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Comparative Measurement of PLMS Between Polysomnography and Actigraphy in Children with Sickle Cell Disease
UMB Dataset

UID: 85

Author(s): Valerie E. Rogers*, Paul R. Gallagher, Carole L. Marcus, Kwaku Ohene-Frempong, Joel T. Traylor, Thornton B.A. Mason * Corresponding Author
Description
Periodic limb movements in sleep (PLMS) are associated with sleep fragmentation and sympathetic nervous system activation, which in turn have been linked to cognitive and behavioral deficits in children and cardiovascular morbidity in both adults and children. Emerging evidence indicates that many children with sickle cell disease (SCD) have elevated PLMS. The specific aims of this prospective, repeated-measures, descriptive study were to assess the agreement between PLMS measurement by actigraphy and concurrent polysomnography (PSG), to test the feasibility of measuring PLMS by actigraphy at home, to evaluate PLMS variability over consecutive nights by actigraphy, and to provide preliminary data on objective and subjective correlates of PLMS. Twenty children with SCD and restless legs syndrome (RLS) symptoms or polysomnography-documented PLMS underwent concurrent attended polysomnography and ankle activity monitoring over one to two nights and home activity monitoring for three nights. Serum iron and ferritin were measured pre- and post-polysomnography. The datasets associated with this study include demographic data, SCD subtype, polysomnographic/activity monitor values, medical history, and clinical measures.
Timeframe
2010 - 2011
Geographic Coverage
Philadelphia (Pa.)
Subject of Study
Subject Domain
Population Age
Child (2 years to 12 years)
Adolescent (13 years to 18 years)
Subject Gender
Female
Male
Keywords
Access via Open Science Framework (OSF)

https://doi.org/10.17605/OSF.IO/4ZHCF

Access Restrictions
Free to All
Access Instructions
Available via the Open Science Framework (OSF)
Associated Publications
Data Type
Equipment Used
PAM-RL Objective Limb Activity Monitor
Software Used
REMbrandt PSG System
SPSS, v. 9.2
Study Type
Observational
Dataset Format(s)
Microsoft Excel, Microsoft Word, PDF, SPSS