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The purpose of this study was to examine the nutritional effects on sleep using actigraphy measures. A repeated-measures, counterbalanced, crossover study design was used to administer treatment diets to 44 adult participants. Participants served as their own control and consumed high-protein, high-fat, high-carbohydrate, and control diets. The study participants wore Motionlogger Actigraph sleep watches while consuming weighed food intakes for 4 days over four different treatment periods. Demographic and laboratory data were also analyzed. Actigraph results showed that the wake episodes and sleep latencies were significantly different when comparing the macronutrient intakes of the participants. Post hoc test results showed that high-protein diets were associated with significantly fewer (p = .03) wake episodes and high-carbohydrate diets were associated with significantly shorter (p < .01) sleep latencies than control diets. Thus, consumption of specific macronutrient intakes may have a significant influence on sleep.
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Western Journal of Nursing
http://wjn.sagepub.com/content/35/4/497
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DOI: 10.1177/0193945911416379
2013 35: 497 originally published online 4 August 2011West J Nurs Res
Glenda Lindseth, Paul Lindseth and Mark Thompson
Nutritional Effects on Sleep
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Western Journal of Nursing Research
35(4) 497 –513
© The Author(s) 2011
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DOI: 10.1177/0193945911416379
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416379WJN35410.1177/0193945911416379Lind
seth et al.Western Journal of Nursing Research
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1University of North Dakota, Grand Forks
2Naverus Corporation, Kent,
WA
Corresponding Author:
Glenda Lindseth, University of North Dakota, P.O. Box 9025, Grand Forks, ND 58201, USA.
Email: glenda_lindseth@und.nodak.edu
Nutritional
Effects on Sleep
Glenda Lindseth1, Paul Lindseth1,
and Mark Thompson2
Abstract
The purpose of this study was to examine the nutritional effects on sleep
using actigraphy measures. A repeated-measures, counterbalanced, cross-
over study design was used to administer treatment diets to 44 adult partici-
pants. Participants served as their own control and consumed high-protein,
high-fat, high-carbohydrate, and control diets. The study participants wore
Motionlogger Actigraph sleep watches while consuming weighed food intakes
for 4 days over four different treatment periods. Demographic and labo-
ratory data were also analyzed. Actigraph results showed that the wake
episodes and sleep latencies were significantly different when comparing the
macronutrient intakes of the participants. Post hoc test results showed that
high-protein diets were associated with significantly fewer (p = .03) wake
episodes and high-carbohydrate diets were associated with significantly
shorter (p < .01) sleep latencies than control diets. Thus, consumption of
specific macronutrient intakes may have a significant influence on sleep.
Keywords
healthy adults, nutrition, sleep
More than two thirds (72%) of adults get less than the recommended 8 hr of
sleep each night and one fifth (20%) of those adults get less than 6 hr each
night (Centers for Disease Control and Prevention, 2011; National Sleep
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498 Western Journal of Nursing Research 35(4)
Foundation, 2009). The consequences of a lack of sleep can result in a poor
performance of daily activities and reductions in functional capacities
(Mahowald, 2007). Furthermore, sleep deprivation is seen as an unmet pub-
lic health problem (Colten & Altevogt, 2006). Some studies have shown
that dietary intakes may significantly affect sleep when macronutrient intakes
are manipulated (Husain, Yancy, Carwile, Miller, & Westman, 2004;
Szentirmai, Kapás, Sun, Smith, & Krueger, 2010). However, there is a pau-
city of research on this topic, and study results are mixed and inconclusive.
Electroencephalographic sleep changes were studied in eight young healthy
male participants using a repeated-measures, within-subjects study design
(Phillips et al., 1975). A high-carbohydrate/low-fat diet resulted in signifi-
cantly less slow-wave (nonrapid eye movement [non-REM]) sleep in com-
parison with a normal balanced diet or a low-carbohydrate/high-fat diet.
Similarly, in an animal-based study, consumption of a protein-rich albumin
diet significantly enhanced non-REM sleep (Obál, Kapás, & Krueger, 1998).
The most restorative sleep is thought to occur during the deep, REM stage
(Bonnet, 1986). Another study of individuals with no previous history of sleep
disorders found that those who consumed a high-carbohydrate, low-fat diet
spent less time in slow-wave (non-REM) sleep than those who consumed
either a control balanced diet or a low-carbohydrate, high-fat diet (Husain et
al., 2004).
