Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 10; p. 4605
• Main Authors: Abdul Jabbar, Khalid, Sarvestan, Javad, Zia Ur Rehman, Rana, Lord, Sue, Kerse, Ngaire, Teh, Ruth, Del Din, Silvia
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09.05.2023; MDPI
• Subjects: Accelerometers; Adhesives; Adults; Aged; Algorithms; Artificial intelligence; Back; digital health; Humans; Independent Living; Machine learning; Measurement; older adults; Older people; Orthopedic apparatus; real-world; Sedentary Behavior; sedentary behaviour; Sitting Position; Software; validation; Walking; Wearable computers; wearable device; United Kingdom; United Kingdom > UK; wearable device; older adults; real-world; sedentary behaviour; digital health; validation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23104605

Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorithm to identify sitting, lying, and upright activities in community-dwelling older people in real-world conditions. Eighteen older adults wore a single triaxial accelerometer with an onboard triaxial gyroscope on their lower back and performed a range of scripted and non-scripted activities in their homes/retirement villages whilst being videoed. A novel algorithm was developed to identify sitting, lying, and upright activities. The algorithm's sensitivity, specificity, positive predictive value, and negative predictive value for identifying scripted sitting activities ranged from 76.9% to 94.8%. For scripted lying activities: 70.4% to 95.7%. For scripted upright activities: 75.9% to 93.1%. For non-scripted sitting activities: 92.3% to 99.5%. No non-scripted lying activities were captured. For non-scripted upright activities: 94.3% to 99.5%. The algorithm could, at worst, overestimate or underestimate sedentary behaviour bouts by ±40 s, which is within a 5% error for sedentary behaviour bouts. These results indicate good to excellent agreement for the novel algorithm, providing a valid measure of sedentary behaviour in community-dwelling older adults.