Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Validation and Comparison of Accelerometers Worn on the Hip, Thigh, and Wrists for Measuring Physical Activity and Sedentary Behavior

1 Clinical Exercise Physiology Program, School of Kinesiology, Ball State University, Muncie, IN, USA
2 Human Energy Research Laboratory, Department of Kinesiology, Michigan State University, East Lansing, MI, USA
3 Networked Embedded & Wireless Systems Laboratory, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA

Special Issues: Advances in sedentary behavior research and translation

Background: Recent evidence suggests that physical activity (PA) and sedentary behavior (SB) exert independent effects on health. Therefore, measurement methods that can accurately assess both constructs are needed. Objective: To compare the accuracy of accelerometers placed on the hip, thigh, and wrists, coupled with machine learning models, for measurement of PA intensity category (SB, light-intensity PA [LPA], and moderate- to vigorous-intensity PA [MVPA]) and breaks in SB. Methods: Forty young adults (21 female; age 22.0 ± 4.2 years) participated in a 90-minute semi-structured protocol, performing 13 activities (three sedentary, 10 non-sedentary) for 3–10 minutes each. Participants chose activity order, duration, and intensity. Direct observation (DO) was used as a criterion measure of PA intensity category, and transitions from SB to a non-sedentary activity were breaks in SB. Participants wore four accelerometers (right hip, right thigh, and both wrists), and a machine learning model was created for each accelerometer to predict PA intensity category. Sensitivity and specificity for PA intensity category classification were calculated and compared across accelerometers using repeated measures analysis of variance, and the number of breaks in SB was compared using repeated measures analysis of variance. Results: Sensitivity and specificity values for the thigh-worn accelerometer were higher than for wrist- or hip-worn accelerometers, > 99% for all PA intensity categories. Sensitivity and specificity for the hip-worn accelerometer were 87–95% and 93–97%. The left wrist-worn accelerometer had sensitivities and specificities of > 97% for SB and LPA and 91–95% for MVPA, whereas the right wrist-worn accelerometer had sensitivities and specificities of 93–99% for SB and LPA but 67–84% for MVPA. The thigh-worn accelerometer had high accuracy for breaks in SB; all other accelerometers overestimated breaks in SB. Conclusion: Coupled with machine learning modeling, the thigh-worn accelerometer should be considered when objectively assessing PA and SB.
  Figure/Table
  Supplementary
  Article Metrics

Keywords machine learning; artificial neural network; pattern recognition; activity monitor; activity tracker; energy expenditure

Citation: Alexander H.K. Montoye, James M. Pivarnik, Lanay M. Mudd, Subir Biswas, Karin A. Pfeiffer. Validation and Comparison of Accelerometers Worn on the Hip, Thigh, and Wrists for Measuring Physical Activity and Sedentary Behavior. AIMS Public Health , 2016, 3(2): 298-312. doi: 10.3934/publichealth.2016.2.298

References

  • 1. U.S. Department of Health and Human Services. Physical Activity Guidelines Advisory Committee: 2008. Physical Activity Guidelines for Americans.
  • 2. Sedentary Behavior Research Network. (2012) Letter to the Editor: Standardized use of the terms "sedentary" and "sedentary behaviours". Appl Physiol Nutr Metab 37: 540-542.    
  • 3. Hamilton MT, Hamilton DG, Zderic TW. (2007) Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes 56: 2655-2667.    
  • 4. Hu FB, Li TY, Colditz GA, et al. (2003) Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA 289: 1785-1791.
  • 5. Katzmarzyk PT, Church TS, Craig CL, et al. (2009) Sitting time and mortality from all causes, cardiovascular disease, and cancer. Med Sci Sports Exerc 41: 998-1005.    
  • 6. Owen N, Healy GN, Matthews CE, et al. (2010) Too much sitting: the population health science of sedentary behavior. Ex Sport Sci Rev 38: 105-113.    
  • 7. Healy GN, Dunstan DW, Salmon J, et al. (2008) Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care 31: 661-666.    
  • 8. Matthews CE, Moore SC, Sampson J, et al. (2015) Mortality Benefits for Replacing Sitting Time with Different Physical Activities. Med Sci Sports Exerc 47: 1833-1840.    
  • 9. Stamatakis E, Rogers K, Ding D, et al. (2015) All-cause mortality effects of replacing sedentary time with physical activity and sleeping using an isotemporal substitution model: a prospective study of 201,129 mid-aged and older adults. Int J Behav Nutr Phys Act 12: 121.    
  • 10. Treuth MS, Schmitz K, Catellier DJ, et al. (2004) Defining accelerometer thresholds for activity intensities in adolescent girls. Med Sci Sports Exerc 36: 1259-1266.
  • 11. Freedson PS, Melanson E, Sirard J. (1998) Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc 30: 777-781.    
  • 12. Pedisic Z, Bauman A. (2015) Accelerometer-based measures in physical activity surveillance: current practices and issues. Br J Sports Med 49: 219-223.
  • 13. Healy GN, Clark BK, Winkler EA, et al. (2011) Measurement of adults' sedentary time in population-based studies. Am J Prev Med 41: 216-227.    
  • 14. Kozey-Keadle S, Libertine A, Lyden K, et al. (2011) Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc 43: 1561-1567.    
  • 15. Lyden K, Kozey Keadle SL, Staudenmayer JW, et al. (2012) Validity of two wearable monitors to estimate breaks from sedentary time. Med Sci Sports Exerc 44: 2243-2252.    
  • 16. Hendelman D, Miller K, Baggett C, et al. (2000) Validity of accelerometry for the assessment of moderate intensity physical activity in the field. Med Sci Sports Exerc 32: S442-449.    
  • 17. Strath SJ, Bassett DR, Jr., Swartz AM. (2003) Comparison of MTI accelerometer cut-points for predicting time spent in physical activity. Int J Sports Med 24: 298-303.    
  • 18. Swartz AM, Strath SJ, Bassett DR, Jr., et al. (2000) Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Med Sci Sports Exerc 32: S450-456.    
  • 19. Bey L, Hamilton MT. (2003) Suppression of skeletal muscle lipoprotein lipase activity during physical inactivity: a molecular reason to maintain daily low-intensity activity. J Physiol 551: 673-682.
  • 20. Katzmarzyk PT. (2014) Standing and mortality in a prospective cohort of Canadian adults. Med Sci Sports Exerc 46: 940-946.    
  • 21. Lyden K, Kozey SL, Staudenmeyer JW, et al. (2011) A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations. Eur J Appl Physiol 111: 187-201.    
  • 22. Staudenmayer J, Pober D, Crouter S, et al. (2009) An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Apple Physiol 107: 1300-1307.    
  • 23. Montoye AH, Mudd LM, Biswas S, et al. (2015) Energy Expenditure Prediction Using Raw Accelerometer Data in Simulated Free Living. Med Sci Sports Exerc 47: 1735-1746.    
