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. 2020 Jul 28;20:1179. doi: 10.1186/s12889-020-09275-3

The relationship between sleep duration and all-cause mortality in the older people: an updated and dose-response meta-analysis

Mengyang He 1,#, Xiangling Deng 1,#, Yuqing Zhu 2, Luyao Huan 1, Wenquan Niu 3,✉
PMCID: PMC7389345  PMID: 32723316

Abstract

Background

Short or long sleep duration is proposed as a potential risk factor for all-cause mortality in the older people, yet the results of published studies are not often reproducible.

Methods

Literature retrieval, study selection and data extraction were completed independently and in duplicate. Only prospective cohort studies were included. Effect-size estimates are expressed as hazard ratio (HR) and 95% confidence interval (CI).

Results

Summary data from 28 articles, involving a total of 95,259 older people, were meta-analyzed. Overall analyses revealed a remarkably significant association between long sleep duration and all-cause mortality (adjusted HR = 1.24, 95% CI: 1.16–1.33, P < .001), whereas only marginal significance was observed for short sleep duration (adjusted HR = 1.04; 95% CI: 1.00–1.09; P = .033). Funnel plots suggested no publication bias for short sleep duration (P = .392). The probability of publication bias was high for long sleep duration (P = .020), yet the trim-and-fill method strengthened its significance in predicting all-cause mortality. In subgroup analyses, the association of long sleep duration with all-cause mortality was statistically significant in both women (HR = 1.48; 95% CI: 1.18–1.86; P = .001) and men (HR = 1.31; 95% CI: 1.10–1.58; P = .003). By contrast, with regard to short sleep duration, statistical significance was observed in men (HR = 1.13; 95% CI: 1.04–1.24; P = .007), but not in women (HR = 1.00; 95% CI: 0.85–1.18; P = .999) (Two-sample Z test P = .099). Besides gender, geographic region, sleep survey method, baseline age and follow-up interval were identified as possible causes of between-study heterogeneity in subgroup analyses. Further dose-response regression analyses revealed that trend estimation was more obvious for long sleep duration (regression coefficient: 0.13; P < .001) than for short sleep duration (regression coefficient: 0.02; P = .046).

Conclusions

Our findings indicate a significantly increased risk of all-cause mortality associated with long sleep duration, especially in women, as well as with short sleep duration in men only.

Keywords: All-cause mortality, Meta-analysis, Older people, Sleep duration

Background

It is widely recognized that sleep plays an important role in human mental and physical health [1, 2]. Experimental studies indicated that sleep deprivation and excessive sleep duration can exert an adverse effect on hormones, metabolism and immune function [3–5]. From epidemiological aspects, although dozens of studies reported that inappropriate sleep duration and poor sleep quality are reported to be associated with high risk of some common diseases, including diabetes [6], cardiovascular diseases [7] and cancer [8], as well as to increased all-cause and cause-specific mortality rates [9], these associations are not often reproducible.

Over the past decades, many prospective studies have reported a U-shaped relationship between sleep duration and all-cause mortality, with the nadir at 7–8 h of sleep per night [10–17]. In 2016, da Silva and colleagues conducted a meta-analysis by pooling the results of 27 cohort studies, and they found a significant association of both long and short sleep duration with increased all-cause mortality risk in the older people, and the association was more evident for long sleep duration [18]. However, the results of other studies have failed to provide any supportive data on sleep duration and mortality in the older people [19–21]. The reasons for these inconsistent reports are multifactorial, possibly relating to inadequate statistical power of individual studies, different backgrounds and characteristics of study groups, and lack of adjustment for confounding factors. Given the accumulating data afterwards, there is a need to reexamine this association in a more comprehensive manner.

To yield more information for future studies, we synthesized the results of prospective cohort studies in the older people, aiming to evaluate the association between sleep duration and all-cause mortality. Meanwhile, we also intended to explore possible causes of between-study heterogeneity.

Methods

This meta-analysis was conducted according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement [22], and the PRISMA checklist is presented in Supplementary Table 1.

Search strategy

We completed literature search by scanning PubMed, EMBASE and Web of Science databases as of November 30, 2019. The following medical topic terms are used: (sleep OR sleep disorders OR sleep duration OR drowse OR napping OR naps OR nap OR Siesta OR drowsiness OR drowse OR insomnia OR actigraphy sleep OR self-reported sleep [Title/Abstract], AND mortality OR death OR deaths OR premature death OR all-cause mortality [Title/Abstract]), AND (aged OR geriatrics OR older people OR older age OR older adult OR older adult OR older persons OR older people OR older men OR older women OR aging OR aging women OR aging men OR the older people OR aging individuals [Title/Abstract]). We also scanned the reference lists of retrieved articles and systematic reviews to avoid potential missing hits.

Two investigators (M.H. and X.D.) independently reviewed all retrieved articles, and, they carefully evaluated preliminary qualification based on their titles or abstracts and full texts if necessary.

Inclusion/exclusion criteria

Our analyses were restricted to the articles that met the following criteria: (1) participants aged ≥60 years old; (2) all-cause mortality as the outcome; (3) prospective cohort studies; (4) clear classification of sleep duration; (5) at least 70% follow-up rate. Studies with subgroup analysis in older people on sleep duration and all-cause mortality were also included in this meta-analysis. Articles were excluded if they focused on cause-specific mortality or involved participants with serious diseases, or if they are case reports/series, editorials, and narrative comments.

Data extraction

Two investigators (M.H. and X.D.) independently extracted data from each qualified article, and typed them into a standardized Excel spreadsheet, including name of the first author, year of publication, country where study was conducted, race, sample size, sex, baseline age, follow-up period, ascertainment of sleep duration, death certificate, adjusted confounders, sleep duration, effect estimation, and other traditional risk factors, if available. The divergences were resolved through joint reevaluation of original articles, and, if necessary, by a third author (W.N).

Statistical analysis

We used the Stata software version 14.1 for Windows (Stata Corp, College Station, TX) to manage and analyze data. Irrespective of the magnitude of between-study heterogeneity, the random-effects model was employed. Effect size estimates are expressed as hazard ratio (HR) and its 95% confidence interval (CI), and the difference between two estimated was tested by the Z-test as reported by Altman and Bland [23]. The dose-response association was examined by the generalized least squares regression proposed by Greenland and Longnecker [24] for trend estimation of summarized dose-response data. Additionally, the restricted cubic splines of exposure distribution with 3 knots (25th, 50th, and 75th percentiles) were used to conduct nonlinearity test between sleep duration and all-cause mortality.

