Influence of sedentary behavior and physical activity in leisure and work on sleep duration: Data from NHANES 2017-2018

doi:10.21203/rs.3.rs-4914244/v1

Download PDF

Research Article

Influence of sedentary behavior and physical activity in leisure and work on sleep duration: Data from NHANES 2017-2018

https://doi.org/10.21203/rs.3.rs-4914244/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Objective

To evaluate the association between sedentary behavior (SB), moderate to vigorous physical activity (MVPA), and sleep duration.

Methods

Data from the 2017–2018 National Health and Nutrition Examination Survey (NHANES) was analyzed. SB was assessed based on the average daily sitting time, while MVPA was estimated by the frequency and duration of leisure and work-related activities. The ratio of time spent in MVPA to time in SB was analyzed, and a threshold of ≥ 2.5 minutes of MVPA per sedentary hour was used to determine sufficiency for mitigating the effects of a sedentary lifestyle. Sleep duration was measured by the average hours slept on weekdays and weekends, classified according to National Sleep Foundation guidelines. Descriptive statistics were used to characterize the sample, and multivariate logistic regression was applied to assess the associations between movement behaviors and sleep duration.

Results

The study included 5,533 participants, with 51.7% women, predominantly aged 26–64 years (61.8%). Insufficient physical activity was reported by 59.6% at work and 62.5% during leisure time. Sleep duration was considered recommended or acceptable in 84.4% of the sample. Adjusted multivariate analysis revealed that individuals engaging in ≥ 2.5 minutes of MVPA during leisure-time for each sedentary hour were 37.0% less likely to experience short sleep duration. Conversely, those who performed the same amount of MVPA at work were 32% more likely to have short sleep spells.

Conclusion

Meeting the MVPA threshold during leisure-time reduces the likelihood of short-term sleep, while higher MVPA levels at work increase the likelihood of short-term sleep.

Insomnia

Sedentary lifestyle

Physical activity

Short-term sleep

Intensity of physical activity

Sleep is a physiological state marked by altered consciousness, decreased sensitivity to external stimuli, and reduced respiratory rate, accompanied by characteristic motor and postural changes¹,². Like diet and exercise, sleep significantly impacts various aspects of physical, cognitive, and emotional health, with sleep restriction being a known risk factor for adverse health outcomes. For adults, 7 to 9 hours of sleep are recommended³. Moreover, evidence suggests that going to bed earlier and maintaining regular, consistent sleep and wake times are beneficial to overall health⁴.

Sleep quality and duration are influenced by a complex interplay of factors, which can be broadly categorized into four groups: biological, environmental, personal/socioeconomic, and behavioral. Biological factors are inherent and generally stable, while environmental factors include exposure to light and noise. Personal/socioeconomic factors encompass socioeconomic status and living conditions. Behavioral factors, which offer substantial potential for intervention, include habits that can be modified through lifestyle changes, such as moderating alcohol and caffeine consumption, reducing sedentary behavior (SB), and promoting regular physical activity (PA). Unlike biological, environmental, and socioeconomic factors, which are often challenging to alter, behavioral factors are more malleable and can significantly impact sleep hygiene⁵.

The positive relationship between PA and health is well-established, with higher levels of PA associated with improved overall health⁶. However, it is essential to differentiate between PA during leisure time and that performed at work. Leisure-time PA is generally linked to enhanced well-being and better sleep quality, while work-related PA, often mandatory and potentially stressful, may not offer the same benefits. Epidemiological studies consistently highlight the role of both acute and chronic physical exercise in promoting sleep⁷. Given the broad impact of sleep health on various clinical conditions, such as cardiovascular disorders, understanding the factors that shape sleep health is crucial for developing effective interventions⁸.

With the shift in human lifestyles from nomadic to sedentary, SB has become pervasive in various settings, including work, study, home, transportation, and even leisure, exacerbated by technological advancements that reduce mobility⁶. Many of the factors contributing to SB are difficult to change, especially when related to work and study.

Given the challenges in reducing SB associated with work and academic demands, it is necessary to recommend some level of PA that can mitigate the effects of a sedentary lifestyle. Therefore, this study aims to evaluate the hypothesis that individuals with prolonged SB and lower levels of MVPA are more likely to experience sleep disorders, while those with shorter periods of SB and higher levels of MVPA may have a protective effect.

Study design and population

This cross-sectional study used population data from the National Center for Health Statistics (NHANES) survey from 2017 to 2018. The NHANES survey aims to assess the health and nutritional status of the US population and generate data for the world's largest public database. NHANES has been approved by the ethics committee, and details of its methodology and database are available at https://www.cdc.gov/nchs/nhanes/index.htm⁹.

