Sedentary behavior in middle-aged adults:measurement method development and associations with lipid and glucose metabolism

• Main Author: Tjurin, P. (Petra)
• Other Authors: Jämsä, T. (Timo), Korpelainen, R. (Raija), Kangas, M. (Maarit)
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Oulun yliopisto 2023
• Subjects: accelerometer; cardiometabolic health; glucose metabolism; insulin resistance; machine learning; physical activity; sedentary behavior; fyysinen aktiivisuus; glukoosiaineenvaihdunta; insuliiniresistenssi; kiihtyvyysanturi; koneoppiminen; paikallaanolo; sydänterveys; Northern Finland
• Online Access: http://urn.fi/urn:isbn:9789526236810

Abstract The adverse health effects of sedentary behavior and prolonged sedentary bouts are well-known. However, it is still unknown how physical activity can modify adverse health impacts related to sedentary behavior. The purpose of this study was to develop signal analysis methodology for sedentary behavior and physical activity classification from raw data of a hip-worn accelerometer and to investigate associations of patterns of sedentary behavior with lipid and glucose metabolism. A machine learning model was developed and validated using acceleration data, which included nine predefined and controlled typical daily activities ranging in intensity from sedentary to vigorous physical activity. Acceleration data was collected from 22 Finnish adults using a triaxial accelerometer attached to an elastic belt on a hip. The data were classified into five categories (lying down, sitting, and light, moderate, and vigorous physical activity). Thirty-six middle-aged Finnish adults wore an accelerometer for 14 days, and their sedentary behavior and sitting characteristics were determined. In addition, associations of sedentary behavior, sitting, and physical activity with glucose and lipid metabolism were investigated in the Northern Finland Birth Cohort 1966 46-year follow-up (n=5,832). Participants completed health and lifestyle questionnaires and attended clinical examinations and two weeks of sedentary behavior and physical activity measurements. Isotemporal substitution modeling was used for investigating time reallocations from sedentary to physical activities. The developed machine learning model provided acceptable accuracy for sedentary behavior and physical activity classifications. The method can be used for describing characteristics of sedentary behavior and sitting separately. Patterns of SB were more consistently associated with lipid metabolism than those of sitting. Associations between sedentary behavior and cardiometabolic health depended on moderate-to-vigorous physical activity levels. ...