Using a 7-day sleep diary and a subjective sleep quality index, changes in
sleep were measured as part of a double-blind, placebo-controlled study of 49
male and female participants. A protein source food bar of tryptophan with
carbohydrate was most effective in significantly reducing awake time during
the night (Hudson, Hudson, Hecht, & MacKenzie, 2005). In comparison,
dietary carbohydrate consumption significantly affected sleep onset in com-
parison with dietary intakes from a control diet (Krauchi, Cajochen, Werth,
& Wirz-Justice, 2002).
A study of 12 healthy young men showed that when comparing very
high (90.4%) carbohydrate meals with high- and low-glycemic indices,
consumption of high-glycemic index meals consumed 4 hr before bedtime
resulted in shorter sleep latencies
(Afaghi, O’Connor, & Chow, 2007).
Polysomnographic measures were used to measure the sleep status of the
study participants. A significant reduction in the mean sleep-onset latencies
of healthy sleepers was observed when a high-glycemic index diet was con-
sumed in comparison with a low-glycemic index meal 4 hr before bedtime.
Thus, the investigators felt that the glycemic index of high-carbohydrate
foods eaten by study participants may be a factor in sleep latency results. In
another study of 14 healthy men by these same scientists (Afaghi, O’Connor,
& Chow, 2008), very low-carbohydrate meals were found to significantly
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Lindseth et al. 499
decrease sleep latency. Thus, their results contradicted their previous find-
ings showing that high-carbohydrate meals resulted in a significant decrease
in sleep latency. The study authors also noted that the higher fat intake levels
associated with the low-carbohydrate intakes may have been a factor in the
sleepiness of the study participants.
Other research has shown that one of the physiological responses to a meal
is the thermic effect of food (TEF): a rise in body temperature after food
intake (Driver, Shulman, Baker, & Buffenstein, 1999). TEF occurs because
the digestion and absorption of dietary nutrients incur energy expenditure
that is released as heat (Tappy, 1996). A lower energy diet is related to a
lower TEF (Zammit, Ackerman, Shindledecker, Fauci, & Smith, 1992).
Meals inducing postprandial sleepiness may act through the production of a
high TEF and subsequent heat loss. As a result, the rate of heat loss may be a
good predictor of sleep latency (Krauchi et al., 2002). Driver et al. (1999)
found that high-energy food intakes appeared to be related to a long-lasting
TEF. Consequently, these factors influenced sleep for up to 2 hr after meal
consumption.
Previous studies have also showed improvements in sleep following con-
sumption of a high-protein diet, especially a diet rich in tryptophan (Markus
et al., 2005). However, Landström, Knutsson, and Lennernäs (2000) did not
discover a significant relationship between diet and the onset of drowsiness.
Another study measured sleep duration in 240 adolescents using food recall
and wrist-actigraphy devices (Weiss et al., 2010). Adolescents who slept an
average of 8 or more hours on weekdays consumed a larger proportion of
their calories from high-fat foods than those who slept less. In contrast,
a large cross-sectional study from China showed a positive association
between decreased sleep duration and increased fat intake (Shi, McEvoy,
Luu, & Attia, 2008). Using a national health and nutrition survey, a sample
of 2,828 adults in China was studied. Those who slept for less than 7 hr a day
consumed a significantly higher (p = .005) percentage of their calories from
fat than those sleeping more than 7 hr per day. In addition, a study by
Rontoyanni, Baic, and Cooper (2007) of 30 healthy Greek women also
found a weak, positive relationship between sleeping less and consumption
of dietary fat intake. This observational cross-sectional study used a Sleep
Habits Questionnaire, a 7-day sleep diary, and two 24-hr dietary recall inter-
views for measurement.
Finally, foods and beverages containing central nervous system stimulants
and depressants, such as caffeine and alcohol, are often discouraged because
of their adverse effects on quantity and quality of sleep. Although alcohol
is often used to “self-medicate” and to shorten sleep latency, research also
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500 Western Journal of Nursing Research 35(4)
indicates that it has been found to affect the quantity of slow-wave sleep and
has also affected REM sleep (Danel, Libersa, & Touitou, 2001; Feige et al.,
2006). Caffeine, found in beverages and foods such as tea, coffee, or choco-
late, is a neurologic stimulant, which has been found to lower the need to
sleep and produces sleep disruption when taken just prior to going to bed
(Nehlig, Daval, & Debry, 1992).
