scholarly journals Latent profile analysis of anxiety disorder among left-behind children in rural Southern China: a cross-sectional study

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e029331 ◽  
Author(s):  
Haining Liao ◽  
Minyi Pan ◽  
Weinan Li ◽  
Changqi Lin ◽  
Xuhao Zhu ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Multinomial Logistic Regression ◽  
School Factors ◽  
Cross Sectional ◽  
Effective Response ◽  
Left Behind ◽  
Children’S Anxiety ◽  
Latent Profile

ObjectivesPrevious studies have used latent profile analysis (LPA) to examine rural left-behind children’s anxiety. Further study is needed to identify the heterogeneous characteristics of rural left-behind children’s anxiety and explore the related factors.SettingA cross-sectional survey using a school-based sample was conducted in January 2018 in Qingxin district, Qingyuan city, Guangdong province.Participants1026 left-behind children (effective response rate of the questionnaire: 95.39%).Main outcome measuresProfile latent classes (LC) and anxiety disorder.ResultsThe LPA identified three anxiety LC: ‘low anxiety’ (56.6%), ‘medium anxiety’ (34.8%) and ‘severe anxiety’ (8.6%). The multinomial logistic regression model was used to predict the relationship between personal, family, school factors and anxiety. We found that the variables directly related to lower anxiety classes included age (12–14 years), harmonious or fair relationship with classmates, no neglect, harmonious parental relationship and the duration of mother migration <6 months.ConclusionsThese findings suggested the need for careful consideration of differences in anxieties among rural left-behind children. Identifying latent subgroups may provide an empirical basis for teachers and public health practitioners to implement anxiety intervention efforts.

scholarly journals Typologies of Family Functioning and 24-Hour Movement Behaviors

2020 ◽  
Author(s):  
Michelle Guerrero ◽  
Joel Barnes ◽  
Mark Tremblay ◽  
Laura Pulkki-Råback
Keyword(s):  
Family Functioning ◽  
Family Environment ◽  
Parental Monitoring ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
The United States ◽  
Movement Behavior ◽  
Cross Sectional ◽  
Family Typologies ◽  
Latent Profile

Abstract Objective: The purpose of the current study was to use latent profile analysis to identify family typologies characterized by parental acceptance, parental monitoring, and family conflict, and to examine whether such typologies were associated with the number of movement behavior recommendations (i.e., physical activity, screen time, and sleep) met by children. Methods: Data for this cross-sectional observational study were part of the baseline data from the Adolescent Brain Cognitive Development (ABCD) study. Data were collected from September 1, 2016 to September 15, 2018, across 21 study sites in the United States. Participants included 11,875 children aged 9 and 10 years. Results: Results from latent profile analysis showed that children were meaningfully classified into one of five family typologies, ranging from ideal (high acceptance, high monitoring, and low conflict) to poor (medium acceptance, low monitoring, and high conflict) functioning. Children from good (OR= 0.54; 95% CI, 0.39-0.76), average (OR=0.28; 95% CI, 0.20, 0.40), fair (OR=0.24; 95% CI, 0.16, 0.36), and poor (OR=0.19; 95% CI, 0.12-0.29) functioning families were less likely to meet all three movement behavior recommendations compared to children from ideal functioning families. The odds of meeting all recommendations progressively decreased as family functioning worsened. Similar findings and pattern of results were found for meeting ≥2 recommendations and ≥1 recommendation. Conclusions: These findings highlight the importance of the family environment for promoting healthy movement behaviors among children.

Latent Profile Analysis of Left-behind Adolescents’ Psychosocial Adaptation in Rural China

2019 ◽  
Vol 48 (6) ◽  
pp. 1146-1160 ◽  
Author(s):  
Jingxin Zhao ◽  
Qianyu Li ◽  
Liwei Wang ◽  
Lingyu Lin ◽  
Wenxin Zhang
Keyword(s):  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Rural China ◽  
Psychosocial Adaptation ◽  
Left Behind ◽  
Latent Profile

scholarly journals Sources of COVID-19-Related Information in People with Various Levels of Risk Perception and Preventive Behaviors in Taiwan: A Latent Profile Analysis

2021 ◽  
Vol 18 (4) ◽  
pp. 2091
Author(s):  
Peng-Wei Wang ◽  
Yi-Lung Chen ◽  
Yu-Ping Chang ◽  
Chia-Fen Wu ◽  
Wei-Hsin Lu ◽  
...  
Keyword(s):  
Risk Perception ◽  
Online Survey ◽  
Latent Profile Analysis ◽  
Latent Class ◽  
Information Sources ◽  
Profile Analysis ◽  
Multinomial Logistic Regression ◽  
Preventive Behaviors ◽  
Related Information ◽  
Latent Profile

