Smartphone-based, high-yield active and passive self-monitoring of multiple sclerosis (MS) may enhance clinical disease assessments when compared to conventional methods for detecting subtle progressive clinical changes, according to a study presented in the poster exhibition this week during the 34th ECTRIMS congress in Berlin.
FLOODLIGHT is a prospective pilot study to assess the adherence of smartphone- and smartwatch-based remote patient monitoring (RPM), utilising feedback from MS patients and healthy controls (HCs). Patients were assessed clinically at baseline, and after 12- and 24 weeks.
At enrolment, eligible patients (n = 76) and HCs (n = 35) were given a preconfigured smartphone/smartwatch containing an app that prompts the user to perform “active tests”, while passively recording sensor data to measure gait and posture, referred to as “passive monitoring” (Figure).
Figure depicting the smartphone assessment schedule. Available as supplemental material, courteously provided by Roche.
Results from FLOODLIGHT revealed an overall adherence to the active and passive tests of 76.06% and 70.83%, respectively. Adherence was measured as a proportion of study weeks with at least three days of completed testing. User satisfaction was “good to excellent”, with patients who completed the full term of the study (n = 57) reporting average satisfaction scores of 72.91 out of 100 at week 12, and 74.74 at week 24 (Wilcoxon signed-rank test, p = 0.18).
All FLOODLIGHT smartphone-based measures, except for the Static Balance Test (SBT), demonstrated significant differences between MS patients and HCs, and/or significant agreement with corresponding conventional clinical outcome measures (according to the Mann-Whitney U test). The nonsignificant results in the SBT outcomes were postulated to be, in part, due to the near complete absence of clinically apparent balance impairment in the MS patients studied, and a ceiling effect of the comparative measure used (Berg Balance Scale).
According to the app-based Symbol Digit Modalities Test (SDMT) – a simple and effective test that detects cognitive impairment in less than five minutes – used for cognition evaluation, MS patients had expectedly lower SDMT scores when compared to HCs. This correlated well with clinical SDMT scores, and showed significant association with the Multiple Sclerosis Impact Scale (MSIS)-29 Psychological Score at baseline.
Hand and arm assessment included simple pinching and shape-drawing tests. The former revealed that MS patients with otherwise normal hand/arm function could be distinguished from HCs by a significantly longer mean time between pinching attempts. Significant correlation was also seen with the 9-Hole Peg Test (9HPT). In the Draw a Shape test, patients were asked to draw increasingly complex shapes as “fast and as accurately as possible” within 30 seconds. HCs predictably showed better celerity. However, the overall mean swiftness of movement was not significantly correlated with 9HPT time in patients with MS. This finding suggests that the heuristic feature of the shape test may measure a different aspect of hand or finger motor function when compared to the 9HPT.
The final metric, gait and posture, included the Five-U-Turn Test (5UTT), which uses a turn-detection algorithm to measure gait and balance difficulties and unusual patterns in U-turn performance while walking short distances. The baseline turn speed in the 5UTT was not significantly different between MS and HC participants – a result likely to be related to the low level of ambulation impairment in the study population, the authors note. Turn speed correlated well with clinically based Timed 25-Foot Walk (T25FW) data, as well as the patient’s perceived lower limb and ambulation function.
The Two-Minute Walk Test (2MWT) – a more demanding walking test designed to assess ambulation speed, fatigability or abnormal gait – revealed significant differences in step power between MS patients and HCs, correlating well with T25FW time.
Offering their conclusions, the authors of FLOODLIGHT underlined that their results suggest that patients are highly engaged and satisfied with smartphone-based self-assessments. The outcomes correlate with conventional in-clinic outcome measures of MS disability, they add, and with patients’ perceptions of their mental and psychological status, and hand/arm/ambulation function.
“Smartphone-based FLOODLIGHT outcomes may represent a promising avenue to enable precise continuous assessment of MS disease in clinical trials, and real-world practice settings” they conclude.
Montalban X, Mulero P, Midaglia L et al. FLOODLIGHT: Smartphone-based self-monitoring is accepted by patients and provides meaningful, continuous digital outcomes augmenting conventional in-clinic multiple sclerosis measures. Poster presentation at the 34th Congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 10–12 October 2018, Berlin, Germany. Supplemental material is available at: https://medically.roche.com/en/search/pdfviewer.21f80a53-a098-4d38-b1a8-c228796302a8.html?cid=qrprxx1810nexxectrims2018floodlightsupplementary. Accessed October 2018.