Prevalence and association of sleep duration and different intensities of physical activity with type 2 diabetes: The first evidence from CHARLS

doi:10.21203/rs.3.rs-5028806/v1

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Prevalence and association of sleep duration and different intensities of physical activity with type 2 diabetes: The first evidence from CHARLS

https://doi.org/10.21203/rs.3.rs-5028806/v1

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Objectives: The aim of the current study was to examine the prevalence and the independent and joint association between sleep duration and different intensities of physical activity (PA) with type 2 diabetes (T2D) in the China Health and Retirement Longitudinal Study (CHARLS).

Methods: We used data spanning all five years to evaluate the changes in T2D prevalence. Data from 2020 were used to examine the independent and joint associations between sleep duration and different intensities of PA with T2D. Sleep duration was classified into three categories: short (< 6 hours/day), normal (6 - 8 hours/day), and long (> 8 hours/day). PA levels were classified based on the IPAQ recommendations as follows: light-intensity PA (LPA, < 600 MET-minutes/week), moderate-intensity PA (MPA, 600- 3000 MET-minutes/week), and vigorous-intensity PA (VPA, > 3000 MET minutes/week).

Results: The prevalence of T2D in the LPA and short sleep groups increased from 13.35 (95% CI = 10.41 - 16.75) and 11.52 (95% CI = 10.01 - 13.15) in 2011 to 17.27 ( 95% CI = 15.09 - 19.62) and 16.28 (95% CI = 15.34 - 17.25) in 2020, respectively (p< 0.01). Compared with LPA, VPA was associated with lower odds of T2D (OR = 0.80, 95%CI = 0.68 - 0.95). Compared with normal sleep duration, short (OR = 1.19, 95%CI = 1.08 - 1.21) but not long sleep duration (OR = 1.02, 95%CI = 0.85 - 1.22) was more likely to have T2D. The odds of T2D were approximately 40% lower for individuals with LPA and normal sleep duration compared to those with LPA and short sleep duration (OR = 0.65, 95% CI = 0.46 - 0.91). In the MVP groups, combined with any sleep duration, the mitigation effect of exercise on T2D was observed (short: OR = 0.73, 95% CI = 0.56 - 0.95; normal: OR = 0.65, 95% CI = 0.51 - 0.8; long: OR = 0.63, 95% CI = 0.45 - 0.895).

Conclusions: The current study highlights the high prevalence of T2D in the LPA and short sleep groups. Short sleep duration, rather than long sleep duration, was identified as a risk factor for T2D. VPA serve as a protective factor in reducing the high prevalence of T2D associated with sleep disorders.

Type 2 diabetes

Light-intensity physical activity

Moderate-intensity activity

Vigorous-intensity activity

China Health and Retirement Longitudinal Study

Type 2 diabetes (T2D) accounts for 90% of all diabetes cases [1] and is one of the leading causes of disability and mortality worldwide [2], and interacts with and exacerbates many other diseases [3]. The burden of T2D can largely be attributed to modifiable social risk factors [3], including physical inactivity [4], disrupted sleep patterns [5], prolonged sedentary behavior [6], dietary risks [7], and environmental and occupational risks [1]. It is well documented that both short and long sleep durations adversely impact the development of diabetes through various mechanisms [8–10]. In this context, physical activity (PA) serves as a potent protective factor that can significantly reduce the risk of T2D [11, 12]. The benefits of vigorous-intensity PA (VPA) have been well established [13, 14] whereas evidence regarding moderate-intensity PA (MPA) and light-intensity PA (LPA) remains controversial [15].

Sleep and PA are widely reported to have a bidirectional relationship, influencing health through overlapping biological pathways [16, 17]. The relationship between these two factors is highlighted by the notion that improving one behavior can enhance the other [18]. PA improves sleep quality [19], helps regulate blood glucose, enhances insulin sensitivity, and reduces inflammation [20]. Therefore, we speculate that increasing daily PA could effectively counteract the adverse effects of poor sleep patterns on diabetes development. Evidence from cohort studies suggests that moderate-to-vigorous PA (MVPA) mitigates the negative effects of insufficient sleep on key cardiometabolic health outcomes [17]. However, it remains unclear whether VPA and MPA can protect short and long sleepers from the elevated risks associated with T2D. Therefore, it is necessary to conduct a nationwide population-based study to investigate the prevalence of T2D across varying intensities of physical activity and different sleep durations among middle-aged and elderly populations in China, as well as to explore the independent and joint associations of these two factors with T2D, as their hypothesized mechanisms are complementary [18]. The purpose of the present study was to (1) estimate the prevalence of T2D under different intensities of PA and varying sleep durations from 2011 to 2020; and (2) examine the independent and joint association of sleep duration and PA with the prevalence of T2D.

