Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity

Shang Ming Zhou; Rebecca A. Hill; Kelly Morgan; Gareth Stratton; Mike B. Gravenor; Gunnar Bijlsma; Sinead Brophy

doi:10.1136/bmjopen-2014-007447

Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity

Shang Ming Zhou^*, Rebecca A. Hill, Kelly Morgan, Gareth Stratton, Mike B. Gravenor, Gunnar Bijlsma, Sinead Brophy

^*Corresponding author for this work

School of Nursing and Midwifery

Swansea University

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Pages (from-to)	e007447-e007447
Number of pages	0
Journal	BMJ Open
Volume	5
Issue number	5
Early online date	11 May 2015
DOIs	https://doi.org/10.1136/bmjopen-2014-007447
Publication status	Published - May 2015

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Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activityLicence: CC BY-NC

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title = "Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity",

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.",

author = "Zhou, {Shang Ming} and Hill, {Rebecca A.} and Kelly Morgan and Gareth Stratton and Gravenor, {Mike B.} and Gunnar Bijlsma and Sinead Brophy",

year = "2015",

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doi = "10.1136/bmjopen-2014-007447",

language = "English",

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T1 - Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity

AU - Zhou, Shang Ming

AU - Hill, Rebecca A.

AU - Morgan, Kelly

AU - Stratton, Gareth

AU - Gravenor, Mike B.

AU - Bijlsma, Gunnar

AU - Brophy, Sinead

PY - 2015/5

Y1 - 2015/5

N2 - 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.

AB - 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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DO - 10.1136/bmjopen-2014-007447

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SP - e007447-e007447

JO - BMJ Open

JF - BMJ Open

IS - 5

ER -

Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity

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