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Relationship between Life Satisfaction and Sleep Quality and Its Dimensions among Older Adults in City of Qom, Iran.

Shahab PapiMaria Cheraghi
Published in: Social work in public health (2021)
Background and Objective: Assessment of sleep quality is necessary in the older adults' management program so that good sleep quality will lead to a successful older adult's life. The present study aims to determine the relationship between life satisfaction and quality of sleep and its dimensions among the older adults' population residing in Qom city, Iran.Method: This was a cross-sectional descriptive-analytic study conducted in 2018. The population of the study consisted of older adults resident Qom, of whom 679 were selected by simple sampling. The data were collected using demographic characteristics, Life Satisfaction Inventory-Z(LSI-Z) and Pittsburgh Sleep Quality Index (PSQI). Data were analyzed with statistical package SPSS (version 22) using independent t-test, and one-way analysis of variance (ANOVA).Results: The mean ± standard deviation (SD)of the older adults was 70.43 ± 7.62 years. Mean of life satisfaction score and sleep quality were 13.00 ± 3.00 and 10.00 ± 3.00, respectively. There was a significant relationship between life satisfaction and sleep quality (P < .001). Moreover, life satisfaction was associated with the use of hypnotic drugs, mental sleep quality, daily functional disorders, and sleep disorders (P < .001). There was a significant relationship between sleep duration (P = .003) and delay to fall asleep (P = .048) with life satisfaction. However, there was no significant relationship between life satisfaction and sleep efficiency (P = .226).Conclusion: Our findings showed that sleep quality was not desirable in older adults. On the other hand, sleep quality and its dimensions were related to the satisfaction of life among the older adults. It is necessary to utilize solutions to improve the sleep quality in the older adults' community.
Keyphrases
  • sleep quality
  • physical activity
  • depressive symptoms
  • mental health
  • healthcare
  • quality improvement
  • deep learning
  • patient safety
  • machine learning
  • middle aged