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EPIDEMIOLOGY

No Evidence of Reciprocal Associations between Daily Sleep and Physical Activity

MITCHELL, JONATHAN A.; GODBOLE, SUNEETA; MORAN, KEVIN; MURRAY, KATE; JAMES, PETER; LADEN, FRANCINE; HIPP, J. AARON; KERR, JACQUELINE; GLANZ, KAREN

Author Information
Medicine & Science in Sports & Exercise: October 2016 - Volume 48 - Issue 10 - p 1950-1956
doi: 10.1249/MSS.0000000000001000

Abstract

Sleep and physical activity are among the most important lifestyle factors that can help prevent the leading chronic diseases in adulthood (5,6,26). U.S. adults are recommended to sleep for 7–9 h per night and to accumulate 150 min of moderate to vigorous physical activity (MVPA) each week (17,29). However, 30% of U.S. adults sleep for ≤6 h each night (16), and an estimated 57% of U.S. adults do not meet the physical activity guidelines (7). The self-reported physical inactivity prevalence is notably high among adult women (60%) (7), and accelerometry estimates indicate that up to 97% of U.S. adult women are physically inactive (33).

It has been hypothesized that nighttime sleep and daytime physical activity are linked in a reciprocal or bidirectional manner (11,21). However, a review published in 2012 indicated that the association between total sleep time (TST) and physical activity energy expenditure was unclear and limited (24). Establishing if this hypothesized relationship exists in adult women is particularly important given their high prevalence of short sleep and physical inactivity (7,16,33), high prevalence of chronic disease, and high use of medical services (8). Moderate-intensity aerobic exercise training (typically involving four 30-min sessions per week, for 16 wk) has been shown to improve certain self-reported sleep outcomes among women, including improved sleep quality, shorter sleep onset latency (SOL), longer TST, improved apnea–hypopnea index, and reduction in reported sleep disturbances (20,22,23,30,34). Conversely, short-term experimental sleep restriction, by 2 h per night for 14 d, was found to lead to a reduction in physical activity levels in adult men and women (3). These experimental and intervention studies support a bidirectional relationship between sleep and physical activity. However, they do not necessarily provide insight into sleep and physical activity patterns in free-living populations. They also do not specifically address if sedentary behavior (SB) is bidirectionally associated with sleep. This is of importance given that SB is associated with chronic diseases in adult women, independent of MVPA (32), and there is some evidence that SB (self-reported sitting and television viewing) is associated with poorer self-reported sleep outcomes in adult men and women (4).

It is feasible to prospectively collect objective measures of sleep, physical activity, and SB concurrently, in the free-living setting, on a day-to-day basis, using accelerometry. Studies dating back to 2003 have used accelerometry to test for daily associations between sleep and physical activity (38). However, to the best of our knowledge, only one study has used accelerometry to prospectively measure both physical activity and sleep exclusively in free-living adult women (mean age 73 yr) (25). In that study, more physical activity during the day was associated with shorter TST at night, and greater sleep efficiency (SE) at night was associated with higher physical activity during the day (25). Importantly, the sample size was relatively small (n = 175), and SB was not measured (25). We therefore tested whether evening sleep was associated with physical activity and SB on the following day, and if physical activity and SB were associated with nighttime sleep, in a sample of 353 adult women who wore wrist and hip accelerometers concurrently for 7 d. We hypothesized that higher physical activity levels would be associated with improved actigraphy-estimated sleep outcomes at night. Conversely, we hypothesized that longer and more efficient sleep at night would be associated with higher physical activity levels on the following day.

