Dataset Information

Physical Activity Assessment with the ActiGraph GT3X and Doubly Labeled Water.

ABSTRACT: PURPOSE:To compare the degree to which four accelerometer metrics-total activity counts per day (TAC per day), steps per day (steps per day), physical activity energy expenditure (PAEE) (kcal·kg·d), and moderate- to vigorous-intensity physical activity (MVPA) (min·d)-were correlated with PAEE measured by doubly labeled water (DLW). Additionally, accelerometer metrics based on vertical axis counts and triaxial counts were compared. METHODS:This analysis included 684 women and 611 men age 43 to 83 yr. Participants wore the Actigraph GT3X on the hip for 7 d twice during the study and the average of the two measurements was used. Each participant also completed one DLW measurement, with a subset having a repeat. PAEE was estimated by subtracting resting metabolic rate and the thermic effect of food from total daily energy expenditure estimated by DLW. Partial Spearman correlations were used to estimate associations between PAEE and each accelerometer metric. RESULTS:Correlations between the accelerometer metrics and DLW-determined PAEE were higher for triaxial counts than vertical axis counts. After adjusting for weight, age, accelerometer wear time, and fat free mass, the correlation between TAC per day based on triaxial counts and DLW-determined PAEE was 0.44 in women and 0.41 in men. Correlations for steps per day and accelerometer-estimated PAEE with DLW-determined PAEE were similar. After adjustment for within-person variation in DLW-determined PAEE, the correlations for TAC per day increased to 0.61 and 0.49, respectively. Correlations between MVPA and DLW-determined PAEE were lower, particularly for modified bouts of ?10 min. CONCLUSIONS:Accelerometer measures that represent total activity volume, including TAC per day, steps per day, and PAEE, were more highly correlated with DLW-determined PAEE than MVPA using traditional thresholds and should be considered by researchers seeking to reduce accelerometer data to a single metric.

SUBMITTER: Chomistek AK

PROVIDER: S-EPMC5561512 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Physical Activity Assessment with the ActiGraph GT3X and Doubly Labeled Water.

Chomistek Andrea K AK Yuan Changzheng C Matthews Charles E CE Troiano Richard P RP Bowles Heather R HR Rood Jennifer J Barnett Junaidah B JB Willett Walter C WC Rimm Eric B EB Bassett David R DR

Medicine and science in sports and exercise 20170901 9

<h4>Purpose</h4>To compare the degree to which four accelerometer metrics-total activity counts per day (TAC per day), steps per day (steps per day), physical activity energy expenditure (PAEE) (kcal·kg·d), and moderate- to vigorous-intensity physical activity (MVPA) (min·d)-were correlated with PAEE measured by doubly labeled water (DLW). Additionally, accelerometer metrics based on vertical axis counts and triaxial counts were compared.<h4>Methods</h4>This analysis included 684 women and 611 m ...[more]

PMID: 28419028

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Project description:Background and objectives: There is a lack of studies investigating associations of physical activity level (PAL) and activity energy expenditure (AEE) using the doubly-labeled water (DLW) method with body composition and physical fitness in young children. Thus, we aimed to examine cross-sectional associations of PAL and AEE with body composition indices and physical fitness components in Swedish preschool children. Materials and methods: PAL was calculated as total energy expenditure measured using DLW divided by the predicted basal metabolic rate in 40 children aged 5.5 (standard deviation 0.2) years. AEE was calculated as total energy expenditure minus basal metabolic rate and the thermic effect of food, and divided by fat-free mass. Body composition was assessed using the 3-component model by combining measurements based on isotope dilution and air-displacement plethysmography. Physical fitness (muscular strength, motor fitness, and cardiorespiratory fitness) was evaluated using the PREFIT test battery. Multiple linear regression models were conducted. Results: PAL and AEE were negatively associated with body mass index, percent body fat, and fat mass index (PAL: standardized β -0.35, -0.41, and -0.45, all p < 0.036; AEE: standardized β -0.44, -0.44, and -0.47, all p < 0.006, respectively). Furthermore, PAL and AEE were positively associated with the standing long jump test (PAL: standardized β 0.37, p = 0.017; AEE: standardized β 0.38, p = 0.014). There were no statistically significant associations found regarding PAL or AEE with fat-free mass index or any other physical fitness test. Conclusions: Greater PAL and AEE at the age 5.5 were significantly associated with body fatness and improved lower-body muscular strength. Therefore, increasing physical activity, and thus energy expenditure, at young ages may be beneficial for preventing overweight/obesity. However, further studies with larger sample sizes are needed to confirm the results.

