Identification of comprehensive geriatric assessment‐based risk factors for insomnia in elderly Chinese hospitalized patients

Abstract

Objective Insomnia is a common problem in older persons and is associated with poor prognosis from a functional or clinical perspective. The purpose of this study was to investigate the prevalence of insomnia and identify comprehensive geriatric assessment (CGA) based clinical factors associated with insomnia in elderly hospitalized patients. Methods Standardized face‐to‐face interviews were conducted and CGA data were collected from 356 Chinese hospitalized patients aged 60 years or older. Insomnia was defined as self‐reported sleep poor quality according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM‐Ⅴ). Multivariate logistic regression analysis was applied to assess the association between patient clinical factors together with domains within the CGA and insomnia. Results Among the 365 patients, insomnia was found in 48.31% of the participants. Difficulty in initiating sleep (DIS), early morning awakening (EMA), difficulty in maintaining sleep (DMS), and snoring were found in 33.99%, 9.55%, 13.48%, and 1.69% of patients, respectively. Significant associations were found between insomnia and several covariates: female gender (P = 0.034), depression (P = 0.001), activities of daily living (ADL) (P = 0.034), instrumental activities of daily living (IADL; P = 0.009), falling (P = 0.003), chronic pain (P = 0.001), and poor nutritional status (P = 0.038). According to the results of the adjustment multivariate logistic regression analysis, female sex (odds ratio [OR] = 2.057, confidence interval [CI] = 1.179‐3.588, P = 0.011), depression (OR = 1.889, CI = 1.080‐3.304, P = 0.026), and chronic pain (OR = 1.779, CI = 1.103‐2.868, P = 0.018) were significant independently predictors associated with insomnia. Conclusion Our study revealed that female sex, depression, and chronic pain were independently predictors of insomnia in hospitalized patients. Early identification of elderly patients with these risk factors using the CGA may improve the quality of life and treatment outcomes.