BACKGROUND AND AIM: With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid motor biomarkers are highly warranted, which detect longitudinal changes in short, trial-like time-frames. In this observational study, we aim to unravel biomarkers of ataxic gait which are sensitive for longitudinal changes in real life by using wearable sensors. We hypothesize that, gait measures captured in patients' real life could be more sensitive to progression in short, trial-like time-frames compared to lab-based gait assessments and clinical rating scales. However, in real life walking, gait measures are substantially influenced by contextual and environmental factors, as it has been shown in healthy subjects as well as for different patient populations. Thus, we introduce a context-sensitive matching procedure of individual walking bouts to reveal disease-related rather than purely context-driven longitudinal changes in variability measures. METHODS: We assessed longitudinal gait changes of 24 subjects with degenerative cerebellar disease (SARA:9.4±4.1) at baseline and 1-year and 2-year follow-up assessment by 3 body-worn inertial sensors in two conditions: (1) laboratory-based walking; (2) real-life walking during everyday living. In the real-life walking condition, a context-sensitive analysis was performed by selecting comparable walking bouts according to macroscopic gait characteristics namely bout length and number of turns within a two-minutes time interval. Movement analysis focussed on measures of spatio-temporal variability, in particular lateral step deviation (LD) and a compound measure of spatial variability (SPcmp). RESULTS: Cross-sectional analyses revealed high correlation to ataxia severity (SARA) and patients subjective balance confidence (ABC Scale) in both conditions (r > 0.8). While clinical ataxia score and gait measure in lab-based gait assessments identified changes after two years only (SARA: rprb = 0.71; LD: rprb = 0.67) in real life gait assessment the features of lateral step deviation and a compound measure of spatial step variability identified changes already prb after one year with high effect sizes (LD: rprb = 0.66; SPcmp: rprb = 0.68) and increased effect sizes after two years (LD: rprb = 0.77; SPcmp: rprb = 0.82). CONCLUSIONS: Utilizing a context-sensitive matching procedure, real-life gait measures capture longitudinal change within short trial-like time frames like 1 year with high effect size. In contrast, clinical scores like the SARA as well as lab-based gait measures show longitudinal change only after two years. Thus, features of real-life gait constitute promising biomarkers for upcoming therapeutical trials, delivering ecologically validity as well as increased effect sizes in comparison with clinical scores and lab-based gait assessment.