The growing popularity of wearable devices for continuous sensing has made personal health data increasingly available, yet methods for data interpretation are still a work in progress. This paper investigates potential under-utilization of wearable device data in diabetes management and develops an analytic approach - GlucoMine - to uncover individualized patterns in extended periods of such data to support and improve care. In addition, we conduct a user study with clinicians to assess and compare conventional tools used for reviewing wearable device data in diabetes management with the proposed solution. Using 3 - 6 months of continuous glucose monitor (CGM) data from 54 patients with type 1 diabetes, we found that: 1) the recommended practice of reviewing only short periods (e.g., the most recent 2-weeks) of CGM data based on correlation analysis is not sufficient for finding hidden patterns of poor management; 2) majority of subjects (96% in this study) had clinically-recognized episodes of recurrent adverse glycemic events observable from analysis of extended periods of their CGM data; 3) majority of clinicians (89% in this study) believe there is benefit to be gained in having an algorithm for extracting patterns of adverse glycemic events from longer periods of wearable device data. Findings from our user study also provides insights, including strengths and weakness of various data presentation tools, to guide development of better solutions that improve the use of wearable device data for patient care.