Establishing the Minimal Clinically Important Difference (MCID) for Smartphone-Derived Gait Measures in Multiple Sclerosis

Agne Kazlauskaite; Bernhard Fehlmann; Cedric Simillion; Dimitar Stanev; Ernst Bos; Gary Cutter; Helmut Butzkueven; Letizia Leocani; Licinio Craveiro; Mike D Rinderknecht

arxiv: 2606.28449 · v1 · pith:AQPPK3JVnew · submitted 2026-06-26 · 🧬 q-bio.QM · cs.CV· physics.med-ph

Establishing the Minimal Clinically Important Difference (MCID) for Smartphone-Derived Gait Measures in Multiple Sclerosis

Mike D Rinderknecht , Bernhard Fehlmann , Dimitar Stanev , Cedric Simillion , Ernst Bos , Letizia Leocani , Agne Kazlauskaite , Gary Cutter

show 2 more authors

Helmut Butzkueven Licinio Craveiro

This is my paper

Pith reviewed 2026-06-30 01:25 UTC · model grok-4.3

classification 🧬 q-bio.QM cs.CVphysics.med-ph

keywords minimal clinically important differencegait measuresmultiple sclerosissmartphonedigital healthanchor-based approachtwo-minute walk testremote monitoring

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The pith

Smartphone gait measures in progressive MS have triangulated MCIDs of -0.16 m/s step velocity, 0.06 s step duration, and similar values for other metrics.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper determines minimal clinically important differences for digital gait metrics captured by smartphone Two-Minute Walk Tests in people with progressive multiple sclerosis. An anchor-based method identifies patients with clinically meaningful worsening on four established scales and computes the median changes in their gait measures at week 96. These medians are triangulated across anchors to produce specific MCID thresholds. A sympathetic reader would care because the thresholds separate daily variability from actual progression in frequent remote monitoring. The approach supports both trial endpoints and real-world patient tracking.

Core claim

Using data from 243 patients, median changes from baseline at Week 96 on digital gait measures were computed for those showing clinically meaningful worsening on the Timed 25-Foot Walk, Ambulation Score, Expanded Disability Status Scale, or 12-item Multiple Sclerosis Walking Scale; triangulation of these changes yields MCIDs of Step Velocity = -0.16 m/s, Step Velocity Scaled to Walking Time = -0.18 m/s, Step Duration = 0.06 s, Step Length = -0.07 m, Total Number of Steps = -28, and Total Distance Walked = -24 m.

What carries the argument

Anchor-based triangulation of median gait changes observed in the subgroup of patients worsening on any of four clinical anchors.

If this is right

These MCIDs allow longitudinal smartphone data to flag meaningful worsening rather than normal fluctuation.
Digital gait endpoints in future MS trials can use the thresholds to assess treatment effects.
Remote monitoring gains an objective basis for detecting progression between clinic visits.
Personalized care plans can incorporate frequent gait tracking with interpretable change criteria.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Independent validation in relapsing-remitting MS or other gait disorders would test generalizability of the thresholds.
Frequent remote testing enabled by these MCIDs could detect progression earlier than infrequent in-clinic assessments.
Integration with other digital biomarkers might increase sensitivity for tracking disease course.

Load-bearing premise

The four clinical anchors validly identify patients with clinically meaningful worsening, and the median digital gait changes in that subgroup equal the minimal important difference.

What would settle it

A replication study that applies the same four anchors to a comparable progressive MS cohort and finds median gait changes at the corresponding time point differing substantially from the reported values would falsify the MCIDs.

Figures

Figures reproduced from arXiv: 2606.28449 by Agne Kazlauskaite, Bernhard Fehlmann, Cedric Simillion, Dimitar Stanev, Ernst Bos, Gary Cutter, Helmut Butzkueven, Letizia Leocani, Licinio Craveiro, Mike D Rinderknecht.

