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Objective: To compare activity counts from the ActiGraph GT3X to those from the ActiGraph GT1M during treadmill walking/running. A secondary aim was to develop tri-axial vector magnitude (VM3) cut-points to classify physical activity (PA) intensity. Methods: Fifty participants wore the GT3X and the GT1M on the non-dominant hip and exercised at 4 treadmill speeds (4.8, 6.4, 9.7, and 12 km h −1). Vertical (VT) and antero-posterior (AP) activity counts (counts min −1) as well as the vector magnitudes of the two axes (VM2) from both monitors were tested for significant differences using two-way ANOVA's. Bland–Altman plots were used to assess agreement between activity counts from the GT3X and GT1M. Linear regression analysis between VM3 counts min −1 and oxygen consumption data was conducted to develop VM3 cut-points for moderate, hard and very hard PA. Results: There were no significant inter-monitor differences in VT activity counts at any speed. AP and VM2 activity counts from the GT1M were significantly higher ( p < 0.01) than those from the GT3X at 4.8, 9.7 and 12 km h −1. High inter-monitor agreement was found for VT activity counts but not for AP and VM2 activity counts. VM3 cut-points for moderate, hard, and very hard PA intensities were 2690–6166, 6167–9642, >9642 counts min −1. Conclusion: Due to the lack of congruence between the AP and VM2 activity counts from the GT1M and the GT3X, comparisons of data obtained with these two monitors should be avoided when using more than just the VT axis. VM3 cut-points may be used to classify PA in future studies.

Background Accelerometers objectively assess physical activity (PA) and are currently used in several large-scale epidemiological studies, but there is no consensus for processing the data. This study compared the impact of wear-time assessment methods and using either vertical (V)-axis or vector magnitude (VM) cut-points on accelerometer output. Methods Participants (7,650 women, mean age 71.4 y) were mailed an accelerometer (ActiGraph GT3X+), instructed to wear it for 7 days, record dates and times the monitor was worn on a log, and return the monitor and log via mail. Data were processed using three wear-time methods (logs, Troiano or Choi algorithms) and V-axis or VM cut-points. Results Using algorithms alone resulted in \"mail-days\" incorrectly identified as \"wear-days\" (27-79% of subjects had >7-days of valid data). Using only dates from the log and the Choi algorithm yielded: 1) larger samples with valid data than using log dates and times, 2) similar wear-times as using log dates and times, 3) more wear-time (V, 48.1 min more; VM, 29.5 min more) than only log dates and Troiano algorithm. Wear-time algorithm impacted sedentary time (~30-60 min lower for Troiano vs. Choi) but not moderate-to-vigorous (MV) PA time. Using V-axis cut-points yielded ~60 min more sedentary time and ~10 min less MVPA time than using VM cut-points. Conclusions Combining log-dates and the Choi algorithm was optimal, minimizing missing data and researcher burden. Estimates of time in physical activity and sedentary behavior are not directly comparable between V-axis and VM cut-points. These findings will inform consensus development for accelerometer data processing in ongoing epidemiologic studies.

Numerous accelerometers and prediction methods are used to estimate energy expenditure (EE). Validation studies have been limited to small sample sizes in which participants complete a narrow range of activities and typically validate only one or two prediction models for one particular accelerometer. The purpose of this study was to evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Two hundred and seventy-seven participants completed an average of six treadmill (TRD) (1.34, 1.56, 2.23 ms −1 each at 0 and 3% grade) and five self-paced activities of daily living (ADLs). EE estimates were compared with indirect calorimetry. Accelerometers were worn while EE was measured using a portable metabolic unit. To estimate EE, 4 ActiGraph prediction models were used, 5 Actical models, and 2 RT3 proprietary models. Across all activities, each equation underestimated EE (bias −0.1 to −1.4 METs and −0.5 to −1.3 kcal, respectively). For ADLs EE was underestimated by all prediction models (bias −0.2 to −2.0 and −0.2 to −2.8, respectively), while TRD activities were underestimated by seven equations, and overestimated by four equations (bias −0.8 to 0.2 METs and −0.4 to 0.5 kcal, respectively). Misclassification rates ranged from 21.7 (95% CI 20.4, 24.2%) to 34.3% (95% CI 32.3, 36.3%), with vigorous intensity activities being most often misclassified. Prediction equations did not yield accurate point estimates of EE across a broad range of activities nor were they accurate at classifying activities across a range of intensities (light <3 METs, moderate 3–5.99 METs, vigorous ≥6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE.

