1. Sleep duration, sleep quality, and body composition among older adults from six middle-income countries: Findings from the Study on global AGEing and adult health (SAGE) Theresa E. Gildner1, Melissa A. Liebert1, Paul Kowal2,3, Somnath Chatterji2, and J. Josh Snodgrass1 1Department of Anthropology, University of Oregon, Eugene, OR; 2World Health Organization, Geneva, Switzerland; 3University of Newcastle Research Centre for Gender, Health and Ageing, Newcastle, NSW, Australia
Introduction
Global obesity levels are on the rise (Lakdawalla et al., 2005), necessitating new intervention programs to reduce disease burden. Previous studies suggest that altering sleep patterns may represent such an intervention. However, this remains poorly tested among older adults, despite an increased prevalence of sleep disorders (Bombois et al., 2010). Short sleep durations often result in daytime drowsiness, decreased physical activity levels, and can increase appetite by altering hormone profiles (Gangwisch et al., 2005; Knutson and Van Cauter, 2008). Further, disrupted sleep and lower subjective sleep quality ratings are associated with higher obesity rates due to potentially impaired glucose regulation (Spiegel et al., 2009). These associations are influenced by both age and sex. Women typically report more sleep disturbances and higher obesity rates (Baldwin et al., 2004). In addition, body fat deposition patterns changes with age, and evidence suggests that obesity rates may increase with age (Oreopoulos, 2009). Still, the relationship between sleep patterns and obesity risk remains poorly studied in older adults. Therefore, cross-cultural studies are needed to distinguish between universal risk factors and those influenced by environmental determinants.
Table 2. Waist circumference (WC; cm) category prevalence data for men, women, and sexes combined in each country with sample size (n). The number of asterisks indicates the level of significance (*= p < 0.05, **= p < 0.01, ***= p < 0.001) in the chi-square test.
Normal, as % (n=13,862)
Increased risk, as % (n=14,855)
China Total***
51.2 (6081)
48.8 (5886)
Men
70.7 (3963)
29.8 (1651)
Women
31.9 (2118)
68.1 (4235)
Ghana Total***
59.7 (2297)
40.3 (1480)
82.9 (1721)
17.1 (299)
32.6 (576)
67.4 (1181)
India Total***
63.8 (3308)
36.2 (1976)
79.5 (2128)
20.5 (595)
46.9 (1180)
53.1 (1381)
Mexico Total***
24.4 (342)
75.6 (1395)
37.8 (248)
62.2 (480)
10.3 (94)
89.7 (915)
Russia Total***
31.9 (694)
68.1 (1983)
46.9 (436)
53.1 (491)
21.9 (258)
78.1 (1492)
South Africa Total***
34.9 (1140)
65.1 (2135)
53.9 (776)
46.1 (624)
20.0 (364)
80.0 (1511)
Figure 2. SAGE participants in Ghana and China being administered an individual questionnaire
Key Findings
Hypothesis One: In all countries a significantly higher percentage of women were classified as obese compared to men using both BMI (p < 0.05) and WC measurements (p < 0.001) (Tables 1 & 2).
Hypotheses
This study focused on older adults and tested four hypotheses:
One: Women will exhibit higher obesity levels than men
Two: Obesity levels will increase linearly with age
Three: Shorter average sleep durations will be correlated with obesity risk
Four: Sleep quality ratings will be inversely related to obesity rates
Hypothesis Two: Significant differences in body composition between age groups were observed in both sexes (all countries pooled).
Men: BMI and WC decreased linearly across age groups (p < 0.001).
Women: BMI decreased linearly across age groups (p < 0.05).
Hypothesis Three: Significant differences in body composition were observed for different average sleep durations (all countries pooled).
Men: Longer sleep durations were significantly associated with lower BMI and WC measures (p < 0.001).
Women: Longer sleep durations were significantly associated with lower BMI and WC measures (p = and p = 0.030, respectively).
Discussion
The present study found support for two of the four hypotheses:
Women in all six countries exhibited higher obesity rates compared to men. These sex differences were apparent in all six countries, perhaps due to differences in fat deposition and occupational workload between the sexes.
Shorter sleep durations were significantly associated with higher BMI and WC measures, supporting findings from high-income nations.
Poor sleep quality and increased age did not contribute to obesity risk.
