Heart rate variability (HRV) has been studied in the context of human behavior analysis and many features have been extracted from the inter-beat interval (RR) time series and tested as correlates of constructs such as mental workload, stress and anxiety. Most studies, however, have been conducted in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate settings (i.e., "in-the-wild") remains unknown. In this paper, we explore the use of motif-based multi-scale HRV features to predict anxiety and stress in-the-wild. To further improve their robustness to artifacts, we propose a quality-aware feature aggregation method. The new quality-aware features are tested on a dataset collected using a wearable biometric sensor from over 200 hospital workers (nurses and staff) during their work shifts. Results show improved stress/anxiety measurement over using conventional time- and frequency-domain HRV measures.