A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study

Reza Daryabeygi-Khotbehsara; Jonathan C Rawstorn; David W Dunstan; Sheikh Mohammed Shariful Islam; Mohamed Abdelrazek; Abbas Z Kouzani; Poojith Thummala; Jenna McVicar; Ralph Maddison

doi:10.2196/47157

A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study

JMIR Form Res. 2024 Jan 24:8:e47157. doi: 10.2196/47157.

Authors

Reza Daryabeygi-Khotbehsara¹, Jonathan C Rawstorn¹, David W Dunstan², Sheikh Mohammed Shariful Islam¹, Mohamed Abdelrazek³, Abbas Z Kouzani⁴, Poojith Thummala³, Jenna McVicar¹, Ralph Maddison¹

Affiliations

¹ Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne Burwood, Australia.
² Baker-Deakin Department of Lifestyle and Diabetes, Melbourne Burwood, Australia.
³ School of Information Technology, Deakin University, Melbourne Burwood, Australia.
⁴ School of Engineering, Deakin University, Geelong, Australia.

PMID: 38265864
PMCID: PMC10851128
DOI: 10.2196/47157

Abstract

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.

Objective: The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL.

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.

Results: Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=-0.3% to 0.9%), 1.19% for standing (LoA=-1.5% to 3.42%), and -4.71% for walking (LoA=-9.26% to -0.16%). The mean biases between SORD and activPAL were -3.45% for sitting and reclining (LoA=-11.59% to 4.68%), 7.45% for standing (LoA=-5.04% to 19.95%), and -5.40% for walking (LoA=-11.44% to 0.64%).

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.

Keywords: SORD; Sedentary behaviOR Detector; activity tracker; algorithms; deep neural network; machine learning; real-time data; sedentary behavior; standing; validation; walking; wearables.

©Reza Daryabeygi-Khotbehsara, Jonathan C Rawstorn, David W Dunstan, Sheikh Mohammed Shariful Islam, Mohamed Abdelrazek, Abbas Z Kouzani, Poojith Thummala, Jenna McVicar, Ralph Maddison. Originally published in JMIR Formative Research (https://formative.jmir.org), 24.01.2024.