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      A survey on sleep assessment methods

      research-article
      1 , , 2 , 3
      PeerJ
      PeerJ Inc.
      Sleep, Sleep assessment, Sleep disorders, Sleep assessment methods

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          Abstract

          Purpose

          A literature review is presented that aims to summarize and compare current methods to evaluate sleep.

          Methods

          Current sleep assessment methods have been classified according to different criteria; e.g., objective (polysomnography, actigraphy…) vs. subjective (sleep questionnaires, diaries…), contact vs. contactless devices, and need for medical assistance vs. self-assessment. A comparison of validation studies is carried out for each method, identifying their sensitivity and specificity reported in the literature. Finally, the state of the market has also been reviewed with respect to customers’ opinions about current sleep apps.

          Results

          A taxonomy that classifies the sleep detection methods. A description of each method that includes the tendencies of their underlying technologies analyzed in accordance with the literature. A comparison in terms of precision of existing validation studies and reports.

          Discussion

          In order of accuracy, sleep detection methods may be arranged as follows:

          Questionnaire < Sleep diary < Contactless devices < Contact devices < Polysomnography

          A literature review suggests that current subjective methods present a sensitivity between 73% and 97.7%, while their specificity ranges in the interval 50%–96%. Objective methods such as actigraphy present a sensibility higher than 90%. However, their specificity is low compared to their sensitivity, being one of the limitations of such technology. Moreover, there are other factors, such as the patient’s perception of her or his sleep, that can be provided only by subjective methods. Therefore, sleep detection methods should be combined to produce a synergy between objective and subjective methods. The review of the market indicates the most valued sleep apps, but it also identifies problems and gaps, e.g., many hardware devices have not been validated and (especially software apps) should be studied before their clinical use.

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          Most cited references51

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          Wrist actigraphy.

          To record sleep, actigraph devices are worn on the wrist and record movements that can be used to estimate sleep parameters with specialized algorithms in computer software programs. With the recent establishment of a Current Procedural Terminology code for wrist actigraphy, this technology is being used increasingly in clinical settings as actigraphy has the advantage of providing objective information on sleep habits in the patient's natural sleep environment. Actigraphy has been well validated for the estimation of nighttime sleep parameters across age groups, but the validity of the estimation of sleep-onset latency and daytime sleeping is limited. Clinical guidelines and research suggest that wrist actigraphy is particularly useful in the documentation of sleep patterns prior to a multiple sleep latency test, in the evaluation of circadian rhythm sleep disorders, to evaluate treatment outcomes, and as an adjunct to home monitoring of sleep-disordered breathing. Actigraphy has also been well studied in the evaluation of sleep in the context of depression and dementia. Although actigraphy should not be viewed as a substitute for clinical interviews, sleep diaries, or overnight polysomnography when indicated, it can provide useful information about sleep in the natural sleep environment and/or when extended monitoring is clinically indicated.
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            Guidelines for the multiple sleep latency test (MSLT): a standard measure of sleepiness.

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              A new scale for measuring insomnia: the Bergen Insomnia Scale.

              A new scale for the measurement of insomnia, the Bergen Insomnia Scale, was constructed on the basis of current formal and clinical diagnostic criteria for insomnia. There are six items, of which the first three pertain to sleep onset, maintenance, and early morning wakening insomnia, respectively. The last three items refer to not feeling adequately rested, experiencing daytime impairment, and being dissatisfied with current sleep. This scale was validated in three samples, 320 students, 2645 community persons, and 225 patients. Cronbach alphas in the three samples were .79, .87, and .80, respectively. The 2-wk. test-retest reliability for students was .77. In the student and the patient samples, a two-factor solution was found, nocturnal symptoms and daytime symptoms, but in the community sample, a one-factor solution was found. The Bergen Insomnia Scale discriminated well between the patient sample and the other two. In all three, values of convergent and discriminative validity in relation to other self-report measures were good, as well as in relation to polysomnographic data for patients. It is concluded that the Bergen Insomnia Scale has good psychometric properties. It is one of very few insomnia scales which provide normative data for comparisons and which has been validated against subjective as well as polysomnographic data.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                25 May 2018
                2018
                : 6
                : e4849
                Affiliations
                [1 ]Facultad de Enfermería, Universidad Católica de Valencia “San Vicente Mártir” , Valencia, Spain
                [2 ]Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València , Valencia, Spain
                [3 ]Departamento de Enfermería; Universidad de Valencia , Valencia, Spain
                Article
                4849
                10.7717/peerj.4849
                5971842
                29844990
                a7d23697-c03c-4f5a-b2c1-1adf5f83825e
                ©2018 Ibáñez et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 17 February 2018
                : 7 May 2018
                Funding
                The authors received no funding for this work.
                Categories
                Global Health
                Neurology

                sleep,sleep assessment,sleep disorders,sleep assessment methods

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