The present study aimed to identify the distinct levels of risk perception and preventive behaviors during the coronavirus disease 2019 (COVID-19) outbreak among people in Taiwan and to examine the roles of information sources in various levels of risk perception and preventive behavior. The online survey recruited 1984 participants through a Facebook advertisement. Their self-reported risk perception, adopted preventive behaviors and COVID-19-related information were collected. We analyzed individuals’ risk perception and adopted preventive behaviors by using latent profile analysis and conducted multinomial logistic regression of latent class membership on COVID-19-related information sources. Four latent classes were identified, including the risk neutrals with high preventive behaviors, the risk exaggerators with high preventive behaviors, the risk deniers with moderate preventive behaviors, and the risk deniers with low preventive behaviors. Compared with the risk neutrals, the risk exaggerators with high preventive behaviors were more likely to obtain COVID-19 information from multiple sources, whereas the risk deniers with moderate preventive behaviors and risk deniers with low preventive behaviors were less likely to obtain COVID-19 information compared with the risk neutrals. Governments and health professions should take the variety of risk perception and adopted preventive behaviors into consideration when disseminating information on COVID-19 to the general public.

scholarly journals Applying Latent Profile Analysis to Identify Lifestyle Profiles and Their Association with Loneliness and Quality of Life among Community-Dwelling Middle- and Older-Aged Adults in South Korea

2021 ◽  
Vol 18 (23) ◽  
pp. 12374
Author(s):  
Kang-Hyun Park ◽  
Eun-Young Yoo ◽  
Jongbae Kim ◽  
Ickpyo Hong ◽  
Jae-Shin Lee ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
South Korea ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Multinomial Logistic Regression ◽  
Community Dwelling ◽  
Large Distribution ◽  
Latent Profile

This study aimed to examine the multi-faceted lifestyle profiles of community-dwelling middle- and older-aged adults based on their physical activity, participation in various activities, and nutrition. It identified the association of lifestyle profiles with demographic variables, quality of life, and mental health. The analysis included 569 participants (mean age = 60.2; SD = 4.3). Latent profile analysis identified three distinctive lifestyle profiles: “inactive and unbalanced” (36.4%), “basic life maintenance” (54.6%), and “active and balanced” (9.1%). Sex (p < 0.001), age (p < 0.001), and regular medication intake (p < 0.01) were statistically significantly different among the three profiles. Of the “inactive and unbalanced” lifestyle group, 63.3% of it was comprised of by females, and a relatively large distribution was aged over 65. In the “basic life maintenance” subgroup, males showed a relatively large distribution, and 92.6% of participants were aged 55–64. People with active and balanced lifestyles demonstrated high quality of life levels (p < 0.001) and low loneliness levels (p < 0.01). Multinomial logistic regression revealed a statistically significant positive association between lifestyle profiles and quality of life (p < 0.001) as well as mental health (p < 0.01). Therefore, health promotion that considers multi-faceted lifestyle factors would need to improve health and quality of life among community-dwelling middle- and older-aged adults in South Korea.

scholarly journals Toward a Multidimensional Understanding of Later Life Disability: A Latent Profile Analysis

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 203-204
Author(s):  
Natasha Peterson ◽  
Jeongeun Lee ◽  
Eva Kahana
Keyword(s):  
Older Adults ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Multinomial Logistic Regression ◽  
Community Dwelling ◽  
Physical Impairment ◽  
Sensory Impairment ◽  
Class Membership ◽  
Participation Restrictions ◽  
Latent Profile

Abstract Disability is difficult to define succinctly. Current literature on disability has primarily focused on physical functional limitations. However, relying on a single dimension or index cannot accurately represent disability as the experience of disability is nuanced and complex. To address these gaps, this study aims to understand the multidimensional nature of disability among retired, community-dwelling older adults. Using a sample of 414 older adults between the ages of 72 and 106 years (M=84.84, SD=4.56), latent profile analysis was employed to identify classes based on five indicators of disability across three domains. The five indicators of disability included difficulties with activities of daily living (ADLs), cognitive impairment, physical impairment, sensory impairment, and participation restrictions. Three classes were found to represent the data best. The most favorable and highly functioning group comprised the highest number of participants (n=242, 59.5%). The next group, class 2 (n=157, 37.9%), was characterized by high physical impairment and ADL-difficulty. The smallest group, class 3 (n=15, 3.6%), had the highest ADL-difficulty and participation restrictions but drastically lower cognitive and sensory impairment. Multinomial logistic regression revealed that class membership was related to sociodemographic characteristics. Finally, class membership predicted several mental health outcomes such as depressive symptoms, positive affect, and life satisfaction in the expected direction. If supported by future work, these findings could inform practitioners in developing more specific interventions relevant to older adults based on their disability profiles. Understanding various combinations of disablement has potential implications for services and interventions to be tailored to individuals’ distinct disability-related needs.

