The Use of Actigraphy to Study Sleep Disorders in Preschoolers: Some Concerns about Detection of Nighttime Awakenings

Sitnick, Stephanie L.; Goodlin‐Jones, Beth L.; Anders, Thomas F.

doi:10.1093/sleep/31.3.395

Cited by 171 publications

(142 citation statements)

References 26 publications

Supporting

Mentioning

134

Contrasting

Unclassified

Order By: Relevance

“…The CSHQ is a 33 item questionnaire for children aged 4 to 12 years. It has also been used for younger children from the age of 2 onwards (Goodlin-Jones., Sitnick., et al, 2008). It has recently been administered in Hollway, Aman, and Butter (2013) and Mannion et al (2013).…”

Section: Objective Measures Used To Assess Sleepmentioning

confidence: 99%

Sleep Problems in Autism Spectrum Disorder: A Literature Review

Mannion

Leader

2013

Rev J Autism Dev Disord

View full text Add to dashboard Cite

Section: Objective Measures Used To Assess Sleepmentioning

confidence: 99%

Sleep Problems in Autism Spectrum Disorder: A Literature Review

Mannion

Leader

2013

Rev J Autism Dev Disord

View full text Add to dashboard Cite

“…Sitnick et al 19 showed that an algorithm commonly used with adults is too sensitive with a population of young children, resulting in high false negative rates (i.e., actigraphy detects wakefulness when the child is probably sleeping). In fact, although several studies with infants and children have reported that various actigraphy devices are highly correlated (> 80%) with PSG or videosomnography, 14,[16][17][18][19][20][21][22][23] most of these studies have shown very low ability to correctly identify wakefulness 14,17,19,22 and hence, sleep fragmentation. 24 Nevertheless, the American Academy of Sleep Medicine (AASM) states that the use of actigraphy in normal children and special pediatric populations is indicated for the assessment of sleep patterns and response to treatment.…”

mentioning

confidence: 99%

“…19 However, this algorithm has not been validated in preschoolers against PSG. The present study aims to compare the validity of three algorithms for detecting sleep with actigraphy by comparing them to PSG in preschoolers.…”

mentioning

confidence: 99%

Validating Actigraphy as a Measure of Sleep for Preschool Children

Bélanger

Bernier²,

Paquet³

et al. 2013

Journal of Clinical Sleep Medicine

115

View full text Add to dashboard Cite

Study Objectives:The algorithms used to derive sleep variables from actigraphy were developed with adults. Because children change position during sleep more often than adults, algorithms may detect wakefulness when the child is actually sleeping (false negative). This study compares the validity of three algorithms for detecting sleep with actigraphy by comparing them to PSG in preschoolers. The putative infl uence of device location (wrist or ankle) is also examined. Methods: Twelve children aged 2 to 5 years simultaneously wore an actigraph on an ankle and a wrist (Actiwatch-L, MiniMitter/Respironics) during a night of PSG recording at home. Three algorithms were tested: one recommended for adults and two designed to decrease false negative detection of sleep in children. Results: Actigraphy generally showed good sensitivity (> 95%; PSG sleep detection) but low specifi city (± 50%; PSG wake detection). Intraclass correlations between PSG and actigraphy variables were strong (> 0.80) for sleep latency, sleep duration, and sleep effi ciency, but weak for number of awakenings (< 0.40). The two algorithms designed for children enhanced the validity of actigraphy in preschoolers and increased the proportion of actigraphyscored wake epochs scored that were also PSG-identifi ed as wake. Sleep variables derived from the ankle and wrist were not statistically different. Conclusion: Despite the weak detection of wakefulness, Actiwatch-L appears to be a useful instrument for assessing sleep in preschoolers when used with an adapted algorithm. S C I E N T I F I C I N V E S T I G A T I O N SS leep is considered to be of paramount importance for brain development during the fi rst two years of life.1 In fact, children spend over half of their fi rst two years of life sleeping, with daily sleep duration decreasing from 14.5 to about 13 hours between 6 months and 2 years of age. 2,3 In the preschool years, daily sleep needs remain high, decreasing from 13 hours at 2 years to about 11 hours at 5 years.2-4 During infancy and childhood, frequent night awakenings or diffi culty falling asleep are among the most frequent developmental complaints. Studies estimate that from 10% to 75% of parents report that their children have sleep problems.5 Importantly, sleep problems tend to persist during childhood 6 and are associated with several adverse consequences for behavioral, cognitive, and emotional health. For example, it has been shown that sleep problems are associated with behavioral and emotional self-regulation problems.7 In fact, results suggest that when the sleep of preschoolers is insuffi cient or fragmented by wakefulness, they show more diffi culty inhibiting emotional responses and more frequent impulsive and aggressive behavior.8 Poor sleep quality also seems associated with obesity in preschool children. 9Studies further suggest that sleep problems, whether occurring in infancy 10 or at school age, 4,11 are associated with lower cognitive performance. In light of the prevalence and the serious consequences of pediatr...

show abstract

“…Actigraphs are used to detect body movements using a build-in accelerometer and give indices of awakenings. A number of studies have been presented that focus on the detection of awakenings based on activity (Lotjonen et al, 2003;Paquet et al, 2007;Sitnick et al, 2008). The use of actigraphy as a sleep-wake indicator is subject to discussion (Pollak et al, 2001;Tryon, 2004).…”

Section: Automatic Detection Of Awakeningsmentioning

confidence: 99%

“…An important shortcoming of these methods is their failure to detect an awakening when a person lies immobile in bed. In some extreme cases even a transition from supine to sitting position can sometimes be undetected (Sitnick et al, 2008). During the course of the EASI project, an algorithm has been developed that is able to automatically detect every time the user is awake during the sleeping period (Bulckaert et al, 2010).…”

Section: Automatic Detection Of Awakeningsmentioning

confidence: 99%