Reply to J Cedernaes and C Benedict

Dear Sir:

We appreciate Cedernaes and Benedict’s interest in our recent work on eating behaviors in relation to duration of sleep (1). The focus of the letter by Cedernaes and Benedict is metabolic and health concerns due to disruption of circadian eating and sleeping norms in shift work, a promising area of study, which was not the subject of our article.

In their summary of findings of our study, Cedernaes and Benedict state that “self-reported eating behavior did not differ between average and long sleepers (ie, ≥9 h).” Our article makes no such assertions. In fact, both tabular and narrative results show that many eating behaviors of long-duration sleepers were similar to those of short-duration sleepers but different from average-duration sleepers (eg, less likely to report breakfast, less energy from main meals, more energy from snacks, etc). These findings are also mentioned in the Discussion.

Cedernaes and Benedict also state that we did not examine “whether short sleepers also eat at times of the day when the circadian system is not metabolically adjusted to process ingested nutrients or stimulants such as caffeine (ie, primarily during the night).” Our study addressed this question in at least 3 ways by examination of the following: 1) the percentage of 24-h energy that was reported at or after 2000 h (shown in our Table 2), 2) the number of snack episodes (and % of energy) reported after dinner by dinner reporters (shown in our Table 3), and 3) the clock time of the last reported eating episode of the recalled day (shown in our Table 4). Short-duration sleepers did report modestly higher energy intakes at or after 2000 h and a later time of the last eating episode of the day.

Last, we want to emphasize that the exposure in our study was hours of self-reported weekday/workday nighttime sleep. During our preliminary analyses we found employment status (yes or no) to be a correlate of sleep duration as well as of energy intake and eating behaviors. Hence, all reported analyses in the article were adjusted for employment status. It is unlikely that additional control for shift work will alter our findings. Overall, in our analytic sample, 10.87% (95% CI: 10.13%, 11.65%) of adult Americans reported evening/night/rotating work schedules. The prevalences (sample weighted %) of evening, night, and rotating work schedules in short-, average-, and long-duration sleepers were 13.45% (12.19%, 14.82%), 9.20% (8.34%, 10.14%), and 10.78% (7.92%, 14.51%), respectively. The multiple variable–adjusted mean hours of sleep reported by regular day schedule reporters (6.82 h; 95% CI: 6.77, 6.87 h) were not significantly different from those of evening/night/rotating schedule reporters (6.76 h; 95% CI: 6.65, 6.88 h). Mean hours of sleep reported by nonemployed Americans, however, were higher at 7.01 h (95% CI: 6.94, 7.08 h) and significantly different from those who worked day or evening/night/rotating schedules. Interestingly, in the NHANES 2007–2008, the metabolic profiles (eg, BMI, waist circumference, serum triglycerides, LDL cholesterol, and glycated hemoglobin) of Americans reporting evening/night/rotating work schedules did not differ from those of regular day shift workers (2).

Nevertheless, to address the concern about possible confounding of sleep duration and eating behavior association by shift work, we conducted further sensitivity analyses. We repeated analyses for key eating behavior outcomes after exclusion of evening/night/rotating shift reporters from the analytic sample and after addition of a variable to indicate evening/night/rotating schedule to the regression models. With both approaches, as shown in Table 1, the substantive findings of our study were unchanged.

TABLE 1.

Adjusted mean (95% CI) intakes of total energy, percentages of 24-h energy from main and non–main meals at or after 2000 h, and clock times of first and last episodes of the recalled day reported by adult Americans by categories of weekday/workday duration of nighttime sleep: NHANES 2005–2010¹

	Duration of sleep
	≤6 h	7–8 h	≥9 h	P2
Estimates of key eating behaviors after exclusion of reporters of evening/night/rotating work schedules from the analytic sample3 (n = 13,532)
Energy (kcal)	2143 (2096, 2190)	2135 (2098, 2172)	2153 (2080, 2226)	0.8
24-h energy from main meals4 (%)	77.6* (76.9, 78.3)	79.0 (78.4, 79.7)	77.6* (76.3, 78.9)	0.004
24-h energy from snack episodes5 (%)	22.4* (21.7, 23.1)	20.9 (20.3, 21.6)	22.4 (21.0, 23.7)	0.004
24-h energy from episodes reported at or after 2000 h (%)	15.7* (14.7, 16.7)	14.6 (13.8, 15.3)	15.6 (13.9, 17.4)	0.05
Clock time of the first eating episode of the day	07505 (0742, 0759)	0802 (0755, 0809)	0823* (0810, 0836)	0.0004
Clock time of the last eating episode of the day	2019* (2014, 2025)	2013 (2008, 2017)	2009 (2000, 2018)	0.02
Estimates of key eating behaviors with adjustment for work schedule6 (n = 14,989)
Energy (kcal)	2158 (2113, 2202)	2149 (2114, 2184)	2140 (2063, 2218)	0.9
24-h energy from main meals4 (%)	77.0* (76.4, 77.7)	78.8 (78.1,79.4)	77.3* (75.9, 78.6)	0.0003
24-h energy from snack episodes5 (%)	23.0* (22.3, 23.6)	21.2 (20.6, 21.9)	22.7* (21.4, 24.0)	0.0003
24-h energy from episodes reported at or after 2000 h (%)	16.1 (15.2, 17.1)	15.1 (14.4, 15.9)	16.7 (15.0, 18.4)	0.047
Clock time of the first eating episode of the day	0750* (0741, 0759)	0804 (0757, 0811)	0823* (0810, 0836)	0.0002
Clock time of the last eating episode of the day	2021* (2016, 2027)	2016 (2012, 2020)	2014 (2006, 2022)	0.04

¹Values were derived from linear regression models with each dietary variable as a continuous outcome; independent variables included hours of sleep duration (≤6, 7–8, or ≥9 h), sex, race-ethnicity (non-Hispanic white, non-Hispanic black, Mexican American, or other), poverty-income ratio (<1.3, 1.3–3.5, >3.5, or unknown), years of education (<12 y, 12 y, some college, or college), BMI (in kg/m²; <25, 25 to <30, or ≥30), smoking status (never, former, or current smoker), alcohol use status (never, former, or current drinker or unknown), day of recall (Monday–Thursday, Friday–Sunday), month of mobile examination center examination (November–April, May–October), chronic disease (yes or no), and employed (yes or no).* Significantly different from the reference category of 7–8 h of sleep, P < 0.05.

²P values of Wald’s global F test for differences between categories of hours of sleep duration.

³Respondents (n = 1468) reporting evening/night/rotating work schedules were excluded.

⁴“Main meals” included eating episodes named by the respondent as breakfast, brunch, lunch, dinner, and supper and their Spanish equivalents.

⁵“Snack” included all eating episodes that were not main meals (as defined above).

⁶The employed (yes/no) variable was replaced by work schedule variable (not employed, regular day/other, evening/night/rotating schedule).

The review by Lowden et al (3), also cited by Cedernaes and Benedict, found the available evidence on the association of shift work and eating to be “complex” and “contradictory.” Thus, we look forward to more research on norms of circadian rhythms of eating and sleeping behaviors and individual adaptations to dyssynchrony resulting from constraints of work and leisure in modern societies. We agree with Cedernaes and Benedict about the value of population surveys (such as the NHANES) to provide useful information about eating behaviors of shift workers. However, given the cross-sectional nature of such surveys, the complexity of eating and physical activity behaviors, and measurement errors in self-reports of behaviors of free-living humans, we caution against causal inferences about “energy surplus” and “metabolic consequences.”