SLEEP 2011 Abstract Supplement

B. Clinical Sleep Science XV. Healthcare Services, Research and Education
CES-D-10 was + 0.36 (p < 0.001). Both scales were dichotomized into
high and low groups with low < 10 and high ≥ 10. This yielded four
groups: high depression, high sleepiness (N = 180); high depression,
low sleepiness (N = 60); low depression, high sleepiness (N = 204);
low depression, low sleepiness (N = 167). Analyses of variance show a
significant linear trend across these groups, with the highest indices of
behavioral changes, conflict with others, working while ill or impaired,
reports of problems during residency, and reports of five types of medical
errors occurring in the high depression, high sleepiness group.
Conclusion: Sleepiness and Depression together predict residency experience
better than either sleep hours or sleepiness by themselves.
1000
VARIATIONS IN SLEEP HOURS AND EPWORTH
SLEEPINESS SCORES IN 36 RESIDENCY PROGRAMS
Baldwin DC 1 , Daugherty SR 2 , Yaghmour NA 3
1
Education, ACGME, Chicago, IL, USA, 2 Psychology, Rush Medical
College, Chicago, IL, USA, 3 Harris School of Public Policy Studies,
University of Chicago, Chicago, IL, USA
Introduction: Neurobehavioral sleep research has identified genetic
polymorphisms determining individual variations in vulnerability to
sleep loss. Yet, most discussions about sleep deprivation during residency
training are based on the “average resident.” Averages obscure the
empirical variation in individual sleep experiences found under real-life
conditions.
Methods: In late 2009, we surveyed residents about their first and second
years of residency training in internal medicine, surgery, pediatrics,
and ob/gyn, in 36 residency programs at 15 hospitals to record the variability
of individual sleep and work experiences within programs. The
overall response rate was 83.37% (N = 634).
Results: Wide variations in sleep were found within as well as between
programs. Within-program sleep hours had standard deviations ranging
from 3.26 hours to 9.68 hours. Using ± 2 standard deviations, 95% of the
residents in the program with the lowest variation reported sleep times
within a 13.04 hour range, while in the program with the highest variation,
the range was 38.72 hours. Within-program standard deviations for
Epworth Sleepiness scores ranged from a low of 1.89 to a high of 6.94.
Using ± 2 standard deviations, 95% of the residents in the program with
the lowest variation had scores in a range of 7.56, while in the program
with the highest variation, 95% reported scores in a range of 27.76. Pearson
correlation between reported sleep hours and Epworth scores was
-0.33 (p < 0.001). Specialty accounted for 6% and 7% of the variation
in reported sleep hours and Epworth scores respectively, while program
accounted for 13% and 14% respectively. Within-program standard deviations
in work hours are not significantly related to within-program
standard deviations for sleep hours or Epworth scores.
Conclusion: Reports from residents in real-work settings show substantial
variations in both sleep time and Epworth scores within the same
residency programs.
1001
SLEEP DEPRIVATION IN MEDICAL RESIDENTS: EFFECTS
ON SUBJECTIVE AND OBJECTIVE MEASURES OF
ATTENTION AND ALERTNESS
Zarrouf FA 1 , Shaer M 4 , Bellapravalu S 2 , Sirbu C 3 , DiPino R 3 , Griffith J 3
1
Sleep Medicine, AnMed Health, Anderson, SC, USA, 2 Psychiatry,
University of Missouri, Columbia, MO, USA, 3 Med/Psych, West
Virginia University, Charleston, WV, USA, 4 Medicine, University of
Damascus, Damascus, Syrian Arab Republic
Introduction: The impact of sleep deprivation (SD) on human cognitive
functioning has received considerable research attention. The impact of
SD on residents’ neurocognitive functions such as vigilance, attention,
and memory is extremely important for their and their patients’ well being.
