Published on Authors of this article:. Background: This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector SORD device in identifying sedentary, standing, and walking behaviors in a group of adult participants. Methods: A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities lying, reclining, sitting, standing, and walking.
Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement. In this model, accuracy, sensitivity, and specificity were all above 0. Conclusions: Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation.
SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior. SB is an independent risk factor for many noncommunicable diseases, with the risk being most pronounced in those who are also physically inactive ie, not meeting physical activity [PA] guidelines [ 3 - 5 ].
Reducing SB for all people, including those who are physically active, can assist in producing health benefits [ 6 ]. Interrupting SB with standing or light or moderate intensity PA can improve chronic risk factors including glucose homeostasis, insulin sensitivity, blood lipid concentrations, and diastolic blood pressure [ 7 - 10 ]. Recent World Health Organization guidelines on PA and SB explicitly state the importance of reducing sedentary time in addition to promoting PA for adults and older adults, including those with chronic conditions [ 11 ].
This has subsequently led to the development of interventions targeting SB reduction, although interventions to date have been compromised by the lack of a tool that can capture SB accurately and in real time. Accurate measurement of sitting, standing, and walking in real time will enable the design of interventions that can adapt to changes in the activity state and can be delivered at times when an individual is most responsive to the intervention, therefore maximizing the potential opportunity for reducing SB and increasing PA [ 12 ].