In summary, although good nutrition intake is prescribed for a variety of
health conditions, the literature shows that the potential connection between
dietary intake and sleep has not been translated into a sufficient number of
clinical studies given the mixed results (Michalsen et al., 2003). Especially
noteworthy is the limited number of studies measuring nutrient intakes through
the use of weighed food intakes rather than dietary recall methods.
Purpose
As a result, the purpose of this study was to test the effects of weighed
macronutrient food intakes on sleep. The specific aims for this study were as
follows: (a) determine actigraph sleep/activity measures of participants
receiving a nonmanipulated (control) diet, a high-protein diet, a high-fat diet,
or a high-carbohydrate diet; (b) analyze for differences in sleep/activity
scores for the groups of participants receiving a nonmanipulated diet, a high-
protein diet, a high-fat diet, or a high-carbohydrate diet; and (c) examine
relationships among participants’ sleep scores and dietary intakes.
Sleep variables were determined by the following measures: sleep effi-
ciency (the amount of time asleep divided by the amount of time spent in
bed), sleep latency (the time between going to bed and falling asleep), and
wake episodes (the number of awakenings between falling asleep and rising).
A macronutrient is referred to an essential nutrient—either protein, fat, or
carbohydrate—that is consumed in sufficient quantities to provide energy for
the body (Ambulatory Monitoring, Inc).
Method
Design
Using a repeated-measures, counterbalanced, crossover study design, par-
ticipants served as their own control for each of four dietary treatment ses-
sions that included a high-protein, high-carbohydrate, high-fat, and a control
diet. The session order was counterbalanced across dietary groups. A double-
blind intervention was used so neither the participant nor the researchers
knew when the participant was receiving control or treatment diets.
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Lindseth et al. 501
Setting and Sample
The sample was comprised of 44 healthy adults recruited through a midwest-
ern university. A power analysis, calculated with Borenstein and Cohen’s
methodology, determined the required sample size (Borenstein, Rothstein, &
Cohen, 2001), based on multiple analysis of variance statistics. The possible
range of effects of dietary interventions on sleep was based on previous
dietary studies conducted by these investigators with a similar population.
The effect size for the power analysis was estimated to be “medium.” The
power for this sample was set at 0.80, with a confidence level, α = .05.
Therefore, a minimum of 35 participants was estimated for each treatment
group to achieve statistical power. Additional participants were entered into
the study to allow for attrition. Thus, 44 participants completed the study.
Inclusion criteria for the study were as follows: (a) being between the ages
of 18 and 50 years and (b) having an ability to read, understand, and speak
English. Exclusion criteria for the study included (a) a self-reported diagnosis
of diabetes or pregnancy, (b) reported sleep problems, and (c) taking
prescription/over-the-counter medications other than Aspirin or non-Codeine
Tylenol. Diabetes and pregnancy were exclusionary because of the special
dietary requirements necessary for prenatal or diabetic conditions. Illicit drug
use and alcohol use were forbidden during the study. The participants’ aca-
demic program has a strict policy against illicit drug use as well as a random
drug testing program is in effect to deter illicit drug use.
During the first 2 weeks of university classes, participants who met sam-
ple selection criteria were invited to participate in this study by the researchers.
The purpose and details of the study were explained to potential study
participants. Participants’ questions were answered by the investigators.
Individuals that signed consent forms and met the inclusion criteria were
randomly selected for participation in the study by drawing names from a
container. Study participants were considered to be free-living participants
because they were allowed to attend classes and work in the community,
although all meals for the study were to be eaten in the study’s dining room.
Ethical considerations for participants in the study were reviewed and
approved by the University and the U.S. Army Human Research Protection
Office.
Measures
Measurements for this study were accomplished through use of the following
study measures: demographic questions, weighed food intakes, sleep acti-
graph measures, the Pittsburgh Sleep Quality Index (PSQI), for its seven
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502 Western Journal of Nursing Research 35(4)
components (Buysee, Reynolds, Monk, Berman, & Kupfer, 1989), and
biochemical laboratory tests.
Demographics. Participants completed a questionnaire that included place
of residence, age, education, marital or social living status, employment sta-
tus, and ethnic identification.
Anthropometric measurements. Each participant was weighed twice per
visit using a Cardinal-Detecto balance beam scale, and averages of the two
measures were recorded. Weights were measured at the beginning of each
study week when the participant received a sleep watch and when they turned
in the sleep watch for evaluation at the end of each 4-day treatment session.
