Top

Published in:

01-07-2021 | Systematic Review

Does Site Matter? Impact of Inertial Measurement Unit Placement on the Validity and Reliability of Stride Variables During Running: A Systematic Review and Meta-analysis

Authors: Benjamin J. Horsley, Paul J. Tofari, Shona L. Halson, Justin G. Kemp, Jessica Dickson, Nirav Maniar, Stuart J. Cormack

Published in: Sports Medicine | Issue 7/2021

Abstract

Background

Inertial measurement units (IMUs) are used for running gait analysis in a variety of sports. These sensors have been attached at various locations to capture stride data. However, it is unclear if different placement sites affect the derived outcome measures.

Objective

The aim of this systematic review and meta-analysis was to investigate the impact of placement on the validity and reliability of IMU-derived measures of running gait.

Methods

Online databases SPORTDiscus with Full Text, CINAHL Complete, MEDLINE (EBSCOhost), EMBASE (Ovid) and Scopus were searched from the earliest record to 6 August 2020. Articles were included if they (1) used an IMU during running (2) reported spatiotemporal variables, peak ground reaction force (GRF) or vertical stiffness and (3) assessed validity or reliability. Meta-analyses were performed for a pooled validity estimate when (1) studies reported means and standard deviation for variables derived from the IMU and criterion (2) used the same IMU placement and (3) determined validity at a comparable running velocity (≤ 1 m·s⁻¹ difference).

Results

Thirty-nine articles were included, where placement varied between the foot, tibia, hip, sacrum, lumbar spine (LS), torso and thoracic spine (TS). Initial contact, toe-off, contact time (CT), flight time (FT), step time, stride time, swing time, step frequency (SF), step length (SL), stride length, peak vertical and resultant GRF and vertical stiffness were analysed. Four variables (CT, FT, SF and SL) were meta-analysed, where CT was compared between the foot, tibia and LS placements and SF was compared between foot and LS. Foot placement data were meta-analysed for FT and SL. All data are the mean difference (MD [95%CI]). No significant difference was observed for any site compared to the criterion for CT (foot: − 11.47 ms [− 45.68, 22.74], p = 0.43; tibia: 22.34 ms [− 18.59, 63.27], p = 0.18; LS: − 48.74 ms [− 120.33, 22.85], p = 0.12), FT (foot: 11.93 ms [− 8.88, 32.74], p = 0.13), SF (foot: 0.45 step·min⁻¹ [− 1.75, 2.66], p = 0.47; LS: − 3.45 step·min⁻¹ [− 16.28, 9.39], p = 0.37) and SL (foot: 0.21 cm [− 1.76, 2.18], p = 0.69). Reliable derivations of CT (coefficient of variation [CV] < 9.9%), FT (CV < 11.6%) and SF (CV < 4.4%) were shown using foot- and LS-worn IMUs, while the CV was < 7.8% for foot-determined stride time, SL and stride length. Vertical GRF was reliable from the LS (CV = 4.2%) and TS (CV = 3.3%) using a spring-mass model, while vertical stiffness was moderately (r = 0.66) and nearly perfectly (r = 0.98) correlated with criterion measures from the TS.

Conclusion

Placement of IMUs on the foot, tibia and LS is suitable to derive valid and reliable stride data, suggesting measurement site may not be a critical factor. However, evidence regarding the ability to accurately detect stride events from the TS is unclear and this warrants further investigation.

Available only for authorised users

Vanrenterghem J, Nedergaard N, Robinson MA, Drust B. Training load monitoring in team sports: a novel framework separating physiological and biomechanical load-adaptation pathways. Sports Med. 2017;47(11):2135–42.PubMedCrossRef

Spencer M, Lawrence S, Rechichi C, Bishop D, Dawson B, Goodman C. Time–motion analysis of elite field hockey, with special reference to repeated-sprint activity. J Sport Sci. 2004;22(9):843–50.CrossRef

Aughey RJ. Applications of GPS technologies to field sports. Int J Sports Physiol Perform. 2011;6(3):295–310.PubMedCrossRef

Buchheit M, Allen A, Poon TK, Modonutti M, Gregson W, Di Salvo V. Integrating different tracking systems in football: multiple camera semi-automatic system, local position measurement and GPS technologies. J Sport Sci. 2014;32(20):1844–57.CrossRef

