Background:
People living with amyotrophic lateral sclerosis (PlwALS) typically experience progressive functional decline, assessed using the ALS Functional Rating Scale-Revised (ALSFRS-R), an approvable endpoint. However, the ALSFRS-R may not capture subtle functional changes in early-phase clinical trials, even when participants experience meaningful declines. Digital health technologies (DHTs) offer objective, frequent, and remote assessments that may provide greater sensitivity to early functional changes through measures of speech and physical activity.
Objectives:
This project aimed to (1) identify DHTs capable of detecting functional change over six months in PlwALS and (2) evaluate their correlation with ALSFRS-R, assessing whether they are more sensitive to change, thereby justifying their use as exploratory endpoints in early-phase studies.
Methods:
Actigraphy and digital speech analysis were selected based on longitudinal data availability and their relevance to PlwALS:
●Actigraphy data from the ALS Therapy Development Institute (TDI) captured continuous, real-world movement data via wearable sensors (Gupta et al., 2023);
●Multimodal speech and facial video data from EverythingALS and Modality.AI (Bingham et al, 2024; Neumann et al, 2024).
Both datasets were analyzed to determine whether these DHTs could detect functional changes earlier or with greater sensitivity than the ALSFRS-R among participants who met key eligibility criteria for a planned Phase 1/2 clinical trial.
Results:
Analysis showed that both actigraphy and speech-based DHTs were more sensitive to functional decline over six months than the ALSFRS-R. These findings support integrating these tools into the upcoming trial. Trial design and data informing DHT selection will be presented.
Discussion:
Incorporating DHTs into ALS trials can enable earlier detection of treatment effects when conventional endpoints lack sensitivity. The high-dimensional nature of DHT data supports refining and prioritizing analytical methods for use in interventional clinical trials.
References:
1.Gupta AS, Patel S, Premasiri A, Vieira F. Nature Communications. 2023; vol. 14, n°1, p. 5080.
2.Bingham IN, Norel R, Roitberg EG, et al. medRxiv. 2024; p. 2024.05.31.24308140.
3.Neumann M, Kothare H, and Ramanarayanan V. Computers in Biology and Medicine. 2024; vol. 180, p. 108949.