Comparison of 3 Different Analytic Approaches for Determining Risk-Related Active and Sedentary Behavioral Patterns in Adolescents

2010 ◽  
Vol 7 (3) ◽  
pp. 381-392 ◽  
Author(s):  
Michael W. Beets ◽  
John T. Foley
Keyword(s):  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Multinomial Logistic Regression ◽  
Analytical Techniques ◽  
Demographic Characteristics ◽  
Behavioral Patterns ◽  
Cross Sectional ◽  
Analytical Approaches ◽  
Selection Of ◽  
Sedentary Activities

Background:Much of the research conducted to date implies overweight youth exhibit uniform active and sedentary behavioral patterns. This approach negates the possibility that multiple co-occurring, and seemingly contrasting, behaviors may manifest within the same individual. We present a substantive dialogue on alternative analytical approaches to identifying risk-related active/sedentary behavioral patterns associated with overweight in adolescents.Methods:Comparisons were made among latent profile analysis (LPA), cluster analysis (CA), and multinomial logistic regression (MLR). A cross sectional sample of youth (N = 6603; 12−18 yrs) completed a questionnaire assessing: physical activity (PA); competing activities (COMP); and sedentary activities (SED). Demographics associated with PA (age, sex, BMI) were used as covariates/predictors.Results:Comparisons among methods revealed that LPA and CA detected subgroupings of behavioral patterns associated with overweight, each unique in regards to behaviors and demographic characteristics, whereas MLR results followed established associations of low PA and high SED without subgroup separation.Conclusions:Use of LPA and CA provides a rich understanding of behavioral patterns and the related demographic characteristics. Decisions guiding the selection of analytical techniques are discussed.

scholarly journals Age-Friendly Communities and Aging in Place: Findings From Latent Profile Analysis

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 559-560
Author(s):  
Seon Kim ◽  
Kyeongmo Kim ◽  
Junpyo Kim
Keyword(s):  
Older Adults ◽  
Successful Aging ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Multinomial Logistic Regression ◽  
Aging In Place ◽  
Community Or ◽  
Current Residence ◽  
The Cost ◽  
Latent Profile

Abstract Older adults prefer to live in their current home or community and ‘Aging in place’ has been shown to reduce the cost of caring for older adults and help their successful aging. Although age-friendly communities (AFC) initiatives have been helpful to aging in place, little has been known about the relationship between the types of AFC and aging in place. Using the 2017 AARP Age-Friendly Community Survey, we included 1,079 adults aged 65 or older. We measured aging in place as ‘move to a different community’, ‘move into a different residence within your current community’, and ‘stay in your current residence’, and included eight AFC constructs. We identified the type of AFC using Latent Profile Analysis: low-friendly, mid-friendly, and high-friendly. We also ran multinomial logistic regression to examine whether the types of AFC were associated with aging in place. Of the total participants, 26.0% lived in the low-friendly community, 23.7% in the mid-friendly community, and 50.3% in the high-friendly community. Older adults living in the high-friendly community were more likely to stay in the current residence (64.7%) than those in the low-friendly (47.1%) (χ2=28.680, p&lt;.001). Also, older adults living in the low-friendly community (OR=3.05, p&lt;.001) and the mid-friendly community (OR=1.42, p&lt;.10) were more likely to move to a different community compared to those living in the high-friendly community. This result suggests that it is important to build an AFC to promote aging in place. For the growing number of older adults' lives, policymakers should consider expanding the AFC initiatives.

scholarly journals Use of latent profile analysis and k-means clustering to identify student anxiety profiles

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Fang Liu ◽  
Dan Yang ◽  
Yueguang Liu ◽  
Qin Zhang ◽  
Shiyu Chen ◽  
...  
Keyword(s):  
Mental Health ◽  
High Risk ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Risk Groups ◽  
High Risk Group ◽  
Cross Sectional ◽  
Student Anxiety ◽  
Targeted Interventions ◽  
Latent Profile

Abstract Background Anxiety disorders are often the first presentation of psychopathology in youth and are considered the most common psychiatric disorders in children and adolescents. This study aimed to identify distinct student anxiety profiles to develop targeted interventions. Methods A cross-sectional study was conducted with 9738 students in Yingshan County. Background characteristics were collected and Mental Health Test (MHT) were completed. Latent profile analysis (LPA) was applied to define student anxiety profiles, and then the analysis was repeated using k-means clustering. Results LPA yielded 3 profiles: the low-risk, mild-risk and high-risk groups, which comprised 29.5, 38.1 and 32.4% of the sample, respectively. Repeating the analysis using k-means clustering resulted in similar groupings. Most students in a particular k-means cluster were primarily in a single LPA-derived student profile. The multinomial ordinal logistic regression results showed that the high-risk group was more likely to be female, junior, and introverted, to live in a town, to have lower or average academic performance, to have heavy or average academic pressure, and to be in schools that have never or occasionally have organized mental health education activities. Conclusions The findings suggest that students with anxiety symptoms may be categorized into distinct profiles that are amenable to varying strategies for coordinated interventions.

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