Original article| Volume 93, ISSUE 11, P2022-2028, November 2012

Comparative Validity of Accelerometer-Based Measures of Physical Activity for People With Multiple Sclerosis


      Coote S, O'Dwyer C. Comparative validity of accelerometer-based measures of physical activity for people with multiple sclerosis.


      To estimate the criterion validity of accelerometer-based devices as measures of steps and energy expenditure in healthy controls and people with multiple sclerosis (MS) with varying disability levels during everyday activities.


      Cross-sectional study.


      University research room.


      People with MS who used at most a stick to walk outdoors (MS-A; n=19), people with MS who used bilateral support for gait (MS-B; n=11), and healthy controls (n=15).


      Participants completed 85 minutes of 9 scripted everyday activities.

      Main Outcome Measures

      Estimates of metabolic equivalent thresholds (METs) and kilocalories from a portable metabolic system, and steps counted from video of the activities. Step and MET estimates from an integrative accelerometer and from a uniaxial accelerometer, and kilocalorie estimates from the integrative accelerometer.


      The uniaxial accelerometer had >30% error for steps for all groups. MET estimates had an intraclass correlation coefficient (ICC) <0.2 for all groups. For the integrative accelerometer, step estimates for controls had an ICC of .69 and <1% error. The step estimates for MS-A and MS-B groups had >20% error. The MET estimates had an ICC of .50 to .65 and 6% to 15% error. Kilocalorie estimates had 2.9% error for controls, 8.16% for MS-A, and 2.56% for MS-B groups. ICCs were all >.67, and mean differences from criterion were <20kcal.


      The agreement between steps and MET estimates from both devices and the criterion was poor, particularly for people with MS. Only the step and MET estimates for the control group for the integrative accelerometer were not significantly different from the criterion. Kilocalorie estimates from the integrative accelerometer using the proprietary algorithms of the device provide the most valid estimate of physical activity during activities of daily living for people with a range of walking disabilities resulting from MS.

      Key Words

      List of Abbreviations:

      APAL (ActivPAL), EE (energy expenditure), GNDS (Guy's Neurological Disability Scale), ICC (intraclass correlation coefficient), MET (metabolic equivalent threshold), MS (multiple sclerosis), PA (physical activity), PwMS (people with multiple sclerosis), SWA (SenseWear Armband)
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        • WHO
        Global recommendations on physical activity for health.
        WHO Pr, Geneva2010
        • Haskell W.L.
        • Lee I.M.
        • Pate R.R.
        • 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; 39: 1423-1434
        • Godfrey A.
        • Conway R.
        • Meagher D.
        • ÓLaighin G.
        Direct measurement of human movement by accelerometry.
        Med Eng Phys. 2008; 30: 1364-1386
        • Bassett D.R.
        • John D.
        Use of pedometers and accelerometers in clinical populations: validity and reliability issues.
        Phys Ther Rev. 2010; 15: 135-142
        • Caspersen C.
        • Powell K.
        • Christenson G.
        Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research.
        Public Health Rep. 1985; 100: 126-131
        • Bauman A.
        Updating the evidence that physical activity is good for health: an epidemiological review 2000–2003.
        J Sci Med Sport. 2004; 7: 6-19
        • Freedson P.
        • Pober D.
        • Janz K.F.
        Calibration of accelerometer output for children.
        Med Sci Sports Exerc. 2005; 37: S523-S530
        • Currie A.
        • Knox K.
        • Glazebrook K.
        • Brawley L.
        Physical activity levels in people with multiple sclerosis in Saskatchewan.
        Int J MS Care. 2009; 11: 114-120
        • Motl R.W.
        • Snook E.M.
        • Agiovlasitis S.
        Does an accelerometer accurately measure steps taken under controlled conditions in adults with mild multiple sclerosis?.
        Disabil Health J. 2011; 4: 52-57
        • Gosney J.
        • Scott J.
        • Snook E.
        • Motl R.
        Physical activity and multiple sclerosis: validity of self-report and objective measures.
        Fam Community Health. 2007; 30: 144-150
        • Motl R.
        • McAuley E.
        • Snook E.
        • Scott J.
        Validity of physical activity measures in ambulatory individuals with multiple sclerosis.
        Disabil Rehabil. 2006; 28: 1151-1156
        • Klassen L.
        • Schachter C.
        • Scudds R.
        An exploratory study of two measures of free-living physical activity for people with multiple sclerosis.
        Clin Rehabil. 2008; 22: 260-271
        • Motl R.W.
        • Dlugonski D.
        • Suh Y.
        • Weikert M.
        • Fernhall B.
        • Goldman M.
        Accelerometry and its association with objective markers of walking limitations in ambulatory adults with multiple sclerosis.
        Arch Phys Med Rehabil. 2010; 91: 1942-1947
        • Busse M.E.
        • Pearson O.R.
        • Van Deursen R.
        • Wiles C.
        Quantified measurement of activity provides insight into motor function and recovery in neurological disease.
        J Neurol Neurosurg Psychiatry. 2004; 75: 884-888
        • Motl R.
        • Snook E.
        • Agiovlasitis S.
        • Suh Y.
        Calibration of accelerometer output for ambulatory adults with multiple sclerosis.
        Arch Phys Med Rehabil. 2009; 90: 1778-1784
        • Kayes N.M.
        • Schluter P.J.
        • McPherson K.M.
        • Leete M.
        • Mawston G.
        • Taylor D.
        Exploring Actical accelerometers as an objective measure of physical activity in people with multiple sclerosis.
        Arch Phys Med Rehabil. 2009; 90: 594-601
        • Calabro M.
        • Welk G.
        • Eisenmann J.
        Validation of the SenseWear Pro Armband algorithms in children.
        Med Sci Sports Exerc. 2009; 41: 1714-1720
        • Cole P.
        • LeMura L.
        • Klinger T.
        • Strohecker K.
        • McConnell T.
        Measuring energy expenditure in cardiac patients using the Body Media Armband versus indirect calorimetry.
        J Sports Med Phys Fitness. 2004; 44: 262-271
        • Kelleher K.J.
        • Spence W.
        • Solomonidis S.
        • Apatsidis D.
        The characterisation of gait patterns of people with multiple sclerosis.
        Disabil Rehabil. 2010; 32: 1242-1250
        • Benedetti M.G.
        • Piperno R.
        • Simoncini L.
        • Bonato P.
        • Tonini A.
        • Giannini S.
        Gait abnormalities in minimally impaired multiple sclerosis patients.
        Mult Scler. 1999; 5: 363-368
        • Goldman M.D.
        • Marrie R.A.
        • Cohen J.A.
        Evaluation of the six-minute walk in multiple sclerosis subjects and healthy controls.
        Mult Scler. 2008; 14: 383-390
        • Gijbels D.
        • Alders G.
        • Van Hoof E.
        • et al.
        Predicting habitual walking performance in multiple sclerosis: relevance of capacity and self-report measures.
        Mult Scler. 2010; 16: 618-626
        • Johannsen D.
        • Calabro M.
        • Stewart J.
        • Franke W.
        • Rood J.
        • Welk G.
        Accuracy of armband monitors for measuring daily energy expenditure in healthy adults.
        Med Sci Sports Exerc. 2010; 42: 2134-2145
        • Hale L.
        • Williams K.
        • Ashton C.
        • Connole T.
        • McDowell H.
        • Taylor C.
        Reliability of RT3 accelerometer for measuring mobility in people with multiple sclerosis: pilot study.
        J Rehabil Res Dev. 2007; 44: 619-628
        • Ryan C.
        • Grant P.
        • Tigbe W.
        • Granat M.
        The validity and reliability of a novel activity monitor as a measure of walking.
        Br J Sports Med. 2006; 40: 779-784
        • King G.
        • Torres N.
        • Potter C.
        • Brooks T.
        • Coleman K.
        Comparison of activity monitors to estimate energy cost of treadmill exercise.
        Med Sci Sports Exerc. 2004; 36: 1244-1251
        • Thomas S.
        • Reading J.
        • Shephard R.
        Revision of the Physical Activity Readiness Questionnaire (PAR-Q).
        Can J Sport Sci. 1992; 17: 338-345
        • Arvidsson D.
        • Slinde F.
        • Larsson S.
        • Hulthen L.
        Energy cost of physical activities in children: validation of SenseWear Armband.
        Med Sci Sports Exerc. 2007; 39: 2076-2084
        • Perret C.
        • Mueller G.
        Validation of a new portable ergospirometric device (Oxycon Mobile) during exercise.
        Int J Sports Med. 2006; 27: 363-367
        • Rosdahl H.
        • Gullstrand L.
        • Salier-Eriksson J.
        • Johansson P.
        • Schantz P.
        Evaluation of the Oxycon Mobile metabolic system against the Douglas bag method.
        Eur J Appl Physiol. 2010; 109: 159-171
        • Maddocks M.
        • Petrou A.
        • Skipper L.
        • Wilcock A.
        Validity of three accelerometers during treadmill walking and motor vehicle travel.
        Br J Sports Med. 2010; 44: 606-608
        • Grant P.
        • Dall P.
        • Mitchell S.
        • Granat M.
        Activity-monitor accuracy in measuring step number and cadence in community-dwelling older adults.
        J Aging Phys Act. 2008; 16: 201-214
        • St-Onge M.
        • Mignault D.
        • Allison D.
        • Rabasa-Lhoret R.
        Evaluation of a portable device to measure daily energy expenditure in free-living adults.
        Am J Clin Nutr. 2007; 85: 742-749
        • Fruin M.
        • Rankin J.
        Validity of a multi-sensor armband in estimating rest and exercise energy expenditure.
        Med Sci Sports Exerc. 2004; 36: 1063-1069
        • Jakicic J.
        • Marcus M.
        • Gallagher K.
        • et al.
        Evaluation of the SenseWear Pro Armband (TM) to assess energy expenditure during exercise.
        Med Sci Sports Exerc. 2004; 36: 897-904
        • Dijkstra B.
        • Zijlstra W.
        • Scherder E.
        • Kamsma Y.
        Detection of walking periods and number of steps in older adults and patients with Parkinson's disease: accuracy of a pedometer and an accelerometry-based method.
        Age Ageing. 2008; 37: 436-441
        • Crouter S.
        • Schneider P.
        • Karabulut M.
        • Bassett Jr, D.R.
        Validity of 10 electronic pedometers for measuring steps, distance, and energy cost.
        Med Sci Sports Exerc. 2003; 35: 1455-1460
        • Schneider P.
        • Crouter S.
        • Lukajic O.
        • Bassett Jr, D.R.
        Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk.
        Med Sci Sports Exerc. 2003; 35: 1779-1784
        • Sharrack B.
        • Hughes R.
        The Guy's Neurological Disability Scale (GNDS): a new disability measure for multiple sclerosis.
        Mult Scler. 1999; 5: 223-233
        • Rossier P.
        • Wade D.T.
        The Guy's Neurological Disability Scale in patients with multiple sclerosis: a clinical evaluation of its reliability and validity.
        Clin Rehabil. 2002; 16: 75-95
        • Coenen M.
        • Cieza A.
        • Freeman J.
        • et al.
        The development of ICF Core Sets for multiple sclerosis: results of the International Consensus Conference.
        J Neurol. 2011; 258: 1477-1488
        • Rankin G.
        • Stokes M.
        Reliability of assessment tools in rehabilitation: an illustration of appropriate statistical analyses.
        Clin Rehabil. 1998; 12: 187-199
        • Bland M.
        • Altman D.
        Statistical methods for assessing agreement between two methods of clinical measurement.
        Lancet. 1986; 327: 307-310
        • Furlanetto K.
        • Bisca G.
        • 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; 91: 261-267
        • Cavalheri V.
        • Donária L.
        • Ferreira T.
        • et al.
        Energy expenditure during daily activities as measured by two motion sensors in patients with COPD.
        Respir Med. 2011; 105: 922-929
        • Taraldsen K.
        • Askim T.
        • Sletvold O.
        • et al.
        Evaluation of a body-worn sensor system to measure physical activity in older people with impaired function.
        Phys Ther. 2011; 91: 277-285
        • Grant P.
        • Ryan C.
        • Tigbe W.
        • Granat M.
        The validation of a novel activity monitor in the measurement of posture and motion during everyday activities.
        Br J Sports Med. 2006; 40: 992-997
        • Kozey-Keadle S.
        • Libertine A.
        • Lyden K.
        • Staudenmayer J.
        • Freedson P.S.
        Validation of wearable monitors for assessing sedentary behavior.
        Med Sci Sports Exerc. 2011; 43: 1561-1567
        • Portney L.G.
        • Watkins M.P.
        Foundations of clinical research: applications to practice.
        Prentice Hall, Upper Saddle River2000
        • Welk G.
        Principles of design and analyses for the calibration of accelerometry-based activity monitors.
        Med Sci Sports Exerc. 2005; 37: S501-S511
        • Harrington D.M.
        • Welk G.J.
        • Donnelly A.E.
        Validation of MET estimates and step measurement using the ActivPAL physical activity logger.
        J Sports Sci. 2011; 29: 627-633
        • Berntsen S.
        • Hageberg R.
        • Aandstad A.
        • et al.
        Validity of physical activity monitors in adults participating in free-living activities.
        Br J Sports Med. 2008; 44: 657-664
        • Coote S.
        • O'Dwyer C.
        Comparing energy expenditure during activities of daily living of people with varying mobility limitations due to multiple sclerosis and healthy controls.
        Physiotherapy. 2011; 97: eS235