The inconsistency index (I2) is used to assess heterogeneity between studies, and it represents the percentage of diversity observed between studies that results from chance rather than an accidental result. If the I2 value is greater than 50%, significant heterogeneity is recorded, and a higher value indicates a higher degree of heterogeneity. Because of diverse sources of heterogeneity possibly from clinical and methodological aspects, a large number of prespecified subgroups were analyzed according to baseline age, sex, region, race, follow-up, short sleep duration and long sleep duration, respectively.

The probability of publication bias was evaluated by both Begg’s funnel plots and Egger regression asymmetry tests at a significance level of 10%. The trim-and-fill method was used to estimate the number of theoretically missing studies.

Results

Eligible studies

After searching prespecified public databases using predefined medical subject terms, a total of 2098 articles were initially identified, and 28 of them with data on sleep duration and all-cause mortality were eligible for inclusion [10, 14, 17, 19, 21, 25–47], including 95,259 older persons in the final analysis. The detailed selection process including specific reasons for exclusion is schematized in Fig. 1. Since most articles provided data according to different age groups at baseline or follow-up periods, they are processed separately in subgroup analyses.

Fig. 1.

Fig. 1

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Flow chart of records retrieved, screened and included in this meta-analysis

Study characteristics

Table 1 and Table 2 show the baseline characteristics of all cohort studies involved in this meta-analysis. Of 28 eligible articles, 2 in older women [17, 19], and 6 specifically described the number of men and women and the number of deaths of men and women [27, 30, 35, 38–40]. Five articles provided data on the association between sleep duration and all-cause mortality by gender [30, 35, 36, 38, 42]. Of all eligible articles, 9 investigated the total sleep duration of 24 h in the older people [19, 26, 31, 33, 38, 39, 41–43], and the others focused on the nighttime. One article adopted the actigraphy method to collect sleep time [43], and 2 articles simultaneously used actigraphy method and questionnaires [17, 47]. Based on geographic locations, all eligible articles were classified into America [14, 17, 19, 26, 32, 33, 40, 42], Europe [10, 21, 34, 37, 42–45], and Asia [27–31, 35, 36, 38, 39, 41].

Table 1.

The baseline characteristics of all cohort studies involved in this meta-analysis

First author (year) Baseline year Country Age (years) Ascertainment of sleep TST Comparison Mortality ascertainment Adjustment
Kaplan (1987) 1965 USA 60–94 Questionnaire Nighttime sleep 7-8 h vs. ≤7 h Death certificate YES
7-8 h vs. > 8 h All-cause
Seki (2001) 1990 Japan 60–74 Questionnaire 24 h sleep 7 h vs. ≤6 h Death certificate YES
7 h vs. ≥9 h All-cause
Goto (2003) 1987 Japan ≥65 Questionnaire Nighttime sleep 6-7 h vs. < 6 h Death certificate YES
6-7 h vs. ≥7 h All-cause
Lan (2007) 1993 China ≥64 Questionnaire Nighttime sleep 7–7.9 h vs. < 7 h Death certificate YES
7–7.9 h vs. ≥10 h All-cause, CVD, cancer
Gangwisch (2008) 1982 USA 60–86 Questionnaire Nighttime sleep 7 h vs. ≤5 h Death certificate and proxy interviews YES
7 h vs. ≥9 h All-cause
Stone (2009) 1986 USA ≥68 Questionnaire Nighttime sleep 6-8 h vs. < 6 h Death certificate YES
6-8 h vs. ≥8 h All-cause, CVD, cancer and other
Suzuki (2009) 1999 Japan 65–85 Questionnaire 24 h sleep 7 h vs. ≤5 h Death certificate YES
7 h vs. ≥10 h All-cause and CVD
Castro-Costa (2011) 1997 Brazil > 60 Questionnaire 24 h sleep 7–7.9 h vs. < 6 h Death certificate and proxy interviews YES
7–7.9 h vs. ≥9 h All-cause
Kripke (2011) 1995 USA 60–81 Questionnaire & actigraphy Nighttime sleep 7 h vs. ≤5 h Proxy interviews and social security death index NO
7 h vs. ≥9 h All-cause
Kronholm (2011) 1972 Finland 60–64 Questionnaire Nighttime sleep 7-8 h vs. ≤5 h Death certificate and hospital discharge register NO
7-8 h vs. ≥10 h All-cause and CVD
Qiu (2011) 2005 China ≥65 Questionnaire 24 h sleep 6-8 h vs. ≤5 h Death certificate YES
6-8 h vs. ≥9 h All-cause
Werle (2011) 1994 Brazil ≥80 Questionnaire Nighttime sleep & 24 h sleep ≤8 h vs. > 8 h Death certificate, proxy interviews and patient records YES
All-cause and CVD
Cohen-Mansfield (2012) 1989 Israel 75–94 Questionnaire Nighttime sleep 7-9 h vs. < 7 h Death certificate YES
7-9 h vs. ≥9 h All-cause
Chen (2013) 1999 China > 65 Questionnaire Nighttime sleep 7 h vs. ≤4 h Death certificate YES
7 h vs. ≥9 h All-cause, CVD, cancer
Jung (2013) 1984 USA 60–96 Questionnaire Nighttime sleep 7-9 h vs. < 6 h Death certificate or notice from a family member or published obituary YES
7-9 h vs. ≥9 h All-cause
Kakizaki (2013) 1994 Japan ≥70 Questionnaire 24 h sleep 7 h vs. ≤6 h Death certificate YES
7 h vs. ≥10 h All-cause, CVD, cancer and the other
Kim (2013) 1990 USA ≥65 Questionnaire 24 h sleep 7 h vs. ≤5 h Death certificate YES
7 h vs. ≥9 h All-cause and CVD
Yeo (2013) 1993 Korea ≥60 Questionnaire 24 h sleep 7 h vs. ≤5 h Death certificate YES
7 h vs. ≥10 h All-cause, CVD, cancer
Lee (2014) 2001 China > 65 Questionnaire Nighttime sleep < 10 h vs. ≥10 h Death certificate YES
All-cause
Hall (2015) – USA 70–79 Questionnaire Nighttime sleep 7 h vs. < 6 h Death certificates, hospital records, informant interviews and autopsy data YES
7 h vs. > 8 h All-cause
Zuurbier (2015) 2004 Holland 60–98 Questionnaire & actigraphy 24 h sleep 6–7.5 hvs. < 6 h Death certificate and patient records YES
6–7.5 hvs. > 7.5 h All-cause
Smagula (2016) 2003 USA ≥65 Questionnaire & actigraphy 24 h sleep 5-8 h vs. < 5 h Death certificate YES
5-8 h vs. > 8 h All-cause, CVD, cancer
Akersted (2017) 1997 Swedish ≥65 Questionnaire Nighttime sleep 7 h vs. ≤5 h Death certificate YES
7 h vs. ≥8 h All-cause, CVD, cancer
Beydoun (2017) 2005 USA ≥65 Questionnaire Nighttime sleep 7 h vs. < 7 h Death certificate YES
7 h vs. > 8 h All-cause
Brostrom (2018) 2003 Sweden 65–82 Questionnaire Nighttime sleep 7-8 h vs. ≤6 h Death certificate NO
7-8 h vs. ≥9 h All-cause
Cheng (2018) 2009 Singapore ≥60 Questionnaire Nighttime sleep 7-8 h vs. ≤6 h Death certificate YES
7-8 h vs. ≥9 h All-cause
Akersted (2019) 1997 Swedish ≥65 Questionnaire Nighttime sleep 7 h vs. ≤4 h Death certificate, YES
7 h vs. ≥8 h All-cause, CVD, cancer
Morgan (2019) 1985 UK ≥65 Questionnaire Nighttime sleep 7 h vs. ≤4 h Death certificate YES
7 h vs. ≥9 h All-cause
4–9.9 h vs. ≥10 h All-cause
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Abbreviations: CVD, cardiovascular disease; TST, total sleep time