METHODS

Participants

Our analytical sample was composed of 353 adult women. This convenience sample comprised participants who were living in/near San Diego, CA (n = 72); Philadelphia, PA (n = 119); and St. Louis, MO (n = 71), at the time of recruitment (2012–2013). Investigators at the University of California San Diego (UCSD), the University of Pennsylvania (UPenn), and the Washington University in St. Louis (WUSTL) recruited these participants. In addition, investigators from Harvard University recruited adult women living throughout the United States (n = 91). At all sites, the participants had to meet the following eligibility criteria: 21–75 yr old, self-reported body mass index (BMI) between 21.0 and 39.9 kg·m−2, and be able to ambulate unassisted. Site-specific eligibility criteria included the following: currently employed full or part time (WUSTL) and previously diagnosed with breast cancer (UPenn). The Harvard sample was selected from participants in the Nurses’ Health Study II with a BMI less than 40, which evenly represents varying population densities and all Census regions of the United States (African Americans were oversampled). Furthermore, 17% of the UCSD sample was composed of confirmed breast cancer survivors. The measurement protocols were identical across all sites. The participants provided informed consent, and an institutional review board approval for this study was granted at each study site.

Accelerometer protocol

Each participant received two accelerometers in the mail to wear for 7 d (ActiGraph GT3X+; ActiGraph, Pensacola, FL). They were instructed to wear one of the accelerometers on their nondominant wrist for 24 h·d−1 and to wear the second accelerometer around their waist by using an elastic belt at the right hip during waking hours. The accelerometers were removed when swimming or bathing, and the participants were provided with a 7-d sleep diary so that they could report any times they removed their wrist or hip accelerometers and the time they went to bed each night and woke each morning. Upon completion, the participants mailed back their devices, and the stored data were downloaded using ActiLife (Version 6.7, ActiGraph). All downloaded data files were visually screened for wear time compliance at each study site before being sent to UCSD for full, standardized processing.

Accelerometer data processing: sleep

The data downloaded from the wrist accelerometers were used to derive estimates of TST (hours per night), SOL (minutes per night), wake after sleep onset (minutes per night), and SE (the percentage of time asleep during each sleep period was categorized as <85% or ≥85%) (10,31). The sleep period was defined using the self-reported bed and wake times and the Cole–Kripke algorithm was applied to generate all sleep parameters (13). These sleep parameters were used to describe the characteristics of the study sample. In addition, TST and SE were used as primary predictors or primary exposures in the analytical models. In general, wrist accelerometry has high sensitivity (>90%) and moderate specificity (30%–40%) for measuring TST in comparison to polysomnography (15).

Accelerometer data processing: physical activity and SB

The data downloaded from the hip accelerometers were used to derive estimates of MVPA (min·d−1) and SB (h·d−1). The hip accelerometers had to be worn for at least 10 h·d−1 to be included in our analyses. Non–wear time was determined using the algorithm developed by Choi et al. (12). A cut point of >1040 counts per minute was used to define MVPA, and a cut point of <100 counts per minute was used to define SB (14,27). Sensitivity analyses were performed using a less conservative cut point of 760 counts per minute to define MVPA and a more conservative MVPA cut point of 2020 counts per minute to define MVPA. The sensitivity analyses involving the less conservative cut point allowed us to make more direct comparisons with the findings of Lambiase et al. (25).

Covariates

We included age, race (White or other), education (high school, some college, college degree, or graduate degree), and employment (employed ≥35 h·wk−1, employed <35 h·wk−1, or not in regular employment [unemployed, seasonal, homemaker, or retired]) as covariates. Differences in sleep and physical activity patterns have been reported across each of these categorical variables (18,33). We also included BMI (kg·m−2), calculated using self-report height and weights, and self-reported health status (1 = poor to 5 = excellent) as covariates because obese and less healthy participants are more likely to have shorter TST and lower physical activity levels (19,36). Finally, daily hip accelerometer wear time was included as covariate to control for variations in accelerometer wear times among participants.