**Figure 1.** Figure 1: Study design and the digital 2MWT. (A) The CONSONANCE study included daily digital 2MWT (blue) performed with a smartphone remotely in an home environment as well as performancebased, clinician-rated, and patient-reported measures used as anchors (red). (B) For the 2MWT, participants were instructed to walk as fast as possible but safely while avoiding sharp turns. Walking aid and/or orthotics were permit… view at source ↗

**Figure 2.** Figure 2: Empirical cumulative distribution functions (ECDFs) for changes on digital gait measures from baseline at Week 96 are shown for anchor-specific worsening groups. Each curve represents the subset of patients who met the respective anchor-defined threshold for meaningful worsening reported in [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗

read the original abstract

Background: Digital health technologies allow for frequent, remote gait monitoring in people with multiple sclerosis (MS). However, to differentiate daily variability from actual disease progression in longitudinal data, established minimal clinically important differences (MCID) are required. Currently, there is limited literature defining these thresholds for digital gait metrics. Objective: To establish MCIDs for digital gait measures reflecting progression in MS. Methods: Digital gait measures were captured via daily, remote, smartphone-based Two-Minute Walk Tests in CONSONANCE (NCT03523858), a phase 3b study of ocrelizumab in progressive MS. Using an anchor-based approach, median changes from baseline at Week 96 on digital gait measures were computed for patients showing clinically meaningful worsening on either Timed 25-Foot Walk, Ambulation Score, Expanded Disability Status Scale, or 12-item Multiple Sclerosis Walking Scale. These changes were subsequently triangulated to derive the MCID estimates. Results: 243 patients with progressive MS (female: n=125 (51%); mean [SD] age: 49.3 [9.3]; mean [SD] EDSS: 4.8 [1.4]) had digital gait data available at baseline and Week 96. Median changes were generally consistent across anchors. Triangulated MCIDs are: Step Velocity = -0.16 m/s, Step Velocity Scaled to Walking Time = -0.18 m/s, Step Duration = 0.06 s, Step Length = -0.07 m, Total Number of Steps = -28, and Total Distance Walked = -24 m. Conclusion: These MCIDs provide a framework for interpreting meaningful gait changes and integrating digital measures into MS outcome evaluation. Beyond facilitating novel clinical trial endpoints to evaluate treatment efficacy, they enable objective, real-world monitoring to advance personalized patient care.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper supplies usable MCID numbers for smartphone gait metrics in MS from a trial cohort but the anchor method leaves the usual questions about validation unanswered.

read the letter

The paper's main contribution is a set of triangulated MCID values for smartphone-derived gait measures in progressive MS: step velocity -0.16 m/s, scaled velocity -0.18 m/s, step duration 0.06 s, step length -0.07 m, total steps -28, and distance -24 m. These come from median changes at week 96 in 243 patients from the CONSONANCE study who showed worsening on at least one of four clinical anchors.

The work applies the established anchor-based method to daily remote smartphone two-minute walk test data. It is new in providing these specific thresholds for digital metrics where prior literature was limited. The study uses a relevant population of progressive MS patients with mean EDSS 4.8 and collects data in a way that aligns with real-world monitoring goals. The fact that median changes were generally consistent across the anchors is a strength, suggesting the estimates have some robustness.

The soft spot is the central assumption in the anchor-based approach. By taking the median digital change in the pooled 'worsening on any anchor' group, the paper equates that observed change directly to the MCID. This requires that the anchors validly identify meaningful worsening in gait and that the median is the right summary for the minimal difference. The abstract does not report correlations between digital and clinical measures, does not test the performance of the combined anchor rule, and does not compare to stable patients. These are common issues in MCID work, but they mean the numbers are plausible estimates rather than tightly validated ones. Data quality and exclusion criteria are also not detailed here.

This paper is for people working on digital endpoints in MS trials or real-world evidence who need starting thresholds for gait measures. A reader focused on practical application in neurology digital health would find the specific numbers and the study context helpful. The paper shows clear thinking in addressing a stated gap with available trial data.

I recommend sending it to peer review. The results are new for these measures and come from a solid cohort size, so referees can evaluate the methods and any additional analyses in the full text.

Referee Report

2 major / 1 minor

Summary. The manuscript claims to establish MCIDs for six smartphone-derived gait measures (step velocity, step velocity scaled to walking time, step duration, step length, total steps, total distance) in progressive MS via an anchor-based triangulation. Using daily remote 2MWT data from 243 patients in the CONSONANCE trial, the authors compute median changes from baseline to Week 96 among those showing worsening on at least one of four clinical anchors (T25FW, Ambulation Score, EDSS, MSWS-12) and triangulate these to obtain the reported thresholds (e.g., -0.16 m/s for step velocity).

