scholarly journals Activity Profiles among Older Adults: Latent Class Analysis Using the Korean Time Use Survey

Author(s):  
Yungsoo Lee

This study empirically explored the activity profiles of Korean older adults by considering a wide range of activities simultaneously and further investigated the socioeconomic factors associated with activity profiles. Gender differences in activity profiles were examined in-depth. Latent class analysis (LCA) was used to identify activity profiles based on a nationally representative sample of older adults from the most recent two waves of the Korean Time Use Survey (n = 3034 for 2014 and n = 3960 for 2019). Multinomial logistic regression analysis was employed to further examine the factors associated with the activity profiles. The findings revealed four distinct activity groups, although there were differences in activity profiles between the two waves. Several sociodemographic factors, such as gender, age, assets and income, were significantly associated with the activity profiles. Findings from this study can inform policy makers seeking interventions that enhance the overall well-being of older adults through activity engagement.


2018 ◽  
Vol 13 (2) ◽  
pp. 103-105
Author(s):  
Ann Glusker

A Review of: van Boekel, L.C., Peek, S. T., & Luijkx, K.G. (2017). Diversity in older adults’ use of the Internet: Identifying subgroups through latent class analysis. Journal of Medical Internet Research, 19(5:e180), 1-10. doi: 10.2196/jmir.6853 Abstract Objective – To determine the amount and types of variation in Internet use among older adults, and to test its relationship to social and health factors. Design – Representative longitudinal survey panel of households Setting – The Netherlands Subjects – A panel with 1,418 members who were over 65 years of age had answered the survey questionnaire that included Internet use questions, and who reported access to and use of the Internet. Methods – Using information about the Internet activities the respondents reported, the authors conducted latent class analysis and extracted a best-fitting model including four clusters of respondent Internet use types.  The four groups were analyzed using descriptive statistics and compared using ANOVA and chi-square tests.  Analysis and comparisons were conducted both between groups, and on the relationship of the groups with a range of social and health variables. Main Results – The four clusters identified included: 1) practical users using the Internet for practical purposes such as financial transactions; 2) social users using the Internet for activities such as social media and gaming; 3) minimizers, who spent the least time on the Internet and were the oldest group; and 4) maximizers, who used the Internet for the widest range of purposes, for the most time, and who were the youngest group.  Once the clusters were delineated, social and health factors were examined (specifically social and emotional loneliness, psychological well-being, and two activities of daily living (ADL) measures).  There were significant differences between groups, but the effect sizes were small.  Practical users had higher psychological well-being, whereas minimizers had the lowest scores related to ADLs and overall health (however, they were also the oldest group). Conclusions – The establishment of four clusters of Internet use types demonstrates that older adults are not homogeneous in their Internet practices.  However, there were no marked findings showing differences between the clusters in social and health-related variables (the minimizers reported lower health status, but they were also the oldest group).  Nevertheless, the finding of Internet use heterogeneity is an important one for those who wish to connect with older adults through Internet-based programming.  The different patterns evidenced in each cluster will require differing outreach strategies. It also highlights the need for ongoing longitudinal research, to determine whether those who are currently younger and more technologically savvy will age into similar patterns that these authors found, or whether a new set of older adult Internet use profiles will emerge as younger generations with more Internet experience and affinity become older.





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 ◽  
...  

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.





2020 ◽  
Author(s):  
Fei Wang

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.



BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e028179 ◽  
Author(s):  
Louisa Picco ◽  
Sherilyn Chang ◽  
Edimansyah Abdin ◽  
Boon Yiang Chua ◽  
Qi Yuan ◽  
...  

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.



Author(s):  
Jing Huang ◽  
Pui Hing Chau ◽  
Edmond Pui Hang Choi ◽  
Bei Wu ◽  
Vivian W Q Lou

Abstract Objectives This study identified the classes (i.e., patterns) of caregivers’ activities, based on their engagements in caregiving activities, and explored the characteristics and the caregiver burden of these classes. Methods This study was a secondary analysis of a cross-sectional survey on the profiles of family caregivers of older adults in Hong Kong. A latent class analysis approach was adopted to classify family caregivers (N = 932) according to their routine involvements in 17 daily caregiving activities: 6 activities of daily living (ADLs) and 8 instrumental activities of daily living activities (IADLs) in addition to emotional support, decision making, and financial support. Multinomial logistic regression and multiple linear regression illuminated the characteristics of the classes and compared their levels of caregiver burden. Results The family caregivers fell into 5 classes: All-Round Care (High Demand, 19.5%), All-Round Care (Moderate Demand, 8.2%), Predominant IADLs Care (High Demand, 23.8%), Predominant IADLs Care (Moderate Demand, 32.5%), and Minimal ADLs and IADLs Care (Low Demand, 16.0%). These classes exhibited different characteristics in terms of care recipients’ cognitive statuses and caregiver backgrounds. The levels of caregiver burden differed across classes; the All-Round Care (High Demand) class experienced the highest levels of caregiver burden. Discussion This study contributes to existing scholarship by turning away from a predefined category of care tasks to explore the patterns of caregiving activities. By identifying caregiving activity patterns and understanding their associated characteristics and caregiver burden, prioritizing and targeting caregiver support interventions better is possible.



Author(s):  
Min Kyung Song ◽  
Ju Young Yoon ◽  
Eunjoo Kim

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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