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MS affects a range of body systems (for example, urinary, visual, motor and so on) and the nature and severity of these symptoms fluctuate over time.1 In clinical practice, patient contact is often infrequent, and this means the MS specialist only ever has a snapshot of the patient’s status and rarely has a complete picture of his/her patient’s functioning and symptoms over weeks and months.1 Digital tools offer the potential to track multiple aspects of functioning (e.g. walking, cognition, vision and motricity), and when these data are used in consultation, the doctor will have detailed information for a thorough follow-up.2 Following patients closely to measure patient functioning could provide clinical evidence of disease activity, function and disability, and this could inform treatment decisions.3 Such continuous assessment may also prove to be useful in clinical trials to evaluate treatment effects.4

Several presentations and posters at this year’s ECTRIMS provided an insight into the digital tools that are being developed to improve the recording of functioning in patients with MS. Some of these tools are being developed for use with smartphones and harness technology built into these devices, particularly accelerometers (motion sensors), orientation sensors, GPS, cameras and touchscreens.


Smartphone-based digital tools are well received by patients.4,5

The FLOODLIGHT study assessed the feasibility of using a smartphone and smartwatch with software that prompted patients to complete assessments.4 These included a measure of mobility (2-minute walk test), cognition (symbol digit modalities test), hand–motor function (pinching test and draw-a-shape test), and gait/posture (five U-turn test), as well as other assessments.4 The system also passively monitors sensor data to record mobility patterns. Overall, adherence to the testing was high, with 76% completing at least 3 days of complete active testing, and mean patient satisfaction at week 24 was 75%.4 Another study evaluated the acceptance of the digital tool MSCopilot®, which assessed four MS functional parameters: walking perimeter, upper-limb dexterity, cognition and low-contrast visual acuity. Overall, there was an excellent acceptance rate, with 87% of patients willing to use MSCopilot® at home, and 84% agreeing that the data collected could improve the management of their disease.5


Results from digital assessment tools correlate with those from standard in-clinic assessment tools.4,6

In a further analysis of the smartphone/smartwatch system in FLOODLIGHT, there was a significant correlation between conventional in-clinic measures and smartphone assessments. For example, results of the pinching test (where patients pinched as many objects on screen as possible in 30 seconds) correlated with the standard 9-hole peg test; similarly, results of the 2-minute walk test correlated with those from the timed 25-foot walk.4 In a separate study, a high correlation was observed between the smartphone accelerometer outputs and current in-clinical mobility assessment, and in fact the smartphone outputs were better correlated with in-clinic measures than the research-grade accelerometer.6 Smartphone accelerometery was also able to differentiate between relapsing-remitting MS and progressive forms of MS.6 Taken together, these results suggest that smartphone-based monitoring of function is clinically valid and could provide a convenient alternative to in-clinic assessment, allowing more frequent, comprehensive assessment of what patients can and cannot do.

Please see the accompanying videos of Prof Marcus D’Souza and Prof Elisabeth Maillart giving a summary of the current role of digital tools in assessing functioning in patients with MS.


  1. Ziemssen T. Patient-reported outcomes as the next frontier. Oral presentation at ECTRIMS 2018, 10 October 2018, Berlin.

  2. Ad Scientiam Real World Patient Data. MSCopilot - Digital Assessment in Multiple Sclerosis. Available at: http://www.adscientiam.com/what/digital-assessment-multiple-sclerosis/. Accessed on: 12/10/18

  3. Ziemssen T. et al. BMC Neurology. 2016 16:124.

  4. Montalban X et al. Poster presentation P624. ECTRIMS On Lib, 2018; 228468. Available at: https://onlinelibrary.ectrims-congress.eu/ectrims/2018/ectrims-2018/228468/xavier.montalban.floodlight.smartphone-based.self-monitoring.is.accepted.by.html?f=media=2*search=p624*listing=3*browseby=8. Accessed on: 12/10/18

  5. Maillart E et al. Poster presentation P702. ECTRIMS On Lib, 2018; 228545. https://onlinelibrary.ectrims-congress.eu/ectrims/2018/ectrims-2018/228545/elisabeth.maillart.acceptability.in.clinical.practice.of.mscopilot.a.html?f=media=2*search=228545*listing=3*browseby=8. Accessed on: 12/10/18

  6. Zhai Y. et al. P388. Poster presentation at ECTRIMS 2018, 10 October 2018, Berlin