This review paper, (read it here) published in the journal Sleep Medicine Reviews, tells the story of what we know about short sleep and the negative health outcomes. The paper has 5 main sections: (1) an explanation of terminology that is often used to describe "short sleep," (2) an overview of laboratory-based studies of sleep deprivation, (3) an overview of population-based studies that looked at self-reported short sleep, (4) a review of the few studies that actually verify that those who report short sleep really do sleep short durations, and (5) some take-home points.
For the specific details and findings, you should read the paper. It outlines the current evidence, suggesting that short sleep may place individuals at increased risk for a number of negative health problems, including heart disease, diabetes, obesity, depression, poor performance, etc.
In addition to providing a summary of the current knowledge about short sleep, there are a few important messages that the paper brings to light:
1. Our terms are inconsistent and confusing. "Short sleep" has been used to describe everything from sleep for a few nights in a lab to habitual sleep at home, as well as sleep less than 8 hours, less than 5 hours, or any other amount. Also, reasons for short sleep (is this when you naturally sleep?) are largely ignored. For that reason, we, as a field, need to get our terms straight. Because lab studies do not generalize to the population, and population studies do not always describe effects that are reliable.
2. Short sleepers need to be better characterized. By lumping all people in a "short sleep" category together -- which we do no matter what our definition or cutoff is -- is automatically limited. This group could include lots of types of short sleepers, including those who are sleep deprived and those who are not. Also it includes people who choose to sleep less, and those who are forced to.
3. By talking about problems associated with short sleep, we assume that these problems could be fixed by simply sleeping more. Both the laboratory and population studies imply this in their language. But habitual short sleepers, who these studies are trying to generalize to, don't know if this is the case. First of all, do they even need more sleep? Second, CAN they sleep more? If so, how much more is needed? This has never actually been tested.
In general, the evidence suggests that not getting "enough sleep" -- which for most people is probably 7-8 hours, might be associated with a number of bad outcomes. However, research needs to explore these in more detail, including getting a better understanding of which short sleepers are sleep deprived and which are simply natural, "true" short sleepers.
Read the paper on this site: http://www.michaelgrandner.com/pages/research-publications.html.
Saturday, October 16, 2010
Wednesday, May 12, 2010
Sleep Problems associated with Race/Ethnicity, Income, Education, Marital Status and Employment Status
In our recent paper, entitled "Who Gets the Best Sleep? Ethnic and Socioeconomic Factors Related to Sleep Complaints" published in the journal Sleep Medicine, we performed a set of analyses on some data that was gathered by the CDC in 2006 on over 150,000 people all across the USA.
These people were asked, "In the past 2 weeks, how many nights did you have trouble falling asleep, staying asleep, or sleeping too much?" The wording of this question is important, because it is not asking for any particular sleep disorder (like insomnia or sleep apnea), but rather a general sleep complaint. Also, it addresses issues that can exist in any sleep disorder, and involves "sleeping too much" which opens the possibility that it is not just measuring insufficient sleep. So this is a very broad question. When we looked at the data, we quickly realized that most people reported values at the very low end or very high end of the scale. Because of this, we re-coded the data so that those whose values were less than 6 were rated as not having a complaint and those with values 6 or higher were rated as having a complaint. This translates to an average of 3 nights per week across 2 weeks.
Next, we realized that this is a very large and diverse group of people. We wanted to know if there was any group of people that was more or less likely to report problems with their sleep. So we chose a number of variables, including Race/Ethnicity (White, Black/African-American, Hispanic/Latino, and Asian/Other), Income (<$10k, $10-15k, $15-20k, $20-25k, $25-35k, $35-50k, $50-75k, and $75k+), Education (Did not finish high school, High school graduate, Some College, and College graduate), Marital Status (Married, Never Married, In a relationship, Divorced, Widowed, and Separated), and Employment Status (Employed, Self-employed, Retired, Student, Homemaker, Unemployed <1 year, Unemployed >1 year, and Unable to work). We analyzed the data separately for men and women.