Study design and population

The China Health and Retirement Longitudinal Study (CHARLS) is an ongoing longitudinal survey that investigates the social, economic, and health conditions of the Chinese population aged 45 and above [21]. The baseline survey, employing a multi-stage probability sampling strategy, commenced in 2011 (Wave 1), with subsequent follow-up surveys conducted in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5). The CHARLS questionnaire includes various content areas such as personal demographics, family structure and economic support, health status, physical measurements, healthcare utilization and insurance, employment, retirement and pensions, income, consumption, assets, and basic community characteristics. For detailed information on the objectives, design, sample, and questionnaires of CHARLS, see Supplement and Zhao et al. [21]. This study used data from the 2020 CHARLS (19395 participants). CHARLS has received ethical approval from the Institutional Review Board of Peking University. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

We used data from all five years (Wave 1 - Wave 5) to assess changes in T2D prevalence. After controlling for data quality, a total of 51724 participants were included in the current study, including 5737 participants in 2011, 4955 in 2013, 6,820 in 2015, 17477 in 2018, and 16735 in 2020. We selected data from 2020 to examine the independent and joint associations between sleep duration and different intensities of PA with T2D. Details of the inclusion and exclusion criteria are presented in Fig. 1.

Exposure

The exposures in the current study included questionnaire-measured sleep duration and PA. The raw data were processed by the CHARLS working group. The included participants were asked about the intensity, duration, and frequency (days per week) of their PA, as well as their typical daily sleep duration.

The PA questionnaire was derived from pages 70–71 of the CHARLS questionnaire, with its structure and wording similar to the International Physical Activity Questionnaire (IPAQ). The volume of different intensity levels was calculated using the corresponding metabolic equivalent (MET) [22] values and IPAQ scoring standards [23]. CHARLS did not collect the precise duration of PA from respondents; instead, it inquired about PA in interval categories. Based on the questionnaire responses, we classified daily PA duration into 5 groups: 0 min, 10–29 min, 30–119 min, 120–239 min, and ≥ 240 min, using the midpoint values for analysis. PA types were further classified into VPA (MET = 8.0, e.g., climbing, running, farming), MPA (MET = 4.0, e.g., brisk walking, Tai Chi), and LPA (MET = 3.3, e.g., casual walking) based on their corresponding MET values. MET-minutes/week = MET value * days * duration [24]. According to IPAQ standards, weekly PA levels were categorized into LPA (< 600 MET-minutes/week), MPA (600–3000 MET-minutes /week), and VPA (> 3000 MET-minutes/week).

Following prior research [25] and the consensus recommendations of the American Academy of Sleep Medicine [26], participants were divided into three groups based on sleep duration: short (< 6 hours/day), normal (6–8 hours/day), and long (> 8 hours/day).

Covariates

Due to the global pandemic in 2020, only partial information could be collected that year. The following variables were considered likely confounding factors: gender (male/female), marital status (marry or others), education (less than primary/primary school/middle school/high school and above), smoking and drinking status (yes or no), retirement status (with/without), residential area (urban or rural), household size (number of members) and other diabetes-related comorbidities (hypertension, cancer, stomach diseases, liver diseases, kidney diseases).

Statistics analysis

Baseline characteristics are presented as the mean (standard deviation, SD) for continuous variables or number (percentage) for categorical variables, as appropriate. At baseline, we used Pearson's chi-square test (for homogeneous variances) or Fisher's exact test (for heterogeneous variances) to analyze the differences in categorical variables across the physical activity or sleep duration groups. For continuous variables, we employed one-way analysis of variance (ANOVA, for homogeneous variances) or the Kruskal-Wallis test (for heterogeneous variances) to examine the differences in the prevalence of T2D among the different PA and sleep duration categories.

We employed three multivariable logistic regression models to estimate the associations between PA (LPA, MPA, VPA) and sleep duration (short, normal, long) with T2D. For independent associations of PA and sleep with T2D. Model 1 was unadjusted. Model 2 was adjusted for age and sex. Model 3 was further adjusted for marital status, education level, smoking and drinking status, residential area, retirement status, and household size based on Model 2. Then, we subdivided the entire sample into 9 groups based on PA level and sleep duration categories. For joint associations, Model 1 was adjusted for age and sex. Model 2 was further adjusted for marital status, education level, smoking and drinking status, residential area, retirement status, and household size based on Model 1. Model 3 was adjusted for Model 2 plus hypertension, cancer, stomach diseases, liver diseases and kidney diseases. Statistical analyses were performed using Stata MP software Version 18.00. Statistical significance was defined as p < 0.05.

Characteristics

Table 1 presents the prevalence of T2D across different levels of PA and sleep duration from 2011 to 2020. The present study included a total of 51724 patients, with 5737 participants in 2011, 4955 in 2013, 6820 in 2015, 17477 in 2018, and 16735 in 2020. The overall prevalence of T2D across the different groups is as follows: LPA: 15.01%, MPA: 16.17%, VPA: 11.66%; Short sleep: 14.33%, Normal sleep: 12.49%, and Long sleep: 13.03%.

