Objective: This study aimed to assess the agreement between the total energy expenditure (TEE) estimated by the activPAL triaxial accelerometers (ACC) and the TEE measured by the doubly labeled water method (DLW), as well as to assess if these values differ between the classifications of body mass index (BMI).
Materials And Methods: This is a cross-sectional study. Low-income adult women (19-45y) with BMI ≥ 18.5 kg/m2 were included. Accelerometry data (activPAL ) were collected over 7 consecutive days, which were used to calculate TEE-ACC and compared with DLW data. The Bland-Altman method, concordance correlation coefficient and root mean square error were used to assess agreement between methods.
Results: The sample consisted of 55 women with a mean age of 31 ± 5 years. The agreement between TEE-ACC and TEE-DLW showed a bias of -142.5 kcal (-7.1%). Among the BMI classifications, participants with normal weight show a bias of -417.1 kcal (-21.0%), participants with overweight, -87.5 kcal (-3.9%) and participants with obesity, 97.5 kcal (4.3%). Furthermore, the bias between the methods showed a significant and positive correlation with the body weight (r = 0.49; p < 0.01).
Conclusion: The TEE-ACC estimates from activPAL were reasonably accurate when compared to the TEE-DLW, especially in women with overweight and obesity, being much less accurate in individuals with normal weight.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665053 | PMC |
http://dx.doi.org/10.20945/2359-3997000000616 | DOI Listing |
Contemp Clin Trials
November 2024
Division of Nephrology and Hypertension, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; Cardio-Renal & Metabolism Center, University of Utah School of Medicine, Salt Lake City, UT, USA; Medical Service, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA.
Background: Sedentary behavior is highly prevalent and associated with morbidity and mortality in chronic kidney disease (CKD). A Sit Less, Interact and Move More (SLIMM) sedentary activity coaching intervention can reduce sedentary duration among persons with CKD, but preliminary data suggest that effects may not persist. Prior studies have suggested that moderate/vigorous intensity physical activities are not sustainable in persons with CKD.
View Article and Find Full Text PDFJ Sports Sci
October 2024
School of Physiotherapy, Faculty of Health, Dalhousie University, Halifax, NS, Canada.
Thigh-worn accelerometry is commonly implemented to measure step cadence. The default activPAL CREA algorithm is a valid measure of cadence during walking, but its validity during running is unknown. The ActiPASS software is designed to analyse tri-axial accelerometry data from various brands.
View Article and Find Full Text PDFJ Cachexia Sarcopenia Muscle
June 2024
Institut Hospitalo-Universitaire (IHU) HealthAge, Toulouse, France.
Background: The way physical activity (PA) and sedentary behaviour (SB) independently and interactively modify the age-related decline in physical capacity remains poorly understood. This cross-sectional study investigated the independent and interactive associations of PA and SB with physical function and performance throughout the adult life course.
Methods: Data from 499 community-dwelling adults (63% female) aged 20-92 years, involved in the INSPIRE Human Translational Cohort, were used in this cross-sectional study.
Arch Endocrinol Metab
May 2023
Faculdade de Nutrição, Universidade Federal de Alagoas, Maceió, AL, Brasil, n
Objective: This study aimed to assess the agreement between the total energy expenditure (TEE) estimated by the activPAL triaxial accelerometers (ACC) and the TEE measured by the doubly labeled water method (DLW), as well as to assess if these values differ between the classifications of body mass index (BMI).
Materials And Methods: This is a cross-sectional study. Low-income adult women (19-45y) with BMI ≥ 18.
J Meas Phys Behav
December 2022
Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA.
Background: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults.
Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!