Personalized monitoring of ambulatory function with a smartphone 2-minute walk test in multiple sclerosis

Ka-Hoo Lam, Ioan Gabriel Bucur, Pim van Oirschot, Frank de Graaf, Eva Strijbis, Bernard Uitdehaag, Tom Heskes, Joep Killestein, Vincent de Groot

Research output: Contribution to journalArticleAcademicpeer-review


Background: Remote smartphone-based 2-minute walking tests (s2MWTs) allow frequent and potentially sensitive measurements of ambulatory function. Objective: To investigate the s2MWT on assessment of, and responsiveness to change in ambulatory function in MS. Methods: One hundred two multiple sclerosis (MS) patients and 24 healthy controls (HCs) performed weekly s2MWTs on self-owned smartphones for 12 and 3 months, respectively. The timed 25-foot walk test (T25FW) and Expanded Disability Status Scale (EDSS) were assessed at 3-month intervals. Anchor-based (using T25FW and EDSS) and distribution-based (curve fitting) methods were used to assess responsiveness of the s2MWT. A local linear trend model was used to fit weekly s2MWT scores of individual patients. Results: A total of 4811 and 355 s2MWT scores were obtained in patients (n = 94) and HC (n = 22), respectively. s2MWT demonstrated large variability (65.6 m) compared to the average score (129.5 m), and was inadequately responsive to anchor-based change in clinical outcomes. Curve fitting separated the trend from noise in high temporal resolution individual-level data, and statistically reliable changes were detected in 45% of patients. Conclusions: In group-level analyses, clinically relevant change was insufficiently detected due to large variability with sporadic measurements. Individual-level curve fitting reduced the variability in s2MWT, enabling the detection of statistically reliable change in ambulatory function.
Original languageEnglish
Pages (from-to)606-614
Number of pages9
Issue number4-5
Early online date2023
Publication statusPublished - Apr 2023


  • Multiple sclerosis
  • ambulatory function
  • digital technology
  • outpatient monitoring
  • patient-specific modeling
  • smartphone

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