Sleep in America

Do you prioritize your sleep? If you do, you are among the 10% of American adults who make sleep a priority. However, if sleep is not your priority, you may relate more to the 33% of American adults who currently sleep less than seven hours per night, which may have health consequences.

Potential Consequences of Neglecting Your Sleep 

Poor sleep habits can be detrimental to your overall health.  Short sleepers (<7 hours) and those with low sleep quality (sleep efficiency < 85%) are at risk for weight gain, obesity, hypertension and cardiovascular disease. Lack of sleep and low sleep quality have been associated with an increase in cravings and an increase in appetite. Current research has focused on how sleep duration and quality may influence or be influenced by nutrition and eating behavior.

Behavior Influences Sleep 

In one weight loss study, researchers observed sleep changes in overweight and obese participants over a ten-month period. Participants lost weight and slept longer at the end of the two-month weight loss plan and continued to sleep longer up to their 3-month follow up appointment. The researchers concluded that successful weight loss is accompanied by an increase in sleep time.

Another study focused on the timing of food intake and how it relates to fat mass and circadian rhythm (your 24-hour internal clock) in college-aged participants.  The findings of this study showed that participants with a higher body fat percentage (32.4% body fat) consumed more calories later in the day and closer to their biological sleeping time than the lean group (22.2% body fat).

Sleep has also been shown to influence food choices. Recently, a study found that when adults who were short sleepers (sleeping 5 to less than 7 hours a night) increased their sleep time by 21 minutes per night, they consumed less sugar and less fat when compared to a group that did not extend their sleeping hours.

Nutrition and Sleep

It is not yet clear if sleep is a driver of food intake or if food intake is a driver of sleep. Increases in dietary protein, fish and vegetables have been shown to elicit many health benefits including benefits related to sleep.  For example, in a weight loss study, dietary protein intake above the current dietary recommendations of 0.8g protein per kilogram of body weight daily, improved sleep quality in overweight and obese middle-aged and older adults when compared to a normal protein diet.

Foods such as milk obtained from cows at night, fatty fish (>5% fat), kiwi (2 kiwi fruits/day 1 hour before bed), and cherries (tart cherry juice or whole fruit) have been labeled as “sleep promoting foods”, but further research is needed to justify these claims.

Nighttime milk is obtained by milking cows at nighttime. Nighttime milk is naturally higher in the sleep promoting hormone melatonin and the essential amino acid tryptophan. More research is needed to support the sleep promoting benefits of nighttime milk.


Sleep has been shown to impact various aspects of behavior and well-being. If you are looking to improve your health and nutrition, it may be time to put sleep on your priority list.


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By Caitlin Dow, Student Blogger

Body Mass Index. BMI, for short. Those three words tend to conjure up some intense feelings in scientists and the general public, alike. In 1832, a Belgian statistician named Adolphe Quetelet created his namesake index, the Quetelet Index, to describe the normal variation seen in weight relative to height across populations. That index got its new name “Body Mass Index” in 1972 from Ancel Keys (1) and was  by the World Health Organization as a clinical tool to be used easily and effectively to determine levels of obesity.


As Cyndi Thomson, PhD, RD, a professor in the College of Public Health at the University of Arizona points out, “BMI was meant for population evaluation and we keep applying it to individuals.” BMI is useful when we study populations. It predicts risk for development of a number of chronic diseases (2). However, applying BMI to individuals, which is likely not what Quetelet had in mind when he created it, creates a number of issues. While BMI correlates well with fat mass on a population (but not necessarily on an individual level), it certainly does not consider distribution of fat. This is important because plenty of data indicate that abdominal fat predisposes people to a number of health risks more so than fat distributed evenly throughout the body (2). Furthermore, associations between BMI and various outcomes like risk for disease or mortality are assumed to be linear. That is, as BMI increases, risk for disease also increases. However, some cross-sectional, epidemiological studies have shown a “U-shaped” relation between BMI and mortality (3,4), meaning that people with very low or very high BMIs are at elevated risk of dying within a given period of time than those in the middle (generally overweight) range. The increased mortality risk with normal BMIs later in life is actually probably due to smoking or weight loss due to disease (like cancer), but this hasn’t stopped the media from concluding that “being overweight is good for you!” Due to these shortcomings of BMI, it is high time to consider/develop some type of index that (a) has a linear relation with mortality for ease of interpretation; (b) considers fat mass and/or distribution; and (c) can be used easily in both research and clinical settings.


To address this need, new adiposity indices are being studied that may provide more clinical and scientific utility than BMI. A body shape index (ABSI) considers waist circumference (a surrogate measure of abdominal adiposity), adjusted for height and weight and was first developed by Krakauer, et al (5). Cyndi Thomson and colleagues recently published a paper in Obesity evaluating the relation between ABSI and mortality risk in a very large cohort study (6). The analysis included over 77,500 postmenopausal women enrolled in the Women’s Health Initiative Observational Study. Anthropometrics were measured at baseline and the women were followed for an average of 13.5 years. Similar to previous findings, a U-shaped association between BMI and mortality was demonstrated. However, ABSI was strongly and positively associated with mortality, such that those in the highest quintile of ABSI had a 37% increased risk of death compared with those in the lowest quintile.

I discussed the implications of these findings with Dr. Thomson over the phone. The results from this study that indicate that ABSI is associated with mortality in postmenopausal women support similar findings from a smaller cohort from the British Health and Lifestyle Survey (7). However, while ABSI may be a more robust index describing the effect of adiposity on mortality risk, it’s not ready for clinical implementation. First, because it is so new, there are no standard reference values or categorical values that correspond with normal or excessive adiposity. As Dr. Thomson says, “ABSI may provide some additional information that informs on risk, but I think we still have the issue of people not measuring waist circumference [clinically].” Because waist circumference requires more than standing on a scale, it has been difficult to implement. Clinicians have to be trained on how to properly measure waist circumference, and while it is inexpensive and not overly complicated to learn, accuracy and inter-individual measurement techniques are an issue. Despite these current setbacks, she remains optimistic: “The measurements haven’t gotten there, but they will.”


Another important aspect of using ABSI (or any index of body composition) will be validating it across a range of races and ethnicities. Thomson notes that in a preliminary analysis that has yet to be published, the ABSI and mortality risk does indeed differ between races and ethnicities. Because of that, “one clinician may use one [adiposity index] while another may use something else, depending on their patient population.”


Although still in the preliminary stages of research, ABSI may pan out as a useful measure of adiposity that could replace or complement BMI. It will need to be rigorously tested across age groups, race/ethnicities, genders and in its associations with a variety of chronic diseases. Stay tuned as this very young area of research unfolds!


1. Keys A,  Fidanza F, Karvonen M, Kimura N, Taylor H. Indices of relative weight and obesity. Journal of Chronic Diseases 1972; 25 (6–7): 329–43.
3.Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA 2013; 309:71-82.
4.Winter JE, Macinnis RJ, Wattanapenpaiboon N, Nowson CA. BMI and all-cause mortality in older adults: a meta-analysis. Am J Clin Nutr 2014;99:875-890.
5.Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PloS One 2012;7:e39504
6.Thomson CA, Garcia DO, Wertheim BC, Hingle MD, Bea JW, Zaslavsky O, Caire-Juvera G, Rohan T, Vitolins MZ, Thompson PA, Lewis CE. Body shape, adiposity index, and mortality in postmenopausal women: Findings from the Women’s Health Initiative. Obesity; 2016; 1061-9.
7.Krakauer NY, Krakauer JC. Dynamic association of mortality hazard with body shape. PloS One 2014;9:e8879.