Conclusions

We found that a majority (64%) of elderly active people

complaining of poor sleep quality has undiagnosed

obstructive sleep apnea that cannot be predicted by clinical

findings or questionnaires.

This high incidence occurs in a woman predominant

population with a multitude of medical problems

(hypertension, heart disease, diabetes) and use of various

sleep promoting medications.

Efficient treatment of the OSA in these patients should have

positive impact on their general health and function and

improve sleep quality.

Since clinical evaluation is unreliable at this age, elderly

should have simple, cost effective home diagnosis of their

disorder, allowing for treatment, when needed.

Prevalence of Sleep Related Breathing Disorderin Elderly Complaining of Poor Sleep

Clement Cahan1, Yair D. Fuxman2, Shuli Eyal2, Jonathan Halpern3, Armanda Baharav1,2

(1)Shaare Zedek Medical Center, Sleep Disorders Clinic, Jerusalem, Israel;(2)HypnoCore, Yehud, Israel; (3) RMIT, Melbourne, Australia.

Methods

Active subjects over 60 years old complaining of poor night time

sleep were recruited for a yoga treatment study for insomnia.

Prior to enrollment, they were interviewed and examined by an

experienced sleep physician. Subjects with proved or suspected

OSA were excluded.

77 subjects enrolled, age 74.2±7.0 years, 80.6% females,

BMI 25.9±3.8.

67 had at least one home sleep study, and 51 had two studies,

performed with an Embletta x30 device.

ECG, oxygen saturation and pulse wave were continuously

recorded throughout the night.

The HC1000P diagnostic software :

• Detects respiratory events by ECG morphology (ECG

Derived Respiration-EDR) and pulse oximetry.3,4

• In addition the system identifies sleep architecture,

arousals and awakenings. This is based on the connection

between sleep and differential autonomic nervous system

modulation of instantaneous heart rate during different

sleep stages.5,6

Results

Sleep Architecture:

Total sleep time 357 min±71; sleep efficiency 84.8%±4.2;

REM% 17.5±6.0; NREM% 69.3±14.2, arousals index 18.3±20.3.

Similar results were obtained during the second study (51

subjects).

Respiration During Sleep:

12% had no OSA, AHI<5

48% had 5≤AHI<15

28% had 15 ≤ AHI<30

12% had AHI≥30

40% of all subjects (N=77) were found to have latent moderate to

severe obstructive sleep apnea, 64% of subjects had AHI greater

than 10 events per hour of sleep.

No statistical correlation was found between BMI, age and AHI.

Introduction

Deterioration in sleep quality, efficiency and increased sleep

related breathing disorders occur with aging.1

Respiratory events cause arousals that have additional

adverse effects on sleep quality and daytime function.2

Objective: The evaluation of obstructive sleep apnea (OSA) in

an elderly population complaining of poor sleep/insomnia.

Fig 1: Distribution of AHI among group of elderly suffering from insomnia.

Fig 2: Distribution of AHI by age, divided into 3 BMI groups. Line indicatesAHI=15.

AHI Distribution

Healthy

12%

Mild

48%

Moderate

28%

Severe

12%

0.0

10.0

20.0

30.0

40.0

50.0

60.0

55 60 65 70 75 80 85 90

Age

AH

I

BMI<25

BMI 25-30

BMI≥30

References

1. Bixler, E.O., A.N. Vgontzas, T. Ten Have, et al., Effects of age on sleep apnea in men: I. Prevalence and severity. Am J Respir Crit Care Med, 1998. 157(1): p. 144-8.

2. Ancoli-Israel, S. and T. Coy, Are breathing disturbances in elderly equivalent to sleep apnea syndrome? Sleep, 1994. 17(1): p. 77-83.

3. Moody, G., R. Mark, A. Zoccola, et al., Derivation of respiratory signals from multi-lead ECGs. COMPUTERS IN CARDIOLOGY, 1985. 12: p. 113-116.

4. Cooper, B.G., D. Veale, C.J. Griffiths, et al., Value of nocturnal oxygen saturation as a screening test for sleep apnoea. Thorax, 1991. 46(8): p. 586-8.

5. Akselrod, S., D. Gordon, F.A. Ubel, et al., Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science, 1981. 213(4504): p. 220-2.

6. Baharav, A., S. Kotagal, V. Gibbons, et al., Fluctuations in autonomic nervous activity during sleep displayed by power spectrum analysis of heart rate variability. Neurology, 1995. 45(6): p. 1183-7.

Automatic scoring based on HC1000P software yielded sleep

architecture information, arousals, sleep efficiency and apnea

and hypopnea events per hour of sleep (AHI).

Diagnosis was based on clinical information obtained at

enrollment and test results.