Objective measurements of sleep for non-laboratory settings as alternatives to polysomnography—a systemic review
ABSTRACT Sleep disturbance influences human health. To examine sleep patterns, it is advisable to utilize valid subjective and objective measures. Laboratory-based polysomnography (PSG) is deemed the gold standard to measure sleep objectively, but is impractical for long-term and home utilization (e.g. resource-demanding, difficult to use). Hence, alternative devices have been developed. This study aimed to review the literature systematically, providing an overview of available objective sleep measures in non-laboratory settings as an alternative to PSG. To identify relevant articles, a specific search strategy was run in EMBASE, PubMed, CINAHL, PsycInfo and Compendex (Engineering Village 2). In addition, reference lists of retrieved articles were screened and experts within this research field were contacted. Two researchers, using specified in/exclusion criteria, screened identified citations independently in three stages: on title, abstract and full text. Data from included articles were extracted and inserted into summarizing tables outlining the results. Of the 2217 electronically identified citations, 35 studies met the inclusion criteria. Additional searches revealed eight papers. Psychometric characteristics of nine different objective measures of sleep pattern alternatives to PSG [(bed) actigraphy, observation, bed sensors, eyelid movement- and non-invasive arm sensors, a sleep switch and a remote device] were evaluated. Actigraphy is used widely and has been validated in several populations. Alternative devices to measure sleep patterns are becoming available, but most remain at prototype stage and are validated insufficiently. Future research should concentrate on the development and further validation of non-invasive, inexpensive and user-friendly sleep measures for non-laboratory settings.
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- "The epoch-by-epoch comparison between Jawbone UP and PSG data confirms that similar to standard actigraphy (Ancoli-Israel et al., 2003; de Souza et al., 2003; Marino et al., 2013; Sadeh, 2011; Van de Water et al., 2011) and to Fitbit Õ (Montgomery-Downs et al., 2012), Jawbone UP had high sensitivity and low specificity , and therefore may be less accurate in evaluating sleep quality in people with fragmented sleep. Interestingly, in our study, two participants had PLMI410, and two participants had PLMI410 and apnea–hypopnea index 45, but none of them showed extreme values in the overall distribution of Jawbone UP-PSG discrepancies. "
ABSTRACT: Wearable fitness-tracker devices are becoming increasingly available. We evaluated the agreement between Jawbone UP and polysomnography (PSG) in assessing sleep in a sample of 28 midlife women. As shown previously, for standard actigraphy, Jawbone UP had high sensitivity in detecting sleep (0.97) and low specificity in detecting wake (0.37). However, it showed good overall agreement with PSG with a maximum of two women falling outside Bland–Altman plot agreement limits. Jawbone UP overestimated PSG total sleep time (26.6 ± 35.3 min) and sleep onset latency (5.2 ± 9.6 min), and underestimated wake after sleep onset (31.2 ± 32.3 min) (p’s < 0.05), with greater discrepancies in nights with more disrupted sleep. The low-cost and wide-availability of these fitness-tracker devices may make them an attractive alternative to standard actigraphy in monitoring daily sleep–wake rhythms over several days.Chronobiology International 07/2015; DOI:10.3109/07420528.2015.1054395 · 2.88 Impact Factor
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- "The data it captures are downloaded and analysed to map activity and inactivity segments from which wake-sleep periods are then inferred (Chesson et al., 2007). In their review of 25 actigraphy studies, Van de water et al. (2011) determined that actigraphy was the most appropriate objective measure available to inform general sleep patterns in a non-laboratory setting. Sleep is also measured subjectively. "
ABSTRACT: Sleep is a dynamic and essential part of human life and health. In healthcare settings, nurses are strategically placed to promote sleep and sleep health. In this regard, nursing actions should be based upon effective methods of assessment of patient sleep. Standardised sleep assessment does not currently occur in the care of acute hospitalised patients. Use of an appropriate measurement tool would help evaluate inpatient sleep. An effective, efficient sleep assessment tool is needed to aid clinicians. Such assessment would enable specific nursing intervention to be tailored to individual patients.International journal of nursing studies 09/2014; 51(9). DOI:10.1016/j.ijnurstu.2014.02.001 · 2.25 Impact Factor
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- "Unfortunately, for many cases, it lacks detail (such as the REM–NREM distinction) and accuracy , . No other methods reached the point beyond prototype or have been sufficiently validated to fill the gap between PSG and ACT . Over the years, however, extensive research has been performed on changes in heart rate (HR) and breathing rate (BR) across sleep stages and other related events. "
ABSTRACT: Polysomnography (PSG) is considered the gold standard to assess sleep accurately, but it can be expensive, time-consuming, and uncomfortable, specifically in long-term sleep studies. Actigraphy, on the other hand, is both cheap and user-friendly, but depending on the application lacks detail and accuracy. Our aim was to evaluate cardiorespiratory and movement signals in discriminating between wake, rapid-eye-movement (REM), light (N1N2), and deep (N3) sleep. The dataset comprised 85 nights of PSG from a healthy population. Starting from a total of 750 characteristic variables (features), problem-specific subsets of 40 features were forwardly selected using the combination of a wrapper method (Cohen's kappa statistic on radial basis function (RBF)-kernel support vector machine (SVM) classifier) and filter method (minimum redundancy maximum relevance criterion on mutual information). Final classification was performed using an RBF-kernel SVM. Non-subject-specific wake versus sleep classification resulted in a Cohen's kappa value of 0.695, while REM versus NREM resulted in 0.558 and N3 versus N1N2 in 0.553. The broad pool of initial features gave insight in which features discriminated best between the different classes. The classification results demonstrate the possibility of making long-term sleep monitoring more widely available.IEEE Journal of Biomedical and Health Informatics 03/2014; 18(2):661-669. DOI:10.1109/JBHI.2013.2276083 · 1.98 Impact Factor