Abstract
Background
Recently, poor sleep was closely related to physical function decline in older adults. To investigate the possible mediation pathway between them, the relationships among sleep status, lower limb composition, and physical performance were analyzed.
Methods
The cross-sectional study included 323 adults (46% women) aged 60 years and above (mean age 71.7 years) from the community of China. Sleep duration and sleep satisfaction were collected by questionnaire. The composition of lower limbs was quantified by dual-energy X-ray absorptiometry. Gait speed was measured as a physical performance indicator. The mediation role of low lean mass and high fat mass in the relationship between sleep status and physical performance was analyzed by bootstrap mediation analysis.
Results
The average sleep duration of participants was 6.8 h, about 43% participants were dissatisfied with their sleep. Compared with medium sleep duration, long sleep duration was associated with low lean mass (OR 2.630, 95% CI 1.390–5.013), high fat mass (OR 2.151, 95% CI 1.155–4.010), and slow gait speed (OR 3.143, 95% CI 1.700–5.880). Participants with long sleep duration and unsatisfactory sleep had the worst lower limb composition and physical performance among them. The mediation effect of lower limb composition on the relationship between long sleep and gait speed was significant, with 15.3% of the total effect being mediated by lean mass and 19.9% mediated by fat mass.
Conclusions
Long and unsatisfactory sleep had a great relationship with poor physical performance, and the change of lower limb composition was one of the mediate factors involved in this relationship. The combination of sleep duration and sleep satisfaction together was a good indicator for sleep assessment, which provide a new strategy for the intervention of physical function decline in older adults.
Clinical trial number
Not applicable.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-025-06084-4.
Keywords: Sleep duration, Sleep satisfaction, Lean mass, Fat mass, Gait speed
Introduction
As one of representative disease of physical function changes associated with aging, sarcopenia emphasizes loss of muscle mass, descent of strength, and poor physical performance, which has received gaining increasing attention [1, 2]. Sarcopenia increases the risk of falls, fractures and even death in older adults [3, 4]. To prevent the occurrence of sarcopenia has become an important issue, researches show risk factors for sarcopenia including poor sleep status [5].
Extreme sleep duration or poor sleep quality was associated with high incidence of sarcopenia in older adults [6–9]. Adults with less than 6 h sleep increased 3-fold risk of sarcopenia; and with more than eight hours sleep increased 2-fold risk of sarcopenia [9]. In sarcopenia adults, long sleep duration or short sleep duration was related to low grip strength and slow gait speed; however, the relationship between sleep duration and muscle mass was uncertain [10, 11]. The relationship among sleep, muscle mass and physical performance was complex, which need to be investigated further.
In addition to muscle mass, sleep was also associated with fat mass. Short sleep duration (≤ 5 h/day) was related to body fat mass increase in Korean adults aged 18–70 years old [12]. And poor sleep quality was positively associated with body fat mass in middle-aged and older adults [13–15]. After a five-year follow-up, ≤ 5 h/day of sleep or ≥ 8 h/day of sleep was related to a greater abdominal fat accumulation in young-aged adults [16]. Fat mass accumulation in skeletal muscle was an important influencing factor for muscle function decline, which contributed to the occurrence of sarcopenia [17]. The pathogenesis of poor sleep-induced physical function decline is multifactorial; the change of body composition may be an important modulator involved in it. The crosstalk among sleep status, lower limb composition, and physical performance can provide a new way for sarcopenia prevention or treatment.
The change of body composition simply refers to a decrease in lean mass and an increase in fat mass during ageing [18]. In sarcopenia, the loss of lean mass had been discussed to be correlated with physical function decline induced by sleep change [19]; however, whether the change of fat mass was involved was unknown. Here, we integrated sleep status, lean mass, fat mass, and physical performance together, and tried to explore the possible role of lower limb composition in the regulation of sleep and physical function, which may supply us with some clues for future research on mechanism exploration of sarcopenia.
