Behaviorally Assessed Sleep and Susceptibility to the Common Cold

Abstract

Short sleep duration and poor sleep continuity have been implicated in the susceptibility to infectious illness. However, prior research has relied on subjective measures of sleep, which are subject to recall bias. The aim of this study was to determine whether sleep, measured behaviorally using wrist actigraphy, predicted cold incidence following experimental viral exposure.
A total of 164 healthy men and women (age range, 18 to 55 y) volunteered for this study. Wrist actigraphy and sleep diaries assessed sleep duration and sleep continuity over 7 consecutive days. Participants were then quarantined and administered nasal drops containing the rhinovirus, and monitored over 5 days for the development of a clinical cold (defined by infection in the presence of objective signs of illness). Logistic regression analysis revealed that actigraphy- assessed shorter sleep duration was associated with an increased likelihood of development of a clinical cold. Specifically, those sleeping <5 h (odds ratio [OR]= 4.50, 95% confidence interval [CI], 1.08-18.69) or sleeping between 5 to 6 h (OR = 4.24, 95% CI, 1.08-16.71) were at greater risk of developing the cold compared to those sleeping >7 h per night; those sleeping 6.01 to 7 h were at no greater risk (OR = 1.66; 95% CI 0.40-6.95). This association was independent of prechallenge antibody levels, demographics, season of the year, body mass index, psychological variables, and health practices. Sleep fragmentation was unrelated to cold susceptibility. Other sleep variables obtained using diary and actigraphy were not strong predictors of cold susceptibility.
Shorter sleep duration, measured behaviorally using actigraphy prior to viral exposure, was associated with increased susceptibility to the common cold.
Copyright © 2015 Associated Professional Sleep Societies, LLC. All rights reserved.

Full text

SLEEP, Vol. 38, No. 9, 2015 1353 Sleep and the Common Cold—Prather et al.
INTRODUCTION
Growing evidence demonstrates that short sleep duration
(< 6 or 7 h/night) and poor sleep continuity are associated with
the onset and development of a number of chronic illnesses,1–4
susceptibility to acute infectious illness,5 –7 and premature
mo r talit y.8 –11 Experimental evidence in animals and humans
suggests that the immune system serves as a key biological
pa t h way.12 –14 For instance, total and partial sleep deprivation in
humans results in modulation of immune parameters critical
to host resistance, including diminished T cell proliferation,15
shifts in T helper cell cytokine responses,16,17 decreases in nat-
ural killer (NK) cell cytotoxicity,18,1 9 and increased activation
of proinammatory pathways.20–23
Sleep related modulation of the immune system is also ob-
served when sleep is measured in the natural environment,
with implications for clinical outcomes.6,24 We recently re-
ported that short sleep duration and poor sleep continuity, mea-
sured by sleep diary over 14 consecutive days, predicted the
incidence of developing a biologically veried cold following
viral exposure.6 One of the limitations of this prior study was
Study Objectives: Short sleep duration and poor sleep continuity have been implicated in the susceptibility to infectious illness. However, prior
research has relied on subjective measures of sleep, which are subject to recall bias. The aim of this study was to determine whether sleep,
measured behaviorally using wrist actigraphy, predicted cold incidence following experimental viral exposure.
Design, Measurements, and Results: A total of 164 healthy men and women (age range, 18 to 55 y) volunteered for this study. Wrist
actigraphy and sleep diaries assessed sleep duration and sleep continuity over 7 consecutive days. Participants were then quarantined and
administered nasal drops containing the rhinovirus, and monitored over 5 days for the development of a clinical cold (dened by infection in the
presence of objective signs of illness). Logistic regression analysis revealed that actigraphy- assessed shorter sleep duration was associated
with an increased likelihood of development of a clinical cold. Specically, those sleeping < 5 h (odds ratio [OR] = 4.50, 95% condence inter val
[CI], 1.08–18.69) or sleeping between 5 to 6 h (OR = 4.24, 95% CI, 1.08–16.71) were at greater risk of developing the cold compared to those
sleeping > 7 h per night; those sleeping 6.01 to 7 h were at no greater risk (OR = 1.66; 95% CI 0.40–6.95). This association was independent
of prechallenge antibody levels, demographics, season of the year, body mass index, psychological variables, and health practices. Sleep
fragmentation was unrelated to cold susceptibility. Other sleep variables obtained using diary and actigraphy were not strong predictors of cold
susceptibility.
Conclusions: Shorter sleep duration, measured behaviorally using actigraphy prior to viral exposure, was associated with increased susceptibility
to the common cold.
Keywords: common cold, immunity, rhinovirus, sleep continuity, sleep duration
Citation: Prather AA, Janicki-Deverts D, Hall MH, Cohen S. Behaviorally assessed sleep and susceptibility to the common cold. SLEEP
2015;38(9):1353–1359.
BEHAVIORALLY ASSESSED SLEEP AND SUSCEPTIBILITY TO THE COMMON COLD
Behaviorally Assessed Sleep and Susceptibility to the Common Cold
Aric A. Prather, PhD1; Denise Janicki-Deverts, PhD2; Martica H. Hall, PhD3; Sheldon Cohen, PhD2
1Department of Psychiatr y, University of California, San Francisco, CA; 2Department of Psychology, Carnegie Mellon University, Pittsburgh, PA;
3Depart ment of Psychiatr y, Universit y of Pittsburgh Medical Center, Pittsburgh, PA
pii: sp-00619-14 http://dx.doi.org/10.5665/sleep.4968
a reliance on self-reported sleep, which is subject to recall
bias leading to inaccurate sleep estimates. Indeed, individuals
often overestimate duration and underestimate minutes awake
across the night.25 Whether objectively measured sleep indices
represent signicant predictors of acute infectious illness fol-
lowing viral exposure remains unknown.