Height was measured on the first visit using an Accustat wall-mounted height
board. Body mass indices (BMIs) were calculated as the weight divided by
the height squared based on the weight measured on admission to the study.
Weight assessments were recorded to establish whether the participant was
considered underweight or overweight at the beginning of the study, and
serial weights were completed to monitor weight gains or losses during the
four treatment sessions.
Sleep and activity measures. The PSQI is a self-report questionnaire. The
questionnaire is used to assess sleep quality of participants for the month
prior to completing the sleep inventory. The 19-item questionnaire measured
important descriptive sleep characteristics. These characteristics were then
used to produce seven-component scores (Buysee et al., 1989). The compo-
nents were summed into a global PSQI score using a Likert-type scale of 0
to 5 (0 = good quality sleep, 5 = poor quality sleep). Internal consistency of
the PSQI score was reported as r = .83 and test–retest reliability (4 weeks)
was r = .85.
Sleep was measured using the Motionlogger Actigraph (Ambulatory
Monitoring, Inc.), a small portable wristwatch type of device that digitally
records integrated measures of gross motor activity. A reliability coefficient
of .92 and a validity of .99 were established for the Motionlogger model
(Tryon, 2005). The actigraph computer interface used ACT Millennium
Graphs Software Version 2K3.0 to analyze data. Physical activity was
recorded as activity counts on the actigraph. Sleep measures included the fol-
lowing variables: sleep efficiency (the amount of time asleep divided by the
amount of time spent in bed), sleep latency (the time between going to bed
and falling asleep), and wake episodes (the number of awakenings between
falling asleep and rising). The participants wore their actigraph watch con-
tinuously throughout each 4-day treatment session. The actigraphs were
approved for showering, although participants could remove the watch
just to wash their wrist. The actigraph recorded sleep and wake patterns,
bed and rise times, sleep efficiency, sleep and wake bouts, mean physical
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Lindseth et al. 503
activity scores, nap analysis, sleep latency, wake episodes, and other activity
patterns.
Health assessment data. A health status assessment modified from Doenges’
Health Assessment Checklist was collected and recorded for each participant
(Doenges, Geissler, & Moorhouse, 1989). The checklist consisted of nine fac-
tors ascertained during a medical history assessment of the participants,
including history of chronic systemic disease such as respiratory, cardiac, gas-
trointestinal, genitourinary, neurological, musculoskeletal, metabolic/endo-
crine, audiovisual, and integumentary system disorders. Depending on the
severity of these disorders, the condition was considered an exclusionary fac-
tor for participating in this study to include pregnancy and ingestion of oral
contraceptives.
Biochemical and laboratory measures. The laboratory data for each partici-
pant included serum glucose and serum cholesterol. Serum cholesterol was
analyzed to monitor for serum lipids due to the effects of the high-fat dietary
treatment meals. Blood glucose testing was completed before the study to
assess for potential hypoglycemic episodes or glucose intolerance and, after
the 4-day dietary treatments, to assess for possible glucose intolerance due to
the high-carbohydrate intakes. Subsequent high-glucose test results were veri-
fied by a 1-hr postprandial glucose test. An abnormal postprandial glucose
test would result in referral to the university student health services for medi-
cal care as well as being excluded from the study. However, no participants
needed to be excluded from the study for this reason. To ensure validity,
licensed personnel supervised collection of all lab tests.
Nutrition analysis. The Food Processor Nutrition Analysis Program, a soft-
ware program used to analyze nutrient intakes for research or clinical appli-
cations and to present the data in a variety of formats, was used to analyze the
nutrient data for this study (ESHA Research, 2002). Although the Food Pro-
cessor Program analyzed for more nutrients, the nutrients selected for assess-
ment and analysis for this study were based on study objectives focusing on
macronutrients. Kilocalories and content of protein, carbohydrate, and fat
nutrients were the primary dietary variables analyzed for relationships to
sleep efficiency, sleep latency, wake episodes, and physical activity. Amounts
of caffeine, resting metabolic rates, kilocalories consumed, and anthropomet-
ric and laboratory test variables were also analyzed for potential effects on
sleep variables (Smith, Kendrick, & Maben, 1992).
