White Paper

Real-World Sleep Assessments Using Wearable Technologies

Source: ActiGraph
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Sleep is an important aspect of health and essential to the quality of life.1 Poor sleep patterns are linked to the progression of many diseases, including depression2 , hypertension3, obesity4, and neurodegenerative diseases.5 Accurate and reliable assessment of sleep patterns is the first step to treatment development and proper care for people living with sleep disorders.

Sleep is a multifaceted construct, and sleep disorders can manifest in different pathological patterns of sleep. Depending on the aspects of sleep that are of interest, different techniques can be used. Polysomnography (PSG) in a sleep laboratory is considered the gold standard for identifying apneas, hypopneas, and REM disorders.

However, in-lab PSG has low ecological validity for understanding sleeping behavior, due to the fact that patients are sleeping with EEG electrodes, ECG electrodes, a breathing belt, a SpO2 monitor, and other sensors in a controlled laboratory environment, which is not typically representative of sleeping behavior in the home environment. Thus, to assess the overall sleeping behaviors, which are more relevant to circadian rhythm disorders and insomnia, sleep log or diary is typically used. However, sleep diaries are subjective and susceptible to recall bias and placebo effect, and they are known to show poor correlation with objective sleep data.6

Wrist-worn wearable devices allow for objective and reliable sleep assessments in the ecologically-valid home setting. This approach reduces the need for costly sleep lab visits and complements self-reported sleep diaries with valuable objective information. However, due to the multifaceted nature of sleep and the use of automated algorithms, sleep assessments using wearable data can be confusing to clinical researchers and physicians. To help sleep researchers leverage the benefit of wearable devices, we hereby summarize the state-of-the art data processing steps and algorithms used to obtain valid sleep outcomes from wrist acceleration data.

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