Table 1

The prevalence of T2D under varying levels of PA and sleep duration from 2011 to 2020.
		LPA	MPA	VPA	Short sleep	Normal sleep	Long sleep
2011	Events/n	63	169	432	190	422	52
2011	Prevalence (95% CI)	13.35% (10.41–16.75)	12.88% (11.11–14.81)	10.93% (9.97–11.94)	11.52% (10.01–13.15)	11.58% (10.56–12.66)	11.76% (8.91–15.14)
2013	Events/n	34	125	173	103	201	28
2013	Prevalence (95% CI)	8.52% (5.97–11.70)	9.54% (8.01–11.26)	5.33% (4.58–6.16)	6.56% (5.38–7.90)	6.63% (5.77–7.58)	7.93% (5.33–11.26)
2015	Events/n	93	330	708	356	674	101
2015	Prevalence (95% CI)	19.42% (15.97–23.25)	19.35% (17.50-21.31)	15.27% (14.24–16.33)	16.85% (15.28–18.51)	16.61% (15.47–17.78)	15.59% (12.87–18.61)
2018	Events/n	167	810	1243	837	1183	200
2018	Prevalence (95% CI)	13.96% (12.05–16.06)	16.40% (15.37–17.46)	10.96% (10.39–11.55)	14.37% (13.48–15.29)	11.74% (11.11–12.38)	12.71% (11.10-14.45)
2020	Events/n	192	876	1382	950	1337	163
2020	Prevalence (95% CI)	17.27% (15.09–19.62)	17.47% (16.42–18.55)	13.03% (12.39–13.68)	16.28% (15.34–17.25)	13.73% (13.05–14.43)	14.06% (12.11–16.20)
Total	Events/n	549	2310	3938	2436	3817	544
Total	Prevalence (95% CI)	15.01% (13.88–16.18)	16.17% (15.57–16.79)	11.66% (11.32–12.01)	14.33% (13.77–14.91)	12.49% (12.14–12.84)	13.03% (12.06–14.05)
Abbreviations: 95% CI = 95% confidence interval; LPA = light-intensity physical activity; MPA = moderate-intensity physical activity; VPA = vigorous-intensity physical activity.

Table 2 shows the baseline characteristics of the study participants stratified by PA level in 2020. A total of 16735 participants were included, with a mean age of 62.83 ± 9.52 years and 52.35% females. Of these, 1112 were in the LPA group (mean age, 66.63 ± 10.98 years; 55.58%% females), 5015 were in the MPA group (mean age, 64.13 ± 9.90 years; 52.08% % females), and 10608 were in the VPA group (mean age, 61.81 ± 8.98; 52.14% females). The prevalence of participants in the LPA, MPA, and VPA groups was 6.64%, 29.96%, and 63.4%, respectively. More than half of the participants engaged in MVPA levels.

Table 2

Baseline characteristics of the study participants stratified by PA (n = 16735) in 2020, data are presented as mean ± SD or n (%).
Characteristic	LPA (n = 1112)	MPA (n = 5015)	VPA (n = 10608)	Total (n = 16735)	χ² P
Age (years)	66.63 ± 10.98	64.13 ± 9.90	61.81 ± 8.98	62.83 ± 9.52	617.73 (p < 0.001)
Male Female	494 (44.42%) 618 (55.58%)	2403 (47.92%) 2612 (52.08%)	5077 (47.86%) 5531(52.14%)	7974 (47.65%) 8761 (52.35%)	4.97 (p = 0.083)
Sleep duration Short (hours/day) Normal (hours/day) Long (hours/day)	407 (36.60%) 608 (54.68%) 97 (8.72%)	1719 (34.28%) 2991 (59.64%) 305 (6.08%)	3711 (34.98%) 6140 (57.88%) 757 (7.14%)	5837 (34.88%) 9739 (58.20%) 1159 (6.93%)	16.87 (p < 0.05)
Education Less than Primary Primary School Middle School High School and Above	498 (44.78%) 247 (22.21%) 222 (19.96%) 145 (13.04%)	1722 (34.34%) 1112 (22.17%) 1236 (24.65%) 945 (18.84%)	4588(43.25%) 2372 (22.36%) 2411 (22.73%) 1237 (11.66%)	6808 (40.68%) 3731 (22.29%) 3869 (23.12%) 2327 (13.90%)	209.00 (p < 0.001)
Marry	868 (78.06%)	4157 (82.89%)	9289 (87.57%)	14314 (85.53%)	113.93 (p < 0.001)
Smoke	263 (23.65%)	1208 (24.09%)	2841 (26.78%)	4312 (25.77%)	15.73 (p < 0.001)
Drink	363 (32.64%)	1767 (35.23%)	4133 (38.96%)	6263 (37.43%)	31.87 (p < 0.001)
Type 2 Diabetes	192 (17.27%)	876 (17.47%)	1382 (13.03%)	2450 (14.64%)	60.28 (p < 0.001)
Dyslipidemia	381 (34.26%)	1529 (30.49%)	2550 (24.04%)	4460 (26.65%)	107.78 (p < 0.001)
Hypertension	502 (45.14%)	2169 (43.25%)	3834 (36.14%)	6505 (38.87%)	92.12 (p < 0.001)
Heart Disease	315 (28.33%)	1153 (22.99%)	1910 (18.01%)	3378 (20.19%)	101.55 (p < 0.001)
Cancer	41 (3.69%)	160 (3.19%)	216 (2.04%)	417 (2.49%)	25.68 (p < 0.001)
Lung Disease	200 (17.99%)	711 (14.18%)	1420 (13.39%)	2331 (13.93%)	18.13 (p < 0.001)
Liver Disease	96 (8.63%)	389 (7.76%)	738 (6.96%)	1223 (7.31%)	6.30 (p < 0.05)
Stroke	104 (9.35%)	416 (8.30%)	548 (5.17%)	1068 (6.38%)	73.40 (p < 0.001)
Kidney Disease	131 (11.78%)	568 (11.33%)	1052 (9.92%)	1751 (10.46%)	9.42 (p < 0.05)
Mental Disorders	55 (4.95%)	150 (2.99%)	233 (2.20%)	438 (2.62%)	33.78 (p < 0.001)
Stomach Disease	403 (36.24%)	1604 (31.98%)	3346 (31.54%)	5353 (31.99%)	10.21 (p < 0.05)
Asthma	109 (9.80%)	303 (6.04%)	527 (4.97%)	939 (5.61%)	46.92 (p < 0.001)
Note: Percentages add up not to 100% due to rounding.

