DOI: https://doi.org/10.21203/rs.3.rs-1381187/v1
Background: Single measures of self-reported nighttime sleep duration and daytime napping duration are associated with adverse health outcomes; However, the association between longitudinal patterns of self-reported nighttime sleep duration and daytime napping duration and multimorbidity remains unclear. We therefore undertook cox regression analyses to explore linkages between sleep trajectories and multimorbidity.
Methods: The current study included 5262 participants from China Health and Retirement Longitudinal Study. Self-reported nighttime sleep duration and daytime napping duration were collected from 2011 to 2015. The 4-year sleep duration trajectories were conducted by group-based trajectory modelling. The fourteen medical conditions were diagnosed by self-reported physicians or identified by combining health assessment and medication data. Multimorbidity was diagnosed as Participants with two or more of the 14 chronic diseases after 2015. Associations between sleep trajectories and multimorbidity were assessed by cox regression models.
Results: During 6.69 years of follow-up, we observed multimorbidity in 785 participants. Four nighttime sleep duration trajectories and three daytime napping duration trajectories were identified. Participants with persistent short nighttime sleep duration trajectory had the higher risk of multimorbidity (Hazard ratio (HR)=1.24, 95% confidence interval (CI): 1.04-1.49), compared with those with persistent normal nighttime sleep duration trajectory. We have not found a benefit of daytime napping on the development of multimorbidity. Participants with persistent normal nighttime sleep duration and persistent seldom daytime napping duration had a lower risk of multimorbidity.
Conclusions: In this study, persistent short nighttime sleep duration trajectory was associated with subsequent multimorbidity risk. Daytime napping cannot fully compensate for the risk of insufficient night sleep.
Sleep plays an important role in maintaining normal physiological functions throughout the life cycle of the body, involving inflammation, immune response, glucose regulation, energy balance, and more[1–3]. With the increase of age, sleep duration, sleep structure and sleep rhythm of the elderly will change correspondingly[1]. Altered sleep patterns and sleep disorders are very common in the elderly, which generally present as insomnia and disruption of sleep duration and quality[4, 5]. Moreover, altered sleep patterns and sleep disorders are associated with many health problems, such as cardiovascular diseases[6], cognitive impairment[7], and mortality[8].
A person suffering from two or more chronic diseases simultaneously or successively can be diagnosed as having multimorbidity[9]. With the aging of the world's population, the prevalence of multimorbidity is increasing, leading to a decline of patients' quality of life, increasing the difficulty of disease treatment and care, and bringing greater burdens and challenges. So far, some studies have explored the association between nighttime sleep duration and multimorbidity[10–15], while few studies had explored the association of daytime napping duration with multimorbidity. These studies used a single measure of sleep duration as exposure to examined the relationship between sleep duration and multimorbidity. However, sleep patterns have found to change continuously, especially in elderly[16]. A single measure of sleep may skew the relationship between sleep and adverse outcomes. In addition, there are no studies on the relationship between nighttime sleep patterns and daytime napping patterns and multimorbidity. Several current studies have found that the association of sustained short sleep with the risk of cardiovascular events, dementia, depression, and other diseases[17–19]. Another study found that the long and erratic trajectory of weekly naps increased the risk of obesity and hypertension[20]. These studies highlighted the role of long-term sleep patterns in disease development. Moreover, the combined effects of nighttime sleep trajectories and daytime napping trajectories on adverse health outcome have not been explored.
Therefore, we utilized the association of self-reported nighttime sleep duration and daytime napping duration trajectory investigated in China Health and Retirement Longitudinal Study (CHARLS) with subsequent multimorbidity events. To be compared with conventional studies, we also assessed the association of baseline-measured nighttime sleep duration and daytime napping duration with multimorbidity risk. We also evaluated the combined effect of nighttime sleep time trajectories and daytime napping duration trajectories on the occurrence of multimorbidity events.
