Sleep Science Friday: Optimizing Sleep Estimation from Actigraphy

13 August 2021

By Dr. Sarah Schoch

Postdoctoral Research Fellow at Donders Institute for Brain, Cognition and Behaviour

 

Sleep can be defined on levels of behavior, brain activity or cellular dynamics. In humans, the gold standard is to measure sleep by using electroencephalography (EEG).  But behavioral measures can complement in-lab investigations to track sleep across several days and in the natural sleep environment. In a recent video, Dr. Sarah Schoch and Prof. Dr. Salome Kurth explain an approach to get more comparable and stable sleep variables.

While several methods exist for assessing behavioral sleep (videosomnography, observation, diaries), the most common is actigraphy. Actigraphs measure movement and other signals such as temperature and light. Algorithms are then applied to compute and estimate sleep and wake states from these movement-based measures. Despite the benefits that actigraphy yields in complementing laboratory EEG-studies, some aspects of actigraphy remain to be optimized for sleep estimation from actigraphs. Three particular challenges have hindered a global generalizability from actigraphy data.

 

Limited Applicability of Algorithms

A first obstacle is that several different algorithms are available, some only applicable to specific age groups (Haghayegh et al., 2019; Schoch et al., 2021). The use of different algorithms can lead to different sleep estimates, especially in populations with changed sleep and movement patterns, such as infants (Schoch et al., 2019). Differences in estimating, e.g., sleep duration based on different algorithms, are significant when considering normative values across development. While certain adjustments to sleep estimates from actigraphy have been suggested that reduce the deviance between algorithms (e.g., rescoring rules Webster et al., 1982), these are only selectively applied. One solution we propose for infant research is an analysis pipeline that incorporates different adjustments to ensure the generalizability of the results (Oakley, 1997; Sadeh et al., 1995; Schoch et al., 2019).

 

Unstandardized Variables

The second obstacle is that variables estimated from actigraphy are not applied in a standardized manner. Instead, several different variables are used, sometimes with distinct names or overlapping concepts (e.g., Sleep Opportunity and Sleep Period). If too many variables are incorporated in analysis, we run into the multiple testing problem; if we select only certain variables, we might overlook important dimensions of sleep. In a new article published in Sensors (Schoch et al., 2020), we applied a principal component analysis on 48 variables frequently reported in actigraphy studies and identified five sleep composites in infants. Sleep composites are Sleep Timing, Sleep Night, Sleep Day, Sleep Activity, and Sleep Variability. In this video, we explain our approach:

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This approach had previously been successfully applied in toddlers and parents (Staples et al., 2019). A more widespread adaption of sleep composites to different populations such as adults or clinical populations would enlarge comparability across studies.

 

Accurate Reporting

A third and last crucial step is to accurately report each specific step of the applied methodology and processing. Unfortunately, essential information, such as the framing of sleep variables or the computation algorithm thereof is often lacking (Schoch et al., 2021). This hinders the assimilation of existing literature and sadly the replication of results. However, improvement in standardized reporting of actimetry-based sleep methodology could move the field forward through allowing big data initiatives across different age groups, healthy and clinical populations, ethnicities and cultures. Such initiatives promise great insight into normative and pathological sleep behavior across the life span and could track dynamics of societies across decades and pandemics. While several big data actimetry studies are ongoing, the lack of generalizability between different analysis methods prevents the combination of data sets. We advocate a centralized database with collected information about actimetry data and methodologies to preserve data for reuse in the coming decades.

References

  1. Haghayegh et al. (2019). Performance comparison of different interpretative algorithms utilized to derive sleep parameters from wrist actigraphy data. Chronobiology International.
  2. Oakley (1997). Validation with polysomnography of the Sleepwatch sleep/wake scoring algorithm used by the Actiwatch activity monitoring system. Cambridge Neurotechnology.
  3. Sadeh et al. (1995). Activity-based assessment of sleep-wake patterns during the 1st year of life. Infant Behavior and Development.
  4. Schoch et al. (2020). Which Are the Central Aspects of Infant Sleep? The Dynamics of Sleep Composites across Infancy. Sensors.
  5. Schoch et al. (2019). Actimetry in infant sleep research: an approach to facilitate comparability. Sleep.
  6. Schoch et al. (2021). Actigraphy in sleep research with infants and young children: Current practices and future benefits of standardized reporting. J Sleep Res.
  7. Staples et al. (2019). Measuring sleep in young children and their mothers: Identifying actigraphic sleep composites. Int J Behav Dev.
  8. Webster et al. (1982). An Activity-Based Sleep Monitor System for Ambulatory Use. Sleep.

ESRS Announcements

Sleep Europe 2022 Call for Symposia 

We’d like to invite all members to contribute to the program by proposing a symposium. Submissions are now being accepted until 27 September 2021. Find out more on the criteria and procedures here.  

Recent publications from ESRS members: 

  1. Acker et al. (2021). Wrist actigraphic approach in primary, secondary and tertiary care based on the principles of predictive, preventive and personalised (3P) medicine. EPMA J.
  2. Karhu et al. (2021). Diabetes and cardiovascular diseases are associated with the worsening of intermittent hypoxaemia. J Sleep Res.
  3. Tarokh L. (2021). Sleep: Twitch in tempo. Curr Biol.
  4. Pfaff & Schlarb (2021). Child maltreatment and sleep: Two pathways explaining the link. J Sleep Res.
  5. Sousouri et al. (2021). Neuromodulation by means of phase-locked auditory stimulation affects key marker of excitability and connectivity during sleep.
  6. Putilov et al. (2021). When early and late risers were left to their own devices: six distinct chronotypes under “lockdown” remained dissimilar on their sleep and health problems. Chronobiol Int.
  7. Danilenko et al. (2021). Winter-summer difference in post-awakening salivary α-amylase and sleepiness depending on sleep and melatonin. Physiol Behav.
  8. Hamill et al. (2021). Does Daylength Affect Sleep and Mental Health Symptoms during Behavioral Interventions for Insomnia? Behav Sleep Med.
  9. Currò et al. (2021). Chronic migraine in the first COVID-19 lockdown: the impact of sleep, remote working, and other life/psychological changes. Neurol Sci.
  10. Pfaff & Schlarb (2021). Consequences of child maltreatment: A glimpse at stress and sleep. J Sleep Res.
  11. Kushida et al. (2021). Once-Nightly Sodium Oxybate (FT218) Demonstrated Improvement of Symptoms in a Phase 3 Randomized Clinical Trial in Patients With Narcolepsy.
  12. Schroder et al. (2021). Pediatric prolonged-release melatonin for insomnia in children and adolescents with autism spectrum disorders. Expert Opin Pharmacother.
  13. Gaig et al. (2021). Frequency and Characterization of Movement Disorders in Anti-IgLON5 Disease.
  14. Blume et al. (2021). Association of transportation noise with sleep during the first year of life: A longitudinal study. Environ Res.

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