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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

Assessment of fluctuation in fatigue scores using environmental dataThe relationship between fatigue (as measured by the Modified Fatigue Impact Scale [MFIS]) and hours of daylight is presented for patients with multiple sclerosis (MS) in (A) and cohabitants in (B). Each participant is represented by one line describing the relation between MFIS and daylight hours. For most participants, there is no significant correlation between fatigue and hours of daylight (represented by gray lines); however, 4 participants represented by colored lines do show a significant increase in MFIS with more hours of daylight, and 1 participant shows the opposite, highlighting the symptomatic heterogeneity among patients with MS. (C) The MFIS total score for patients with MS is presented over the course of the calendar year. Each MS study completer is represented by a different line.
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Figure 2: Assessment of fluctuation in fatigue scores using environmental dataThe relationship between fatigue (as measured by the Modified Fatigue Impact Scale [MFIS]) and hours of daylight is presented for patients with multiple sclerosis (MS) in (A) and cohabitants in (B). Each participant is represented by one line describing the relation between MFIS and daylight hours. For most participants, there is no significant correlation between fatigue and hours of daylight (represented by gray lines); however, 4 participants represented by colored lines do show a significant increase in MFIS with more hours of daylight, and 1 participant shows the opposite, highlighting the symptomatic heterogeneity among patients with MS. (C) The MFIS total score for patients with MS is presented over the course of the calendar year. Each MS study completer is represented by a different line.

Mentions: Leveraging one of the advantages of frequent data collection afforded by the smartphone, we assessed whether self-reported fatigue, which patients with MS often link to higher temperatures, varied in relation to external factors. We linked the time and date stamp for each recorded data point for the Modified Fatigue Inventory Scale (MFIS) total score to the ambient temperature (uploaded to the smartphone) and daylight hours (as estimated by a publicly available sinusoidal function for Boston, MA) at the exact time and day of survey completion. Many patients with MS did display fluctuations in perceived level of fatigue over the year (figure 2C), but we found no significant evidence of a fixed effect of daylight hours (p = 0.091) or ambient temperature (p = 0.18) on MFIS (linear mixed-effects regression with random intercepts and slope adjusted for age, sex, and disease duration). As seen in figure 2A, there is clearly heterogeneity in the MS patient population, with a subset of patients displaying significant correlations between MFIS and daylight hours. This structure in the patient population needs to be explored further in larger studies.


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)

Assessment of fluctuation in fatigue scores using environmental dataThe relationship between fatigue (as measured by the Modified Fatigue Impact Scale [MFIS]) and hours of daylight is presented for patients with multiple sclerosis (MS) in (A) and cohabitants in (B). Each participant is represented by one line describing the relation between MFIS and daylight hours. For most participants, there is no significant correlation between fatigue and hours of daylight (represented by gray lines); however, 4 participants represented by colored lines do show a significant increase in MFIS with more hours of daylight, and 1 participant shows the opposite, highlighting the symptomatic heterogeneity among patients with MS. (C) The MFIS total score for patients with MS is presented over the course of the calendar year. Each MS study completer is represented by a different line.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Assessment of fluctuation in fatigue scores using environmental dataThe relationship between fatigue (as measured by the Modified Fatigue Impact Scale [MFIS]) and hours of daylight is presented for patients with multiple sclerosis (MS) in (A) and cohabitants in (B). Each participant is represented by one line describing the relation between MFIS and daylight hours. For most participants, there is no significant correlation between fatigue and hours of daylight (represented by gray lines); however, 4 participants represented by colored lines do show a significant increase in MFIS with more hours of daylight, and 1 participant shows the opposite, highlighting the symptomatic heterogeneity among patients with MS. (C) The MFIS total score for patients with MS is presented over the course of the calendar year. Each MS study completer is represented by a different line.
Mentions: Leveraging one of the advantages of frequent data collection afforded by the smartphone, we assessed whether self-reported fatigue, which patients with MS often link to higher temperatures, varied in relation to external factors. We linked the time and date stamp for each recorded data point for the Modified Fatigue Inventory Scale (MFIS) total score to the ambient temperature (uploaded to the smartphone) and daylight hours (as estimated by a publicly available sinusoidal function for Boston, MA) at the exact time and day of survey completion. Many patients with MS did display fluctuations in perceived level of fatigue over the year (figure 2C), but we found no significant evidence of a fixed effect of daylight hours (p = 0.091) or ambient temperature (p = 0.18) on MFIS (linear mixed-effects regression with random intercepts and slope adjusted for age, sex, and disease duration). As seen in figure 2A, there is clearly heterogeneity in the MS patient population, with a subset of patients displaying significant correlations between MFIS and daylight hours. This structure in the patient population needs to be explored further in larger studies.

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