From Total Volume To Sequence Maps: Sophisticated Accelerometer Data Analysis

Purpose: To date, epidemiological studies have focused on the potential health effects of total volume of physical activity (PA) or sedentary behavior (SB). However, two persons may have the same volume of PA or SB but accumulated in a completely different sequence. The pattern of accumulating PA and SB might be more important for health effects than the total volume. Therefore, the aim was to develop a sophisticated algorithm translating accelerometer data into detailed sequence maps considering how PA and SB are accumulated throughout the day. Methods: We developed a novel algorithm to convert accelerometer counts into a sequence map based on behavior states defined by a combination of intensity (SB, light, moderate, and vigorous intensity) and duration (sporadic accumulation or in bouts of different duration). In addition, hierarchical cluster analysis was applied to identify clusters of children with similar behavioral sequence maps. Results: Clustering resulted in seven clusters of children with similar PA and SB sequence maps: an average cluster (33% of children); a cluster with relatively more SB, light, and moderate PA in bouts (SB and PA bouters, 31%); a cluster characterized by more sporadic SB and light PA (light activity breakers, 26%); and four smaller clusters with 7% of the children or less. Conclusion: This novel algorithm is a next step in more sophisticated analyses of accelerometer data considering how PA and SB are accumulated throughout the day. The next step is identifying whether specific patterns of accumulating PA and SB are associated with improved health outcomes.