Clinical Investigation of Sleep Status in the Elderly Occupational Population

Authors: testHsi-Che Shen1, 2, Wan-Ching Chang3, Zi-Hao Zhao4, Yi-Chun Hu1,5, Yu-Fen Chen6-8 and Tao-Hsin Tung4,9,10

Citation: Hsi-Che Shen, Wan-Ching Chang, Zi-Hao Zhao, Yi-Chun Hu, Yu-Fen Chen and Tao-Hsin Tung, ”Clinical Investigation of Sleep Status in the Elderly Occupational Population”, Global Scientific Research Journal of Public Health, 1(1), 2018, pp. 1-8.

Copyright: Copyright © 2018 Hsi-Che Shen et al. (2018). This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Purpose: From the clinical viewpoint, numerous factors may interfere with sleep- wake patterns in elderly population. Mild changes in sleep quality could be expected with aging, but the consequences of chronic sleep problems also should be considered and evaluated. The purpose of this study is conducted to explore the sleep status and associated factors in the elderly occupational population in Taipei, Taiwan.

Methods: A total of 3,828 (2,382 males and 1,446 females) healthy adults voluntarily admitted to a teaching hospital for a physical check-up in 2013 in New Taipei City, Taiwan. Fasting blood samples were drawn via venipuncture and interviewed with a structured questionnaire included sleeping time and sleep status from study participants by clinical nurses.

Results: The mean age of the study participants is 69.91±7.1 years. The prevalence of poor sleep status was 31.1% (95% CI: 29.6-32.6%). Female did exhibit a higher prevalence of poor sleep status than males (33.7% vs. 29.5%, p-value=0.006). Based on the logistic regression, age (OR=0.96, 95%CI: 0.93-0.99), male sex (OR=0.81, 95%CI: 0.75-0.90), sleep time less than 6 hours per day (OR=3.01, 95%CI: 2.13-4.09), alcohol drinking (OR=1.14, 95%CI: 1.03-1.27), higher BMI (OR=1.49, 95%CI:1.08-1.95), and metabolic syndrome (OR=1.67, 95%CI: 1.23-2.07) were significantly associated with poor sleep status.

Conclusion: Several associated factors were indicated pertaining to the prevalence of poor sleep status among elderly specific occupational population.

Key Words: sleep status, prevalence, elderly, agricultural and fishing population



Insomnia and obstructive sleep apnea are two common disorders affecting elderly population [1]. Previous results indicated that 50% of elder individuals suffer from insomnia, with a higher prevalence amongst women [2]. Sleep disturbances could decrease total time of nocturnal sleep time, delay sleep onset, advance circadian phase, reduce rapid-eye-movement sleep, short daytime nap, and somnolence [3]. The subjects with sleep disturbances are typically combined with medical conditions include diabetes, hypertension, depression, cardiovascular and cerebrovasculardisease, and further diminish quality of life [1,3,4]. Due to the delayed diagnosis or appropriate treatment for this sleep disorder may account for poor prognosis and long-term utilization of medical services then increase healthcare expenditures [1], the early detection by routine screening followed by appropriate clinical intervention would offer a practical means for the prevention of condition-associated damage.

From the evidence-based medicine viewpoint, sleep disorder is matched the Wilson criteria for screening due to it is an important health problem for the elderly population [5]; the disease natural course should be explored; a recognizable latent or early symptomatic stage; a screening test is easy to perform and interpret, acceptable,accurate, reliable, sensitive and specific; an accepted treatment recognized for the disease; clinical intervention is more effective if started early; a health policy on who should be treated; diagnosis and treatment are cost-effectiveness; and case-finding should be a continuous process. With population aging, the burden of healthcare service will certainly increase amongst this demographic group at risk for developing and/or deteriorating indispositions. To the best of our knowledge, however, few clinical evidence-based studies attempted to determine the possible etiology between associated factors and poor sleep status for the elderly agricultural and fishing population of Taiwan, which also faced to the burden of health-related disease. The purpose of this study is to explore in the context of prevalence of and associated factors and poor sleep status amongst the elderly agricultural and fishing population, as determined by the application of a healthy volunteer subjects screening program health examination in New Taipei, Taiwan.