To address this gap in the literature, the current study mea-
sured sleep behavior objectively using wrist actigraphy and
subjectively using sleep diaries over 7 consecutive days and
investigated whether measures of sleep duration and continuity
predicted susceptibility to the common cold in participants
subsequently exposed to a virus (rhinovirus) that causes the
common cold. Following exposure to the cold virus, partici-
pants were quarantined and monitored for cold symptoms and
development of clinical illness. We hypothesized that shorter
sleep duration and poorer sleep continuity would be associated
with increased incidence of a biologically veried cold and that
these associations would be independent of sociodemographic,
psychological, and behavioral factors previously shown to pre-
dict cold incidence using this paradigm.6, 7,26 – 28
METHODS
Participants
Data were collected between 2007 and 2011. Study par-
ticipants for these analyses included 94 men and 70 women,
aged between 18 and 55 y (mean age = 29.9, standard devia-
tion [SD] = 10.9) from the Pittsburgh, Pennsylvania metro-
politan area who responded to study advertisements and
were judged to be in good health. Volunteers were excluded
if they had a history of nasal surgery or any other chronic ill-
ness (e.g., asthma, coronary heart disease, or obstructive sleep
A commentary on this article appears in this issue on page 1341.
Submitted for publication October, 2014
Submitted in nal revised form December, 2014
Accepted for publication January, 2015
Address correspondence to: Aric A. Prather, PhD, Center for Health and
Community, Department of Psychiatry, University of California, San Fran-
cisco, CA; Email: aric.prather@ucsf.edu or Sheldon Cohen, PhD, Depar t-
ment of Psychology, Carnegie Mellon University, Pittsburgh, PA; Email:
scohen@cmu.edu

SLEEP, Vol. 38, No. 9, 2015 1354 Sleep and the Common Cold—Prather et al.
apnea); abnormal ndings based on urinalysis, complete blood
count, or blood enzyme levels; were pregnant or currently lac-
tating; were positive for the human immunodeciency virus;
or taking medications regularly, including sleep medications
and oral contraceptives. They were also excluded if they had
been hospitalized in the past 5 y or were currently taking medi-
cations for psychiatric conditions. In order to maximize the
rate of infection by the virus, specic levels of serum antibody
to the challenge virus were obtained at screening and partici-
pants were excluded with titers higher than 4. Each participant
was paid $1,000 for their participation at the conclusion of the
study. This study received institutional review board approval,
and written, informed consent was obtained for each study
participant.
Procedures
Volunteers presenting for possible enrollment underwent
medical screening, including a blood draw to assess specic
serum neutralizing antibody titer for rhinovirus 39 (RV39).
Qualifying participants were enrolled and during the approxi-
mately 2 mo that preceded viral challenge they completed
questionnaire batteries, 2 w of daily interviews to assess posi-
tive emotions, and a subsequent 1 w of wrist actigraphy and
concurrent sleep diary to objectively and subjectively measure
sleep behavior. Another sample of blood was collected to as-
sess antibody level just before (3–5 days) viral exposure, which
provided an estimate of prechallenge antibody titers.
Participants were then isolated in a local hotel for a 6-day
period. During the rst 24 h of the quarantine, prior to viral ex-
posure, participants underwent a nasal examination and nasal
lavage; baseline nasal mucociliary and nasal mucus production
were assessed at this time. Those showing signs or symptoms
of a cold on this day were dismissed. Then, participants re-
ceived nasal drops containing approximately 150 tissue cul-
ture infectious dose (TCID50)/mL of RV39. Volunteers were
subsequently quarantined for 5 days. On each day, nasal la-
vage samples were collected to assess infection (virus culture).
Additionally, daily nasal mucociliary clearance function and
nasal mucus production were assessed as objective markers of
illness. Approximately 28 days after viral exposure, blood was
collected for serological testing.
Sleep Measures
Participants wore an Actiwatch-(64) (Philips Respironics
Inc, McMurray, PA) on their nondominant wrists for 7 con-
secutive nights. Data were stored in 1-min epochs and vali-
dated software algorithms (Philips Respironics Inc) were
used to estimate sleep parameters. The two actigraphy vari-
ables included in these analyses were total sleep time and
fragmentation index. Total sleep time, which was used to
estimate sleep duration, was dened as the total amount of
minutes scored as sleep by the software algorithm in a given
dened sleep interval. Fragmentation index is a measure of
restlessness during sleep as measured by sleep epochs as-
sociated with movement (range 0 to 150, with higher values
indicating poorer sleep continuity). As expected, actigraphy-
assessed sleep efciency, dened as the percentage of the
sleep interval scored as sleep, was inversely correlated with
fragmentation index in this sample (r = −0.59, P < 0.001). We
chose fragmentation index instead of sleep efciency given
the documented poor specicity associated with actigraphy-
assessed sleep efciency.29
Self-report sleep diaries were obtained concurrently with
actigraphy collection. Each morning, participants reported in
their sleep diary what time they went to sleep, what time they
woke up, and the min it took to fall asleep. Sleep time was
calculated as the time a participant reported waking up minus
the time the participant went to sleep. Self-reported sleep dura-
tion was computed by sleep time minus the minutes required
to fall asleep. Sleep efciency was calculated as sleep duration
divided by sleep time multiplied by 100. Actigraphy and diary
estimates for sleep (actigraphy: sleep duration and fragmen-
tation; sleep diary: sleep duration and sleep efciency) were
obtained by averaging over the collection period for all partici-
pants with data for at least 5 of the 7 days.
