1.0 BACKGROUND ACTIGRAPHY HAS BEEN USED TO STUDY SLEEP/WAKE PATTERNS FOR OVER 20 YEARS. The advantage of actigraphy over traditional polysomnography (PSG) is that actigraphy can conveniently record continuously for 24-hours a day for days, weeks or even longer. In 1995, Sadeh et al.,1 under the auspices of the American Sleep Disorders Association (now called the American Academy of Sleep Medicine, AASM), reviewed the current knowledge about the role of actigraphy in the evaluation of sleep disorders. They concluded that actigraphy does provide useful information and that it may be a “cost-effective method for assessing specific sleep disorders...[but that] methodological issues have not been systematically addressed in clinical research and practice.” Based on that task force’s report, the AASM Standards of Practice Committee concluded that actigraphy was not indicated for routine diagnosis or for assessment of severity or management of sleep disorders, but might be a useful adjunct for diagnosing insomnia, circadian rhythm disorders or excessive sleepiness.2 Since that time, actigraph technology has improved, and many more studies have been conducted. Several review papers have concluded that wrist actigraphy can usefully approximate sleep versus wake state during 24 hours and have noted that actigraphy has been used for monitoring insomnia, circadian sleep/wake disturbances, and periodic limb movement disorder.3,4 This paper begins where the 1995 paper left off. Under the auspices of the AASM, a new task force was established to review the current state of the art of this technology.

The Role of Actigraphy in the Study of Sleep and Circadian Rhythms AMERICAN ACADEMY OF SLEEP MEDICINE REVIEW PAPER

Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus. The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified. Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015. The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific. This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data. CRD42016039991.

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Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations

Comparison of actigraphic, polysomnographic, and subjective assessment of sleep parameters in sleep-disordered patients

OBJECTIVES We validated actigraphy for detecting sleep and wakefulness versus polysomnography (PSG). DESIGN Actigraphy and polysomnography were simultaneously collected during sleep laboratory admissions. All studies involved 8.5 h time in bed, except for sleep restriction studies. Epochs (30-sec; n = 232,849) were characterized for sensitivity (actigraphy = sleep when PSG = sleep), specificity (actigraphy = wake when PSG = wake), and accuracy (total proportion correct); the amount of wakefulness after sleep onset (WASO) was also assessed. A generalized estimating equation (GEE) model included age, gender, insomnia diagnosis, and daytime/nighttime sleep timing factors. SETTING Controlled sleep laboratory conditions. PARTICIPANTS Young and older adults, healthy or chronic primary insomniac (PI) patients, and daytime sleep of 23 night-workers (n = 77, age 35.0 ± 12.5, 30F, mean nights = 3.2). INTERVENTIONS N/A. MEASUREMENTS AND RESULTS Overall, sensitivity (0.965) and accuracy (0.863) were high, whereas specificity (0.329) was low; each was only slightly modified by gender, insomnia, day/night sleep timing (magnitude of change 30 min/night. CONCLUSIONS This validation quantifies strengths and weaknesses of actigraphy as a tool measuring sleep in clinical and population studies. Overall, the participant-specific accuracy is relatively high, and for most participants, above 80%. We validate this finding across multiple nights and a variety of adults across much of the young to midlife years, in both men and women, in those with and without insomnia, and in 77 participants. We conclude that actigraphy is overall a useful and valid means for estimating total sleep time and wakefulness after sleep onset in field and workplace studies, with some limitations in specificity.

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Measuring sleep: accuracy, sensitivity, and specificity of wrist actigraphy compared to polysomnography.

https://northumbria-test.eprints-hosting.org/id/document/263680

Assessment of the cortisol awakening response: Expert consensus guidelines.

The purpose of this study was to develop and validate automatic scoring methods to distinguish sleep from wakefulness based on wrist activity. Forty-one subjects (18 normals and 23 with sleep or psychiatric disorders) wore a wrist actigraph during overnight polysomnography. In a randomly selected subsample of 20 subjects, candidate sleep/wake prediction algorithms were iteratively optimized against standard sleep/wake scores. The optimal algorithms obtained for various data collection epoch lengths were then prospectively tested on the remaining 21 subjects. The final algorithms correctly distinguished sleep from wakefulness approximately 88% of the time. Actigraphic sleep percentage and sleep latency estimates correlated 0.82 and 0.90, respectively, with corresponding parameters scored from the polysomnogram (p < 0.0001). Automatic scoring of wrist activity provides valuable information about sleep and wakefulness that could be useful in both clinical and research applications.

W Gruen

Papers

Automatic sleep/wake identification from wrist activity