Table 2.

The baseline characteristics of all cohort studies involved in this meta-analysis

Published year First author Gender Sample size Age Men Women Follow up (years) Total deaths Men deaths Women deaths Exposure (h) Ref (h) Adjusted HR, 95% CI
1987 Kaplan Men, Women 4174 60–94 – – 17 – – – > 8 7–8 YES 1.02, 0.87–1.19
1987 Kaplan Men, Women 4174 60–94 – – 17 – – – < 7 7–8 YES 1.02, 0.87–1.19
1987 Kaplan Men, Women 4174 60–94 – – 17 – – – > 8 7–8 NO 1.02, 0.87–1.21
1987 Kaplan Men, Women 4174 60–94 – – 17 – – – < 7 7–8 NO 1.02, 0.87–1.21
2011 Kronholm Men, Women 1210 60–64 – – 35 1065 – – ≥10 7–8 NO 1.11, 1.05–1.18
2011 Kronholm Men, Women 1210 60–64 – – 35 1065 – – ≤5 7–8 NO 1.07, 1.01–1.14
2008 Gangwisch Men, Women 3983 60–86 – – 10 1604 – – ≥9 7 YES 1.36, 1.15–1.6
2008 Gangwisch Men, Women 3983 60–86 – – 10 1604 – – ≤5 7 YES 1.27, 1.06–1.53
2008 Gangwisch Men, Women 3983 60–86 – – 10 1604 – – ≥9 7 NO 1.98, 1.68–2.32
2008 Gangwisch Men, Women 3983 60–86 – – 10 1604 – – ≤5 7 NO 1.72, 1.44–2.06
2013 Jung Men 2001 60–96 889 1112 19 1224 632 592 ≥9 7 YES 1.09, 0.82–1.45
2013 Jung Women 2001 60–96 889 1112 19 1224 632 592 ≥9 7 YES 1.51, 1.05–2.18
2013 Jung Men 2001 60–96 889 1112 19 1224 632 592 ≤5 7 YES 0.98, 0.67–1.43
2013 Jung Women 2001 60–96 889 1112 19 1224 632 592 ≤5 7 YES 1.11, 0.77–1.6
2013 Jung Men 2001 60–96 889 1112 19 1224 632 592 ≥9 7 NO 1.18, 0.92–1.52
2013 Jung Women 2001 60–96 889 1112 19 1224 632 592 ≥9 7 NO 1.50, 1.12–2.00
2013 Jung Men 2001 60–96 889 1112 19 1224 632 592 ≤5 7 NO 1.10, 0.79–1.55
2013 Jung Women 2001 60–96 889 1112 19 1224 632 592 ≤5 7 NO 1.07, 0.79–1.44
2019 Morgan Men, Women 960 ≥65 375 585 27 927 – – ≥9 7 YES 1.18, 0.85–1.63
2019 Morgan Men, Women 960 ≥65 375 585 27 927 – – ≤4 7 YES 1.08, 0.83–1.40
2019 Morgan Men, Women 960 ≥65 375 585 27 927 – – ≥9 7 NO 1.40, 1.08–1.83
2019 Morgan Men, Women 960 ≥65 375 585 27 927 – – ≤4 7 NO 1.02, 0.80–1.29
2009 Stone Women 8101 ≥68 0 8101 6.9 1922 0 1922 > 8 6–8 YES 1.16, 0.97–1.39
2009 Stone Women 8101 ≥68 0 8101 6.9 1922 0 1922 < 6 6–8 YES 1.02, 0.87–1.19
2009 Stone Women 8101 ≥68 0 8101 6.9 1922 0 1922 ≥10 8–9 YES 1.58, 1.27–1.95
2009 Stone Women 8101 ≥68 0 8101 6.9 1922 0 1922 < 6 8–9 YES 0.95, 0.76–1.18
2003 Goto Men 724 ≥65 251 473 12 305 139 166 > 7 6–7 YES 1.54, 0.92–2.58
2003 Goto Women 724 ≥65 251 473 12 305 139 166 > 7 6–7 YES 1.40, 0.91–2.15
2003 Goto Men 724 ≥65 251 473 12 305 139 166 < 6 6–7 YES 1.29, 0.50–3.34
2003 Goto Women 724 ≥65 251 473 12 305 139 166 < 6 6–7 YES 2.62, 1.36–5.07
2003 Goto Men 724 ≥65 251 473 12 305 139 166 > 7 6–7 NO 1.62, 0.99–2.66
2003 Goto Women 724 ≥65 251 473 12 305 139 166 > 7 6–7 NO 1.60, 1.06–2.42
2003 Goto Men 724 ≥65 251 473 12 305 139 166 < 6 6–7 NO 1.42, 0.61–3.27
2003 Goto Women 724 ≥65 251 473 12 305 139 166 < 6 6–7 NO 2.65, 1.42–4.95
2012 Cohen-Mansfield Men, Women 1166 ≥75 – – 20 1108 – – > 9 7–9 YES 1.32, 1.09–1.58
2012 Cohen-Mansfield Men, Women 1166 ≥75 – – 20 1108 – – < 7 7–9 YES 0.98, 0.84–1.13
2012 Cohen-Mansfield Men, Women 1166 ≥75 – – 20 1108 – – > 9 7–9 NO 1.29, 1.11–1.52
2012 Cohen-Mansfield Men, Women 1166 ≥75 – – 20 1108 – – < 7 7–9 NO 0.81, 0.71–0.93
2013 Kim Men, Women – 65–69 – – 12.9 4764 – – ≥9 7 YES 1.25, 1.14–1.38
2013 Kim Men, Women – 65–69 – – 12.9 4764 – – ≤5 7 YES 1.13, 1.02–1.26
2013 Kim Men, Women – ≥70 – – 12.9 6444 – – ≥9 7 YES 1.14, 1.05–1.24
2013 Kim Men, Women – ≥70 – – 12.9 6444 – – ≤5 7 YES 1.09, 0.99–1.19
2001 Seki Men, Women 1065 60–74 440 625 7.5 123 77 46 ≥9 7 YES 0.97, 0.50–1.90
2001 Seki Men, Women 1065 60–74 440 625 7.5 123 77 46 < 6 7 YES 1.74, 0.72–4.24