  • 24. Lyden K, Keadle SK, Staudenmayer J, et al. (2013) A Method to Estimate Free-Living Active and Sedentary Behavior from an Accelerometer. Med Sci Sports Exerc 46: 386-397.
  • 25. Preece SJ, Goulermas JY, Kenney LP, et al. (2009) Activity identification using body-mounted sensors--a review of classification techniques. Physiol Meas 30: R1-33.    
  • 26. Rowlands AV, Olds TS, Hillsdon M, et al. (2014) Assessing sedentary behavior with the GENEActiv: introducing the sedentary sphere. Med Sci Sports Exerc 46: 1235-1247.    
  • 27. Steeves JA, Bowles HR, McClain JJ, et al. (2015) Ability of thigh-worn ActiGraph and activPAL monitors to classify posture and motion. Med Sci Sports Exerc 47: 952-959.    
  • 28. Malina R. (1995) Anthropometry. Physiological assessment of human fitness: 205-219.
  • 29. Dong B, Montoye A, Moore R, et al. (2013) Energy-aware activity classification using wearable sensor networks. 87230Y-87230Y.
  • 30. Montoye A, Dong B, Biswas S, et al. (2014) Use of a wireless network of accelerometers for improved measurement of human energy expenditure. Electronics 3: 205-220.    
  • 31. Skotte J, Korshoj M, Kristiansen J, et al. (2014) Detection of physical activity types using triaxial accelerometers. J Phys Act Health 11: 76-84.    
  • 32. Cleland I, Kikhia B, Nugent C, et al. (2013) Optimal placement of accelerometers for the detection of everyday activities. Sensors 13: 9183-9200.    
  • 33. Montoye AHK, Mudd LM, Pivarnik JM, et al. (2016) Comparison of activity type classification accuracy from accelerometers worn on the hip, wrists, and thigh in young, apparently healthy adults. Meas Phys Ed Exerc Sci In Press.
  • 34. Ainsworth BE, Haskell WL, Herrmann SD, et al. (2011) Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc 43: 1575-1581.    
  • 35. Altman D. (1991) Practical Statistics for Medical Research. London, UK: Chapman & Hall.
  • 36. Di Pietro L, Dziura J, Blair SN. (2004) Estimated change in physical activity level (PAL) and prediction of 5-year weight change in men: the Aerobics Center Longitudinal Study. Int J Obes Relat Metab Disord 28: 1541-1547.    
  • 37. Staudenmayer J, He S, Hickey A, et al. (2015) Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements. J Appl Physiol 119: 396-403.    
  • 38. Edwardson C, Winkler E, Bodicoat D, et al. (2016) Considerations when using the activPAL monitor in field based research with adult populations. J Sport Health Sci In Press.
  • 39. Esliger DW, Rowlands AV, Hurst TL, et al. (2011) Validation of the GENEA Accelerometer. Med Sci Sports Exerc 43: 1085-1093.    
  • 40. Rowlands AV, Yates T, Olds TS, et al. (2016) Sedentary Sphere: Wrist-Worn Accelerometer-Brand Independent Posture Classification. Med Sci Sports Exerc 48: 748-754.
  • 41. Pavey TG, Gomersall SR, Clark BK, et al. (2016) The validity of the GENEActiv wrist-worn accelerometer for measuring adult sedentary time in free living. J Sci Med Sport 19: 395-399.
  • 42. Troiano RP, McClain JJ, Brychta RJ, et al. (2014) Evolution of accelerometer methods for physical activity research. Br J Sports Med 48: 1019-1023.    
  • 43. Orme M, Wijndaele K, Sharp SJ, et al. (2014) Combined influence of epoch length, cut-point and bout duration on accelerometry-derived physical activity. Int J Behav Nutr Phys Act 11: 34.    
  • 44. Ayabe M, Kumahara H, Morimura K, et al. (2013) Epoch length and the physical activity bout analysis: an accelerometry research issue. BMC Res Notes 6: 20.    
  • 45. John D, Sasaki J, Staudenmayer J, et al. (2013) Comparison of raw acceleration from the GENEA and ActiGraph GT3X+ activity monitors. Sensors 13: 14754-14763.    