The initial sample consisted of 9,254 participants, but after applying exclusion criteria, 550 participants were removed because they were not in the 18–65 age group or older, 3171 did not provide information on body mass index (BMI), PA or sleep-related variables, which were the variables of interest for this study. Thus, the study included a total of 5,533 participants for analysis, as shown in the flowchart (Fig. 1).

Sedentary behavior

The SB was assessed through a respondent-level interview using the Global Physical Activity Questionnaire (GPAQ)¹⁰. The interview was also conducted at the MEC by trained interviewers using the CAPI system. The duration of a sedentary lifestyle was assessed using a question: "How much time do you usually spend sitting down on a typical day?". The reported sedentary time was recorded in minutes and then transformed into hours for later analysis.

Physical activity during leisure time and at work

PA during both leisure time and work was evaluated using questions from the PA and fitness questionnaire. To assess moderate PA during leisure time, participants were asked the following: "In a typical week, do you engage in any moderate-intensity sports, fitness, or recreational activities that cause a small increase in breathing or heart rate, such as brisk walking, cycling, swimming, or golf, for at least 10 minutes continuously?" They were also asked, "On how many days during a typical week do you engage in such activities?" and "How much time do you spend doing these activities on a typical day?" For vigorous activities, the questions were: "In a typical week, do you engage in any vigorous-intensity sports, fitness, or recreational activities that cause large increases in breathing or heart rate, such as running or basketball, for at least 10 minutes continuously?" followed by "On how many days during a typical week do you engage in these activities?" and "How much time do you spend doing these activities on a typical day?"

To assess moderate PA at work, participants were asked: "Does your job involve moderate-intensity activity that causes small increases in breathing or heart rate, such as brisk walking or carrying light loads for at least 10 minutes continuously?". They were further asked, "On how many days during a typical week do you engage in such activities as part of your work?" and "How much time do you spend doing these activities on a typical day?" For vigorous work-related activity, participants were asked: "Does your job involve vigorous-intensity activity that causes large increases in breathing or heart rate, such as carrying or lifting heavy loads, digging, or construction work for at least 10 minutes continuously?" followed by "On how many days during a typical week do you engage in these activities as part of your job?" and "How much time do you spend doing these activities on a typical day?"

The intensities of these activities were combined to create a new variable, MVPA. To estimate the average daily activity time, the number of active days per week was multiplied by the daily duration of activity and then divided by 7⁹. Additionally, a ratio was calculated between the time spent in PA (both during leisure and work, in minutes per day) and time spent in SB (in hours per day). Subsequent classification followed the method suggested by Chastin et al. (2021)¹², with a cut-off point of 2.5 minutes of activity per sedentary hour to mitigate the impact of SB.

Sleep

Sleep duration was assessed using a standardized approach, where participants answered specific questions about the amount of sleep they got during the night. The questions included: 'How many hours of sleep do you get on average during the night on weekdays?' and 'How many hours of sleep do you get on average during the night on weekends?'. The answers were used to calculate the average number of hours of sleep per night, combining weekdays and weekends. Sleep was classified according to the sleep duration recommendations of the National Sleep Foundation. The population was divided into two groups: young adults aged 18 to 25 and adults aged 26 to 64. For the first group, 7 to 9 hours of sleep were considered recommended or optimal sleep duration, while 6 hours and 10 to 11 hours could be appropriate or reasonable, and finally, less than 6 hours and more than 11 hours were not recommended or not appropriate. For the second group, the recommendation range was the same, but 6h and 10h could be appropriate, and 6h and more than 10h were not recommended³.

Covariates

Demographic and anthropometric covariates were collected using standardized methods to ensure data accuracy and comparability. Participants' sex was self-reported and recorded as either female or male. Age was determined from the participants' reported date of birth and categorized into three groups: 18 to 25 years, 26 to 64 years, and 65 years or older. Ethnicity was self-reported and aligned with NHANES categories, including Hispanics (Mexican American and Other Hispanic) and Non-Hispanics (Non-Hispanic White, Non-Hispanic Black, and Other Ethnicity-Multiracial). Income was assessed based on household income relative to the U.S. Department of Health and Human Services poverty guidelines, classifying participants into two groups: poverty (income ≤ $5/hour) and non-poverty (income ≥ $5/hour).

Participants' weight and height were measured by trained professionals at NHANES mobile examination centers (MEC) using calibrated scales and stadiometers. Weight was measured with participants in light clothing and without shoes to ensure accuracy, while height was measured with participants barefoot and standing with their heads positioned in the Frankfurt plane (a standard head position where the line of sight is parallel to the ground). These measurements were used to calculate BMI using the formula BMI = weight(kg)/height(m)². BMI was then classified according to World Health Organization cut-off points: non-obesity (BMI < 30 kg/m²) and obesity (BMI ≥ 30 kg/m²) ¹³.

