The impact of the COVID-19 pandemic on physical activity and sedentary behavior during pregnancy: a prospective study.

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.

Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography.

This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a wrist-worn ActiGraph GT3X+ (AG). Criterion sleep measures included PSG-derived Total Sleep Time (TST), Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), and Efficiency Once Asleep (SE_ASLEEP). Analogous variables were derived from temporally aligned AG data using the Cole-Kripke algorithm. For PSG, SO was defined as the first score of 'sleep'. For AG, SO was defined three ways: 1-, 5-, and 10-consecutive minutes of 'sleep'. Agreement statistics and linear mixed effects regression models were used to analyze 'Device' and 'Sleep Onset Rule' main effects and interactions. Sleep-wake agreement and sensitivity for all AG methods were high (89.0-89.5% and 97.2%, respectively); specificity was low (23.6-25.1%). There were no significant interactions or main effects of 'Sleep Onset Rule' for any variable. The AG underestimated SOL (19.7 min) and WASO (6.5 min), and overestimated TST (26.2 min), SE (6.5%), and SE_ASLEEP (1.9%). Future research should focus on developing sleep-wake detection algorithms and incorporating biometric signals (e.g., heart rate).

A Framework to Evaluate Devices That Assess Physical Behavior.

Body-worn devices that estimate physical behavior have tremendous potential to address key research gaps. However, there is no consensus on how devices and processing methods should be developed and evaluated, resulting in large differences in summary estimates and confusion for end users. We propose a phase-based framework for developing and evaluating devices that emphasizes robust validation studies in naturalistic conditions.

Validation of Wearable Camera Still Images to Assess Posture in Free-Living Conditions.

PURPOSE: To assess the convergent validity of body worn wearable camera (WC) still-images (IMGs) for determining posture compared with activPAL (AP) classifications. METHODS: Participants (n=16, mean age 46.7±23.8yrs, 9F) wore an Autographer WC above the xyphoid process and an AP during three, 2hr free-living visits. IMGs were captured on average 8.47 seconds apart and were annotated with output consisting of events, transitory states, unknown and gaps. Events were annotations that matched AP classifications (sit, stand and move) consisting of at least 3 IMGs, transitory states were posture annotations fewer than 3 IMGs, unknown were IMGs that could not be accurately classified, and gaps were time between annotations. For analyses, annotation and AP output were converted to one-sec epochs and matched second-by-second. Total and average length of visits and events are reported in minutes. Bias and 95% CIs for event posture times from IMGs to AP posture times were calculated to determine accuracy and precision. Confusion matrices using total AP posture times were computed to determine misclassification. RESULTS: 43 visits were analyzed with a total visit and event time of 5027.73 and 4237.23 minutes and average visit and event lengths being 116.92 and 98.54 minutes, respectively. Bias was not statistically significant for sitting but significant for standing and movement (0.84, -6.87 and 6.04 minutes). From confusion matrices, IMGs correctly classified sitting, standing and movement 85.69%, 54.87%, and 69.41% of total AP time, respectively. CONCLUSION: WC IMGs provide a good estimation of overall sitting time but underestimate standing and overestimate movement time. Future work is warranted to improve posture classifications and examine IMG accuracy and precision in assessing activity type behaviors.

Accelerometer Calibration: The Importance of Considering Functionality.

PURPOSE: To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions. METHODS: 118 adults self-identifying as healthy (n = 44) and those with arthritis (n = 23), multiple sclerosis (n = 18), Parkinson's disease (n = 17), and stroke (n =18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit. RESULTS: All activities' accelerometer counts per minute (CPM) were 29.5-72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, -0.49 (-0.71, -0.27) for arthritis, -0.38 (-0.53, -0.22) for healthy, -0.44 (-0.68, -0.20) for MS, -0.34 (-0.58, -0.09) for Parkinson's, and -0.30 (-0.54, -0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, -0.37 METs (-0.52, -0.23); Group 2, -0.44 METs (-0.60, -0.28); and Group 3, -0.33 METs (-0.55, -0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition. CONCLUSION: Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.

Estimating Sedentary Time from a Hip- and Wrist-Worn Accelerometer.