Table 3 shows the baseline characteristics of the study participants stratified by sleep duration in 2020. The percentage of participants with short, normal, and long sleep duration were 34.8%, 58.2%, and 6.9%, respectively. Approximately 40% of the study population did not meet the recommended sleep duration guidelines, indicating that insufficient sleep is a prevalent issue.

Table 3

Baseline characteristics of the study participants stratified by sleep duration (n = 16735) in 2020, data are presented as mean ± SD or n (%).
Characteristic	Short (n = 5837)	Normal (n = 9739)	Long (n = 1159)	Total (n = 16735)	χ² P
Age (years)	64.11 ± 9.38	61.63 ± 9.30	66.45 ± 10.20	62.83 ± 9.52	629.70 (p < 0.001)
Male Female	2403 (41.17%) 3434 (58.83%)	4973 (51.06%) 4766 (48.94%)	598 (51.60%) 561 (48.40%)	7974 (47.65%) 8761 (52.35%)	151.01 (p < 0.001)
LPA MPA VPA	407(6.97%) 1719 ( 29.45%) 3711 (63.58%)	608 (6.24%) 2991 (30.71%) 6140 (63.05%)	97 (8.37%) 305 (26.32%) 757 (65.31%)	1112 (6.64%) 5015 (29.97%) 10608 (63.39%)	16.87 (p < 0.05)
Education Less than Primary Primary School Middle School High School and Above	2743 (46.99%) 1274 (21.83%) 1179 (20.20%) 641 (10.98%)	3423 (35.15%) 2207 (22.66%) 2509 (25.76%) 1600 (16.43%)	642 (55.39%) 250 (21.57%) 181 (15.62%) 86 (7.42%)	6808 (40.68%) 3731 (22.29%) 3869 (23.12%) 2327 (13.90%)	388.29 (p < 0.001)
Marry	4802 (82.27%)	8575 (88.05%)	937 (80.85%)	14314 (85.53%)	120.64 (p < 0.001)
Smoke	1317 (22.57%)	2668 (27.40%)	327 (28.21%)	4312 (25.77%)	48.37 (p < 0.001)
Drink	2014 (34.51%)	3874 (39.78%)	375 (32.36%)	6263 (37.43%)	56.92 (p < 0.001)
Type 2 Diabetes	950 (16.28%)	1337 (13.73%)	163 (14.06%)	2450 (14.64%)	19.27 (p < 0.001 )
Dyslipidemia	1761 (30.17%)	2461 (25.27%)	238 (20.53%)	4460 (26.65%)	68.65 (p < 0.001)
Hypertension	2434 (41.70%)	3575 (36.71%)	496 (42.80%)	6505 (38.87%)	46.34 (p < 0.001)
Heart Disease	1468 (25.15%)	1691 (17.36%)	219 (18.90%)	3378 (20.19%)	138.63 (p < 0.001)
Cancer	179 (3.07%)	214 (2.20%)	24 (2.07%)	417 (2.49%)	12.26 (p < 0.001)
Lung Disease	1037 (17.77%)	1121 (11.51%)	173 (14.93%)	2331 (13.93%)	120.16 (p < 0.001)
Liver Disease	540 (8.63%)	621 (7.76%)	62 (6.96%)	1223 (7.31%)	51.58 (p < 0.001)
Stroke	483 (8.27%)	489 (5.02%)	96 (8.28%)	1068 (6.38%)	72.20 (p < 0.001)
Kidney Disease	786 (13.47%)	878 (9.02%)	87 (7.51%)	1751 (10.46%)	88.78 (p < 0.001)
Mental Disorders	217 (3.72%)	189 (1.94%)	32 (2.76%)	438 (2.62%)	45.31 (p < 0.001)
Stomach Disease	2287 (39.18%)	2748 (28.22%)	318 (27.44%)	5353 (31.99%)	213.53 (p < 0.001)
Asthma	446 (7.64%)	415 (4.26%)	78 (6.73%)	939 (5.61%)	81.66 (p < 0.001)
Note: Percentages add up not to 100% due to rounding.
Abbreviations: 95% CI = 95% confidence interval; LPA = light-intensity physical activity; MPA = moderate-intensity physical activity; VPA = vigorous-intensity physical activity.

