Amyotrophic lateral sclerosis (ALS) is a progressive and debilitating neurodegenerative disease. Digital biomarkers derived from smartphone data can enable scalable, low-cost, remote, unobtrusive, and quantitative measurement of physical activity (PA). These biomarkers offer opportunities for quasi-continuous assessment of PA levels, which may provide new methods for monitoring ALS disease progression in real time. In this exploratory study, we analyzed data from 31 individuals with ALS (including 16 deaths) with up to 9 years of follow-up (median 3 years) to assess the impact of incorporating smartphone-derived PA measures into survival prediction models. We examine whether the strength of the statistical association with survival differs when PA is summarized as (i) a simple metric, such as the mean daily step count, vs. (ii) distributional representations of PA. The exploratory results suggest that the addition of PA variables defined via distributional representations improves the performance of the model, as reflected by higher C-score values ( 0.68 vs. 0.55 , estimated as the median over bootstrap replicas B = 1 , 000 ). A bootstrap-based hypothesis test shows statistically significant differences between the two models at the confidence level of 90%. These exploratory results indicate that the use of more advanced metrics to summarize PA time series can produce more accurate digital biomarkers to monitor the progression of ALS, although larger studies with larger sample sizes are required to confirm these findings.
Matabuena et al. (Thu,) studied this question.