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

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 - Neurol Neuroimmunol Neuroinflamm (2015)

Bottom Line: Among patients with MS, low scores on PROs relating to mental and visual function were associated with dropout (p < 0.05).Finally, averaging repeated measures over the study yielded the most robust correlation matrix of the different outcome measures.A smartphone platform may therefore enable large-scale naturalistic studies of patients with MS or other neurologic diseases.

View Article: PubMed Central - PubMed

Affiliation: 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.

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.

No MeSH data available.


Related in: MedlinePlus

Pairwise correlations between selected patient-reported outcomes and performance tests in patients with MS(A) The number of pairwise correlations meeting a nominal p < 0.05 significance when the first measure obtained from each participant is used. (B) The number of pairwise correlations meeting the nominal p < 0.05 threshold when the mean of all responses for an individual patient with multiple sclerosis (MS) is used, omitting a 3-test run-in. The number of significant correlations increases from 46 to 88. The intensity of the color is proportional to the strength of the correlation, with positive correlations denoted in blue and inverse correlations in red.
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Figure 5: Pairwise correlations between selected patient-reported outcomes and performance tests in patients with MS(A) The number of pairwise correlations meeting a nominal p < 0.05 significance when the first measure obtained from each participant is used. (B) The number of pairwise correlations meeting the nominal p < 0.05 threshold when the mean of all responses for an individual patient with multiple sclerosis (MS) is used, omitting a 3-test run-in. The number of significant correlations increases from 46 to 88. The intensity of the color is proportional to the strength of the correlation, with positive correlations denoted in blue and inverse correlations in red.

Mentions: Our hypothesis was that serial measures would enable us to overcome the environmental noise found in any one cross-sectional measure. We compared the correlation matrix for each MS patient's first entry for each of the smartphone suite's measures (figure 5A) to the correlation matrix for each patient's mean score for each measure calculated after removing the first 3 tests (to minimize practice effects) (figure 5B). The number of pairwise correlations among the different tests meeting a suggestive p < 0.05 significance threshold increased from 44 (when using the first measure of each test) to 85 (when using the mean of each test after minimizing the practice effect). Additional thresholds are reported in appendix e-1 and figure e-6.


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

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 - Neurol Neuroimmunol Neuroinflamm (2015)

Pairwise correlations between selected patient-reported outcomes and performance tests in patients with MS(A) The number of pairwise correlations meeting a nominal p < 0.05 significance when the first measure obtained from each participant is used. (B) The number of pairwise correlations meeting the nominal p < 0.05 threshold when the mean of all responses for an individual patient with multiple sclerosis (MS) is used, omitting a 3-test run-in. The number of significant correlations increases from 46 to 88. The intensity of the color is proportional to the strength of the correlation, with positive correlations denoted in blue and inverse correlations in red.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608760&req=5

Figure 5: Pairwise correlations between selected patient-reported outcomes and performance tests in patients with MS(A) The number of pairwise correlations meeting a nominal p < 0.05 significance when the first measure obtained from each participant is used. (B) The number of pairwise correlations meeting the nominal p < 0.05 threshold when the mean of all responses for an individual patient with multiple sclerosis (MS) is used, omitting a 3-test run-in. The number of significant correlations increases from 46 to 88. The intensity of the color is proportional to the strength of the correlation, with positive correlations denoted in blue and inverse correlations in red.
Mentions: Our hypothesis was that serial measures would enable us to overcome the environmental noise found in any one cross-sectional measure. We compared the correlation matrix for each MS patient's first entry for each of the smartphone suite's measures (figure 5A) to the correlation matrix for each patient's mean score for each measure calculated after removing the first 3 tests (to minimize practice effects) (figure 5B). The number of pairwise correlations among the different tests meeting a suggestive p < 0.05 significance threshold increased from 44 (when using the first measure of each test) to 85 (when using the mean of each test after minimizing the practice effect). Additional thresholds are reported in appendix e-1 and figure e-6.

Bottom Line: Among patients with MS, low scores on PROs relating to mental and visual function were associated with dropout (p < 0.05).Finally, averaging repeated measures over the study yielded the most robust correlation matrix of the different outcome measures.A smartphone platform may therefore enable large-scale naturalistic studies of patients with MS or other neurologic diseases.

View Article: PubMed Central - PubMed

Affiliation: 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.

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.

No MeSH data available.


Related in: MedlinePlus