Actigraphy is an objective, non-intrusive method for estimating sleep–wake patterns using activity-based monitoring. The use of actigraphy in research has gained significant popularity over the past 20 years, to the extent that the recent growth in published research studies that used actigraphy have outpaced studies that used polysomnography (PSG).1 Actigraphy can be a particularly valuable methodology for use among pediatric populations, where the common reliance on parental report alone may limit the range and accuracy of information about children's sleep. Consistent with the growth of actigraphy use across sleep research domains reported by Sadeh,1 there has also been significant growth in the report of actigraphy specific to pediatric studies (Fig. 1), with the number of studies published in 2010 alone (n = 41) similar to the total number of studies published from 1991 to 2001 (n = 38).
Another area of particular concern and utmost importance is the validity of actigraphy as a measure of sleep–wake patterns among children and adolescents of all ages. Many studies discuss the validity of actigraphy compared to PSG, but then cite studies validated on adult samples.3, 4, 5 Actigraphy output provides valuable information about activity levels that are ideal for visual analysis, which is useful for evaluating clinical treatment efficacy or to corroborate parental report of child sleep. However, actigraphically measured sleep estimates among pediatric samples (e.g., total sleep time or wake after sleep onset) should only be used when the recording device and particular sleep value are established as valid in comparison to a ‘gold standard’ measure such as PSG or direct observation.
Correlation statistics are often used to evaluate the validity of actigraphy when compared to a gold standard like PSG. However, correlations alone are not an appropriate way to validate these devices. A perfect correlation can be found between any two instruments, even if they have widely divergent measurement scales, as long as both measures increase at the same proportional rate. Some validation studies have relied only on correlation analyses, while others have overemphasized high sensitivity and underemphasized low specificity. Sensitivity and specificity bear further explanation because they are the most appropriate statistical method for validity assessment.
Sensitivity and specificity are most commonly used in the biomedical field for determining the quality of a novel diagnostic instrument. Sensitivity describes how accurately an instrument identifies people with a disorder (“true positive” cases); the more people who are inaccurately identified as not having the disorder (“false negative” cases), the less sensitive the instrument. On the other hand, specificity describes how accurately an instrument identifies people who do not have the disorder (“true negative” cases); the more people inaccurately identified as having the disorder (“false positive” cases), the less specific the instrument.
An ideal test accurately identifies 100% of both positive cases (is highly sensitive) and negative cases (is highly specific). When it comes to actigraphy, researchers have established a convention of considering sensitivity to be the proportion of epochs scored as sleep using polysomnography that are accurately identified as sleep by actigraphy. Specificity, on the other hand, is the proportion of polysomnography-scored wake epochs accurately identified as wake by actigraphy. An actigraph, or algorithm, that incorrectly scores sleep as wake has low sensitivity, and an actigraph, or algorithm, that incorrectly scores wake as sleep has low specificity. For example, if an actigraph scores an entire sleep period as sleep, it would be 100% sensitive at the expense of poor specificity in identifying wake during the sleep period. Both sensitivity and specificity are inherently important since the incorrect scoring of sleep or wake can result in under or overestimates of reported sleep variables (e.g., total sleep time, wake after sleep onset).
Thus, examinations of sensitivity and specificity, and the relation between the two (represented by the “likelihood ratio”), are necessary for establishing the extent of actigraphy's validity. Another assessment technique commonly used to examine instrument validity is the Bland–Altman concordance technique.*6, 7 This approach provides a visual representation of agreement by plotting the new method against the gold standard. The difference between the measures for each participant are fitted to lines that represent the ideal (no difference), plus either standard deviations or time discrepancies to show each participant's deviation from the ideal.
For a more detailed description of validation and reliability issues pertaining to use of actigraphy, the reader is referred to several comprehensive reviews.*1, 8, 9