ArticleBMJ open2015
Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity.
Article in BMJ open, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Physical activity measurements in individuals with interstitial lung disease: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023Pooled it
- Interrelations of work with health and wellbeing on a 50+ year old workforce assessed using longitudinal self-reports and actigraphy.Scientific reports · 2026Article
- 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
- Development of an alcohol biosensor non-wear algorithm: laboratory-based machine learning and field-based deployment.Scientific reports · 2025Article
- Insights into endometriosis symptom trajectories and assessment of surgical intervention outcomes using longitudinal actigraphy.NPJ digital medicine · 2025Article
- Using wearable technology to objectively investigate physical behaviour and determine health outcomes of a physical activity intervention in patients with psoriasis.Skin health and disease · 2024Article
- Article
- Gait, physical activity and tibiofemoral cartilage damage: a longitudinal machine learning analysis in the Multicenter Osteoarthritis Study.British journal of sports medicine · 2023Article
- Generalizability and performance of methods to detect non-wear with free-living accelerometer recordings.Scientific reports · 2023Article
- Detecting accelerometer non-wear periods using change in acceleration combined with rate-of-change in temperature.BMC medical research methodology · 2022Article
- 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
- Detection of Food Intake Sensor's Wear Compliance in Free-Living.IEEE sensors journal · 2021Article
- Identifying Prenatal and Postnatal Determinants of Infant Growth: A Structural Equation Modelling Based Cohort Analysis.International journal of environmental research and public health · 2021Article
- Objective Characterization of Activity, Sleep, and Circadian Rhythm Patterns Using a Wrist-Worn Actigraphy Sensor: Insights Into Posttraumatic Stress Disorder.JMIR mHealth and uHealth · 2020Article
- Evaluating the performance of raw and epoch non-wear algorithms using multiple accelerometers and electrocardiogram recordings.Scientific reports · 2020Article
- Sensors Capabilities, Performance, and Use of Consumer Sleep Technology.Sleep medicine clinics · 2020Review
- Ambulatory sleep scoring using accelerometers-distinguishing between nonwear and sleep/wake states.PeerJ · 2020Article
- Predictors of objectively measured physical activity in 12-month-old infants: A study of linked birth cohort data with electronic health records.Pediatric obesity · 2019Article
- Validation of automatic wear-time detection algorithms in a free-living setting of wrist-worn and hip-worn ActiGraph GT3X.BMC public health · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveTo classify wear and non-wear time of accelerometer data for accurately quantifying physical activity in public health or population level research.
designA bi-moving-window-based approach was used to combine acceleration and skin temperature data to identify wear and non-wear time events in triaxial accelerometer data that monitor physical activity.
settingLocal residents in Swansea, Wales, UK.
participants50 participants aged under 16 years (n=23) and over 17 years (n=27) were recruited in two phases: phase 1: design of the wear/non-wear algorithm (n=20) and phase 2: validation of the algorithm (n=30).
methodsParticipants wore a triaxial accelerometer (GeneActiv) against the skin surface on the wrist (adults) or ankle (children). Participants kept a diary to record the timings of wear and non-wear and were asked to ensure that events of wear/non-wear last for a minimum of 15 min.
resultsThe overall sensitivity of the proposed method was 0.94 (95% CI 0.90 to 0.98) and specificity 0.91 (95% CI 0.88 to 0.94). It performed equally well for children compared with adults, and females compared with males. Using surface skin temperature data in combination with acceleration data significantly improved the classification of wear/non-wear time when compared with methods that used acceleration data only (p<0.01).
conclusionsUsing either accelerometer seismic information or temperature information alone is prone to considerable error. Combining both sources of data can give accurate estimates of non-wear periods thus giving better classification of sedentary behaviour. This method can be used in population studies of physical activity in free-living environments.
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