Significance. If the anchor-based medians are shown to be robust, the MCIDs would provide a practical framework for interpreting longitudinal digital gait data in MS, enabling differentiation of progression from variability and supporting both trial endpoints and real-world monitoring. The sample size and remote smartphone collection are strengths for applicability.

major comments (2)

[Abstract/Methods] Abstract/Methods: The central derivation pools patients who worsen on any of the four anchors and equates the median digital-gait change in this subgroup directly to the MCID. No anchor–digital correlations, overlap statistics, or specificity of the 'worsening on ≥1' rule are reported, so it is unclear whether the resulting values (-0.16 m/s, -0.07 m, etc.) represent minimal clinically important thresholds or averages across heterogeneous subgroups.
[Results] Results: The abstract states median changes were 'generally consistent across anchors' but provides no IQR, standard errors, or sensitivity analyses (e.g., requiring worsening on ≥2 anchors or restricting to gait-specific anchors). Without these, the triangulation step and the claim that the values are suitable for distinguishing progression remain insufficiently supported.

minor comments (1)

[Abstract] Abstract: The triangulation procedure (how the anchor-specific medians are combined into the final MCID) is not described; a brief statement of the rule used would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which have helped us identify areas to strengthen the clarity and robustness of our MCID estimates. We respond to each major comment below.

read point-by-point responses

Referee: [Abstract/Methods] Abstract/Methods: The central derivation pools patients who worsen on any of the four anchors and equates the median digital-gait change in this subgroup directly to the MCID. No anchor–digital correlations, overlap statistics, or specificity of the 'worsening on ≥1' rule are reported, so it is unclear whether the resulting values (-0.16 m/s, -0.07 m, etc.) represent minimal clinically important thresholds or averages across heterogeneous subgroups.

Authors: The anchor-based MCID method we employed is standard in the literature, defining the MCID as the median change observed in patients who demonstrate worsening on established clinical anchors. The use of 'worsening on at least one anchor' captures patients with any form of clinical deterioration relevant to gait. While we did not report correlations or overlap statistics in the original submission, these can be computed from the available data. We will add Spearman correlations between the digital gait measures and the anchors, as well as the distribution of patients worsening on one versus multiple anchors, to a revised manuscript. This will help demonstrate the specificity and support the triangulation approach. revision: yes
Referee: [Results] Results: The abstract states median changes were 'generally consistent across anchors' but provides no IQR, standard errors, or sensitivity analyses (e.g., requiring worsening on ≥2 anchors or restricting to gait-specific anchors). Without these, the triangulation step and the claim that the values are suitable for distinguishing progression remain insufficiently supported.

Authors: We agree that additional details on variability and sensitivity would enhance the support for our findings. In the revised manuscript, we will report the interquartile ranges and, where appropriate, standard errors for the median changes stratified by each anchor. Furthermore, we will conduct and present sensitivity analyses, including those requiring worsening on at least two anchors and those limited to gait-specific anchors (T25FW, Ambulation Score, and MSWS-12). These additions will be included in the Results section and a new supplementary table to better substantiate the triangulation and the applicability of the MCIDs for distinguishing progression. revision: yes

Circularity Check

0 steps flagged

No circularity; MCIDs derived empirically from external clinical anchors

full rationale

The derivation selects patients worsening on independent external anchors (T25FW, Ambulation Score, EDSS, MSWS-12), computes observed median changes in digital gait metrics within that subgroup at Week 96, and triangulates those medians. This is a direct empirical calculation from data; no equation reduces the MCID values to a fitted parameter, self-referential definition, or self-citation chain. The anchors are not derived from the digital measures, and no uniqueness theorem or ansatz is imported. The result is self-contained against external benchmarks and receives a normal non-finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Central claim rests on the domain assumption that the selected clinical anchors capture meaningful worsening and that median changes in the digital measures among those patients define the MCID; no free parameters or invented entities are introduced in the abstract.

axioms (1)

domain assumption Clinical anchors (T25FW, Ambulation Score, EDSS, MSWS-12) accurately identify patients with clinically meaningful worsening
Used to select the subgroup whose median digital changes are taken as the MCID

pith-pipeline@v0.9.1-grok · 5928 in / 1276 out tokens · 34200 ms · 2026-06-30T01:25:24.784511+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

8 extracted references · 3 canonical work pages

[1]

Multiple sclerosis

Thompson AJ, Baranzini SE, Geurts J, Hemmer B, Ciccarelli O. Multiple sclerosis. Lancet. 2018;391:1622–1636. Available at: https://doi.org/10.1016/S0140-6736(18)30481-1

work page doi:10.1016/s0140-6736(18)30481-1 2018
[2]

New insights into the burden and costs of multiple sclerosis in Europe

Kobelt G, Thompson A, Berg J, Gannedahl M, Eriksson J. New insights into the burden and costs of multiple sclerosis in Europe. Mult. Scler. J. 2017;23:1123–1136

