Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants

Matthew Willetts, Sven Hollowell, Louis Aslett, Chris Holmes, Aiden Doherty, Matthew Willetts, Sven Hollowell, Louis Aslett, Chris Holmes, Aiden Doherty

Abstract

Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants. For example, we find that men spend more time in both low- and high- intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Variation in accelerometer-measured behaviour types across the day by participant characteristics (measured 2007–2010) and weekday/weekend (2013–2015): the UK Biobank study (n = 96,220).
Figure 2
Figure 2
Variation in accelerometer-measured time by activity type: the UK Biobank study 2013–2015 (n = 96,220).

References

    1. Lee I-M, et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380:219–29. doi: 10.1016/S0140-6736(12)61031-9.
    1. Celis-Morales, C. A. et al. Association between active commuting and incident cardiovascular disease, cancer, and mortality: prospective cohort study. BMJ357, (2017).
    1. Shan, Z. et al. Sleep Duration and Risk of Type 2 Diabetes: A Meta-analysis of Prospective Studies. Diabetes Care38 (2015).
    1. Kelly P, et al. Systematic review and meta-analysis of reduction in all-cause mortality from walking and cycling and shape of dose response relationship. Int. J. Behav. Nutr. Phys. Act. 2014;11:132. doi: 10.1186/s12966-014-0132-x.
    1. Wilmot EG, et al. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia. 2012;55:2895–2905. doi: 10.1007/s00125-012-2677-z.
    1. Colbert LH, Matthews CE, Havighurst TC, Kim K, Schoeller DA. Comparative validity of physical activity measures in older adults. Med. Sci. Sports Exerc. 2011;43:867–76. doi: 10.1249/MSS.0b013e3181fc7162.
    1. Althoff T, et al. Large-scale physical activity data reveal worldwide activity inequality. Nature. 2017;547:336–339. doi: 10.1038/nature23018.
    1. Sudlow C, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. doi: 10.1371/journal.pmed.1001779.
    1. Littlejohns TJ, Sudlow C, Allen NE, Collins R. UK Biobank: opportunities for cardiovascular research. Eur. Heart J. 2017 doi: 10.1093/eurheartj/ehx254.
    1. Doherty A, et al. Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study. PLoS One. 2017;12:e0169649. doi: 10.1371/journal.pone.0169649.
    1. Troiano RP, McClain JJ, Brychta RJ, Chen KY. Evolution of accelerometer methods for physical activity research. Br. J. Sports Med. 2014;48:1019–23. doi: 10.1136/bjsports-2014-093546.
    1. Menai M, et al. Accelerometer assessed moderate-to-vigorous physical activity and successful ageing: results from the Whitehall II study. Sci. Rep. 2017;8:45772. doi: 10.1038/srep45772.
    1. Kerr J, et al. Objective Assessment of Physical Activity: Classifiers for Public Health. Med. Sci. Sports Exerc. 2016;48:951–7. doi: 10.1249/MSS.0000000000000841.
    1. Intille SS, Lester J, Sallis JF, Duncan G. New horizons in sensor development. Med Sci Sport. Exerc. 2012;44:24–31.
    1. Mannini A, Intille SS, Rosenberger M, Sabatini AM, Haskell W. Activity recognition using a single accelerometer placed at the wrist or ankle. Med. Sci. Sports Exerc. 2013;45:2193–203. doi: 10.1249/MSS.0b013e31829736d6.
    1. Welch WA, et al. Classification accuracy of the wrist-worn gravity estimator of normal everyday activity accelerometer. Med. Sci. Sports Exerc. 2013;45:2012–9. doi: 10.1249/MSS.0b013e3182965249.
    1. van Hees VT, Golubic R, Ekelund U, Brage S. Impact of study design on development and evaluation of an activity-type classifier. J. Appl. Physiol. 2013;114:1042–51. doi: 10.1152/japplphysiol.00984.2012.
    1. Zhang S, Rowlands AV, Murray P, Hurst TL. Physical activity classification using the GENEA wrist-worn accelerometer. Med Sci Sport. Exerc. 2012;44:742–748.
    1. Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J. Appl. Physiol. 2009;107:1300–1307. doi: 10.1152/japplphysiol.00465.2009.
    1. Ellis, K., Godbole, S., Kerr, J. & Lanckriet, G. Multi-sensor physical activity recognition in free-living. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct Publication - UbiComp’14 Adjunct. 431–440, 10.1145/2638728.2641673,(ACM Press, 2014).
    1. Miller, N. E., Welch, W. A., Doherty, A. R. & Strath, S. J. Accuracy of Behavioral Assessment with a Wearable Camera in Semi-Structured and Free Living Conditions in Older Adults. in American College of Sports Medicine Annual Meeting, 30 May–03 June (2017).
    1. Montoye AHK, Begum M, Henning Z, Pfeiffer KA. Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data. Physiol. Meas. 2017;38:343–357. doi: 10.1088/1361-6579/38/2/343.
    1. Sasaki JE, et al. Performance of Activity Classification Algorithms in Free-Living Older Adults. Med. Sci. Sports Exerc. 2016;48:941–50. doi: 10.1249/MSS.0000000000000844.
    1. Pavzey, T. G., Gilson, N. D., Gomersall, S. R., Clark, B. & Trost, S. G. Field evaluation of a random forest activity classifier for wrist-worn accelerometer data. J. Sci. Med. Sport, 10.1016/j.jsams.2016.06.003 (2016).