Study design and study population
The data used in this study is from CHARLS, which is a representative longitudinal survey of the elderly in China, consisting of people aged 45 and above, conducted by the National Development Research Institute of Peking University. Ethical approval for collecting data on human subjects was received at Peking University by their institutional review board (IRB). The CHARLS data are publicly available from the China Health and Retirement Longitudinal Study website: http://charls.pku.edu.cn/en. The CHARLS baseline survey was carried out in 2011. 150 county-level units were randomly selected using probability and scale sampling counts (pps), including 450 village-level units, 17,000 out of 10,000 households from the sampling frame of all county-level units except Tibet. Respondents use computer-assisted personal interview (CAPI) technology to conduct face-to-face interviews at home[21]. A detailed description of the investigation objectives and methods has been reported elsewhere[21]. We used data from four rounds of the CHARLS conducted from 2011 to 2018. To investigate the association between sleep duration and multimorbidity, we limited the sample to respondents who did not have multimorbidity by 2015, had at least two sleep data in the previous three rounds, and had no missing covariates in the baseline survey. A total of 5262 respondents were included, and the sample screening process is shown in Figure 1.
Exposure Assessment
Respondents were asked to report their average actual sleep time per night over the past month, as well as how long they typically took a nap during the past month. Sleep duration was recoded as short nighttime sleep duration (<7 hour), normal nighttime sleep duration (7~9 hour) and long nighttime sleep duration (>9 hour) according to the American Academy of Sleep Medicine recommended the sleep duration[22]. The daytime napping duration were recoded as non-daytime napping (0 hour), short daytime napping duration (0~1 hour), and long napping duration (>1 hour). We used sleep data investigated from 2011 (baseline) to 2015(second follow-up) to assess the trajectory of persistent changes in nighttime sleep duration and daytime napping duration through a group-based trajectory model calculated using traj commands in STATA[18, 23]. Grouping selection was based on model fitting statistics (Bayesian Information Criterion, Akaike Information Criterion, and mean posterior probability) and meaningful interpretation[24, 25].
Assessment of multimorbidity Outcomes
In this study, hypertension, dyslipidemia, diabetes, heart disease, stroke, chronic lung disease, asthma, liver disease, cancer, digestive disease, kidney disease, arthritis, psychiatric disease, and memory-related disease were defined by self-reported physician diagnoses or in combination with health assessment and medication data. To reduce reverse causality, participants with two or more of the 14 chronic diseases after 2015 can be diagnosed as having multimorbidity. We used the time span between the onset of the second disease and 2011 as the time scale.
Covariate Assessment
We used the following variables as covariates in the main analysis: Age (<60 and older), sex (male and female), marital status (married and others), residence determined by household registration, including urban (non-agricultural) and rural (agricultural), education (illiterate, no finish primary school, elementary school, and middle school and above), smoking (yes/no), drinking refer to whether have consumed alcoholic beverages in the past year (more than once a month, less than once a month, and none) ,income assistance (never, a few time, and most).
Statistical analysis
We used the nighttime sleep duration trajectory group and multimorbidity at the end of follow-up to explore sociodemographic differences in baseline data. Cox regression was used for all analyses, with the time span between the onset of the second disease and 2011 as the time scale. Firstly, we explored the association between group-based trajectory model of nighttime sleep and daytime napping and multimorbidity. Secondly, we explore the combined effect of night sleep duration trajectories and daytime napping trajectories on multimorbidity. Stratification analysis was performed to explore this association by age and sex. For comparison with the result of previous studies, sensitivity analyses were also performed to find the correlation of nighttime sleep duration and daytime napping duration with multimorbidity. Restrictive cubic splines were used to examine the shape of the association between the two types of sleep duration and the incidence of multimorbidity. All analyses were performed in R language, Cox regression was performed using "survival" package and "survminer" package, and restricted cubic splines were performed using "rms" package. P values on both sides < 0.05 were considered statistically significant.
CHARLS data were used in this study, with a total of 17,956 subjects enrolled in the baseline study. A total of 5262 subjects were included in the 7-year follow-up study, excluding age <45 years, multimorbidity diagnosed before 2015, more than 1 missing value of sleep duration (both day and night) in 3 surveys, and missing value of corrected variables (flow chart shown in Figure 1). Of the included subjects, the mean follow-up time for 785 participants with multimorbidity was 6.01±0.77 years and the mean follow-up time for 4477 participants with non-multimorbidity was 6.81±0.72 years.