Methods
Subjects
346 volunteers aged 60 and above were recruited through advertisement from the Beijing community in 2015 [20]. All the participants were healthy without physical disabilities. Inclusion criteria included: (1) no overt diseases; (2) no mental and physical disorders. Exclusion Criteria included individuals with any physical or cognitive impairments preventing the investigation completion. The subjects completed a short questionnaire that included basic demographic details, life style, and sleep metrics. For the partial absence of sleep duration data, we chose to use the complete data of the corresponding metric for analysis to ensure the accuracy of the analysis. A total of 323 data were used in the analysis. Informed consent has been obtained from subjects for the use of their information in this study. The study was approved by the Ethics Committee of Beijing Hospital (Grant No. 2012BJYYEC-052-02).
Sleep parameters
Sleep parameters included subjective sleep duration and sleep satisfaction which were assessed by questionnaires. Subjects were asked to answer the question, “How many hours do you usually sleep per night?” Subjective sleep duration was measured in units of 0.5 h and divided into three groups: short sleep duration (≤ 5 h), medium sleep duration (5.5–7.5 h), and long sleep duration (≥ 8 h) [16]. Sleep satisfaction relied on subjective sleep reports in the form of a question in which the subject was asked to answer “Are you satisfied with your sleep?” Sleep medication was conducted by asking the question “Do you take sleeping pills?” Detailed sleep survey questions were provided as supplementary materials.
Lower limb composition
Lower limb composition was measured in Beijing Hospital by dual-energy X-ray absorptiometry (DEXA, Hologic QDR 4500 A, Hologic). The data were analyzed using QDR software. Total mass, lean mass, and fat mass of each lower limb were detected, and the mean values of two lower limbs were used for analysis. Lean mass index was a measure of lower limb lean mass adjusted for the square of an individual’s height [21]; fat mass percentage was divided by lower limb fat mass by lower limb total mass [22]. According to the method used by the Asian Working Group for Sarcopenia (AWGS) [23, 24], participants with lean mass index below 20% (men ≤ 2.40 kg/m², women ≤ 1.98 kg/m²) was defined as low lean mass and participants with fat mass percentage above 80% (men ≥ 31.10%, women ≥ 43.43%) was defined as high fat mass in this study.
Physical performance
Gait speed reflects overall physical performance [25, 26]. Subjects underwent a 6-meter walk experiment to obtain measurements of gait speed. Set up markers at the beginning and end of a flat, straight and barrier-free 6-metre corridor. The subjects stood at the starting point, and walked in a straight line at their normal pace. The tester used a stopwatch to record the time the subject crossed the finish line. Gait speed was calculated by dividing the 6 m by the time (m/s). Gait speed < 1.0 m/s was defined as slow gait speed [23].
Statistical analysis
Data for continuous and categorical variables were described using mean and standard deviation or count and percentage, respectively. Unpaired Student’s t-test and chi-square test were used to compare mean and frequency differences in descriptive sex characteristics between men and women.
Multivariate logistic regression analysis was used to estimate the correlation of sleep duration and sleep satisfaction with adverse lower limb composition and slow gait speed, with odds ratios (ORs) and 95% confidence intervals (CIs) as outputs. The crude model did not regulate other variables. The adjusted model incorporated age, sex, BMI, sleep medication and physical activity that may affect the dependent variables.
The interaction terms of sleep duration and sleep satisfaction was added to the linear model to analyze their interaction on lower limb composition and gait speed, and all models adjusted for age and sex. The relationship between sleep duration, lower limb composition and physical performance under different sleep satisfaction was plotted by locally weighted regression scatter smoothing method. The generalized additive model was used to fit the pace by sleep duration and lower limb composition, and the relationship among them was visualized using a 3D surface diagram.