Statistical analysis

The statistical analysis was meticulously conducted in accordance with the guidelines of the Centers for Disease Control and Prevention (CDC), utilizing data from NHANES 2017–2018. Analyses were performed using Stata statistical software, version 18.0, with a significance level of 5%. The Stata syntax incorporated the 'svy' prefix, which accounts for sample weights, stratification, and clustering inherent in the NHANES complex sampling design, thereby allowing results to be generalized to the U.S. population. Initially, the sample was characterized using absolute and relative frequencies for categorical variables and measures of central tendency and dispersion for continuous variables. The commands 'svy: tabulate' were used to organize categorical variables, and 'svy: mean' was applied to continuous variables, generating tables with 95% confidence intervals and conducting Pearson's chi-squared tests to assess associations.

To explore the relationships between movement behaviors and sleep duration, multivariate logistic regression analysis was applied. The adjusted model estimated odds ratios (OR) and confidence intervals (CI) to determine whether the ratio of MVPA to SB could predict adequate sleep duration. In constructing the multivariate model, careful selection of variables was based on prior knowledge and causal inference. Sociodemographic, economic, and behavioral variables were considered for adjustment, given their potential influence as confounding factors. Consequently, the multivariate model was adjusted for sex, age group, ethnicity, income, and BMI. The Akaike Information Criterion (AIC) was then used to optimize the model's fit. Supplementary Table S1, "Overview of potential confounders in the association between movement behavior and sleep duration," presents the theoretical model of confounders developed for this study.

The sample consisted of 51.7% females, with 61.8% of participants aged between 26 and 64 years, representing a diverse range of ethnicities. A significant portion of the sample (82.9%) reported an income of less than 5 dollars per hour. Regarding nutritional status, 57.8% of the participants had a BMI below 30, classifying them as non-obese (Table 1).

Table 1

Sociodemographic characterization, lifestyle and its relationship with sleep time in the population, NHANES 2017 − 201.
Variables	Total	Sleep duration
Variables	Total	Optimal	Reasonable	Not appropriate	p-value
Sex
Male	48.0 (46.3–50.0)	46.7 (43.8–49.7)	50.1 (44.8–55.4)	51.0 (43.7–58.3)	0.395
Female	52.0 (50.0–53.7)	53.3 (50.3–56.2)	49.9 (44.6–55.2)	49.0 (41.7–56.3)	0.395
Age (years)
18–25	14.4 (12.3–16.7)	15.2 (13.1–15.5)	15.3 (12.4–18.8)	8.5 (5.7–12.6)	< 0.001
26–65	66.0 (63.1–68.9)	70.7 (67.7–73.6)	57.2 (53.2–61.1)	62.1 (54.5–69.2)
≥65	19.6 (17.0– 22.4)	14.1 (12.0–16.5)	27.5 (23.5–31.8)	29.4 (22.5–37.3)
Obesity
Not obese (< 30 Kg/m²)	57.4 (53.5–61.1)	58.0 (54.0–62.0)	58.0 (37.0–47.3)	52.9 (47–52.1)	0.187
Obesity (≥ 30 Kg/m²)	42.6 (39.0–46.5)	42.0 (38.0–46.0)	42.0 (37.0–47.3)	47.1 (41.0–53.4)	0.187
Ethnicity
Mexican-American	9.1 (6.1–13.5)	9.4 (6.5–14.0)	8.4 (5.1–14.0)	8.7 (5.3–14.0)	< 0.001
Other Hispanics	7.0 (5.2–8.8)	6.8 (5.2–8.8)	7.5 (5.3–10.5)	7.2 (5.6–9.2)
Non-Hispanic white	62.1 (56.5–67.2)	62.8 (56.9–68.3)	63.0 (57.1–68.5)	56.5 (50.0–64.0)
Non-Hispanic black	11.3 (8.32–15.2)	9.5 (6.7–13.1)	12.0 (8.7–16.4)	18.5 (14.2–24.0)
Other races - multiracial	10.5 (8.2–13.5)	11.5 (8.5–15.3)	9.1 (7.0–15.3)	9.1 (7.0–12.0)
Income
Poverty (< 5 dollars/hour)	73.4 (70.0–76.8))	71.3 (67.4–75.0)	73.4 (68.2–78.0)	83.0 (76.5–88.4)	0.001
No poverty (≥ 5 dollars/hour)	26.6 (23.2–30.3)	28.7 (25.0–32.6)	26.6 (22.0–31.8)	17.0 (12.0–23.5)	0.001
Leisure-time physical activity
<2.5 min/hour	57.1 (53.4–60.3)	55.4 (52.4–58.7)	56.4 (52.0–60.7)	66.2 (59.1–72.6)
≥2.5 min/hour	42.9 (39.7–46.3)	44.6 (41.3–47.9)	43.6 (39.3–48.1)	33.8 (27.4–40.9)	0.002
Physical activity at work
<2.5 min/hour	54.1 (50.9–57.2)	54.6 (50.5–58.5)	54.7 (49.7–59.7)	50.4 (48.8–56.0)	0.404
≥2.5 min/hour	45.9 (42.8–49.1)	45.4 (41.5–49.5)	45.3 (40.3–50.3)	49.6 (44.0–55.2)	0.404
*Chi-square test for the association between the study's categorized variables and sleep time. A value of p < 0.005 was considered significant.

