ArticlePeerJ2020
Ambulatory sleep scoring using accelerometers-distinguishing between nonwear and sleep/wake states.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Do subjective and objective baseline sleep disturbances predict post-traumatic stress disorder treatment response? A secondary analysis of a randomized controlled trial.European journal of psychotraumatology · 2026Trial
- Subjective and objective sleep disturbances following trauma-focused treatment.European journal of psychotraumatology · 2025Trial
- An mHealth Intervention Promoting Physical Activity and Healthy Eating in a Family Setting (SMARTFAMILY): Randomized Controlled Trial.JMIR mHealth and uHealth · 2024Trial
- Cleaning and pre-processing of actigraphy data for physical activity and sleep research: a scoping review.Physiological measurement · 2026Article
- Validation and optimisation of wearable accelerometer data pre-processing for digital measure implementation and development.PloS one · 2026Article
- Circadian rhythm parameters differentiate euthymic, manic and depressive mood states in bipolar disorders - an explorative pilot study.International journal of bipolar disorders · 2025Article
- Linking physical activity to workers' low back pain, back health, and theory-based psychological variables: study protocol of the workHealth intensive longitudinal observational study.BMC public health · 2025Article
- Impact of a Semi-Rigid Knee Orthotic Intervention on Pain, Physical Activity, and Functional Capacity in Patients with Medial Knee Osteoarthritis.Journal of clinical medicine · 2024Article
- Determination of cut-off points for the Move4 accelerometer in children aged 8-13 years.BMC sports science, medicine & rehabilitation · 2023Article
- Generalizability and performance of methods to detect non-wear with free-living accelerometer recordings.Scientific reports · 2023Article
- A wearable device for at-home obstructive sleep apnea assessment: State-of-the-art and research challenges.Frontiers in neurology · 2023Review
- Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors.Sensors (Basel, Switzerland) · 2022Article
- Manual Annotation of Time in Bed Using Free-Living Recordings of Accelerometry Data.Sensors (Basel, Switzerland) · 2021Article
- Comparison of Self-Reported and Device-Based Measured Physical Activity Using Measures of Stability, Reliability, and Validity in Adults and Children.Sensors (Basel, Switzerland) · 2021Article
- Sleep classification from wrist-worn accelerometer data using random forests.Scientific reports · 2021Article
- Emfit Bed Sensor Activity Shows Strong Agreement with Wrist Actigraphy for the Assessment of Sleep in the Home Setting.Nature and science of sleep · 2021Article
- Accuracy of Sedentary Behavior-Triggered Ecological Momentary Assessment for Collecting Contextual Information: Development and Feasibility Study.JMIR mHealth and uHealth · 2020Article
- Does Cardiorespiratory Fitness Buffer Stress Reactivity and Stress Recovery in Police Officers? A Real-Life Study.Frontiers in psychiatry · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDifferentiating nonwear time from sleep and wake times is essential for the estimation of sleep duration based on actigraphy data. To efficiently analyze large-scale data sets, an automatic method of identifying these three different states is required. Therefore, we developed a classification algorithm to determine nonwear, sleep and wake periods from accelerometer data. Our work aimed to (I) develop a new pattern recognition algorithm for identifying nonwear periods from actigraphy data based on the influence of respiration rate on the power spectrum of the acceleration signal and implement it in an automatic classification algorithm for nonwear/sleep/wake states; (II) address motion artifacts that occur during nonwear periods and are known to cause misclassification of these periods; (III) adjust the algorithm depending on the sensor position (wrist, chest); and (IV) validate the algorithm on both healthy individuals and patients with sleep disorders.
methodsThe study involved 98 participants who wore wrist and chest acceleration sensors for one day of measurements. They spent one night in the sleep laboratory and continued to wear the sensors outside of the laboratory for the remainder of the day. The results of the classification algorithm were compared to those of the reference source: polysomnography for wake/sleep and manual annotations for nonwear/wear classification.
resultsThe median kappa values for the two locations were 0.83 (wrist) and 0.84 (chest). The level of agreement did not vary significantly by sleep health (good sleepers vs. subjects with sleep disorders) ( DISCUSSION: We studied the influence of the respiration wave on the power spectrum of the acceleration signal for the differentiation of nonwear periods from sleep and wake periods. The algorithm combined both spectral analysis of the acceleration signal and rescoring. Based on the Bland-Altman analysis, the chest-worn accelerometer showed better results than the wrist-worn accelerometer.
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