Control Variables
We controlled for a number of covariates previously asso-
ciated with susceptibility to the common cold, including pre-
challenge viral-specic antibody levels to RV; age; sex; race;
body mass index (BMI); the season in which the trial occurred;
years of education; household income, health habits including
current smoking status, physical activity, and alcohol con-
sumption; and psychological variables including perceived
socioeconomic status, perceived stress, extraversion, agree-
ableness, and positive emotional style. These covariates were
assessed either during eligibility screening or in the interval
between screening and the viral challenge.
Participants self-reported their age, sex, and race. They de-
scribed their primary racial and ethnic group by choosing from
six categories (white, Caucasian; black, African American;
Native American, Eskimo, Aleut; Asian or Pacic Islander;
Hispanic, Latino; Other). For the analyses, the racial or ethnic
groups were dummy coded, with all but whites and blacks col-
lapsed into a single “other” category. BMI (weight in kg/height
in m2) was computed based on measurements of participants’
weight and height.
Income was assessed by having participants endorse one of
13 household income categories (before taxes) that best repre-
sented them. These categories ranged from less than $5,000 to
$150,000 or more; income was dened as the median income
for the identied category and treated as a continuous vari-
able. Participants’ education was assessed by asking them to
report on their highest educational attainment. Nine response
items were provided, ranging from “didn’t nish high school”
to “doctoral degree.” Answers were converted into number of
years of education based on their responses (e.g., high school,
12 y; PhD, 20 y). Perceived socioeconomic rank was assessed
by participants placing themselves on a nine-rung of a ladder
in terms of where they stand in their country based on income,
education, and occupation.26
Health habits were obtained through self-report question-
naires. Participants were deemed current smokers if they an-
swered “yes” to being asked whether they currently smoked
cigarettes, cigars, or pipes on a daily basis. Physical activity
was assessed by asking participants whether they engaged in
regular activity at least once per week (1, yes; 0, no). Alcohol
consumption was obtained by asking participants the average

SLEEP, Vol. 38, No. 9, 2015 1355 Sleep and the Common Cold—Prather et al.
number of drinks they consumed per day (one drink = one
glass of wine, 12 oz of beer, or one shot of hard liquor).
Psychological variables that were assessed by questionnaire
included a 10-item perception of stress over the past month30;
extraversion and agreeableness were assessed using the 10-
item versions from the International Personality Item Pool
(IPIP) Big Five Factor Markers.31 Finally, positive emotional
style was measured as part of an evening interview assessment
that was conducted over 14 consecutive days. During each
of the 14 daily interviews participants reported the extent to
which they felt happy, calm, lively, full of pep, and cheerful
throughout the preceding day; ratings for each item were av-
eraged to create a daily total positive affect score across the
interview period.27
Virus Culture and Antibody Response
Virus-specic neutralizing antibody titers were measured
in serum samples obtained before and approximately 28 days
after viral exposure. The results were expressed as reciprocals
of the nal dilution of serum.32 Daily nasal lavage samples
were frozen at −80°C and later cultured for RV using standard
techniques.32
Signs of Illness
Daily mucus production was obtained by collecting used tis-
sues in sealed plastic bags.33 The bags were weighed and the
weights of the tissues and bags were subtracted. Nasal muco-
ciliary clearance function was measured by administering a
dye into the anterior area of the nose and calculating the time
taken for the dye to reach the nasopharynx.33
Clinical Cold Criteria
Study participants were considered to have a clinical cold
if they were both infected and met illness criteria. Infection
was dened as the recovery of the challenge virus on any of
the postchallenge days or a fourfold or greater increase in the
virus-specic serum neutralizing antibody titer measured pre-
exposure to 28 days post-exposure.33 Illness criterion for an
objective cold required a total adjusted mucus weight of ≥ 10 g
or a total adjusted nasal clearance time of ≥ 35 min.7
Statistical Analysis
All analyses were carried out using SPSS version 22 (SPSS
Inc., Chicago, IL). Data were drawn from 212 volunteers who
participated in this study. Of those, actigraphy measures were
collected from 165 participants. One participant was identi-
ed as a clear outlier (> 9 standard deviations above the mean
on sleep duration) and excluded, yielding 164 participants for
these analyses. Self-report sleep measures obtained by sleep
diary were available on 159 participants. Income, BMI, and al-
cohol consumption were log (base-10) transformed to better ap-
proximate a normal distribution. Logistic regression was used
to predict colds (1, yes; 0, no). Sleep measures were treated as
continuous variables with the exception of self-reported sleep
efciency, which was negatively skewed, and was modeled as
a categorical (quartile) predictor. We reported regression coef-
cients with standard errors and probability values.
