Validity of Actigraphy in Young Adults With Insomnia

Williams, J. H.; Taylor, Daniel J.; Slavish, Danica C.; Gardner, Christie; Zimmerman, Marian R.; Patel, Kruti; Reichenberger, David; Francetich, Jade M.; Dietch, Jessica R.; Estevez, Rosemary

doi:10.1080/15402002.2018.1545653

Cited by 37 publications

(26 citation statements)

References 36 publications

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“…Actigraphy is based on body movements while polysomnography is based on a combination of electroencephalogram, electrooculogram and electromyogram, which also permits derivation of sleep architecture information. It has been observed in validation studies, that TST and SE derived from the two methods of assessment correlate ( r = 0.87 for TST and r = 0.56 for SE; e.g., Lichstein et al, 2006; Williams et al, 2018). However, contrasting evidence also suggested limited validity of actigraphy when compared to polysomnography (e.g., Sànchez-Ortun o et al, 2010; Natale et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Executive Functions in Insomnia Disorder: A Systematic Review and Exploratory Meta-Analysis

et al. 2019

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Background: Executive functions (EFs) are involved in the control of basic psychological processes such as attention and memory and also contribute to emotion regulation. Research on the presence of EFs impairments in insomnia yielded inconsistent results. Therefore, we performed a systematic review of the literature on three EFs: inhibitory control, working memory, and cognitive flexibility in adults with insomnia in order to investigate the presence and magnitude of insomnia-related EFs impairments. Methods: PubMed, Scopus, Medline, and PsycINFO were searched. Risk of bias assessment of included studies was performed by two independent researchers. Findings were summarised using both a narrative approach and meta-analysis. Cohen's d was calculated at 95% confidence interval (CI) as effect size of between groups differences. Results: Twenty-eight studies comparing adult individuals with a diagnosis of insomnia and healthy controls on neuropsychological measures of EFs were included. Narrative synthesis revealed substantial variability across study findings. Factors that were primarily hypothesised to account for this variability are: objective sleep impairments and test sensitivity. Exploratory meta-analysis showed impaired performance of small to moderate magnitude in individuals with insomnia as compared to controls in reaction times, but not accuracy rates, of inhibitory control (d = −0.32, 95% CI: −0.52 to −0.13) and cognitive flexibility tasks (d = −0.30, 95% CI: −0.59 to −0.01). Performance in working memory tasks was also significantly impacted (d = −0.19, 95% CI: −0.38 to −0.00). Effects sizes were larger when insomnia was associated with objective sleep impairments, rather than normal sleep. Conclusions: We gathered evidence supporting small to moderate deficits in EFs in individuals with insomnia. Due to the small sample size results should be considered preliminary and interpreted carefully.

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Section: Discussionmentioning

confidence: 99%

Executive Functions in Insomnia Disorder: A Systematic Review and Exploratory Meta-Analysis

et al. 2019

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“…Natale et al [19] have studied primary insomnia using actiwatches. However, studies using actigraphy to understand insomnia have been largely inconsistent in terms of the choice of metrics and detailed analysis of the signal [16], [18]- [20], [30]. Some of the potential reasons for the lack of consistency are that the methodology, and in particular, the statistical methods of analysis, may not be sensitive and have never been done in acute insomnia except for our studies [10], [11], [13].…”

Section: Introductionmentioning

confidence: 99%

Automated Method for Detecting Acute Insomnia Using Multi-Night Actigraphy Data

et al. 2020

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In this paper we propose a new machine learning model for classification of nocturnal awakenings in acute insomnia and normal sleep. The model does not require sleep diaries or any other subjective information from the individuals who took part of the study. It is based on nocturnal actigraphy collected from pre-medicated individuals with acute insomnia and normal sleep controls. We have derived dynamical and statistical features from the actigraphy time series data. These features are combined using two machine learning techniques namely Random Forest (RF) and Support Vector Machine (SVM). RF shows better performance (accuracy-84%) than SVM (73%) in classifying individuals with insomnia from healthy sleepers. The developed model provides a signature of the condition of acute insomnia obtained from actigraphy only and is very promising as a tool to detect the condition in a non-invasive way and without sleep diaries or any other subjective information. INDEX TERMS Acute insomnia, actigraphy, machine learning, insomnia detection, dynamical features.

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“…As a rule, actigraphy measurements are used in the diagnosis and therapy of sleep disorders. For example, actigraphy has shown to be a valid objective measure of sleep onset latency, wake after sleep onset, TST and sleep efficiency SE in a young adult insomnia sample, as compared to ambulatory PSG [17]. But the available devices are expensive, require complex evaluation software and are usually only intended for professionals in sleep medicine.…”

Section: Discussionmentioning

confidence: 99%

Measuring Sleep Quality and Efficiency With an Activity Monitoring Device in Comparison to Polysomnography

et al. 2019

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BackgroundMonitoring for physical activity becomes popular and actually many devices are available. Some physical activity monitors (PAMs) provide data about sleep quality for the user, but there are scarce data concerning validity and usability of these measurements. This study compared the data of sleep parameters generated by a PAM with the polysomnography (PSG).MethodsIn 2016, data of 26 patients in two consecutive PSGs as well as in two daytime and nighttime measurements with a PAM according to physical activity and sleep quality were collected. Furthermore, sleep quality, using the Pittsburgh sleep quality index (PSQI), daytime fatigue, using the multidimensional fatigue inventory (MFI-20) and additionally data of a sleep diary were collected.ResultsThere were positive correlations of both methods with respect to total sleep time (TST) (r = 0.76, P < 0.01) and sleep efficiency (r = 0.71, P < 0.01). Data analysis over two nights showed that over 90% of the TST (95% confidence interval (CI) -1.59 to 0.82) and of the sleep efficiency (95% CI -8.28 to 15.51) were within the limits of agreement. The analysis of the PSQI and the sleep efficiency of the PAM showed no significant correlations. The daytime fatigue correlated negatively with the physical activity (r = -0.72, P < 0.01).ConclusionThe sleep efficiency and TST measured with the PAM sufficiently reflect the PSG sleep parameters and the subjects’ subjective feelings. At the same time, PAM results are also correlated with the subjectively perceived quality of sleep. Further investigations to assess the long-term results are pending.

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Validity of Actigraphy in Young Adults With Insomnia

Cited by 37 publications

References 36 publications

Executive Functions in Insomnia Disorder: A Systematic Review and Exploratory Meta-Analysis

Executive Functions in Insomnia Disorder: A Systematic Review and Exploratory Meta-Analysis

Automated Method for Detecting Acute Insomnia Using Multi-Night Actigraphy Data

Measuring Sleep Quality and Efficiency With an Activity Monitoring Device in Comparison to Polysomnography

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