To perform our analyses, we used a technique called logistic regression, which computes the odds ration (OR) that a certain outcome will occur. The "outcome" in question was having sleep problems. So we created an equation (actually 2 equations -- one for each gender) that included age (to control for age), as well as the variables mentioned above. The equation computes the OR simultaneously for all of the levels of all of the variables, so they all control for each other. So, for example, when we report the OR for being Married, it is controlling for gender (separate analysis), age, race/ethnicity, income, education, and employment all at the same time. So we know the effects are not due to any of those other factors, which makes them much more reliable. Also, for this type of analysis, when you have multiple categories (like in Race/Ethnicity), one category is set to be the "reference" and all others are compared to it.
What we found was surprising.
For Race/Ethnicity (reference = White): For women, if you were Black/African-American or Hispanic/Latina, you were less likely to complain (26% less likely than White in both cases), and if you were Asian/Other, you were much less likely to complain (58% less likely than White), but if you were Multiracial, you were more likely to complain (67% more likely). For men, there were no differences among groups (none was different from White) except for men who were Asian/Other, who were 57% less likely to complain of sleep problems than White men.
For Education (reference = College Graduate): College graduates slept best. For both men and women, the more education you had, the less likely you were to report sleep problems. In men, those with some college were 22% more likely to complain, those who graduated high school were 27% more likely to complain, and those that did not complete high school were 35% more likely to complain (all of these were significantly different from college graduates). For women, those with some college were 30% more likely to complain, those who graduated high school were 31% more likely to complain, and those that did not complete high school were 64% more likely to complain (again, all of these were significantly different from college graduates).
For Marital Status (reference = Married): Married people slept best. Those that slept worse were those that were never married (62% more likely for women and 74% more likely for men) or in a relationship but not married (39% more likely for women and 102% more likely for men). Men who were divorced or separated were more likely to report problems (52% more likely for divorced and 43% more likely for separated) and this increase was greater than in women (divorced women 24% more likely and separated women 15% more likely). Widowed men and women did not sleep better than married, after controlling for all of the other variables.
For Employment (reference = Employed): Employed people slept best. If you were self-employed, you were 21% (women) or 25% (men) more likely to report sleep problems. If you were retired and male, you were 22% more likely to report problems (no difference in women). If you were a student, there was such a wide range of responses that it was impossible to tell if they were different (some were less likely to report problems and some were almost twice as likely). If you were unemployed <1 year, you were 97% more likely to report problems if you were a woman and 191% more likely if you were a man. If you were unemployed >1 year, you were 122% more likely if you were a woman and 197% more likely if you were a man. If you were unable to work, you were 319% more likely to report problems as a woman and 454% more likely as a man. Although these patterns were similar for men and women, there was a large difference for homemakers. Women homemakers were 18% more likely to report problems (similar to self-employed), but male homemakers were 244% more likely to report sleep problems (similar to unemployed).
For Income (reference $75k+): For women, all categories reported significantly more sleep problems than those making the most money. Generally, the less money, the more complaint (15% more likely for $50-75k, 32% for $35-50k, 39% for $25-35k, 58% for $20-25k, 75% for $15-20k, 84% for $10-15k, and 52% for <$10k). For men, the pattern was a little different. Although the least amount of complaint was still in the highest-earning group, only those making <$25k were significantly different with those making $20-25k being 45% more likely to complain, $15-20k 53% more likely, $10-15k 88% more likely and <$10k 47% more likely.
We know that there is a very strong relationship between sleep and health. Overall, these results show that social and economic factors can play an important role in that relationship.
For more, see the complete paper at http://www.michaelgrandner.com/pages/research-publications.html.
These people were asked, "In the past 2 weeks, how many nights did you have trouble falling asleep, staying asleep, or sleeping too much?" The wording of this question is important, because it is not asking for any particular sleep disorder (like insomnia or sleep apnea), but rather a general sleep complaint. Also, it addresses issues that can exist in any sleep disorder, and involves "sleeping too much" which opens the possibility that it is not just measuring insufficient sleep. So this is a very broad question. When we looked at the data, we quickly realized that most people reported values at the very low end or very high end of the scale. Because of this, we re-coded the data so that those whose values were less than 6 were rated as not having a complaint and those with values 6 or higher were rated as having a complaint. This translates to an average of 3 nights per week across 2 weeks.
