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Methods Inf Med 2016; 55(06): 525-532
DOI: 10.3414/ME15-05-0013
Original Articles
Schattauer GmbH

Challenges and Opportunities for Harmonizing Research Methodology: Raw Accelerometry

Vincent T. van Hees
1   Netherlands eScience Center, Amsterdam, The Netherlands
,
Kathrin Thaler-Kall
2   Institute of Medical Statistics and Epidemiology, Technical University of Munich, Klinikum rechts der Isar, Munich, Germany
,
Klaus-Hendrik Wolf
3   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hanover Medical School, Hanover, Germany
,
Jan C. Brønd
4   University of Southern Denmark, Odense, Denmark
,
Alberto Bonomi
5   Philips Research Laboratories, Eindhoven, The Netherlands
,
Mareike Schulze
3   Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hanover Medical School, Hanover, Germany
,
Matthäus Vigl
6   German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Potsdam, Germany
,
Bente Morseth
7   Department of Community Medicine, UiT – The Arctic University of Norway, Tromsø, Norway
8   School of Sport Sciences, UiT – The Arctic University of Norway, Tromsø, Norway
,
Laila Arnesdatter Hopstock
7   Department of Community Medicine, UiT – The Arctic University of Norway, Tromsø, Norway
,
Lukas Gorzelniak
5   Philips Research Laboratories, Eindhoven, The Netherlands
,
Holger Schulz
9   Institute of Epidemiology I, Helmholtz Zentrum München, Munich, Germany
,
Søren Brage
10   Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
,
Alexander Horsch
2   Institute of Medical Statistics and Epidemiology, Technical University of Munich, Klinikum rechts der Isar, Munich, Germany
11   Department of Computer Science, UiT – The Arctic University of Norway, Tromsø, Norway
› Author Affiliations Funding This work was supported by the German Research Foundation (DFG).
Further Information

Publication History

received: 06 December 2015

accepted: 07 June 2016

Publication Date:
08 January 2018 (online)

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Summary

Objectives: Raw accelerometry is increasingly being used in physical activity research, but diversity in sensor design, attachment and signal processing challenges the comparability of research results. Therefore, efforts are needed to harmonize the methodology. In this article we reflect on how increased methodological harmonization may be achieved.

Methods: The authors of this work convened for a two-day workshop (March 2014) themed on methodological harmonization of raw accelerometry. The discussions at the workshop were used as a basis for this review.

Results: Key stakeholders were identified as manufacturers, method developers, method users (application), publishers, and funders. To facilitate methodological harmonization in raw accelerometry the following action points were proposed: i) Manufacturers are encouraged to provide a detailed specification of their sensors, ii) Each fundamental step of algorithms for processing raw accelerometer data should be documented, and ideally also motivated, to facilitate interpretation and discussion, iii) Algorithm developers and method users should be open about uncertainties in the description of data and the uncertainty of the inference itself, iv) All new algorithms which are pitched as “ready for implementation” should be shared with the community to facilitate replication and ongoing evaluation by independent groups, and v) A dynamic interaction between method stakeholders should be encouraged to facilitate a well-informed harmonization process.

Conclusions: The workshop led to the iden -tification of a number of opportunities for harmonizing methodological practice. The discussion as well as the practical checklists proposed in this review should provide guidance for stakeholders on how to contribute to increased harmonization.

