Reliability and validity of the physical activity monitor for assessing energy expenditures in sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults

Abstract

Background Inertial sensors, such as accelerometers, serve as convenient devices to predict the energy expenditures (EEs) during physical activities by a predictive equation. Although the accuracy of estimate EEs especially matter to athletes receive physical training, most EE predictive equations adopted in accelerometers are based on the general population, not athletes. This study included the heart rate reserve (HRR) as a compensatory parameter for physical intensity and derived new equations customized for sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults. Methods With indirect calorimetry as the criterion measure (CM), the EEs of participants on a treadmill were measured, and vector magnitudes (VM), as well as HRR, were simultaneously recorded by a waist-worn accelerometer with a heart rate monitor. Participants comprised a sedentary group (SG), an exercise-habit group (EHG), a non-endurance group (NEG), and an endurance group (EG), with 30 adults in each group. Results EE predictive equations were revised using linear regression with cross-validation on VM, HRR, and body mass (BM). The modified model demonstrates valid and reliable predictions across four populations (Pearson correlation coefficient, r: 0.922 to 0.932; intraclass correlation coefficient, ICC: 0.919 to 0.930). Conclusion Using accelerometers with a heart rate monitorcan accurately predict EEs of athletes and non-athletes with an optimized predictive equation integrating the VM, HRR, and BM parameters.

Concurrent validity of the combined HRV/ACC sensor and physical activity diary when monitoring physical activity in university students during free-living days

“…Such finding was concordant with previous studies that analyzed the consistency between self-reported levels of PA (similar to Bouchard) and single ACC ( 21 , 31 ). However, previous studies ( 32 – 34 ) showed high-percentage error and wide limits of agreement in EE estimation using Actigraph when compared with the golden standard tests, while only a few studies showed good validity in predicting EE in controlled treadmill activities or using light intensity stepping exercise ( 35 , 36 ). Therefore, it remains inconclusive to determine which method was better due to the lack of the gold standard method of EE estimation in the study.…”

Current tendencies in implementation of biomedical technologies in sports medicine practice

“…Окремим напрямів впровадження біомедичних технологій за авторством Chang, Hsu, Li, Tu, Jhang, Hsu, Huang, Ho, (2020) [6] є використання біомедичних тренажерів, які дозволяють вирішувати конкретні фізичні завдання і контролювати їх результати (рис. 2).…”

“…• системна фізіологія -використання інженерних стратегій для аналізу експериментальних даних і формулюванні математичного опису фізіологічних подій для отримання комплексного та інтегрованого розуміння функції живих організмів та прогнозування фізіологічних реакцій при плануванні експериментів [6];…”

Disparities in Physical Activity and Depressive Symptoms among Young Adults in the Early Stage of the COVID-19 Pandemic

“…Participants' average percentage of time per day on LPA, MVPA, and sedentary behavior were calculated based on the total wearing time and days and were then used in the data analysis. It was well-documented that ActiGraph accelerometer is a valid and reliable tool in measuring different intensi-ty levels of PA among adults (Chang et al, 2020;Clevenger et al, 2020;Montoye et al, 2017).…”