2001 Seki Men, Women 1065 60–74 440 625 7.5 123 77 46 ≥9 7 NO 1.00, 0.52–1.96
2001 Seki Men, Women 1065 60–74 440 625 7.5 123 77 46 < 6 7 NO 2.17, 0.91–5.21
2007 Lan Men 3079 ≥64 1748 1331 10 1338 816 522 ≥10 7–7.9 YES 1.51, 1.19–1.92
2007 Lan Women 3079 ≥64 1748 1331 10 1338 816 522 ≥10 7–7.9 YES 2.06, 1.50–2.83
2007 Lan Men 3079 ≥64 1748 1331 10 1338 816 522 < 7 7–7.9 YES 0.98, 0.76–1.25
2007 Lan Women 3079 ≥64 1748 1331 10 1338 816 522 < 7 7–7.9 YES 1.14, 0.77–1.67
2007 Lan Men 3079 ≥64 1748 1331 10 1338 816 522 ≥10 7–7.9 NO 1.86, 1.48–2.34
2007 Lan Women 3079 ≥64 1748 1331 10 1338 816 522 ≥10 7–7.9 NO 2.49, 1.84–3.37
2007 Lan Men 3079 ≥64 1748 1331 10 1338 816 522 < 7 7–7.9 NO 0.97, 0.76–1.23
2007 Lan Women 3079 ≥64 1748 1331 10 1338 816 522 < 7 7–7.9 NO 1.04, 0.71–1.51
2013 Yeo Men, Women 5538 ≥60 – – 9.4 1223 – – ≥10 7 YES 1.48, 1.13–1.93
2013 Yeo Men, Women 5538 ≥60 – – 9.4 1223 – – ≤5 7 YES 1.23, 1.03–1.47
2013 Kakizaki Men, Women 9690 ≥70 – – 10.8 3960 – – ≥9 7–7.9 YES 1.33, 1.24–1.43
2013 Kakizaki Men, Women 9690 ≥70 – – 10.8 3960 – – < 6 7–7.9 YES 0.98, 0.87–1.10
2011 Werle Men, Women 187 ≥80 68 119 8.7 141 56 85 > 8 7 YES 0.95, 0.89–1.02
2011 Werle Men, Women 187 ≥80 68 119 8.7 141 56 85 > 8 7 NO 0.95, 0.90–1.01
2011 Kripke Women 355 60–81 – – 10.5 79 – – ≥9 7–7.9 NO 0.93, 0.37–2.35
2011 Kripke Women 355 60–81 – – 10.5 79 – – ≤5 7 NO 0.83, 0.4–1.73
2017 Akersted Men, Women 8089 ≥65 3879 4210 13 2337 – – ≥8 7 YES 1.01, 0.90–1.14
2017 Akersted Men, Women 8089 ≥65 3879 4210 13 2337 – – ≤5 7 YES 1.05, 0.90–1.22
2017 Akersted Men, Women 8089 ≥65 3879 4210 13 2337 – – ≥8 7 NO 1.06, 0.96–1.19
2017 Akersted Men, Women 8089 ≥65 3879 4210 13 2337 – – ≤5 7 NO 1.02, 0.90–1.16
2011 Castro-Costa Men, Women 1512 > 60 – – 7.5 440 – – ≥9 7–7.9 YES 1.56, 1.12–2.18
2011 Castro-Costa Men, Women 1512 > 60 – – 7.5 440 – – < 6 7–7.9 YES 0.88, 0.61–1.28
2011 Castro-Costa Men, Women 1512 > 60 – – 7.5 440 – – ≥9 7–7.9 NO 1.84, 1.40–2.43
2011 Castro-Costa Men, Women 1512 > 60 – – 7.5 440 – – < 6 7–7.9 NO 1.01, 0.75–1.37
2013 Chen Men, Women 4064 > 65 2269 1795 9 1004 336 668 ≥9 7 YES 1.66, 1.28–2.17
2013 Chen Men, Women 4064 > 65 2269 1795 9 1004 336 668 ≤4 7 YES 1.00, 0.75–1.33
2009 Suzuki Men, Women 11,395 65–85 5825 5570 7 1004 689 315 ≥10 7 YES 1.96, 1.49–2.57
2009 Suzuki Men, Women 11,395 65–85 5825 5570 7 1004 689 315 ≤5 7 YES 0.92, 0.66–1.28
2009 Suzuki Men 11,395 65–85 5825 5570 7 1004 689 315 ≥10 7 YES 1.86, 1.34–2.56
2009 Suzuki Women 11,395 65–85 5825 5570 7 1004 689 315 ≥10 7 YES 2.27, 1.37–3.76
2009 Suzuki Men 11,395 65–85 5825 5570 7 1004 689 315 ≤5 7 YES 1.08, 0.72–1.61
2009 Suzuki Women 11,395 65–85 5825 5570 7 1004 689 315 ≤5 7 YES 0.71, 0.39–1.29
2009 Suzuki Men, Women 11,395 65–85 5825 5570 7 1004 689 315 ≥10 7 NO 2.29, 1.75–3.00
2009 Suzuki Men, Women 11,395 65–85 5825 5570 7 1004 689 315 ≤5 7 NO 1.03, 0.74–1.43
2009 Suzuki Men 11,395 65–85 5825 5570 7 1004 689 315 ≥10 7 NO 2.16, 1.57–2.98
2009 Suzuki Women 11,395 65–85 5825 5570 7 1004 689 315 ≥10 7 NO 2.65, 1.61–4.37
2009 Suzuki Men 11,395 65–85 5825 5570 7 1004 689 315 ≤5 7 NO 1.16, 0.78–1.73
2009 Suzuki Women 11,395 65–85 5825 5570 7 1004 689 315 ≤5 7 NO 0.82, 0.46–1.48
2014 Lee Men 3427 > 65 1745 1682 5 297 221 76 ≥10 < 10 YES 1.75, 1.09–2.81
2014 Lee Women 3427 > 65 1745 1682 5 297 221 76 ≥10 < 10 YES 2.88, 1.01–8.20
2014 Lee Men 3427 > 65 1745 1682 5 297 221 76 ≥10 < 10 NO 2.10, 1.33–3.33
2014 Lee Women 3427 > 65 1745 1682 5 297 221 76 ≥10 < 10 NO 2.70, 0.98–7.46
2018 Brostrom Men 630 65–82 301 329 6 144 86 58 ≥9 7–8 YES 1.10, 0.1–10.30