Keywords

Standardization - accelerometer - physical activity - raw data
 

  • References

  • 1 Wijndaele K, Westgate K, Stephens SK, Blair SN, Bull FC, Chastin SFM. et al. Utilization and Harmonization of Adult Accelerometry Data: Review and Expert Consensus. Med Sci Sports Exerc 2015; 47 (Suppl. 10) 2129-2139.
    CrossrefPubMedGoogle Scholar
  • 2 van Hees VT, Pias M, Taherian S, Ekelund U, Brage S. A method to compare new and traditional accelerometry data in physical activity monitoring. 2010 IEEE Int Symp “A World Wireless, Mob Multimed Networks.” IEEE 2010; 1-6.
    PubMedGoogle Scholar
  • 3 Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG. Validation of the GENEA Accelerometer. Med Sci Sports Exerc 2011; 43 (Suppl. 06) 1085-1093.
    CrossrefPubMedGoogle Scholar
  • 4 van Hees VT, Renström F, Wright A, Gradmark A, Catt M, Chen KY. et al. Estimation of daily energy expenditure in pregnant and non-pregnant women using a wrist-worn tri-axial accelerometer. PLoS One 2011; 6 (Suppl. 07) e22922.
    CrossrefPubMedGoogle Scholar
  • 5 Zijlstra W, Hof AL. Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait Posture 2003; 18 (Suppl. 02) 1-10.
    CrossrefPubMedGoogle Scholar
  • 6 Veltink PH, Bussmann HB, de Vries W, Martens WL, Van Lummel RC. Detection of static and dynamic activities using uniaxial accelerometers. IEEE Trans Rehabil Eng 1996; 4 (Suppl. 04) 375-385.
    CrossrefPubMedGoogle Scholar
  • 7 van Hees V. The challenge of assessing physical activity in populations. Lancet 2012; 380 9853 1555 author reply 1555–1556.
    PubMedGoogle Scholar
  • 8 Brønd JC, Arvidsson D. Sampling frequency affects the processing of Actigraph raw acceleration data to activity counts. J Appl Physiol 2016; 120 (Suppl. 03) 362-369.
    CrossrefPubMedGoogle Scholar
  • 9 Hildebrand M, Van Hees VT, Hansen BH, Ekelund U. Age-Group Comparability of Raw Accelerometer Output from Wrist- and Hip-Worn Monitors. Med Sci Sports Exerc 2014; 46 (Suppl. 09) 1816-24.
    CrossrefPubMedGoogle Scholar
  • 10 UK Biobank.. Category 2 enhanced phenotyping at baseline assessment visit in last 100–150,000 participants [Internet]. 2009 p. 33. Available from: http://www.ukbiobank.ac.uk/wp-content/uploads/2011/06/Protocol_addendum_2.pdf
    PubMedGoogle Scholar
  • 11 Troiano RP, McClain JJ, Brychta RJ, Chen KY. Evolution of accelerometer methods for physical activity research. Br J Sports Med 2014; 48 (Suppl. 13) 1019-1023.
    CrossrefPubMedGoogle Scholar
  • 12 Pedišić Ž, Bauman A. Accelerometer-based measures in physical activity surveillance: current practices and issues. Br J Sports Med 2015; 49 (Suppl. 04) 219-223.
    CrossrefPubMedGoogle Scholar
  • 13 GSISH website [Internet].. 2014 [cited 2014 Jul 23]. Available from: http://gsish.tum.edu
    PubMed
  • 14 Bonomi AG, Goris AH, Yin B, Westerterp KR. Detection of type, duration, and intensity of physical activity using an accelerometer. Med Sci Sport Exerc 2009; 41 (Suppl. 09) 1770-1777.
    CrossrefPubMedGoogle Scholar
  • 15 Gyllensten IC, Bonomi AG. Identifying types of physical activity with a single accelerometer: evaluating laboratory-trained algorithms in daily life. IEEE Trans Biomed Eng 2011; 58 (Suppl. 09) 2656-2663.
    CrossrefPubMedGoogle Scholar
  • 16 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 (Suppl. 08) 1042-1051.
    CrossrefPubMedGoogle Scholar
  • 17 Bonomi AG, Plasqui G, Goris AH, Westerterp KR. Estimation of free-living energy expenditure using a novel activity monitor designed to minimize obtrusiveness. Obes (Silver Spring) 2010; 18 (Suppl. 09) 1845-1851.
    CrossrefPubMedGoogle Scholar
  • 18 Schulze M, Song B, Gietzelt M, Wolf K-H, Kayser R, Tegtbur U. et al. Supporting rehabilitation training of COPD patients through multivariate sensor-based monitoring and autonomous control using a Bayesian network: prototype and results of a feasibility study. Inform Health Soc Care 2010; 35 3–4 144-156.
    CrossrefPubMedGoogle Scholar
  • 19 Faurholt-Jepsen D, Hansen KB, van Hees VT, Christensen LB, Girma T, Friis H. et al. Children treated for severe acute malnutrition experience a rapid increase in physical activity a few days after admission. J Pediatr 2014; 164 (Suppl. 06) 1421-1424.
    CrossrefPubMedGoogle Scholar