Intervention
Dietary treatments. In this design, participants served as their own control and
were randomly rotated through each of three dietary treatments: a high-protein
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504 Western Journal of Nursing Research 35(4)
diet (56% protein, 22% carbohydrate, and 22% fat), a high-carbohydrate diet
(56% carbohydrate, 22% protein, and 22% fat), a high-fat diet (56% fat, 22%
carbohydrate, and 22% protein), and a control diet (50% carbohydrate, 35%
fat, and 15% protein). The macronutrient percentage for the control diet was
based on percentages of macronutrients commonly used in other nutrition
intervention studies (Blumenthal et al., 2010; McDowell et al., 1994). The
dietary percentage protocols resulted in two nutrients being controlled for
dietary treatment comparisons for all of the diets. (For example, in the high-
carbohydrate diet, fat remained constant at 22% for both the high-protein and
high-carbohydrate diet comparisons. Similarly, the carbohydrate percentages
also remained constant at 22% for the high-fat and high-protein diets.) The
four separate diet conditions were selected to test macronutrients that may
affect participants’ sleep. Participants were fed diets containing daily kilo-
calorie levels based on the participant’s individual indirect calorimetry
measurement.
Intervention procedures. Only foods prescribed for the study were to be
eaten by the study participants. Preparation of foods for the study was com-
pleted under the guidance and in consultation with a research dietitian using
standardized recipes and exact portion sizes. Meals prepared for the dietary
interventions were served by a research team member directly to each partici-
pant. The study dietitian confirmed that each meal was prepared properly
before administering the meal to the participants. A double-blind intervention
plan resulted in neither the participant nor the researchers knowing when the
participant was receiving the control diet or the treatment diets. Food con-
sumption was controlled both in terms of what was consumed and how much
was consumed. Each participant was given preweighed meals that were
required to be eaten in the study dining room. Meal consumption was moni-
tored by the dietitian and the study staff. Food intakes were weighed and
recorded before and after eating by the research staff. Weighing food intakes
is considered the most accurate method for measuring human food consump-
tion (Gibson, 1990). After the final meal was eaten in the dining room each
day, participants signed a food/beverage intake sheet to verify that no foods
or beverages from outside of the study were consumed. Space was provided
on the sheet to allow participants to record any foods, beverages, or other
substances that may have been inadvertently consumed outside of the study
within the previous 24-hr period. If a participant was still hungry, they were
given additional portions of the foods in the same exact macronutrient per-
centages required by study protocols.
Beverages were also issued to the participants as part of the study meals.
Beverages planned for consumption included (a) beverages, such as water
or noncaloric drinks with controlled caffeine, that were not offset by the
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Lindseth et al. 505
percentages of fat, protein, or carbohydrates for the meal and (b) beverages
that contributed to the fat, protein, and carbohydrate percentages for the meal
and snack (this included milk for participants not indicating lactose intoler-
ance). Amounts of caffeine were carefully controlled for in the study because
of the potential effects on sleep. Individuals with a history of moderate caf-
feine consumption were allowed caffeinated drinks with a limit of <200 mg/
day. A preliminary food habit questionnaire was given to all selected partici-
pants to be sure that they would be willing to consume the prescribed diets/
beverages. Anyone who objected to the prescribed foods was advised they
should not participate. Participants were given snacks and beverages in insu-
lated lunch bags to take home for consumption between the evening meal and
bedtime. Any uneaten foods or beverages along with the respective contain-
ers were returned for weighing when the participant came back for the next
meal.
Data Collection
An initial pilot study was conducted 4 months prior to the full study and was
used to provide an evaluation and justification for inclusion of planned
instrumentation and variables within this study. The psychometric properties
of the instruments in this study had been tested by the original authors or
researchers as indicated.
A week prior to starting study treatments, a mutually agreed-on time was
set for the nurse researcher to meet with the consenting participants. At this
meeting, the researcher completed health assessments and anthropometric
measurements. Indirect calorimetry values were also obtained. Each study
participant also completed a demographic questionnaire. Directions for the
dietary treatment sessions, laboratory testing, and wearing the Actiwatch
sleep watches were given. Study participants were instructed on the impor-
tance of consuming only the food and beverages as prescribed by the study.
To ensure that order of treatment was not a concern for the interpretation
of findings, any potential effects were mitigated in two ways: (a) allowing 2
weeks of “washout” time to lapse between treatments and (b) randomly
assigning order of treatment to the participants. Participants were randomly
assigned to dietary treatment groups according to two sets of 24 possible
treatment orders. The order of treatment and control diets was drawn from a
container for random assignment without replacement for 44 participants.