Next, we realized that this is a very large and diverse group of people. We wanted to know if there was any group of people that was more or less likely to report problems with their sleep. So we chose a number of variables, including Race/Ethnicity (White, Black/African-American, Hispanic/Latino, and Asian/Other), Income (<$10k, $10-15k, $15-20k, $20-25k, $25-35k, $35-50k, $50-75k, and $75k+), Education (Did not finish high school, High school graduate, Some College, and College graduate), Marital Status (Married, Never Married, In a relationship, Divorced, Widowed, and Separated), and Employment Status (Employed, Self-employed, Retired, Student, Homemaker, Unemployed <1 year, Unemployed >1 year, and Unable to work). We analyzed the data separately for men and women.
To perform our analyses, we used a technique called logistic regression, which computes the odds ration (OR) that a certain outcome will occur. The "outcome" in question was having sleep problems. So we created an equation (actually 2 equations -- one for each gender) that included age (to control for age), as well as the variables mentioned above. The equation computes the OR simultaneously for all of the levels of all of the variables, so they all control for each other. So, for example, when we report the OR for being Married, it is controlling for gender (separate analysis), age, race/ethnicity, income, education, and employment all at the same time. So we know the effects are not due to any of those other factors, which makes them much more reliable. Also, for this type of analysis, when you have multiple categories (like in Race/Ethnicity), one category is set to be the "reference" and all others are compared to it.
What we found was surprising.
For Race/Ethnicity (reference = White): For women, if you were Black/African-American or Hispanic/Latina, you were less likely to complain (26% less likely than White in both cases), and if you were Asian/Other, you were much less likely to complain (58% less likely than White), but if you were Multiracial, you were more likely to complain (67% more likely). For men, there were no differences among groups (none was different from White) except for men who were Asian/Other, who were 57% less likely to complain of sleep problems than White men.
For Education (reference = College Graduate): College graduates slept best. For both men and women, the more education you had, the less likely you were to report sleep problems. In men, those with some college were 22% more likely to complain, those who graduated high school were 27% more likely to complain, and those that did not complete high school were 35% more likely to complain (all of these were significantly different from college graduates). For women, those with some college were 30% more likely to complain, those who graduated high school were 31% more likely to complain, and those that did not complete high school were 64% more likely to complain (again, all of these were significantly different from college graduates).
For Marital Status (reference = Married): Married people slept best. Those that slept worse were those that were never married (62% more likely for women and 74% more likely for men) or in a relationship but not married (39% more likely for women and 102% more likely for men). Men who were divorced or separated were more likely to report problems (52% more likely for divorced and 43% more likely for separated) and this increase was greater than in women (divorced women 24% more likely and separated women 15% more likely). Widowed men and women did not sleep better than married, after controlling for all of the other variables.
For Employment (reference = Employed): Employed people slept best. If you were self-employed, you were 21% (women) or 25% (men) more likely to report sleep problems. If you were retired and male, you were 22% more likely to report problems (no difference in women). If you were a student, there was such a wide range of responses that it was impossible to tell if they were different (some were less likely to report problems and some were almost twice as likely). If you were unemployed <1 year, you were 97% more likely to report problems if you were a woman and 191% more likely if you were a man. If you were unemployed >1 year, you were 122% more likely if you were a woman and 197% more likely if you were a man. If you were unable to work, you were 319% more likely to report problems as a woman and 454% more likely as a man. Although these patterns were similar for men and women, there was a large difference for homemakers. Women homemakers were 18% more likely to report problems (similar to self-employed), but male homemakers were 244% more likely to report sleep problems (similar to unemployed).
For Income (reference $75k+): For women, all categories reported significantly more sleep problems than those making the most money. Generally, the less money, the more complaint (15% more likely for $50-75k, 32% for $35-50k, 39% for $25-35k, 58% for $20-25k, 75% for $15-20k, 84% for $10-15k, and 52% for <$10k). For men, the pattern was a little different. Although the least amount of complaint was still in the highest-earning group, only those making <$25k were significantly different with those making $20-25k being 45% more likely to complain, $15-20k 53% more likely, $10-15k 88% more likely and <$10k 47% more likely.
We know that there is a very strong relationship between sleep and health. Overall, these results show that social and economic factors can play an important role in that relationship.