  • 20 Ortlieb S, Dias A, Gorzelniak L, Nowak D, Karrasch S, Peters A. et al. Exploring patterns of accelerometry-assessed physical activity in elderly people. Int J Behav Nutr Phys Act 2014; 11 (Suppl. 01) 28.
    CrossrefPubMedGoogle Scholar
  • 21 Morseth B, Ahmed LA, Bjørnerem Å, Emaus N, Jacobsen BK, Joakimsen R. et al. Leisure time physical activity and risk of non-vertebral fracture in men and women aged 55 years and older: the Tromsø Study. Eur J Epidemiol 2012; 27 (Suppl. 06) 463-471.
    CrossrefPubMedGoogle Scholar
  • 22 da Silva IC, van Hees VT, Ramires V V, Knuth AG, Bielemann RM, Ekelund U. et al. Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry. Int J Epidemiol 2014; 43 (Suppl. 06) 1959-1968.
    CrossrefPubMedGoogle Scholar
  • 23 Sabia S, van Hees VT, Shipley MJ, Trenell MI, Hagger-Johnson G, Elbaz A. et al. Association Between Questionnaire- and Accelerometer-Assessed Physical Activity: The Role of Sociodemo-graphic Factors. Am J Epidemiol 2014; 179 (Suppl. 06) 781-790.
    CrossrefPubMedGoogle Scholar
  • 24 Ortlieb S, Gorzelniak L, Nowak D, Strobl R, Grill E, Thorand B. et al. Associations between multiple accelerometry-assessed physical activity parameters and selected health outcomes in elderly people – results from the KORA-age study. PLoS One 2014; 9 (Suppl. 11) e111206.
    CrossrefPubMedGoogle Scholar
  • 25 Emaus A, Degerstrøm J, Wilsgaard T, Hansen BH, Dieli-Conwright CM, Furberg A-S. et al. Does a variation in self-reported physical activity reflect variation in objectively measured physical activity, resting heart rate, and physical fitness? Results from the Tromso study. Scand J Public Health 2010; 38 (Suppl. 05) 105-118.
    CrossrefPubMedGoogle Scholar
  • 26 van der Berg JD, Stehouwer CDA, Bosma H, van der Velde JHPM, Willems PJB, Savelberg HHCM. et al. Associations of total amount and patterns of sedentary behaviour with type 2 diabetes and the metabolic syndrome: The Maastricht Study. Diabetologia 2016; 59 (Suppl. 04) 709-718.
    CrossrefPubMedGoogle Scholar
  • 27 Troiano R, Mc Clain J. Objective measures of physical activity, sleep, and strength in U.S. National Health and Nutrition Examination Survey (NHANES) 2011–2014. In Proceedings of the 8th International Conference on Diet and Activity Methods. Rome, Italy: 2012
    PubMedGoogle Scholar
  • 28 Knuth AG, Assunção MCF, Gonçalves H, Menezes AMB, Santos IS, Barros AJD. et al. [Methodological description of accelerometry for measuring physical activity in the 1993 and 2004 Pelotas (Brazil) birth cohorts]. Cad Saude Publica 2013; 29: 557-565. Portuguese.
    CrossrefPubMedGoogle Scholar
  • 29 Ried-Larsen M, Brønd JC, Brage S, Hansen BH, Grydeland M, Andersen LB. et al. Mechanical and free living comparisons of four generations of the Actigraph activity monitor. Int J Behav Nutr Phys Act 2012; 9: 113.
    CrossrefPubMedGoogle Scholar
  • 30 Rowlands A V, Stone MR, Eston RG. Influence of speed and step frequency during walking and running on motion sensor output. Med Sci Sport Exerc 2007; 39 (Suppl. 04) 716-727.
    CrossrefPubMedGoogle Scholar
  • 31 Brage S, Wedderkopp N, Franks PW, Andersen LB, Froberg K. Reexamination of validity and reliability of the CSA monitor in walking and running. Med Sci Sports Exerc 2003; 35 (Suppl. 08) 1447-1454.
    CrossrefPubMedGoogle Scholar
  • 32 Brage S, Brage N, Wedderkopp N, Froberg K. Reliability and Validity of the Computer Science and Applications Accelerometer in a Mechanical Setting. Meas Phys Educ Exerc Sci 2003; 7 (Suppl. 02) 101-119.
    CrossrefPubMedGoogle Scholar
  • 33 Godfrey A, Barry G, Mathers JC, Rochester L. A comparison of methods to detect postural transitions using a single tri-axial accelerometer. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE 2014. p. 6234-6237.
    CrossrefGoogle Scholar
  • 34 Lukowicz P, Junker H, Troster G. Automatic Calibration of Body Worn Acceleration Sensors. Lect Notes Comput Sci 2004; 3001 176-181.
    PubMedGoogle Scholar
  • 35 van Hees VT, Fang Z, Langford J, Assah F, Mohammad A, da Silva IC. et al. Auto-calibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J Appl Physiol (1985) 2014; 117 (Suppl. 07) 738-744.
    CrossrefPubMedGoogle Scholar
