Introduction to Actigraphy

Introduction to Actigraphy

Get an in-depth introduction to actigraphy, including a discussion on its advantages/disadvantages over other sleep assessment methods, clinically-relevant actigraphic measures, history, clinical indications (specifically focused on sleep medicine), and limitations.

Sep 16, 2022
By Drs. Han-Ping Huang, Francis Hsu and Andrew Ahn
Intro to Actigraphy


This document introduces actigraphy and discusses its advantages/disadvantages over other sleep assessment methods, clinically-relevant actigraphic measures, history, clinical indications (specifically focused on sleep medicine),  and limitations, according to the “American Academy of Sleep Medicine Clinical Practice Guideline” in 2018 [1-2].

1. Advantages of Actigraphy in Sleep Medicine

Actigraphy is a procedure that records and integrates the occurrence and degree of limb movement activity over time. Actigraphic devices can be worn on the wrist, ankle, or waist relatively unobtrusively over a period of days to weeks. For assessing sleep quality, the devices are typically worn on the wrist or ankle. Mathematical algorithms are then applied to these data to estimate wakefulness and sleep. 

In addition to providing a graphical summary of wakefulness and sleep patterns over time, actigraphy generates estimates of certain sleep parameters that are also commonly estimated by using sleep logs, or measured directly by polysomnography (PSG), the gold standard measure of sleep. Table 1 compares the advantages and disadvantages of sleep logs, actigraphy, and PSG.

table showing advantages and disadvantages of actigraphy vs other sleep assessment methods

It is important to recognize that actigraphy is not a substitute for in-laboratory PSG when there is an indication for in-laboratory testing, however, it can provide useful objective metrics across a variety of sleep-wake disorders to assist in the assessment and monitoring of treatment response. In general, for many sleep parameters, actigraphy yields significantly distinct information from sleep logs and in some instances provides parameter estimates that are sufficiently similar to PSG.

2. Clinically Relevant Outcomes based on Sleep Parameters

Table 2 lists the clinically relevant outcomes that actigraphy provides for each sleep disorder. Actigraphy is useful in the assessment of related diseases and in monitoring the treatment response. For example, a home sleep apnea test can help detect obstructive sleep apnea (OSA), and a multiple sleep latency test can help detect hypersomnia.

table showing Clinical outcomes by patient population

3. Development History

Table 3 lists the development history of actigraphy reviewed by the American Academy of Sleep Medicine (AASM).

Table showing Development History of Actigraphy Reviewed by AASM

4. Recommendations on the clinical use

Table 4 lists the AASM’s guidelines of clinical practice recommendations for the use of actigraphy in adult and pediatric patients with suspected or diagnosed sleep disorders or circadian rhythm sleep-wake disorders.

table showing ASM recommendations on clinical use

5. Limitation and Future Direction

  • When compared with PSG, actigraphy is fairly valid and reliable in healthy normal subjects. It is best at estimating total sleep time. As sleep becomes more disturbed, the actigraphy recording becomes less accurate. In general, actigraphy may overestimate sleep and underestimate wake, particularly during the day [1-2,7-8].

  • Sleep parameters derived from actigraphy are useful in evaluating patients with sleep disorders and circadian rhythm sleep-wake disorders. However, the detailed methodology for the derivation of sleep parameters is usually lacking [9]. Future scientific reports using actigraphy should uniformly publish detailed technical and scoring procedures including sensitivity settings, scoring algorithms, and scoring procedures so that future research can more fully establish validity, particularly in special patient populations [1-2].

  • Actigraphs with different algorithms are now commercially available; accordingly, reliability has become a major question. Ideally, each actigraph needs its own reliability studies, and results from one population may not be generalizable to other populations [8].

To conclude, sleep parameters derived from different devices and algorithms may lead to different results. Transparency of the methodology for the derivation of sleep parameters may help to identify the most suitable algorithm for the special patient population as well as individuals.

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6. References

[1] M. T. Smith et al., "Use of actigraphy for the evaluation of sleep disorders and circadian rhythm sleep-wake disorders: an American Academy of Sleep Medicine clinical practice guideline," Journal of Clinical Sleep Medicine, vol. 14, no. 7, pp. 1231-1237, 2018.

[2] M. T. Smith et al., "Use of actigraphy for the evaluation of sleep disorders and circadian rhythm sleep-wake disorders: an American Academy of Sleep Medicine systematic review, meta-analysis, and GRADE assessment," Journal of Clinical Sleep Medicine, vol. 14, no. 7, pp. 1209-1230, 2018.

[3] J. L. Martin and A. D. Hakim, "Wrist actigraphy," Chest, vol. 139, no. 6, pp. 1514-1527, 2011.

[4] J. G. Acker et al., "The role of actigraphy in sleep medicine," Somnologie, vol. 25, no. 2, pp. 89-98, 2021.

[5] A. S. D. Association, "Practice parameters for the use of actigraphy in the clinical assessment of sleep disorders," Sleep, vol. 18, pp. 285-287, 1995.

[6] M. Littner et al., "Practice parameters for the role of actigraphy in the study of sleep and circadian rhythms: an update for 2002," Sleep, vol. 26, no. 3, pp. 337-341, 2003.

[7] T. Morgenthaler et al., "Practice parameters for the use of actigraphy in the assessment of sleep and sleep disorders: an update for 2007," Sleep, vol. 30, no. 4, pp. 519-529, 2007.

[8] K. L Stone, S. Ancoli-Israel. In: M. H. Kryger, T. Roth, and W. C. Dement, Principles and Practice of Sleep Medicine. Elsevier Health Sciences, pp 1671–1678. e6, 2017.

[9] D. Fekedulegn, M. E. Andrew, M. Shi, J. M. Violanti, S. Knox, and K. E. Innes, "Actigraphy-based assessment of sleep parameters," Annals of Work Exposures and Health, vol. 64, no. 4, pp. 350-367, 2020.

Dr. Francis Hsu, PhD
Dr. Francis Hsu, PhD
Research Lead

Francis is a research Lead at Labfront, responsible for data validation and analysis. He is interested in applying physics or math to medical research.

Dr. Han-Ping Huang, PhD
Dr. Han-Ping Huang, PhD
Research Lead

Han-Ping is the senior research lead (and chief plant caretaker) at Labfront, specializing in physiological data analysis. An alumnus of the BIDMC/Harvard's Center for Dynamical Biomarkers, Han-Ping uses his PhD in electrophysics to help Labfront customers convert raw physiological data into health insights. He does his best Python coding while powered by arm massages from his spiky-tongued cat, Pi.

Dr. Andrew Ahn, MD
Dr. Andrew Ahn, MD
Chief Medical/Science Officer

Dr. Ahn is an internal medicine physician with a background in physics/engineering and physiological signal analyses. He is the Chief Medical Officer at Labfront and an Assistant Professor in Medicine & Radiology at Harvard Medical School. Dr. Ahn is passionate about democratizing health sciences and exploring health from an anti-disciplinary perspective.

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