The participants reported an average sleep of 7.4 hours per day (95%CI:7.3–7.5), and according to the categories of recommended sleep duration by age group, 86.9% (95%CI:84.6–89.8) of the population had a sleep duration classified as either recommended or acceptable, while 13.1% had an inadequate sleep duration (Fig. 2). In terms of movement behaviors, the participants had an average SB of 8.94 hours per day (95%CI:8.61–9.09), while the practice of MVPA, measured in minutes per day, averaged 88.49 minutes (95%CI:77.90-99.39) at work and 23.62 minutes (95%CI:21.85–25.43) at leisure (Fig. 3). In the combined analysis, 59.6% and 62.5% demonstrated insufficient activity levels per hour of SB at work and during leisure time, respectively (Table 1).

Furthermore, Table 1 presents a statistically significant association between age group and sleep duration (X²=182.2889, p < 0.001). Significant associations were also found with race/ethnicity (X²=57.3333, p < 0.001), income (X²=37.2340, p < 0.001), and leisure-related PA (X²=27.7697, p = 0.0028). However, no significant associations were observed between sleep duration and other variables analyzed: gender (X²=7.2829, p = 0.3959), obesity (X²=6.8617, p = 0.187), and work-related PA (X² =4.3836, p = 0.4048).

In the multivariate model, individuals who engaged in at least 2.5 minutes of MVPA during leisure time for every hour of SB were 37.0% less likely to experience short sleep duration (OR:0.73;95%CI:0.56–0.96; p-value = 0.027). In contrast, those who reported the same amount of MVPA at work per hour of SB were 1.33 times more likely to have a short sleep duration (OR:1.33;95%CI:1.06–1.67; p-value = 0.019) (Table 2).

Table 2

Association between sleep duration and moderate to vigorous leisure-time physical activity for each hour in sedentary behavior, according to the domain of physical activity, NHANES 2017–2018.
Association with short-sleep duration*	Univariate			Multivariate
Association with short-sleep duration*	OR	95%CI	p-value	OR	95%CI	p-value
Leisure-time physical activity
< 2,5 min of MVPA per hour of SB	1.00	-	-	1.00	-	-
≥ 2,5 min of MVPA per hour of SB	0.64	0.51–0.81	0.001	0.73	0.56–0.96	0.027
Physical activity at work
< 2,5 min of MVPA per hour of SB	1.00	-	-	1.00	-	-
≥ 2,5 min of MVPA per hour of SB	1.18	0.91–1.55	0.198	1.33	1.06–1.67	0.019
MVPA: Moderate to vigorous leisure-time physical activity. SB: Sedentary behavior. OR: Odds ratio; CI: Confidence interval. The ratio MVPA/SB was calculated by dividing the minutes of moderate to vigorous leisure-time physical activity (MVPA) per day by the hours of sedentary behavior (SB) per day. *Multivariate logistic regression adjusted for sex, age group, ethnicity, income, and body mass index Values in bold indicates statistical significance (p-value < 0.05).

The findings of this study support the initial hypothesis that engaging in MVPA during leisure time, even when coupled with SB at work, is associated with a lower likelihood of experiencing short sleep duration. Conversely, MVPA performed in the work context was found to be negatively associated with sleep duration. These results emphasize the importance of considering the context in which PA occurs, as it has distinct effects on sleep depending on whether it is performed during leisure or work hours.

Sleep is a vital physiological process, and the prevalence of short sleep duration observed in our study, where 13.1% of participants did not meet the recommended sleep duration, underscores the importance of understanding the factors contributing to this condition. Short sleep duration has been linked to a range of adverse health outcomes, including increased cardiovascular risk, obesity, and metabolic disorders¹⁴. Given the significant role that sleep plays in overall health, identifying and addressing the determinants of insufficient sleep is crucial.

There are several factors that can directly interfere with sleep quality. Psychological factors, such as anxiety, stress and depression, are some of the main ones that have a major influence on the sleep-wake cycle¹⁵. In addition to psychological factors, there are other well-known risk factors, such as gender, age^16,17, excess weight¹⁸ and also movement behaviors, such as PA¹⁹, and SB, more recently¹¹. Studies have shown that high levels of SB are associated with poorer sleep quality and a greater prevalence of poor sleep quality¹¹. Conversely, regular engagement in MVPA, particularly during leisure time, has been associated with improved sleep quality, including shorter sleep latency (time to fall asleep) and increased sleep duration²⁰. This suggests that the context and nature of PA play a significant role in its impact on sleep.

Traditionally, studies have tended to evaluate PA and SB separately. However, the interaction between these two behaviors is critical for a more comprehensive understanding of how they affect sleep, as demonstrated by Menezes-Júnior, et al. 2023, where regular leisure-time PA can mitigate the harmful effects of a sedentary lifestyle and improve general health, including sleep quality¹¹. Studies suggest that more daily hours of sedentary time are associated with an increased risk of all-cause mortality and worsened sleep quality^21,22. The need for refined recommendations on SB to maintain sleep quality is evident²². In our study, we examined the ratio of time spent in MVPA to time in SB, using a threshold of ≥ 2.5 minutes of MVPA per hour of SB as a measure of adequacy. This approach allows for a more integrated assessment of movement behaviors, reflecting the reality of daily interactions between these behaviors and their combined impact on health. The significance of examining the ratio between MVPA and SB lies in its ability to capture the balance between activity and inactivity. A higher ratio indicates that an individual is engaging in sufficient PA to potentially offset the negative effects of prolonged SB.

In our findings, participants who engaged in at least 2.5 minutes of MVPA during leisure time for every hour of SB were 37% less likely to experience short sleep duration (OR: 0.73; 95%CI: 0.56–0.96; p-value = 0.027). This result is similar to that found in other studies, as presented by Menezes-Júnior et al. 2023, where, evaluating the cut-off point suggested in the literature, the chance of poor sleep quality was 3 times lower in individuals who performed 2.5 min or more of MVPA in leisure-time per hour of SB compared to individuals who performed less than 2.5 min of MVPA per hour of SB (OR:0.33;95%CI:0.16–0.72)^11,23 also found similar results with the mental health outcome. Young adults who did not engage in 2.5 min of MVPA per hour of SB had higher odds of anxiety symptoms (OR:1.44;95%CI:1.31–1.58) and depression (OR:1.74;95%CI:1.59–1.92)²³. Despite the protective results of greater leisure-time PA for sleep duration, our study found the opposite results when assessing PA at work. Individuals who reported the same level of MVPA during work hours were 1.33 times more likely to have short sleep duration (OR: 1.33; 95%CI: 1.06–1.67; p-value = 0.019).

The differential impact of PA during leisure versus work on sleep underscores the importance of the context in which PA occurs. Leisure-time PA is often self-directed, performed in less stressful environments, and may be associated with relaxation and enjoyment, all of which can promote better sleep quality²⁴. Furthermore, leisure-time PA helps regulate circadian rhythms, improves mood, and reduces stress levels, all of which contribute to enhanced sleep quality^25,26. Physiologically, leisure-time PA may help reduce muscle tension and lower cortisol levels, both of which are conducive to better sleep²⁷. Socially and culturally, leisure-time activity is often viewed as a form of self-care²⁸, which may reinforce its positive effects on sleep.

In contrast, MVPA performed during work hours was negatively associated with sleep duration. This negative relationship may stem from the physical and psychological demands associated with work, which can lead to increased stress and fatigue, thereby adversely affecting sleep ¹⁴. For example, in highly stressful work environments such as hospitals, more than half of the workers report sleep difficulties, a situation that may be exacerbated by the physically demanding nature of their jobs²⁹. PA at work is often viewed as a mandatory task, potentially leading to added stress and reduced recovery time, thus explaining the observed negative effects on sleep. The physiological stress from work-related PA, such as elevated heart rate and persistent muscle strain, can hinder the body’s ability to transition into restful sleep³⁰.

Few studies have explored the association between occupational PA and sleep disturbances. Research indicates that physically demanding jobs are often linked to sleep problems, while moderate to high levels of occupational PA have been associated with reduced sleep duration and quality ¹⁸. These findings suggest that the nature and context of occupational PA warrant further investigation to better understand their impact on sleep. The differential impact of PA during leisure versus work on sleep also has significant implications for public health interventions and policies. Encouraging PA during leisure time while addressing the negative impacts of work-related PA could be an effective strategy for improving sleep quality and overall health.

While encouraging PA during leisure time is beneficial for sleep quality, addressing the negative impacts of work-related PA presents a considerable challenge. This challenge is particularly complex due to the intertwining of sociocultural and economic factors. Individuals in lower socioeconomic positions often hold jobs that are physically demanding yet offer little flexibility or support for recovery, exacerbating sleep problems. These workers may lack access to resources that promote restful sleep, such as sufficient time off, stress management programs, or ergonomic adjustments to reduce physical strain. Improving the sleep quality of these individuals requires comprehensive strategies that go beyond simply promoting PA. It necessitates policy-level interventions that address the broader social determinants of health, ensuring equitable access to sleep-promoting environments and practices. Future research should focus on identifying specific interventions that not only enhance sleep quality by considering these contextual factors but also address the underlying sociocultural and economic disparities that contribute to poor sleep outcomes in physically demanding occupations.

