sleepmonitoring

Sleep monitoring refers to the collection and analysis of data about a person’s sleep patterns to support clinical assessment, treatment, and research. It ranges from specialized clinical studies conducted in laboratories to home-based assessments and consumer wearables.

Clinical sleep studies, or polysomnography, record brain activity (EEG), eye movements (EOG), and muscle tone (EMG) along with heart rate, respiratory effort, airflow, oxygen saturation, snoring, and sometimes limb movements. These data provide a comprehensive view of sleep architecture, breathing disorders, and body movements over one night. Actigraphy uses a wrist-worn accelerometer to estimate sleep-wake cycles over several days to weeks, offering a practical view of habitual sleep patterns. Home sleep apnea testing uses simplified sensors to assess breathing and oxygen levels outside a lab setting. Consumer devices, such as smartwatches and fitness trackers, often estimate sleep duration, awakenings, heart rate, and sometimes SpO2, based on movement and physiology.

Key metrics used in sleep monitoring include total sleep time, sleep onset latency, wake after sleep onset, sleep efficiency, and the distribution of sleep stages (light, deep, and REM sleep). In diagnostic contexts, indices such as the apnea-hypopnea index and periodic limb movement index help classify sleep disorders. Data interpretation supports diagnosing obstructive sleep apnea, narcolepsy, insomnia, restless legs syndrome, and other sleep-related conditions, and it aids treatment planning and outcome monitoring.

Limitations include varying accuracy across methods, with polysomnography as the reference standard and actigraphy or consumer devices offering estimates rather than precise measurements. Privacy, data security, and accessibility considerations are important in the use of sleep-monitoring technologies. Future directions emphasize remote monitoring, AI-driven analysis, multimodal sensing, and standardized clinical guidelines.