The Mental Health of the Peruvian Older Adult during the COVID-19 Pandemic.
Jack Roberto Silva FhonMaritza Evangelina Villanueva-BenitesMaria Del Pilar Goméz-LujánMaria Rosario Mocarro-AguilarOrfelina Arpasi-QuispeReyna Ysmelia Peralta-GómezSofia Sabina Lavado-HuarcayaZoila Esperanza Leitón-EspinozaPublished in: International journal of environmental research and public health (2022)
During the pandemic, the elderly population was the most exposed to disease and changes in their daily lives. The objective was to determine the association between demographic factors, access to health services, sources of information, and physical symptoms in the mental health of the elderly during the COVID-19 pandemic-a study with 3828 older adults residing in nine cities in Peru. The data was collected using a web-based survey, and the instruments of demographic data; exposure to information (radio, television, and internet); and presence of physical symptoms, anxiety, and perceived stress were used. Descriptive and analytical analysis was performed. Female sex, those aged between 60 and 79 years old, those with secondary education, those in their own home, those residing in an urban area, and those using public services of health predominated in this study. Likewise, 62.9% presented depressive symptoms; on the stress scale, an average of 27.81 (SD = 8.71), and on the anxiety scale, an average of 27.24 (SD = 6.04). Moreover, 65.1% reported fatigue, 62.2% had a headache, and 61.2% lack of energy. There is an association between demographic variables and the physical and psychological symptoms of stress, anxiety, and depressive symptoms in the elderly during the pandemic.
Keyphrases
- mental health
- sleep quality
- depressive symptoms
- physical activity
- middle aged
- community dwelling
- healthcare
- social support
- coronavirus disease
- sars cov
- mental illness
- health information
- cross sectional
- electronic health record
- stress induced
- big data
- primary care
- drinking water
- public health
- emergency department
- machine learning
- climate change
- risk assessment
- deep learning