Group based trajectory modeling
In this study, the average posterior probability of group assignment, Bayesian Information Criterion, Akaike Information Criterion, and combined with the actual situation were used to judge the trajectories of the night sleep duration and daytime napping duration of middle-aged and elderly Chinese. It was found that the nighttime sleep of the middle-aged and elderly can be divided into four patterns (Figure 2 and Table 2).1554 (29.5%) participants maintained normal nighttime sleep duration throughout the procedure ( persistent normal group, mean range, 7.9 to 8.0 hours), 1594 participants (30.3%) had a relatively short nighttime sleep duration (persistent short group, mean range, 4.9 to 5.4 hours), 1096 (20.8%) had a short to normal nighttime sleep duration, (change from short to normal group, mean nighttime sleep duration increase, 6.1 to 7.9 hours), and 1018 (19.3%) had a nighttime sleep duration changed from normal to short (change from short to normal group, mean nighttime sleep duration decrease, 7.7 to 5.3 hour). Daytime napping duration can be divided into three patterns. 2414 participants (45.9%) sustained seldom daytime napping duration throughout (mean range, 0.1-0.1 hours), 2107 (40.0%) maintained short daytime napping duration (mean range, 0.5 to 1.0 hours), and 741 (14.8%) had a longer daytime napping sleep duration (mean range, 1.4 to 1.7 hours).
Characteristics of the study population
People with multimorbidity were more likely to be older than those who did not have multimorbidity (P<0.05). The baseline data were compared by the nighttime sleep duration trajectory group, and the results showed that there were differences in sex, age, education level and residence composition among each group (P <0.05) (Table 1).
Sleep trajectories and multimorbidity
We first explored the relationship between nighttime sleep duration trajectories and multimorbidity (Figure 3). After adjusting potential confounders, participants who maintained persistent short nighttime sleep duration had a higher risk of multimorbidity than those who continued to maintain persistent normal nighttime sleep duration (HR: 1.24, 95% CI: 1.04-1.49). Participants whose nighttime sleep duration changed from short to normal or from normal to short had an increased risk of multimorbidity, but it was not statistically significant. Subgroup analysis showed that participants whose nighttime sleep duration maintained short or changed from normal to short had increased risk of developing multimorbidity compared with those who had normal nighttime sleep duration in the female crowd, HR (95% CI) were 1.52(1.17-1.98) and 1.35(1.01-1.81), respectively (Table 3). When we explored the association between daytime napping duration trajectories and multimorbidity, we found that participants with persistent short or long daytime napping duration had an increased risk of developing multimorbidity compared with those with persistent seldom daytime napping duration, but unfortunately it was not statistically significant. In order to explore the combined effect of nighttime sleep duration trajectories and daytime napping duration trajectories on occurrence of multimorbidity, we foundthat participants who maintained persistent normal nighttime sleep duration and persistent seldom daytime napping duration had a lower risk of multimorbidity (Figure 4).
Sensitivity analysis
In comparison with previous studies, baseline sleep duration was used to further validate the association between sleep and occurrence of multimorbidity. As shown in Figure 5, people who slept less than 4 hours had higher risk of multimorbidity than those who slept 7-9 hours at night (HR:1.34, 95% CI:1.07-1.66), while those who slept 4-7 hours and more than 9 hours at night had higher risk of multimorbidity, but it has no statistical significance. The exposure response curve of baseline nighttime sleep duration and multimorbidity was shown in Figure 6A, which is U-shaped, that is, short or long nighttime sleep duration, especially with short one can increase the risk of multimorbidity. Stratified analysis by age and sex, the results showed that the participants who slept ≤4 hours at night had higher risk of multimorbidity both in <60 years group and ≥60 years group, and there was an interaction between age and nighttime sleep duration (Table 3). In exploring the association of baseline daytime napping duration with multimorbidity, we found only in the 60 years and older crowd that people with 0-1 hours at noon had a higher risk of developing multimorbidity, compared with non-nappers (HR:1.32, 95% CI:1.03-1.69) (Table 4). Baseline daytime napping duration and multimorbidity exposure curves are shown in figure 6B.
