Device-guided slow breathing alters postprandial oxidative stress in young adult males: A randomized sham-controlled crossover trial.

BACKGROUND AND AIMS: Slow, deep breathing (SDB) lowers blood pressure (BP) though the underlying mechanisms are unknown. Redox improvements could facilitate hemodynamic adjustments with SDB though this has not been investigated. The purpose of this randomized, sham-controlled trial was to examine the acute effects of SDB on oxidative stress and endothelial function during a physiological perturbation (high-fat meal) known to induce oxidative stress. METHODS AND RESULTS: Seventeen males (ages 18-35 years) were enrolled, and anthropometric measurements and 7-day physical activity monitoring were completed. Testing sessions consisted of 24-h diet recalls (ASA24), blood sample collection for superoxide dismutase (SOD) and thiobarbituric acid reactive substances (TBARS) analysis, and flow-mediated dilation (FMD). High-fat meals were ingested and 2-min breathing exercises (SDB or sham control breathing) were completed every 15 min during the 4-h postprandial phase. Blood sample collection and FMD were repeated 1-, 2-, and 4-h post meal consumption. Mean body mass index and step counts were 25.6 ± 4.3 kg/m2 and 8165 ± 4405 steps per day, respectively. Systolic and diastolic BP and nutrient intake 24 h prior were similar between conditions. No time or time by condition interaction effects were observed for FMD. The total area under the curve (AUC) for SOD was significantly lower during SDB compared to the sham breathing condition (p < 0.01). No differences were observed in TBARS AUC (p = 0.538). CONCLUSIONS: Findings from the current investigation suggest that SDB alters postprandial redox in the absence of changes in endothelial function in young, healthy males. CLINICAL TRIAL REGISTRATION NUMBER: NCT04864184. CLINICAL TRIALS IDENTIFIER: NCT04864184.

Impact of Sit-to-Stand and Treadmill Desks on Patterns of Daily Waking Physical Behaviors Among Overweight and Obese Seated Office Workers: Cluster Randomized Controlled Trial.

BACKGROUND: Sit-to-stand and treadmill desks may help sedentary office workers meet the physical activity guideline to "move more and sit less," but little is known about their long-term impact on altering the accumulation patterns of physical behaviors. OBJECTIVE: This study explores the impact of sit-to-stand and treadmill desks on physical behavior accumulation patterns during a 12-month multicomponent intervention with an intent-to-treat design in overweight and obese seated office workers. METHODS: In total, 66 office workers were cluster randomized into a seated desk control (n=21, 32%; 8 clusters), sit-to-stand desk (n=23, 35%; 9 clusters), or treadmill desk (n=22, 33%; 7 clusters) group. Participants wore an activPAL (PAL Technologies Ltd) accelerometer for 7 days at baseline, 3-month follow-up (M3), 6-month follow-up (M6), and 12-month follow-up (M12) and received periodic feedback on their physical behaviors. Analyses of physical behavior patterns included total day and workday number of sedentary, standing, and stepping bouts categorized into durations ranging from 1 to 60 and >60 minutes and usual sedentary, standing, and stepping bout durations. Intervention trends were analyzed using random-intercept mixed linear models accounting for repeated measures and clustering effects. RESULTS: The treadmill desk group favored prolonged sedentary bouts (>60 min), whereas the sit-to-stand desk group accrued more short-duration sedentary bouts (<20 min). Therefore, compared with controls, sit-to-stand desk users had shorter usual sedentary bout durations short-term (total day ΔM3: -10.1 min/bout, 95% CI -17.9 to -2.2; P=.01; workday ΔM3: -20.3 min/bout, 95% CI -37.7 to -2.9; P=.02), whereas treadmill desk users had longer usual sedentary bout durations long-term (total day ΔM12: 9.0 min/bout, 95% CI 1.6-16.4; P=.02). The treadmill desk group favored prolonged standing bouts (30-60 min and >60 min), whereas the sit-to-stand desk group accrued more short-duration standing bouts (<20 min). As such, relative to controls, treadmill desk users had longer usual standing bout durations short-term (total day ΔM3: 6.9 min/bout, 95% CI 2.5-11.4; P=.002; workday ΔM3: 8.9 min/bout, 95% CI 2.1-15.7; P=.01) and sustained this long-term (total day ΔM12: 4.5 min/bout, 95% CI 0.7-8.4; P=.02; workday ΔM12: 5.8 min/bout, 95% CI 0.9-10.6; P=.02), whereas sit-to-stand desk users showed this trend only in the long-term (total day ΔM12: 4.2 min/bout, 95% CI 0.1-8.3; P=.046). The treadmill desk group accumulated more stepping bouts across various bins of duration (5-50 min), primarily at M3. Thus, treadmill desk users had longer usual stepping bout durations in the short-term compared with controls (workday ΔM3: 4.8 min/bout, 95% CI 1.3-8.3; P=.007) and in the short- and long-term compared with sit-to-stand desk users (workday ΔM3: 4.7 min/bout, 95% CI 1.6-7.8; P=.003; workday ΔM12: 3.0 min/bout, 95% CI 0.1-5.9; P=.04). CONCLUSIONS: Sit-to-stand desks exerted potentially more favorable physical behavior accumulation patterns than treadmill desks. Future active workstation trials should consider strategies to promote more frequent long-term movement bouts and dissuade prolonged static postural fixity. TRIAL REGISTRATION: ClinicalTrials.gov NCT02376504; https://clinicaltrials.gov/ct2/show/NCT02376504.

Companion: A Pilot Randomized Clinical Trial to Test an Integrated Two-Way Communication and Near-Real-Time Sensing System for Detecting and Modifying Daily Inactivity among Adults >60 Years-Design and Protocol.