PURPOSE: This study aimed to determine the validity of existing methods to estimate sedentary behavior (SB) under free-living conditions using ActiGraph GT3X+ accelerometers (AG). METHODS: Forty-eight young (18-25 yr) adults wore an AG on the right hip and nondominant wrist and were video recorded during four 1-h sessions in free-living settings (home, community, school, and exercise). Direct observation videos were coded for postural orientation, activity type (e.g., walking), and METs derived from the Compendium of Physical Activities, which served as the criterion measure of SB (sitting or lying posture, <1.5 METs). Thirteen methods using cut points from vertical counts per minute (CPM), counts per 15-s (CP15s), and vector magnitude (VM) counts (e.g., CPM1853VM), raw acceleration and arm angle (sedentary sphere), Euclidean norm minus one (ENMO) corrected for gravity (mg) thresholds, uni- or triaxial sojourn hybrid machine learning models (Soj1x and Soj3x), random forest (RF), and decision tree (TR) models were used to estimate SB minutes from AG data. Method bias, mean absolute percent error, and their 95% confidence intervals were estimated using repeated-measures linear mixed models. RESULTS: On average, participants spent 34.1 min per session in SB. CPM100, CPM150, Soj1x, and Soj3x were the only methods to accurately estimate SB from the hip. Sedentary sphere and ENMO44.8 overestimated SB by 3.9 and 6.1 min, respectively, whereas the remaining wrist methods underestimated SB (range, 9.5-2.5 min). In general, mean absolute percent error was lower using hip methods compared with wrist methods. CONCLUSION: Accurate group-level estimates of SB from a hip-worn AG can be achieved using either simpler count-based approaches (CPM100 and CPM150) or machine learning models (Soj1x and Soj3x). Wrist methods did not provide accurate or precise estimates of SB. The development of large open-source free-living calibration data sets may lead to improvements in SB estimates.

Drug development and hormesis: changing conceptual understanding of the dose response creates new challenges and opportunities for more effective drugs.

This review proposes that the emerging acceptance of the hormetic dose-response model in toxicology and pharmacology has the potential to significantly change important aspects of drug development. Two situations where the hormesis concept may affect drug development are considered: one in which low-dose stimulation may represent an adverse/unwanted effect (eg, stimulation of tumor cell proliferation by antitumor drugs), the other in which low-dose stimulation defines the therapeutic zone (ie, a beneficial or intended effect; eg, cognition enhancement in Alzheimer's disease treatment). Examples are used to demonstrate that the hormetic dose-response model has implications for the definition of an ideal candidate for a therapeutic agent, as well as implications for study designs needed to assess the quantitative features of the dose-response relationship.

Hormesis outperforms threshold model in National Cancer Institute antitumor drug screening database.

Which dose-response model best explains low-dose responses is a critical issue in toxicology, pharmacology, and risk assessment. The present paper utilized the U.S. National Cancer Institute yeast screening database that contains 56,914 dose-response studies representing the replicated effects of 2189 chemically diverse possible antitumor drugs on cell proliferation in 13 different yeast strains. Multiple evaluation methods indicated that the observed data are inconsistent with the threshold model while supporting the hormetic model. Hormetic response patterns were observed approximately four times more often than would be expected by chance alone. The data call for the rejection of the threshold model for low-dose prediction, and they support the hormetic model as the default model for scientific interpretation of low-dose toxicological responses.

Performance of Activity Classification Algorithms in Free-Living Older Adults.

PURPOSE: The objective of this study is to compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults. METHODS: Thirty-five older adults (21 females and 14 males, 70.8 ± 4.9 yr) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors (in the dominant hip, wrist, and ankle; ActiGraph, LLC, Pensacola, FL). Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz. Fifteen of the participants also wore GT3X+ in free-living settings and were directly observed for 2-3 h. Time- and frequency-domain features from acceleration signals of each monitor were used to train random forest (RF) and support vector machine (SVM) models to classify five activity types: sedentary, standing, household, locomotion, and recreational activities. All algorithms were trained on laboratory data (RFLab and SVMLab) and free-living data (RFFL and SVMFL) using 20-s signal sampling windows. Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique. RESULTS: Overall classification accuracy rates for the algorithms developed from laboratory data were between 49% (wrist) and 55% (ankle) for the SVMLab algorithms and 49% (wrist) to 54% (ankle) for the RFLab algorithms. The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58% (wrist) to 69% (ankle) and from 61% (wrist) to 67% (ankle), respectively. CONCLUSIONS: Our algorithms developed on free-living accelerometer data were more accurate in classifying the activity type in free-living older adults than those on our algorithms developed on laboratory accelerometer data. Future studies should consider using free-living accelerometer data to train machine learning algorithms in older adults.

Classification accuracy of the wrist-worn gravity estimator of normal everyday activity accelerometer.

