Study on the Sleep Quality Classification and Influencing Factors of Adult ICU Awake Patients Based on Latent Profile Analysis: A Cross-Sectional Survey

Yanting Zhang,Yuan Zhang, Manting Teng, Bingxue Wang, Miying Cheng, Si Zhong, Xiao Zhang,Xinbo Ding,Chang Liu, Jing Ma

Nature and Science of Sleep(2023)

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摘要
Objective: To explore the potential category characteristics of sleep quality in conscious adult patients in the intensive care unit (ICU) and to analyze the differences in characteristics of different categories of conscious adult patients in the ICU. Methods: From 5/4/2023 to 30/6/2023, we selected ICU patients of a Class iii Grade A hospital in Wuhan as subjects by simple random sampling. The general information questionnaire, Acute Physiology and Chronic Health Evaluation II, Numerical Rating Scale (NRS)/Critical Care Pain Observation Tool (CPOT) and Richards-Campbell Sleep Questionnaire (RCSQ) were used for investigation. To analyze the latent profile of sleep quality characteristics of adult ICU awake patients and to identify the influencing factors of the latent profile through univariate analysis and logistic regression analysis. Results: A total of 298 awake ICU patients were investigated. There were three potential profiles of sleep quality characteristics, namely "low sleep quality" type (16.31%), "medium sleep quality" type (52.11%), and "high sleep quality" type (31.57%). Logistic regression analysis showed that age (36-60), disease type admitted to ICU, oxygen administration method, CPOT or NRS score, postoperative or not, use of analgesic drugs or not, and use of sleep-promoting drugs or not were the factors affecting the sleep quality of adult ICU awake patients (P < 0.05). Conclusion: The sleep quality of adult ICU awake patients has obvious classification characteristics. Health professionals can carry out individualized interventions according to the influencing factors of different potential profiles to improve the sleep quality of adult ICU awake patients.
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关键词
ICU patients, sleep quality, sleep disorders, latent profile analysis, influencing factors
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