Background: Sedentary behavior (SED) and moderate-to-vigorous intensity physical activity (MVPA) have important implications for health; however, little is known about predictors of these behaviors during pregnancy. Methods: This cohort study measured SED (activPAL) and MVPA (GT3X) in each trimester of pregnancy. Univariate associations of demographic, socioeconomic, and pregnancy health-related factors with SED or MVPA were calculated. Associations with P < .10 were included in stepwise linear regression models to determine independent predictors in each trimester. Results: Pregnant women (n = 127) were age 31.0 (4.9) years and 78% white. In regression models across trimesters, fewer children ≤ age 5 in the household (P < .04) and primarily sitting job activity (P < .008) were related to higher SED and use of assisted reproductive technology (P < .05) was associated with higher MVPA. In at least one trimester, younger age was related to higher SED (P = .014); no history of pregnancy loss (P < .04), being married (P = .003), employed (P < .004, full time or student), white race (P = .006), and higher education (P = .010) were associated with higher MVPA. Conclusions: Predictors of SED in pregnancy were more consistent, and differed from predictors of MVPA. These findings may help identify women at risk of high SED or low MVPA, though future research in larger samples is needed.
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Demographic, Socioeconomic, and Health-Related Predictors of Objectively Measured Sedentary Time and Physical Activity During Pregnancy
Melissa A. Jones, Kara Whitaker, McKenzie Wallace, and Bethany Barone Gibbs
Patterns in Prenatal Physical Activity and Sedentary Behavior: Associations With Blood Pressure and Placental Features in the MoMHealth Cohort
Abbi Lane, Janet Catov, Melissa A. Jones, and Bethany Barone Gibbs
Background: Moderate to vigorous physical activity (MVPA) and sedentary behavior (SED) are associated with blood pressure (BP) and adverse pregnancy outcomes. The authors investigated associations of prenatal MVPA and SED patterns with BP and with placental malperfusion features. Methods: Women enrolled in this prospective cohort study in the first trimester. MVPA, SED, and BP were measured objectively each trimester. MVPA and SED trajectories were constructed. Placental examinations were conducted in a subset. Associations of trajectories with BPs were assessed with linear regression adjusted for age, race, education, prepregnancy body mass index, and gestational age. Associations with placental malperfusion lesions and weight were adjusted for key covariates. Results: One hundred eleven participants were included; placental exams were available in 50. Participants with high (vs low) SED were younger and more likely to have adverse pregnancy outcomes. High SED (vs low) was associated with higher first trimester systolic (β = 5.3; 95% confidence interval, 0.0 to 10.6) and diastolic (β = 5.0; 95% confidence interval, 1.4 to 8.6) and higher second trimester diastolic (β = 4.9; 95% confidence interval, 1.6 to 8.2) BP. Medium and high MVPA groups were associated with lower postpartum diastolic BP. Trajectories were not associated with placental malperfusion. Conclusions: MVPA and SED patterns were differentially associated with prenatal and postpartum BP. Encouraging favorable levels of both might help women achieve lower BP during and after pregnancy.
Context Matters: The Importance of Physical Activity Domains for Public Health
Tyler D. Quinn and Bethany Barone Gibbs
Physical activity can be performed across several domains, including leisure, occupation, household, and transportation, but physical activity research, measurement, and surveillance have historically been focused on leisure-time physical activity. Emerging evidence suggests differential health effects across these domains. In particular, occupational physical activity may be associated with adverse health outcomes. We argue that to adequately consider and evaluate such impacts, physical activity researchers and public health practitioners engaging in measurement, surveillance, and guideline creation should measure and consider all relevant physical activity domains where possible. We describe why physical activity science is often limited to the leisure-time domain and provide a rationale for expanding research and public health efforts to include all physical activity domains.