In this seven-year longitudinal cohort study of 5,262 subjects, we identified four different nighttime sleep trajectories and three different daytime sleep trajectories in Chinese adults over 45 years. We found that nighttime sleep trajectories were significantly correlated with the occurrence of multimorbidity and daytime napping cannot compensate for the risk of insufficient night sleep. We also found a correlation of baseline nighttime sleep duration and daytime napping duration with the occurrence of multimorbidity, which supports conclusion of traditional studies.
Our study found that participants with persistent short nighttime sleep duration had a higher risk of multimorbidity. While exploring the relationship between the baseline nighttime sleep duration and the incidence of multimorbidity, we found that participants with a baseline nighttime sleep duration of ≤4 hours had an increased the risk of developing multimorbidity compared with those with a baseline nighttime sleep duration of 7-9 hours. The dose-response curve showed a U-shaped relationship between baseline nighttime sleep duration and the risk of multimorbidity. At present, few studies have linked nighttime sleep duration to risk of multimorbidity, but some studies have found an association between nighttime sleep patterns and certain diseases. For example, a study found that people with consistently high nighttime sleep efficiency had lower prevalence of hypertension, circulatory system problems, arthritis, respiratory problems, and depression than those with consistently low nighttime efficiency[26]. Sustained short sleep duration and sleep variability in adulthood were associated with increased risk of type 2 diabetes later in life[27]. Another study found the association of persistent poor sleep quality and persistent sleep problems with continued deterioration in physical health[28]. Other studies have identified associations between persistently short nighttime sleep duration and the prevalence of hypertension[29], the risk of diabetes[27], and the risk of a first cardiovascular event[17]. In addition, the Whitehall II study and a Korean study respectively found an association of consistently short sleep duration with the risk of dementia and depression[18, 19]. Several cross-sectional studies[10, 11, 30] and a longitudinal cohort study[14] supported our findings that short baseline nighttime sleep increases the risk of multimorbidity.
We were the first to examined the association between daytime napping duration trajectories and the occurrence of multimorbidity. We found that people with persistent short or long duration of daytime naps had an increased the risk of multimorbidity, but unfortunately this association was not statistically significance. In addition, sensitivity analysis indicated that compared with no naps, those who took 0~1 hour daytime naps increased the risk of multimorbidity in people aged 60 years and above. So far, there are few studies on daytime nap patterns and multimorbidity, but a recent study found that individuals with the high-concave daytime napping duration pattern had a higher risk of hypertension, compared with individuals with a low-stable napping duration pattern[20]. Past studies have found the association of baseline daytime napping duration with metabolic syndrome[31], stroke[32], heart failure[33]. In contrast, a meta-analysis of cohort studies only found an association between daytime napping duration and cardiovascular in women crowds[34]. These studies support our findings to some extent that longer daytime nap may be associated with adverse outcomes.
Our study also found that compared with those with persistent normal nighttime duration and those with persistent seldom daytime napping duration, other sleep trajectory combinations increased the risk of developing multimorbidity to varying degrees, especially in those with persistent short nighttime sleep duration and persistent short daytime napping duration. Only a few studies have examined association between the combined effects of a single measure of nighttime and daytime sleep duration and adverse outcomes. For example, studies found that people who slept less at night and take longer naps had an increased risk of diabetes[35] and stroke [36]. Another study found that shorter total sleep duration combining with longer naps were associated with higher mortality in colorectal cancer survivors[37].