For mediation analysis, the dependent variable X was sleep duration and sleep satisfaction, the mediating variable M was lower limb composition (lean mass index or fat mass percentage), and the dependent variable Y was gait speed. Linear models were used to analyze and adjusted for age and sex. The Bootstrap sampling method was used to estimate the total effect, average causal mediation effect (ACME), and average direct effect (ADE), and the proportion of the mediation effect to total effect was calculated.
All statistical analyses were performed using R version 4.4.0 (2024-04-24 ucrt). A P-value of < 0.05 was considered statistically significant.
Results
Basic information of participants
The basic information of the study subjects was shown in Table 1. The mean age of the 323 participants (150 women) was 71.74 years. The average sleep duration was 6.82 h. Compared to the sleep duration of men (7.03 ± 1.16 h), women slept for shorter duration (6.58 ± 1.41 h). 43% participants were dissatisfied with sleep, and women were more dissatisfied with their sleep than men. There was a significant difference in lower limb composition between two sexes, with men having more lean mass (7.57 kg for men and 5.63 kg for women, p < 0.001) and less fat mass (2.97 kg for men and 3.60 kg for women, p < 0.001). The average gait speed was 1.16 m/s for men and 1.10 m/s for women.
Table 1.
Characteristics of participants by sex
| Characteristic | Total (n = 323) |
Men (n = 173) |
Women (n = 150) |
P-value |
|---|---|---|---|---|
| Age (years) | 71.74 ± 7.07 | 71.82 ± 6.65 | 71.65 ± 7.55 | 0.837 |
| Height (cm) | 163.36 ± 7.94 | 168.64 ± 5.69 | 157.26 ± 5.39 | < 0.001 |
| Weight (kg) | 64.22 ± 10.34 | 68.93 ± 9.20 | 58.79 ± 8.81 | < 0.001 |
| Body mass index (kg/m²) | 24.00 ± 3.00 | 24.22 ± 2.81 | 23.75 ± 3.21 | 0.173 |
| Sleep parameters | ||||
| Sleep duration (hours) | 6.82 ± 1.30 | 7.03 ± 1.16 | 6.58 ± 1.41 | 0.002 |
| Sleep duration groups (%) | 0.003 | |||
| Short | 40 (12.38) | 13 (7.51) | 27 (18.00) | |
| Medium | 193 (59.75) | 102 (58.96) | 91 (60.67) | |
| Long | 90 (27.86) | 58 (33.53) | 32 (21.33) | |
| Sleep satisfaction (%) | < 0.001 | |||
| Satisfied | 184 (56.97) | 115 (66.47) | 69 (46.00) | |
| Unsatisfied | 139 (43.03) | 58 (33.53) | 81 (54.00) | |
| Sleep medication (%) | < 0.001 | |||
| Yes | 82 (25.39) | 24 (13.87) | 58 (38.67) | |
| No | 241 (74.61) | 149 (86.13) | 92 (61.33) | |
| Lower extremity measurements | ||||
| Total mass (kg) | 9.93 ± 1.65 | 10.54 ± 1.47 | 9.23 ± 1.57 | < 0.001 |
| Lean mass (kg) | 6.67 ± 1.41 | 7.57 ± 1.13 | 5.63 ± 0.90 | < 0.001 |
| Fat mass (kg) | 3.27 ± 0.91 | 2.97 ± 0.71 | 3.60 ± 1.00 | < 0.001 |
| Lean mass index (kg/m²) | 2.48 ± 0.38 | 2.66 ± 0.34 | 2.27 ± 0.32 | < 0.001 |
| Fat mass percentage (%) | 33.00 ± 7.80 | 28.13 ± 5.22 | 38.62 ± 6.39 | < 0.001 |
| Physical performance | ||||
| Gait speed (m/s) | 1.13 ± 0.25 | 1.16 ± 0.24 | 1.10 ± 0.25 | 0.024 |
P-value: Differences between men and women. Lean mass index: lower limb lean mass divided by the square of an individual’s height. Fat mass percentage: lower limb fat mass divided by lower limb total mass
Relationship of sleep status with lower limb composition and physical performance
Multivariate logistic regression analysis showed that compared to medium sleep duration, long sleep duration was significantly associated with low muscle mass index (OR 2.689, 95% CI 1.486–4.881), high fat mass percentage (OR 2.156, 95% CI 1.192–3.889), and slow gait speed (OR 3.147, 95% CI 1.793–5.558, Table 2). After adjustments for potential confounders, the results were also statistically significant: the adjusted ORs of long sleep duration for low muscle mass index was 2.630 (95% CI 1.390–5.013), for high fat mass percentage was 2.151 (95% CI 1.155–4.010), and for low gait speed was 3.143 (95% CI 1.700–5.880). There was no relationship of sleep satisfaction with lower limb composition and gait speed.