    1. Ainsworth BE, et al. 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sport. Exerc. 2011;43:1575–1581. doi: 10.1249/MSS.0b013e31821ece12.
    1. Ellis K, et al. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers. Physiol. Meas. 2014;35:2191–203. doi: 10.1088/0967-3334/35/11/2191.
    1. Landis JR, Koch GC. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–174. doi: 10.2307/2529310.
    1. Doherty AR, et al. Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity. Int. J. Behav. Nutr. Phys. Act. 2013;10:22. doi: 10.1186/1479-5868-10-22.
    1. Kerr J, et al. Using the SenseCam to improve classifications of sedentary behavior in free-living settings. Am. J. Prev. Med. 2013;44:290–296. doi: 10.1016/j.amepre.2012.11.004.
    1. Ellis K, Kerr J, Godbole S, Staudenmayer J, Lanckriet G. Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification. Med. Sci. Sports Exerc. 2016;48:933–40. doi: 10.1249/MSS.0000000000000840.
    1. Bonomi, A. G., Plasqui, G., Goris, A. H. C. & Westerterp, K. R. Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. J. Appl. Physiol. 107 (2009).
    1. Sabia S, et al. Association Between Questionnaire- and Accelerometer-Assessed Physical Activity: The Role of Sociodemographic Factors. Am. J. Epidemiol. 2014;179:781–790. doi: 10.1093/aje/kwt330.
    1. da Silva IC, et al. Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry. Int. J. Epidemiol. 2014;43:1959–68. doi: 10.1093/ije/dyu203.
    1. Borazio, M., Berlin, E., Kucukyildiz, N., Scholl, P. & Laerhoven, K. Van. Towards Benchmarked Sleep Detection with Wrist-Worn Sensing Units. In 2014 IEEE International Conference on Healthcare Informatics. 125–134, 10.1109/ICHI.2014.24 (IEEE, 2014),.
    1. Strath SJ, et al. Guide to the assessment of physical activity: Clinical and research applications: a scientific statement from the American Heart Association. Circulation. 2013;128:2259–79. doi: 10.1161/01.cir.0000435708.67487.da.
    1. White, T., Westgate, K., Wareham, N. J. & Brage, S. Estimation of physical activity energy expenditure during free-living from wrist accelerometry in UK adults. PLoS One (in press) (2016).
    1. Kelly P, et al. Developing a Method to Test the Validity of 24 Hour Time Use Diaries Using Wearable Cameras: A Feasibility Pilot. PLoS One. 2015;10:e0142198. doi: 10.1371/journal.pone.0142198.
    1. Ladha, C., Ladha, K., Jackson, D. & Olivier, P. Shaker Table Validation Of Openmovement Ax3 Accelerometer. in 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement 69–70 (2013).
    1. Esliger DW, et al. Validation of the GENEA Accelerometer. Med Sci Sport. Exerc. 2011;43:1085–1093.
    1. Doherty AR, et al. Wearable cameras in health: The state of the art and future possibilities. Am. J. Prev. Med. 2013;44:320–323. doi: 10.1016/j.amepre.2012.11.008.
    1. Hodges, S. et al. SenseCam: A Retrospective Memory Aid. in UbiComp: 8th International Conference on Ubiquitous Computing4602, 177–193 (Springer, 2006).
    1. Kelly, P. et al. High group level validity but high random error of a self-report travel diary, as assessed by wearable cameras. J. Transp. Heal, 10.1016/j.jth.2014.04.003 (2014).
    1. Doherty, A. R. et al. Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity. Int. J. Behav. Nutr. Phys. Act. 10 (2013).
    1. Kelly P, et al. Ethics of using wearable cameras devices in health behaviour research. Am J Prev Med. 2013;44:314–319. doi: 10.1016/j.amepre.2012.11.006.
    1. Doherty AR, Moulin CJA, Smeaton AF. Automatically assisting human memory: A SenseCam browser. Memory. 2011;19:785–795. doi: 10.1080/09658211.2010.509732.
    1. van Hees VT, et al. A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer. PLoS One. 2015;10:e0142533. doi: 10.1371/journal.pone.0142533.
    1. Eurostat. Harmonised European Time Use Surveys: 2008 Guidelines (2008).
    1. van Hees VT, et al. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J. Appl. Physiol. 2014;117:738–44. doi: 10.1152/japplphysiol.00421.2014.
    1. Vähä-Ypyä H, Vasankari T, Husu P, Suni J, Sievänen H. A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. Clin. Physiol. Funct. Imaging. 2015;35:64–70. doi: 10.1111/cpf.12127.
    1. Breiman L. Random Forests. Mach. Learn. 2001;45:5–32. doi: 10.1023/A:1010933404324.
    1. Fernández-Delgado M, Cernadas E, Barro S, Amorim D. Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? J. Mach. Learn. Res. 2014;15:3133–3181.
    1. Chen, C., Liaw, A. & Breiman, L. Using random forest to learn imbalanced data. University of California, Berkeley (2004).
    1. Rabiner L, Juang B. An introduction to hidden Markov models. IEEE ASSP Mag. 1986;3:4–16. doi: 10.1109/MASSP.1986.1165342.
    1. Forney GD. The viterbi algorithm. Proc. IEEE. 1973;61:268–278. doi: 10.1109/PROC.1973.9030.
    1. Viera AJ, Garrett JM. Understanding interobserver agreement: the kappa statistic. Fam. Med. 2005;37:360–3.
    1. R Core Team. R: A Language and Environment for Statistical Computing (2016).

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir