SLEEP 2011 Abstract Supplement

B. Clinical Sleep Science XIV. Instrumentation and Methodology
0970
FUNCTIONAL DATA ANALYSIS OF ACTIGRAPHY REVEALS
THAT AHI AND BMI IMPACT CIRCADIAN ACTIVITY
Wang J 1 , Licis A 2 , Deych E 1 , Ding J 3 , McLeland JS 2 , Toedebusch C 2 ,
Li T 1 , Duntley S 2 , Shannon B 1
1
Medicine, Washington University School of Medicine, St Louis, MO,
USA, 2 Neurology, Washington University School of Medicine, St.
Louis, MO, USA, 3 Mathematics, Washington University, St. Louis,
MO, USA
Introduction: Actigraphy is used extensively in sleep medicine to record
movements and assess sleep/wake patterns. Analysis of this data
often reduces this time series data to a single summary statistic (eg, total
sleep time) and comparing those values across groups. We introduce
more powerful functional data analysis (FDA) methods for analyzing
circadian activity patterns of patients categorized according to apneahypopnea
index (AHI) and body mass index (BMI), age and gender.
Methods: 138 patients having PSG volunteered to undergo 7 days of actigraphy
recording. Actigraphy data was collected at 15 second intervals
and converted to functional data activity profiles over time. Functional
Linear Models, a subset of FDA, was used to correlate circadian activity
patterns with AHI, BMI, age and gender.
Results: Circadian activity pattern differences are seen across high and
low AHI and BMI patient subgroups, and showed statistically significant
different throughout different times of the day. When analyzed jointly in
combination with age and gender, high AHI results in low activity from
10 am - 6 pm, independent of BMI, and high BMI results in low activity
between midnight and 10 am, independent of AHI.
Conclusion: Functional data analysis is a new statistical tool for analyzing
and extracting more information from actigraphy data than is currently
extracted with data reduction techniques. Unexpected patterns
of actigraphy across clinically important subgroups were discovered.
Further research and application of FDA statistical approaches for actigraphy
analysis could have important consequences for understanding
how sleep disturbances impact activity, fatigue, and treatment response.
Support (If Any): This work was supported through R01HL092347
“New Data Analysis Methods For Actigraphy In Sleep Medicine”
(Shannon, PI), and the WUSM Dept. of Medicine Biostatistical Consulting
Center (Shannon, Director) and Dept. of Neurology Sleep Center
(Duntley, Director).
0971
FUNCTIONAL DATA ANALYSIS OF ACTIGRAPHY SHOWS
PERIODIC LIMB MOVEMENTS IMPACT DAYTIME
ACTIVITY LEVELS
Ju YS 1,2 , Wang J 3 , McLeland JS 1 , Toedebusch C 1 , Duntley S 1,2 ,
Shannon B 3
1
Neurology, Washington University School of Medicine, St. Louis,
MO, USA, 2 Multidisciplinary Sleep Medicine Center, Washington
University School of Medicine, St. Louis, MO, USA, 3 Medicine,
Washington University School of Medicine, St Louis, MO, USA
Introduction: Periodic limb movements of sleep (PLMS) can disrupt
sleep leading to daytime fatigue. It is unknown whether PLMS, or associated
arousals, objectively affect daytime function. We used functional
data analysis (FDA), a recently-developed statistical method for actigraphy,
to assess the effect of PLMS on 24-hour activity.
Methods: Subjects from the Washington University Sleep Center underwent
wrist actigraphy for 7 days. PLMS index (PLMI) and PLMS
arousal index (PLMAI) were scored during an overnight polysomnogram.
Subjects were grouped into those with PLMI of 0, and the rest
into quintiles; PLMAI groups were assigned identically. FDA was used
to derive functions describing the activity level of each group. Age, gender,
apnea-hypopnea index, and body mass index were added into FDA.
Results: Of 155 subjects (female 52.6%, median age 52.5), 63.2% had
PLMI 0, and the rest had PLMI quintiles 0.3-3.1, 3.1-6.8, 6.8-16.1, 16.1-
38.5, and 38.5-120.4. With each quintile increase in PLMI, morning activity
increased, and afternoon-evening activity decreased (statistically
significant ~6:30-7pm). PLMAI was 0 in 68.4%, and similar analysis
showed each PLMAI quintile increase was associated with increased
activity in the morning and decreased activity at all other times (statistically
significant 6am-12pm, 1pm-3am). Younger (