Table 2.
The relationship between sleep status, lower limb composition, and gait speed in older adults
| Low lean mass index | High fat mass percentage | Low gait speed | |||||
|---|---|---|---|---|---|---|---|
| Crude model | Adjusted model | Crude model | Adjusted model | Crude model | Adjusted model | ||
| Variable | Group | OR (95%CI) | OR (95%CI) | OR (95%CI) | OR (95%CI) | OR (95%CI) | OR (95%CI) |
| Age | < 70 years | ref. | ref. | ref. | ref. | ref. | ref. |
| ≥ 70 years | 1.499 (0.856–2.687) | 1.397 (0.757–2.628) | 1.947 (1.096–3.574) * | 1.882 (1.031–3.538) * | 3.410 (1.971–6.112) * | 3.305 (1.836–6.156) * | |
| Sex | Men | ref. | ref. | ref. | ref. | ref. | ref. |
| Women | 0.986 (0.569 -1.700) | 1.099 (0.585–2.064) | 0.986 (0.569 -1.700) | 1.110 (0.606–2.030) | 1.746 (1.062–2.889) * | 1.965 (1.092–3.575) * | |
| BMI | < 24 kg/m² | ref. | ref. | ref. | ref. | ref. | ref. |
| ≥ 24 kg/m² | 0.267 (0.140–0.484) * | 0.261 (0.134–0.487) * | 1.619 (0.937–2.829) | 1.902 (1.073–3.427) * | 0.677 (0.409–1.113) | 0.805 (0.463–1.395) | |
| Physical activity | No | ref. | ref. | ref. | ref. | ref. | ref. |
| Yes | 0.342 (0.145–0.715) * | 0.372 (0.152–0.809) * | 0.752 (0.380–1.412) | 0.838 (0.412–1.625) | 0.310 (0.148–0.597) * | 0.347 (0.160–0.699) * | |
| Sleep duration | Short | 1.200 (0.452–2.841) | 1.193 (0.416–3.139) | 0.888 (0.315–2.161) | 0.679 (0.229–1.760) | 3.041 (1.442–6.338) * | 2.003 (0.861–4.607) |
| Medium | ref. | ref. | ref. | ref. | ref. | ref. | |
| Long | 2.689 (1.486–4.881) * | 2.630 (1.390–5.013) * | 2.156 (1.192–3.889) * | 2.151 (1.155–4.010) * | 3.147 (1.793–5.558) * | 3.143 (1.700–5.880) * | |
| Sleep satisfaction | Satisfied | ref. | ref. | ref. | ref. | ref. | ref. |
| Unsatisfied | 1.084 (0.624–1.872) | 1.074 (0.554–2.067) | 1.266 (0.731–2.187) | 1.425 (0.755–2.688) | 1.561 (0.948–2.574) | 1.188 (0.642–2.188) | |
| Sleep medication | No | ref. | ref. | ref. | ref. | ref. | ref. |
| Yes | 0.855 (0.439–1.592) | 0.556 (0.245–1.216) | 1.278 (0.686–2.315) | 1.132 (0.545–2.311) | 2.004 (1.155–3.456) * | 1.049 (0.518–2.095) | |
*P-value < 0.05. Lean mass index: lower limb lean mass divided by the square of an individual’s height. Fat mass percentage: lower limb fat mass divided by lower limb total mass
Combination analysis of sleep duration and sleep satisfaction
Combined sleep duration and sleep satisfaction together to analysis the relationship between sleep status, lower limb composition and gait speed. Participants who slept ≥ 8 h/day and sleep unsatisfied had the worst lower limb composition, which showed low lean mass and high fat mass (Fig. 1). This phenomenon was more obvious in men than in women. The relationship between sleep status and gait speed showed an inverted U-shaped curve (Fig. 1). In the condition of sleep unsatisfied, both short sleep duration and long sleep duration showed a decrease in gait speed.