Project description:BackgroundPhysical activity (PA) is reduced in persons with multiple sclerosis (MS), though it is known to aid in symptom and fatigue management. Methods for measuring PA are diverse and the impact of this heterogeneity on study outcomes is unclear. We aimed to clarify this impact by comparing common methods for deriving PA metrics in MS populations.MethodsFirst, a rapid review of existing literature identified methods for calculating PA in studies which used the Actigraph GT3X in populations with MS. We then compared methods in a prospective study on 42 persons with MS [EDSS 4.5 (3.5-6)] during a voluntary course of inpatient neurorehabilitation. Mixed-effects linear regression identified methodological factors which influenced PA measurements. Non-parametric hypothesis tests, correlations, and agreement statistics assessed overall and pairwise differences between methods.ResultsIn the rapid review, searches identified 421 unique records. Sixty-nine records representing 51 eligible studies exhibited substantial heterogeneity in methodology and reporting practices. In a subsequent comparative study, multiple methods for deriving six PA metrics (step count, activity counts, total time in PA, sedentary time, time in light PA, time in moderate to vigorous PA), were identified and directly compared. All metrics were sensitive to methodological factors such as the selected preprocessing filter, data source (vertical vs. vector magnitude counts), and cutpoint. Additionally, sedentary time was sensitive to wear time definitions. Pairwise correlation and agreement between methods varied from weak (minimum correlation: 0.15, minimum agreement: 0.03) to perfect (maximum correlation: 1.00, maximum agreement: 1.00). Methodological factors biased both point estimates of PA and correlations between PA and clinical assessments.ConclusionsMethodological heterogeneity of existing literature is high, and this heterogeneity may confound studies which use the Actigraph GT3X. Step counts were highly sensitive to the filter used to process raw accelerometer data. Sedentary time was particularly sensitive to methodology, and we recommend using total time in PA instead. Several, though not all, methods for deriving light PA and moderate to vigorous PA yielded nearly identical results. PA metrics based on vertical axis counts tended to outperform those based on vector magnitude counts. Additional research is needed to establish the relative validity of existing methods.

Project description:BackgroundPhysical activity is recommended to promote healthy aging. Defining the importance of activities such as walking in achieving higher levels of physical activity might provide indications for interventions.ObjectiveTo describe the importance of walking in achieving higher levels of physical activity in older adults.MethodsThe study included 42 healthy subjects aged between 51 and 84 years (mean body mass index 25.6 kg/m(2) [SD 2.6]). Physical activity, walking, and nonwalking activity were monitored with an accelerometer for 2 weeks. Physical activity was quantified by accelerometer-derived activity counts. An algorithm based on template matching and signal power was developed to classify activity counts into nonwalking counts, short walk counts, and long walk counts. Additionally, in a subgroup of 31 subjects energy expenditure was measured using doubly labeled water to derive physical activity level (PAL).ResultsSubjects had a mean PAL of 1.84 (SD 0.19, range 1.43-2.36). About 20% of the activity time (21% [SD 8]) was spent walking, which accounted for about 40% of the total counts (43% [SD 11]). Short bouts composed 83% (SD 9) of walking time, providing 81% (SD 11) of walking counts. A stepwise regression model to predict PAL included nonwalking counts and short walk counts, explaining 58% of the variance of PAL (standard error of the estimate=0.12). Walking activities produced more counts per minute than nonwalking activities (P<.001). Long walks produced more counts per minute than short walks (P=.001). Nonwalking counts were independent of walking counts (r=-.05, P=.38).ConclusionsWalking activities are a major contributor to physical activity in older adults. Walking activities occur at higher intensities than nonwalking activities, which might prevent individuals from engaging in more walking activity. Finally, subjects who engage in more walking activities do not tend to compensate by limiting nonwalking activities.Trial registrationClinicalTrials.gov NCT01609764; https://clinicaltrials.gov/ct2/show/NCT01609764 (Archived by WebCite at http://www.webcitation.org/6grls0wAp).

Dataset Information

Physical Activity Assessment with the ActiGraph GT3X and Doubly Labeled Water.

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Physical Activity Assessment with the ActiGraph GT3X and Doubly Labeled Water.

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