Much More Than the Malady: The Promise of a Web-Based Digital Platform Incorporating Self-Report for Research and Clinical Care in Mild Cognitive Impairment - Andrew McGarry, MD; Oliver Roesler, PhD; Jackson Liscombe, PhD; Michael Neumann, PhD; Hardik Kothare, PhD; Abhishek Hosamath, MBM; Lakshmi Arbatti, MS; Anusha Badathala, MD; Stephen Ruhmel, BS, MPH; Bryan J. Hansen, PhD; Madeline Quall, BA; Sandrine Istas, MBio; et al. (Mayo Clinic Proceedings: Digital Health)
Traditional clinical trials in neurodegenerative disorders have utilized combinations of examination-based outcomes, global assessments by investigators and participants, and scales aimed at function, some of which are patient-reported outcomes. It is debatable whether these tools optimally convey therapeutic efficacy. A complementary approach using digital biomarkers to surpass exam-based limitations for detecting physical change coupled with a direct report from participants on what their sources of suffering are could be a useful advance in reporting beneficial effects of interventions, particularly if changes track together. We sought to determine the feasibility of remotely assessing speech, facial features, and cognition in an mild cognitive impairment (MCI) population, whether those extracted features could distinguish MCI from controls, and to explore what self-reported problems could reveal about the MCI experience. Our web-based platform was easy to use and revealed facial features in particular as capable of discriminating MCI from controls. Using the features that showed a statistically significant difference between cohorts (P<.01) produced an area under the receiver operating curve of 0.75. Self-reported problems with cognition, gait, sleep, and behavior were more common in the MCI group. The MCI was associated with 6 times more difficulty with falls (n=6 vs 1). These data support the feasibility and discriminative utility of using remote monitoring technology in combination with participant self-report in an MCI population. Future work will investigate the extent to which multimodal biomarkers combined with self-report can characterize MCI longitudinally and for potential research applications as a measure of therapeutic effect.