Fig. 1.
The change in lower limb lean mass index, lower limb fat mass percentage and gait speed in participants with different sleep duration and sleep satisfaction
Interaction effect of sleep duration and sleep satisfaction
A significant interaction effect of sleep duration and sleep satisfaction on gait speed was shown in Table 3. Model 2 including the interaction between sleep duration and sleep satisfaction explained 23% of the variance in gait speed (p < 0.001, F = 14.32, adjusted R2 = 0.2301), which was much higher than model 1. When adding lower limb fat mass percentage and lean mass index into the model, model 4 can explain 30% of the variance in gait speed (p < 0.001, F = 16.01, adjusted R² =0.3021).
Table 3.
The interaction effect of sleep duration and sleep satisfaction
| Estimate | Standard Error | t-stat | P-value | Lower 95% | Upper 95% | ||
|---|---|---|---|---|---|---|---|
| Model 1 | (R² =0.1932, ΔR² =0.1801, F = 14.70, p < 0.001) | ||||||
| Unsatisfied sleep | -0.002 | 0.028 | -0.075 | 0.940 | -0.056 | 0.052 | |
| Short sleep | -0.078 | 0.041 | -1.879 | 0.061 | -0.159 | 0.004 | |
| Long sleep | -0.121 | 0.030 | -4.039 | < 0.001 | -0.180 | -0.062 | |
| Model 2 | (R² =0.2474, ΔR² =0.2301, F = 14.32, p < 0.001) | ||||||
| Unsatisfied sleep | 0.080 | 0.033 | 2.411 | 0.017 | 0.015 | 0.145 | |
| Short sleep | -0.036 | 0.073 | -0.496 | 0.620 | -0.181 | 0.108 | |
| Long sleep | -0.029 | 0.035 | -0.824 | 0.410 | -0.098 | 0.040 | |
| Unsatisfied: Short sleep | -0.100 | 0.087 | -1.141 | 0.255 | -0.271 | 0.072 | |
| Unsatisfied: Long sleep | -0.293 | 0.063 | -4.678 | < 0.001 | -0.417 | -0.170 | |
| Model 3 | (R² =0.2886, ΔR² =0.2723, F = 17.68, p < 0.001) | ||||||
| Unsatisfied sleep | 0.007 | 0.026 | 0.254 | 0.800 | -0.045 | 0.058 | |
| Short sleep | -0.100 | 0.039 | -2.551 | 0.011 | -0.177 | -0.023 | |
| Long sleep | -0.090 | 0.029 | -3.122 | 0.002 | -0.147 | -0.033 | |
| Fat mass percentage | -0.013 | 0.002 | -5.558 | < 0.001 | -0.018 | -0.009 | |
| Lean mass index | 0.091 | 0.042 | 2.183 | 0.030 | 0.009 | 0.173 | |
| Model 4 | (R² =0.3222, ΔR² =0.3021, F = 16.01, p < 0.001) | ||||||
| Unsatisfied sleep | 0.076 | 0.032 | 2.397 | 0.017 | 0.014 | 0.138 | |
| Short sleep | -0.027 | 0.070 | -0.388 | 0.698 | -0.165 | 0.110 | |
| Long sleep | -0.021 | 0.034 | -0.628 | 0.530 | -0.087 | 0.045 | |
| Unsatisfied: Short sleep | -0.132 | 0.083 | -1.587 | 0.114 | -0.296 | 0.032 | |
| Unsatisfied: Long sleep | -0.230 | 0.061 | -3.764 | < 0.001 | -0.351 | -0.110 | |
| Fat mass percentage | -0.012 | 0.002 | -5.288 | < 0.001 | -0.017 | -0.008 | |
| Lean mass index | 0.065 | 0.042 | 1.557 | 0.121 | -0.017 | 0.146 | |
All models were adjusted for sex and age. Lean mass index: lower limb lean mass divided by the square of an individual’s height. Fat mass percentage: lower limb fat mass divided by lower limb total mass
Integration analysis of sleep status, lower limb composition and physical performance