For more, see the complete paper at http://www.michaelgrandner.com/pages/research-publications.html.
Friday, April 9, 2010
Are you Angry or Happy? Sleep Deprivation Makes it Harder to Tell
In a paper recently published in the journal SLEEP (2010;33(3):335-342), a group of researchers from the University of California at Berkeley report a very provocative finding: being sleep deprived makes it harder to read emotions on strangers' faces.
Who they studied: 37 young (age 18-25), healthy adults who were randomly assigned to either total sleep deprivation (no sleep at all for one night) followed by two nights of recovery sleep or three nights of restful sleep. The group was mostly female (21/35) and, importantly, the authors did not report the ethnicities of the participants.
What they did: To measure ability to detect emotions, subjects were shown a series of pictures of a face that was either (a) Sad, (b) Angry, or (c) Happy. The intensity of the emotions on the face varied from "definitely neutral" to "definitely happy" (or angry or sad). All pictures were of the same person and were in black and white, taken from the standard set of faces developed by Ekman. Importantly, all subjects knew which emotion was being shown -- they were just supposed to rate how intense the emotion was. Presumably, if the rating was more neutral, they saw less of that emotion in the face.
What they found: Across all emotions, in general, the sleep-deprived group was less able to pick up on the intensity of the emotions used -- they were more likely to rate the faces more neutral overall. With respect to specific emotions, the effect was largest for the Angry and Happy faces. In both cases, both groups could identify the most extreme faces at the same rate, but those faces that were not extreme were generally rated as more neutral by those that are sleep deprived. All of these effects disappeared after 1 night of recovery.
What do the findings mean? After a night of no sleep, you tend to see other people's faces as less emotionally intense than if you had slept. And this difference will go away once you get a good night of rest. So if someone is very angry (potentially a dangerous situation), you are less likely to be able to tell. Also if someone is happy, you will be less likely to tell that as well.
Some questions that remain:
1. Since we know that certain cultures are more sensitive to facial emotions, how do these results apply across cultures?
2. Since the subjects in this study were all young, healthy college-aged people, how much of this also applies to adults over 25?
3. Since the only sleep deprivation condition was total sleep deprivation, how does this apply to partial sleep deprivation -- where you get less sleep than you need, but more than none at all?
Paper: Sleep Deprivation Impairs the Accurate Recognition of Human Emotions
Authors: Els van der Helm, MSc; Ninad Gujar, MSc; Matthew P. Walker, PhD
Link: http://www.journalsleep.org/ViewAbstract.aspx?pid=27729
Who they studied: 37 young (age 18-25), healthy adults who were randomly assigned to either total sleep deprivation (no sleep at all for one night) followed by two nights of recovery sleep or three nights of restful sleep. The group was mostly female (21/35) and, importantly, the authors did not report the ethnicities of the participants.
What they did: To measure ability to detect emotions, subjects were shown a series of pictures of a face that was either (a) Sad, (b) Angry, or (c) Happy. The intensity of the emotions on the face varied from "definitely neutral" to "definitely happy" (or angry or sad). All pictures were of the same person and were in black and white, taken from the standard set of faces developed by Ekman. Importantly, all subjects knew which emotion was being shown -- they were just supposed to rate how intense the emotion was. Presumably, if the rating was more neutral, they saw less of that emotion in the face.
What they found: Across all emotions, in general, the sleep-deprived group was less able to pick up on the intensity of the emotions used -- they were more likely to rate the faces more neutral overall. With respect to specific emotions, the effect was largest for the Angry and Happy faces. In both cases, both groups could identify the most extreme faces at the same rate, but those faces that were not extreme were generally rated as more neutral by those that are sleep deprived. All of these effects disappeared after 1 night of recovery.
What do the findings mean? After a night of no sleep, you tend to see other people's faces as less emotionally intense than if you had slept. And this difference will go away once you get a good night of rest. So if someone is very angry (potentially a dangerous situation), you are less likely to be able to tell. Also if someone is happy, you will be less likely to tell that as well.
Some questions that remain:
1. Since we know that certain cultures are more sensitive to facial emotions, how do these results apply across cultures?
2. Since the subjects in this study were all young, healthy college-aged people, how much of this also applies to adults over 25?
