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Bove R, White CC, Giovannoni G, et al. Evaluating more naturalistic outcome measures: A 1-year smartphone study in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm. 2015;2(6):e162doi: 10.1212/NXI.0000000000000162.
Bove, R., White, C. C., Giovannoni, G., Glanz, B., Golubchikov, V., Hujol, J., Jennings, C., Langdon, D., Lee, M., Legedza, A., Paskavitz, J., Prasad, S., Richert, J., Robbins, A., Roberts, S., Weiner, H., Ramachandran, R., Botfield, M., & De Jager, P. L. (2015). Evaluating more naturalistic outcome measures: A 1-year smartphone study in multiple sclerosis. Neurology(R) neuroimmunology & neuroinflammation, 2(6), e162. https://doi.org/10.1212/NXI.0000000000000162
Bove, Riley, et al. "Evaluating more naturalistic outcome measures: A 1-year smartphone study in multiple sclerosis." Neurology(R) neuroimmunology & neuroinflammation vol. 2,6 (2015): e162. doi: https://doi.org/10.1212/NXI.0000000000000162
Bove R, White CC, Giovannoni G, Glanz B, Golubchikov V, Hujol J, Jennings C, Langdon D, Lee M, Legedza A, Paskavitz J, Prasad S, Richert J, Robbins A, Roberts S, Weiner H, Ramachandran R, Botfield M, De Jager PL. Evaluating more naturalistic outcome measures: A 1-year smartphone study in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm. 2015 Oct 15;2(6):e162. doi: 10.1212/NXI.0000000000000162. eCollection 2015 Dec. PMID: 26516627; PMCID: PMC4608760.
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Neurol Neuroimmunol Neuroinflamm. 2015 Oct 15;2(6):e162. doi: 10.1212/NXI.0000000000000162. eCollection 2015 Dec.

Evaluating more naturalistic outcome measures: A 1-year smartphone study in multiple sclerosis.

Neurology(R) neuroimmunology & neuroinflammation

Riley Bove, Charles C White, Gavin Giovannoni, Bonnie Glanz, Victor Golubchikov, Johnny Hujol, Charles Jennings, Dawn Langdon, Michelle Lee, Anna Legedza, James Paskavitz, Sashank Prasad, John Richert, Allison Robbins, Susan Roberts, Howard Weiner, Ravi Ramachandran, Martyn Botfield, Philip L De Jager

Affiliations

  1. Program in Translational Neuropsychiatric Genomics (R.B., C.C.W., B.G., M.L., S.P., A.R., H.W., P.L.D.J.), Ann Romney Center for Neurologic Diseases, and the Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women's Hospital, Brookline, MA; Harvard Medical School (R.B., B.G., S.P., H.W., P.L.D.G.), Boston, MA; Blizard Institute (G.G.) and Royal Holloway (D.L.), University College London, London, UK; Vertex Pharmaceuticals Incorporated (V.G., A.L., S.R., R.R., M.B.), Boston MA; Woo Sports (J.H.), Boston, MA; McGovern Institute Neurotechnology Program (C.J.), MIT, Cambridge, MA; and Biogen-Idec (J.P., J.R.), Cambridge, MA.

PMID: 26516627 PMCID: PMC4608760 DOI: 10.1212/NXI.0000000000000162

Abstract

OBJECTIVE: In this cohort of individuals with and without multiple sclerosis (MS), we illustrate some of the novel approaches that smartphones provide to monitor patients with chronic neurologic disorders in their natural setting.

METHODS: Thirty-eight participant pairs (MS and cohabitant) aged 18-55 years participated in the study. Each participant received an Android HTC Sensation 4G smartphone containing a custom application suite of 19 tests capturing participant performance and patient-reported outcomes (PROs). Over 1 year, participants were prompted daily to complete one assigned test.

RESULTS: A total of 22 patients with MS and 17 cohabitants completed the entire study. Among patients with MS, low scores on PROs relating to mental and visual function were associated with dropout (p < 0.05). We illustrate several novel features of a smartphone platform. First, fluctuations in MS outcomes (e.g., fatigue) were assessed against an individual's ambient environment by linking responses to meteorological data. Second, both response accuracy and speed for the Ishihara color vision test were captured, highlighting the benefits of both active and passive data collection. Third, a new trait, a person-specific learning curve in neuropsychological testing, was identified using spline analysis. Finally, averaging repeated measures over the study yielded the most robust correlation matrix of the different outcome measures.

CONCLUSIONS: We report the feasibility of, and barriers to, deploying a smartphone platform to gather useful passive and active performance data at high frequency in an unstructured manner in the field. A smartphone platform may therefore enable large-scale naturalistic studies of patients with MS or other neurologic diseases.

References

  1. Mult Scler. 2005 Oct;11(5):583-4 - PubMed
  2. Biomed Eng Online. 2014 Feb 06;13:10 - PubMed
  3. Ann Neurol. 2014 Nov;76(5):633-42 - PubMed
  4. Mult Scler. 2013 Aug;19(9):1240-1 - PubMed
  5. PLoS One. 2013;8(3):e59707 - PubMed
  6. Int J Epidemiol. 2001 Dec;30(6):1332-41 - PubMed
  7. Trials. 2015 Jul 07;16:293 - PubMed
  8. Stroke. 2012 Dec;43(12):3271-7 - PubMed
  9. BMC Neurol. 2015 Jun 10;15:89 - PubMed
  10. Ann Neurol. 2005 Dec;58(6):840-6 - PubMed
  11. J Rehabil Res Dev. 2010;47(5):477-84 - PubMed
  12. J Vis Exp. 2014 Jun 30;(88):e51318 - PubMed
  13. Mult Scler. 2007 Jul;13(6):770-5 - PubMed
  14. Mult Scler. 2010 Jul;16(7):868-77 - PubMed
  15. Mult Scler. 2005 Jun;11(3):338-42 - PubMed
  16. Neurol Clin. 2010 May;28(2):395-410 - PubMed

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