The change in the prevalence of T2D across varying levels of PA and sleep duration from 2011–2020

Figure 2 and Fig. 3 illustrate the change in T2D prevalence over the period from 2011–2020. All groups showed a significant increase in T2D prevalence during this time. In the LPA group, the prevalence of T2D rose from 13.35% in 2011 to 17.27% in 2020. A similar upward trend was observed in the MPA, with an increase from 12.88–17.47% over the same period. Meanwhile, the VPA group showed a relatively smaller increase from 10.93–13.03%.

The short sleep group experienced a clear upward trend, with the prevalence of T2D rising from 11.52% in 2011 to 16.28% in 2020. In contrast, the increase was relatively small in the normal sleep group, from 11.58–13.73%, as well as in the long sleep group, from 11.76–14.06%. In summary, the prevalence of T2D was higher in the LPA and short sleep duration groups, while it was relatively lower in the VPA and normal/long sleep duration groups.

Independent association of PA and sleep duration with T2D in 2020

A coefficient of 0.01 suggested a very small, non-significant difference in T2D prevalence between the LPA and MPA groups (p > 0.05). A negative coefficient of -0.33 indicated that those engaging in VPA had a significantly lower prevalence of T2D compared to those in the LPA group (p < 0.01). A coefficient of -0.35 showed a significant decrease in T2D prevalence in VPA compared to MPA (p < 0.001). Regarding sleep duration, a negative coefficient of -0.20 indicated that shorter sleep duration was associated with a higher prevalence of T2D compared to normal sleep duration (p < 0.001). However, there were no significant differences between the normal and long sleep duration groups, nor between short and long sleep duration groups (p > 0.05, see Table 4).

Table 4

Differences in the prevalence of T2D across varying levels of PA and sleep duration.
	Coefficient	Standard Error	95% CI	P	Z value
PA volume
LPA VS.MPA	0.01	0.09	(-0.16–0.19)	p > 0.05	0.16
LPA VS.VPA	-0.33	0.08	(-0.50 - -0.17)	p < 0.05	-3.93
MPA VS.VPA	-0.35	0.05	(-0.43 - -0.25)	p < 0.001	-7.34
Sleep duration
Normal VS. Short	-0.20	0.46	(-0.36 - -0.29)	p < 0.001	-4.34
Normal VS. Long	-0.03	0.90	(-0.02–0.15)	p > 0.05	-0.31
Short VS. Long	0.17	0.01	(-0.01–0.35)	p > 0.05	1.88
Abbreviations: 95% CI = 95% confidence interval; LPA = light-intensity physical activity; MPA = moderate-intensity physical activity; VPA = vigorous-intensity physical activity; PA = physical activity.

Moreover, we further constructed three weighted logistic regression models. After full adjustment in Model 3, participants with VPA had a lower OR for T2D (0.80, 95% CI = 0.68–0.95; Fig. 4). In contrast, no statistically significant association was observed between MPA and LPA across all models (Model 3: OR = 1.00, 95% CI = 0.84–1.19). These results indicate that, compared with LPA, VPA can serve as a protective factor against the risk of T2D. Additional results from other models are presented in Table 5. Participants with normal sleep duration had a lower OR (0.84, 95% CI = 0.76–0.92, Fig. 4) for T2D compared to short sleep duration. Long sleep duration showed a trend toward a lower risk of T2D compared to short sleep duration, but this was only statistically significant in Model 2 (OR = 0.81, 95% CI = 0.68–0.97). Short (OR = 1.19, 95%CI = 1.08–1.21) but not long sleep duration (OR = 1.02, 95%CI = 0.85–1.22) was associated with excessive T2D prevalence. Those results indicated that short sleep duration serves as a risk factor for T2D. The results of other models are detailed in Table 5.

Table 5

Independent associations between PA level, sleep duration, and T2D.
	Events/n	Model 1 OR (95% CI)	Model 2 OR (95% CI)	Model 3 OR (95% CI)
PA volume
LPA	192/1112	1.00 (reference)	1.00 (reference)	1.00 (reference)
MPA	876/5015	1.10 (0.85–1.20) p > 0.05	1.07 (0.90–1.27) p > 0.05	1.00 (0.84–1.19) p > 0.05
VPA	1382/10608	0.73 (0.61–0.85) p < 0.001	0.79 (0.67–0.93) p < 0.05	0.80 (0.68–0.95) p < 0.05
Sleep duration
Short	950/5837	1.00 (reference)	1.00 (reference)	1.00 (reference)
Normal	1337/9739	0.82 (0.75–0.90) p < 0.001	0.87 (0.79–0.95) p < 0.001	0.84 (0.76–0.92) p < 0.001
Long	163/1159	0.84 (0.71–1.01) p > 0.05	0.81 (0.68–0.97) p < 0.05	0.86 (0.71–1.02) p > 0.05
Note: Model 1 did not include any adjustments and purely examined the relationship between the two variables. Model 2 was adjusted for Model 1 plus age and gender. Model 3 was adjusted for Model 2 plus marital status, education level, smoking status, alcohol consumption, place of residence, retirement status and family size.
Abbreviations: 95% CI = 95% confidence interval; LPA = light-intensity physical activity; MPA = moderate-intensity physical activity; VPA = vigorous-intensity physical activity; OR = odds ration; PA = physical activity.