This study benefits from a large, diverse sample from the NHANES 2017–2018, providing broad generalizability across various demographic groups. It comprehensively assesses movement behaviors by examining both SB and MVPA during work and leisure, using an innovative MVPA-to-SB ratio that offers new insights into mitigating the effects of sedentary lifestyles. The contextual differentiation between work and leisure activity adds valuable depth to the findings. However, the cross-sectional design limits causal inferences, and reliance on self-reported data may introduce bias. The absence of objective measures for PA and sleep, potential unmeasured confounders, and a primary focus on sleep duration rather than overall sleep quality also present limitations. Additionally, the study does not fully explore socioeconomic and occupational factors, which are crucial for understanding the broader context of these associations.

This study highlights the complex relationship between the movement behaviors and sleep duration, emphasizing the importance of context in which PA occurs. The findings suggest that while leisure-time MVPA is associated with longer sleep duration, work-related MVPA may have the opposite effect, potentially due to the physical and psychological demands of the workplace. These results underscore the need for public health interventions that encourage PA in leisure time while addressing the challenges posed by work-related physical demands, particularly in socioeconomically disadvantaged populations. Further research should aim to explore the mechanisms underlying these associations and develop targeted strategies to improve sleep health through balanced movement behaviors across different life contexts.

Ethical questions

All procedures were carried out according to the guidelines and standards for research involving human beings of the Declaration of Helsinki and were approved by the Research Ethics of the National Center for Health Statistics (NCHS) (reference number #2018-01), and each participant provided written informed consent. After anonymization, NHANES data is publicly available. Therefore, as NHANES data is publicly accessible, this study did not require additional ethical approval. Full information on NHANES study designs and data is available at www.cdc.gov/nchs/nhanes/.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Acknowledgments

We thank the scientists at the Division of Environmental Health Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control, and more generally the scientists and participants who made NHANES possible. We also extend our gratitude to our colleagues and teacher on the course "Sleep, Health, and Nutrition (NUT440)" in the Postgraduate Program in Health and Nutrition (PPGSN) at the Federal University of Ouro Preto (UFOP).

Funding

The National Council for Scientific and Technological Development (CNPq/Brazil), FAPEMIG/Brazil, and the Coordination for the Improvement of Higher Education Personnel (CAPES/Brazil) provided scholarships for undergraduate and graduate students.

Authors' contribution

LTP, LAN, MVD, and LAAMJ supported or carried out the data collection. LTP, LAN and MVD carried out the statistical analyses with the support of LAAMJ. LTP, LAN and MVD drafted the first version of the manuscript. LTP, LAN, and LAAMJ carried out a substantive revision of the work. LAAMJ supervised the work. All the authors approved the submitted version.

Declaration of interests

We declare no competing interests.

Availability of data and materials declarations

The data used in this study were taken from the National Health and Nutrition Examination Survey database. The public data of NHANES are available at https://wwwn.cdc.gov/nchs/nhanes/.

