Amyotrophic lateral sclerosis (ALS) is a degenerative disease which causes death of neurons controlling voluntary muscles. It is currently assessed with subjective clinical measurements, but it would benefit from alternative surrogate biomarkers that can better estimate disease progression. This work analyzes speech and fine motor coordination of subjects recruited by the Answer ALS foundation using data from a mobile app. In addition, clinical variables such as speech, writing and total ALSFRS-R scores are also acquired along with forced and slow vital capacity. Cross-sectional and longitudinal analyses were performed using speech and fine motor features. Results show that both types of features are useful to infer clinical variables especially for males (R2=0.79 for ALSFRS-R total score), but their initial values are not helpful to predict speech and motor decline. However, we found that longitudinal progression for bulbar and spinal ALS onset are different and they can be identified with high accuracy by the extracted features.