Joint associations of PA and sleep duration with T2D in 2020

Figure 5 and Table 6 present the results of the joint analyses of the association of sleep duration and PA with T2D. Compared to the combination of short sleep duration and LPA, individuals with normal sleep duration who engaged in LPA had a lower OR (0.65, 95% CI = 0.46–0.91). MPA, when combined with any sleep duration (short: OR = 0.83, 95% CI = 0.63–1.10; normal: OR = 0.80, 95% CI = 0.62–1.05; long: OR = 0.82, 95% CI = 0.55–1.21), did not significantly reduce the risk of T2D, although marginal significance was observed in some models (Model 2). In contrast, VPA, when combined with any sleep duration (short: OR = 0.73, 95% CI = 0.56–0.95; normal: OR = 0.65, 95% CI = 0.51–0.85), consistently showed a lower OR. This protective effect was especially pronounced among those with long sleep duration (OR = 0.63, 95% CI = 0.45–0.89). This results indicate that VPA can serve as a protective factor in reducing the risk of T2D associated with sleep disorders.

Table 6

Joint associations between PA, sleep duration and T2D.
	Events/n	Model 1 OR (95% CI)	Model 2 OR (95% CI)	Model 3 OR (95% CI)
LPA
Short sleep duration	89/407	1.00 (reference)	1.00 (reference)	1.00 (reference)
Normal sleep duration	83/608	0.61 (0.44–0.84) p < 0.05	0.59 (0.42–0 .82) p < 0.05	0.65 ( 0.46–0.91) p < 0.05
Long sleep duration	20/97	0.89 (0.51–1.53) p > 0.05	0.93 (0.53–1.60) p > 0.05	0.99 (0.56–1.74) p > 0.05
MPA
Short sleep duration	322/1719	0.87 (0.67–1.14) p > 0.05	0.82 ( 0.62–1.07) p > 0.05	0.83 (0.63–1.10) p > 0.05
Normal sleep duration	501/2991	0.80 (0.62–1.03) p = 0.087	0.73 (0.56–0.94) p < 0.05	0.80 (0.62–1.05) p = 0.087
Long sleep duration	53/305	0.76 (0.52–1.11) p > 0.05	0.75 (0.51–1.09) p > 0.05	0.82 (0.55–1.21) p > 0.05
VPA
Short sleep duration	539/3711	0.67 (0.52–0.86) p < 0.05	0.69 (0.53–0.88) p < 0.05	0.73 ( 0.56–0.95) p < 0.05
Normal sleep duration	753/6140	0.58 (0.45–0.74) p < 0.001	0.57 (0.44–0.74) p < 0.001	0.65 (0.51–0.85) p < 0.05
Long sleep duration	90/757	0.52 (0.37–0.72) p < 0.001	0.55 (0.40–0.76) p < 0.001	0.63 ( 0.45–0.89) p < 0.05
Note: Model 1 was adjusted for age and gender. Model 2 was adjusted for Model 1 plus marital status, education level, smoking status, alcohol consumption, place of residence, retirement status and family size. Model 3 was adjusted for Model 2 plus hypertension, cancer, stomach diseases, liver diseases, kidney diseases.
Abbreviations: 95% CI = 95% confidence interval; LPA = light-intensity physical activity; MPA = moderate-intensity physical activity; VPA = vigorous-intensity physical activity; OR = odds ration; PA = physical activity.

To our knowledge, this is the first and largest survey to examine the prevalence and the independent and joint associations of sleep duration and different intensities of PA with T2D. The current study highlights the high prevalence of T2D in the LPA and short sleep groups. Short sleep duration (< 6 hours/day), rather than long sleep duration (> 8 hours/day), was identified as a risk factor for T2D. Additionally, VPA serves as a protective factor in reducing the risk of T2D associated with sleep disorders.

Higher levels of PA intensity were associated with a lower prevalence of T2D [27]. The benefits of MVPA are well established and it is recognized and recommended by guidelines as a robust preventive factor for T2D [28]. Consequently, both the public and researchers may focus more on MVPA, potentially overlooking the independent effects of MPA. This tendency could result in insufficient attention to the independent effects of MPA in research and public health recommendations. An analysis of PA intensity in this study revealed that VPA has a negative association with T2D prevalence, while MPA has no association with T2D. The results indicate that MPA can not serve as a protective factor against the risk of T2D.

The findings of this study further reinforced the widely recognized association between short sleep duration and an increased risk of T2D [10]. However, in contrast to existing findings [10], this study found no significant association between prolonged sleep duration and T2D. The inconsistent findings may be attributed to potential biases in self-reported sleep data, as participants might have overestimated their actual sleep duration, thus influencing the study outcomes. In fact, prolonged sleep duration is typically positively associated with an increased risk of T2D [29]. Prolonged sleep can potentially disrupt the body's circadian rhythm and metabolic processes, thereby affecting insulin sensitivity and glucose homeostasis [30]. Furthermore, prolonged sleep is often accompanied by extended bed rest and sedentary behavior, as well as sleep fragmentation, all of which are behavioral risk factors for the development of T2D [31, 32]. This suggests that the measurement of sleep duration should rely more on objective assessments.