Stevens TG, de Ruiter CJ, van Niel C, van de Rhee R, Beek PJ, Savelsbergh GJ. Measuring acceleration and deceleration in soccer-specific movements using a local position measurement (LPM) system. Int J Sports Physiol Perform. 2014;9(3):446–56.CrossRef

Duffield R, Reid M, Baker J, Spratford W. Accuracy and reliability of GPS devices for measurement of movement patterns in confined spaces for court-based sports. J Sci Med Sport. 2010;13(5):523–5.PubMedCrossRef

Jennings D, Cormack S, Coutts AJ, Boyd L, Aughey RJ. The validity and reliability of GPS units for measuring distance in team sport specific running patterns. Int J Sports Physiol Perform. 2010;5(3):328–41.PubMedCrossRef

Varley MC, Fairweather IH, Aughey RJ. Validity and reliability of GPS for measuring instantaneous velocity during acceleration, deceleration, and constant motion. J Sport Sci. 2012;30(2):121–7.CrossRef

Gabbett T, Jenkins D, Abernethy B. Physical collisions and injury during professional rugby league skills training. J Sci Med Sport. 2010;13(6):578–83.PubMedCrossRef

10.

Boyd LJ, Ball K, Aughey RJ. Quantifying external load in Australian football matches and training using accelerometers. Int J Sports Physiol Perform. 2013;8(1):44–51.PubMedCrossRef

11.

Gastin PB, McLean O, Spittle M, Breed RV. Quantification of tackling demands in professional Australian football using integrated wearable athlete tracking technology. J Sci Med Sport. 2013;16(6):589–93.PubMedCrossRef

12.

Gastin PB, Mclean OC, Breed RV, Spittle M. Tackle and impact detection in elite Australian football using wearable microsensor technology. J Sport Sci. 2014;32(10):947–53.CrossRef

13.

Glassbrook DJ, Fuller JT, Alderson JA, Doyle TL. Foot accelerations are larger than tibia accelerations during sprinting when measured with inertial measurement units. J Sport Sci. 2019;38:1–8. https://doi.org/10.1080/02640414.2019.1692997.CrossRef

14.

Warman GE, Cole MH, Johnston RD, Chalkley D, Pepping G-J. Using microtechnology to quantify torso angle during match-play in field hockey. J Strength Cond Res. 2019;33(10):2648–54.PubMedCrossRef

15.

Boyd LJ, Ball K, Aughey RJ. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int J Sports Physiol Perform. 2011;6(3):311–21.PubMedCrossRef

16.

Ammann R, Taube W, Wyss T. Accuracy of PARTwear inertial sensor and Optojump optical measurement system for measuring ground contact time during running. J Strength Cond Res. 2016;30(7):2057–63.PubMedCrossRef

17.

Gabbett TJ. Quantifying the physical demands of collision sports: does microsensor technology measure what it claims to measure? J Strength Cond Res. 2013;27(8):2319–22.PubMedCrossRef

18.

Bouten CV, Westerterp KR, Verduin M, Janssen JD. Assessment of energy expenditure for physical activity using a triaxial accelerometer. Med Sci Sports Exerc. 1994;26(12):1516–23.PubMedCrossRef

19.

Bouten CV, Koekkoek KT, Verduin M, Kodde R, Janssen JD. A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Trans Biomed Eng. 1997;44(3):136–47.PubMedCrossRef

20.

Swartz AM, Strath SJ, Bassett DR, O’Brien WL, King GA, Ainsworth BE. Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Med Sci Sports Exerc. 2000;32(9):450–6.CrossRef

21.

Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–8.CrossRef

22.

Skotte J, Korshøj M, Kristiansen J, Hanisch C, Holtermann A. Detection of physical activity types using triaxial accelerometers. J Phys Act Health. 2014;11(1):76–84.PubMedCrossRef

23.

McNamara DJ, Gabbett TJ, Chapman P, Naughton G, Farhart P. The validity of microsensors to automatically detect bowling events and counts in cricket fast bowlers. Int J Sports Physiol Perform. 2015;10(1):71–5.PubMedCrossRef

24.