PURPOSE: The purpose of this study was to determine whether the published left-wrist cut points for the triaxial Gravity Estimator of Normal Everyday Activity (GENEA) accelerometer are accurate for predicting intensity categories during structured activity bouts. METHODS: A convenience sample of 130 adults wore a GENEA accelerometer on their left wrist while performing 14 different lifestyle activities. During each activity, oxygen consumption was continuously measured using the Oxycon mobile. Statistical analysis used Spearman's rank correlations to determine the relationship between measured and estimated intensity classifications. Cross tabulations were constructed to show the under- or overestimation of misclassified intensities. One-way χ2 tests were used to determine whether the intensity classification accuracy for each activity differed from 80%. RESULTS: For all activities, the GENEA accelerometer-based physical activity monitor explained 41.1% of the variance in energy expenditure. The intensity classification accuracy was 69.8% for sedentary activities, 44.9% for light activities, 46.2% for moderate activities, and 77.7% for vigorous activities. The GENEA correctly classified intensity for 52.9% of observations when all activities were examined; this increased to 61.5% with stationary cycling removed. CONCLUSIONS: A wrist-worn triaxial accelerometer has modest-intensity classification accuracy across a broad range of activities when using the cut points of Esliger et al. Although the sensitivity and the specificity are less than those reported by Esliger et al., they are generally in the same range as those reported for waist-worn, uniaxial accelerometer cut points.

Multisensor data fusion for physical activity assessment.

This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multisensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which activity type and energy expenditure are derived. The results show that the method correctly recognized the 13 activity types 88.1% of the time, which is 12.3% higher than using a hip accelerometer alone. Also, the method predicted energy expenditure with a root mean square error of 0.42 METs, 22.2% lower than using a hip accelerometer alone. Furthermore, the fusion method was effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition, especially when data from the ventilation sensor were added to the fusion model. These results demonstrate that the multisensor fusion technique presented is more effective in identifying activity type and energy expenditure than the traditional accelerometer-alone-based methods.

Validity of two wearable monitors to estimate breaks from sedentary time.

UNLABELLED: Investigations using wearable monitors have begun to examine how sedentary time behaviors influence health. PURPOSE: The objective of this study is to demonstrate the use of a measure of sedentary behavior and to validate the activPAL (PAL Technologies Ltd., Glasgow, Scotland) and ActiGraph GT3X (Actigraph, Pensacola, FL) for estimating measures of sedentary behavior: absolute number of breaks and break rate. METHODS: Thirteen participants completed two 10-h conditions. During the baseline condition, participants performed normal daily activity, and during the treatment condition, participants were asked to reduce and break up their sedentary time. In each condition, participants wore two ActiGraph GT3X monitors and one activPAL. The ActiGraph was tested using the low-frequency extension filter (AG-LFE) and the normal filter (AG-Norm). For both ActiGraph monitors, two count cut points to estimate sedentary time were examined: 100 and 150 counts per minute. Direct observation served as the criterion measure of total sedentary time, absolute number of breaks from sedentary time, and break rate (number of breaks per sedentary hour (brk·sed-h)). RESULTS: Break rate was the only metric sensitive to changes in behavior between baseline (5.1 [3.3-6.8] brk·sed-h) and treatment conditions (7.3 [4.7-9.8] brk·sed-h) (mean (95% confidence interval)). The activPAL produced valid estimates of all sedentary behavior measures and was sensitive to changes in break rate between conditions (baseline, 5.1 [2.8-7.1] brk·sed-h; treatment, 8.0 [5.8-10.2] brk·sed-h). In general, the AG-LFE and AG-Norm were not accurate in estimating break rate or the absolute number of breaks and were not sensitive to changes between conditions. CONCLUSION: This study demonstrates the use of expressing breaks from sedentary time as a rate per sedentary hour, a metric specifically relevant to free-living behavior, and provides further evidence that the activPAL is a valid tool to measure components of sedentary behavior in free-living environments.

Wireless design of a multisensor system for physical activity monitoring.

Real-time monitoring of human physical activity (PA) is important for assessing the intensity of activity and exposure to environmental pollutions. A wireless wearable multisenor integrated measurement system (WIMS) has been designed for real-time measurement of the energy expenditure and breathing volume of human subjects under free-living conditions. To address challenges posted by the limited battery life and data synchronization requirement among multiple sensors in the system, the ZigBee communication platform has been explored for energy-efficient design. Two algorithms have been developed (multiData packaging and slot-data-synchronization) and coded into a microcontroller (MCU)-based sensor circuitry for real-time control of wireless data communication. Experiments have shown that the design enables continued operation of the wearable system for up to 68 h, with the maximum error for data synchronization among the various sensor nodes (SNs) being less than 24 ms. Experiment under free-living conditions have shown that the WIMS is able to correctly recognize the activity intensity level 86% of the time. The results demonstrate the effectiveness of the energy-efficient wireless design for human PA monitoring.

Comparison of the ActiGraph 7164 and the ActiGraph GT1M during self-paced locomotion.

