Sleep Efficiency as a Digital Biomarker
Sleep efficiency is the percentage of time in bed actually spent asleep. Wearables estimate it every night at home, making it a core digital biomarker of sleep health.
Evidence maturity
Graded with the V3 framework: whether the sensor measures accurately, whether the algorithm has been validated against a reference standard, and whether the measure has been shown to matter clinically.
A DATAcc core digital sleep measure: actigraphy-based estimates are validated against polysomnography and accepted in sleep medicine, while consumer wearable staging algorithms vary in accuracy between devices.
What is Sleep Efficiency
Sleep efficiency is the proportion of time in bed that a person actually spends asleep, calculated as total sleep time divided by time in bed and expressed as a percentage. A night with 6.8 hours of sleep across 8 hours in bed gives a sleep efficiency of 85 percent, which is the threshold commonly treated as healthy in adults. Values well below that suggest fragmented sleep or difficulty falling and staying asleep, which is why sleep efficiency is a core outcome in insomnia research and one of the six core digital sleep measures defined by DATAcc, the digital measurement collaborative led by the Digital Medicine Society. As a digital biomarker it is estimated passively every night in a person's own bed, avoiding both the artificial setting of a sleep laboratory and the recall bias of sleep diaries.
How it is measured
Wearables estimate sleep efficiency by detecting sleep and wake from movement and physiology. Actigraphy, the long-established research method, classifies each time window as sleep or wake from wrist accelerometer data. Consumer devices such as rings and smartwatches add optical heart rate, heart rate variability and skin temperature signals to improve detection of brief awakenings. The device or algorithm then divides estimated total sleep time by time in bed to produce the nightly percentage. The reference standard is polysomnography, the instrumented laboratory sleep study, against which both actigraphy and consumer wearables are validated. Because single nights vary, research protocols typically average at least a week of nights for a stable estimate.
Clinical use
Sleep efficiency is a standard outcome in insomnia trials, where treatments such as cognitive behavioural therapy for insomnia aim to consolidate sleep, and a monitoring measure in depression, chronic pain and neurodegenerative disease research where sleep disruption tracks symptom burden. In sleep apnoea studies it complements respiratory measures by showing whether therapy actually improves sleep continuity. Because wearables collect it passively for months, it also supports decentralised and real-world studies that could never bring participants into a laboratory repeatedly. Trials commonly pair nightly sleep efficiency with validated questionnaires such as the Pittsburgh Sleep Quality Index to combine objective and subjective views of sleep.
Regulatory status
No standalone regulatory qualification to date. Actigraphy-derived sleep measures are widely accepted in clinical research, and several wearable sleep monitoring functions hold device-level FDA clearances.
Limitations
Wearables tend to overestimate sleep compared with polysomnography, because lying still while awake can be misclassified as sleep, and this bias is largest in people with insomnia. Time in bed is defined differently across algorithms, which shifts the calculated percentage. Vendor algorithm updates can change values mid-study, threatening longitudinal comparability. Single-night values are unreliable, and normative ranges vary with age, so interpretation needs multi-night averages and population context.
References
- Smith MT, 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. J Clin Sleep Med. 2018. pubmed.ncbi.nlm.nih.gov
- Reed DL, Sacco WP. Measuring sleep efficiency: what should the denominator be? J Clin Sleep Med. 2016. pubmed.ncbi.nlm.nih.gov
- DATAcc by DiMe. Core digital measures for sleep. 2024. dimesociety.org
- de Zambotti M, et al. Wearable sleep technology in clinical and research settings. Med Sci Sports Exerc. 2019. ncbi.nlm.nih.gov
Objective counterpart of self-reported sleep questionnaires such as the Pittsburgh Sleep Quality Index and the Insomnia Severity Index.
Collect sleep efficiency data with WeGuide, the all in one patient engagement platform
Combine nightly wearable sleep data with validated questionnaires like the PSQI in a single study workflow.