3. Since the only sleep deprivation condition was total sleep deprivation, how does this apply to partial sleep deprivation -- where you get less sleep than you need, but more than none at all?
Paper: Sleep Deprivation Impairs the Accurate Recognition of Human Emotions
Authors: Els van der Helm, MSc; Ninad Gujar, MSc; Matthew P. Walker, PhD
Link: http://www.journalsleep.org/ViewAbstract.aspx?pid=27729
Wednesday, March 31, 2010
Sleep and Nutrition
A number of studies have found a connection between shorter sleep durations and increased risk of obesity (and diabetes), especially in women. Some have even gone into detail showing that short sleepers were more likely to show abnormalities in hormones that control hunger and satiety, which may partly explain this.
However, there is one major part of this equation that has not been looked at until now: diet.
A recent study published in the journal Sleep Medicine details some of my recent work exploring relationships between habitual sleep and health. This study examined nutritional profiles from ~450 women and compared the dietary data to a number of sleep-related variables. We studied both subjective sleep (measured with sleep diary) and objective sleep (measured with actigraphy). We looked at both nighttime sleep and daytime naps. What we found was surprising.
Basically, our study found two things:
1. There was a significant relationship between less sleep (verified objectively) and more intake of fat. It didn't matter which kind of fat -- all fats were associated with less sleep.
2. There was an even stronger relationship between more reports of napping (but not actual naps verified by objective methods) with fat intake, as well as with intake of nutrients found in meat. We think that since this relationship was with subjective but not objective naps, this could mean that people who eat more fat and meat are experiencing more sleepiness, even if they are actually sleeping less.
What was especially interesting about this pattern was that this controlled for age (not just one age group driving results), socioeconomic status (not just poorer people eating worse and sleeping worse), BMI (this was independent of weight), total gram amount eaten (it wasn't just that they were eating more), and exercise (it wasn't just that they were less active).
Also of note, there was NO relationship with intake of carbohydrates, even though previous data suggests that sleep-deprived people crave more carbohydrates.
Since these findings were correlational, we can't determine whether the short sleep (or sleepiness) caused more fat and meat intake or eating fat and meat makes you sleep less (and feel sleepier).
However, we conclude that less sleep is associated with eating more fat -- independent of the relationship with obesity. Also, eating more fat (and meat) was associated with feeling more sleepy.
See more in PUBLICATIONS.
However, there is one major part of this equation that has not been looked at until now: diet.
A recent study published in the journal Sleep Medicine details some of my recent work exploring relationships between habitual sleep and health. This study examined nutritional profiles from ~450 women and compared the dietary data to a number of sleep-related variables. We studied both subjective sleep (measured with sleep diary) and objective sleep (measured with actigraphy). We looked at both nighttime sleep and daytime naps. What we found was surprising.
Basically, our study found two things:
1. There was a significant relationship between less sleep (verified objectively) and more intake of fat. It didn't matter which kind of fat -- all fats were associated with less sleep.
2. There was an even stronger relationship between more reports of napping (but not actual naps verified by objective methods) with fat intake, as well as with intake of nutrients found in meat. We think that since this relationship was with subjective but not objective naps, this could mean that people who eat more fat and meat are experiencing more sleepiness, even if they are actually sleeping less.
What was especially interesting about this pattern was that this controlled for age (not just one age group driving results), socioeconomic status (not just poorer people eating worse and sleeping worse), BMI (this was independent of weight), total gram amount eaten (it wasn't just that they were eating more), and exercise (it wasn't just that they were less active).
Also of note, there was NO relationship with intake of carbohydrates, even though previous data suggests that sleep-deprived people crave more carbohydrates.
Since these findings were correlational, we can't determine whether the short sleep (or sleepiness) caused more fat and meat intake or eating fat and meat makes you sleep less (and feel sleepier).
However, we conclude that less sleep is associated with eating more fat -- independent of the relationship with obesity. Also, eating more fat (and meat) was associated with feeling more sleepy.
See more in PUBLICATIONS.
Monday, March 15, 2010
First Post
This is the first post; hopefully, the first of many.
For more information, see http://www.michaelgrandner.com.
For more information, see http://www.michaelgrandner.com.
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