Statistical analyses

To describe the characteristics of our sample, we used mean and SD values for the continuous variables and frequencies and percentages for the categorical variables. The descriptive data are presented for the entire sample and by tertiles of TST and MVPA. An overview of our study analyses is provided in Table 1. To test whether daytime physical activity was associated with nighttime sleep, sleep on night i was aligned with physical activity on day I, whereas for the analyses testing, if nighttime sleep was associated with physical activity the following day, sleep on night i was aligned with physical activity on day i + 1. Because we analyzed our data at the daily level, participants with at least 1 d of sleep and physical activity data were included in our analyses. Once the data were appropriately aligned, we used linear mixed models to account for correlation of data within a participant. Logistic mixed models were used when SE (<85% [referent] vs ≥85%) was the outcome variable. In the mixed-effects analyses, random intercepts, a maximum likelihood estimation, and an unstructured correlation structure were applied. The fixed effects of the model included the time varying predictors MVPA and SB when TST or SE were the outcome variables. When MVPA or SB was the outcome variable, the fixed effects of the model included TST and SE as time varying predictors. For all models, the fixed effects included the time-constant covariates: age, race, BMI, education, employment status, marital status, and self-reported health status. We also conducted sensitivity analyses to test if any bidirectional associations between the sleep and the physical activity variables differed as a function of age, BMI, and breast cancer survivor status (i.e., predictor–age, predictor–BMI, and predictor–survivor interactions, respectively). Note that breast cancer survivor status was not confirmed among the participants recruited at WUSTL and Harvard; therefore, the related sensitivity analysis assumes that all unconfirmed participants are not breast cancer survivors.

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TABLE 1:
Overview of the study analyses.

Quantile regression was also used to extend our data analysis beyond the mean of the outcome variables (37). In contrast to linear mixed models, quantile regression tests associations at the median and any other percentile of the frequency distribution (2). In the context of MVPA and TST, it is especially important to know if a predictor associates with changes in MVPA or TST for those in the sample with lower MVPA and TST (i.e., below the 50th percentiles). By contrast, it is especially important to know if a predictor associates with changes in SB for those in the sample with higher SB (i.e., >50th percentile). Quantile regression also has the advantage of modeling the outcome as a continuous variable and so does not require categorization, which has limitations (1), and this regression method is robust to outliers. Using quantile regression, we specifically tested if our predictors were associated with the 5th, 10th, 15th, …, 85th, 90th, and 95th percentiles of the outcome variables. The related beta coefficients, the 95% confidence intervals, and the P values are interpreted in the same way as standard regression methods (i.e., difference in outcome per unit increase in the predictor). The 95% confidence intervals were estimated using 100 cluster bootstrap samples to account for the correlation between daily accelerometry measures (37). All analyses were performed using Stata 14.1 (StataCorp, College Station, TX).

RESULTS

A total of 353 women completed the study protocol and had complete sleep, MVPA, SB, and covariate data (Table 2). More than 92% of the sample (N = 326) provided four or more consecutive days of data (see Table, Supplemental Digital Content 1, Description of data collection patterns of the 353 participants with valid accelerometry data, https://links.lww.com/MSS/A711). On average, the participants were 55 yr old and had a BMI of 27.6 kg·m−2. The majority of the sample was White (79%) and married (72%), with approximately half in full-time employment (49%). On average, the participants spent 9 h·d−1 in SB, 1 h·d−1 in MVPA, and 7 h per night asleep. The average time spent in SB and MVPA remained relatively constant across the TST tertiles; similarly, TST remained relatively constant across the MVPA tertiles (Table 2).

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TABLE 2:
Descriptive characteristics of the analytical sample (N = 353 women).