Murray NB, Black GM, Whiteley RJ, Gahan P, Cole MH, Utting A, et al. Automatic detection of pitching and throwing events in baseball with inertial measurement sensors. Int J Sports Physiol Perform. 2017;12(4):533–7.PubMedCrossRef

25.

Spangler R, Rantalainen T, Gastin PB, Wundersitz D. Inertial sensors are a valid tool to detect and consistently quantify jumping. Int J Sports Med. 2018;39(10):802–8. https://doi.org/10.1055/s-0044-100793.CrossRefPubMed

26.

Barrett S, Midgley A, Lovell R. PlayerLoad(TM): reliability, convergent validity, and influence of unit position during treadmill running. Int J Sports Physiol Perform. 2014;9(6):945–52.PubMedCrossRef

27.

Mooney MG, Cormack S, O’Brien BJ, Morgan WM, McGuigan M. Impact of neuromuscular fatigue on match exercise intensity and performance in elite Australian football. J Strength Cond Res. 2013;27(1):166–73.PubMedCrossRef

28.

Cormack SJ, Mooney MG, Morgan W, McGuigan MR. Influence of neuromuscular fatigue on accelerometer load in elite Australian football players. Int J Sports Physiol Perform. 2013;8(4):373–8.PubMedCrossRef

29.

McGrath D, Greene BR, O’Donovan KJ, Caulfield B. Gyroscope-based assessment of temporal gait parameters during treadmill walking and running. Sports Eng. 2012;15(4):207–13.CrossRef

30.

Benson LC, Clermont CA, Watari R, Exley T, Ferber R. Automated accelerometer-based gait event detection during multiple running conditions. Sensors. 2019;19(7):1483. https://doi.org/10.3390/s19071483.CrossRefPubMedCentral

31.

Aubol KG, Milner CE. Foot contact identification using a single triaxial accelerometer during running. J Biomech. 2020;105:109768. https://doi.org/10.1016/j.jbiomech.2020.109768.CrossRefPubMed

32.

Lee JB, Mellifont RB, Burkett BJ. The use of a single inertial sensor to identify stride, step, and stance durations of running gait. J Sci Med Sport. 2010;13(2):270–3.PubMedCrossRef

33.

Bergamini E, Picerno P, Pillet H, Natta F, Thoreux P, Camomilla V. Estimation of temporal parameters during sprint running using a trunk-mounted inertial measurement unit. J Biomech. 2012;45(6):1123–6.PubMedCrossRef

34.

Falbriard M, Meyer F, Mariani B, Millet GP, Aminian K. Accurate estimation of running temporal parameters using foot-worn inertial sensors. Front Physiol. 2018. https://doi.org/10.3389/fphys.2018.00610.CrossRefPubMedPubMedCentral

35.

García-Pinillos F, Latorre-Román PÁ, Soto-Hermoso VM, Párraga-Montilla JA, Pantoja-Vallejo A, Ramírez-Campillo R, et al. Agreement between the spatiotemporal gait parameters from two different wearable devices and high-speed video analysis. PLoS ONE. 2019. https://doi.org/10.1371/journal.pone.0222872.CrossRefPubMedPubMedCentral

36.

Wundersitz DWT, Netto KJ, Aisbett B, Gastin PB. Validity of an upper-body-mounted accelerometer to measure peak vertical and resultant force during running and change-of-direction tasks. Sports Biomech. 2013;12(4):403–12.PubMedCrossRef

37.

Buchheit M, Gray A, Morin JB. Assessing stride variables and vertical stiffness with GPS-embedded accelerometers: preliminary insights for the monitoring of neuromuscular fatigue on the field. J Sports Sci Med. 2015;14:698–701.PubMedPubMedCentral

38.

Eggers TM, Massard TI, Clothier PJ, Lovell R. Measuring vertical stiffness in sport with accelerometers: exercise caution! J Strength Cond Res. 2018;32(7):1919–22.PubMedCrossRef

39.

Nedergaard NJ, Verheul J, Drust B, Etchells T, Lisboa P, Robinson MA, et al. The feasibility of predicting ground reaction forces during running from a trunk accelerometry driven mass-spring-damper model. PeerJ. 2018. https://doi.org/10.7717/peerj.6105.CrossRefPubMedPubMedCentral

40.

Raper DP, Witchalls J, Philips EJ, Knight E, Drew MK, Waddington G. Use of a tibial accelerometer to measure ground reaction force in running: a reliability and validity comparison with force plates. J Sci Med Sport. 2018;21(1):84–8.PubMedCrossRef

41.

Dugan SA, Bhat KP. Biomechanics and analysis of running gait. Phys Med Rehabil Clin N Am. 2005;16(3):603–21.PubMedCrossRef

42.

Nedergaard NJ, Robinson MA, Eusterwiemann E, Drust B, Lisboa PJ, Vanrenterghem J. The relationship between whole-body external loading and body-worn accelerometry during team-sport movements. Int J Sports Physiol Perform. 2017;12(1):18–26.PubMedCrossRef

43.

Edwards S, White S, Humphreys S, Robergs R, O’Dwyer N. Caution using data from triaxial accelerometers housed in player tracking units during running. J Sport Sci. 2019;37(7):810–8.CrossRef

44.

Glassbrook DJ, Fuller JT, Alderson JA, Doyle TL. Measurement of lower-limb asymmetry in professional rugby league: a technical note describing the use of inertial measurement units. PeerJ. 2020;8:e9366. https://doi.org/10.7717/peerj.9366.CrossRefPubMedPubMedCentral

45.

Lafortune MA, Lake MJ, Hennig EM. Differential shock transmission response of the human body to impact severity and lower limb posture. J Biomech. 1996;29(12):1531–7.PubMedCrossRef

46.

Derrick TR, Hamill J, Caldwell GE. Energy absorption of impacts during running at various stride lengths. Med Sci Sports Exerc. 1998;30(1):128–35. https://doi.org/10.1097/00005768-199801000-00018.CrossRefPubMed

47.

Lucas-Cuevas AG, Encarnación-Martínez A, Camacho-García A, Llana-Belloch S, Pérez-Soriano P. The location of the tibial accelerometer does influence impact acceleration parameters during running. J Sport Sci. 2017;35(17):1734–8. https://doi.org/10.1080/02640414.2016.1235792.CrossRef

48.

Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–9.CrossRef

49.

Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998;52(6):377–84.PubMedPubMedCentralCrossRef

50.

Cummins C, Orr R, O’Connor H, West C. Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review. Sports Med. 2013;43(10):1025–42.PubMedCrossRef

51.

Glassbrook DJ, Doyle TL, Alderson JA, Fuller JT. The demands of professional rugby league match-play: a meta-analysis. Sports Med. 2019;5:1–20. https://doi.org/10.1186/s40798-019-0197-9.CrossRef

52.

Chew D-K, Ngoh KJ, Gouwanda D, Gopalai AA. Estimating running spatial and temporal parameters using an inertial sensor. Sports Eng. 2018;21(2):115–22.CrossRef

53.

Watari R, Hettinga B, Osis S, Ferber R. Validation of a torso-mounted accelerometer for measures of vertical oscillation and ground contact time during treadmill running. J Appl Biomech. 2016;32(3):306–10. https://doi.org/10.1123/jab.2015-0200.CrossRefPubMed

54.

Kenneally-Dabrowski CJ, Serpell BG, Spratford W. Are accelerometers a valid tool for measuring overground sprinting symmetry? Int J Sports Sci Coach. 2018;13(2):270–7.CrossRef

55.

Dorschky E, Nitschke M, Seifer A-K, van den Bogert AJ, Eskofier BM. Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models. J Biomech. 2019;95:109278. https://doi.org/10.1016/j.jbiomech.2019.07.022.CrossRefPubMed

56.

Ngoh KJ, Gouwanda D, Gopalai AA, Chong YZ. Estimation of vertical ground reaction force during running using neural network model and uniaxial accelerometer. J Biomech. 2018;76:269–73. https://doi.org/10.1016/j.jbiomech.2018.06.006.CrossRefPubMed

57.

Gurchiek RD, McGinnis RS, Needle AR, McBride JM, van Werkhoven H. The use of a single inertial sensor to estimate 3-dimensional ground reaction force during accelerative running tasks. J Biomech. 2017;61:263–8. https://doi.org/10.1016/j.jbiomech.2017.07.035.CrossRefPubMed

58.

Mitschke C, Heß T, Milani TL. Which method detects foot strike in rearfoot and forefoot runners accurately when using an inertial measurement unit? Appl Sci. 2017;7(9):959. https://doi.org/10.3390/app7090959.CrossRef

59.

Mitschke C, Zaumseil F, Milani TL. The influence of inertial sensor sampling frequency on the accuracy of measurement parameters in rearfoot running. Comput Methods Biomech Biomed Engin. 2017;20(14):1502–11. https://doi.org/10.1080/10255842.2017.1382482.CrossRefPubMed

60.

Pairot de Fontenay B, Roy J, Dubois B, Bouyer L, Esculier J. Validating commercial wearable sensors for running gait parameters estimation. IEEE Sens J. 2020;20(14):7783–91. https://doi.org/10.1109/JSEN.2020.2982568.CrossRef

61.

Pogson M, Verheul J, Robinson MA, Vanrenterghem J, Lisboa P. A neural network method to predict task- and step-specific ground reaction force magnitudes from trunk accelerations during running activities. Med Eng Phys. 2020;78:82–9. https://doi.org/10.1016/j.medengphy.2020.02.002.CrossRefPubMed

62.

Sinclair J, Hobbs SJ, Protheroe L, Edmundson CJ, Greenhalgh A. Determination of gait events using an externally mounted shank accelerometer. J Appl Biomech. 2013;29(1):118–22.PubMedCrossRef

63.

Tan HX, Aung NN, Tian J, Chua MCH, Yang YO. Time series classification using a modified LSTM approach from accelerometer-based data: a comparative study for gait cycle detection. Gait Posture. 2019;74:128–34. https://doi.org/10.1016/j.gaitpost.2019.09.007.CrossRefPubMed

64.

Winter SC, Lee JB, Leadbetter RI, Gordon SJ. Validation of a single inertial sensor for measuring running kinematics overground during a prolonged run. J Fit Res. 2016;5(1):14–23.

65.

Zrenner M, Gradl S, Jensen U, Ullrich M, Eskofier BM. Comparison of different algorithms for calculating velocity and stride length in running using inertial measurement units. Sensors. 2018;18(12):4194. https://doi.org/10.3390/s18124194.CrossRefPubMedCentral

66.

Neugebauer JM, Collins KH, Hawkins DA. Ground reaction force estimates from ActiGraph GT3X+ hip accelerations. PLoS ONE. 2014;9(6):e99023. https://doi.org/10.1371/journal.pone.0099023.CrossRefPubMedPubMedCentral

67.

Hall JP, Barton C, Jones PR, Morrissey D. The biomechanical differences between barefoot and shod distance running: a systematic review and preliminary meta-analysis. Sports Med. 2013;43(12):1335–53.PubMedCrossRef

68.

Gindre C, Lussiana T, Hebert-Losier K, Morin J-B. Reliability and validity of the Myotest for measuring running stride kinematics. J Sport Sci. 2016;34(7):664–70.CrossRef

69.

Mo S, Chow DH. Accuracy of three methods in gait event detection during overground running. Gait Posture. 2018;59:93–8. https://doi.org/10.1016/j.gaitpost.2017.10.009.CrossRefPubMed

70.

Fadillioglu C, Stetter BJ, Ringhof S, Krafft FC, Sell S, Stein T. Automated gait event detection for a variety of locomotion tasks using a novel gyroscope-based algorithm. Gait Posture. 2020;81:102–8. https://doi.org/10.1016/j.gaitpost.2020.06.019.CrossRefPubMed

71.

García-Pinillos F, Roche-Seruendo LE, Marcén-Cinca N, Marco-Contreras LA, Latorre-Román PA. Absolute reliability and concurrent validity of the Stryd system for the assessment of running stride kinematics at different velocities. J Strength Cond Res. 2018. https://doi.org/10.1519/jsc.0000000000002595.CrossRefPubMed

72.

Machulik C, Hamacher D, Lindlein K, Zech A, Hollander K. Validation of an inertial measurement unit based magnetic timing gate system during running and sprinting. Ger J Sports Med. 2020;71(3):69–75. https://doi.org/10.5960/dzsm.2020.426.CrossRef

73.

Simon SR. Quantification of human motion: gait analysis—benefits and limitations to its application to clinical problems. J Biomech. 2004;37(12):1869–80.PubMedCrossRef

74.

Lienhard K, Schneider D, Maffiuletti NA. Validity of the Optogait photoelectric system for the assessment of spatiotemporal gait parameters. Med Eng Phys. 2013;35(4):500–4.PubMedCrossRef

75.

Muro-de-la-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A. Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors. 2014;14(2):3362–94.PubMedPubMedCentralCrossRef

76.

Schwarzer G. meta: an R package for meta-analysis. R News. 2007;7(3):40–5.

77.

Borenstein M, Higgins JP. Meta-analysis and subgroups. Prev Sci. 2013;14(2):134–43.PubMedCrossRef

78.

Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Br Med J. 2003;327(7414):557–60.CrossRef

79.

Viechtbauer W, Cheung MWL. Outlier and influence diagnostics for meta-analysis. Res Synth Methods. 2010;1(2):112–25.PubMedCrossRef

80.

Quintana DS. From pre-registration to publication: a non-technical primer for conducting a meta-analysis to synthesize correlational data. Front Physiol. 2015. https://doi.org/10.3389/fpsyg.2015.01549.CrossRef

81.

Schwarzer G, Carpenter JR, Rücker G. Meta-analysis with R. Use R! 1st ed. Cham: Springer International Publishing Switzerland; 2015.CrossRef

82.

Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. Br Med J. 2001;322(7300):1479–80.CrossRef

83.

Gouttebarge V, Wolfard R, Griek N, de Ruiter CJ, Boschman JS, van Dieën JH. Reproducibility and validity of the Myotest for measuring step frequency and ground contact time in recreational runners. J Hum Kinet. 2015;45:19–26.PubMedPubMedCentralCrossRef

84.

Norris M, Kenny IC, Anderson R. Comparison of accelerometry stride time calculation methods. J Biomech. 2016;49(13):3031–4.PubMedCrossRef

85.

Brahms C, Zhao Y, Gerhard D, Barden JM. Stride length determination during overground running using a single foot-mounted inertial measurement unit. J Biomech. 2018;71:302–5.PubMedCrossRef

86.

Buchheit M, Lacome M, Cholley Y, Simpson BM. Neuromuscular responses to conditioned soccer sessions assessed via GPS-embedded accelerometers: insights into tactical periodization. Int J Sports Physiol Perform. 2018;13:577–83.PubMedCrossRef

87.

Wouda FJ, Giuberti M, Bellusci G, Maartens E, Reenalda J, van Beijnum B-JF, et al. Estimation of vertical ground reaction forces and sagittal knee kinematics during running using three inertial sensors. Front Physiol. 2018;9:218. https://doi.org/10.3389/fphys.2018.00218.CrossRefPubMedPubMedCentral

88.

Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Technol. 2011;2(1):37–63.

89.

Hreljac A, Marshall RN. Algorithms to determine event timing during normal walking using kinematic data. J Biomech. 2000;33(6):783–6.PubMedCrossRef

90.

Zeni J Jr, Richards J, Higginson J. Two simple methods for determining gait events during treadmill and overground walking using kinematic data. Gait Posture. 2008;27(4):710–4.PubMedCrossRef

91.

Borenstein M, Hedges LV, Higgins JP, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. 2010;1(2):97–111. https://doi.org/10.1002/jrsm.12.CrossRefPubMed

92.

Cavanagh PR, Lafortune MA. Ground reaction forces in distance running. J Biomech. 1980;13(5):397–406. https://doi.org/10.1016/0021-9290(80)90033-0.CrossRefPubMed

93.

Keller TS, Weisberger A, Ray J, Hasan SS, Shiavi RG, Spengler DM. Relationship between vertical ground reaction force and speed during walking, slow jogging, and running. Clin Biomech. 1996;11(5):253–9.CrossRef

94.

Hunter JP, Marshall RN, McNair PJ. Relationships between ground reaction force impulse and kinematics of sprint-running acceleration. J Appl Biomech. 2005;21(1):31–43.PubMedCrossRef

95.

Verheul J, Nedergaard NJ, Vanrenterghem J, Robinson MA. Measuring biomechanical loads in team sports—from lab to field. Sci Med Footb. 2020;4:1–7.

96.

Morin J-B, Dalleau G, Kyröläinen H, Jeannin T, Belli A. A simple method for measuring stiffness during running. J Appl Biomech. 2005;21(2):167–80.PubMedCrossRef

97.

Ruddy JD, Cormack SJ, Whiteley R, Williams MD, Timmins RG, Opar DA. Modeling the risk of team sport injuries: a narrative review of different statistical approaches. Front Physiol. 2019. https://doi.org/10.3389/fphys.2019.00829.CrossRefPubMedPubMedCentral

98.

Hopkins WG. Measures of reliability in sports medicine and science. Sports Med. 2000;30(1):1–15.PubMedCrossRef

99.

Cormack SJ, Newton RU, McGuigan MR, Doyle TL. Reliability of measures obtained during single and repeated countermovement jumps. Int J Sports Physiol Perform. 2008;3(2):131–44.PubMedCrossRef

100.

Thorborg K, Petersen J, Magnusson SP, Hölmich P. Clinical assessment of hip strength using a hand-held dynamometer is reliable. Scand J Med Sci Sports. 2010;20(3):493–501.PubMedCrossRef

101.

Garrett J, Graham SR, Eston RG, Burgess DJ, Garrett LJ, Jakeman J, et al. A novel method of assessment for monitoring neuromuscular fatigue in Australian rules football players. Int J Sports Physiol Perform. 2018;14(5):598–605.CrossRef

102.

Rowell AE, Aughey RJ, Clubb J, Cormack SJ. A standardized small sided game can be used to monitor neuromuscular fatigue in professional A-League football players. Front Physiol. 2018. https://doi.org/10.3389/fphys.2018.01011.CrossRefPubMedPubMedCentral

103.

Walker EJ, McAinch AJ, Sweeting A, Aughey RJ. Inertial sensors to estimate the energy expenditure of team-sport athletes. J Sci Med Sport. 2016;19(2):177–81.PubMedCrossRef

104.

Wundersitz DW, Gastin PB, Richter C, Robertson SJ, Netto KJ. Validity of a trunk-mounted accelerometer to assess peak accelerations during walking, jogging and running. Eur J Sport Sci. 2015;15(5):382–90.PubMedCrossRef

105.

Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. Br Med J. 2016;352:i1981. https://doi.org/10.1136/bmj.i1981.CrossRef

106.

Lin L. Bias caused by sampling error in meta-analysis with small sample sizes. PLoS ONE. 2018;13(9):e0204056. https://doi.org/10.1371/journal.pone.0204056.CrossRefPubMedPubMedCentral

Title: Does Site Matter? Impact of Inertial Measurement Unit Placement on the Validity and Reliability of Stride Variables During Running: A Systematic Review and Meta-analysis
Authors: Benjamin J. Horsley
Paul J. Tofari
Shona L. Halson
Justin G. Kemp
Jessica Dickson
Nirav Maniar
Stuart J. Cormack
Publication date: 01-07-2021
Publisher: Springer International Publishing
Published in: Sports Medicine / Issue 7/2021
Print ISSN: 0112-1642
Electronic ISSN: 1179-2035
DOI: https://doi.org/10.1007/s40279-021-01443-8

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Does Site Matter? Impact of Inertial Measurement Unit Placement on the Validity and Reliability of Stride Variables During Running: A Systematic Review and Meta-analysis

Abstract

Background

Objective

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Objective

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 7/2021

Strengthening the Case for Cluster Set Resistance Training in Aged and Clinical Settings: Emerging Evidence, Proposed Benefits and Suggestions

Correction to: Mental Fatigue and Sport‑Specific Psychomotor Performance: A Systematic Review

The Impact of Coronavirus (COVID-19) Related Public-Health Measures on Training Behaviours of Individuals Previously Participating in Resistance Training: A Cross-Sectional Survey Study

Developing a New Curvilinear Allometric Model to Improve the Fit and Validity of the 20-m Shuttle Run Test as a Predictor of Cardiorespiratory Fitness in Adults and Youth

Mental Fatigue and Sport-Specific Psychomotor Performance: A Systematic Review

Comment on ‘Developing a New Curvilinear Allometric Model to Improve the Fit and Validity of the 20-m Shuttle Run Test as a Predictor of Cardiorespiratory Fitness in Adults and Youth’