Study design and data collection: This hospital-based, cross-sectional health screening was conducted with a total of 3,828 elder agricultural and fishing professional (2,382 males and 1,446 females) voluntarily admitted to one teaching hospital in New Taipei City for an annual physical check-up between January 2013 and December 2013.

The medical histories and measurements of the participants were obtained by well-trained nurses. Personal and family histories of hypertension, type 2 diabetes, cardiovascular diseases, and other chronic diseases were obtained by a structured health interview questionnaire. The personal life habits such as smoking, alcohol drinking and areca nut use were also collected. The study participants were asked to take off the shoes and any other belongings that could possibly add extra weight when they were weighed. Heights and weights were evaluated according to body mass index (BMI). Also the waist circumference was also measured at the level of the iliac processes and the umbilicus with a soft tape measure to estimate abdominal obesity.

Blood pressures for each subject were measured twice in the sitting position with an interval of 15 minutes between the measurements, by means of standard sphygmomanometers of appropriate width, after a rest period for 30 minutes. Those who taking antihypertensive therapy were considered to be known hypertension [6]. 

Fasting blood samples were drawn via venipuncture from study participants by clinical nurses. Overnight-fasting serum and plasma samples (from whole blood preserved with EDTA and NaF) were kept frozen (-20°C) until ready for analysis. All procedures were performed in accordance with the guidelines of our institutional ethics committee and adhered to the tenets of the Declaration of Helsinki. All patients’information were anonymous. The anonymity of participants and confidentiality of the responses were ensured by using numerical codes for questionnaires and destroying the data at the end of study.

Measurement of sleep quality:

The primary outcome measure was sleep quality assessed using a structured questionnaire. For the assessment of sleep quality, the question is “Over the past month, the status of your night time sleep?” The poor sleep quality was defied as when the answer included light sleep, bad dream recall, or frequent dreaming. In addition, for the evaluation of sleep duration, the question is “How much sleep time do you get per night?” Ranging from <4 hrs, 4-6 hrs, 6-8 hrs, to ≧8 hrs.

Diagnosis of metabolic syndrome:

In this study, metabolic syndrome was diagnosed by the Adult Treatment Panel III (ATP III) criteria, based on the presence of at least 3 of the following 5 metabolic factors: (1) central obesity (waist circumference 90 cm in Asian men and 80 cm in Asian women); (2) decreased HDL-C: fasting HDL-C < 40 mg/dL or drug treatment for reduced HDL-C; (3) elevated blood pressure: systolic blood pressure 130 mmHg and/or diastolic blood pressure 85 mmHg, or antihypertensive drug treatment in a patient with a history of hypertension; (4) hypertriglyceridemia: fasting plasma triglycerides 150 mg/dL or drug treatment for elevated triglycerides; and (5) hyperglycemia: fasting glucose level 100 mg/dL or drug treatment for elevated glucose [7].

Statistical analysis :

Statistical analysis was performed using SAS for Windows, (SAS version 9.3; SAS Institute Inc., Cary, NC, USA). For univariate analysis, the χ2-test and unadjusted odds ratio (OR) was adopted to assess differences of categorical variables. Binary logistic regression was also performed to provide a set of coefficients of poor sleep status and to investigate the independence of factors associated with the prevalence of poor sleep status. A p-value of <0.05 was considered to represent a statistically significant difference among test populations.