We first investigated if SB and MVPA were associated with TST at night (Table 3). MVPA was not associated with TST (beta = −0.03, 95% confidence interval [CI] = −0.12 to 0.06); similarly, we observed no association between SB and TST (beta = 0.002, 95% CI = −0.04 to 0.04). Time spent engaged in MVPA and SB was not associated with SE at night (odds ratio = 0.88, 95% CI = 0.70–1.10, and odds ratio = 1.07, 95% CI = 0.97–1.18, respectively). There was no statistical evidence that the findings in Table 3 differed by age, BMI, or breast cancer survivor status (interaction P values >0.05). Furthermore, the MVPA results did not change when we reassessed using cut points of 760 and 2020 counts per minute to define MVPA (see Table, Supplemental Digital Content 2, Associations between physical activity [760 counts per minute and 2020 counts per minute cut points] with nighttime sleep outcomes [N = 353], https://links.lww.com/MSS/A712; see Table, Supplemental Digital Content 3, Associations between sleep exposures at nighttime and physical activity [760 counts per minute cut point] behavior the following day [N = 353], https://links.lww.com/MSS/A713).

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TABLE 3:
Associations between physical activity and SB with nighttime sleep outcomes (N = 353).

We then investigated if TST was associated with MVPA and SB during waking hours the next day (Table 4). We did not observe any associations between TST and MVPA (beta = 0.01, 95% CI = −0.01 to 0.04) or between TST and SB (beta = −0.02, 95% CI = −0.07 to 0.04). Similarly, SE at night was not associated with MVPA (beta = 0.06, 95% CI = −0.001 to 0.12) or SB on the following day (beta = −0.05, 95% CI = −0.19 to 0.09). There was no evidence that the findings in Table 4 differed by age, BMI, or breast cancer survivor status (interaction P values >0.05). Again, the MVPA-related results did not change when we reassessed using cut points of 760 and 2020 counts per minute to define MVPA (see Table, Supplemental Digital Content 2, Associations between physical activity [760 counts per minute and 2020 counts per minute cut points] with nighttime sleep outcomes [N = 353], https://links.lww.com/MSS/A712; see Table, Supplemental Digital Content 3, Associations between sleep exposures at nighttime and physical activity [760 counts per minute cut point] behavior the following day [N = 353], https://links.lww.com/MSS/A713). However, SE at night was associated with higher MVPA when using the 2020 cut point definition (see Table, Supplemental Digital Content 3, Associations between sleep exposures at nighttime and physical activity [760 counts per minute cut point] behavior the following day [N = 353], https://links.lww.com/MSS/A713).

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TABLE 4:
Associations between sleep exposures at nighttime and physical activity and SB the following day (N = 353).

We further analyzed our data using quantile regression models. Time spent engaged in MVPA was not associated with TST at any of the specified percentiles (Fig. 1A). SB was not associated with TST at any percentile below the 85th percentile; however, SB during waking hours was associated with higher TST at night at the 85th (beta = 0.09, 95% CI = 0.003–0.12), 90th (beta = 0.07, 95% CI = 0.01–0.14), and 95th TST percentiles (beta = 0.10, 95% CI = 0.03–0.18) (Fig. 1B). Nighttime TST and SE were not associated with MVPA, at any of the specified percentiles, on the following day (Figs. 1C and 1E), or with SB, at any of the specified percentiles, on the following day on the following day (Figs. 1D and 1F). The MVPA-related quantile regression results did not change when we reassessed using cut points of 760 and 2020 counts per minute to define MVPA (see Figure, Supplemental Digital Content 4, Quantile regression associations between physical activity, SB, and sleep [N = 353], https://links.lww.com/MSS/A714).

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FIGURE 1:
Quantile regression associations between physical activity, SB, and sleep (N = 353). A, Change in TST (h·d−1) per additional unit increase in MVPA (h·d−1). B, Change in TST (h·d−1) per additional unit increase in SB (h·d−1). C, Change in MVPA (h·d−1) per unit increase in TST (h·d−1). D, Change in SB (h·d−1) per unit increase in TST (h·d−1). E, Change in MVPA (h·d−1) for those with SE ≥85%, relative to those with SE <85%. F, Change in SB (h·d−1) for those with SE ≥85%, relative to those with SE <85%. All models included the covariates: age, race, education level, employment status, BMI, marital status, self-report heath status, and hip accelerometer wear time.