Purpose: To compare raw acceleration output of the ActiGraph™ GT3X+ and GENEA activity monitors. Methods: A GT3X+ and GENEA were oscillated in an orbital shaker at frequencies ranging from 0.7 to 4.0 Hz (ten 2-min trials/frequency) on a fixed radius of 5.08 cm. Additionally, 10 participants (age = 23.8 ± 5.4 years) wore the GT3X+ and GENEA on the dominant wrist and performed treadmill walking (2.0 and 3.5 mph) and running (5.5 and 7.5 mph) and simulated free-living activities (computer work, cleaning a room, vacuuming and throwing a ball) for 2-min each. A linear mixed model was used to compare the mean triaxial vector magnitude (VM) from the GT3X+ and GENEA at each oscillation frequency. For the human testing protocol, random forest machine-learning technique was used to develop two models using frequency domain (FD) and time domain (TD) features for each monitor. We compared activity type recognition accuracy between the GT3X+ and GENEA when the prediction model was fit using one monitor and then applied to the other. Z-statistics were used to compare the proportion of accurate predictions from the GT3X+ and GENEA for each model. Results: GENEA produced significantly higher (p < 0.05, 3.5 to 6.2%) mean VM than GT3X+ at all frequencies during shaker testing. Training the model using TD input features on the GENEA and applied to GT3X+ data yielded significantly lower (p < 0.05) prediction accuracy. Prediction accuracy was not compromised when interchangeably using FD models between monitors. Conclusions: It may be inappropriate to apply a model developed on the GENEA to predict activity type using GT3X+ data when input features are TD attributes of raw acceleration.

Background Prior studies evaluating the impact of the COVID-19 pandemic on pregnancy physical activity (PA) have largely been limited to internet-based surveys not validated for use in pregnancy. Methods This study used data from the Pregnancy PA Questionnaire Validation study conducted from 2019–2021. A prospective cohort of 50 pregnant women completed the Pregnancy PA Questionnaire (PPAQ), validated for use in pregnancy, in early, mid, and late pregnancy and wore an ActiGraph GT3X-BT for seven days. COVID-19 impact was defined using a fixed date of onset (March 13, 2020) and a self-reported date. Multivariable linear mixed effects regression models adjusted for age, early pregnancy BMI, gestational age, and parity. Results Higher sedentary behavior (14.2 MET-hrs/wk, 95% CI: 2.3, 26.0) and household/caregiving PA (34.4 MET-hrs/wk, 95% CI: 8.5, 60.3 and 25.9 MET-hrs/wk, 95% CI: 0.9, 50.9) and lower locomotion (-8.0 h/wk, 95% CI: -15.7, -0.3) and occupational PA (-34.5 MET-hrs/wk, 95% CI: -61.9, -7.0 and -30.6 MET-hrs/wk, 95% CI: -51.4, -9.8) was observed in middle and late pregnancy, respectively, after COVID-19 vs. before. There was no impact on steps/day or meeting American College of Obstetricians and Gynecologists guidelines. Conclusions Proactive approaches for the promotion of pregnancy PA during pandemic-related restrictions are critically needed.

Objective: This study compared the accuracy of four commonly used RMR prediction equations to measured RMR obtained from the MedGem ® metabolic analyzer. Design and Methods: Height, weight and RMR were measured in 362 healthy individuals [51% female; body mass index (BMI): 17.6–50.6 kg m −2; ages: 18–60 years; 17.4% non-white]. Following a 4 h fast, participants rested in the supine position after which RMR was measured. RMR was estimated using four commonly used prediction equations: Harris–Benedict, Mifflin–St. Jeor, Owen, and WHO/FAO/UNU. Accuracy was determined by calculating the percentage of predicted RMR values that were within ±10% of measured RMR values. Main effects of sex, BMI, age, and race/ethnicity were assessed using repeated measures ANCOVAs. Results: For all participants combined, the Harris–Benedict, Mifflin, and WHO/FAU/UNU equations similarly predicted RMR values within ±10% of measured RMR values (57.5, 56.4, and 55.2% of the sample, respectively). When participant data were stratified by sex, BMI, age, and race/ethnicity, the accuracy of each regression equation varied dramatically. The Harris–Benedict equation over-predicted RMR in 18–29 year olds. The Owen equation under-predicted RMR in both sexes, all three BMI categories, 18–49 year olds and White participants. The Mifflin under-predicted RMR in both sexes, normal weight individuals, 40–60 year olds, and non-Hispanic White participants. The WHO/FAO/UNU over-predicted RMR in males, overweight participants, and 50–60 year olds. Conclusions: When examining the entire sample, the Harris–Benedict, Mifflin, and WHO/FAU/UNU equations yielded similar levels of agreement with the MedGem ® measured RMR. However, clinical judgment and caution should be used when applying these prediction equations to special populations or small groups.

Although neither motion sensors nor heart rate are perfect markers of physical activity, they certainly eliminate subjectivity of obtaining physical activity information. The objective method of choice depends on how the measurement will be used. For example, if walking behavior is the desired outcome, then a pedometer may be sufficient. If patterns and intensity of activity over longer periods of times such as a week or longer are needed, then an accelerometer with large memory capacity should be selected. In the future, efforts should be directed towards developing an objective motion sensor as inexpensive as a pedometer but with the data acquisition capabilities of the CSA or Tritrac accelerometer. Providing simultaneous heart rate with motion is also recommended to further verify that elevated heart rate does in fact represent a physical activity response. As the cost of the electronic components continues to decrease, these activity monitor configurations may become possible.