Several potential mechanisms may explain the association between reduced nighttime sleep duration and increased daytime napping duration and the risk of multimorbidity. Experimental evidence suggests that sleep is involved in inflammation, immune response, glucose regulation, and energy balance. It has been demonstrated that inflammation is one of the important pathogenesis of multimorbidity[38, 39]. Chronic sleep deprivation leads to chronic systemic low-level inflammation, whose persistence has been linked to obesity, cardiovascular disease, asthma, chronic pain, cancer, and neurodegenerative diseases[40]。A meta-study has found that napping itself is not harmful, and that inflammation is the mediator linking napping to later poor health[41]. After sleep restriction, napping or sleep extension could improve the recovery processes of alertness and immune and inflammatory parameters[42]. However, daytime napping appears to have limited compensation for the risk of nightime sleep deprivation[41, 43]. Thus, consistent with previous studies, we found that daytime napping may have a protective effect on short-night sleepers, but only in a limited way.
Strengths and Limitations
The advantage of this study is that subjects were selected from a nationally representative longitudinal cohort. In this study, we assessed the longitudinal daytime napping habits and nighttime sleep habits and explored the association between longitudinal sleep habits and occurrence of the multimorbidity. We also examined the combined effects of nighttime sleep habits and daytime napping habits on multimorbidity. Potential confounding factors were adjusted, such as sex, age, marital status, education, smoking, alcohol consumption, and financial support.
The study has several notable limitations. First, using self-reported sleep duration took place of objective measures, which may create bias. Second, there is a lack of measurements of other latitude sleep variables, such as sleep apnea, insomnia, and sleep quality. Moreover, the association between sleep duration trajectories and the occurrence of multimorbidity need to be validified by a study with longer time follow-up and larger sample size. Finally, although multiple potential covariables were adjusted, other confounding could not be ruled out.
Overall, this study provides a new perspective on the association between sleep duration and multimorbidity, highlighting the role of longitudinal sleep habits in the occurrence and development of multimorbidity. Our study found that persistent short nighttime sleep duration was association with the higher risk of multimorbidity, while daytime napping cannot compensate for negative effects of insufficient nighttime sleep. However, the role of changes in long-term sleep trajectory in the pathogenesis of multimorbidity needs to be further investigated.
HR: Hazard Ratio; CI: Confidence Interval.
Acknowledgments
We would like to thank the National School of Development together with the Institute for Social Science Survey at Peking University for providing the CHARLS data.
Authors’ contributions
SYW and XXX conceptualized the study, SYW, XXX, and JHG took responsibility for data handling and statistical analysis. JHG, ANL, and MJC drafted the manuscript and DHW, JYW, TGW, YDH, YYW, XYX, LY, HYL contributed to interpretation of data, critical revision of the manuscript, and study supervision. All authors read and approved the final manuscript.
Funding
This research was supported by Natural Science Foundation of Fujian (2019J01315), Fujian medical university talent research funding (XRCZX2019031) and Joint Funds for the Innovation of Science and Technology, Fujian Province (2018Y9089).
Availability of data and materials
The datasets used and analyzed during the current study are available in http://forum.charls.pku.edu.cn/.
Ethics approval and consent to participate
The China Health and Retirement Longitudinal Study (CHARLS) was a survey approved by the Ethical Review Committee of Beijing University, and this research involving human participants, human material, or human data was performed in accordance with the Declaration of Helsinki. Data from all participants analyzed in this study were anonymous.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Table 1. Characteristics of the participants by nighttime sleep duration trajectory group and multimorbidity status at end of follow-up*.
