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Bok Nam Seo1
, Inja Kim2
, Inja Kim2
Abstract
Purpose
To provide basic data for the health management of adults with prolonged fatigue, demographic characteristics, stress, sleep quality, and metabolic syndrome were compared between groups with and without prolonged fatigue.
Methods
Data of 370 adults (197 with Prolonged Fatigue [PF] and 173 without PF) from the 2014~2016 database of Korea Medicine Data Center were used. Fatigue, stress and sleep quality were measured with the revised Chalder Fatigue Scale, Psychosocial Well-being Index-Short Form, and Pittsburgh Sleep Quality Index, respectively. Metabolic syndrome was diagnosed according to the National Cholesterol Education Program Adult Treatment Panel III criteria.
Results
First, regarding demographic characteristics, there were significant differences in occupation, smoking, and Body Mass Index (BMI) between groups with and without PF. More of the participants with PF than those without PF were employed, and smokers. They were also more likely to be obese than the participants in the group without PF. Second, there were significant differences between the groups in terms of stress and sleep quality; the participants with PF showed higher stress and lower sleep quality than those without PF. Finally, the prevalence of metabolic syndrome was found to be higher in the participants with PF than those without PF.
Figures and Tables
Table 1
Demographic Characteristics of Participants with and without Prolonged Fatigue in Early Adulthood (N=370)
Table 3
Differences of Metabolic Syndrome by Prolonged Fatigue in Early Adulthood (N=370)
PF=prolonged fatigue; OR=odds ratio, reference: Group without PF; CI=confidence interval; †Waist circumference: Yes: if men, ≥90 cm; if women, ≥85 cm; ‡LDL=low density cholesterol, Yes: ≥150 mg/dL or under treatment; §HDL=high density cholesterol, Yes: if men<40 mg/dL, if women, <50 mg/dL, or under treatment; ∥BP=blood pressure, Yes: systolic ≥130 mmHg or diastolic ≥85 mmHg or under treatment; ¶FBS=fasting blood sugar, FBS ≥100 mg/dL or under treatment.
ACKNOWLEDGEMENT
This article is a condensed form of the first author's master's thesis from Daejeon University.
This study was supported by the “Development of Korean Medicine Original Technology for Preventive Treatment based on Integrative Big Data” project (K18091) and “The Construction of Database of Mibyeong Clinical Research Based on Healthy Koreans” (NRF-2014M3A9D7034351) of the Korea Institute of Oriental Medicine.
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