Da, M., Gomes, M., Schmidt Quinhones, M., & Engelhardt, E. (2008). RBN_44_4_2008-rev. In Revista Brasileira de Neurologia (Vol. 46).
Zielinski, M. R., McKenna, J. T., & McCarley, R. W. (2016). Functions and mechanisms of sleep. In AIMS Neuroscience (Vol. 3, Issue 1, pp. 67–104). AIMS Press. https://doi.org/10.3934/Neuroscience.2016.1.67
Hirshkowitz, M., Whiton, K., Albert, S. M., Alessi, C., Bruni, O., DonCarlos, L., Hazen, N., Herman, J., Katz, E. S., Kheirandish-Gozal, L., Neubauer, D. N., O’Donnell, A. E., Ohayon, M., Peever, J., Rawding, R., Sachdeva, R. C., Setters, B., Vitiello, M. V., Ware, J. C., & Adams Hillard, P. J. (2015). National sleep foundation’s sleep time duration recommendations: Methodology and results summary. Sleep Health, 1(1), 40–43. https://doi.org/10.1016/j.sleh.2014.12.010
Chaput, J. P., Dutil, C., Featherstone, R., Ross, R., Giangregorio, L., Saunders, T. J., Janssen, I., Poitras, V. J., Kho, M. E., Ross-White, A., Zankar, S., & Carrier, J. (2020). Sleep timing, sleep consistency, and health in adults: a systematic review. Applied Physiology, Nutrition, and Metabolism = Physiologie Appliquee, Nutrition et Metabolisme, 45(10), S232–S247. https://doi.org/10.1139/apnm-2020-0032
Philippens, N., Janssen, E., Kremers, S., & Crutzen, R. (2022). Determinants of natural adult sleep: An umbrella review. In PLoS ONE (Vol. 17, Issue 11 November). Public Library of Science. https://doi.org/10.1371/journal.pone.0277323ç
Ildefonzo, J., & Rodulfo, A. (2019). Sedentarism, a disease from xxi century. Clínica e Investigación En Arteriosclerosis (English Edition), 31(5), 233–240. https://doi.org/10.1016/j.arteri.2019
Driver, H. S., & Taylor, S. R. (2000). Exercise and sleep. In Sleep Medicine Reviews (Vol. 4, Issue 4, pp. 387–402). https://doi.org/10.1053/smrv.2000.0110
Grandner, M. A., & Fernandez, F. X. (2021). The translational neuroscience of sleep: A contextual framework. In Science (Vol. 374, Issue 6567, pp. 568–573). American Association for the Advancement of Science. https://doi.org/10.1126/SCIENCE.ABJ8188
National Center for Health Statistics (NHANES). Disponível em: https://www.cdc.gov/nchs/nhanes/index.htm. Acesso em 29 de abril de 2024.
Keating, X.D., et al., A meta-analysis of college students' physical activity behaviors. J Am Coll Health, 2005. 54(2): p. 116-25.
Menezes-Júnior, L. A. A. de, de Moura, S. S., Miranda, A. G., de Souza Andrade, A. C., Machado Coelho, G. L. L., & Meireles, A. L. (2023). Sedentary behavior is associated with poor sleep quality during the COVID-19 pandemic, and physical activity mitigates its adverse effects. BMC Public Health, 23(1). https://doi.org/10.1186/s12889-023-16041-8
Chastin, S. F. M., McGregor, D. E., Biddle, S. J. H., Cardon, G., Chaput, J. P., Dall, P. M., Dempsey, P. C., DiPietro, L., Ekelund, U., Katzmarzyk, P. T., Leitzmann, M., Stamatakis, E., & van der Ploeg, H. P. (2021). Striking the right balance: Evidence to inform combined physical activity and sedentary behavior recommendations. Journal of Physical Activity and Health, 18(6), 631–637. https://doi.org/10.1123/jpah.2020-0635
Organização Mundial de Saúde – OMS. (1995). Physical status: the use and interpretation of anthropometry. Organização Mundial de Saúde – OMS. Physical Status: The Use and Interpretation of Anthropometry. Geneva: WHO, 1995.
Li, J., Cao, D., Huang, Y., Chen, Z., Wang, R., Dong, Q., Wei, Q., & Liu, L. (2022). Sleep duration and health outcomes: an umbrella review. Sleep and Breathing, 26(3), 1479–1501. https://doi.org/10.1007/s11325-021-02458-1
Colten, H. R., & Altevogt, B. M. (2006). Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem. Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem, 1–404. https://doi.org/10.17226/11617
Chaput, J. P., Dutil, C., & Sampasa-Kanyinga, H. (2018). Sleeping hours: What is the ideal number and how does age impact this? In Nature and Science of Sleep (Vol. 10, pp. 421–430). Dove Medical Press Ltd. https://doi.org/10.2147/NSS.S163071
Mong, J. A., & Cusmano, D. M. (2016). Sex differences in sleep: impact of biological sex and sex steroids. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1688), 20150110. https://doi.org/10.1098/rstb.2015.0110
Muscogiuri, G., Barrea, L., Annunziata, G., Di Somma, C., Laudisio, D., Colao, A., & Savastano, S. (2019). Obesity and sleep disturbance: the chicken or the egg? Critical Reviews in Food Science and Nutrition, 59(13), 2158–2165. https://doi.org/10.1080/10408398.2018.1506979
Wang, F., & Boros, S. (2021). The effect of physical activity on sleep quality: a systematic review. In European Journal of Physiotherapy (Vol. 23, Issue 1, pp. 11–18). Taylor and Francis Ltd. https://doi.org/10.1080/21679169.2019.1623314
Kredlow, M. A., Capozzoli, M. C., Hearon, B. A., Calkins, A. W., & Otto, M. W. (2015). The effects of physical activity on sleep: a meta-analytic review. Journal of Behavioral Medicine, 38(3), 427–449. https://doi.org/10.1007/s10865-015-9617-6
