The experience of psychological distress in family caregivers of people with dementia: A cross-sectional study

2017 ◽  
Vol 54 (2) ◽  
pp. 317-323 ◽  
Author(s):  
Wilson Abreu ◽  
Teresa Rodrigues ◽  
Carlos Sequeira ◽  
Regina Pires ◽  
Ana Sanhudo
Keyword(s):  
Psychological Distress ◽  
Family Caregivers ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
People With Dementia

scholarly journals A cross-sectional study of family caregiver burden and psychological distress linked to frailty and functional dependency of a relative with advanced dementia

Dementia ◽  
2018 ◽  
Vol 19 (2) ◽  
pp. 301-318 ◽  
Author(s):  
Wilson Abreu ◽  
Debbie Tolson ◽  
Graham A Jackson ◽  
Nilza Costa
Keyword(s):  
Psychological Distress ◽  
Family Caregivers ◽  
Caregiver Burden ◽  
Well Being ◽  
Cross Sectional Study ◽  
Functional Dependency ◽  
Advanced Dementia ◽  
Sectional Study ◽  
Cross Sectional ◽  
People With Dementia

Psychological health of caregivers of people with dementia is a major public concern. This study sought to determine the relationship between caregiver burden, psychological distress, frailty and functional dependency of a relative with advanced dementia. Persons with dementia and their caregivers (102 dyads) participated in this Portuguese community based cross-sectional study. Data were collected using the Clinical Dementia Rating Scale, a sociodemographic questionnaire, the Zarit Burden Interview, the Brief Symptoms Inventory and the Edmonton Frail Scale. Alzheimer's disease was the most common type of dementia among the recipients of care, who showed moderate (42.2%) to severe (52.9%) dementia. Among them 35.3% exhibited moderate and 45.1% severe frailty. Family caregivers reported moderate (76.5%) to severe burden (18.6%). Psychological distress was very high among family caregivers. Results show that people with dementia exhibited moderate (35.3%) or severe frailty (45.1%) and that a severe frailty was found in people with moderate dementia. A one-way ANOVA was conducted between the Global Severity Index and some sociodemographic variables. ANOVA reached p < .01 for employment status of the caregiver, assistance and professional support, and psychiatric history; and p = 0.01 for caregiver age and years of caregiving. Although caregivers reported benefit from the supportive approach offered by the multidisciplinary home care team, high levels of distress and associated burden were found, which might decrease their capacity to care for the person with dementia and their own health and well-being.

Clustering Analysis of the Care Problems of People with Dementia Based on the Minimum Spanning Tree Algorithm: A Cross-sectional Study (Preprint)

2021 ◽  
Author(s):  
Minmin Leng ◽  
Yue Sun ◽  
Hui Chang ◽  
Zhiwen Wang
Keyword(s):  
Family Caregivers ◽  
Clustering Analysis ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
The Core ◽  
People With Dementia ◽  
Care Problems

BACKGROUND Recognizing the correlations between care problems of people with dementia could be beneficial, as it may help clinicians choose treatment methods because related symptom groups may respond to the same treatment intervention. However, generalizable data on the prevalence of care problems and potential clusters of care problems in people with dementia in China remain unavailable. OBJECTIVE This study aimed to (1) evaluate the prevalence of various care problems of people with dementia, and (2) explore the core care problems and the correlation between care problems of people with dementia. METHODS A cross-sectional study design was adopted to identify the care problems of people with dementia reported by family caregivers. The questionnaire consisted of two parts. The first part was mainly socio-demographic questions of people with dementia. The second part was the care problems evaluation sheet which involved three aspects: daily living care problems, behavioral and psychological symptoms, and safety risks. Care problems of people with dementia were measured with this care problems evaluation sheet. Clustering analysis of the care problems based on Kruskal's minimum spanning tree (MST) algorithm was performed in the Jupyter Notebook software to explore the core care problems and the correlation between care problems. RESULTS A total of 687 participants were included in the analysis. In general, the prevalence of having difficulty in language performance, agitated behavior, incidence of falls was relatively higher in people with dementia, which distressed their family caregivers. Through the clustering analysis based on the Kruskal's MST algorithm, the 63 care problems were clustered into 7 clusters. The 7 core care problems were “Don't know how to dress in order”, “Refusing to take a bath”, “Bedridden”, “Hitting, kicking, pushing, or biting others”, “Pacing and aimless wandering”, “Complaining”, and “Choking on food”. CONCLUSIONS The prevalence of various care problems was high. Through the clustering analysis, care problems were clustered into 7 clusters and 7 core care problems were identified. The identity of just a few core care problems instead of a large number of them might have relevant clinical implications, in the sense that it may lead to a greater ease in the identification of underlying etiologies and to more rational treatments in people with dementia.

Predictive factors associated with psychological distress of caregivers of people with dementia in Japan: a cross-sectional study

2017 ◽  
Vol 30 (8) ◽  
pp. 1089-1098 ◽  
Author(s):  
Ryo Shikimoto ◽  
Mitsuhiro Sado ◽  
Akira Ninomiya ◽  
Kimio Yoshimura ◽  
Baku Ikeda ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Psychological Distress ◽  
Informal Care ◽  
Predictive Factors ◽  
Cross Sectional Study ◽  
Lower Number ◽  
Sectional Study ◽  
Cross Sectional ◽  
People With Dementia

ABSTRACTBackground:Caregivers of people with dementia are likely to have psychological distress that sometimes results in mental health problems, such as depression. The objective of this study was to examine some predictive factors that are thought to be associated with psychological distress of caregivers of people with dementia in Japan.Methods:Design: A cross-sectional study. Sample: As part of a study to estimate the cost of dementia in Japan, 1,437 people with dementia-caregiver dyads were enrolled in the current informal care time study. The measurements in the study included were the basic characteristics of the caregivers and the people with dementia, and the informal care time during a week.Analysis:Factors that predict caregivers’ psychological distress, which was measured by Kessler's Psychological Distress scale (K6) score, were evaluated using univariate and multivariate regression analyses.Results:Approximately 69% of the caregivers recorded a K6 score higher than 4, while 18% scored higher than 12. According to the results of the logistic regression analysis (cut-off 4/5), the K6 score was associated with mental and comorbid diseases of people with dementia, informal care time, its lower number of caregivers, and the level of nursing care. According to the results of logistic regression analysis (cut-off 12/13), the K6 score was associated with mental symptoms and comorbid disease of people with dementia, sex of caregivers, informal care time, and its lower number of caregivers.Conclusion:Our findings indicated that the psychological distress of the caregivers is quite high and that informal care time and behavioral and psychological symptoms of dementia are associated with it. These results corroborate with previous findings.

scholarly journals Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Psychological Distress ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
The Neural Network ◽  
Significant Difference ◽  
Total Data

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.

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