Both lean mass and fat mass were associated with gait speed but the relationships among them were more significant in sleep unsatisfied group than those in sleep satisfied group. Participants with “long sleep duration, unsatisfied sleep and low lean mass index” or “long sleep duration, unsatisfied sleep and high fat mass percentage” showed the slowest gait speed in Fig. 2. Considering the interaction effect of sleep duration and sleep satisfaction, the effect of fat mass on gait speed was more significant than the effect of lean mass (model 4 in Table 3).
Fig. 2.
3D graphical representation of the relationship among sleep status, lower limb composition and gait speed
Mediation effect of lower limb composition
The results of mediating analysis were shown in Fig. 3. Both lean mass and fat mass of lower limbs involved in the association between long sleep and gait speed, the mediating proportion were 15.30% of the total effect for lean mass index (p = 0.006) and 19.91% of the total effect for fat mass percentage (p = 0.034). Considering long sleep duration and sleep satisfaction together, only fat mass percentage mediated the relationship between sleep unsatisfied and gait speed with 17.58% of the total effect (p = 0.034).
Fig. 3.
The mediation effect of lean mass index of lower limbs and fat mass percentage of lower limbs on the relationship between sleep status and gait speed
Discussion
Sleep plays an essential role in physical function maintenance, which is important for older adults’ independent living. Here, we talked about the possible pathway involved in the modulation between poor sleep and physical function decline from lower limb composition, and found that (1) long sleep duration was associated with adverse lower limb composition and slow gait speed. (2) older adults who slept ≥ 8 h per night but still unsatisfied showed a significant decline in gait speed. (3) the change of lower limb composition (both high fat mass percentage and low lean mass index) is involved in the regulatory process between sleep and gait speed.
Gait speed is a commonly indicator for physical performance assessment in older adults [1]. A significant relationship between long sleep duration and poor gait speed was observed in this study, which was consistent with previous studies [27, 28]. In previous studies, sleep duration or sleep quality assessed by Pittsburgh Sleep Quality Index (PSQI) were always used to investigate the linkages between sleep and gait speed [27, 29]. Recently, sleep satisfaction, which emphasizes the individual’s subjective feelings, has received widespread attention in the studies of sleep health [30]. Sleep satisfaction had been proved to be positive associated with physical activity and physical function [31, 32]. Here, we also investigated the personal sleep satisfaction, and nearly half of participants were dissatisfied with their sleep. Although there was no significant association between self-report sleep satisfaction and gait speed, the combination of sleep satisfaction and sleep duration together showed a more efficient effect on gait speed than each of them respectively. To our knowledge, this was the first study to integrate these two sleep indicators with gait speed together. And according to our results, older adults with long duration and unsatisfactory sleep should pay more attention to their physical function.