To our knowledge, this is the first study to systematically examine the joint association between sleep duration, PA and T2D. LPA demonstrated a certain protective effect when combined with normal sleep duration, suggesting that even lower-intensity activity, when coupled with good sleep habits, can moderately reduce the risk of T2D. However, the protective effect of prolonged sleep duration was not significant in the LPA group, which may reflect the detrimental impact of excessive sleep, such as the potential association between oversleeping and metabolic abnormalities [33]. Conversely, MPA did not exhibit a significant risk-reducing effect in this study, particularly in the short and long sleep duration groups. VPA showed a significant protective effect across all sleep duration groups, with the effect being most pronounced when combined with prolonged sleep. Taken together, these results suggest that high levels of PA (> 3000 MET minutes/week) may protect short sleepers from developing T2D.

Short sleep duration is positively associated with T2D. Lifestyle interventions aimed at increasing PA levels may help mitigate this excess risk. These findings on the protective effect of increasing PA for short sleepers against T2D are largely consistent with existing evidence. For instance, evidence from cohort studies has indicated that VPA can help mitigate the inflammatory effects induced by short sleep duration [34]. Cross-sectional studies have found that among individuals with short sleep duration, those with higher levels of PA have a lower risk of developing diabetes or insulin resistance compared to their age-matched counterparts [35]. A previous small-sample experimental study (n = 10) found that PA can partially protect sleep-deprived men from a decline in insulin sensitivity [36]. The present study provides direct and robust evidence supporting the hypothesis that achieving 3000 MET-minutes/week of PA may help reduce the excessive risk of T2D in individuals with short sleep duration.

This study has several notable strengths. First, the large sample size of 16,735 participants and the detailed epidemiological profile allowed for high-quality data analysis and accurate estimation of the exposure-disease relationship. Furthermore, the study not only focused on sleep duration but also distinguished between different intensities of PA (e.g., light, moderate, and vigorous), providing more detailed and specific health recommendations. However, the study has some limitations. Sleep duration and PA intensity were primarily obtained through self-reported questionnaires, which may introduce recall bias or social desirability bias, potentially affecting data accuracy. Furthermore, PA was estimated in intervals, which may have led to overestimation or underestimation of activity duration. In addition. the specific timing of measurements also limited the collection of blood samples, preventing the inclusion of key variables such as blood glucose levels as covariates. Finally, as PA intensity was presented in interval form, the optimal dose of PA matched to sleep duration could not be determined.

The current study highlights the high prevalence in LPA and short sleep groups. The present study demonstrated that short sleep duration (< 6 hours/day), rather than long sleep duration (> 8 hours/day), was positively associated with T2D. VPA combined with any sleep duration significantly reduced the risk of T2D, serving as strong protective factors. Surprisingly, LPA exhibited a certain protective effect when combined with normal sleep duration.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation. Further inquiries can be directed to the first author. The link to my data is “https://figshare.com/s/0f98434ed228dcdec2b9”.

Ethics statement

Ethical approval for the CHARLS was obtained from the institutional review board at Peking University. Each respondent who agreed to participate in the survey provided written informed consent.

Author contributions

TX conceived and supervised the study. TX and ZG designed the experiments. ZG analyzed the data. ZG wrote the manuscript. All authors approved the final version of the submitted manuscript.

