Using structural equation modeling to detect response shift in quality of life in patients with Alzheimer's disease

2018 ◽  
Vol 31 (1) ◽  
pp. 123-132
Author(s):  
Xuxia Wang ◽  
Xiaomeng Xu ◽  
Hongjuan Han ◽  
Runlian He ◽  
Liye Zhou ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Short Form ◽  
Bodily Pain ◽  
Equation Modeling

ABSTRACTBackground:Our study aims to detect different types of response shifts (RS) and true changes of quality of life (QOL) measurement in patients with Alzheimer's disease (AD) using structural equation modeling (SEM) in domain level.Methods:Patients with AD aged over 60 years old were collected from the Department of Neurology and Geriatrics in Taiyuan Central Hospital, China. The 12-item Short Form (SF-12) Health Survey was measured in 238 patients with AD prior to hospitalization and one month following discharge. RS was detected by SEM approach. The statistical process consisted of four steps and fitted four models. We interpreted changes of parameters in models to detect RS and to assess true change.Results:The results showed reprioritization of social functioning (SF) (χ2 = 4.13, p < 0.05), reconceptualization of role limitations due to emotional problems (RE) (χ2 = 17.03, p < 0.001), uniform recalibration of bodily pain (BP) (χ2 = 12.24, p < 0.001), and non-uniform recalibration of mental health (MH) (χ2 = 4.41, p < 0.05), respectively. The true changes of common factors were deteriorated in general physical health (PHYS) (−0.10, χ2 = 8.30, p < 0.005) and improved in general mental health (MENT) (+0.29, χ2 = 20.95, p < 0.001). The effect-sizes of RS were only small.Conclusion:This study showed that patients with AD occurred three types of RS and true changes one month following discharge. RS had effects on the QOL of patients. Better understanding of potential changes in QOL in patients with AD is crucial.

scholarly journals Association between social capital, health-related quality of life, and mental health: a structural-equation modeling approach

2016 ◽  
Vol 57 (1) ◽  
pp. 58-65 ◽  
Author(s):  
Jafar Hassanzadeh ◽  
Mohsen Asadi-Lari ◽  
Abdolvahab Baghbanian ◽  
Haleh Ghaem ◽  
Aziz Kassani ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Social Capital ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Health Related Quality ◽  
Related Quality ◽  
Health Related

scholarly journals Predictors of Quality of Life in Portuguese Obese Patients: A Structural Equation Modeling Application

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Estela Vilhena ◽  
José Pais-Ribeiro ◽  
Isabel Silva ◽  
Helena Cardoso ◽  
Denisa Mendonça
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Structural Equation Modeling ◽  
Negative Affect ◽  
Physical Health ◽  
Structural Equation ◽  
Equation Modeling ◽  
Obese Patients ◽  
Mediation Effects

Living with obesity is an experience that may affect multiple aspects of an individual’s life. Obesity is considered a relevant public health problem in modern societies. To determine the comparative efficacy of different treatments and to assess their impact on patients’ everyday life, it is important to identify factors that are relevant to the quality of life of obese patients. The present study aims to evaluate, in Portuguese obese patients, the simultaneous impact of several psychosocial factors on quality of life. This study also explores the mediating role of stigma in the relationship between positive/negative affect and quality of life. A sample of 215 obese patients selected from the main hospitals in Portugal completed self-report questionnaires to assess sociodemographic, clinical, psychosocial, and quality of life variables. Data were analysed using structural equation modeling. The model fitted the data reasonably well, CFI = 0.9, RMSEA = 0.06. More enthusiastic and more active patients had a better quality of life. Those who reflect lower perception of stigma had a better physical and mental health. Partial mediation effects of stigma between positive affect and mental health and between negative affect and physical health were found. The stigma is pervasive and causes consequences for psychological and physical health.

scholarly journals Workplace Violence Against Chinese Frontline Clinicians During the COVID-19 Pandemic and Its Associations With Demographic and Clinical Characteristics and Quality of Life: A Structural Equation Modeling Investigation

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuan Yang ◽  
Yue Li ◽  
Ying An ◽  
Yan-Jie Zhao ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Structural Equation Modeling ◽  
Workplace Violence ◽  
Structural Equation ◽  
Emotional Disturbances ◽  
World Health ◽  
Equation Modeling ◽  
Life Questionnaire ◽  
World Health Organization Quality

Background: Workplace violence is a major concern for clinicians worldwide. There has been little data on the epidemiology of workplace violence against frontline clinicians during the COVID-19 pandemic. This study examined the pattern of workplace violence and its association with quality of life (QOL) against frontline clinicians during the outbreak of COVID-19 pandemic in China.Methods: A cross-sectional online study was conducted in China between March 15 and March 20, 2020. Frontline clinicians' experience with workplace violence was measured with six standardized questions derived from the Workplace Violence Scale, while anxiety, depressive, and insomnia symptoms, and QOL were measured using the General Anxiety Disorder Questionnaire, the Patient Health Questionnaire, the Insomnia Severity Index, and the World Health Organization Quality of Life Questionnaire, respectively. Univariate analyses, multivariable logistic regression analyses, and structural equation modeling (SEM) were conducted.Results: A total of 15,531 clinicians completed the assessment; 2,878 (18.5, 95% CI = 17.92–19.14%) reported workplace violence during the outbreak of the COVID-19 pandemic (verbal violence: 16.1%; physical violence: 6.9%). According to multivariable models, key correlates of workplace violence were male gender, longer work experience, higher education level, smoking, working in the psychiatry or emergency department, working in tertiary hospitals, being involved in direct care of infected patients, having infected family/ friends/ colleagues, and frequently using social communication programs. Clinicians working in inpatient departments were less likely to report workplace violence compared to those working in outpatient departments. SEM analysis revealed that both violence and emotional disturbances (anxiety, depression, and insomnia) directly affected QOL (standardized direct effect = −0.031, and −0.566, respectively, P &lt; 0.05), while emotional disturbances partly mediated the association between work violence and QOL (standardized indirect effect = −0.184, P &lt; 0.05).Conclusion: Frontline clinicians were vulnerable to workplace violence during the COVID-19 pandemic. Due to the negative impact of workplace violence on quality of care and clinicians' QOL, health authorities and policymakers should take effective measures to reduce workplace violence against clinicians.

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