Scheier, Zoe A.

AbstractIntroduction/AimsMany people living with amyotrophic lateral sclerosis (PALS) report restrictions in their day‐to‐day communication (communicative participation). However, little is known about which speech features contribute to these restrictions. This study evaluated the effects of common speech symptoms in PALS (reduced overall speaking rate, slowed articulation rate, and increased pausing) on communicative participation restrictions.MethodsParticipants completed surveys (the Communicative Participation Item Bank‐short form; the self‐entry version of the ALS Functional Rating Scale‐Revised) and recorded themselves reading the Bamboo Passage aloud using a smartphone app. Rate and pause measures were extracted from the recordings. The association of various demographic, clinical, self‐reported, and acoustic speech features with communicative participation was evaluated with bivariate correlations. The contribution of salient rate and pause measures to communicative participation was assessed using multiple linear regression.ResultsFifty seven people living with ALS participated in the study (mean age = 61.1 years). Acoustic and self‐report measures of speech and bulbar function were moderately to highly associated with communicative participation (Spearman rho coefficients ranged from rs = 0.48 to rs = 0.77). A regression model including participant age, sex, articulation rate, and percent pause time accounted for 57% of the variance of communicative participation ratings.DiscussionEven though PALS with slowed articulation rate and increased pausing may convey their message clearly, these speech features predict communicative participation restrictions. The identification of quantitative speech features, such as articulation rate and percent pause time, is critical to facilitating early and targeted intervention and for monitoring bulbar decline in ALS.

AbstractBackgroundPassively collected smartphone sensor data provide an opportunity to study physical activity and mobility unobtrusively over long periods of time and may enable disease monitoring in people with amyotrophic lateral sclerosis (PALS).MethodsWe enrolled 63 PALS who used Beiwe mobile application that collected their smartphone accelerometer and GPS data and administered the self‐entry ALS Functional Rating Scale‐Revised (ALSFRS‐RSE) survey. We identified individual steps from accelerometer data and used the Activity Index to summarize activity at the minute level. Walking, Activity Index, and GPS outcomes were then aggregated into day‐level measures. We used linear mixed effect models (LMMs) to estimate baseline and monthly change for ALSFRS‐RSE scores (total score, subscores Q1–3, Q4–6, Q7–9, Q10–12) and smartphone sensor data measures, as well as the associations between them.FindingsThe analytic sample (N = 45) was 64.4% male with a mean age of 60.1 years. The mean observation period was 292.3 days. The ALSFRS‐RSE total score baseline mean was 35.8 and had a monthly rate of decline of −0.48 (p‐value <0.001). We observed statistically significant change over time and association with ALSFRS‐RSE total score for four smartphone sensor data‐derived measures: walking cadence from top 1 min and log‐transformed step count, step count from top 1 min, and Activity Index from top 1 min.InterpretationSmartphone sensors can unobtrusively track physical changes in PALS, potentially aiding disease monitoring and future research.

Abstract Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought to determine if mobile applications (apps) and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection. Forty ambulatory adults with ALS were followed for 6-months. The Beiwe app was used to administer self-entry ALS functional rating scale-revised (ALSFRS-RSE) and the Rasch Overall ALS Disability Scale (ROADS) surveys every 2-4 weeks. A wrist-worn activity monitor (ActiGraph's Insight Watch) or an ankle-worn activity monitor (Modus' StepWatch) was used continuously by each participant. Wearable device wear and app survey compliance were adequate. ALSFRS-R highly correlated with ALSFRS-RSE. Several wearable data daily physical activity measures demonstrated statistically significant change over time and associations with ALSFRS-RSE and ROADS. Active and passive digital data collection hold promise for novel ALS trial outcomes development.