2018 Brostrom Women 630 65–82 301 329 6 144 86 58 ≥9 7–8 YES 0.35, 0.10–26.90
2018 Brostrom Men 630 65–82 301 329 6 144 86 58 ≤6 7–8 YES 0.60, 0.10–2.90
2018 Brostrom Women 630 65–82 301 329 6 144 86 58 ≤6 7–8 YES 0.34, 0.10–1.90
2016 Smagula Men 2531 ≥65 2531 0 7.4 628 628 0 > 8 5–8 YES 0.83, 0.71–1.31
2016 Smagula Men 2531 ≥65 2531 0 7.4 628 628 0 < 5 5–8 YES 1.12, 0.89–1.42
2016 Smagula Men 2531 ≥65 2531 0 7.4 628 628 0 > 8 5–8 NO 1.02, 0.76–1.37
2016 Smagula Men 2531 ≥65 2531 0 7.4 628 628 0 < 5 5–8 NO 1.28, 1.02–1.62
2015 Zuurbier Men, Women 1073 60–98 – – 7.3 142 – – > 7.5 6–7.5 YES 1.24, 0.73–2.10
2015 Zuurbier Men, Women 1073 60–98 – – 7.3 142 – – < 6 6–7.5 YES 1.12, 0.75–1.68
2011 Qiu Men, Women 12,671 ≥65 5421 7250 3 5199 2067 3132 ≥10 8 YES 1.09, 1.00–1.180
2011 Qiu Men, Women 12,671 ≥65 5421 7250 3 5199 2067 3132 ≤5 8 YES 0.97, 0.88–1.08
2011 Qiu Men 12,671 ≥65 5421 7250 3 5199 2067 3132 ≥10 8 YES 1.22, 1.08–1.38
2011 Qiu Women 12,671 ≥65 5421 7250 3 5199 2067 3132 ≥10 8 YES 1.00, 0.90–1.11
2011 Qiu Men 12,671 ≥65 5421 7250 3 5199 2067 3132 ≤5 8 YES 1.17, 1.01–1.38
2011 Qiu Women 12,671 ≥65 5421 7250 3 5199 2067 3132 ≤5 8 YES 0.85, 0.75–0.98
2011 Qiu Men, Women 12,671 65–79 5421 7250 3 5199 2067 3132 ≥10 8 YES 1.17, 0.88–1.54
2011 Qiu Men, Women 12,671 65–79 5421 7250 3 5199 2067 3132 ≤5 8 YES 1.00, 0.74–1.35
2011 Qiu Men, Women 12,671 ≥80 5421 7250 3 5199 2067 3132 ≥10 8 YES 1.08, 0.99–1.18
2011 Qiu Men, Women 12,671 ≥80 5421 7250 3 5199 2067 3132 ≤5 8 YES 0.97, 0.87–1.08
2011 Qiu Men, Women 12,671 ≥65 5421 7250 3 5199 2067 3132 ≥10 8 NO 1.22, 1.13–1.32
2011 Qiu Men, Women 12,671 ≥65 5421 7250 3 5199 2067 3132 ≤5 8 NO 1.19, 1.08–1.32
2011 Qiu Men 12,671 ≥65 5421 7250 3 5199 2067 3132 ≥10 8 NO 1.36, 1.20–1.54
2011 Qiu Women 12,671 ≥65 5421 7250 3 5199 2067 3132 ≥10 8 NO 1.12, 1.02–1.25
2011 Qiu Men 12,671 ≥65 5421 7250 3 5199 2067 3132 ≤5 8 NO 1.47, 1.26–1.71
2011 Qiu Women 12,671 ≥65 5421 7250 3 5199 2067 3132 ≤5 8 NO 1.03, 0.90–1.17
2011 Qiu Men, Women 12,671 65–79 5421 7250 3 5199 2067 3132 ≥10 8 NO 1.46, 1.11–1.91
2011 Qiu Men, Women 12,671 65–79 5421 7250 3 5199 2067 3132 ≤5 8 NO 1.32, 0.98–1.77
2011 Qiu Men, Women 12,671 ≥80 5421 7250 3 5199 2067 3132 ≥10 8 NO 1.21, 1.12–1.32
2011 Qiu Men, Women 12,671 ≥80 5421 7250 3 5199 2067 3132 ≤5 8 NO 1.18, 1.06–1.31
2017 Beydoun Men, Women 2173 ≥65 – – 4.5 – – – > 8 7–8 YES 1.30, 0.73–2.29
2017 Beydoun Men, Women 2173 ≥65 – – 4.5 – – – < 7 7–8 YES 0.96, 0.68–1.35
2017 Beydoun Men, Women 2173 ≥65 – – 4.5 – – – > 8 7–8 NO 1.90, 1.44–2.50
2017 Beydoun Men, Women 2173 ≥65 – – 4.5 – – – < 7 7–8 NO 1.20, 0.94–1.52
2018 Cheng Men, Women 2448 ≥60 1167 1281 4 274 – – ≥9 7–8 YES 2.24, 1.05–4.77
2018 Cheng Men, Women 2448 ≥60 1167 1281 4 274 – – ≤6 7–8 YES 2.14, 1.12–4.11
2018 Cheng Men, Women 2448 ≥60 1167 1281 4 274 – – ≥9 7–8 NO 2.87, 1.36–6.05
2018 Cheng Men, Women 2448 ≥60 1167 1281 4 274 – – ≤6 7–8 NO 2.69, 1.44–5.03
2015 Hall Men, Women 3013 ≥70 1463 1550 8.2 953 – – > 8 7 YES 1.23, 0.93–1.63
2015 Hall Men, Women 3013 ≥70 1463 1550 8.2 953 – – < 6 7 YES 1.06, 0.83–1.34
2015 Hall Men, Women 3013 ≥70 1463 1550 8.2 953 – – > 8 7 NO 1.49, 1.15–1.93
2015 Hall Men, Women 3013 ≥70 1463 1550 8.2 953 – – < 6 7 NO 1.30, 1.05–1.61
2019 Akersted Men, Women – ≥65 – – 13 – – – ≥9 7 YES 0.99, 0.84–1.09
2019 Akersted Men, Women – ≥65 – – 13 – – – ≤4 7 YES 0.97, 0.81–1.18
2019 Akersted Men, Women – ≥65 – – 13 – – – ≥9 7 YES 0.91, 0.66–1.25
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Abbreviations: Ref, reference; HR, hazard ratio; 95% CI, 95% confidence interval

