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A survey on sleep assessment methods

Read time: 1 mins
Published:24th May 2018
Author: Ibáñez V, Silva J, Cauli O.
Source: PeerJ
Availability: Free full text
Ref.:PeerJ. 2018 May 25;6:e4849.
DOI:10.7717/peerj.4849
A survey on sleep assessment methods


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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