The planned treatment order for each participant was recorded and treatments
were implemented accordingly. Dietary treatment sessions were scheduled
for a Monday through Thursday time period with a 2-week washout period
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506 Western Journal of Nursing Research 35(4)
between the study sessions. In the literature, 2-week washout periods are
commonly used to eliminate carryover effects between dietary interventions
(Carson, Burke, & Hark, 2004; Driskell, 2007; Howarth, Petrisko, Furchner-
Evanson, Nemoseck, & Kern, 2010). During the weeks of the dietary treat-
ments, participants were given directions for using the actigraph. The
importance of strict compliance in participating with the dietary and control
group treatments was stressed. Participants were not allowed to consume any
foods or beverages that were not provided by the study. Meal times were set
according to a desirable and “typical” schedule for each participant. Participants
were carefully observed to be certain that their meals were received and eaten
at the appropriate times. Food intakes were carefully weighed and recorded
(within .05 oz accuracy). The participants completed questionnaires daily to
confirm that they had not consumed foods or beverages that were not issued
by the study.
The PSQI, a self-rating questionnaire, was administered to participants as
they began their dietary treatments. Each participant was given an actigraph
on the first day of treatment to wear continuously during the dietary treatment
sessions. The actigraph was worn continuously for the four dietary treatment
days and returned after the last planned meal was eaten on the fourth treat-
ment day. This provided data on the participants’ sleep experienced during
the study treatments.
On the fourth day of receiving a dietary treatment, the participants returned
their sleep watches to the investigators. The record of weighed food intakes
for each participant was compared with their sleep assessment results that
were gathered on the 4th day of each dietary treatment regimen. Anthropometric
measures and laboratory tests were also completed by the nurse researcher at
this time. Participants received a small compensation of US$25 for comple-
tion of each of the observational interviews. Including baseline measurements,
a total stipend of US$125 was paid as compensation for each participant’s
time in the study, inconvenience, and as an expression of appreciation for
contributing to this body of knowledge.
Analysis
Study data were analyzed using the Statistical Package for Social Science
(SPSS). The SPSS Explore Procedure was used to screen data, visually
examine distributions of group values, and test for normality and homogene-
ity of variance. Response frequencies were tabulated for measures of dietary
intake, demographics, and sleep/activity levels. The data were analyzed by
applicable descriptive and inferential statistics; correlations among key vari-
ables were also analyzed. Missing data were handled by replacing the missing
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Lindseth et al. 507
value with the mean score calculated from the other participants’ data for that
particular variable. Sleep efficiency, sleep latency, wake episodes, and activ-
ity scale scores were calculated for the participants after receiving the non-
manipulated (control) diet and the high-carbohydrate, high-protein, and
high-fat diets. Significant differences among the sleep/activity scores for
participants in the four dietary treatment groups were analyzed by using a
RANOVA. Tukey’s post hoc tests were calculated when the overall omnibus
effect from the RANOVA was statistically significant. The post hoc testing
was used to compare specific diet conditions with each other between the
significantly different groups. An alpha level of .05 was the criterion for
significance.
Results
Sample Demographics
Of the 44 participants, the study sample included 39 European American
participants, 2 Asian participants, 2 Hispanic participants, and 1 African
American participant. Participant ages ranged from 19 to 22 years with a
median age of 20.6 years. The median years of education were 14 years, with
a range from 13 to 16 years. The median BMI was 24.8 (range = 21.0-28.0).
Other variable means and standard deviations are listed in Table 1.
Table 1. Baseline Means and Standard Deviations for Demographics, Health Status,
and Sleep Data (N = 44).
Variable M SD
Demographics of the sample
Age (years) 20.6 2.0
Education (years) 13.6 0.98
Health status of the participants
BMI 24.8 3.5
Sleep and activity data
Sleep index (scores) 91.1 11.0
PSQI (scores) 4.1 1.9
Sleep efficiency (percentages) 92.2 4.3
Wake episodes 14.8 7.0
Sleep latency (minutes) 11.7 11.9
Activity mean (counts) 268.5 177.4
Note: BMI = body mass index; PSQI = Pittsburgh Sleep Quality Index.