Quality assessment

Table 3 shows the quality assessment results by using the Newcastle-Ottawa Scale (NOS) tool for cohort studies, with the total scores (mean: 7.46, standard deviation: 0.74) ranging from 6 to 9 in this meta-analysis.

Table 3.

The Newcastle-Ottawa Scale (NOS) for assessing the quality of all cohort studies involved in this meta-analysis

First author Published year Representative of the exposed cohort Selection of the non-exposed cohort Ascertainment of exposed Demonstration that outcome of interest was no present at start of study Control for important cohort Additional factors Assessment of outcome Follow up Adequacy of follow up Score
Kaplan 1987 1 1 0 1 1 1 1 1 1 8
Seki 2001 1 1 0 1 1 1 1 1 1 8
Goto 2003 1 1 0 1 1 1 1 1 0 7
Lan 2007 1 1 0 1 1 1 1 1 0 7
Gangwisch 2008 1 1 0 1 1 1 1 1 0 7
Stone 2009 1 1 0 1 1 1 1 1 1 8
Suzuki 2009 1 1 0 1 1 1 1 1 1 8
Kronholm 2011 1 1 0 1 1 0 1 1 0 6
Werle 2011 1 1 0 1 0 1 1 1 0 6
Kripke 2011 1 1 1 1 1 0 1 1 1 8
Castro-Costa 2011 1 1 0 1 1 1 1 1 0 7
Qiu 2011 1 1 0 1 1 1 1 1 1 8
Cohen-Mansfield 2012 1 1 0 1 1 1 1 1 1 8
Jung 2013 1 1 0 1 1 1 1 1 1 8
Kim 2013 1 1 0 1 1 1 1 1 0 7
Yeo 2013 1 1 0 1 1 1 1 1 0 7
Kakizaki 2013 1 1 0 1 1 1 1 1 1 8
Chen 2013 1 1 0 1 1 1 1 1 1 8
Lee 2014 1 1 0 1 1 1 1 1 0 7
Zuurbier 2015 1 1 1 1 1 1 1 1 1 9
Hall 2015 1 1 0 1 1 1 1 1 0 7
Smagula 2016 1 1 1 1 1 1 1 1 1 9
Akersted 2017 1 1 0 1 1 1 1 1 0 7
Beydoun 2017 1 1 0 1 1 1 1 1 0 7
Brostrom 2018 1 1 0 1 1 0 1 1 1 7
Cheng 2018 1 1 0 1 1 1 1 1 0 7
Morgan 2019 1 1 0 1 1 1 1 1 1 8
Akersted 2019 1 1 0 1 1 1 1 1 0 7
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Overall analyses

After pooling the results of all qualified prospective cohorts together (Table 4), unadjusted effect-size estimates for the association of the long (HR = 1.43; 95% CI: 1.30–1.58; P < .001; I2 = 88.6%) and short (HR = 1.15; 95% CI: 1.06–1.25; P < .001; I2 = 71.5%) sleep duration with all-cause mortality in the older people were remarkably significant. After adjusting for potential confounders, long sleep duration was significantly associated with an increased risk of all-cause mortality in the older people (HR = 1.24; 95% CI: 1.16–1.33; P < .001), whereas only marginal significance was observed for short sleep duration (HR = 1.04; 95% CI: 1.00–1.09; P = .033) (Table 4). In view of the striking differences before and after adjustment, the following analyses are based on adjusted effect-size estimates for the sake of relative accuracy.

Table 4.

Overall and subgroup analyses of short and long sleep duration with all-cause mortality in the older people

Group Number of qualified studies Short sleep duration Long sleep duration
HR (95% CI); P I2 HR (95% CI); P I2
Overall analyses
 Mortality (unadjusted) 23/26 1.15 (1.06–1.25); <.001 71.5% 1.43 (1.30–1.58); <.001 88.6%
 Mortality (adjusted) 32/36 1.04 (1.00–1.09); .033 16.1% 1.24 (1.16–1.33); <.001 76.2%
Subgroup analyses based on adjusted mortality
By gender
 Both genders 20/23 1.04 (0.99–1.08); .096 11.1% 1.20 (1.11–1.29); <.001 79.0%
 Men 8/8 1.13 (1.04–1.24); .007 0.0% 1.31 (1.10–1.58); .003 62.3%
 Women 8/9 1.00 (0.85–1.18); .999 58.6% 1.48 (1.18–1.86); .001 80.4%
By country
 America 12/13 1.08 (1.03–1.14); .002 0.0% 1.19 (1.07–1.31); .001 78.2%
 Europe 6/7 1.03 (0.93–1.14); .627 0.0% 1.01 (0.93–1.09); .823 0.0%
 Asia 14/16 1.04 (0.96–1.12); .384 40.6% 1.41 (1.26–1.57); <.001 75.4%
By total sleep time
 Nighttime 19/23 1.05 (0.99–1.13); .113 17.8% 1.25 (1.13–1.38); <.001 73.7%
 24 h 13/13 1.04 (0.99–1.10); .146 19.9% 1.25 (1.14–1.36); <.001 76.2%
By ascertainment of sleep
 Questionnaire 30/34 1.04 (1.00–1.09); .055 20.5% 1.26 (1.17–1.35); <.001 76.8%
 Actigraphy 1/1 1.12 (0.89–1.42); .342 ─* 0.83 (0.61–1.13); .233 ─
 Both 1/1 1.12 (0.75–1.68); .582 ─ 1.24 (0.73–2.10); .425 ─
By follow-up (years)
  ≥ 7.5 20/22 1.07 (1.02–1.12); .006 15.2% 1.24 (1.14–1.34); <.001 80.2%
  < 7.5 13/14 0.99 (0.93–1.05); .736 0.0% 1.27 (1.12–1.45); <.001 68.3%
By age
  < 65 11/11 1.21 (1.02–1.23); .018 18.2% 1.38 (1.19–1.60); <.001 61.5%
  ≥ 65 21/25 1.03 (0.99–1.07); .193 4.2% 1.20 (1.11–1.30); <.001 78.2%
Dose-analysis
  ≤ 5 h 15 1.06 (1.01–1.11); .014 12.3% ─ ─
  ≤ 6 h 27 1.05 (1.01–1.10); .031 26.7% ─ ─
  ≤ 7 h 32 1.04 (1.00–1.09); .033 16.1% ─ ─
  ≥ 8 h 33 ─ ─ 1.24 (1.16–1.33); <.001 77.9%
  ≥ 9 h 26 ─ ─ 1.31 (1.21–1.41); <.001 71.9%
  ≥ 10 h 10 ─ ─ 1.45 (1.24–1.70); <.001 82.6%
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Abbreviations: HR, hazard ratio; 95% CI, 95% confidence interval. *Data are not available