  • 36 Bai J, He B, Shou H, Zipunnikov V, Glass TA, Crainiceanu CM. Normalization and extraction of interpretable metrics from raw accelerometry data. Biostatistics 2014; 15 (Suppl. 01) 102-116.
    CrossrefPubMedGoogle Scholar
  • 37 Brandes M, VAN Hees VT, Hannöver V, Brage S. Estimating energy expenditure from raw accelerometry in three types of locomotion. Med Sci Sports Exerc 2012; 44 (Suppl. 11) 2235-2242.
    CrossrefPubMedGoogle Scholar
  • 38 Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sport Exerc 2008; 40 (Suppl. 01) 181-188.
    CrossrefPubMedGoogle Scholar
  • 39 Tudor-Locke C, Brashear MM, Johnson WD, Katzmarzyk PT. Accelerometer profiles of physical activity and inactivity in normal weight, over-weight, and obese U.S. men and women. Int J Behav Nutr Phys Act 2010; 7: 60.
    CrossrefPubMedGoogle Scholar
  • 40 German National Cohort (GNC) Consortium.. The German National Cohort: aims, study design and organization. Eur J Epidemiol 2014; 29 (Suppl. 05) 371-382.
    CrossrefPubMedGoogle Scholar
  • 41 Sabia S, Cogranne P, van Hees VT, Bell Ja., Elbaz A, Kivimaki M. et al. Physical Activity and Adiposity Markers at Older Ages: Accelerometer Vs Questionnaire Data. J Am Med Dir Assoc 2015; 16 (Suppl. 05) 438.e7-13.
    CrossrefPubMedGoogle Scholar
  • 42 Shannon CE. Communication in the Presence of Noise (Classic Paper). Proceedings-IEEE 1998; 86 (Suppl. 02) 447-457.
    PubMedGoogle Scholar
  • 43 Welch WA, Bassett DR, Thompson DL, Freedson PS, Staudenmayer JW, John D. et al. Classification accuracy of the wrist-worn gravity estimator of normal everyday activity accelerometer. Med Sci Sports Exerc 2013; 45 (Suppl. 10) 2012-2019.
    CrossrefPubMedGoogle Scholar
  • 44 Rowlands A V, Fraysse F, Catt M, Stiles VH, Stanley RM, Eston RG. et al. Comparability of Measured Acceleration from Accelerometry-Based Activity Monitors. Med Sci Sports Exerc 2015; 47 (Suppl. 01) 201-210.
    PubMedGoogle Scholar
  • 45 Dannecker KL, Sazonova NA, Melanson EL, Sazonov ES, Browning RC. A comparison of energy expenditure estimation of several physical activity monitors. Med Sci Sports Exerc 2013; 45 (Suppl. 11) 2105-2112.
    CrossrefPubMedGoogle Scholar
  • 46 Vroege DP, Wijsman CA, Broekhuizen K, de Craen AJM, van Heemst D, van der Ouderaa FJG. et al. Dose-response effects of a Web-based physical activity program on body composition and metabolic health in inactive older adults: additional analyses of a randomized controlled trial. J Med Internet Res 2014; 16 (Suppl. 12) e265.
    CrossrefPubMedGoogle Scholar
  • 47 Zhang S, Rowlands A V, Murray P, Hurst TL. Physical activity classification using the GENEA wrist-worn accelerometer. Med Sci Sports Exerc 2012; 44 (Suppl. 04) 742-748.
    CrossrefPubMedGoogle Scholar
  • 48 Stiles VH, Griew PJ, Rowlands A V.. Use of accelerometry to classify activity beneficial to bone in premenopausal women. Med Sci Sports Exerc 2013; 45 (Suppl. 12) 2353-2361.
    CrossrefPubMedGoogle Scholar
  • 49 Bonomi AG, Plasqui G. “Divide and conquer”: assessing energy expenditure following physical activity type classification. J Appl Physiol 2012; 112 (Suppl. 05) 932 author reply 933.
    CrossrefPubMedGoogle Scholar
  • 50 Brazendale K, Beets MW, Bornstein DB, Moore JB, Pate RR, Weaver RG. et al. Equating accelerometer estimates among youth: The Rosetta Stone 2. J Sci Med Sport 2016; 19 (Suppl. 03) 242-249.
    CrossrefPubMedGoogle Scholar
  • 51 Bornstein DB, Beets MW, Byun W, Welk G, Bottai M, Dowda M. et al. Equating accelerometer estimates of moderate-to-vigorous physical activity: in search of the Rosetta Stone. J Sci Med Sport 2011; 14 (Suppl. 05) 404-410.
    CrossrefPubMedGoogle Scholar
  • 52 Kelly P, Fitzsimons C, Baker G. Should we reframe how we think about physical activity and sedentary behaviour measurement?. Validity and reliability reconsidered. Int J Behav Nutr Phys Act 2016; 13 (Suppl. 01) 32.
    PubMedGoogle Scholar
  • 53 Hickey AM, Freedson PS. Utility of Consumer Physical Activity Trackers as an Intervention Tool in Cardiovascular Disease Prevention and Treatment. Prog Cardiovasc Dis 2016; 58 (Suppl. 06) 613-619.
    CrossrefPubMedGoogle Scholar
  • 54 Liu S, Gao RX, Freedson PS. Computational methods for estimating energy expenditure in human physical activities. Med Sci Sports Exerc 2012; 44 (Suppl. 11) 2138-2146.
    CrossrefPubMedGoogle Scholar