Age and prechallenge viral-specic antibody titers were
included as covariates in all analyses. Next, we conducted a
series of regressions entering one of the 14 separate covari-
ates, along with age and prechallenge antibody titers. The
approach reduces the risks of “overtting” the regression
models34,35; however, we also computed single models that
included all study covariates simultaneously. In addition, to
better elucidate the independent and interactive contributions
of duration and continuity measures on cold susceptibility,
we t models that included both actigraphy assessed sleep
duration and fragmentation simultaneously as predictors as
well as tested the interaction between them (sleep duration ×
fragmentation).
Finally, to better clarify associations between actigraphy as-
sessed sleep duration and cold incidence and to provide an esti-
mate effect size, sleep duration was categorized based on hours
of sleep (< 5 h, n = 36; 5 to 6 h, n = 54; 6.01 to 7 h, n = 52; > 7 h,
n = 22). We tted a logistic regression using this categorical
sleep variable and reported odds ratios (OR) with 95% con-
dence intervals (CIs).
Tab le 1 —Sample characteristics (n = 164).
Variable Mean (SD) or % (n)
Sociodemographics
Age 29.9 (10.9)
Sex (% female) 42.7 (70)
Body mass index (kg/m2)27.4 (6.5)
Race (%)
White/Caucasian 68.3 (112)
Black/African American 26.2 (43)
Other 5.5 (9)
Prechallenge antibodies (titer) 2.45 (2.8)
Season (%)
Winter 26.2 (43)
Spring 31.7 (52)
Summer 42.1 (69)
Fall 0 (0)
Education (y) 14.1 (1.9)
Income ($) 21,856 (23,202)
Subjective SES 4.2 (1.8)
Health practices
Smoking status (% current smoker) 32.9 (54)
Physical activity (% engage in regular
activity/w)
84.8 (139)
Alcohol consumption (# of drinks/w) 2.5 (4.8)
Psychological measures
Perceived stress scale 12.3 (5.8)
Agreeableness 38.9 (6.1)
Extraversion 32.5 (7.0)
Positive emotional style 14.3 (4.3)
Sleep measures
Sleep diary
Sleep duration (h) 7.5 (1.2)
Sleep efciency (%) 96.1 (3.6)
Actigraphy
Sleep duration (h) 5.8 (1.1)
Fragmentation index (% fragmented) 33.3 (13.4)
SD, standard deviation; SES, socioeconomic status.

SLEEP, Vol. 38, No. 9, 2015 1356 Sleep and the Common Cold—Prather et al.
RE S ULTS
Sample Characteristics and Sleep Scores
Table 1 presents descriptive data for all variables involved
in the analyses. Of the 164 participants, 124 (75.6%) were in-
fected and 48 (29.3%) developed a biologically veried cold,
which was dened as infection and objective cold criterion.
As expected, sleep measures were intercorrelated (actig-
raphy sleep duration and fragmentation, r = −0.37, P < 0.001;
actigraphy sleep duration and self-reported sleep duration,
r = 0.49, P < 0.001; actigraphy sleep duration and self-reported
sleep efciency, r = 0.27, P < 0.001; actigraphy fragmentation
and self-reported sleep efciency, r = −0.12, P = 0.14).
Sleep and Susceptibility to the Common Cold
Adjusting for age and prechallenge antibody titers, shorter
sleep duration, assessed using actigraphy, was associated with
increased risk for the development of the cold (b = −0.44, stan-
dard error [SE] = 0.17, P = 0.011). In contrast, sleep fragmen-
tation and self-reported sleep duration were not signicant
predictors of cold susceptibility (fragmentation: b = −0.01,
SE = 0.01, P = 0.715; self-reported sleep duration: b = −0.15,
SE = 0.16, P = 0.325). Similarly, participants reporting sleep ef-
ciency in the bottom quartile were no more likely to develop
the cold than individuals in the top quartile (b = 0.57, SE = 0.51,
P = 0.258).
To follow up on the signicant association between actig-
raphy-assessed sleep duration and the likelihood of developing
a biologically veried cold, additional models were computed
adjusting for study covariates. Here, we carried out a set of
regressions that entered each covariate one by one in separate
models (14 separate models). As displayed in Table 2, shorter
sleep duration continued to be associated with increased rates
of developing a cold (all Ps < 0.015). Furthermore, shorter
sleep duration predicted increased odds of developing a cold
when all covariates were included in a single model (b = −0.49,
SE = 0.2 0, P = 0.012). Sle ep fr agment a t ion wa s not signica n tly
related to cold incidence when all covariates were included in a
single model (b = −0.01, SE = 0.02, P = 0.755). This was simi-
larly the case for self-reported sleep duration and efciency
(data not shown).
To better characterize the effect of sleep duration on odds of
developing a cold, sleep categories were created. As illustrated
Tab le 2 —Logistic regression models with actigraphy-based sleep
duration predicting incidence of the cold, adjusting for each study
covariate separately.
Modelab (SE) P
Sex
Male −0.44 (0.37) 0.232
Female Reference
Sleep duration −0.44 (0.17) 0.011
Body mass indexb2.79 (2.01) 0.166
Sleep duration −0.43 (0.17) 0.012
Race
White −0.36 (0.75) 0.629
Black 0.24 (0.79) 0.766
Other Reference
Sleep duration −0.43 (0.17) 0.014
Season of trial
Winter 0.13 (0.42) 0.756
Spring −1.12 (0.49) 0.022
Summer Reference
Sleep duration −0.46 (0.18) 0.010
Education 0.00 (0.09) 0.985
Sleep duration −0.44 (0.17) 0.011
Incomeb0.04 (0.41) 0.926
Sleep duration −0.43 (0.17) 0.013
Subjective SES 0.05 (0.10) 0.602
Sleep duration −0.45 (0.17) 0.010
Current smoker
No −0.64 (0.39) 0.100
Yes Reference
Sleep duration −0.44 (0.17) 0.012
Regular physical activity
No −0.20 (0.54) 0.703
Yes Reference
Sleep duration −0.44 (0.17) 0.011
Alcohol consumptionb0.76 (0.41) 0.065
Sleep duration −0.44 (0.18) 0.013
Perceived stress 0.00 (0.03) 0.943
Sleep duration −0.44 (0.17) 0.011
Agreeableness 0.02 (0.03) 0.559
Sleep duration −0.45 (0.17) 0.011
Extraversion 0.05 (0.03) 0.066
Sleep duration −0.47 (0.18) 0.008
Positive emotional style 0.01 (0.05) 0.793
Sleep duration −0.44 (0.17) 0.012
Sleep duration (full model)c−0.49 (0.20) 0.012
aAll models adjusted for age and prechallenge antibodies. bLog-
10 transformed. cModel included all covariates. SE, standard error;
SES, socioeconomic status.
Figure 1—Sleep duration (measured by wrist actigraphy) averaged over
a 7-day period before virus exposure is associated with percentage of
participants who subsequently developed a cold. The percentage of
colds is based on predicted values (adjusted for age and prechallenge
viral-specic antibody levels).
0
5
10
15
20
25
30
35
40
45
50
< 5 5– 6 6.01–7 > 7
Adjusted % with Objective Colds
Actigraphy Sleep Duration (hours)

SLEEP, Vol. 38, No. 9, 2015 1357 Sleep and the Common Cold—Prather et al.
in Figure 1, the predictive inuence of sleep duration on cold
susceptibility indicates a threshold effect at 6 or fewer hours of
sleep (< 5 h, OR = 4.50; 95% CI 1.08–18.69; 5–6 h, OR = 4.24;
95% CI 1.08 –16.71; 6.01–7 h, OR = 1.66; 95% CI 0.40–6.95; > 7
h, 1 [reference]).
The observed elevated risk of developing the cold in partici-
pants experiencing shorter sleep duration may have been due
to increased susceptibility to infection and/or increased illness
expression among those infected. In this regard, in adjusted
analyses, actigraphy assessed sleep duration was unrelated to
rates of infection (b = −0.11, SE = 0.17, P = 0.543). Similarly,
among those who were infected (n = 124), shorter sleep dura-
tion was not signicantly related to increased odds of meeting
illness criteria for mucus production or nasal clearance time
(b = −0.32, SE = 0.19, P = 0.090). Although there were no sig-
nicant relationships of actigraphy-assessed sleep duration
with either infection or expression of illness, the association
with cold incidence appears to be primarily driven by illness
expression.
Because measures of actigraphy assessed sleep duration
and fragmentation capture different aspects of an individual’s
sleep, we tested whether the effects of duration operated inde-
pendent of fragmentation in predicting risk for a biologically
veried cold. To this end, we t a regression model with both
measures entered together. Analyses revealed that sleep dura-
tion continued to predict cold incidence adjusting for age and
prechallenge antibody levels (b = −0.53, SE = 0.19, P = 0.005)
as well as in the fully adjusted (16 covariates and fragmenta-
tion) model (b = −0.56, SE = 0.21, P = 0.006). There was no
evidence that sleep duration and fragmentation interacted to
predict cold incidence (P = 0.92).
DISCUSSION
Shorter sleep duration, measured by wrist actigraphy over
a 7-day period, was prospectively associated with increased
incidence of the common cold following experimental viral
challenge. This association was independent of a cadre of co-
variates, including age, prechallenge antibody levels, sex, body
mass index, race, season of trial, income, education, perceived
socioeconomic status, smoking, physical activity, alcohol con-
sumption, perceived stress, agreeableness, extraversion, and
positive emotional style. This study provides the rst prospec-
tive evidence that behaviorally assessed sleep duration serves
as a predictor of cold susceptibility.
Analyses revealed a linear association between sleep dura-
tion and cold susceptibility; however, when categorized based
on hours of sleep, a threshold effect was observed such that in-
dividuals sleeping fewer than 6 h of sleep per night were at ele-
vated risk whereas those sleeping more than 6 h were not. This
is consistent with some epidemiologic evidence that nd strong
effects on morbidity and mortality in short sleepers compared
to normal sleepers.1,11,36 For instance, Patel and colleagues
found that in a sample of nearly 57,000 women, those who re-
ported sleeping ≤ 6 h per night were at signicantly greater
risk of developing pneumonia compared to those sleeping 8 h
per night.5 Those sleeping 7 h were at no greater risk than 8-h
sleepers. Emerging evidence also suggests that long sleepers
(≥ 9 h per night) are at increased risk of disease.10,11, 37 The un-
derlying mechanisms linking negative health and long sleep
are poorly understood38–40 ; however, depression and medical
comorbidities have been implicated.38 Very few participants in
this study reported sleeping more than 9 h per night (11.3% by
sleep diary, 0.6% by actigraphy), making it difcult to deter-
mine whether long sleep was a risk factor of cold incidence.
The small sample of long sleepers in this study may be due to
the fact that the study sample was carefully screened to meet
good health standards, including being free from psychiatric
illness.
Self-reported diary measures of duration and sleep ef-
ciency were unrelated to cold incidence. This is in contrast
to our prior work that found that poorer sleep efciency and
shorter sleep duration, measured via a 14-day daily interview,
predicted cold susceptibility.6 There are several possible ex-
planations for differences across studies. First, fewer partici-
pants became infected in this sample, which may have limited
our power to detect effects using self-report measures. Second,
this study relied on a shorter 7-day sleep diary rather than a
14-day daily interview, which may have produced less stable
averages as well as less accurate estimates of sleep. In regard
to sleep estimates, employment of a daily interview in the prior
study helped ensure timely assessments of self-reported sleep,
which potentially decreased recall bias. Third, given that ac-
tigraphy has been well correlated with polysomnography,41
the gold standard of measurement in sleep research, it is also
possible that had our prior study included actigraphy assess-
ment concurrently with the daily interview sleep data, those
ndings would have been even more robust. Future studies
incorporating both actigraphy and sleep diaries are needed to
understand when and why certain sleep measures signicantly
predict immune function.
What are the mechanisms that might link sleep and suscep-
tibility to acute infectious illness? Sleep, along with circadian
rhythms, exerts substantial regulatory effects on the immune
system.42,43 Circulating immune cells, including T and B cells,
peak early in the night and then decline throughout the noc-
turnal hours moving out of circulation into lymphoid organs
where exposure to virally infected cells occur.43– 45 Studies
employing experimental sleep loss also support functional
changes relevant to host resistance. Sleep deprivation results
in down regulation in T cell production of interleukin-219,44 and
a shift away from T-helper 1 responses, marked by a reduc-
tion in the ratio of interferon-γ/IL-4 production.16 Sleep loss
is associated with diminished proliferative capacity of T cells
in vitro15 as well as modulation of the function of antigen pre-
senting cells critical to virus uptake.46
Illness expression in colds is generally attributed to blunted
downregulation of local inammatory responses.47,4 8 Emerging
evidence demonstrates bidirectional links between sleep and
inammation.14,42,49 Proinammatory activity has a role in the
homeostatic regulation of sleep.50, 51 Likewise, some but not
all studies that employ partial and total sleep restriction nd
substantial increases in systemic levels of proinammatory
cytokines52 as well as enhanced inammatory gene expres-
sion and transcriptional pathways that support inammatory
processes.20 ,21 In addition, recent evidence suggests that el-
evated systemic inammation mediates prospective associa-
tions between short sleep duration and premature mortality.53
Future studies characterizing the immunologic mediators of

SLEEP, Vol. 38, No. 9, 2015 1358 Sleep and the Common Cold—Prather et al.
cold incidence in the context of sleep duration and our viral
challenge paradigm are needed to clarify when in the infection
process sleep has the most potent effects.
Like prior work, we nd that infectious risk is strongest in
the shortest of sleepers, suggesting that “normal” sleepers (e.g.,
7 to 9 h per night for adults) would be protected in this context.
Whether sleep interventions aimed at increasing sleep dura-
tion would protect individuals from cold incidence remains
an open question. In this regard, recent ndings that cognitive
behavioral therapy for older adults with insomnia resulted in
decreased levels of systemic inammation54 raises the possi-
bility that a similar enhancement in cell-mediated immunity
could also be observed. Given that infectious illness (i.e., inu-
enza and pneumonia) remains one of the top 10 leading causes
of death in the United States,55 the current data suggest that a
greater focus on sleep duration, as well as sleep health more
br oadly, 56 is indicated.
In summary, these novel ndings provide the rst evidence
that sleep duration assessed behaviorally through actigraphy
predicts incidence of infectious illness using an experimental
viral challenge. Although this study does not provide direct
evidence of causality, the prospective nature of the viral chal-
lenge design does eliminate concerns of reverse causation. It
is recognized that actigraphy is a behavioral measure of rest/
activity patterns and is not an objective measure of sleep per
se. Although actigraphy has been shown to correlate well with
polysomnography in healthy samples,41 actigraphy-assessed
indices of sleep duration cannot identify specic dimensions of
sleep (e.g., decreased slow wave sleep) that may be contributing
to infectious risk. In addition, future studies investigating the
immunologic mechanisms underlying these effects as well as
generalizability of these ndings to other samples (i.e., older
adults; sleep disordered patients) are warranted.
DISCLOSURE STATEMENT
This was not an industry supported study. Preparation of
this paper was supported by the National Center for Comple-
mentary and Alternative Medicine (AT006694), and data col-
lection by the National Institute of Allergy and Infectious
Diseases (AI066367). Clinical and regulatory assistance for the
study was provided by National Institute of Health grants (UL1
RR024153 and UL1 TR000005) to the University of Pittsburgh
Clinical and Translational Science Institute. Dr. Prather’s par-
ticipation was supported by a grant from the National Heart,
Lung, & Blood Institute (K08HL112961). The authors have in-
dicated no nancial conicts of interest.
REFERENCES
1. Cappuccio FP, D’Elia L, Strazzullo P, Miller MA. Quantity and quality
of sleep and incidence of ty pe 2 diabetes: a systematic review and meta-
analysis. Diabetes Care 2010;33:414–20.
2. Mallon L, Broman JE, Het ta J. Sleep complaints predict coronary artery
disease mortality in males: a 12-year follow-up st udy of a middle-aged
Swedish population. J Intern Med 2002;251:207–16.