Variables |
Nighttime sleep duration trajectory |
c2 |
p‡ |
Multimorbidity status at end of follow-up |
c2 |
p‡ |
||||
Persistent normal |
Persistent short |
Change from short to normal |
Change from normal to short |
No Multimorbidity |
Multimorbidity |
|||||
N |
1554 |
1594 |
1096 |
1018 |
|
|
4477 |
785 |
|
|
Sex |
|
|
|
|
11.653 |
0.009 |
|
|
1.844 |
0.174 |
male |
847(54.5) |
824(51.7) |
543(49.5) |
491(48.2) |
|
|
2319(51.8) |
386(49.2) |
|
|
female |
707(45.5) |
770(48.3) |
553(50.5) |
527(51.8) |
|
|
2158(48.2) |
399(50.8) |
|
|
Age |
|
|
|
|
29.902 |
<0.001 |
|
|
6.126 |
0.013 |
<60 years |
1038(66.8) |
955(59.9) |
721(65.8) |
708(69.5) |
|
|
2942(65.7) |
480(61.1) |
|
|
≥60 years |
516(33.2) |
639(40.1) |
375(34.2) |
310(30.5) |
|
|
1535(34.3) |
305(38.9) |
|
|
Marital status |
|
|
|
|
0.364 |
0.061 |
|
|
0.903 |
0.342 |
married |
1327(85.4) |
1312(82.3) |
936(85.4) |
863(84.8) |
|
|
3767(84.1) |
671(85.5) |
|
|
others |
227(14.6) |
282(17.7) |
160(14.6) |
155(15.2) |
|
|
710(15.9) |
114(14.5) |
|
|
Education |
|
|
|
|
29.132 |
0.001 |
|
|
7.202 |
0.066 |
illiterate |
362(23.3) |
409(25.7) |
311(28.4) |
256(25.1) |
|
|
1119(25.0) |
219(27.9) |
|
|
no finish primary school |
230(14.8) |
292(18.3) |
174(15.9) |
159(15.6) |
|
|
722(16.1) |
133(16.9) |
|
|
elementary school |
336(21.6) |
326(20.5) |
260(23.7) |
220(21.6) |
|
|
964(21.5) |
178(22.7) |
|
|
middle school and above |
626(40.3) |
567(35.6) |
351(32.0) |
383(37.6) |
|
|
1672(37.3) |
255(32.5) |
|
|
Residence |
|
|
|
|
11.027 |
0.012 |
|
|
1.015 |
0.314 |
rural |
1259(81.0) |
1280(80.3) |
932(85.0) |
839(82.4) |
|
|
3657(81.7) |
653(83.2) |
|
|
city |
295(19.0) |
314(19.7) |
164(15.0) |
179(17.6) |
|
|
820(18.3) |
132(16.8) |
|
|
Smoking |
|
|
|
|
5.673 |
0.129 |
|
|
0.074 |
0.785 |
yes |
644(41.4) |
668(41.9) |
432(39.4) |
384(37.7) |
|
|
1814(40.5) |
314(40.0) |
|
|
no |
910(58.6) |
926(58.1) |
664(60.6) |
634(62.3) |
|
|
2663(59.5) |
471(60.0) |
|
|
Drinking |
|
|
|
|
8.87 |
0.181 |
|
|
0.989 |
0.610 |
more than once a month |
463(29.8) |
491(30.8) |
293(26.7) |
285(28.0) |
|
|
1294(28.9) |
238(30.3) |
|
|
less than once a month |
139(8.9) |
124(7.8) |
106(9.7) |
82(8.1) |
|
|
389(8.69) |
62(7.90) |
|
|
none |
952(61.3) |
979(61.4) |
697(63.6) |
651(63.9) |
|
|
2794(62.4) |
485(61.8) |
|
|
Income assistance |
|
|
|
|
5.461 |
0.486 |
|
|
0.19 |
0.909 |
never |
1291(83.1) |
1311(82.2) |
915(83.5) |
835(82.0) |
|
|
3707(82.8) |
645(82.2) |
|
|
a few times |
216(13.9) |
249(15.6) |
158(14.4) |
156(15.3) |
|
|
659(14.7) |
120(15.3) |
|
|
most |
47(3.0) |
34(2.1) |
23(2.1) |
27(2.7) |
|
|
111(2.48) |
20(2.55) |
|
|
*Data are presented as absolute number (percentages) for the categorical variables.
‡ Chi-squared tests for the categorical variables.
Table 2. Description of nighttime sleep duration and daytime napping by groups of trajectories.
