Clinical trial NCT02412527: Verification of Computerized Sleep-wake Analysis in Sleep Disorders
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Sleep-wake Analysis
Lead sponsor: Chang Gung Memorial Hospital
Sponsor class: OTHER
Collaborators: name: National Yang Ming Chiao Tung University
First posted: 2015-04-09
Enrollment: 330
Sex: ALL
Minimum age (years): 20
Maximum age (years): 50
Standard ages: ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients ranged from age 20-50 with primary insomnia, periodic limb movement in sleep, simple snores, and obstructive sleep apnea will be enrolled.

Brief summary: Objective: Computerized sleep staging has been verified in a normal population. This study attempts to investigate and verify the interpretation of this computerized sleep staging program in patients with sleep disorders, and improve the accuracy of actigraphy algorithm for sleep duration and postures.

1. Compare the accuracy of computerized sleep-wake analysis with current sleep quality indicators: through actigraphy and heart rate sensors, compare the computerized sleep-wake analysis program with polysomnography, and verify its clinical reliability and validity in sleep disorder patients.
2. Establish the sleep posture detection system: through three-axis accelerometer, develop algorithms for position recording and monitoring during sleep, and upgrade its accuracy according to photography.

Methods: Patients ranged from age 20-50 with primary insomnia, periodic limb movement in sleep, simple snores, and obstructive sleep apnea will be enrolled in our study. Routine polysomnography will be applied in sleep center at Chang Gung Memorial Hospital, Taoyuan, as well as sleep-related questionnaires. At the same time, actigraphy and heart rate sensor patch will be used for sleep posture detection and further computerized sleep-wake analysis. Results will be verified the consistency by comparison with sleep-wake analysis from polysomnographic data and sleep-related questionnaires.


Eligibility criteria: Inclusion Criteria:

* age 20-50
* diagnosed as primary insomnia, periodic limb movement in sleep, simple snores, and obstructive sleep apnea

Exclusion Criteria:

* arrhythmia
* sleep efficiency\<50%
* combined other sleep disorders

Primary outcomes:
measure: Electrocardiogram and body position signals
timeFrame: Data recruited during one-night (PSG study) from Patch III heart rate sensor
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
References:
citation: Yang CC, Lai CW, Lai HY, Kuo TB. Relationship between electroencephalogram slow-wave magnitude and heart rate variability during sleep in humans. Neurosci Lett. 2002 Aug 30;329(2):213-6. doi: 10.1016/s0304-3940(02)00661-4.
citation: Kuo TB, Yang CC. Frequency domain analysis of electrooculogram and its correlation with cardiac sympathetic function. Exp Neurol. 2009 May;217(1):38-45. doi: 10.1016/j.expneurol.2009.01.012. Epub 2009 Jan 31.
citation: Kuo TB, Chen CY, Hsu YC, Yang CC. Performance of the frequency domain indices with respect to sleep staging. Clin Neurophysiol. 2012 Jul;123(7):1338-45. doi: 10.1016/j.clinph.2011.11.003. Epub 2011 Dec 5.
citation: Sadeh A. The role and validity of actigraphy in sleep medicine: an update. Sleep Med Rev. 2011 Aug;15(4):259-67. doi: 10.1016/j.smrv.2010.10.001. Epub 2011 Jan 14.
citation: Lindemann U, Hock A, Stuber M, Keck W, Becker C. Evaluation of a fall detector based on accelerometers: a pilot study. Med Biol Eng Comput. 2005 Sep;43(5):548-51. doi: 10.1007/BF02351026.
citation: Paquet J, Kawinska A, Carrier J. Wake detection capacity of actigraphy during sleep. Sleep. 2007 Oct;30(10):1362-9. doi: 10.1093/sleep/30.10.1362.
citation: Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC. Automatic sleep/wake identification from wrist activity. Sleep. 1992 Oct;15(5):461-9. doi: 10.1093/sleep/15.5.461.
citation: Tryon WW. Issues of validity in actigraphic sleep assessment. Sleep. 2004 Feb 1;27(1):158-65. doi: 10.1093/sleep/27.1.158.
citation: Hedner J, Pillar G, Pittman SD, Zou D, Grote L, White DP. A novel adaptive wrist actigraphy algorithm for sleep-wake assessment in sleep apnea patients. Sleep. 2004 Dec 15;27(8):1560-6. doi: 10.1093/sleep/27.8.1560.
citation: Murayama R, Kubota T, Kogure T, Aoki K. The effects of instruction regarding sleep posture on the postural changes and sleep quality among middle-aged and elderly men: a preliminary study. Biosci Trends. 2011;5(3):111-9. doi: 10.5582/bst.2011.v5.3.111.
citation: Wrzus C, Brandmaier AM, von Oertzen T, Muller V, Wagner GG, Riediger M. A new approach for assessing sleep duration and postures from ambulatory accelerometry. PLoS One. 2012;7(10):e48089. doi: 10.1371/journal.pone.0048089. Epub 2012 Oct 24.
citation: Bignold JJ, Mercer JD, Antic NA, McEvoy RD, Catcheside PG. Accurate position monitoring and improved supine-dependent obstructive sleep apnea with a new position recording and supine avoidance device. J Clin Sleep Med. 2011 Aug 15;7(4):376-83. doi: 10.5664/JCSM.1194.
citation: de Souza L, Benedito-Silva AA, Pires ML, Poyares D, Tufik S, Calil HM. Further validation of actigraphy for sleep studies. Sleep. 2003 Feb 1;26(1):81-5. doi: 10.1093/sleep/26.1.81.
citation: Silber MH, Ancoli-Israel S, Bonnet MH, Chokroverty S, Grigg-Damberger MM, Hirshkowitz M, Kapen S, Keenan SA, Kryger MH, Penzel T, Pressman MR, Iber C. The visual scoring of sleep in adults. J Clin Sleep Med. 2007 Mar 15;3(2):121-31.
citation: Ahlberg K, Savolainen A, Paju S, Hublin C, Partinen M, Kononen M, Ahlberg J. Bruxism and sleep efficiency measured at home with wireless devices. J Oral Rehabil. 2008 Aug;35(8):567-71. doi: 10.1111/j.1365-2842.2008.01875.x. Epub 2008 May 9.