Concurrent Agreement Between ActiGraph and activPAL for Measuring Physical Activity in Pregnant Women and Office Workers
Melissa A. Jones, Sara J. Diesel, Bethany Barone Gibbs, and Kara M. Whitaker
Introduction: Current best practice for objective measurement of sedentary behavior and moderate-to-vigorous intensity physical activity (MVPA) requires two separate devices. This study assessed concurrent agreement between the ActiGraph GT3X and the activPAL3 micro for measuring MVPA to determine if activPAL can accurately measure MVPA in addition to its known capacity to measure sedentary behavior. Methods: Forty participants from two studies, including pregnant women (n = 20) and desk workers (n = 20), provided objective measurement of MVPA from waist-worn ActiGraph GT3X and thigh-worn activPAL micro3. MVPA from the GT3X was compared with MVPA from the activPAL using metabolic equivalents of task (MET)- and step-based data across three epochs. Intraclass correlation coefficient and Bland–Altman analyses, overall and by study sample, compared MVPA minutes per day across methods. Results: Mean estimates of activPAL MVPA ranged from 22.7 to 35.2 (MET based) and 19.7 to 25.8 (step based) minutes per day, compared with 31.4 min/day (GT3X). MET-based MVPA had high agreement with GT3X, intraclass correlation coefficient ranging from .831 to .875. Bland–Altman analyses revealed minimal bias between 15- and 30-s MET-based MVPA and GT3X MVPA (−3.77 to 8.63 min/day, p > .10) but with wide limits of agreement (greater than ±27 min). Step-based MVPA had moderate to high agreement (intraclass correlation coefficient: .681–.810), but consistently underestimated GT3X MVPA (bias: 5.62–11.74 min/day, p < .02). For all methods, activPAL appears to better estimate GT3X at lower quantities of MVPA. Results were similar when repeated separately by pregnant women and desk workers. Conclusion: activPAL can measure MVPA in addition to sedentary behavior, providing an option for concurrent, single device monitoring. MET-based MVPA using 30-s activPAL epochs provided the best estimate of GT3X MVPA in pregnant women and desk workers.
Objective Versus Self-Reported Physical Activity in Overweight and Obese Young Adults
John M. Jakicic, Wendy C. King, Bethany Barone Gibbs, Renee J. Rogers, Amy D. Rickman, Kelliann K. Davis, Abdus Wahed, and Steven H. Belle
Background:
To compare moderate-to-vigorous intensity physical activity (MVPA) assessed via questionnaires to an objective measure of MVPA in overweight or obese young adults.
Methods:
MVPA was assessed in 448 [median BMI = 31.2 (Interquartile Range: 28.5–34.3) kg/m2] young adults [median age: 30.9 (Interquartile Range: 27.8–33.7) years]. Measures included the SenseWear Armband (MVPAOBJ), the Paffenbarger Questionnaire (MVPAPAFF), and the Global Physical Activity Questionnaire (GPAQ). The GPAQ was used to compute total MVPA (MVPAGPAQ-TOTAL) and MVPA from transportation and recreation (MVPAGPAQ-REC).
Results:
The association between MVPAOBJ and MVPAPAFF was r s = 0.40 (P < .0001). Associations between MVPAOBJ and MVPAGPAQ-TOTAL and MVPAGPAQ-REC were r s = 0.19 and r s = 0.32, respectively (P < .0001). MVPAGPAQ-TOTAL was significantly greater than MVPAOBJ (P < .0001). Median differences in MET-min/week between MVPAOBJ and MVPAPAFF or MVPAGPAQ-REC were not significantly different from zero. There was proportional bias between each self-reported measure of MVPA and MVPAOBJ. There were significant associations between all measures of MVPA and fitness. MVPAOBJ was significantly associated with BMI and percent body fat.
Conclusions:
Objective and self-reported measures of MVPA are weakly to moderately correlated, with substantial differences between measures. MVPAOBJ provided predictive validity with fitness, BMI, and percent body fat. Thus, an objective measure of MVPA may be preferred to self-report in young adults.
Objective vs. Self-report Sedentary Behavior in Overweight and Obese Young Adults
Bethany Barone Gibbs, Wendy C. King, Kelliann K. Davis, Amy D. Rickman, Renee J. Rogers, Abdus Wahed, Steven H. Belle, and John Jakicic
Background:
Sedentary behavior (SED) has been measured almost exclusively by self-reported total SED or television time in longitudinal studies. This manuscript aimed to compare self-reported vs. objectively measured SED.