Publication Bias

Figure 2 shows the Begg’s funnel plot to assess publication bias for the association of sleep duration with all-cause mortality, and only the plot of short sleep duration seemed symmetrical. As revealed by the Egger’s test, there was no evidence of publication bias for short sleep duration (P = .392), yet strong evidence of publication bias for long sleep duration (P = .020). Further filled funnel plots showed that there were 9 potentially missing studies due to publication bias to make the plot of long sleep duration symmetrical. After adjusting for these potentially missing studies, effect size estimates were still statistically significant for the association of long sleep duration with all-cause mortality (HR = 1.15; 95% CI: 1.07–1.23, P < .001).

Fig. 2.

Fig. 2

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The Begg’s and filled funnel plots for the association of both short and long sleep duration with all-cause mortality

Subgroup analyses

A series of prespecified subgroup analyses were conducted to account for possible causes of between-study heterogeneity for both short and long sleep duration in the older people (Table 4).

By gender, the association of long sleep duration with all-cause mortality was statistically significant in both women (HR = 1.48; 95% CI: 1.18–1.86; P = .001) and men (HR = 1.31; 95% CI: 1.10–1.58; P = .003) (Two-sample Z test P = .205). By contrast, with regard to short sleep duration, statistical significance was observed in men (HR = 1.13; 95% CI: 1.04–1.24; P = .007), but not in women (HR = 1.00; 95% CI: 0.85–1.18; P = .999) (Two-sample Z test P = .099).

By geographic locations, the association of long sleep duration with all-cause mortality was stronger in Asia (HR = 1.41; 95% CI: 1.26–1.57; P < .001) than in Europe (HR = 1.01; 95% CI: 0.93–1.09; P = .823) (Two-sample Z test P < .001) and America (HR = 1.19; 95% CI: 1.07–1.31; P = .001) (Two-sample Z test P = .013). There was no observable difference for short sleep duration between Asia (HR = 1.04; 95% CI: 0.96–1.12; P = .384) and Europe (HR = 1.03; 95% CI: 0.93–1.14; P = .627).

By total sleep time, significance was only observed for the association of long sleep duration with all-cause mortality, and there was no material difference between the nighttime (HR = 1.25; 95% CI: 1.13–1.38; P < .001) and the 24 h sleep duration (HR = 1.25; 95% CI: 1.14–1.36; P < .001).

By ascertainment of sleep, for long sleep duration, the association was more evident for questionnaire survey (HR = 1.26; 95% CI: 1.17–1.35; P < .001) than for actigraph survey (HR = 0.83; 95% CI: 0.61–1.13; P = .233) (Two-sample Z test P = .004). Contrastingly, for short sleep duration, there was no detectable significance.

By the median value (7.5 years) of follow-up intervals, the association of long sleep duration with all-cause mortality was significant in both long (≥7.5 years) (HR = 1.24; 95% CI: 1.14–1.34; P < .001) and short (< 7.5 years) (HR = 1.27; 95% CI: 1.12–1.45; P < .001) follow-up. As for short sleep duration, the association was only significant in studies with long follow-up intervals (HR = 1.07; 95% CI: 1.02–1.12; P = .006).

By the median value (65 years) of baseline age, long sleep duration was significantly associated with all-cause mortality in both subgroups (≥65 years: HR = 1.20; 95% CI: 1.11–1.30; P < .001, and < 65 years: HR = 1.38; 95% CI: 1.19–1.60; P < .001), and for short sleep duration, only marginal significance was observed for studies with median age < 65 years (HR = 1.21; 95% CI: 1.02–1.23; P = .018).

Dose-response analyses

In the dose-response analysis on short sleep duration, all-cause mortality increased with the decrease of sleep time (≤5 h: HR = 1.06; 95% CI: 1.01–1.11; P = .014, ≤6 h: HR = 1.05; 95% CI: 1.01–1.10; P = .031, and ≤ 7 h: HR = 1.04; 95% CI: 1.00–1.09; P = .033) (Two-sample Z test P = .379 for ≤5 h vs. ≤6 h, and P = .379 for ≤6 h vs. ≤7 h) (Table 4). For long sleep duration, the trend was more evident (≥8 h: HR = 1.24; 95% CI: 1.16–1.33; P < .001, ≥9 h: HR = 1.31; 95% CI: 1.21–1.41; P < .001, and ≥ 10 h: HR = 1.45; 95% CI: 1.24–1.70; P < .001) (Two-sample Z test P = .147 for ≥8 h vs. ≥9 h, and P = .128 for ≥9 h vs. ≥10 h) (Table 4 and Fig. 3A).

Fig. 3.

Fig. 3

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The trend plots of effect-size estimates with the increase of sleep duration in all older persons (A) and by genders (B and C). Abbreviations: HR, hazard ratio; 95% CI, 95% confidence interval

In men, the risk associated with all-cause mortality was significant and increased with both shorter and longer sleep duration, and the increasing trend was more obvious for long sleep duration (Fig. 3B). In women, the risk associated with all-cause mortality was nonsignificant for short sleep duration, yet it was significantly increased with longer sleep duration in a graded manner, which was steeper than men (Fig. 3C).

In both genders, dose-response regression analyses, using log (effect-size estimates) as dependent variable and categorized sleep duration as independent variable, revealed that trend estimation was more obvious for long sleep duration (regression coefficient: 0.13; P < .001) than for short sleep duration (regression coefficient: 0.02; P = .046) (Fig. 4). In men, the regression coefficient for tread estimation was 0.05 (P = .022) and 0.15 (P < .001) for short and long sleep duration, respectively, and the regression coefficient was separately 0.04 (P = .449) and 0.20 (P < .001) in women.