PURPOSE: This study compared the ActiGraph accelerometer model 7164 (AM1) with the ActiGraph GT1M (AM2) during self-paced locomotion. METHODS: Participants (n = 116, aged 18-73 yr, mean body mass index = 26.1 kg x m(-2)) walked at self-selected slow, medium, and fast speeds around an indoor circular hallway (0.47 km). Both activity monitors were attached to a belt secured to the hip and simultaneously collected data in 60-s epochs. To compare differences between monitors, the average difference (bias) in count output and steps output was computed at each speed. Time spent in different activity intensities (light, moderate, and vigorous) based on the cut points of Freedson et al. was compared for each minute. RESULTS: The mean +/- SD walking speed was 0.7 +/- 0.22 m x s(-1) for the slow speed, 1.3 +/- 0.17 m x s(-1) for medium, and 2.1 +/- 0.61 m x s(-1) for fast speeds. Ninety-five percent confidence intervals (95% CI) were used to determine significance. Across all speeds, step output was significantly higher for the AM1 (bias = 19.8%, 95% CI = -23.2% to -16.4%) because of the large differences in step output at slow speed. The count output from AM2 was a significantly higher (2.7%, 95% CI = 0.8%-4.7%) than that from AM1. Overall, 96.1% of the minutes were classified into the same MET intensity category by both monitors. CONCLUSIONS: The step output between models was not comparable at slow speeds, and comparisons of step data collected with both models should be interpreted with caution. The count output from AM2 was slightly but significantly higher than that from AM1 during the self-paced locomotion, but this difference did not result in meaningful differences in activity intensity classifications. Thus, data collected with AM1 should be comparable to AM2 across studies for estimating habitual activity levels.

Accelerometer output and MET values of common physical activities.

PURPOSE: This article 1) provides the calibration procedures and methods for metabolic and activity monitor data collection, 2) compares measured MET values to the MET values from the compendium of physical activities, and 3) examines the relationship between accelerometer output and METs for a range of physical activities. METHODS: Participants (N = 277) completed 11 activities for 7 min each from a menu of 23 physical activities. Oxygen consumption (V O2) was measured using a portable metabolic system, and an accelerometer was worn. MET values were defined as measured METs (V O2/measured resting metabolic rate) and standard METs (V O2/3.5 mL.kg.min). For the total sample and by subgroup (age [young < 40 yr], sex, and body mass index [normal weight < 25 kg.m]), measured METs and standard METs were compared with the compendium, using 95% confidence intervals to determine statistical significance (alpha = 0.05). Average counts per minute for each activity and the linear association between counts per minute and METs are presented. RESULTS: Compendium METs were different than measured METs for 17/21 activities (81%). The number of activities different than the compendium was similar between subgroups or when standard METs were used. The average counts for the activities ranged from 11 counts per minute (dishes) to 7490 counts per minute (treadmill: 2.23 m.s, 3%). The r between counts and METs was 0.65. CONCLUSIONS: This study provides valuable information about data collection, metabolic responses, and accelerometer output for common physical activities in a diverse participant sample. The compendium should be updated with additional empirical data, and linear regression models are inappropriate for accurately predicting METs from accelerometer output.

Accelerometer prediction of energy expenditure: vector magnitude versus vertical axis.

UNLABELLED: It is suggested that triaxial accelerometers (RT3) are superior to single-plane accelerometers for predicting energy expenditure (EE). PURPOSE: To compare the RT3 uniaxial and triaxial prediction of activity EE (AEE) during treadmill activities (TM) and activities of daily living (ADL). METHODS: Two hundred and twelve subjects (aged 20-60 yr) completed TM speeds of 1.34, 1.56, and 2.23 m x s(-1) at 0% and 3% grades, stair ascent/descent, moving a box, and two randomly assigned ADL. Subjects wore a portable indirect calorimeter to measure EE to calculate AEE by subtracting resting metabolic rate. Acceleration counts in the vertical (V), medial-lateral, and anterior-posterior planes were collected in a single RT3 secured to the hip. Predicted AEE (RT3AEE) was estimated from vector magnitude (VM) counts using a proprietary algorithm. A paired t-test compared RT3AEE versus AEE. The relationship among V and VM counts and AEE was examined using linear regression analyses. RESULTS: RT3 overestimated AEE for all activities combined, overestimated for TM (9.0%), and underestimated for ADL (34.3%; P < 0.001). The R2 values between RT3AEE and AEE for TM and ADL were R2 = 0.78 and R2 = 0.15, respectively. The RT3 underestimated activity with greater upper body movements by 24.4%-64.5% (P < 0.001). V and VM counts were similarly related to AEE (R2 = 0.35) and RT3AEE (R2 = 0.83-0.89). CONCLUSIONS: Although the RT3 did not accurately predict AEE from accelerometer counts, stronger relationships existed between predicted and measured AEE for TM compared with ADL. Compared with V counts, using VM counts to predict AEE did not significantly improve the relationship between counts and AEE. Analytic techniques beyond linear regression with VM as a covariate or with counts from each axis entering the model separately may improve estimates of AEE from triaxial accelerometers.