Methods:
Among overweight and obese young adults enrolled in a weight loss trial, baseline SED was assessed by 3 methods: 1) a questionnaire assessing 8 common SEDs (SEDQ), 2) 1 question assessing SED from the Global Physical Activity Questionnaire (SEDGPAQ), and 3) a monitor worn on the arm (SEDOBJ). In addition, television time (SEDTV) was isolated from the SEDQ. SED measures were compared using Spearman’s correlations, signed-rank tests, and Bland-Altman plots.
Results:
In 448 participants, SEDQ and SEDGPAQ were only weakly associated with SEDOBJ (rs = 0.21; P < .001, rs = 0.32; P < .001, respectively). Compared with SEDOBJ, SEDQ more often overestimated SEDOBJ (median difference: 1.1 hours/day; P < .001), while SEDGPAQ more often underestimated SEDOBJ (median difference: –0.7 hours/day; P < .001). The correlation between SEDTV and SEDOBJ was not significantly different from 0 (rs = 0.08; P = .08).
Conclusions:
SEDQ and SEDGPAQ were weakly correlated with, and significantly different from, SEDOBJ in overweight and obese young adults. SEDTV was not related to SEDOBJ. The poor associations of self-reported and objectively measured SED could affect interpretation and comparison across studies relating SED to adverse health outcomes.
Effect of Using a Sit-Stand Desk on Ratings of Discomfort, Fatigue, and Sleepiness Across a Simulated Workday in Overweight and Obese Adults
Robert J. Kowalsky, Sophy J. Perdomo, John M. Taormina, Christopher E. Kline, Andrea L. Hergenroeder, Jeffrey R. Balzer, John M. Jakicic, and Bethany Barone Gibbs
Background: Limited research examines the influence of sit-stand desks on ratings of discomfort, sleepiness, and fatigue. This study evaluated the time course of these outcomes over 1 day. Methods: Adults (N = 25) completed a randomized cross-over study in a laboratory with two 8-hour workday conditions: (1) prolonged sitting (SIT) and (2) alternating sitting and standing every 30 minutes (SIT-STAND). Sleepiness was assessed hourly. Discomfort, physical fatigue, and mental fatigue were measured every other hour. Linear mixed models evaluated whether these measures differed across conditions and the workday. Effect sizes were calculated using Cohen’s d. Results: Participants were primarily white (84%) males (64%), with mean (SD) body mass index of 31.9 (5.0) kg/m2 and age 42 (12) years. SIT-STAND resulted in decreased odds of discomfort (OR = 0.37, P = .01) and lower overall discomfort (β = −0.19, P < .001, d = 0.42) versus SIT. Discomfort during SIT-STAND was lower in the lower and upper back, but higher in the legs (all Ps< .01, d = 0.26–0.42). Sleepiness (β = −0.09, P = .01, d = 0.15) and physical fatigue (β = −0.34, P = .002, d = 0.34) were significantly lower in SIT-STAND. Mental fatigue was similar across conditions. Conclusions: Sit-stand desks may reduce acute levels of sleepiness, physical fatigue, and both overall and back discomfort. However, levels of lower extremity discomfort may be increased with acute exposure.
Accuracy and Acceptability of Commercial-Grade Physical Activity Monitors in Older Adults
Andrea L. Hergenroeder, Bethany Barone Gibbs, Mary P. Kotlarczyk, Subashan Perera, Robert J. Kowalsky, and Jennifer S. Brach
The aim of this study was to evaluate accuracy of seven commercial activity monitors in measuring steps in older adults with varying walking abilities and to assess monitor acceptability and usability. Forty-three participants (age = 87 ± 5.7 years) completed a gait speed assessment, two walking trials while wearing the activity monitors, and questionnaires about usability features and activity monitor preferences. The Accusplit AX2710 Accelerometer Pedometer had the highest accuracy (93.68% ± 13.95%), whereas the Fitbit Charge had the lowest (39.12% ± 40.3%). Device accuracy varied based on assistive device use, and none of the monitors were accurate at gait speeds <0.08 m/s. Barriers to monitor usability included inability to apply monitor and access the step display. Monitor accuracy was rated as the most important feature, and ability to interface with a smart device was the least important feature. This study identified the limitations of the current commercial activity monitors in both step counting accuracy and usability features for older adults.
