The use of wearable monitors to track physical activity and sleep has increased in recent years. Because many devices were specifically designed to measure physical activity, there is a lack of computational tools for processing sleep data, aside from manufacturers’ proprietary software that is not readily available for public use. Developing accessible algorithms to process these sleep data may help researchers and clinicians make more informed decisions about sleep duration and quality. This paper presents a user-friendly SAS Macro that transforms minute-by-minute sleep data from the Sensewear Mini Armband into summary variables indicating length and quality of sleep. These variables include Total Sleep Time, Time in Bed, Wake After Sleep Onset, etc. The Macro incorporates flexibility at decision points that vary by study, including definitions of valid data, continuous nighttime sleep, naps, and more. While the Macro is currently specific to the Sensewear device, it will be adapted to handle other devices. This Macro is currently being applied to sleep data from an 18-Month behavioral weight loss intervention to assess the effect of sleep on weight loss and treatment adherence.