Continuous monitoring of ALS symptoms is crucial to improve quality of life because both survival and progression rate can vary substantially between individuals. Speech and facial biomarkers can serve as useful proxies for disease progression and can be monitored remotely and automatically. The main objective of this longitudinal study is to analyze acoustic and facial speech metrics extracted from a web-based conversational assessment over time. Our hypothesis is that such multimodal remote patient monitoring allows us to measure and track changes in certain speech and facial biomarkers (i) more frequently and cost-effectively, while (ii) remaining as informative (if not more) than current clinical standard scales in capturing differences in ALS disease progression between slow and fast progressors. Our findings demonstrate the efficacy of remote patient monitoring via a cost-effective and scalable dialog platform to extract informative speech and facial biomarkers, allowing the potential capture of information that standard scales like the ALSFRS-R (which is not very granular) might not register.