[논문]연령분류에 따른 성인 수면의 질에 영향을 미치는 요인

최진이; 박영미; 최덕자; 하영옥

doi:10.14370/jewnr.2019.25.1.17

연령분류에 따른 성인 수면의 질에 영향을 미치는 요인
Factors Influencing the Quality of Sleep in Korean Adults by Age Groups 원문보기

동서간호학연구지 = Journal of East-West nursing research, v.25 no.1, 2019년, pp.17 - 25

최진이 (건국대학교 의료생명대학 간호학과) , 박영미 (안산대학교 간호학과) , 최덕자 (서울대학교병원) , 하영옥 (안산대학교 간호학과)

Abstract ▼ AI-Helper

Purpose: This study investigated the factors influencing sleep quality in adults according to age groups. The escriptive study was conducted in metropolitan areas of South Korea to control for possible sleep differences between adults living in rural areas and those living in cities. Participants were 450 adults between 19 and 65 years old. Methods: Self-administered questionnaires consisting of including the Korean Modified Sleep Leeds Evaluation Questionnaire, the International Physical Activity Questionnaire and the Perceived Stress Scale were used to assess the quality of adults' sleep. Results: Sleep quality was poor across the all age groups Stress and subjective health status were influencing factors of sleep quality among participants in their 20s; subjective health status and circadian rhythm (evening-type) were influencing factors of those in their 30s; subjective health status, alcohol consumption, and work days of less than 8 hours were influencing factors among participants in their 40s; and stress, circadian rhythm (evening-type), subjective health status, work days of over 11 hours, past smoking habit, and light physical activity were influencing factors of those aged 50 years and over. Conclusion: The quality of sleep was low across the all adult age groups in South Korea. Although the factors influencing quality of sleep differed by age groups, they in general indicate the need to focus on maintaining and controlling health status in sleep interventions.

주제어

표/그림 (4)

그림 Figure 1. Theoretical framework
표 Table 1. Sociodemographic Characteristics of Participants According to Age Groups
표 Table 2. Correlation between Sleep Quality and Independent Variables According to Age Groups
표 Table 3. Comparison of Factors Influencing Quality of Sleep in Adults According to Age Groups

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문제 정의

The present study investigated the quality of sleep, and its influencing factors, according to adult age groups based on Spielman’s 3P biobehavioral model. The aim was to provide baseline data for developing age-specific care strategies for sleep problems in adults.
This study explored the factors that influence the quality of sleep according to age groups using Spielman’s 3P biobehavioral model. The goal of this study was to provide baseline data aiding in the development of differentiated sleep intervention methods based on age groups.

제안 방법

Circadian rhythms, BMI, work duration, physical activity, and smoking were processed as dummy variables to analyze the factors that affect quality of sleep in adults according to age group. Table 3 shows the results of the regression analysis.
The study only included individuals living in metropolitan areas in order to control for possible sleep differences between adults living in rural areas and those living in cities. The survey used a self-report questionnaire. The exclusion criteria included those who had received a sleep disorder diagnosis from a hospital within the last 6 months, those who were taking medications related to sleep, and those residing in health care facilities.

대상 데이터

80. According to this analysis, at least 376 subjects were needed. After considering the possible dropout rate, a target sample size of 426 was determined.
The data were gathered over 3 months after receiving approval from the institutional review board in January 2016 (confirmation number: AN01-201604-HR-001-01). The participants were adults ranging from 19 to 65 years old, who understood the purpose of the study, and who voluntarily agreed to participate in an internet survey. The study only included individuals living in metropolitan areas in order to control for possible sleep differences between adults living in rural areas and those living in cities.

데이터처리

Age differences in pain disturbance level, subjective health status, perceived stress, and quality of sleep were analyzed using one-way analyses of variance (ANOVAs); Scheffe’s test was used for the post-hoc analysis.
Age differences in pain disturbance level, subjective health status, perceived stress, and quality of sleep were analyzed using one-way analyses of variance (ANOVAs); Scheffe’s test was used for the post-hoc analysis. The relationships between the major factors and quality of sleep were analyzed using the Pearson correlation coefficient in each age group. To explain the independent factors of quality of sleep in adults, all significant factors in the correlation analysis were included as response variables in a stepwise multiple regression analysis.
The relationships between the major factors and quality of sleep were analyzed using the Pearson correlation coefficient in each age group. To explain the independent factors of quality of sleep in adults, all significant factors in the correlation analysis were included as response variables in a stepwise multiple regression analysis. The significance level for the analyses was set at .

이론/모형

Sleep quality was measured using the Korean Modified Sleep Leeds Evaluation Questionnaire (KMLSEQ) [13]. The KMLSEQ measures 4 areas of sleep, including sleep patterns, sleep quality, waking patterns, and behavior after waking.
Physical activity was assessed using the International Physical Activity Questionnaires (IPAQ), which required participants to record the weekly amount of intensive and moderate physical activity, and weekly amount of walking. The collected data was then used to calculate the metabolic equivalent task (MET) minutes based on the IPAQ point conversion method. The participants were subsequently classified into 3 groups: The light activity group comprised those who engaged in less than 600 MET-min of physical activity per week; the moderate activity group comprised those who engaged in at least 600 MET-min of activity for 5 or more days a week; and the vigorous activity group comprised those who engaged in over 3000 MET-min of activity per week [15].
This descriptive survey used Spielman’s 3P biobehavioral model to explore the influencing factors of sleep quality according to age groups (Figure 1).
This study explored the factors that influence the quality of sleep according to age groups using Spielman’s 3P biobehavioral model.

성능/효과

Classification of the sample according to BMI (low body weight, normal body weight, overweight, and obesity) showed that the proportion of participants who were overweight and obese increased with age, and there was a significant difference in proportion according to age (p=.005). The proportion of participants who had a job increased with age, although it began to decrease among those in aged 50 years and over; the proportions significantly differed by age groups (p<.
The demographic characteristics included age, sex, marital status, education level, BMI, job status, work duration, underlying disease, alcohol consumption (times/week), caffeine intake (mg/day), smoking, and pain disturbance level. Caffeine intake was determined based on the average of 400 mg per day among adults in South Korea, according to the Korea Food and Drug Administration [12].
001). These findings indicated that quality of sleep for adults in their 20s, 40s, and 50 years and over decreased as perceived stress and pain disturbance levels increased. However, quality of sleep increased with subjective health status.
This study revealed that the quality of sleep was poor among adults of all age groups in South Korea. Moreover, the factors affecting the quality of sleep differed according to age groups, although they in general suggested that maintaining health status should be included in sleep interventions for all age groups.

후속연구

In addition, some variables such as physical activity and caffeine intake were measured only using a questionnaire; thus, there are limitations in the interpretation of our results. Future research is required including emotional variables and physiological measurements. Finally, we suggest the development of a comprehensive sleep intervention program considering both physical and emotional health states.

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