Jeong, S. H., Jang, B. N., Kim, S. H., Kim, G. R., Park, E.-C., & Jang, S.-I. (2021). Association between sedentary time and sleep quality based on the Pittsburgh Sleep Quality Index among South Korean adults. BMC Public Health, 21(1), 2290. https://doi.org/10.1186/s12889-021-12388-y
Azambuja, V. dos A., Pena, S. B., Pereira, F. H., Santos, V. B., & Santos, M. A. dos. (2023). Avaliação da qualidade do sono em profissionais de saúde da emergência. Acta Paulista de Enfermagem, 37. https://doi.org/10.37689/acta-ape/2024AO0001001
Barbosa, B. C. R., Menezes-Júnior, L. A. A. de, de Paula, W., Chagas, C. M. dos S., Machado, E. L., de Freitas, E. D., Cardoso, C. S., de Carvalho Vidigal, F., Nobre, L. N., Silva, L. S. da, & Meireles, A. L. (2024). Sedentary behavior is associated with the mental health of university students during the Covid-19 pandemic, and not practicing physical activity accentuates its adverse effects: cross-sectional study. BMC Public Health, 24(1), 1860. https://doi.org/10.1186/s12889-024-19345-5
Mahindru, A., Patil, P., & Agrawal, V. (2023). Role of Physical Activity on Mental Health and Well-Being: A Review. Cureus. https://doi.org/10.7759/cureus.33475
Grimaldi-Puyana, M., Fernández-Batanero, J. M., Fennell, C., & Sañudo, B. (2020). Associations of objectively-assessed smartphone use with physical activity, sedentary behavior, mood, and sleep quality in young adults: A cross-sectional study. International Journal of Environmental Research and Public Health, 17(10). https://doi.org/10.3390/ijerph17103499
Weinert, D., & Gubin, D. (2022). The Impact of Physical Activity on the Circadian System: Benefits for Health, Performance and Wellbeing. Applied Sciences, 12(18), 9220. https://doi.org/10.3390/app12189220
Castelli, L., Macdonald, J. H., Innominato, P. F., & Galasso, L. (2024). Editorial: Circadian rhythm, athletic performance, and physical activity. Frontiers in Physiology, 15(July), 1–3. https://doi.org/10.3389/fphys.2024.1466152
Sanchez-Villegas, A., Ara, I., Dierssen, T., de la Fuente, C., Ruano, C., & Martinez-Gonzalez, M. A. (2012). Physical activity during leisure time and quality of life in a Spanish cohort: SUN (Seguimiento Universidad de Navarra) project. British Journal of Sports Medicine, 46(6), 443–448. https://doi.org/10.1136/bjsm.2010.081836
Chiang, S., Tzeng, W., Chiang, L., Lee, M., Lin, C., & Lin, C. (2024). Physical activity patterns, sleep quality, and stress levels among rotating-shift nurses during the COVID-19 pandemic. International Nursing Review. https://doi.org/10.1111/INR.12997
White, R. L., Bennie, J., Abbott, G., & Teychenne, M. (2020). Work-related physical activity and psychological distress among women in different occupations: a cross-sectional study. BMC Public Health, 20(1), 1007. https://doi.org/10.1186/s12889-020-09112-7
Ku, P. W., Steptoe, A., Liao, Y., Hsueh, M. C., & Chen, L. J. (2018). A cut-off of daily sedentary time and all-cause mortality in adults: A meta-regression analysis involving more than 1 million participants. BMC Medicine, 16(1).
Kakinami, L., O’Loughlin, E. K., Brunet, J., Dugas, E. N., Constantin, E., Sabiston, C. M., & O’Loughlin, J. (2017). Associations between physical activity and sedentary behavior with sleep quality and quantity in young adults. Sleep Health, 3(1), 56–61. https://doi.org/10.1016/j.sleh.2016.11.001
Skarpsno, E. S., Mork, P. J., Nilsen, T. I. L., JÃ¸rgensen, M. B., & Holtermann, A. (2017). Objectively measured occupational and leisure-time physical activity: cross-sectional associations with sleep problems. Scandinavian Journal of Work, Environment & Health. https://doi.org/10.5271/sjweh.3688
Hajo, S., Reed, J. L., Hans, H., Tulloch, H. E., Reid, R. D., & Prince, S. A. (2020). Physical activity, sedentary time and sleep and associations with mood states, shift work disorder and absenteeism among nurses: an analysis of the cross-sectional Champlain Nurses’ Study. PeerJ, 8, e8464. https://doi.org/10.7717/peerj.8464

No competing interests reported.

SupplementaryTable1.docx

Download PDF

Editorial decision: Revision requested
22 Sep, 2024
Reviews received at journal
19 Sep, 2024
Reviews received at journal
16 Sep, 2024
Reviewers agreed at journal
16 Sep, 2024
Reviewers agreed at journal
15 Sep, 2024
Reviewers agreed at journal
27 Aug, 2024
Reviewers agreed at journal
26 Aug, 2024
Reviewers invited by journal
25 Aug, 2024
Editor assigned by journal
15 Aug, 2024
Submission checks completed at journal
15 Aug, 2024
First submitted to journal
14 Aug, 2024

You are reading this latest preprint version

Influence of sedentary behavior and physical activity in leisure and work on sleep duration: Data from NHANES 2017-2018

Status:

Version 1

Abstract

Objective

Methods

Results

Conclusion

Figures

Introduction

Methodology

Study design and population

Sedentary behavior

Physical activity during leisure time and at work

Sleep

Covariates

Statistical analysis

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1