Eleven previously sedentary adult males, serving as the experimental (EXP) group [mean (SE) age 36.6 (1.7) years, body mass 87.2 (4.3) kg, body mass index, BMI, 28.4 (1.5) kgm(-2)] participated in a 16-week supervised exercise program (3 days x week(-1), 30 min day(-1), at approximately equal to 80% of heart rate reserve) to determine the temporal effects of a moderate-to-vigorous-intensity exercise program on heart rate variability (HRV). Five sedentary males [mean (SD) age 36.6 (4.2 )years, body mass 83.8 (6.6) kg, BMI 22.8 (1.7) kg x m(-2)] served as non-exercising controls (CON). HRV was measured every 4 weeks from a resting electrocardiogram obtained while subjects paced their breathing at 10 breaths x min(-1) (0.167 Hz). The time-domain measures of HRV recorded were the proportion of adjacent intervals differing by more than 50 ms (pNN50), the root mean square of successive differences (rMSSD), and the standard deviation of the resting interbeat interval. The frequency-domain measures recorded were high (HF) and low (LF) frequency oscillations, as determined using the fast Fourier transform technique. Aerobic capacity (i.e., peak oxygen uptake) increased by 13.8% in EXP (P < 0.001), but did not change in CON. Resting heart rate did not change in either EXP or CON. In EXP, pNN50 at week 12 (P<0.01), rMSSD at weeks 12 (P < 0.01) and 16 (P = 0.05), and HF power at weeks 12 (P < 0.01) and 16 (P = 0.05) were elevated above baseline. Time- and frequency-domain measures of HRV remained unchanged in CON. It is concluded that a moderate-to-vigorous-intensity exercise program produces increases in time- and frequency-domain measures of HRV within 12 weeks.

Insulin resistance is a risk factor for breast cancer recurrence. How exercise training changes fasting and postglucose insulin resistance in breast cancer survivors is unknown. To evaluate exercise-induced changes in postglucose ingestion insulin concentrations, insulin resistance, and their associations with cancer-relevant biomarkers in breast cancer survivors. The University of Massachusetts Kinesiology Department. 15 postmenopausal breast cancer survivors not meeting the physical activity guidelines (150 min/week of exercise). A supervised 12-week aerobic exercise program (60 min/day, 3-4 days/week). Postglucose ingestion insulin was determined by peak insulin and area under the insulin curve (iAUC) during a 5-sample oral glucose tolerance test. Insulin sensitivity was estimated from the Matsuda composite insulin sensitivity index (C-ISI). Changes in fitness and body composition were determined from submaximal VO2peak and dual energy X-ray absorptiometry. Participants averaged 156.8 ± 16.6 min/week of supervised exercise. Estimated VO2peak significantly increased (+2.8 ± 1.4 mL/kg/min, P < .05) and body weight significantly decreased (-1.1 ± 0.8 kg, P < .05) following the intervention. There were no differences in fasting insulin, iAUC, C-ISI, or peak insulin following the intervention. Insulin was only significantly lower 120 min following glucose consumption (68.8 ± 34.5 vs 56.2 ± 31.9 uU/mL, P < .05), and there was a significant interaction with past/present aromatase inhibitor (AI) use for peak insulin (-11.99 non-AI vs +13.91 AI uU/mL) and iAUC (-24.03 non-AI vs +32.73 AI uU/mL). Exercise training had limited overall benefits on insulin concentrations following glucose ingestion in breast cancer survivors but was strongly influenced by AI use.

A person's physical activity has important health implications, so it is important to be able to measure aspects of physical activity objectively. One approach to doing that is to use data from an accelerometer to classify physical activity according to activity type (e.g., lying down, sitting, standing, or walking) or intensity (e.g., sedentary, light, moderate, or vigorous). This can be formulated as a labeled classification problem, where the model relates a feature vector summarizing the accelerometer signal in a window of time to the activity type or intensity in that window. These data exhibit two key characteristics: (1) the activity classes in different time windows are not independent, and (2) the accelerometer features have moderately high dimension and follow complex distributions. Through a simulation study and applications to three datasets, we demonstrate that a model's classification performance is related to how it addresses these aspects of the data. Dynamic methods that account for temporal dependence achieve better performance than static methods that do not. Generative methods that explicitly model the distribution of the accelerometer signal features do not perform as well as methods that take a discriminative approach to establishing the relationship between the accelerometer signal and the activity class. Specifically, Conditional Random Fields consistently have better performance than commonly employed methods that ignore temporal dependence or attempt to model the accelerometer features.