Model |
Sleep duration, mean (standard deviation) (h) |
||
2011 |
2013 |
2015 |
|
Nighttime sleep duration |
|
|
|
Persistent normal |
7.9(1.1) |
7.9(1.1) |
8.0(1.1) |
Persistent short |
5.0(1.2) |
5.4(1.6) |
4.9(1.3) |
Change from short to normal |
6.1(1.4) |
6.0(1.4) |
7.9(1.1) |
Change from normal to short |
7.7(0.9) |
6.1(1.5) |
5.3(1.1) |
Daytime napping |
|
|
|
Seldom |
0.1(0.4) |
0.1(0.3) |
0.1(0.3) |
Short |
0.5(0.5) |
0.9(0.7) |
1.0(0.7) |
Long |
1.7(0.7) |
1.6(0.7) |
1.4(0.8) |
Table 3. Association of nighttime sleep duration trajectories and daytime napping duration trajectories with multimorbidity, stratified by age and sex *.
Trajectory model |
HR (95%CI) |
|||||
Age |
P-interaction |
Sex |
P-interaction |
|||
<60 years |
≥60 years |
Male |
Female |
|||
Nighttime sleep duration |
|
|
0.961 |
|
|
0.825 |
Persistent normal |
1 (reference) |
1 (reference) |
1 (reference) |
1 (reference) |
||
Persistent short |
1.24(0.98, 1.57) |
1.24(0.93, 1.65) |
1.02(0.79, 1.32) |
1.52(1.17, 1.98) |
||
Change from short to normal |
1.07(0.83, 1.39) |
1.05(0.75, 1.46) |
1.16(0.89, 1.53) |
0.97(0.71, 1.32) |
||
Change from normal to short |
1.14(0.88, 1.48) |
1.02(0.71, 1.47) |
0.86(0.63, 1.18) |
1.35(1.01, 1.81) |
||
Daytime napping |
|
|
0.457 |
|
|
0.264 |
Seldom |
1 (reference) |
1 (reference) |
1 (reference) |
1 (reference) |
||
Short |
1.02(0.84, 1.24) |
1.13(0.88, 1.45) |
0.93(0.75, 1.15) |
1.23(0.98, 1.54) |
||
Long |
1.09(0.83, 1.43) |
1.26(0.91, 1.74) |
1.09(0.79, 1.51) |
1.24(0.94, 1.65) |
Abbreviations: HR, Hazard Ratio; CI, Confidence Interval.
* Using cox regression to get hazard ratio after adjusted sex, age, marital status, education, residence, smoking, drinking, and income assistance.
Table 4. Association of nighttime sleep duration and daytime napping duration with multimorbidity, stratified by age and sex *.
Sleep duration |
HR (95%CI) |
|||||
Age |
P-interaction |
Sex |
P-interaction |
|||
<60 years |
≥60 years |
Male |
Female |
|||
Nighttime sleep duration |
|
|
0.013 |
|
|
0.829 |
≤4 h |
1.10(0.79, 1.54) |
1.50(1.11, 2.02) |
1.10(0.77, 1.57) |
1.50(1.13, 1.99) |
||
4~7 h |
1.22(1.00, 1.49) |
0.83(0.63, 1.08) |
1.08(0.86, 1.35) |
1.04(0.82, 1.31) |
||
7~9 h |
1 (reference) |
1 (reference) |
1 (reference) |
1 (reference) |
||
>9h |
1.36(0.89, 2.10) |
0.80(0.43, 1.49) |
1.13(0.69, 1.84) |
1.08(0.65, 1.80) |
||
Daytime napping |
|
|
0.165 |
|
|
0.200 |
0 |
1 (reference) |
1 (reference) |
1 (reference) |
1 (reference) |
||
0~1 |
0.91(0.74, 1.11) |
1.32(1.03, 1.69) |
0.97(0.78, 1.21) |
1.14(0.91, 1.43) |
||
>1 |
0.92(0.69, 1.23) |
1.09(0.77, 1.54) |
0.85(0.60, 1.20) |
1.11(0.83, 1.49) |
Abbreviations: HR, Hazard Ratio; CI, Confidence Interval.
* Using cox regression to get hazard ratio after adjusted sex, age, marital status, education, residence, smoking, drinking, and income assistance.