3. Mallon L, Broma n JE, Hetta J. High incidence of diabetes in men with
sleep complai nts or shor t sleep duration: a 12-year follow-up study of a
middle-aged population. Diabetes Ca re 2005;28:2762–7.
4. King CR, Knutson KL, Rathouz PJ, Sidney S, Liu K, Lauderdale DS.
Short sleep duration and incident coronary ar tery calcication. JAM A
2008;300:2859 –66.
5. Patel SR, Malhot ra A, Gao X, Hu FB, Neuman MI, Fawzi WW. A
prospective study of sleep duration and pneumonia risk in women.
Sle ep 2012;35:97 –101.
6. Cohen S, Doyle WJ, Alper CM, Janicki-Deverts D, Turner RB. Sleep
habits and susceptibility to the com mon cold. Arch Intern Med
20 0 9;169:62 –7.
7. Cohen S, Doyle WJ, Skoner DP, Rabin BS, Gwaltney JM, Jr. Social ties
and susceptibility to the common cold. JAMA 1997;277:1940–4.
8. Dew MA, Hoch CC, Buysse DJ, et al. Healthy older adults’ sleep
predicts all-cause mortality at 4 to 19 years of follow-up. Psychosom
Med 2003;65:63–73.
9. Kripke DF, Garnkel L, Wingard DL, K lauber MR, Marler MR.
Mortality associated w ith sleep duration and insomnia. Arch Gen
Psychiatry 2002;59:131–6.
10. Heslop P, Smith GD, Metcalfe C, Macleod J, Har t C. Sleep duration and
mortality: the effect of shor t or long sleep duration on cardiovascular
and all-cause mor tality in working men and women. Sleep Med
2002;3:305–14.
11. Cappuccio FP, D’Elia L, Strazz ullo P, Miller MA. Sleep duration and all-
cause mor talit y: a systematic review and meta-analysis of prospective
studies. Sleep 2010;33:585–92.
12. Irwi n MR. Why slee p Is import ant for health: a ps ychoneuroim munology
perspective. An nu Rev Psychology 2015;66:143–72.
13. Everson CA. Sustained sleep deprivat ion impairs host defense. Am J
Physiol 1993;265:R1148–54.
14. Bryant PA, Trinder J, Curtis N. Sick and tired: does sleep have a vital
role in the immune system? Nature reviews. Immunology 2004;4:457–
67.
15. Bollinger T, Bollinger A, Skrum L, Dimit rov S, Lange T, Solbach W.
Sleep-dependent activity of T cells and regulatory T cells. Clin Exp
Immunol 2009;155:231–8.
16. Dimitrov S, Lange T, Tieken S, Fehm HL, Born J. Sleep associated
regulation of T helper 1/T helper 2 cytokine balance in humans. Brain
Behav Immunity 2004;18:341–8.
17. Sakami S, Ishi kawa T, Kawakami N, et al. Coemergence of
insomnia and a shift in the Th1/Th2 balance toward T h2 dominance.
Neuroi mmu nomodulation 2002;10:337– 43.
18. Irwin M, Mascovich A, Gilli n JC, Willoughby R, Pike J, Smith TL.
Partial sleep deprivation reduces natural killer cell activity i n humans.
Psychosom Med 1994;56:493 –8.
19. Irw in M, McClintick J, Cost low C, Fort ner M, White J, Gi llin JC. Partia l
night sleep deprivat ion reduces natu ral killer and cellular i mmune
responses in humans. FASEB J 1996;10:643–53.
20. Irwin M, Wang M, Campomayor CO, Collado-Hidalgo A, Cole S. Sleep
deprivation and activation of mor ning levels of cellular and genomic
markers of inam mation. Arch Intern Med 2006;166:1756–62.
21. Irwin MR, Wang M, R ibeiro D, et al. Sleep loss activates cellular
inammatory signaling. Biol Psychiatry 2008;64:538–40.
22. Meier-Ewert HK, Ridker PM, Rifai N, et al. Effect of sleep loss on
C-react ive protein, an inammatory marker of cardiovascular risk. J
Am Coll Cardiol 2004;43:678–83.
23. Moldofsky H, Lue FA, Davidson JR, Gorczynski R. Effects of sleep
deprivation on human immune fu nctions. FASEB J 1989;3:1972–7.
24. Prather AA, Hall M, Fury J M, et al. Sleep and antibody response to
hepatitis B vaccination. Sleep 2012;35:1063–9.
25. Lauderdale DS, Knutson KL, Yan LL, Liu K, Rathouz PJ. Self-reported
and measured sleep duration: how similar are they? Epidemiology
200 8;19:838–45.
26. Cohen S, Alper CM, Doyle WJ, Adler N, Treanor JJ, Turner R B.
Objective and subjective socioeconom ic status and susceptibility to the
common cold. Health Psychol 2008;27:268–74.
27. Cohen S, Alper CM, Doyle WJ, Treanor JJ, Turner R B. Positive
emotiona l style predict s resistance t o illness af ter exper imental expo sure
to rhinovirus or inuenza a virus. Psychosom Med 2006;68:809–15.
28. Cohen S, Janicki-Deverts D, Miller GE. Psychological stress and
disease. JAMA 2007;298:1685–7.
