scholarly journals Patterns of compliance with COVID-19 preventive behaviours: a latent class analysis of 20 000 UK adults

2021 ◽  
pp. jech-2021-216876
Author(s):  
Liam Wright ◽  
Andrew Steptoe ◽  
Daisy Fancourt
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Social Study ◽  
Self Report ◽  
Class Analysis ◽  
Cross Sectional ◽  
Social Distancing ◽  
Personality Predictors ◽  
High Level

BackgroundGovernments have implemented a range of measures to tackle COVID-19, primarily focusing on changing citizens’ behaviours in order to lower the transmission of the virus. Few studies have looked at the patterns of compliance with different measures within individuals: whether people comply with all measures or selectively choose some but not others. Such research is important for designing interventions to increase compliance.MethodsWe used cross-sectional data from 20 947 UK adults in the COVID-19 Social Study collected from 17 November to 23 December 2020. Self-report compliance was assessed with six behaviours: mask wearing, hand washing, indoor household mixing, outdoor household mixing, social distancing and compliance with other guidelines. Patterns of compliance behaviour were identified using latent class analysis, and multinomial logistic regression was used to assess demographic, socioeconomic and personality predictors of behaviour patterns.ResultsWe selected a four-latent class solution. Most individuals reported similar levels of compliance across the six behaviour measures. High level of compliance was the modal response. Lower self-reported compliance was related to young age, high risk-taking behaviour, low confidence in government and low empathy, among other factors. Looking at individual behaviours, mask wearing had the highest level of compliance while compliance with social distancing was relatively low.ConclusionResults suggest that individuals choose to comply with all guidelines, rather than some but not others. Strategies to increase compliance should focus on increasing general motivations to comply alongside specifically encouraging social distancing.

scholarly journals Patterns of compliance with COVID-19 preventive behaviours: a latent class analysis of 20,000 UK adults

2021 ◽  
Author(s):  
Liam Wright ◽  
Andrew Steptoe ◽  
Daisy Fancourt
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Social Study ◽  
Self Report ◽  
Class Analysis ◽  
Cross Sectional ◽  
Social Distancing ◽  
Personality Predictors ◽  
Lower Transmission

AbstractBackgroundGovernments have implemented a range of measure to tackle COVID-19, primarily focusing on changing citizens’ behaviours in order to lower transmission of the virus. Few studies have looked at the patterns of compliance with different measures within individuals: whether people comply with all measures or selectively choose some but not others. Such research is important for designing interventions to increase compliance.MethodsWe used cross-sectional data from 20,947 UK adults in the COVID-19 Social Study collected 17 November – 23 December 2020. Self-report compliance was assessed with six behaviours: mask wearing, hand washing, indoor household mixing, outdoor household mixing, social distancing, and compliance with other guidelines. Patterns of compliance behaviour were identified using latent class analysis, and multinomial logistic regression was used to assess demographic, socioeconomic and personality predictors of behaviour patterns.ResultsWe selected a four latent class solution. Most individuals reported similar levels of compliance across the six behaviour measures. High levels of compliance was the modal response. Lower self-reported compliance was related to young age, high risk-taking behaviour, low confidence in government, and low empathy, among other factors. Looking at individual behaviours, mask wearing had the highest level of compliance whilst compliance with social distancing was relatively low.ConclusionResults suggest that individuals choose to comply with all guidelines, rather than some but not others. Strategies to increase compliance should focus on increasing general motivations to comply alongside specifically encouraging social distancing.

Resilient Coping Types in People With Spinal Cord Injury: Latent Class Analysis

2021 ◽  
pp. 003435522199073
Author(s):  
Chungyi Chiu ◽  
Jessica Brooks ◽  
Alicia Jones ◽  
Kortney Wilcher ◽  
Sa Shen ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Life Satisfaction ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Psychosocial Interventions ◽  
Self Report ◽  
Class Analysis ◽  
Multivariate Tests ◽  
Cord Injury