Poor sleep-induced physical function decline may be achieved by modulating body composition. The underlying mechanisms of sleep and body composition remain to be explored. Sleep plays an important role in regulating the endocrine system, and poor sleep quality or sleep restriction affected the secretion of leptin, adiponectin, growth hormone, etc., which act on fat and muscle production and decomposition, resulting in the loss of muscle mass and the increase of fat mass [33]. The relationship between sleep duration and body composition appears to be a controversial issue. In the latest study, short sleep duration (< 7 h) was positively correlated with BMI and body lean mass in U.S. adults aged 20–80 [34]. Tan et al. [35] had proved that short (≤ 5 h) or long (≥ 8 h) sleep durations led to adverse body composition, abdominal fat accumulation, obesity or sarcopenia in adults aged 45–75 years. Older adults aged ≥ 65 years with < 6 h of sleep or ≥ 8 h of sleep increased 3-fold or 2- fold risk of sarcopenia [9]. Here, we only observed a relationship between long sleep duration and the changes in lower limb composition in older adults. There may be several reasons contributing to these inconsistencies: (1) age group - different age groups had different correlations between sleep and body composition. (2) detection method - the results from bioelectrical impedance analysis (BIA) and DEXA may have some differences. (3) body part - this work detected the lower limb composition, which differed from previous studies focusing on the whole-body composition. (4) sample size - The sample size of short sleep duration group in this study was a little small, which may lead to a decrease in the power of statistical tests. After stratifying the participants based on sleep satisfaction, a slightly lean mass decrease and fat mass increase can be observed in men with short duration and unsatisfactory sleep, which may give us some clue for further research.
In sarcopenia, sleep disorder was associated with low muscle mass and slow gait speed [36, 37]. Consistent with this, a mediation effect of lean mass on the association of long sleep with gait speed was observed in this study. In addition, we also found a significant mediation effect of fat mass on the association between long sleep and gait speed. When considering sleep duration and sleep satisfaction together, the effect of fat mass on gait speed was even more than the effect of lean mass. Increased fat mass in lower limbs may be associated with insulin resistance and chronic inflammation, contributing to a decrease in lean mass and gait speed [38, 39]. Recently, several cytokines involved in the inflammatory crosstalk between muscle and adipose, such as adiponectin, leptin, soluble tumor necrosis factor receptor 1 (sTNFr-1), had been found [40], which supplied us with some new clues for further research on pathogenesis mechanism of sarcopenia.
In addition, there were still several insufficiencies to be desired in this study. First, our study was a cross-sectional study and could not prove a causal relationship between sleep and the composition of lower limbs. Second, both sleep duration and sleep satisfaction were collected in the form of self-report, which may have a certain recall bias. Third, the sample size of this study was somewhat small, some details for stratified analysis can be studied in future.
Conclusions
The combination of sleep duration and sleep satisfaction together was a good and effective indicator for physical function evaluation. It is simple to use in large-scale population survey on sleep health in the community, and we recommend that older adults with long sleep (≥ 8 h/night) but still unsatisfied should focus on their physical function maintenance. The change of lower limb composition, especially the increase in fat mass, may be an important regulatory pathway involved in sleep-induced sarcopenia. We can attempt to explore the possible mechanisms from the perspective of the interaction between muscle and adipose. Self-satisfied sleep is essential for older adults to maintain muscle and physical function, which is beneficial for healthy ageing.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Author contributions
Concept and design: Z.Y.C. and J.P. Investigation, Data acquisition: J.C., Y.W.H., E.Y.Z., Y.Z., J.P. and T.M.Z. Statistical analysis: Z.Y.C. and F.Y.T. Drafting of the manuscript: Z.Y.C. and J.P. Funding acquisition: T.M.Z. and J.P. All authors reviewed and approve the manuscript.
Funding
This work was supported by National High Level Hospital Clinical Research Funding (No. BJ-2024-219), National Key R&D Program of China (No. 2021YFE0111800), the National Natural Science Foundation of China (No. 81970745), and the Research Special Fund for Public Welfare Industry of Health (No. 201302008).
Data availability
The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of Beijing Hospital (Grant No. 2012BJYYEC-052-02). Informed consent has been obtained from subjects for the use of their information in this study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Ziyi Chen, Ju Cui and Fuyi Tu contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.