Table 1 shows the gender- and age-specific prevalence of poor sleep status amongst study-participating elderly subjects. The overall prevalence of poor sleep status for the screened population was 31.1% and revealed a statistically significant decrease with increasing subject age by means of the χ 2 trend test (p<0.0001). The prevalence of poor sleep status for females proved to be substantially higher than males (33.7% vs.29.5%, p value for χ 2 test=0.006). After stratifying data by age into one of four broad (age) groups, study females exhibited a more-pronounced prevalence of poor sleep status for all age groups than was the case for the male group. The age-specific prevalence of poor sleep status revealed a significant inverse relationship with age when applying the χ 2 trend test for both male (p<0.001) and female (p<0.001) study subjects.

Table1. The gender- and age-specific prevalence of poor sleep status among elderly agricultural and fishing screened population subjects (n=3,828)

Table 2 presents the univariate analysis for the association between certain relevant associated factors and poor sleep status. Compared to individuals who exhibited a normal sleep status, subjects featuring a poor sleep status revealed a more-pronounced prevalence of: sleep time less than six hours (OR=6.13, 95%CI: 5.21-7.22), obesity (OR=3.24, 95%CI: 2.80-3.75), elevated bold pressure (OR=2.90,95%CI: 2.48-3.38), central obesity (OR=2.06, 95%CI: 1.79-2.38), hyperhlycemia (OR=1.62, 95%CI: 1.41-1.86), and metabolic factor (1-2 vs. none, OR=1.48, 95%CI: 1.12-1.80; ≧3 vs. none, OR=2.03, 95%CI: 1.68-2.46). In addition, the poor sleep status was tended to be higher in individuals with smoking (yes vs. no, OR=1.37,95%CI: 1.13-1.60) or alcohol drinking (yes vs. no, OR=1.87, 95%CI: 1.62-2.16).

Table2. Univariate analysis of associated factors for poor sleep status among elderly agricultural and fishing screened population subjects (n=3,828)

The effect of independent associated risk factors of poor sleep status was examined using the binary logistic regression model. As is depicted in Table 3, subsequent to adjustment for confounding factors, age (OR=0.97, 95%CI: 0.96-0.99),sex (male vs. female, OR=0.77, 95%CI: 0.64-0.90), sleep time (<6 vs. ≧6 hrs,OR=3.49, 95%CI: 3.03-4.01), alcohol drinking (yes vs. no, OR=1.17, 95%CI: 1.01-1.37), BMI (≧25 vs. <25 Kg/m2, OR=1.56, 95%CI: 1.10-1.98), elevated blood pressure (yes vs. no, OR=1.22, 95%CI: 1.09-1.46), central obesity (yes vs. no,OR=1.78, 95%CI: 1.24- 2.51), and hyperglycemia (yes vs. no, OR=1.48, 95%CI: 1.14-2.02) appeared to be statistically significantly related to mild NAFLD. Table 2 also showed that age (OR=0.96, 95%CI: 0.93-0.99), sex (male vs. female, OR=0.81,95%CI: 0.75- 0.90), sleep time (<6 vs. ≧6 hrs, OR=3.01, 95%CI: 2.13-4.09), alcohol drinking (yes vs. no, OR=1.14, 95%CI: 1.03- 1.27), BMI (≧25 vs. <25 Kg/m2,OR=1.49, 95%CI: 1.08-1.95), and metabolic syndrome (yes, vs. no, OR=1.67,95%CI: 1.23-2.07) appeared to be statistically significantly related to poor sleep status.

Table3. Multiple logistic regression of associated factors for poor sleep status among elderly agricultural and fishing screened population subjects (n=3,828)


Undoubtedly, the maintenances of good health and suitable training for agricultural and fishing professional are necessary. The longer and irregular working hours may cause some adverse health effects for this subpopulation. In Taiwan, there would appear only few published population-based studies attempting to discuss the prevalence and possible etiology of poor sleep status for this elderly Chinese population, which also faced to the burden of sleep disorder. Due to the increased frequency of poor sleep status among elderly subjects, it is essential for identifying needs for medical services, health promotion planning, and implementing comprehensive preventive care programs for sleep disorder. Appropriated preventive health screenings are an important health promotion strategy due to they could help to identify disorder at an early stage, postpone the development of subsequent serious outcomes, and further significantly save healthcare resources and lives [8]. The negative relationship between increased age and poor sleep status in our study supported earlier results from other population-based study [1]. The possible explanation is competing causes of death, that is, a percentage of the oldest-old with poor sleep status and combined other health conditions may have died at earlier ages.In addition, our study observed females tend to have higher prevalence for poor sleep status. Previous studies indicated that estrogen deficiency, particularly during the perimenopausal period, may account for the higher number of women with insomnia [9].