Resilience is central to living well with a spinal cord injury (SCI). To provide a timely, targeted, and individualized intervention supporting resilience, it is necessary to assess an individual’s resilience level and characteristics of resilience on an ongoing basis. We aimed to validate the different types of resilient coping among people with SCI (PwSCI), using the Connor–Davidson resilience scale, and to identify the relationships between resilience and other psychosocial factors among the types of resilient coping. We recruited 93 PwSCI, who took the self-report measures of resilience, depression, life satisfaction, and spirituality. Using latent class analysis, we found three types: (a) goal-pursuing, bouncing-back, and persevering, named GP; (b) uncertainty about coping with setbacks, named UC; and (c) loss of resilient coping, named LOSS. The multivariate tests indicated that the three types differed on a linear combination of resilience, depression, and life satisfaction, with a large effect size. We discussed the three types of resilient coping and the implications for psychosocial interventions. We also recommended that rehabilitation clinicians examine PwSCI’s resilience levels and types of resilience during initial and follow-up visits. In doing so, PwSCI will have timely, targeted supports for developing and/or re-building their resilience.

scholarly journals Latent Class Analysis: a new vision of the phenomenon of depression in elderly men in the Brazilian Northeast

2017 ◽  
Vol 20 (6) ◽  
pp. 814-825
Author(s):  
Rita de Cássia Hoffmann Leão ◽  
Vanessa de Lima Silva ◽  
Rafael da Silveira Moreira
Keyword(s):  
Latent Class Analysis ◽  
Associated Factors ◽  
Latent Class ◽  
Descriptive Analysis ◽  
Geriatric Depression Scale ◽  
Depression Scale ◽  
Class Analysis ◽  
Elderly Men ◽  
Cross Sectional ◽  
Low Levels

Abstract Objective: to identify the prevalence of depression in elderly men and associated factors using Latent Class Analysis. Method: a cross-sectional, epidemiological study evaluating 162 Primary Care users resident in the community in Recife, Brazil, was carried out. The Yesavage Geriatric Depression Scale was used as a screening instrument. The study was based on descriptive analysis and Latent Class Analysis, which allows the indirect measurement of the phenomenon of Depression by measuring the latent phenomenon of depression through 15 directly observed questions/answers from the scale used followed by ordinal logistic regression. Results: Elderly men with up to four years of schooling had a 2.43 times greater chance of developing depression. Those with normal levels of cortisol were less likely to become depressed while elderly men with low levels of Vitamin D and testosterone and high levels of thyroid stimulating hormones (TSH) were more likely to be depressed. The prevalence of the highest level of depression in the study population was 29% and was associated with low levels of education and alterations in the clinical data investigated. Conclusion: The study concluded that Latent Class Analysis allowed an innovative perspective of the phenomenon of depression and its relationship with associated factors, allowing a better and broader approach to this phenomenon in clinical practice.

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.

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.

scholarly journals Which are the Nutritional Supplements Used by Beach-Volleyball Athletes? A Cross-Sectional Study at the Italian National Championship

Sports ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 31 ◽  
Author(s):  
Stefano Amatori ◽  
Davide Sisti ◽  
Fabrizio Perroni ◽  
Samuel Impey ◽  
Michela Lantignotti ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Nutritional Supplements ◽  
Cross Sectional Study ◽  
Class Analysis ◽  
Sports Performance ◽  
Sectional Study ◽  
Pragmatic Approach ◽  
Cross Sectional ◽  
The Face

Beach volleyball is an intermittent team sport played under high temperature and humidity. Given that some nutritional supplements can enhance sports performance, this study aimed to evaluate the quantity and the heterogeneity of the nutritional supplementation practices of amateur (n = 69) and professional (n = 19) beach volley athletes competing in the Italian National Championship; an online form was used to collect data about the supplementation habits. The latent class analysis was used to find sub-groups characterised by different habits regarding supplements consumption. The most frequently used supplements (more than once a week) are vitamins B and C (39.2% of athletes), protein (46.8%), and caffeine (36.9%). The latent class analysis revealed three different sub-groups of athletes: the first class (56.7%) included athletes who were used to take very few supplements, the second class (17.0%) was characterised by higher consumption of supplements and the third class (26.2%) was in the middle between the others two. Groups were characterised not only by the quantity but also by the category of supplements used. Our results highlighted a high heterogeneity in supplementation habits. A pragmatic approach to supplements and sports foods is needed in the face of the evidence that some products can usefully contribute to enhancing performance.

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.

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.

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