Dataset Information

Application of a novel actigraphy algorithm to detect movement and sleep/wake patterns in children with autism spectrum disorder.

ABSTRACT:

Objective

Actigraphy is a non-intrusive method of recording rest/activity cycles as well as a surrogate for sleep/wake activity. Standard actigraphy analysis is limited in ascribing discrete movement events to wake status during sleep. We applied a novel algorithm to overnight actigraphy data recorded simultaneously with video polysomnography-electroencephalography (video PSG-EEG) to determine its ability to define movement and sleep/wake patterns in children with autism spectrum disorder (ASD) and age-comparable typically developing (TD) controls.

Methods

A previously published novel algorithm uses mathematical endpoints to analyze actigraphy data without assumptions about sleep/wake status, and smooths data using moving windows of increasing length. Nighttime activity level "S" events (S1-S5) determined by this algorithm (n = 273) were identified in 15 children ages 3-10 years (nine with ASD and six TD) who wore an AW2 Spectrum Actiwatch (Philips Respironics) while undergoing simultaneous video PSG-EEG. Data were analyzed to identify the time each activity level "S" event occurred, video movement events (movements captured by video and scored based on level of severity), and sleep/wake status defined by PSG-EEG. The relationships among activity level "S" events, video movement events, and sleep/wake status were analyzed statistically.

Results

Activity level "S" events, the presence and severity of video movement events, and sleep-wake status, were significantly associated. These associations were present in both participants with ASD and those who were typically developing.

Conclusion

This actigraphy algorithm shows promise for detecting nighttime movements and sleep/wake status and warrants further study in larger datasets of neurotypical children and those with neurodevelopmental disorders.

SUBMITTER: Alder ML

PROVIDER: S-EPMC7371359 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Application of a novel actigraphy algorithm to detect movement and sleep/wake patterns in children with autism spectrum disorder.

Alder Megan L ML Ye Fei F Run Fan F Bagai Kanika K Fawkes Diane B DB Peterson Barry T BT Malow Beth A BA

Sleep medicine 20200306

<h4>Objective</h4>Actigraphy is a non-intrusive method of recording rest/activity cycles as well as a surrogate for sleep/wake activity. Standard actigraphy analysis is limited in ascribing discrete movement events to wake status during sleep. We applied a novel algorithm to overnight actigraphy data recorded simultaneously with video polysomnography-electroencephalography (video PSG-EEG) to determine its ability to define movement and sleep/wake patterns in children with autism spectrum disorde ...[more]

PMID: 32454300

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Project description:Actigraphy (ACT) is a non-invasive objective assessment tool for the study of sleep-wake rhythms. It is of particular interest in children with autism spectrum disorder (ASD), as sleep disorders are highly prevalent and have a significant impact on both cognitive and behavioral functions. As polysomnography (PSG), the gold standard for the assessment of sleep, is difficult to perform in children with ASD, ACT has become a tool of choice but has not yet been validated against PSG using state-of-the-art methodology. The main objective of this study was to assess, for the first time, the validity of ACT compared to PSG for the measurement of sleep in children with ASD. During the same night of hospitalization, PSG and ACT were conducted in 26 children (6 girls and 20 boys; mean age 5.4 years ± 1.6) diagnosed with ASD according to DSM-5 criteria and standardized diagnostic scales. Sleep parameters were total sleep time (TST), sleep latency (SL), wake after sleep onset (WASO), and sleep efficiency (SE). To compare PSG and ACT, we conducted sleep parameter agreement analyses including: intraclass correlation coefficient (ICC), Bland-Altman plots, and equivalence tests. The comparison also included an epoch-by-epoch (EBE) agreement analysis to determine sensitivity (ability to detect sleep) and specificity (ability to detect wake). According to equivalence tests, the difference between ACT and PSG measures was clinically acceptable for TST (<30 min, p < 0.01), SL (<15 min, p < 0.001), and SE (10%, p < 0.01), but not for WASO (<15 min, p = 0.13). There was a good agreement between methods for SL (ICC = 0.79) and TST (ICC = 0.85) and a moderate agreement for WASO (ICC = 0.73) and SE (ICC = 0.68). The EBE agreement analysis revealed a high sensitivity (0.94 ± 0.06) and moderate specificity (0.5 ± 0.2). Since sleep disorders are one of the most common comorbidities within the ASD population and are highly prevalent, it is essential to validate objective tools of assessment. To our knowledge, our study is the first to validate ACT compared to PSG, using a state-of-the-art methodology, in children with ASD. The results suggest ACT to be a valid method to evaluate sleep within this population, with a good reliability for most sleep parameters.

