Measurement
What we do?
The Assessment of Movement Behaviours (AMBer) Group has been at the forefront of developing methods for describing physical behaviours using data from wearable accelerometers. Our measurement research spans the development of robust methods for cleaning and processing accelerometer data, the establishment of valid and reliable assessment of physical behaviours (physical activity, sedentary behaviour and sleep), and the generation of novel metrics that offer further insights into how patterns of physical behaviours are associated with health. We work with other areas in the Leicester Lifestyle Health & Research Group, the wider BRC and with external collaborators to apply the methods we develop to a wide range of diverse datasets
Members
Highlighted Research
Rowlands, A.V., Orme, M., Maylor, B., Kingsnorth A., Herring, L., Khunti, K., Davies, M., Yates, T. (2023). Can quantifying the relative intensity of a person’s free-living physical activity predict how they respond to a physical activity intervention? Findings from the PACES RCT. British Journal of Sports Medicine. doi: 10.1136/bjsports-2023-106953
Maylor, B., Edwardson, C.L., Dempsey, P.C., Patterson, M., Plekhanova, T., Yates, T., Rowlands, A.V. (2022). Stepping towards more intuitive physical activity metrics with wrist-worn accelerometry: Validity of an open-source step-count algorithm. Sensors. doi: 10.3390/s22249984
Edwardson, C.L., Maylor, B., Dawkins, N., Plekhanova, T., Rowlands, A.V. (2022). Comparability of Postural and Physical Activity Metrics from Different Accelerometer Brands Worn on the Thigh: Data Harmonization Possibilities. Measurement in Physical Education and Exercise Science. 26, 1, 39-50. doi: 10.1080/1091367X.2021.1944154
Plekhanova, T., Rowlands, A.V., Davies, M.J., Hall, A.P., Yates, T., Edwardson, C.L. (2022). Validation of an automated sleep detection algorithm using data from multiple accelerometer brands. Journal of Sleep Research, 32, 3. doi: 10.1111/jsr.13760
Dempsey, P.C., Rowlands, A.V., Strain, T., Zaccardi, F., Dawkins, N.P., Razieh, C., Davies, M.J., Khunti, K., Edwardson, C.L., Wijendale, K., Brage, S., Yates, T. (2022). Physical activity volume, intensity and incident cardiovascular disease. European Heart Journal. doi: 10.1093/eurheartj/ehac613
Plekhanova, T., Rowlands, A.V., Evans, R.A., Edwardson, C.L., Bishop, L., Bolton, C.E., Chalmers, J.D., Davies, M.J., Daynes, E., Dempsey, P., Docherty, A.B., Elneima, O., Greening, N.J., Greenwood, S., Hall, A.P., Harris, V.C., Harrison, E.M., Henson, J., Ho, L-P., Horsley, A., Houchen-Wolloff, L., Khunti, K., Leavy, O.C., Lone, N.I., Man, W.D-C., Marks, M., Maylor, B., McAuley, H.J.C., Nolan, C., Poinasamy, K., Quint, J., Raman, B., Richardson, M., Sargeant, J., Saunders, R.M., Sereno, M., Shikotra, A., Singapuri, A., Steiner, M., Stensel, D., Wain, L.V., Whitney, J., Brightling, C.E., Singh, S.J., Yates, T. (2022). Device-assessed sleep and physical activity in individuals recovering from a hospital admission for COVID-19: a prospective, multicentre study. International Journal of Behavioural Nutrition and Physical Activity. doi: 10.1186/s12966-022-01333-w
Rowlands AV, Edwardson CL, Davies MJ, Khunti K, Harrington D, Yates, T. Beyond cut-points: Accelerometer metrics that capture the physical activity profile. Med Sci Sports Exerc 2018. doi: 10.1249/MSS.0000000000001561
Rowlands AV, Davies MJ, Dempsey PC, Edwardson CE, Razieh C, Yates T. Wrist-worn accelerometers: Recommending ~1.0 mg as the minimum clinically important difference (MCID) in daily average acceleration for inactive adults. BJSM 2020. doi: 10.1136/bjsports-2020-102293
Rowlands AV, Plekhanova T, Yates T, Mirkes EM, Davies M, Khunti K, Edwardson CL. Providing a Basis for Harmonization of Accelerometer-Assessed Physical Activity Outcomes Across Epidemiological Datasets. Journal for the Measurement of Physical Behaviour 2019. doi: 10.1123/jmpb.2018-0073
Edwardson C, Bodicoat, D, Winkler E, Yates T, Dunstan D, Healy G. Considerations when using the activPAL monitor in field based research with adult populations. Journal of Sport and Health Science 2017. doi: 10.1016/j.jshs.2016.02.002
Accelerometer processing methods and code developed by our team
Processing PAL: In 2016, in collaboration with researchers in Australia, we published the validation of an automated algorithm to identify valid waking wear data in activPAL data collected with a continuous wear protocol. Our group then translated this algorithm into a user friendly, freely available, java application for a one stop method of bulk processing, visualising and summarising activPAL data. This can be accessed via github: https://github.com/UOL-COLS/ProcessingPAL
Sedentary Sphere: Two templates are available to estimate posture - one from wrist-worn accelerometer data and one from thigh-worn accelerometer data. These can be accessed here: https://www.researchgate.net/project/AMBer-Assessment-of-Movement-Behaviours
Radar Plot Code: Code written in R to generate a Radar Plot, which is a useful method for visualising and assisting with the interpretation of accelerometer data, as described in Rowlands et al. (2019). This can be accessed here: https://github.com/Maylor8/RadarPlotGenerator
Relative Intensity Gradient: Code written in R to generate the relative intensity gradient, as described in Rowlands et al. (2023). This can be accessed at: www.github.com/Maylor8/Relative-Intensity-Gradient.