DISCUSSION

This is the largest study to date that has used objective measures of sleep and MVPA for a 7-d period to investigate the relationship between sleep and MVPA in free-living adult women. We did not find evidence of reciprocal associations between TST or SE with time spent in MVPA. We took the extra step to analyze our data using quantile regression; importantly, this demonstrated that there were no associations between TST and MVPA specifically among women with below average TST and below average MVPA (i.e., those in the population most in need of increasing TST and increasing MVPA). In addition, we tested for associations between sleep and SB, which have not been previously investigated in a sample of adult women using accelerometry to estimate both sleep and SB. As with MVPA, there was no evidence of reciprocal associations between TST or SE and time spent in SB. However, using quantile regression, we did observe that an additional hour of SB was associated with increased TST among those with the highest TST (≥85th percentile), but these associations translate to no more than a 6-min per night increase in TST.

Our study design closely reflects that used by Lambiase et al. (25). In that study, using an MVPA cut point of 760 counts per minute (average time spent in MVPA was 1 h·d−1), it was observed that more time spent in MVPA associated with shorter TST at night, but greater SE at night was associated with higher MVPA on the following day (25). The average age of the women in our sample was 55 yr, and 72% of the women were in full or part time employment. By contrast, the sample of women studied by Lambiase et al. (25) had an average age of 73 yr and, in the absence of employment data, it is reasonable to assume that less than 72% of this sample was in full or part time employment. Work schedules and child care responsibilities are possible factors that could have a greater influence on sleep–wake cycles in our sample, and this may have made it more difficult to observe associations between nighttime sleep and physical activity patterns. Indeed, McClain et al. (28) used the 2005–2006 National Health and Nutrition Examination Survey data to determine whether accelerometry-estimated MVPA and SB were associated with self-reported TST. There were no reciprocal associations observed in women age 40–59 yr; however, among the older women (≥60 yr), those sleeping for ≤5 h per night had lower MVPA levels compared with those sleeping between 6 or 7 h per night (28). Importantly, in our study, we did not find statistical evidence that our results differed as a function of age.

Differences in accelerometry methods may also have contributed to our inability to replicate the findings by Lambiase et al. (25). In both studies, the ActiGraph GT3X+ was worn at the hip to estimate MVPA, but we used a cut point of 1041 counts per minute to define MVPA, and Lambiase et al. (25) used a cut point of 760 counts per minute. We conducted sensitivity analyses using the lower 760 counts per minute cut point to enable a more direct comparison, but we did not observe any reciprocal associations between sleep and MVPA with this lower cut point. Furthermore, we used the ActiGraph GT3X+ (worn on the wrist) to estimate sleep, whereas Lambiase et al. (25) used the Actiwatch-64m accelerometer to estimate sleep. It has been shown that the Actiwatch performs slightly better than the ActiGraph at estimating TST in the lab setting when compared against polysomnography (9).

We also conducted sensitivity analyses using the higher 2020 counts per minute cut point to define MVPA. This more conservative cut point measures “more vigorous” physical activity, and interestingly, using this definition, we did observe an association between more efficient nighttime sleep and higher MVPA the following day. This is consistent with the Lambiase et al. (25) finding, but it only translates to an additional 4 min·d−1 increase in MVPA.

Our findings have important public health implications for adult women. Addressing high SB, physical inactivity, and short TST is an important public health goal, but based on our findings, targeting daily increases in TST and SE may not necessarily increase MVPA and lower SB in the short term (and vice versa) on the basis of our specific sample. However, from a clinical standpoint, there is growing evidence that aerobic exercise training could be beneficial for adults with a diagnosed sleep disorder (21). Furthermore, aerobic exercise training among adult women has been demonstrated to improve self-reported sleep outcomes, including improved sleep quality, shorter SOL, longer TST, improved apnea–hypopnea index, and reduction in reported sleep disturbances/problems (20,22,23,30,34). Importantly, these sleep outcomes were self-reported, and sleep was not a primary outcome in any of these exercise trials. In addition, the women enrolled in these studies were all overweight/obese, physically inactive, and postmenopausal at enrollment (20,22,23,30,34), and in one study, the women were diagnosed with insomnia (30). Our observational study sample included normal, overweight, and obese women and women considered physically active and inactive. Although our findings do not support daily relationships between evening TST and SE with MVPA and SB (and there was no evidence of age or BMI statistical interactions), this does not detract from the mounting evidence of a relationship between longer-term aerobic exercise training and improved sleep outcomes in adult women with sleep disorder or who are overweight/obese and physically inactive (20,22,23,30,34). However, the findings from these aerobic exercise trials need to be replicated in trials where more stringently measured sleep parameters are the primary outcome.

