Skip to main content

Advertisement

Springer Nature Link
Log in

Using Cadence to Study Free-Living Ambulatory Behaviour

  • Review Article
  • Free-Living Cadence
  • Published:
Sports Medicine Aims and scope Submit manuscript

Abstract

The health benefits of a physically active lifestyle across a person’s lifespan have been established. If there is any single physical activity behaviour that we should measure well and promote effectively, it is ambulatory activity and, more specifically, walking. Since public health physical activity guidelines include statements related to intensity of activity, it follows that we need to measure and promote free-living patterns of ambulatory activity that are congruent with this intent. The purpose of this review article is to present and summarize the potential for using cadence (steps/minute) to represent such behavioural patterns of ambulatory activity in free-living. Cadence is one of the spatio-temporal parameters of gait or walking speed. It is typically assessed using short-distance walks in clinical research and practice, but freeliving cadence can be captured with a number of commercially available accelerometers that possess time-stamping technology. This presents a unique opportunity to use the same metric to communicate both ambulatory performance (assessed under testing conditions) and behaviour (assessed in the real world). Ranges for normal walking cadence assessed under laboratory conditions are 96–138 steps/minute for women and 81–135 steps/minute for men across their lifespan. The correlation between mean cadence and intensity (assessed with indirect calorimetry and expressed as metabolic equivalents [METs]) based on five treadmill/overground walking studies, is r = 0.93 and 100 steps/minute is considered to be a reasonable heuristic value indicative of walking at least at absolutely-defined moderate intensity (i.e. minimally, 3 METs) in adults. The weighted mean cadence derived from eight studies that have observed pedestrian cadence under natural conditions was 115.2 steps/minute, demonstrating that achieving 100 steps/minute is realistic in specific settings that occur in real life. However, accelerometer data collected in a large, representative sample suggest that self-selected walking at a cadence equivalent to ≥100 steps/minute is a rare occurrence in free-living adults. Specifically, the National Health and Nutrition Examination Survey (NHANES) data show that US adults spent ≅4.8 hours/day in non-movement (i.e. zero cadence) during wearing time, ≅8.7 hours at 1–59 steps/minute, ≅16 minutes/day at cadences of 60–79 steps/minute,≅8 minutes at 80–99 steps/minute,≅5 minutes at 100–119 steps/minute, and ≅2 minutes at 120+ steps/minute. Cadence appears to be sensitive to change with intervention, and capitalizing on the natural tempo of music is an obvious means of targeting cadence. Cadence could potentially be used effectively in epidemiological study, intervention and behavioural research, dose-response studies, determinants studies and in prescription and practice. It is easily interpretable by researchers, clinicians, programme staff and the lay public, and therefore offers the potential to bridge science, practice and real life.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Table I
Fig. 1
Table II
Table III

Similar content being viewed by others

References

  1. Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Report, 2008. Washington, DC: U.S. Department of Health and Human Services, 2008

    Google Scholar 

  2. Ham SA, Kruger J, Tudor-Locke C. Participation by US adults in sports, exercise, and recreational physical activities. J Phys Act Health 2009; 6: 1–10

    Google Scholar 

  3. Rafferty AP, Reeves MJ, McGee HB, et al. Physical activity patterns among walkers and compliance with public health recommendations. Med Sci Sports Exerc 2002 Aug; 34 (8): 1255–61

    Article  PubMed  Google Scholar 

  4. Tudor-Locke C, Ham SA. Walking behaviors reported in the American Time Use Survey 2003-2005. J Phys Act Health 2008 Sep; 5 (5): 633–47

    PubMed  Google Scholar 

  5. Miller R, Brown W, Tudor-Locke C. But what about swimming and cycling? How to ‘count’ non-ambulatory activity when using pedometers to assess physical activity. J Phys Act Health 2006; 3 (3): 257–66

    Google Scholar 

  6. Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Report, 2008. Washington, DC: U.S. Department of Health and Human Services, 2008

    Google Scholar 

  7. Kemi OJ, Wisloff U. High-intensity aerobic exercise training improves the heart in health and disease. J Cardiopulm Rehabil Prev 2010 Jan-Feb; 30 (1): 2–11

    PubMed  Google Scholar 

  8. Waters RL, Lunsford BR, Perry J, et al. Energy-speed relationship of walking: standard tables. J Orthop Res 1988; 6: 215–22

    Article  PubMed  CAS  Google Scholar 

  9. Healy GN, Dunstan DW, Salmon J, et al. Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care 2008 Apr; 31 (4): 661–6

    Article  PubMed  Google Scholar 

  10. Powell KE, Paluch AE, Blair SN. Physical activity for health: what kind? How much? How intense? On top of what? Annu Rev Public Health 2011 Apr 21; 32: 349–65

    Article  PubMed  Google Scholar 

  11. Ainsworth BE, Haskell WL, Whitt MC, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000 Sep; 32 (9 Suppl.): S498–504

    PubMed  CAS  Google Scholar 

  12. Mekary RA, Willett WC, Hu FB, et al. Isotemporal substitution paradigm for physical activity epidemiology and weight change. Am J Epidemiol 2009 Aug 15; 170 (4): 519–27

    Article  PubMed  Google Scholar 

  13. Troiano RP, Berrigan D, Berrigan D, et al. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008 Jan; 40 (1): 181–8

    PubMed  Google Scholar 

  14. Puthoff ML, Darter BJ, Nielsen DH, et al. The effect of weighted vest walking on metabolic responses and ground reaction forces. Med Sci Sports Exerc 2006 Apr; 38 (4): 746–52

    Article  PubMed  Google Scholar 

  15. Church TS, Earnest CP, Morss GM. Field testing of physiological responses associated with Nordic walking. Res Q Exerc Sport 2002 Sep; 73 (3): 296–300

    Article  PubMed  Google Scholar 

  16. Zatsiorky VM, Werner SL, Kaimin MA. Basic kinematics of walking. Step length and step frequency: a review J Sports Med Phys Fitness. 1994 Jun; 34 (2): 109–34

    PubMed  CAS  Google Scholar 

  17. Beets MW, Agiovlasitis S, Fahs CA, et al. Adjusting step count recommendations for anthropometric variations in leg length. J Sci Med Sport 2010 Jan 21; 13 (5): 509–12

    Article  PubMed  Google Scholar 

  18. Lubans DR, Morgan PJ, Collins CE, et al. The relationship between heart rate intensity and pedometer step counts in adolescents. J Sports Sci 2009 Apr; 27 (6): 591–7

    Article  PubMed  Google Scholar 

  19. Auvinet B, Berrut G, Touzard C, et al. Reference data for normal subjects obtained with an accelerometric device. Gait Posture 2002; 16 (2): 124–34

    Article  PubMed  Google Scholar 

  20. Saunders PU, Pyne DB, Telford RD, et al. Reliability and variability of running economy in elite distance runners. Med Sci Sports Exerc 2004 Nov; 36 (11): 1972–6

    Article  PubMed  Google Scholar 

  21. Styns F, van Noorden L, Moelants D, et al. Walking on music. Hum Mov Sci 2007 Oct; 26 (5): 769–85

    Article  PubMed  Google Scholar 

  22. Hatano Y. Prevalence and use of the pedometer. Res J Walking 1997; 1: 45–54

    Google Scholar 

  23. Terrier P, Schutz Y. Variability of gait patterns during unconstrained walking assessed by satellite positioning (GPS). Eur J Appl Physiol 2003 Nov; 90 (5-6): 554–61

    Article  PubMed  Google Scholar 

  24. Graham JE, Ostir GV, Fisher SR, et al. Assessing walking speed in clinical research: a systematic review. J Eval Clin Pract 2008 Aug; 14 (4): 552–62

    Article  PubMed  Google Scholar 

  25. Himann JE, Cunningham DA, Rechnitzer PA, et al. Agerelated changes in speed of walking. Med Sci Sports Exerc 1988 Apr; 20 (2): 161–6

    Article  PubMed  CAS  Google Scholar 

  26. Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 1994 Mar; 49 (2): M85–94

    Article  PubMed  CAS  Google Scholar 

  27. Stillman B, McMeeken J. Use of a video time display in determining general gait measures. Aust J Physiother 1996; 42 (3): 213–7

    PubMed  Google Scholar 

  28. McDonough AL, Batavia M, Chen FC, et al. The validity and reliability of the GAIT Rite system’s measurements: A preliminary evaluation. Arch Phys Med Rehabil 2001 Mar; 82 (3): 419–25

    Article  PubMed  CAS  Google Scholar 

  29. Whittle MW. Gait analysis: an introduction. Edinburgh: Elselvier, 2007

    Google Scholar 

  30. Norton K, Norton L, Sadgrove D. Position statement on physical activity and exercise intensity terminology. J Sci Med Sport 2010 Sep; 13 (5): 496–502

    Article  PubMed  Google Scholar 

  31. American College of Sports Medicine. ACSM’s Guidelines for exercise testing and prescription. 8th ed. New York (NY): Lippincott, Williams & Wilkins, 2010

    Google Scholar 

  32. Haskell WL, Lee IM, Pate RR, et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 2007 Aug; 39 (8): 1423–34

    Article  PubMed  Google Scholar 

  33. O’Donovan G, Blazevich AJ, Boreham C, et al. The ABC of physical activity for health: a consensus statement from the British Association of Sport and Exercise Sciences. J Sports Sci 2010 Apr; 28 (6): 573–91

    Article  PubMed  Google Scholar 

  34. Hatano Y. Use of the pedometer for promoting daily walking exercise. ICHPER 1993; 29: 4–8

    Google Scholar 

  35. Tudor-Locke C, Sisson SB, Collova T, et al. Pedometerdetermined step count guidelines for classifying walking intensity in a young ostensibly healthy population. Can J Appl Physiol 2005 Dec; 30 (6): 666–76

    Article  PubMed  Google Scholar 

  36. Marshall SJ, Levy SS, Tudor-Locke CE, et al. Translating physical activity recommendations into a pedometerbased step goal: 3000 steps in 30 minutes. Am J Prev Med 2009 May; 36 (5): 410–5

    Article  PubMed  Google Scholar 

  37. Rowe DA, Welk GJ, Heil DP, et al. Stride rate recommendations for moderate intensity walking. Med Sci Sports Exerc 2011 Jun 11; 43 (2): 312–8

    Article  PubMed  Google Scholar 

  38. Abel M, Hannon J, Mullineaux D, et al. Determination of step rate thresholds corresponding to physical activity classifications in adults. J Phys Act Health 2011; 8: 45–51

    PubMed  Google Scholar 

  39. Park J, Ishikawa-Takata K, Tanaka S, et al. Effects of walking speed and step frequency on estimation of physical activity using accelerometers. J Physiol Anthropol 2011; 30 (3): 119–27

    Article  PubMed  Google Scholar 

  40. Spelman CC, Pate RR, Macera CA, et al. Self-selected exercise intensity of habitual walkers. Med Sci Sports Exerc 1993 Oct; 25 (10): 1174–9

    PubMed  CAS  Google Scholar 

  41. Murtagh EM, Boreham CA, Murphy MH. Speed and exercise intensity of recreational walkers. Prev Med 2002 Oct; 35 (4): 397–400

    Article  PubMed  Google Scholar 

  42. Drillis RJ. Objective recording and biomechanics of pathological gait. Ann New York Acad Sciences 1958; 74: 86–109

    Article  CAS  Google Scholar 

  43. Molen NH, Rozendal RH. Some factors of human gait. Proc K Ned Akad Wet C 1966; 69 (4): 522–7

    PubMed  CAS  Google Scholar 

  44. Finley FR, Cody KA. Locomotive characteristics of urban pedestrians. Arch Phys Med Rehabil 1970 Jul; 51 (7): 423–6

    PubMed  CAS  Google Scholar 

  45. Molen NH, Rozendal RH, Boon W. Fundamental characteristics of human gait in relation to sex and location. Proc K Ned Akad Wet C 1972; 45 (3): 215–23

    PubMed  CAS  Google Scholar 

  46. Eke-Okoro ST, Sandlund B. The effects of load, shoes, sex and direction on the gait characteristics of street pedestrians. J Hum Movement Stud 1984; 10: 107–14

    Google Scholar 

  47. Sato H, Ishizu K. Gait patterns of Japanese pedestrians. J Hum Ergol (Tokyo) 1990 Jun; 19 (1): 13–22

    CAS  Google Scholar 

  48. Sato H, Sako H, Mukae H, et al. Gait patterns of young Japanese women. J Hum Ergol (Tokyo) 1991 Jun; 20 (1): 85–8

    CAS  Google Scholar 

  49. Hangland A, Cimbalo RS. Human ethology: age and sex differences in mall walking. Percept Mot Skills 1997 Dec; 85 (3Pt1): 845–6

    Article  PubMed  CAS  Google Scholar 

  50. Johnson ST, Tudor-Locke C, McCargar LJ, et al. Measuring habitual walking speed of people with type 2 diabetes: are they meeting recommendations? Diabetes Care 2005 Jun; 28 (6): 1503–4

    Article  PubMed  Google Scholar 

  51. Lord SE, Rochester L, Weatherall M, et al. The effect of environment and task on gait parameters after stroke: a randomized comparison of measurement conditions. Arch Phys Med Rehabil 2006 Jul; 87 (7): 967–73

    Article  PubMed  Google Scholar 

  52. Donovan K, Lord SE, McNaughton HK, et al. Mobility beyond the clinic: the effect of environment on gait and its measurement in community-ambulant stroke survivors. Clin Rehabil 2008 Jun; 22 (6): 556–63

    Article  PubMed  Google Scholar 

  53. Tudor-Locke C, Camhi SM, Leonardi C, et al. Patterns of adults stepping cadence in the 2005-2006 NHANES. Prev Med 2011; 53: 178–81

    Article  PubMed  Google Scholar 

  54. Cavanaugh JT, Coleman KL, Gaines JM, et al. Using step activity monitoring to characterize ambulatory activity in community-dwelling older adults. J Am Geriatr Soc 2007 Jan; 55 (1): 120–4

    Article  PubMed  Google Scholar 

  55. Gardner AW, Montgomery PS, Scott KJ, et al. Patterns of ambulatory activity in subjects with and without intermittent claudication. J Vasc Surg 2007 Dec; 46 (6): 1208–14

    Article  PubMed  Google Scholar 

  56. Tyo BM, Fitzhugh EC, Bassett Jr DR, et al. Effects of body mass index and step rate on pedometer error in a freeliving environment. Med Sci Sports Exerc 2011; 43 (2): 350–6

    Article  PubMed  Google Scholar 

  57. Ayabe M, Aoki J, Kumahara H, et al. Assessment of minute- by-minute stepping rate of physical activity under free-living conditions in female adults. Gait Posture 2011; 34 (2): 292–4

    Article  PubMed  Google Scholar 

  58. Orendurff MS, Schoen JA, Bernatz GC, et al. How humans walk: bout duration, steps per bout, and rest duration. J Rehabil Res Dev 2008; 45 (7): 1077–90

    Article  PubMed  Google Scholar 

  59. Benedetti MG, Di Gioia A, Conti L, et al. Physical activity monitoring in obese people in the real life environment. J Neuroeng Rehabil 2009; 6: 47

    Article  PubMed  Google Scholar 

  60. Ayabe M, Aoki J, Kumahara H, et al. Minute-by-minute stepping rate of daily physical activity in normal and overweight/obese adults. Obes Res Clin Pract 2011; 5: e151–6

    Article  Google Scholar 

  61. Aoyagi Y, Park H, Watanabe E, et al. Habitual physical activity and physical fitness in older Japanese adults: the Nakanojo Study. Gerontology 2009; 55 (5): 523–31

    Article  PubMed  Google Scholar 

  62. Mudge S, Stott NS. Timed walking tests correlate with daily step activity in persons with stroke. Arch Phys Med Rehabil 2009 Feb; 90 (2): 296–301

    Article  PubMed  Google Scholar 

  63. Gerdhem P, Dencker M, Ringsberg K, et al. Accelerometer- measured daily physical activity among octogenerians: results and associations to other indices of physical performance and bone density. Eur J Appl Physiol 2008 Jan; 102 (2): 173–80

    Article  PubMed  Google Scholar 

  64. Fiser WM, Hays NP, Rogers SC, et al. Energetics of walking in elderly people: factors related to gait speed. J Gerontol A Biol Sci Med Sci 2010; 65 (12): 1332–7

    Article  PubMed  Google Scholar 

  65. Pruitt LA, Glynn NW, King AC, et al. Use of accelerometry to measure physical activity in older adults at risk for mobility disability. J Aging Phys Act 2008 Oct; 16 (4): 416–34

    PubMed  Google Scholar 

  66. Barnett A, Cerin E. Individual calibration for estimating free-living walking speed using the MTI monitor. Med Sci Sports Exerc 2006 Apr; 38 (4): 761–7

    Article  PubMed  Google Scholar 

  67. Bereket S. Effects of anthropometric parameters and stride frequency on estimation of energy cost of walking. J Sports Med Phys Fitness 2005 Jun; 45 (2): 152–61

    PubMed  CAS  Google Scholar 

  68. Rodgers CD, Paterson DH, Cunningham DA, et al. Sleep deprivation: effects on work capacity, self-paced walking, contractile properties and perceived exertion. Sleep 1995 Jan; 18 (1): 30–8

    PubMed  CAS  Google Scholar 

  69. Malatesta D, Simar D, Saad HB, et al. Effect of an overground walking training on gait performance in healthy 65- to 80-year-olds. Exp Gerontol 2010 Jun; 45 (6): 427–34

    Article  PubMed  Google Scholar 

  70. Hunter GR, Treuth MS, Weinsier RL, et al. The effects of strength conditioning on older women’s ability to perform daily tasks. J Am Geriatr Soc 1995 Jul; 43 (7): 756–60

    PubMed  CAS  Google Scholar 

  71. Lord SR, Lloyd DG, Nirui M, et al. The effect of exercise on gait patterns in older women: a randomized controlled trial. J Gerontol A Biol Sci Med Sci 1996 Mar; 51 (2): M64–70

    Article  PubMed  CAS  Google Scholar 

  72. Allet L, Armand S, Aminian K, et al. An exercise intervention to improve diabetic patients’ gait in a real-life environment. Gait Posture 2010 Jun; 32 (2): 185–90

    Article  PubMed  CAS  Google Scholar 

  73. Kirk A, Mutrie N, MacIntyre P, et al. Increasing physical activity in people with type 2 diabetes. Diabetes Care 2003 Apr; 26 (4): 1186–92

    Article  PubMed  Google Scholar 

  74. Rogers LQ, Hopkins-Price P, Vicari S, et al. Physical activity and health outcomes three months after completing a physical activity behavior change intervention: persistent and delayed effects. Cancer Epidemiol Biomarkers Prev 2009 May; 18 (5): 1410–8

    Article  PubMed  Google Scholar 

  75. Mudge S, Barber PA, Stott NS. Circuit-based rehabilitation improves gait endurance but not usual walking activity in chronic stroke: a randomized controlled trial. Arch Phys Med Rehabil 2009 Dec; 90 (12): 1989–96

    Article  PubMed  Google Scholar 

  76. Gardner AW, Parker DE, Montgomery PS, et al. Efficacy of quantified home-based exercise and supervised exercise in patients with intermittent claudication: a randomized controlled trial. Circulation 2011 Feb 8; 123 (5): 491–8

    Article  PubMed  Google Scholar 

  77. Hartmann A, Luzi S, Murer K, et al. Concurrent validity of a trunk tri-axial accelerometer system for gait analysis in older adults. Gait Posture 2009 Apr; 29 (3): 444–8

    Article  PubMed  Google Scholar 

  78. Lord S, Rochester L, Baker K, et al. Concurrent validity of accelerometry to measure gait in Parkinsons Disease. Gait Posture 2008 Feb; 27 (2): 357–9

    Article  PubMed  Google Scholar 

  79. McClain JJ, Hart TL, Getz RS, et al. Convergent validity of 3 low cost motion sensors with the Acti Graph accelerometer. J Phys Act Health 2010 Sep; 7 (5): 662–70

    PubMed  Google Scholar 

  80. Holbrook EA, Barreira TV, Kang M. Validity and reliability of Omron pedometers for prescribed and self-paced walking. Med Sci Sports Exerc 2009 Mar; 41 (3): 670–4

    Article  PubMed  Google Scholar 

  81. Hasson RE, Haller J, Pober DM, et al. Validity of the Omron HJ-112 pedometer during treadmill walking. Med Sci Sports Exerc 2009 Apr; 41 (4): 805–9

    Article  PubMed  Google Scholar 

  82. Schneider PL, Crouter SE, Lukajic O, et al. Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk. Med Sci Sports Exerc 2003 Oct; 35 (10): 1779–84

    Article  PubMed  Google Scholar 

  83. Abel M. The effect of pedometer tilt angle on pedometer accuracy. Int J Fit 2008; 4 (1): 51–7

    Google Scholar 

  84. Kanoun N. Validation of the ActivPAL activity monitor as a measure of walking at pre-determined slow walking speeds in a healthy population in a controlled setting. Reinvention: a Journal of Undergraduate Research 2009; 2 (2) [online]. Available from URL: http://www.warwick.ac.uk/go/reinventionjournal/issues/Vol.2issue2/kanoun [Accessed 2012 Feb 29]

    Google Scholar 

  85. Ryan CG, Grant PM, Tigbe WW, et al. The validity and reliability of a novel activity monitor as a measure of walking. Br J Sports Med 2006 Sep; 40 (9): 779–84

    Article  PubMed  CAS  Google Scholar 

  86. Maddocks M, Petrou A, Skipper L, et al. Validity of three accelerometers during treadmill walking and motor vehicle travel. Br J Sports Med 2010 Jun; 44 (8): 606–8

    Article  PubMed  Google Scholar 

  87. Dwyer TJ, Alison JA, McKeough ZJ, et al. Evaluation of the Sense Wear activity monitor during exercise in cystic fibrosis and in health. Respir Med 2009 Oct; 103 (10): 1511–7

    Article  PubMed  Google Scholar 

  88. Furlanetto KC, Bisca GW, Oldemberg N, et al. Step counting and energy expenditure estimation in patients with chronic obstructive pulmonary disease and healthy elderly: accuracy of 2 motion sensors. Arch Phys Med Rehabil 2010 Feb; 91 (2): 261–7

    Article  PubMed  Google Scholar 

  89. Foster RC, Lanningham-Foster LM, Manohar C, et al. Precision and accuracy of an ankle-worn accelerometerbased pedometer in step counting and energy expenditure. Prev Med 2005 Sep-Oct; 41 (3-4): 778–83

    Article  PubMed  Google Scholar 

  90. Abel MG, Hannon JC, Sell K, et al. Validation of the Kenz Lifecorder EX and ActiGraph GT1M accelerometers for walking and running in adults. Appl Physiol Nutr Metab 2008 Dec; 33 (6): 1155–64

    Article  PubMed  Google Scholar 

  91. Esliger DW, Probert A, Gorber SC, et al. Validity of the Actical accelerometer step-count function. Med Sci Sports Exerc 2007 Jul; 39 (7): 1200–4

    Article  PubMed  Google Scholar 

  92. Le Masurier GC, Lee SM, Tudor-Locke C. Motion sensor accuracy under controlled and free-living conditions. Med Sci Sports Exerc 2004 May; 36 (5): 905–10

    PubMed  Google Scholar 

  93. Le Masurier GC, Tudor-Locke C. Comparison of pedometer and accelerometer accuracy under controlled conditions. Med Sci Sports Exerc 2003 May; 35 (5): 867–71

    Article  PubMed  Google Scholar 

  94. Crouter SE, Schneider PL, Karabulut M, et al. Validity of 10 electronic pedometers for measuring steps, distance, and energy cost. Med Sci Sports Exerc 2003 Aug; 35 (8): 1455–60

    Article  PubMed  Google Scholar 

  95. McClain JJ, Craig CL, Sisson SB, et al. Comparison of Lifecorder EX and Acti Graph accelerometers under freeliving conditions. Appl Physiol Nutr Metab 2007 Aug; 32 (4): 753–61

    Article  PubMed  Google Scholar 

  96. Johnson ST, McCargar LJ, Bell GJ, et al. Walking faster: distilling a complex prescription for type 2 diabetes management through pedometry. Diabetes Care 2006 Jul; 29 (7): 1654–5

    Article  PubMed  Google Scholar 

  97. Lim I, van Wegen E, de Goede C, et al. Effects of external rhythmical cueing on gait in patients with Parkinson’s disease: a systematic review. Clin Rehabil 2005 Oct; 19 (7): 695–713

    Article  PubMed  CAS  Google Scholar 

  98. Conklyn D, Stough D, Novak E, et al. A home-based walking program using rhythmic auditory stimulation improves gait performance in patients with multiple sclerosis: a pilot study. Neurorehabil Neural Repair 2010; 24 (9): 835–42

    Article  PubMed  Google Scholar 

  99. Bauldoff GS, Hoffman LA, Zullo TG, et al. Exercise maintenance following pulmonary rehabilitation: effect of distractive stimuli. Chest 2002 Sep; 122 (3): 948–54

    Article  PubMed  Google Scholar 

  100. Yamashita S, Iwai K, Akimoto T, et al. Effects of music during exercise on RPE, heart rate and the autonomic nervous system. J Sports Med Phys Fitness 2006 Sep; 46 (3): 425–30

    PubMed  CAS  Google Scholar 

  101. Potteiger JA, Schroeder JM, Goff KL. Influence of music on ratings of perceived exertion during 20 minutes of moderate intensity exercise. Percept Mot Skills 2000 Dec; 91 (3Pt1): 848–54

    Article  PubMed  CAS  Google Scholar 

  102. Thaut MH, McIntosh GC, Rice RR, et al. Rhythmic auditory stimulation in gait training for Parkinson’s disease patients. Mov Disord 1996 Mar; 11 (2): 193–200

    Article  PubMed  CAS  Google Scholar 

  103. Sun J, Walters M, Svensson N, et al. The influence of surface slope on human gait characteristics: a study of urban pedestrians walking on an inclined surface. Ergonomics 1996 Apr; 39 (4): 677–92

    Article  PubMed  CAS  Google Scholar 

  104. Tudor-Locke C, Brashear MM, Katzmarzyk PT, et al. Peak stepping cadence in free-living adults: 2005-2006 NHANES. J Phys Act Health. In press

Download references

Acknowledgements

No funding was provided for the preparation of this paper. The authors have no conflicts of interest to report. The listed authors are publicly responsible for the content of this article.

Author information

Authors and Affiliations

  1. Walking Behavior Laboratory, Pennington Biomedical Research Center, 6400 Perkins Road, Baton Rouge, LA, 70808, USA

    Catrine Tudor-Locke PhD (FACSM, Director)

  2. School of Psychological Sciences and Health, University of Strathclyde, Glasgow, UK

    David A. Rowe

Authors
  1. Catrine Tudor-Locke PhD
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. David A. Rowe
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Catrine Tudor-Locke PhD.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tudor-Locke, C., Rowe, D.A. Using Cadence to Study Free-Living Ambulatory Behaviour. Sports Med 42, 381–398 (2012). https://doi.org/10.2165/11599170-000000000-00000

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2165/11599170-000000000-00000

Keywords