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508 Western Journal of Nursing Research 35(4)
Sleep and Activity in Dietary Groups
Mean sleep and activity scores were compared among the high-fat, high-
protein, high-carbohydrate, and control diet groups. The sleep scores were
based on actigraph measures of sleep efficiency, sleep latency, wake epi-
sodes, and actigraph activity scores. Table 2 illustrates the intraparticipant
analysis using RANOVA to detect differences in a single participant’s sleep
and activity scores with different dietary intakes. Statistically significant dif-
ferences were based on the mean sleep and activity scores compared among
the different dietary groups. The wake episode scores were significantly dif-
ferent, F = 3.6; df = 3, 44; p =. 02, when comparing the control group with
the fat, protein, and carbohydrate groups. Also, the sleep latency scores were
Table 2. A Repeated-Measures ANOVA of Sleep and Activity Scores According to
Dietary Intakes of Participants (N = 44).
M SD F p
Physical activity 0.01 ns
High-fat diet 270.3 170.2
High-protein diet 271.5 194.7
High-carbohydrate diet 268.2 184.4
Control diet 274.8 180.8
Wake episodes 3.6 .02
High-fat diet 14.5 6.9
High-protein dieta13.5 7.2
High-carbohydrate diet 14.1 7.3
Control diet 16.7 6.4
Sleep efficiency 1.6 ns
High-fat diet 92.2 4.2
High-protein diet 92.9 4.1
High-carbohydrate diet 92.5 4.4
Control diet 91.3 4.8
Sleep latency 5.3 .004
High-fat diet 11.3 12.7
High-protein diet 12.8 15.1
High-carbohydrate dietb9.1 7.6
Control diet 13.9 11.7
Note: Post hoc test results are as follows:
aProtein diet score was significantly (p = .03) lower than the control diet score.
bCarbohydrate diet score was significantly (p < .01) lower than the control diet score.
(df = 3), p < .05.
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Lindseth et al. 509
significantly different among control and the other dietary groups, F = 5.3;
df = 3, 44; p = .004. Differences among the dietary groups were further ana-
lyzed using a post hoc test to detect significant differences between specific
groups. Post hoc testing indicated that the wake episodes were significantly
lower (p = .03) for the protein diet as compared with the control diet. Also
the sleep latency scores were significantly lower (p = .01) for the carbohy-
drate diet as compared with the control diet.
Other significant correlations were found between sleep efficiency and the
following: basal metabolic kilocalories per day (r = .26, p = .04), random
glucose (r = –.45, p = .001), and total cholesterol (r = –.60, p < .001).
Discussion
This study investigated the effects of manipulated macronutrient intakes on
sleep and activity variables. Results indicated significant differences in
sleep latency and wake episodes when comparing participants’ composition
of dietary macronutrients. For example, participants who fed on high-
carbohydrate diets had significantly shorter (p =. 004) mean sleep latencies
in comparison with those who fed on control diets. However, these results
contradict the work from Afaghi et al’s. (2008) study, indicating that low-
carbohydrate meals decreased sleep latency. However, the results are consistent
with results of another study that compared the effects of carbohydrate-rich
meals with control meals on sleep and body thermoregulation (Krauchi
et al., 2002).
Also, the results of our study indicated that consuming a high-protein diet
produced significantly fewer (p < .03) wake episodes in comparison with
consuming a control diet. Some related studies have shown that low-protein
diets alone did not significantly affect sleep, although other studies showed
that tryptophan depletion with subsequent brain serotonin reductions corre-
lated with more wake periods and greater wake percentages than controls
(Voderholzer et al., 1998). Our findings were similar to those studies that
showed sleep improvements with high-protein diets, especially diets rich in
tryptophan (Markus et al., 2005).
However, our results contrast with the findings of Landström et al. (2000),
who did not discover a significant relationship between diet and the onset of
drowsiness. The Landström et al. study relied heavily on subjective ratings of
the levels of fatigue. In contrast, our study applied actigraphy for more objec-
tive measurements of activity and sleep states. Sleep loss has an adverse
effect on the body’s ability to use glucose. For example, in 1 week of severe
sleep deprivation (about 4 hr per night), a healthy, lean, fit individual can be
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510 Western Journal of Nursing Research 35(4)
in a prediabetic state (Van Cauter et al., 2007). Consistent with this, our
study showed that lower sleep efficiency was associated with high-glucose
and high–serum cholesterol levels. In particular, sleep efficiency correlated
significantly (p < .05) with random glucose, total cholesterol, and resting
metabolic kilocalories per day. In a related study, serum lipids and total cho-
lesterol were significantly related (p < .001) to the arousal frequency (number
of sleep arousals per hour; Ekstedt, Akerstedt, & Soderstrom, 2004).
