Floodlight App Captures MS Functional Impairment, Proof-of-Concept Supports Clinical Use

Stephen L. Hauser, MD

The Floodlight Proof-of-Concept (PoC) application, a sensor-based monitoring tool, was shown to effectively capture reliable and clinically relevant measures of functional impairment in patients with multiple sclerosis (MS) across a 24-week study (NCT02952911). Investigators concluded that these results support the app’s use in clinical research and practice.

The app was designed to assess functional abilities across 3 key domains affected by MS: cognition, upper extremity function, and gait and balance. Cognition was assessed using the electronic Symbol Digit Modalities Test (e-SDMT) while upper extremity function was evaluated using the Pinching Test and Draw a Shape Test. Lastly, the Static Balance Test (SBT), U-Turn Test (UTT), Walk Test, and Passive Monitoring examined gait and balance.

Senior author Stephen L. Hauser, MD, professor of neurology, and director, UCSF Weill Institute for Neurosciences, and colleagues evaluated the app’s performance on 76 people with MS (PwMS) and 25 health controls (HCs). Intraclass correlation coefficients (ICCs) and age- or sex-adjusted Spearman’s rank correlation determined test–retest reliability and correlations with clinical and MRI outcome measures, respectively.

Test–retest reliability was assessed in PwMS and HC with valid assessments in all consecutive 2-week windows. In PwMS, ICCs were moderate or good compared to HCs, where the group sizes were lower and ICCs were mostly poor to good. These data suggest that reliable data can be captured with the Floodlight PoC app.

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Overall, strongest correlations of test features were observed with the respective domain-specific standard clinical disability measures. Investigators observed good-to-excellent correlations in the cognitive domain (r = 0.82) and fair or moderate-to-good in the upper extremity function domain (absolute value |r| = 0.40-0.64) and gait and balance domain (r = -0.25 to -0.52; all P <.05).

"The higher temporal resolution and multidimensional feature space of functional data collected by this platform hold the potential to capture subtle, potentially disease-related information which are not readily discriminated by clinician-administered assessments,” Hauser et al wrote. "It also has the potential to improve and standardize assessment of MS disease over time, provide PwMS and health care professionals in both specialty and primary care environments a better understanding of disease progression, change the way MS is monitored in clinical trials and daily practice, and ultimately improve patient care.”

Only the SBT did not correlate with its domain-specific standard clinical measure, the Berg Balance Scale (r = -0.20; P >.05). Besides the Draw a Share Test overall mean trace celerity and Passive Monitoring step power, most test features correlated with Expanded Disability Status Scale (all P <.05). Additionally, most test features also correlated with their respective 29-item Multiple Sclerosis Impact Scale subscale or items (all P <.05 except for Draw a Shape Test overall mean trace celerity, Passive Monitoring turn speed, and Passive Monitoring step power). Normalized brain volume correlated significantly with test features across all domains with the strongest association found with e-SDMT (r = 0.54; P <.001).

When evaluating correlations between active gait tests and passive monitoring, UTT turn speed demonstrated moderate-to-good correlation with Passive Monitoring turn speed (r = 0.43; P <.001). Additionally, investigators recorded stronger, good-to-excellent positive correlations between Walk Test step power and Passive Monitoring step power (r = 0.76; P <.001).

Hauser et al noted that the current iteration of the app, Floodlight MS, is available for public use in selected countries, and a rolling release schedule is now in process to provide access soon to the wider MS community across the world. In late 2019 at the European Committee for Treatment and Research in MS annual meeting, NeurologyLive spoke with several experts about the development of Floodlight, including Laura Julian,nhbju PhD, senior director, MS/NMO Disease Area, Genentech. She discussed the need to address the inability to more consistently measure the impact the disease has on patients with MS, and how the Floodlight smartphone tool may help fill in those gaps.

"What we want to try and do is get a really detailed footprint or a picture of what the patient's functionality in between clinic visits is,” Julian told NeurologyLive at the time. “The patient is asked to do a series of measures every day, with the idea to get a nearly continuous measurement of function so that we can track trends over time. Then, the neurologists can potentially, hopefully, use that information in the future to help complement their visits.”

REFERENCE

Montalban X, Graves J, Midaglia L, et al. A smartphone sensor-based digital outcome assessment of multiple sclerosis. Mult Scler J. Published July 14, 2021. doi: 10.1177/13524585211028561