Fig. 4.

Fig. 4

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The dose-response relationship plot for the association of sleep duration with all-cause mortality. Lines with long dashes represent the pointwise 95% confidence intervals for the fitted nonlinear trend (solid line). Lines with short dashes represent the linear trend. The red horizontal line represents the reference line (hazard ratio: 1)

Discussion

To the best of our knowledge, this is thus far the most comprehensive meta-analysis that has explored the dose-response relationship between sleep duration and all-cause mortality in the older people. It is worth noting that long sleep duration was associated with a significantly increased risk of all-cause mortality, especially in women, and the mortality risk associated with short sleep duration was only significant in men. Moreover, besides gender, geographic region, sleep survey method, baseline age and follow-up interval were identified as possible causes of between-study heterogeneity. Our findings highlight the importance and the necessity of closely monitoring the sleep status of elders who have long sleep duration, as well as elderly men of sleep deficiency, to control and prevent all-cause mortality.

In the previous meta-analysis of 27 cohort studies by da Silva and colleagues, both long and short sleep duration were found to be associated with a significantly increased risk of all-cause mortality risk in the older people [18]. Differing from the meta-analysis by da Silva and colleagues [18], we restricted analysis only to prospective cohort studies that reported HRs and 95% CIs to quantify the association between sleep duration and all-cause mortality in elders. After synthesizing the adjusted effect-size estimates from 28 articles including 95,259 older persons, albeit the consistent marginal significance for short sleep duration in overall analyses, extending the findings by da Silva and colleagues [18], we in subsidiary analysis observed a remarkably significant mortality risk associated with short sleep duration in men only. Similarly, da Silva and colleagues [18] and we unanimously supported the significant contribution of long sleep duration to all-cause mortality. The reasons behind above inconsistent observations are manifold. First, the most likely reason is the unaccounted confounding, as our analysis based on unadjusted effect-size estimates indicated that short sleep duration was a significant predictor for all-cause mortality, yet no significance was detected after adjustment.

Another possible reason is the synthesis of different types of effect-size estimates. To minimize this statistical noise, we restricted analysis to only HRs that were calculated after adjusting for confounding factors, despite the varying panels of adjusted factors across each involved study in this meta-analysis. The third reason is the significant heterogeneity across individual studies. To fully account for this, we conducted both subgroup and meta-regression analyses, and found that gender, geographic region, sleep survey method, baseline age and follow-up interval were possible causes of between-study heterogeneity. We agree that future large-scale, well-designed cohort studies were warrant to derive a relatively reliable estimate.

Although the mechanisms for the association between long sleep duration and all-cause mortality are not completely understood, the current possible explanation is that sleep affects the human body through inflammatory processes. When sleep duration is too long, concentrations of inflammatory markers, such as interleukin-6 and C-reactive protein can increase [48, 49]. In addition, it is reported that unstable sleep duration was associated with some common diseases, such as hypertension [50, 51], diabetes [52], and coronary heart disease [53, 54]. It is hence reasonable to speculate that long-term irregular sleep duration is likely to destroy the body’s immune system balance through chronic inflammatory processes, and further increase all-cause mortality risk. There is also evidence showing that sleep has a crucial impact on autonomic nervous system, system dynamics, cardiac function, endothelial function and coagulation [55]. Nevertheless, over sleep duration can accelerate the occurrence or progression of chronic diseases, and further precipitate all-cause mortality.

It is worth noting that we identified strong evidence of between-study heterogeneity for the association of long sleep duration with all-cause mortality, irrespective of adjustment. By contrast, for short sleep duration, heterogeneity was dwindled from strong in the unadjusted model to low in the adjusted model. It is hence reasonable to expect that besides methodological heterogeneity (such as study design), clinical heterogeneity like different baseline characteristics (such as age, sex ratio, dietary habits) of study populations in this meta-analysis may explain the discrepancy. In particular, insufficient adjustment for residual confounding by incompletely measured or unmeasured clinical covariates might exist in our results. As such, translating our findings into clinical practice should be done with caution.

Finally, some limitations should be acknowledged for this present meta-analysis. First, only sleep duration was considered in this study, and other sleep-related indexes, such as sleep quality, are of added interest for explorations in case of sufficient eligible studies. Second, although adjusted effect-size estimates were synthesized in this meta-analysis, some important confounding factors are still not taken into account by all involved studies, such as physical activity and other lifestyle factors. For example, in a long-term follow up of older adults in the UK, physical activity and prefrailty was observed to be significant modifiers for the prediction of long sleep duration for all-cause mortality [40]. Third, although there was a high probability of publication bias for long sleep duration as reflected by Begg’s funnel plot and Egger’s test, we adopted the trim-and-fill method to impute theoretically missing studies and recalculated our pooled effect-sized estimate, which was still statistically significant. Fourth, although a large panel of subgroup and meta-regression analyses were undertaken to account for possible causes of heterogeneity, significant heterogeneity still persisted in some subgroups, limiting the interpretation of pooled effect-size estimates. Last but not the least, the majority of studies involved in this meta-analysis recorded sleep duration based on nighttime, and data on naps are sparse.

Conclusions

Taken together, our findings indicate a significantly increased risk of all-cause mortality associated with long sleep duration, especially in women, as well as with short sleep duration in men only. We agree that the findings of this meta-analysis pose a challenging task for searchers, clinicians, and policy makers to attach importance to monitor the sleep status of elders, especially with long sleep duration. Further investigations on the molecular mechanisms linking sleep duration and all-cause mortality are also warranted.

Supplementary information

Additional file 1. (62KB, doc)

Availability of data and material

The datasets used and/or analyzed during the current meta-analysis are available from the corresponding author upon reasonable request.

Authors’ contributions

All authors read and approved the final manuscript prior to submission. Conceived and designed the experiments: W.N. Performed the experiments: M.H., X.D., L.H. Analyzed the data: M.H., X. D, W.N. Contributed materials/analysis tools: M.H., X.D., Y.Z., H.Y. Wrote and revised the paper: M.H., W.N.

Funding

This work received no financial support.

Ethics approval and consent to participate

Ethics approval and consent to participate were received by each involved study in this meta-analysis.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Mengyang He and Xiangling Deng shared first authors

Supplementary information

Supplementary information accompanies this paper at 10.1186/s12889-020-09275-3.

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