Quantifying Energy Expenditure in Osteoarthritis using ActiGraph and ActivPal accelerometers: A validation Study.

Abstract

Background: Osteoarthritis (OA) is a chronic degenerative disease targeting the body's loadbearing joints. If left untreated, the disease could progress, resulting in an individual being deemed physically disabled due to pain experienced and movement limitations associated with the condition. Physical activity is the gold-standard treatment for OA-associated symptom management and prevention of disease progression. However, sedentary behaviour and physical inactivity are common occurrences within OA. Physiological changes accompanied by the condition (such as additional muscle incorporation to compensate for instability and muscle weakness) result in a higher energy cost during movement in people with OA. Accurate physical activity prescription is known to aid in OA symptom management and improve quality of life. However, current methods of physical activity monitoring may prove inaccurate in an OA population. Current accelerometer algorithms have been validated in a healthy population. However, their accuracy in people with OA remains unclear. The higher energy cost of movement in OA may result in inaccurate energy expenditure estimations using current algorithms. Therefore, the present study aims to validate the use and accuracy of the current accelerometer and associated algorithms in predicting energy expenditure in OA. Methods: 8 OA participants (mean (sd) age 61.62 (9.13) years, BMI 29.13 (4.68) kg/m2 ) were directly observed for 2 hours and instructed to complete activities of sedentary behaviour, at-home mimicked activities and light physical activity. Indirect calorimetry was used to determine actual energy expenditure during activities and compared to accelerometer-derived energy expenditure estimations using hip and wrist-based ActiGraph and thigh-based ActivPal accelerometers. Results: Hip ActiGraph achieved a 37.5% agreement to gas analysis energy expenditure estimations, and wrist ActiGraph achieved a 25% agreement. Thigh ActivPal achieved a near-perfect agreement of 87.5% to actual energy expenditure estimations. Conclusion: The present study found that hip and wrist-worn ActivGraph accelerometers may not be valid in accurately predicting energy expenditure in OA. Research is needed to develop algorithms to adjust for the physiological changes and higher energy costs in OA. Thigh-based ActivPal accelerometers and algorithms are valid and accurate in predicting energy expenditure in OA.