 

This article has been cited by

  • 1. Alexander H.K. Montoye, James M. Pivarnik, Lanay M. Mudd, Subir Biswas, Karin A. Pfeiffer, Evaluation of the activPAL accelerometer for physical activity and energy expenditure estimation in a semi-structured setting, Journal of Science and Medicine in Sport, 2017, 10.1016/j.jsams.2017.04.011
  • 2. Jesus D. Ceron, Diego M. Lopez, Gustavo A. Ramirez, A mobile system for sedentary behaviors classification based on accelerometer and location data, Computers in Industry, 2017, 92-93, 25, 10.1016/j.compind.2017.06.005
  • 3. Alexander H. K. Montoye, Scott A. Conger, Christopher P. Connolly, Mary T. Imboden, M. Benjamin Nelson, Josh M. Bock, Leonard A. Kaminsky, Validation of Accelerometer-Based Energy Expenditure Prediction Models in Structured and Simulated Free-Living Settings, Measurement in Physical Education and Exercise Science, 2017, 1, 10.1080/1091367X.2017.1337638
  • 4. TARRAH MITCHELL, KELSEY BORNER, JONATHAN FINCH, JACQUELINE KERR, JORDAN A. CARLSON, Using Activity Monitors to Measure Sit-to-Stand Transitions in Overweight/Obese Youth, Medicine & Science in Sports & Exercise, 2017, 49, 8, 1592, 10.1249/MSS.0000000000001266
  • 5. Saejong Park, Mihyun Lee, Validation of Physical Activity Measured by Accelerometers Worn on Waist and Wrist, The Korean Journal of Physical Education, 2017, 56, 4, 563, 10.23949/kjpe.2017.07.56.4.38
  • 6. Alexander HK Montoye, Bradford S. Westgate, Morgan R. Fonley, Karin A. Pfeiffer, Cross-validation and out-of-sample testing of physical activity intensity predictions using a wrist-worn accelerometer, Journal of Applied Physiology, 2018, 10.1152/japplphysiol.00760.2017
  • 7. ALEXANDER H. K. MONTOYE, M. BENJAMIN NELSON, JOSHUA M. BOCK, MARY T. IMBODEN, LEONARD A. KAMINSKY, KELLY A. MACKINTOSH, MELITTA A. MCNARRY, KARIN A. PFEIFFER, Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers, Medicine & Science in Sports & Exercise, 2018, 50, 5, 1103, 10.1249/MSS.0000000000001534
  • 8. E. J. Smits, E. A. H. Winkler, G. N. Healy, P. M. Dall, M. H. Granat, P. W. Hodges, Comparison of single- and dual-monitor approaches to differentiate sitting from lying in free-living conditions, Scandinavian Journal of Medicine & Science in Sports, 2018, 10.1111/sms.13203
  • 9. Joost Oomen, Dennis Arts, Michel Sperling, Steven Vos, A stepwise science-industry collaboration to optimize the calculation of energy expenditure during walking and running with a consumer-based activity device, Technology in Society, 2018, 10.1016/j.techsoc.2018.09.001
  • 10. Vahid Farrahi, Maisa Niemelä, Maarit Kangas, Raija Korpelainen, Timo Jämsä, Calibration and Validation of Accelerometer-based Activity Monitors: A Systematic Review of Machine-Learning Approaches, Gait & Posture, 2018, 10.1016/j.gaitpost.2018.12.003
  • 11. Michael I.C. Kingsley, Rashmika Nawaratne, Paul D. O’Halloran, Alexander H.K. Montoye, Damminda Alahakoon, Daswin De Silva, Kiera Staley, Matthew Nicholson, Wrist-specific accelerometry methods for estimating free-living physical activity, Journal of Science and Medicine in Sport, 2018, 10.1016/j.jsams.2018.12.003
  • 12. Jennifer L. Huberty, Jeni L. Matthews, Meynard Toledo, Lindsay Smith, Catherine L. Jarrett, Benjamin Duncan, Matthew P. Buman, Vinyasa Flow: Metabolic Cost and Validation of Hip- and Wrist-Worn Wearable Sensors, Journal for the Measurement of Physical Behaviour, 2018, 1, 10.1123/jmpb.2017-0010
  • 13. Anis Davoudi, Kumar Rohit Malhotra, Benjamin Shickel, Scott Siegel, Seth Williams, Matthew Ruppert, Emel Bihorac, Tezcan Ozrazgat-Baslanti, Patrick J. Tighe, Azra Bihorac, Parisa Rashidi, Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning, Scientific Reports, 2019, 9, 1, 10.1038/s41598-019-44004-w
  • 14. Gil Boudet, Pierre Chausse, David Thivel, Sylvie Rousset, Martial Mermillod, Julien S. Baker, Lenise M. Parreira, Yolande Esquirol, Martine Duclos, Frédéric Dutheil, How to Measure Sedentary Behavior at Work?, Frontiers in Public Health, 2019, 7, 10.3389/fpubh.2019.00167

Reader Comments

your name: *   your email: *  

Copyright Info: 2016, Alexander H.K. Montoye, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved