Advertisement

Journal of Neurology

, Volume 264, Issue 2, pp 316–326 | Cite as

Continuous daily assessment of multiple sclerosis disability using remote step count monitoring

  • V. J. Block
  • A. Lizée
  • E. Crabtree-Hartman
  • C. J. Bevan
  • J. S. Graves
  • R. Bove
  • A. J. Green
  • B. Nourbakhsh
  • M. Tremblay
  • P.-A. Gourraud
  • M. Y. Ng
  • M. J. Pletcher
  • J. E. Olgin
  • G. M. Marcus
  • D. D. Allen
  • B. A. C. Cree
  • J. M. Gelfand
Original Communication

Abstract

Disability measures in multiple sclerosis (MS) rely heavily on ambulatory function, and current metrics fail to capture potentially important variability in walking behavior. We sought to determine whether remote step count monitoring using a consumer-friendly accelerometer (Fitbit Flex) can enhance MS disability assessment. 99 adults with relapsing or progressive MS able to walk ≥2-min were prospectively recruited. At 4 weeks, study retention was 97% and median Fitbit use was 97% of days. Substudy validation resulted in high interclass correlations between Fitbit, ActiGraph and manual step count tally during a 2-minute walk test, and between Fitbit and ActiGraph (ICC = 0.76) during 7-day home monitoring. Over 4 weeks of continuous monitoring, daily steps were lower in progressive versus relapsing MS (mean difference 2546 steps, p < 0.01). Lower average daily step count was associated with greater disability on the Expanded Disability Status Scale (EDSS) (p < 0.001). Within each EDSS category, substantial variability in step count was apparent (i.e., EDSS = 6.0 range 1097–7152). Step count demonstrated moderate-strong correlations with other walking measures. Lower average daily step count is associated with greater MS disability and captures important variability in real-world walking activity otherwise masked by standard disability scales, including the EDSS. These results support remote step count monitoring as an exploratory outcome in MS trials.

Keywords

Multiple sclerosis Outcome measurement Remote physical activity monitoring Accelerometer Progressive Relapsing 

Notes

Acknowledgements

We thank Dan Robeson, DPT, and Patrick Sullivan, DPT, for assistance with data collection in the clinic.

Compliance with ethical standards

Disclosures

Valerie J Block has no disclosures.

Antoine Lizée has no disclosures.

Elizabeth Crabtree-Hartman has received educational grants from the MS Foundation, Teva neurosciences, and Biogen. She has served as a consultant to Genzyme, Teva and Novartis. She is on the Speakers Bureau for Genzyme, Teva and Biogen.

Carolyn J Bevan has no disclosures.

Jennifer Graves has current research grants from Race to Erase MS, National MS Society, Genentech, and Biogen.

Riley Bove has no disclosures.

Ari Green has received research grants from the NMSS, NIH, Novartis and Inception 5 Sciences. He has served on an end point adjudication committee for Mediimmune and a steering committee for OCTIMs. He has served as an expert witness for Mylan and Amneal. He also is on the Scientific Advisory Board of Bionure and Inception Sciences.

Matthew Tremblay has no disclosures.

Bardia Nourbakhsh has received research support from American Brain Foundation, Biogen and National MS Society.

Pierre-Antoine Gourraud has no disclosures.

Madelena Ng has no disclosures.

Jeffrey Olgin has no disclosures.

Gregory M Marcus has no disclosures.

Jeffrey M Pletcher has no disclosures.

Diane D Allen has received compensation as an instructor for the Neurologic Physical Therapy Residency Program at Kaiser Redwood City. She has also received compensation for co-developing an online continuing education course in rehabilitation for people with multiple sclerosis for Western Schools.

Bruce C Cree has received personal compensation for consulting from Abbvie, Biogen, EMD Serono, MedImmune, Novartis, Sanofi Genzyme, Shire and Teva.

Jeffrey M Gelfand has received personal compensation for consulting on a scientific advisory board for MedImmune and Genentech, research support to UCSF from Quest Diagnostics for development of a dementia care pathway, and personal compensation for medical legal consulting as an expert witness.

Study funding

National Center for Advancing Translational Sciences of NIH (KL2TR000143) (JMG).

References

  1. 1.
    Hauser SL, Goodin DS. Multiple Sclerosis and Other Demyelinating Diseases. In: Longo DL, Fauci AS, Kasper DL, et al., eds. Harrison’s Principles of Internal Medicine. 18th edn. McGraw-Hill Global Education Holdings, LLC, 2012:33. http://accessmedicine.mhmedical.com/book.aspx?bookId=331
  2. 2.
    Dlugonski D, Pilutti LA, Sandroff BM et al (2013) Steps per day among persons with multiple sclerosis: variation by demographic, clinical, and device characteristics. Arch Phys Med Rehabil 94(8):1534–1539. doi: 10.1016/j.apmr.2012.12.014 [published Online First: 2013/02/20] CrossRefPubMedGoogle Scholar
  3. 3.
    Klaren RE, Motl RW, Dlugonski D et al (2013) Objectively quantified physical activity in persons with multiple sclerosis. Arch Phys Med Rehabil 94(12):2342–2348. doi: 10.1016/j.apmr.2013.07.011 [published Online First: 2013/08/03] CrossRefPubMedGoogle Scholar
  4. 4.
    Motl RW, Pilutti LA, Learmonth YC et al (2013) Clinical importance of steps taken per day among persons with multiple sclerosis. PLoS One 8(9):e73247. doi: 10.1371/journal.pone.0073247 [published Online First: 2013/09/12] CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Motl RW, Sandroff BM, Suh Y et al (2012) Energy cost of walking and its association with gait parameters, daily activity, and fatigue in persons with mild multiple sclerosis. Neurorehabil Neural Repair 26(8):1015–1021. doi: 10.1177/1545968312437943 [published Online First: 2012/04/03] CrossRefPubMedGoogle Scholar
  6. 6.
    Kurtzke J (1983) Rating neurological impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33:1444–1452CrossRefPubMedGoogle Scholar
  7. 7.
    Hobart J, Freeman J, Thompson A (2000) Kurtzke scales revisited: the application of psychometric methods to clinical intuition. Brain J Neurol 123(Pt 5):1027–1040 [published Online First: 2000/04/25] CrossRefGoogle Scholar
  8. 8.
    Fletcher GF, Balady G, Blair SN et al (1996) Statement on exercise: benefits and recommendations for physical activity programs for all Americans. A statement for health professionals by the Committee on Exercise and Cardiac Rehabilitation of the Council on Clinical Cardiology. American Heart Association. Circulation 94(4):857–862 [published Online First: 1996/08/15] PubMedGoogle Scholar
  9. 9.
    Andersen LK, Knak KL, Witting N et al (2016) Two- and 6-minute walk tests assess walking capability equally in neuromuscular diseases. Neurology 86(5):442–445. doi: 10.1212/wnl.0000000000002332 [published Online First: 2016/01/08] CrossRefPubMedGoogle Scholar
  10. 10.
    Busse ME, Pearson OR, Van Deursen R et al (2004) Quantified measurement of activity provides insight into motor function and recovery in neurological disease. J Neurol Neurosurg Psychiatry 75(6):884–888 [published Online First: 2004/05/18] CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Motl R, Sandroff B, Sosnoff J (2012) Commercially available accelerometry as an ecologically valid measure of ambulation in individuals with multiple sclerosis. Expert Rev Neurother 12:1079–1088CrossRefPubMedGoogle Scholar
  12. 12.
    Motl RW, McAuley E, Sandroff BM (2013) Longitudinal change in physical activity and its correlates in relapsing-remitting multiple sclerosis. Phys Ther 93(8):1037–1048 [published Online First: 2013 Apr 18.] CrossRefPubMedGoogle Scholar
  13. 13.
    Motl RW, Dlugonski D, Suh Y et al (2010) Accelerometry and its association with objective markers of walking limitations in ambulatory adults with multiple sclerosis. Arch Phys Med Rehabil 91(12):1942–1947. doi: 10.1016/j.apmr.2010.08.011 [published Online First: 2010/11/30] CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Block VAJ, Pitsch E, Tahir P et al (2016) Remote physical activity monitoring in neurological disease: a systematic review. PLoS One 11(4):e0154335. doi: 10.1371/journal.pone.0154335 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Motl RW, McAuley E, Dlugonski D (2012) Reactivity in baseline accelerometer data from a physical activity behavioral intervention. Health Psychol 31(2):172–175. doi: 10.1037/a0025965 [published Online First: 2011/10/26] CrossRefPubMedGoogle Scholar
  16. 16.
    Balto JM, Kinnett-Hopkins DL, Motl RW (2016) Accuracy and precision of smartphone applications and commercially available motion sensors in multiple sclerosis. Mult Scler J Exp Transl Clin 2:1–8. doi: 10.1177/2055217316634754 Google Scholar
  17. 17.
    Polman CH, Reingold SC, Banwell B et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292–302. doi: 10.1002/ana.22366 [published Online First: 2011/03/10] CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Lublin FD, Reingold SC, Cohen JA et al (2014) Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology 83(3):278–286. doi: 10.1212/wnl.0000000000000560 [published Online First: 2014/05/30] CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Kieseier BC, Pozzilli C (2012) Assessing walking disability in multiple sclerosis. Mult Scler (Houndmills, Basingstoke, England) 18(7):914–924. doi: 10.1177/1352458512444498 [published Online First: 2012/06/29] CrossRefGoogle Scholar
  20. 20.
    Sebastiao E, Sandroff BM, Learmonth YC et al (2016) Validity of the timed up and go test as a measure of functional mobility in persons with multiple sclerosis. Arch Phys Med Rehabil 97(7):1072–1077. doi: 10.1016/j.apmr.2015.12.031 CrossRefPubMedGoogle Scholar
  21. 21.
    Rossier P, Wade DT (2001) Validity and reliability comparison of 4 mobility measures in patients presenting with neurologic impairment. Arch Phys Med Rehabil 82(1):9–13. doi: 10.1053/apmr.2001.9396 [published Online First: 2001/03/10] CrossRefPubMedGoogle Scholar
  22. 22.
    Harris PA, Taylor R, Thielke R et al (2009) Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42(2):377–381CrossRefPubMedGoogle Scholar
  23. 23.
    Hobart JC, Riazi A, Lamping DL et al (2003) Measuring the impact of MS on walking ability: the 12-Item MS Walking Scale (MSWS-12). Neurology 60(1):31–36 [published Online First: 2003/01/15] CrossRefPubMedGoogle Scholar
  24. 24.
    Fischer JS, LaRocca NG, Miller DM et al (1995) Recent developments in the assessment of quality of life in multiple sclerosis (MS). Mult Scler (Houndmills, Basingstoke, England) 5(4):251–259 [published Online First: 1999/09/01] CrossRefGoogle Scholar
  25. 25.
    Patel DP, Elliott SP, Stoffel JT et al (2016) Patient reported outcomes measures in neurogenic bladder and bowel: a systematic review of the current literature. Neurourol Urodyn 35(1):8–14. doi: 10.1002/nau.22673 [published Online First: 2014/10/21]CrossRefPubMedGoogle Scholar
  26. 26.
    Fisk JD, Ritvo PG, Ross L et al (1994) Measuring the functional impact of fatigue: initial validation of the fatigue impact scale. Clin Infect Dis Off Publ Infect Dis Soc Am 18(Suppl 1):S79–S83 [published Online First: 1994/01/01] CrossRefGoogle Scholar
  27. 27.
    Jones KH, Ford DV, Jones PA et al (2012) A large-scale study of anxiety and depression in people with Multiple Sclerosis: a survey via the web portal of the UK MS Register. PLoS One 7(7):e41910. doi: 10.1371/journal.pone.0041910 [published Online First: 2012/08/04] CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Balantrapu S, Sosnoff JJ, Pula JH et al (2014) Leg spasticity and ambulation in multiple sclerosis. Mult Scler Int 2014:649390. doi: 10.1155/2014/649390 [published Online First: 2014/07/08] PubMedPubMedCentralGoogle Scholar
  29. 29.
    Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet (London, England) 1(8476):307–310 [published Online First: 1986/02/08] CrossRefGoogle Scholar
  30. 30.
    Tudor-Locke C, Bassett DR Jr (2004) How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med (Auckland, NZ) 34(1):1–8 [published Online First: 2004/01/13] CrossRefGoogle Scholar
  31. 31.
    Dalgas U, Stenager E (2012) Exercise and disease progression in multiple sclerosis: can exercise slow down the progression of multiple sclerosis? Ther Adv Neurol Disord 5(2):81–95. doi: 10.1177/1756285611430719 [published Online First: 2012/03/22] CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Cavanaugh JT, Gappmaier VO, Dibble LE et al (2011) Ambulatory activity in individuals with multiple sclerosis. J Neurol Phys Ther JNPT 35(1):26–33. doi: 10.1097/NPT.0b013e3182097190 [published Online First: 2011/04/09] CrossRefPubMedGoogle Scholar
  33. 33.
    Motl RW, Learmonth YC, Pilutti LA et al (2014) Validity of minimal clinically important difference values for the multiple sclerosis walking scale-12? Eur Neurol 71(3–4):196–202. doi: 10.1159/000356116 [published Online First: 2014/01/25] CrossRefPubMedGoogle Scholar
  34. 34.
    Motl RW, Pilutti L, Sandroff BM et al (2013) Accelerometry as a measure of walking behavior in multiple sclerosis. Acta Neurol Scand 127(6):384–390. doi: 10.1111/ane.12036 [published Online First: 2012/12/18] CrossRefPubMedGoogle Scholar
  35. 35.
    McIninch J, Datta S, DasMahapatra P et al (2015) Remote tracking of walking activity in MS patients in a real-world setting. Neurology 84(14 Supplement P3.209),The 67th Annual Meeting took place, April 18–25, 2015, Washington, DCGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • V. J. Block
    • 1
  • A. Lizée
    • 2
  • E. Crabtree-Hartman
    • 2
  • C. J. Bevan
    • 2
  • J. S. Graves
    • 2
  • R. Bove
    • 2
  • A. J. Green
    • 2
  • B. Nourbakhsh
    • 2
  • M. Tremblay
    • 2
  • P.-A. Gourraud
    • 2
  • M. Y. Ng
    • 3
  • M. J. Pletcher
    • 4
  • J. E. Olgin
    • 3
  • G. M. Marcus
    • 3
  • D. D. Allen
    • 1
  • B. A. C. Cree
    • 2
  • J. M. Gelfand
    • 2
  1. 1.Department of Physical Therapy and RehabilitationUniversity of California, San Francisco and San Francisco State UniversitySan FranciscoUSA
  2. 2.Department of NeurologyUniversity of California San FranciscoSan FranciscoUSA
  3. 3.Division of Cardiology, Department of MedicineUniversity of CaliforniaSan FranciscoUSA
  4. 4.Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoUSA

Personalised recommendations