Project description:BackgroundIsolated rapid-eye-movement sleep behavior disorder (iRBD) is in most cases a prodrome of neurodegenerative synucleinopathies, affecting 1% to 2% of middle-aged and older adults; however, accurate ambulatory diagnostic methods are not available. Questionnaires lack specificity in nonclinical populations. Wrist actigraphy can detect characteristic features in individuals with RBD; however, high-frequency actigraphy has been rarely used.ObjectiveThe aim was to develop a machine learning classifier using high-frequency (1-second resolution) actigraphy and a short patient survey for detecting iRBD with high accuracy and precision.MethodsThe method involved analysis of home actigraphy data (for seven nights and more) and a nine-item questionnaire (RBD Innsbruck inventory and three synucleinopathy prodromes of subjective hyposmia, constipation, and orthostatic dizziness) in a data set comprising 42 patients with iRBD, 21 sleep clinic patients with other sleep disorders, and 21 community controls.ResultsThe actigraphy classifier achieved 95.2% (95% confidence interval [CI]: 88.3-98.7) sensitivity and 90.9% (95% CI: 82.1-95.8) precision. The questionnaire classifier achieved 90.6% accuracy and 92.7% precision, exceeding the performance of the Innsbruck RBD Inventory and prodromal questionnaire alone. Concordant predictions between actigraphy and questionnaire reached a specificity and precision of 100% (95% CI: 95.7-100.0) with 88.1% sensitivity (95% CI: 79.2-94.1) and outperformed any combination of actigraphy and a single question on RBD or prodromal symptoms.ConclusionsActigraphy detected iRBD with high accuracy in a mixed clinical and community cohort. This cost-effective fully remote procedure can be used to diagnose iRBD in specialty outpatient settings and has potential for large-scale screening of iRBD in the general population. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Project description:COVID-related lockdown led to a radical modification of daily activities and routines which are known to affect sleep. Compared to the general population, participants with autism may be particularly vulnerable to the repercussions of lockdown on sleep, given their intrinsic inflexible adherence to routines and the high overall prevalence of sleep disturbances in this population. The study is a French nation-wide online survey assessing sleep-wake rhythms and behaviors known to affect sleep (daily screen time, daylight exposure, and physical activity), before and during COVID-related lockdown. Respondents were 207 adults with autism (56% female) and 1652 adults of the general population (77% female), with a mean age 35.3 years (SD 11.3). Before lockdown, the adults with autism displayed on average later bedtime and waking hours, lower sleep quality, more evening screen time, less exposure to daylight, and less exercise (all p < 0.01). Lockdown affected all studied measures of sleep and related exposures in a similar way in both groups: poorer self-rated sleep quality as well as a less regular and delayed sleep-wake rhythm, longer screen time in the evening and less exposure to daylight (all p < 0.001). Adults with autism displayed significantly higher levels of sleep and circadian rhythm disturbances and less favorable daily routines known to regulate sleep. While the effect of confinement on sleep and sleep related behaviors was similar in both groups, the results highlight that the pre-existing shift in circadian rhythms and lifestyles in adults with ASD further deteriorated during lockdown. LAY ABSTRACT: COVID-related lockdown led to a radical modification of daily activities and routines known to affect sleep. In a sample of 1800 adults, we observed that, before lockdown, participants with autism displayed significantly higher levels of sleep disturbances and less favorable daily routines known to regulate sleep, compared to the general population. While the deleterious effect of lockdown on sleep was similar in both groups, pre-existing difficulties in adults with autism reached worrying levels during lockdown.

Dataset Information

Application of a novel actigraphy algorithm to detect movement and sleep/wake patterns in children with autism spectrum disorder.

Objective

Methods

Results

Conclusion

Publications

Application of a novel actigraphy algorithm to detect movement and sleep/wake patterns in children with autism spectrum disorder.

Similar Datasets

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