Meyer, Elijah S.Tran, TanGreenwood, Mark C.2018-10-092018-10-092016Meyer, Elijah S.; Tran, Tan; and Greenwood, Mark (2016) "Statistical methods for detecting groups of patterns in daily step count activity profiles," Skyline - The Big Sky Undergraduate Journal: Vol. 4: Iss. 1, Article 6.https://scholarworks.montana.edu/handle/1/14924The growth of wearable technology is apparent. A total of 2.7 million wearable bands were shipped worldwide in the first quarter of 2014 (Sullivan, 2014). Of this number, Fitbit, a company focused on developing and manufacturing compact, wireless wearable technology devices accounted for half of the shipments alone. A Fitbit device can track steps, distance, calories burned, floors climbed, active minutes, and sleep patterns and provides regular users with daily totals or totals over 15-minute intervals throughout the day. The motivation of staying fit coupled with the convenience of collecting the data through a simple wrist band is fueling this industry. The utility of activity tracking for improved health outcomes is left for other researchers. Here we focus on whether the focus on daily summaries is making dissimilar days look similar, and whether we can group days in such a way that balances total activity as well as timing of that activity.en-USSkyline is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. The Journal is faculty-reviewed and available to any undergraduate student attending a Big Sky Conference institution.Article