Funding

The study was conducted without any external funding support.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10397):203–34.
GBD 2021 Forecasting Collaborators. Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2204–56.
The Lancet. Diabetes: a defining disease of the 21st century. Lancet. 2023;401(10394):2087.
Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.
St-Onge MP, Grandner MA, Brown D, et al. Sleep duration and quality: Impact on lifestyle behaviors and cardiometabolic health: A scientific statement from the American Heart Association. Circulation. 2016;134(18):e367–86.
Balducci S, D'Errico V, Haxhi J, et al. Effect of a behavioral intervention strategy on sustained change in physical activity and sedentary behavior in patients with type 2 diabetes: The IDES_2 Randomized Clinical Trial. JAMA. 2019;321(9):880–90.
Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 201;14(2):88–98.
Larcher S, Benhamou PY, Pépin JL, et al. Sleep habits and diabetes. Diabetes Metab. 2015;41(4):263–71.
Reutrakul S, Van Cauter E. Sleep influences on obesity, insulin resistance, and risk of type 2 diabetes. Metabolism. 2018;84:56–66.
Shan Z, Ma H, Xie M, et al. Sleep duration and risk of type 2 diabetes: a meta-analysis of prospective studies. Diabetes Care. 2015;38(3):529–37.
Strain T, Dempsey PC, Wijndaele K, et al. Quantifying the relationship between physical activity energy expenditure and incident type 2 diabetes: A prospective cohort study of device-measured activity in 90,096 Adults. Diabetes Care. 2023;46(6):1145–55.
Cao Z, Min J, Chen H, et al. Accelerometer-derived physical activity and mortality in individuals with type 2 diabetes. Nat Commun. 2024;15(1):5164.
Boonpor J, Parra-Soto S, Petermann-Rocha F, et al. Dose-response relationship between device-measured physical activity and incident type 2 diabetes: findings from the UK Biobank prospective cohort study. BMC Med. 2023;21(1):191.
Huang Z, Zhuang X, Huang R, et al. Physical activity and weight loss among adults with type 2 diabetes and overweight or obesity: A post hoc analysis of the look ahead trial. JAMA Netw Open. 2024;7(2):e240219.
Gill JM, Cooper AR. Physical activity and prevention of type 2 diabetes mellitus. Sports Med. 2008;38(10):807–24.
Chennaoui M, Arnal PJ, Sauvet F, et al. Sleep and exercise: a reciprocal issue? Sleep Med Rev. 2015;20:59–72.
Huang BH, Duncan MJ, Cistulli PA. Sleep and physical activity in relation to all-cause, cardiovascular disease and cancer mortality risk. Br J Sports Med. 2022;56(13):718–24.
Kredlow MA, Capozzoli MC, Hearon BA, et al. The effects of physical activity on sleep: a meta-analytic review. J Behav Med. 2015;38(3):427–49.
Hartescu I, Morgan K, Stevinson CD. Increased physical activity improves sleep and mood outcomes in inactive people with insomnia: a randomized controlled trial. J Sleep Res. 2015;24(5):526–34.
Sylow L, Kleinert M, Richter EA, et al. Exercise-stimulated glucose uptake - regulation and implications for glycaemic control. Nat Rev Endocrinol. 2017;13(3):133–48.
Zhao Y, Hu Y, Smith JP, et al. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61–8.
Li X, Zhang W, Zhang W, et al. Level of physical activity among middle-aged and older Chinese people: evidence from the China health and retirement longitudinal study. BMC Public Health. 2020;20(1):1682.
The IPAQ group. scoring protocol [EB/OL]. https://docs.google.com/viewer? a = v&pid = sites&srcid = ZGVmYXVsdGRvbWFpbnx0aGVpcGFxfGd4OjE0NDgxMDk3NDU1YWRlZTM. Accesed 20 Oct 2018.
Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.
Yin J, Jin X, Shan Z, et al. Relationship of sleep duration with all-cause mortality and cardiovascular events: A systematic review and dose-response meta-analysis of prospective cohort studies. J Am Heart Assoc. 2017;6(9):e005947.
; Consensus Conference Panel, Watson NF, Badr MS et al. Joint consensus statement of the American academy of sleep medicine and sleep research society on the recommended amount of sleep for a healthy adult: Methodology and discussion. Sleep. 2015;38(8):1161-83.
Jin X, Chen Y, Feng H, et al. Association of accelerometer-measured sleep duration and different intensities of physical activity with incident type 2 diabetes in a population-based cohort study. J Sport Health Sci. 2024;13(2):222–32.
American Diabetes Association Professional Practice Committee. 3. Prevention or delay of type 2 diabetes and associated comorbidities: Standards of medical care in diabetes-2022. Diabetes Care. 2022;45(Suppl 1):S39-S45.
Han H, Wang Y, Li T, et al. Sleep duration and risks of incident cardiovascular disease and mortality among people with type 2 diabetes. Diabetes Care. 2023;46(1):101–10.
Jang JH, Kim W, Moon JS, et al. Association between sleep duration and incident diabetes mellitus in healthy subjects: A 14-year longitudinal cohort study. J Clin Med. 2023;12(8):2899.
Mokhlesi B, Temple KA, Tjaden, et al. Association of self-reported sleep and circadian measures with glycemia in adults with prediabetes or recently diagnosed untreated type 2 diabetes. Diabetes Care. 2019;42(7):1326–32.
Cappuccio FP, D'Elia L, Strazzullo P, et al. Quantity and quality of sleep and incidence of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2010;33(2):414–20.
Jike M, Itani O, Watanabe N, et al. Long sleep duration and health outcomes: A systematic review, meta-analysis and meta-regression. Sleep Med Rev. 2018;39:25–36.
You Y. Accelerometer-measured physical activity and sedentary behaviour are associated with C-reactive protein in US adults who get insufficient sleep: A threshold and isotemporal substitution effect analysis. J Sports Sci. 2024;42(6):527–36.
Zuo H, Shi Z, Yuan B, et al. Interaction between physical activity and sleep duration in relation to insulin resistance among non-diabetic Chinese adults. BMC Public Health. 2012;12:247.
VanHelder T, Symons JD, Radomski MW. Effects of sleep deprivation and exercise on glucose tolerance. Aviat Space Environ Med. 1993;64(6):487–92.

No competing interests reported.

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Prevalence and association of sleep duration and different intensities of physical activity with type 2 diabetes: The first evidence from CHARLS

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Independent association of PA and sleep duration with T2D in 2020

Joint associations of PA and sleep duration with T2D in 2020

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