Owls, larks, swifts, woodcocks and they are not alone: A historical review of methodology for multidimensional self-assessment of individual differences in sleep-wake pattern.

Owls, larks, swifts, woodcocks and they are not alone: A historical review of methodology for multidimensional self-assessment of individual differences in sleep-wake pattern.

Chronobiol Int. 2017 Jan 27;:1-12

Authors: Putilov AA

Abstract
Differences between the so-called larks and owls representing the opposite poles of morningness-eveningness dimension are widely known. However, scientific consensus has not yet been reached on the methodology for ranking and typing people along other dimensions of individual variation in their sleep-wake pattern. This review focused on the history and state-of-the-art of the methodology for self-assessment of individual differences in more than one trait or adaptability of the human sleep-wake cycle. The differences between this and other methodologies for the self-assessment of trait- and state-like variation in the perceived characteristics of daily rhythms were discussed and the critical issues that remained to be addressed in future studies were highlighted. These issues include a) a failure to develop a unidimensional scale for scoring chronotypological differences, b) the inconclusive results of the long-lasting search for objective markers of chronotype, c) a disagreement on both number and content of scales required for multidimensional self-assessment of chronobiological differences, d) a lack of evidence for the reliability and/or external validity of most of the proposed scales and e) an insufficient development of conceptualizations, models and model-based quantitative simulations linking the differences between people in their sleep-wake pattern with the differences in the basic parameters of underlying chronoregulatory processes. It seems that, in the nearest future, the wide implementation of portable actigraphic and somnographic devices might lead to the development of objective methodologies for multidimensional assessment and classification of sleep-wake traits and adaptabilities.

PMID: 28128994 [PubMed - as supplied by publisher]

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