29. Marino M, Li Y, Rueschma n MN, et al. Measuring sleep: accuracy,
sensitivity, and speci city of wr ist actigraphy compared to
polysomnography. Sleep 2013;36:1747–55.
30. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived
stress. J Health Soc Behav 1983;24:385–96.

SLEEP, Vol. 38, No. 9, 2015 1359 Sleep and the Common Cold—Prather et al.
31. Goldberg LR. The intenational personality item pool and the fut ure
of public-domain per sonalit y measures. J Research Personalit y
2006;40:84–96.
32. Gwaltney J M, Jr., Colonno RI, Hampa rian V V, Turner RB. R hinovirus.
In: Schmidt NI, Er nmons RW, eds. Diagnostic procedures for viral,
rickettsial, and chlaymdial infections. 6th ed. Washing ton, DC:
American Public Healh Association, 1989:579–614.
33. Doyle WJ, McBride TP, Skoner DP, Maddern BR, Gwaltney JM Jr,
Uhr in M. A double-blind, placebo-controlled clinical tr ial of the effect
of chlorpheniramine on the response of the nasal air way, middle ear and
eustachian tube to provocative rhinovirus challenge. Pediatr Infect Dis
J 1988;7: 229–38.
34. Bagley SC, White H, Golomb BA. Logistic regression in the medical
literat ure: standards for use and repor ting, with par ticular attention to
one medical domain. J Clin Epidem iol 2001;54:979–85.
35. Babyak MA. W hat you see may not be what you ge t: a brief, nontechn ical
introduction to overtt ing in regression-ty pe models. Psychosom Med
2004;66:411–21.
36. Gangwisch JE, Heymseld SB, Boden-Albala B, et al. Short sleep
duration as a risk factor for hypertension: analyses of the rst National
Health and Nut rition Exami nation Survey. Hyper tension 2006;47:833–9.
37. Ayas NT, White DP, Al-Delaimy WK, et al. A prospective st udy of self-
reported sleep duration and incident diabetes in women. Diabetes Care
2003;26:380– 4.
38. Grandner MA, Drum mond SP. Who are the long sleepers? Towards
an understanding of the mortality relat ionship. Sleep Med Rev
2007;11:341–60.
39. Grand ner MA, Kripke DF. Self-reported sleep complaints with long
and short sleep: a nationally representative sample. Psychosom Med
200 4;66:239– 41.
40. Patel SR, Malhotra A, Gottlieb DJ, White DP, Hu FB. Correlates of long
sleep duration. Sleep 2006;29:881–9.
41. Ancoli-Israel S, Cole R, Alessi C, Chambers M, Moorcroft W, Pollak
CP. The role of actigraphy i n the study of sleep and circadian rhythms.
Sleep 20 03;26:3 42–92.
42. Opp MR, Born J, Irwin MR. Sleep and the immune system. In: Ader R,
ed. Psychoneuroimmunology. 4th ed. New York, NY: Academic Press,
20 07:57 0 – 618.
43. Lange T, Dimitrov S, Born J. Effects of sleep and circadian rhyt hm on
the human immu ne system. Ann N Y Acad Sci 2010;1193:48–59.
44. Born J, Lange T, Hansen K, Molle M, Fehm HL. Effects of sleep and
circadian rhythm on human circulating immune cells. J Immunol
1997;158:4454–64.
45. Besedovsky L, Lange T, Born J. Sleep and i mmune function. Pugers
Arch 2012;463:121–37.
46. Dimitrov S, Lange T, Nohroudi K , Born J. Number and f unction of
circulating human antigen presenting cells regulated by sleep. Sleep
2007;30:401–11.
47. Proud D, Gwaltney J M Jr, Hendley JO, Dinarello CA, Gillis S,
Schleimer RP. Increased levels of interleukin-1 are detected in nasal
secretions of volunteers dur ing experimental rhinovir us colds. J Infect
Dis 1994;169:1007–13.
48. Zhu Z, Tang W, Ray A, et al. Rhinovirus stimulation of interleuk in-6
in vivo and in vitro. Evidence for nuclear factor kappa B-dependent
transcript ional activation. J Clin Invest 1996;97:421–30.
49. Opp MR. Cytokines a nd sleep. Sleep Med Rev 2005;9:355–64.
50. Krueger J M, Obal FJ, Fang J, Kubota T, Taishi P. The role of cytokines
in physiological sleep regulation. Ann N Y Acad Sci 2001;933:211–21.
51. Irwin MR, Cole SW. Reciprocal regulation of the neural and innate
immune systems. Nature reviews. I mmunology 2011;11:625–32.
52. Solarz DE, Mullington JM, Meier-Ewert HK. Sleep, i nammation and
cardiovascular disease. Front Bioscience 2012;4:2490–501.
53. Hall MH, Smagula SF, Boudreau RM, et al. Association between sleep
duration and mortality is mediated by markers of ina mmation and
health in older adults: the health, aging and body composition st udy.
Sleep 2015;38:189 –95.
54. Irwin M R, Olmstead R, Carrillo C, et al. Cognitive behavioral therapy
vs. tai chi for late life insomnia and inammatory risk: a randomized
controlled comparative efcacy trial. Sleep 2014;37:1543–52.
55. Murphy SL, Xu J, Kochanek KD. Deaths: nal data for 2010. Natl Vital
Stat Rep 2013;61:1–117.
56. Buysse DJ. Sleep health: can we dene it? Does it matter? Sleep
2014;37:9–17.