Due to the agricultural and fishing population always face to the hard work, job stress,and reversed working and resting time. Irregular lifestyle and careless their own health are also major problems. This might partially explain the alcohol drinking apparently high prevalence of poor sleep status observed in our study. However, the finding may simply have been related to the different study populations.

Metabolic syndrome remains the most useful and widely accepted description of this cluster of metabolically related cardiovascular risk factors which also predict a high risk of developing diabetes [10]. In this study, the metabolic syndrome is significantly related to poor sleep status. The combination of obstructive sleep apnea and metabolic syndrome has been referred to as “syndrome Z” [11]. Although metabolic syndrome and obstructive sleep apnea may simply be concurrent syndromes, there is growing no consistent conclusion, clinical evidence that the physiological processes of obstructive sleep apnea and metabolic syndrome overlap considerably [11, 12]. The documented prevalence of poor sleep status is still clinically significant and is a wake-up call for government health practitioners and policy makers to be on the alert and also formulate policy to help curtail its impact especially by measures to reduce the components of metabolic syndrome in view of the relationship. In addition, the presence of a higher BMI was also associated with a higher risk for poor sleep status even after adjusting for other confounding factors.

Only 2-3% Asia subjects can be identified as obese according to the Western criteria for obesity and Asians have a higher proportion of visceral fat and a lower proportion of lean body mass than Caucasians with the same BMI condition [5, 10, 13]. Various studies have discussed the impact of sleep disorders on obesity, and are an important link in understanding the relationship between sleep disorders and related chronic disease [14-16]. Physical activity and exercise are essential prognostic tools in obesity and chronic disease, and numerous studies have explored the relationship between obesity, sleep disorders, and exercise [17-20]. Further epidemiological and etiological investigations are needed to clarify the pathophysiological mechanisms with metabolic syndrome, BMI and poor sleep status

Methodological considerations:

Admittedly, there were several limitations in this study. Firstly, the potential impact on the prevalence and the study-observed sleep status-related risk factors were due to the screening of elderly population from one area, in our estimation, inevitable. The study still retained sufficient statistical power to evaluate the various risk factors for poor sleep status given the rather large sample size. Secondly, this study only included subjects who were aged subjects and may have different characteristics compared with whole population. However, this sub-population was more susceptible to have poor sleep status and easily to know the trend happened in Taiwan and take early prevention strategies. Thirdly, we only used questionnaire to identify poor sleep status, the misclassification may be occurred. Finally, our measurements were conducted at only a single point in time and, therefore, may not reflect long-term exposure to important demographic or biochemical factors [5]. The improvement to such a quandary would be to conduct a number of prospective longitudinal analogous studies to see if they would complement the cross-sectional findings of this study.


Several associated factors were indicated pertaining to the prevalence of poor sleep status among elderly specific occupational population. Integrative promotions of this population for metabolic function are important.

Competing Interests:

The authors declare that they have no competing interests.

Authors’ Contributions:

His-Che Shen, Yi-Chun Hu, Yu-Fen Chen, and Tao-Hsin Tung carried out the study and drafted the manuscript. Wan-Ching Chang and Tao-Hsin Tung participated in the design of the study and performed the statistical analysis. Hsi-Che Shen and Tao-Hsin Tung conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.


This study was supported by the grants from the National Science Council (NSC-95-2314-B-002-MY3) and (NSC-98-2314-B-350-002-MY3), and New Taipei City Hospital, Taipei, Taiwan.

