Validation of the VitaBit Sit-Stand Tracker: Detecting Sitting, Standing, and Activity Patterns
Overview
Affiliations
Sedentary behavior (SB) has detrimental consequences and cannot be compensated for through moderate-to-vigorous physical activity (PA). In order to understand and mitigate SB, tools for measuring and monitoring SB are essential. While current direct-to-customer wearables focus on PA, the VitaBit validated in this study was developed to focus on SB. It was tested in a laboratory and in a free-living condition, comparing it to direct observation and to a current best-practice device, the ActiGraph, on a minute-by-minute basis. In the laboratory, the VitaBit yielded specificity and negative predictive rates (NPR) of above 91.2% for sitting and standing, while sensitivity and precision ranged from 74.6% to 85.7%. For walking, all performance values exceeded 97.3%. In the free-living condition, the device revealed performance of over 72.6% for sitting with the ActiGraph as criterion. While sensitivity and precision for standing and walking ranged from 48.2% to 68.7%, specificity and NPR exceeded 83.9%. According to the laboratory findings, high performance for sitting, standing, and walking makes the VitaBit eligible for SB monitoring. As the results are not transferrable to daily life activities, a direct observation study in a free-living setting is recommended.
Berninger N, Crutzen R, Ruiter R, Kok G, Plasqui G, Ten Hoor G Int J Behav Med. 2023; 30(6):849-866.
PMID: 36720773 PMC: 10713801. DOI: 10.1007/s12529-022-10147-w.
Giurgiu M, Timm I, Becker M, Schmidt S, Wunsch K, Nissen R JMIR Mhealth Uhealth. 2022; 10(6):e36377.
PMID: 35679106 PMC: 9227659. DOI: 10.2196/36377.
Berninger N, Plasqui G, Crutzen R, Ruiter R, Kok G, Ten Hoor G Int J Behav Med. 2022; 29(6):728-742.
PMID: 35099779 PMC: 9684295. DOI: 10.1007/s12529-022-10054-0.
Tele-Monitoring System for Chronic Diseases Management: Requirements and Architecture.
Mucchi L, Jayousi S, Gant A, Paoletti E, Zoppi P Int J Environ Res Public Health. 2021; 18(14).
PMID: 34299910 PMC: 8305785. DOI: 10.3390/ijerph18147459.
Activity Segmentation Using Wearable Sensors for DVT/PE Risk Detection.
Gentry A, Mongan W, Lee B, Montgomery O, Dandekar K Proc COMPSAC. 2021; 2019:477-483.
PMID: 33594351 PMC: 7884185. DOI: 10.1109/compsac.2019.10252.