Effect of Types of Dementia Care on Quality of Life and Mental Health Factors in Caregivers of Patients with Dementia: A Cross-Sectional Study.
Seung-Hyun ChoHyun-Se ChoiPublished in: Healthcare (Basel, Switzerland) (2023)
In Eastern cultures, particularly in South Korea, caregiving for dementia patients at home is common, yet even after facility placement, families may experience ongoing burden due to cultural factors. The aim of this study was to examine the burden experienced by caregivers of dementia patients, considering cultural factors influencing in-home care and facility-based care. Using a cross-sectional study design, we compared the quality of life, depression, subjective happiness, and subjective health of family caregivers providing in-home care (FCHC) and informal family caregivers (IFCGs). Data from the 2019 Community Health Survey conducted by the Korea Disease Control and Prevention Agency (KDCA) that met the study criteria were selected and statistically analyzed. The results showed that psychological/emotional and economic burdens were the primary burden factors for both FCHC and IFCGs. Statistically significant differences were found between the two groups in terms of quality of life, depression, subjective happiness, and subjective health. Specifically, FCHC demonstrated a lower quality of life, and both groups experienced moderate to severe depression, indicating the need for mental health management for caregivers of individuals with dementia. As not all FCHC can be transitioned to IFCGs, interventions tailored to specific caregiving types should be developed to improve the quality of life, depression, subjective happiness, and subjective health of caregivers of individuals with dementia.
Keyphrases
- mental health
- sleep quality
- mild cognitive impairment
- healthcare
- palliative care
- cognitive impairment
- end stage renal disease
- depressive symptoms
- newly diagnosed
- public health
- chronic kidney disease
- ejection fraction
- physical activity
- prognostic factors
- health information
- machine learning
- risk factors
- mental illness
- electronic health record
- quality improvement
- pain management
- big data
- deep learning
- climate change
- human health