Our study has several strengths and limitations. We used accelerometry to estimate sleep outcomes, MVPA, and SB prospectively for 7 d. However, accelerometer models and data processing models used vary across different studies, and this makes direct comparisons challenging. Although accelerometry provides an estimate of total SB, this method is not able to measure time spent in specific behaviors, and it has been shown that screen-based SB is a more important predictor of self-reported sleep outcomes than accelerometry-estimated SB (35). Similarly, accelerometry is limited in its ability to capture certain MVPA contexts such as swimming and weight training. We did not prospectively measure screen time on the days when the accelerometers were worn; future studies should consider doing so to test for reciprocal associations between daily screen time and sleep outcomes. We also did not consider the timing of sleep or the timing of MVPA and SB, and follow-up investigation is required to determine whether the timing of these behaviors is more important than the total duration (5). There may be other factors as well, such as time spent outdoors, which may moderate the effects. It is also possible that a 7-d period is not sufficient for capturing more long-term effects of MVPA on sleep (and vice versa). Research examining this relationship for a more prolonged period may identify differences between acute and long-term effects and would allow for the investigation of weekend versus weekday differences. We did not prospectively measure tiredness and fatigue during the week of observations; sleep deprivation cannot be measured using accelerometry, and future studies should consider investigating reciprocal relationships between sleep deprivation and physical activity patterns. We used a sample of adult women recruited at four sites in the United States, which included nurses and confirmed breast cancer survivors (20% and 100% of the UCSD and UPenn samples, respectively). The replication of findings in a larger, more representative sample of women is required, and replication in a more detailed breast cancer survivor sample is required. It would also be of interest to study reciprocal associations between daily sleep and physical activity patterns in other settings; for example, in other chronic disease populations, in children and adolescents, and in those diagnosed with a sleep disorder.

It has been proposed that sleep and physical activity patterns are linked in a reciprocal manner. However, our 7 d of accelerometry data among free-living adult women do not support this hypothesis. Time spent engaged in MVPA and SB did not influence TST and SE at night. Similarly, TST and SE at night did not influence time spent engaged in MVPA or SB on the following day. Therefore, on the basis of our findings among a sample of adult women, daily increases in physical activity will not necessarily lead to sleep improvements in the short term (and vice versa).

This work was supported by the NCI Centers for Transdisciplinary Research on Energetics and Cancer (TREC) (grant nos. U01 CA116850, U54 CA155496, U54 CA155626, U54 CA155435, and U54 CA155850) and the National Institutes of Health (grant nos. UM1 CA176726 and R01 ES017017). The opinions or assertions contained herein are the private ones of the authors and are not considered as official or reflecting the views of the National Institutes of Health. Jonathan Mitchell was supported by award numbers F32CA162847 (NCI) and K01HL123612 (NHLBI). Peter James was supported by the Harvard NHLBI Cardiovascular Epidemiology Training Grant T32 HL 098048. Kate Murray was supported by MRSG-13-069-01-CPPB (American Cancer Society). The results of the present study do not constitute endorsement by the American College of Sports Medicine.

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Keywords:

ACCELEROMETRY; ACTIGRAPHY; FEMALE; SEDENTARY BEHAVIOR

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