Objectively Measured Sedentary Behavior and Physical Activity Across 3 Trimesters of Pregnancy: The Monitoring Movement and Health Study
Bethany Barone Gibbs, Melissa A. Jones, John M. Jakicic, Arun Jeyabalan, Kara M. Whitaker, and Janet M. Catov
Background: Though moderate- to vigorous-intensity physical activity is recommended, limited research exists on sedentary behavior (SED) during pregnancy. Methods: The authors conducted a prospective cohort study to describe objectively measured patterns of SED and activity during each trimester of pregnancy. Women wore thigh- (activPAL3) and waist-mounted (ActiGraph GT3X) activity monitors. SED and activity were compared across trimesters using likelihood ratio tests and described using group-based trajectories. Exploratory analyses associated SED and activity trajectories with adverse pregnancy outcomes and excessive gestational weight gain. Results: Pregnant women (n = 105; mean [SD] age = 31 [5] y; prepregnancy body mass index = 26.2 [6.6] kg/m2) had mean SED of 9.7, 9.5, and 9.5 hours per day (P = .062) across trimesters, respectively. Some activities differed across trimesters: standing (increased, P = .01), stepping (highest in second trimester, P = .04), steps per day (highest in second trimester, P = .008), and moderate- to vigorous-intensity physical activity (decreased, P < .001). Prolonged SED (bouts ≥ 30 min) and bouted moderate- to vigorous-intensity physical activity (≥10 min) were stable (P > .05). In exploratory analyses, higher SED and lower standing, stepping, and steps per day trajectories were associated with increased odds of adverse pregnancy outcomes (P < .05). No trajectories were associated with excessive gestational weight gain. Conclusions: Pregnant women exhibited stable SED of nearly 10 hours per day across pregnancy. Future research evaluating SED across pregnancy and adverse pregnancy outcome risk is warranted.
Distinguishing Passive and Active Standing Behaviors From Accelerometry
Robert J. Kowalsky, Herman van Werkhoven, Marco Meucci, Tyler D. Quinn, Lee Stoner, Christopher M. Hearon, and Bethany Barone Gibbs
Purpose: To investigate whether active standing can be identified separately from passive standing via accelerometry data and to develop and test the accuracy of a machine-learning model to classify active and passive standing. Methods: Ten participants wore a thigh-mounted activPAL monitor and stood for three 5-min periods in the following order: (a) PASSIVE: standing with no movement; (b) ACTIVE: five structured weight-shifting micromovements in the medial–lateral, superior–inferior, and anterior–poster planes while standing; and (c) FREE: participant’s choice of active standing. Averages of absolute resultant acceleration values in 15-s epochs were compared via analysis of variance (Bonferroni adjustment for pairwise comparisons) to confirm the dichotomization ability of the standing behaviors. Absolute resultant acceleration values and SDs in 2- and 5-s epochs were used to develop a machine-learning model using leave-one-subject-out cross validation. The final accuracy of the model was assessed using the area under the curve from a receiver operating characteristic curve. Results: Comparison of resultant accelerations across the three conditions (PASSIVE, ACTIVE, and FREE) resulted in a significant omnibus difference, F(2, 19) = [116], p < .001, η2 = .86, and in all pairwise post hoc comparisons (all p < .001). The machine-learning model using 5-s epochs resulted in 94% accuracy for the classification of PASSIVE versus ACTIVE standing. Model application to the FREE data resulted in an absolute average difference of 4.8% versus direct observation and an area under the curve value of 0.71. Conclusions: Active standing in three planes of movement can be identified from thigh-worn accelerometry via a machine-learning model, yet model refinement is warranted.