We evaluate the effect of bedtime and total sleep time on several
tests of attention, including the ZZ-Test (designed by our team and validated
in previous study), Digit Span, Trail Making Tests, Stroop Color-
Word Test and compare them with subjective measures of sleepiness.
Methods: A prospective study evaluating healthy consenting medical
residents from two institutions. We administered above tests in randomized
orders to residents and evaluated their reported sleep schedules for
the night and the week before the evaluation. Reported sleep schedules,
subjective sleepiness and tests’ results were compared using T-Test and
paired correlations. Statistical analyses were performed using the SPSS
statistical software
Results: We included 28 (14 females and 14 males) subjects from two
different institutions. Mean age was 34.32/3.73, mean sleep time/24h
was 5.96/ 0.96 for the previous night and 6.89/1.17 for the previous
week. Later bedtime (after midnight) correlated significantly with lower
scores on ZZT (6.17/1.08 v.s 8.27/1.88, P= 0.05), TMT and SCW, but
not with ESS scores (7.33/4.13 v.s 6.45/2.824, p= 0.64). Mean sleep
time/24h in the previous week before evaluation correlated significantly
with ZZT (r=0.541, p= 0.003), TMT (r= -0.471, p= 0.011) and SCW but
again not with ESS (r=0.061, p= 0.757).
Conclusion: In medical residents, shorter sleep time and later bedtime
correlated with lower attention and vigilance measured by multiple objective
cognitive tests, but not with subjective sleepiness scales. This
may reflect underestimation of subjective sleepiness in professionals.
Limitations include no objective measures of sleep schedule (Actigraphy)
and lower N at this time.
1002
SIMULATIONS OF ROTATION SCHEDULES FOR TEAMS
OF RESIDENT-PHYSICIANS CAN IDENTIFY POTENTIAL
AREAS OF LOW PERFORMANCE AND GUIDE RESIDENCY
SCHEDULE DESIGN
Dean DA 1 , Beckett SA 1 , Klerman EB 1 , Landrigan CP 1,2
1
Medicine, BWH/Harvard Medical School, Boston, MA, USA,
2
Medicine, Children’s Hospital/Harvard Medical School, Boston, MA,
USA
Introduction: The Accreditation Council for Graduate Medical Education
(ACGME) will require residency training programs to limit PGY1
resident-physicians (interns) to 16 hour shifts beginning July 2011. We
used a mathematical model of circadian rhythms and performance [Jewett
et al. J Biol. Rhythms 1999] to evaluate predicted intern performance
on alternate schedules designed in response to these guidelines.
Methods: Two daytime interns (A,B), with six “cross-cover” night float
interns (X1-6) were assigned to cover a month-long rotation. The residency
leadership wished for Interns A and B to work 5 days “on” (7:00
to 17:30) followed by two days “off”. Two potential schedules were
simulated for cross-cover night-float interns: 5 fourteen-hour shifts between
17:00 and 07:00 (contiguous) plus 2 days off vs. two blocks of 3
on-1 off-2 on-1 off (2-block) at the same hours. Based on prior research,
sleep was estimated to be 6.6 hours per night for daytime interns and 5.5
hours for night-float interns. Their proposed sleep-wake schedules were
simulated with the mathematical model and the quartiles of predicted
performance for each work shift were compared.
Results: Over the entire month, the predicted performance (25%, 50%,
and 75% quartiles) during the work-shift for daytime interns was 90.9%,
91.8%, and 94.1% of maximum performance. When the two possible
night-float schedules were simulated, the quartiles of predicted performance
for the contiguous night-float interns were 63.2%, 85.2%, and
94.2% and for the two-block night-float interns were 54.4%, 82.7%,
and 93.8%, respectively. For model simulations, a value of ~50% corresponds
to > 24 hours of wakefulness, which has been associated with
increased medical errors and occupational injuries [Barger, New England
J. Med 2005].
Conclusion: The revised schedule resulted in good predicted performance
for daytime interns but identified periods of low performance
for the night-float interns. Contiguous scheduling of night-float is better
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