Interestingly, increased sleep quality did not reduce obesity risk, perhaps due to lifestyle changes (e.g., improved sleep environment in conjunction with access to processed food). Obesity rates decreased with age in all countries, suggesting obese individuals lose weight with age or die young. In conclusion, although the effects of sleep on body composition are modest, increasing sleep duration represents a potential intervention strategy.
Study on global AGEing and adult health (SAGE) participants
Data were from the first wave of the World Health Organization’s SAGE project. SAGE is a longitudinal study of nationally-representative samples of older adults (>50 years old) in six middle-income countries (China, Ghana, India, Mexico, Russian Federation, and South Africa) (Fig. 1). A household questionnaire administered to participants (Fig. 2) was used to obtain measures of sleep duration (n = 32,142), sleep quality (n = 33,348), body mass index (BMI) (n = 28,980), and waist circumference (WC) (n = 28,717).
Hypothesis Four: Significant differences in body composition were observed in men as sleep quality ratings increased.
Higher sleep quality ratings were significantly associated with higher BMI and WC measures (p = 0.001). This significant positive relationship was only apparent in men from India (p < 0.001) and China (p = 0.001), suggesting cultural factors may explain this finding.
Table 1. Body mass index (BMI; kg/m2) category prevalence data for men, women, and sexes combined in each country with sample size (n). The number of asterisks indicates the level of significance (*= p < 0.05, **= p < 0.01, ***= p < 0.001) in the chi-square test.
Underweight, as % (n=3,082)
Normal, as % (n=10,971)
Overweight/ Increased risk, as % (n=9,654)
Obese/Higher risk, as % (n=5,273)
China Total***
4.2 (541)
38.0 (4680)
42.7 (5049)
15.1 (1601)
Men
4.2 (247)
42.0 (2397)
42.4 (2348)
11.4 (570)
Women
4.3 (294)
34.1 (2283)
42.9 (2701)
18.6 (1031)
Ghana Total**
14.8 (562)
55.7 (2139)
19.8 (725)
9.7 (343)
14.8 (300)
60.5 (1261)
18.6 (352)
6.1 (102)
14.8 (262)
50.2 (878)
21.2 (373)
13.9 (241)
India Total***
38.4 (1800)
39.3 (2140)
15.7 (933)
6.6 (376)
39.3 (951)
41.6 (1179)
15.1 (465)
4.0 (116)
37.4 (849)
36.9 (961)
16.4 (468)
9.3 (260)
Mexico Total*
0.6 (18)
21.6 (477)
48.7 (709)
29.0 (544)
0.5 (6)
20.7 (220)
57.2 (336)
21.6 (161)
0.8 (12)
22.6 (227)
39.7 (373)
36.9 (383)
Russia Total**
1.1 (26)
23.9 (692)
40.6 (1288)
34.3 (1016)
1 (9)
27.6 (313)
46.2 (564)
25.1 (226)
1.2 (17)
21.3 (379)
36.5 (724)
40.9 (790)
South Africa Total***
3.3 (135)
22.8 (843)
26.9 (950)
47.1 (1393)
4.1 (73)
28.0 (443)
28.5 (429)
39.4 (476)
2.6 (62)
18.6 (400)
25.6 (521)
53.1 (917)
Acknowledgments
We thank Nirmala Naidoo for her efforts in data analysis. Support for the research was provided by NIH NIA Interagency Agreement YA CN-0020; NIH R01-AG
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Figure 1. Map of six SAGE countries, showing study locations.
Methods
Sleep Variables: Participants reported sleep duration and quality (on a scale of 1-5; here 1=poor sleep) the two previous nights. These values were averaged together to create participant summary measures.
Body Composition Variables: Height (cm) and weight (kg) were measured and were used to calculate individual BMI (kg/m2). BMI values were classified as underweight, normal, overweight, and obese. WC was obtained and divided into two categories: normal and increased risk.
Statistics: Depressed individuals were excluded from all analyses. Chi-square analyses were used to determine if significant sex differences were apparent in BMI or WC category by country. A series of linear regressions, separated by sex, were conducted to evaluate the relative contribution of sleep patterns and age to obesity variation, while controlling for lifestyle factors (smoking and drinking frequency, systolic and diastolic blood pressure, physical activity level) and country.