Normative Sleep Data: Introduction

First created | 11/04/2004

Last edited   | 11/19/2011

Written by Maurice M. Ohayon, MD, DSc, PhD

Reference to cite: 1) Ohayon MM. Normative Sleep Data: Introduction. SleepEval.com Website. Oct 2011. 2) Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep. 2004 Nov 1;27(7):1255-73. Free PMC

Sleep patterns evolve across the normal aging process in complex ways. Changes in sleep patterns across childhood and adolescence, for example, are not only related to chronological age but also to maturational stage.

 

Few studies, however, have made comprehensive analyses of these two aspects in adolescents (1). Similarly, chronological age in elderly people does not always match physiological age. Therefore, changes in sleep patterns may happen earlier, i.e., at a younger age, for some individuals or at an older age for others. Further, epidemiological and other studies suggest that much of the sleep disturbance typically seen in old age is likely the result of medical co-morbidities than age per se (2-6).

Nevertheless, four age-related changes have been consistently demonstrated in polysomnographic (PSG) studies of sleep architecture: total sleep time (7-29), sleep efficiency (7,9-14,17-23,25-29,30-36), and slow wave sleep (7,8,10, 12-18, 21-28, 31,33, 35,37-39) all decrease, while wake after sleep onset (12-14,16,17,19,21,23,28,29, 32,33,36,37,40) increases with age.

However, a number of PSG sleep characteristics remain uncertain as regard their evolution with age:

(a) sleep latency has been reported to increase with age in some studies (10,13,26,31,40), while several other studies found no significant changes with age (8,9,12, 14,16,17,20-23,28,29,32,33,35-37,39,41). Likewise, a number of studies found no significant differences with age for (b) percentage of stage 1 (9,25,26,35,39,42) and

(c) stage 2 (9,13,20,22,23,25,33,35,36,42,43) while many others reported an increase with age of these stages (7,8,12,17,27,28,31).

(d) Similarly, REM sleep has reported to decrease with age in several studies (7,8,10-12,14,16-18,20,21,23-26,28,31,33,37,38,44) while many other studies found no such association with age (9,13,15,19,22,27,34-36,39-43).

 

Why such discrepancies between the studies?

Several factors may be responsible for the difficulties identifying age trends in sleep architecture of apparently healthy subjects, for example: small sample sizes; inconsistency in controlling factors that may influence sleep, such as mental or physical illness; uncontrolled use of alcohol, drugs or medications; or insufficient screening for sleep disorders.

 

 

What is normative data of sleep?

 

It is a set a guidelines that described the different sleep characteristics that can observed in healthy individuals at different stages of life.
These distinctions are crucial since as sleep evolves with age, normative data for a given age can be abnormal when applied to another age group.
Therefore deviations to these norms for a given age give indications of potential sleep pathologies.

 

 

Why do we need normative data?

 

Sleep classifications are attempts to provide operational criteria to delineate abnormal sleep in all its forms.
The problem is that abnormality can only exist relative to a norm but these normative data exist only in disparate ways.
Consequently, it is often difficult for a non-sleep specialist to evaluate the magnitude of a sleep complaint when there is no point of normality to refer to.

 

 

How the normative data were obtained?

 

Assessing sleep characteristics, especially when it concerns objective sleep data, is very expansive and cannot be achieved unless having extraordinary budget.
Consequently, a literature review of studies that assessed the sleep characteristics of healthy individuals was done.
The results of these studies were analyzed using a meta-analysis technique.

 

 

What is a meta-analysis?

 

It is a statistical method that allows to combine the results into a single set of analyzes.
It allows to quantify conclusions which cannot be done with traditional literature reviews.

 

How it is calculated?

 

Inside each study that we identified, different effect sizes were calculated.

 

Those effect size indicted the magnitude of the change between two age groups; for example, young adults vs. middle-aged; middle-aged vs. elderly, etc...

 

Subsequently, all these effect sizes are cumulated and an average is calculated.

The final result indicates the magnitude of the effect size:
    • Small (around .20)
    • Medium (around .50)
    • Large (around .80)

 

Procedures, figures, tables, references and analyses of effect sizes are included in the reference paper:
Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV., Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep. 2004 Nov 1;27(7):1255-73. Free PMC

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