Conflicts of Interest:

We certify that all the affiliations with or financial involvement in, within the past 5 years and foreseeable future, any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript are completely disclosed (e.g., employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, royalties).



1. Gamaldo AA, Beydoun MA, Beydoun HA, et al. Sleep disturbances among older adults in the United States, 2002-2012: Nationwide inpatient rates, predictors, and outcomes. Front Aging Neurosci 2016;8:266.

2. Monane M. Insomnia in the elderly. J Clin Psychiatry. 1992;53(Suppl):23-28.

3. Chen L, Bell JS, Visvanathan R, et al. The association between benzodiazepine use and sleep quality in residential aged care facilities: a cross-sectional study. BMC Geriatr 2016;16:196.

4. Bloom HG, Ahmed I, Alessi CA, et al. Evidence-based recommendations for the assessment and management of sleep disorders in older persons. J Am Geriatr Soc. 2009;57:761-789.

5. Shen HC, Zhao ZH, Hu YC, et al. Relationship between obesity, metabolic syndrome, and nonalcoholic fatty liver disease among the elderly agricultural and fishing population in Taiwan.Clin Interv Aging 2014;9:501- 508.

6. Kuo CM, Chien WH, Shen HC, et al. Clinical epidemiology of reduced kidney function among male elderly fishing and agricultural population in Taipei, Taiwan.Biomed Res Int 2013;Article ID 214128

7. Grundy SM, Cleeman JI, Daniels SE, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005;112:2735-2752.

8. Chang WC, Lan TH, Ho WC, et al. Factors affecting the use of health examinations by the elderly in Taiwan. Arch Gerontol Geriatr 2010;50(Suppl 1):S11-S16.

9. Wolkove N, Elkholy O, Baltzan M, et al. Sleep andaging:1. Sleep disorders commonly found in older people. CMAJ 2007;176:1299-1304.

10. Tung TH, Chang TH, Chiu WH, et al. Clinical correlation of nonalcoholic fatty liver disease in a Chinese taxi drivers population in Taiwan: Experience at a teaching hospital. BMC Res Notes 2011;4:315.

11. Calvin AD, Albuquerque FN, Lopez-Jimenez F, et al. Obstructive sleep apnea, inflammation, and the metabolic syndrome. Metab Syndr Relat Disord 2009;7:271-278.

12. Wilcox I, McNamara S, Collins F, et al. “Syndrome Z”: The interaction of sleep apnoea, vascular risk factors and heart disease. Thorax 1998;53:25-28.

13. Fan JG, Farrell GC. Epidemiology of non-alcoholic fatty liver disease in China. J Hepatol 2009;50:204-210.

14. Hargens TA, Kaleth AS, Edwards ES, et al. Association between sleep disorders, obesity, and exercise: a review. Nat Sci Sleep 2013;5:27-35.

15. Lopez-Garcia E, Faubel R, Leon-Munoz L, et al. Sleep duration, general and abdominal obesity, and weight change among the older adult population of Spain.Am J Clin Nutr 2008;87:310-316.

16. Patel SR, Blackwell T, Redline S, et al. The association between sleep duration and obesity in older adults. Int J Obes (Lond). 2008;32:1825-1834.

17. Grandner MA, Patel NP, Perlis ML, et al. Obesity, diabetes, and exercise associated with sleep-related complaints in the American population. Z Gesundh Wiss. 2011;19:463-474.

18. Chasens ER, Yang K. Insomnia and physical activity in adults with prediabetes. Clin Nurs Res. 2012;21:294- 308.

19. Passos GS, Poyares D, Santana MG, et al. Effect of acute physical exercise on patients with chronic primary insomnia. J Clin Sleep Med. 2010;6:270-275.

20. Reid KJ, Baron KG, Lu B, et al. Aerobic exercise improves self-reported sleep and quality of life in older adults with insomnia. Sleep Med. 2010;11:934-940