2017
[3]

The impact of slower walking speed on activities of daily living in patients with multiple sclerosis

Yildiz M. The impact of slower walking speed on activities of daily living in patients with multiple sclerosis. Int. J. Clin. Pr. 2012;66:1088–1094. Available at: https://doi.org/10.1111/ijcp.12003

work page doi:10.1111/ijcp.12003 2012
[4]

Gait deficits in people with multiple sclerosis: A systematic review and meta-analysis

Comber L, Galvin R, Coote S. Gait deficits in people with multiple sclerosis: A systematic review and meta-analysis. Gait Posture. 2017;51:25–35

2017
[5]

Impact of Walking Impairment in Multiple Sclerosis

LaRocca NG. Impact of Walking Impairment in Multiple Sclerosis. Patient: Patient- Centered Outcomes Res. 2011;4:189–201. Available at: https://doi.org/10.2165/11591150- 000000000-00000

work page doi:10.2165/11591150- 2011
[6]

Patient perception of bodily functions in multiple sclerosis: gait and visual function are the most valuable

Heesen C, Böhm J, Reich C, Kasper J, Goebel M, Gold S. Patient perception of bodily functions in multiple sclerosis: gait and visual function are the most valuable. Mult Scler. 2008;14:988–991

2008
[7]

Time between expanded disability status scale (EDSS) scores

Zurawski J, Glanz BI, Chua A, et al. Time between expanded disability status scale (EDSS) scores. Mult. Scler. Relat. Disord. 2019;30:98–103. 28

2019
[8]

I have to walk a lot slower, and I seem to sometimes have to almost limp a little bit

Griffiths P, Sims J, Williams A, et al. How strong should my anchor be for estimating group and individual level meaningful change? A simulation study assessing anchor correlation strength and the impact of sample size, distribution of change scores and methodology on establishing a true meaningful change threshold. Quality of life research : an internati...

2023

[1] [1]

Multiple sclerosis

Thompson AJ, Baranzini SE, Geurts J, Hemmer B, Ciccarelli O. Multiple sclerosis. Lancet. 2018;391:1622–1636. Available at: https://doi.org/10.1016/S0140-6736(18)30481-1

work page doi:10.1016/s0140-6736(18)30481-1 2018

[2] [2]

New insights into the burden and costs of multiple sclerosis in Europe

Kobelt G, Thompson A, Berg J, Gannedahl M, Eriksson J. New insights into the burden and costs of multiple sclerosis in Europe. Mult. Scler. J. 2017;23:1123–1136

2017

[3] [3]

The impact of slower walking speed on activities of daily living in patients with multiple sclerosis

Yildiz M. The impact of slower walking speed on activities of daily living in patients with multiple sclerosis. Int. J. Clin. Pr. 2012;66:1088–1094. Available at: https://doi.org/10.1111/ijcp.12003

work page doi:10.1111/ijcp.12003 2012

[4] [4]

Gait deficits in people with multiple sclerosis: A systematic review and meta-analysis

Comber L, Galvin R, Coote S. Gait deficits in people with multiple sclerosis: A systematic review and meta-analysis. Gait Posture. 2017;51:25–35

2017

[5] [5]

Impact of Walking Impairment in Multiple Sclerosis

LaRocca NG. Impact of Walking Impairment in Multiple Sclerosis. Patient: Patient- Centered Outcomes Res. 2011;4:189–201. Available at: https://doi.org/10.2165/11591150- 000000000-00000

work page doi:10.2165/11591150- 2011

[6] [6]

Patient perception of bodily functions in multiple sclerosis: gait and visual function are the most valuable

Heesen C, Böhm J, Reich C, Kasper J, Goebel M, Gold S. Patient perception of bodily functions in multiple sclerosis: gait and visual function are the most valuable. Mult Scler. 2008;14:988–991

2008

[7] [7]

Time between expanded disability status scale (EDSS) scores

Zurawski J, Glanz BI, Chua A, et al. Time between expanded disability status scale (EDSS) scores. Mult. Scler. Relat. Disord. 2019;30:98–103. 28

2019

[8] [8]

I have to walk a lot slower, and I seem to sometimes have to almost limp a little bit

Griffiths P, Sims J, Williams A, et al. How strong should my anchor be for estimating group and individual level meaningful change? A simulation study assessing anchor correlation strength and the impact of sample size, distribution of change scores and methodology on establishing a true meaningful change threshold. Quality of life research : an internati...

2023