J.K. Buitelaar ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Depressive Disorder ◽  
Latent Class Analysis ◽  
Electroconvulsive Therapy ◽  
Latent Class ◽  
Class Analysis ◽  
Unipolar Depressive Disorder

scholarly journals Application of Latent Class Analysis in Assessing the Mental Health of Medical Students During the COVID-19 Epidemic

2021 ◽  
Author(s):  
Zhuang Liu ◽  
Yue Zhang ◽  
Ran Zhang ◽  
Rongxun Liu ◽  
Lijuan Liang ◽  
...  
Keyword(s):  
Mental Health ◽  
Medical Students ◽  
High Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Risk Group ◽  
Multinomial Logistic Regression ◽  
High Risk Group ◽  
Class Analysis ◽  
Related Factors

Abstract Background: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. Methods: A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. Results: In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. Conclusions: Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.

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