In addition, our study indicated that higher caloric intake resulted in better
sleep efficiency. This result supports the work of Driver et al. (1999) con-
cerning the TEF. Contrary to our study, Zammit et al. (1992) concluded that
there were no significant differences in sleep latency noted in participants
that consumed high- or low-caloric carbohydrate meals.
In summary, the results of this study show that attention to nutritional
intake could be a key to better sleep. For example, consuming a high-protein
diet significantly reduced wake episodes compared with the control diet.
Also, a high-carbohydrate diet significantly reduced sleep latency as com-
pared with the control diet. In addition, glucose and serum cholesterol levels
were negatively correlated, and kilocalorie intake was positively correlated
with sleep efficiency. These results provide evidence that specific macronu-
trient diets may influence a person’s sleep quality. However, our results did
not support any relationships between sleep efficiency or mean activity and
macronutrient diet composition. The participants spent the same percentage
of time sleeping once they fell asleep and maintained the same level of physi-
cal activity regardless of the diet they consumed.
Finally, limitations of this study include homogeneity of the study sample,
generalizability of the results, and the fact that actigraphic monitoring was used
rather than polysomnographic testing. Another shortcoming includes the fact
that although actigraph measurements of sleep are accurate for field research
(nonlaboratory research results), they do not achieve the accuracy that could be
achieved using polysomnographic testing. Therefore, for a more accurate assess-
ment of sleep quality, we recommend that future studies using polysomnogra-
phy be conducted with laboratory participants while testing macronutrient diets.
Acknowledgment
This manuscript is written in memory of Dr. Marcia Gragert, who provided invalu-
able expertise to this work.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
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Lindseth et al. 511
Funding
The author(s) disclosed receipt of the following financial support for the research,
authorship, and/or publication of this article: This work was supported by the Peer
Reviewed Medical Research Program of the U.S. Army Biomedical Research and
Materiel Command (grant number DAMDIT-03-1-0010) and the National Institutes
of Health (grant number 1C06RR022088).
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... Eight studies provided sufficient data for the outcome SQ, 29-35 8 studies were used for the metaanalysis on ST, [30][31][32][33][34]36 7 studies entered meta-analysis on SL, 31,33-35,37 and 6 were used to examine the effect of protein intake on SEff. 31,33,34,37 Meta-analyses were carried out on the outcomes ST and SL, but major conclusions could not be reached because of problems with heterogeneity. To ensure transparency, the forest plots are depicted in Figures S1 and S2 in the Supporting Information online. ...
... Meta-analyses were then carried out using the generic inverse-variance method. For 2 studies, 34,37 MDs and SEs were not available and had to be calculated. Therefore, the correlation coefficient (r ¼ 0.68) from Gwin and Leidy 33 has been imputed; they used the same objective measurement applying the following formula, adapted from Elbourne et al 38 : ...
... 32 After removing duplicates, then screening titles and abstracts, and evaluating full texts of articles deemed relevant, 10 references met the inclusion criteria and were used for systematic review. [29][30][31][32][33][34][35][36][37]39 Two publications were conference abstracts, and 2 reported results from 2 different studies, making a total of 12 studies included in the systematic review. ...
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Clinical practice related to sleep problems and sleep disorders has been expanding rapidly in the last few years, but scientific research is not keeping pace. Sleep apnea, insomnia, and restless legs syndrome are three examples of very common disorders for which we have little biological information. This new book cuts across a variety of medical disciplines such as neurology, pulmonology, pediatrics, internal medicine, psychiatry, psychology, otolaryngology, and nursing, as well as other medical practices with an interest in the management of sleep pathology. This area of research is not limited to very young and old patients-sleep disorders reach across all ages and ethnicities. Sleep Disorders and Sleep Deprivation presents a structured analysis that explores the following: Improving awareness among the general public and health care professionals. Increasing investment in interdisciplinary somnology and sleep medicine research training and mentoring activities. Validating and developing new and existing technologies for diagnosis and treatment. This book will be of interest to those looking to learn more about the enormous public health burden of sleep disorders and sleep deprivation and the strikingly limited capacity of the health care enterprise to identify and treat the majority of individuals suffering from sleep problems. © 2006 by the National Academy of Sciences. All rights reserved.
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