Supervised personal training is most effective in improving the health effects of exercise in older adults. Yet, low frequency (60 min, 1-3 sessions/week) of trainer contact limits influence on behavior change outside sessions. Strategies to extend the effect of trainer contact outside of supervision and that integrate meaningful and intelligent two-way communication to provide complex and interactive problem solving may motivate older adults to "move more and sit less" and sustain positive behaviors to further improve health. This paper describes the experimental protocol of a 16-week pilot RCT (N = 46) that tests the impact of supplementing supervised exercise (i.e., control) with a technology-based behavior-aware text-based virtual "Companion" that integrates a human-in-the-loop approach with wirelessly transmitted sensor-based activity measurement to deliver behavior change strategies using socially engaging, contextually salient, and tailored text message conversations in near-real-time. Primary outcomes are total-daily and patterns of habitual physical behaviors after 16 and 24 weeks. Exploratory analyses aim to understand Companion's longitudinal behavior effects, its user engagement and relationship to behavior, and changes in cardiometabolic and cognitive outcomes. Our findings may allow the development of a more scalable hybrid AI Companion to impact the ever-growing public health epidemic of sedentariness contributing to poor health outcomes, reduced quality of life, and early death.

Validity of proximity sensor-based wear-time detection using the ActiGraph GT9X.

Our study investigated the performance of proximity sensor-based wear-time detection using the GT9X under laboratory and free-living settings. Fifty-two volunteers (23.2 ± 3.8 y; 23.2 ± 3.7 kg/m2) participated in either a laboratory or free-living protocol. Lab participants wore and removed a wrist-worn GT9X on 3-5 occasions during a 3-hour directly observed activity protocol. The 2-day free-living protocol used an independent temperature sensor and self-report as the reference to determine if wrist and hip-worn GT9X accurately determined wear (i.e., sensitivity) and non-wear (i.e., specificity). Free-living estimates of wear/non-wear were also compared to Troiano 2007 and Choi 2012 wear/non-wear algorithms. In lab, sensitivity and specificity of the wrist-worn GT9X in detecting total minutes of wear-on and off was 93% and 49%, respectively. The GT9X detected wear-off more often than wear-on, but with a greater margin of error (4.8 ± 11.6 vs. 1.4 ± 1.4 min). In the free-living protocol, wrist and hip-worn GT9X's yielded sensitivity and specificity of 72 and 90% and 84 and 92%, respectively. GT9X estimations had inferior sensitivity but superior specificity to Troiano 2007 and Choi 2012 algorithms. Due to inaccuracies, it may not be advisable to singularly use the proximity-sensor-based wear-time detection method to detect wear-time.

"What Is a Step?" Differences in How a Step Is Detected among Three Popular Activity Monitors That Have Impacted Physical Activity Research.

(1) Background: This study compared manually-counted treadmill walking steps from the hip-worn DigiwalkerSW200 and OmronHJ720ITC, and hip and wrist-worn ActiGraph GT3X+ and GT9X; determined brand-specific acceleration amplitude (g) and/or frequency (Hz) step-detection thresholds; and quantified key features of the acceleration signal during walking. (2) Methods: Twenty participants (Age: 26.7 ± 4.9 years) performed treadmill walking between 0.89-to-1.79 m/s (2-4 mph) while wearing a hip-worn DigiwalkerSW200, OmronHJ720ITC, GT3X+ and GT9X, and a wrist-worn GT3X+ and GT9X. A DigiwalkerSW200 and OmronHJ720ITC underwent shaker testing to determine device-specific frequency and amplitude step-detection thresholds. Simulated signal testing was used to determine thresholds for the ActiGraph step algorithm. Steps during human testing were compared using bias and confidence intervals. (3) Results: The OmronHJ720ITC was most accurate during treadmill walking. Hip and wrist-worn ActiGraph outputs were significantly different from the criterion. The DigiwalkerSW200 records steps for movements with a total acceleration of ≥1.21 g. The OmronHJ720ITC detects a step when movement has an acceleration ≥0.10 g with a dominant frequency of ≥1 Hz. The step-threshold for the ActiLife algorithm is variable based on signal frequency. Acceleration signals at the hip and wrist have distinctive patterns during treadmill walking. (4) Conclusions: Three common research-grade physical activity monitors employ different step-detection strategies, which causes variability in step output.

Free phenytoin toxicity.

Phenytoin has a narrow therapeutic window, and when managing cases of toxicity, clinicians are very wary of this fact. Typically, if patient presents with symptoms suggestive of phenytoin toxicity, total serum phenytoin is promptly ordered. That could be falsely low especially in elderly or critically ill patients, which may lead to a low albumin level resulting in this discrepancy. The free phenytoin can be best estimated using the Sheiner-Tozer equation. Herein, we describe a case of an elderly male patient who presented with drowsiness, gait changes, and elevated liver enzymes and a normal total serum phenytoin level of 18 ng/dL (normal, 10-20 ng/dL).After taking his albumin level into account, his free phenytoin level was calculated to be 27 ng/dL, and the phenytoin was discontinued leading to resolution of his symptoms as well as a return of his liver function panel values to baseline.

Comparison of raw acceleration from the GENEA and ActiGraph™ GT3X+ activity monitors.

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.

Detecting Sleep and Nonwear in 24-h Wrist Accelerometer Data from the National Health and Nutrition Examination Survey.

INTRODUCTION: Estimating physical activity, sedentary behavior, and sleep from wrist-worn accelerometer data requires reliable detection of sensor nonwear and sensor wear during both sleep and wake. PURPOSE: This study aimed to develop an algorithm that simultaneously identifies sensor wake-wear, sleep-wear, and nonwear in 24-h wrist accelerometer data collected with or without filtering. METHODS: Using sensor data labeled with polysomnography ( n = 21) and directly observed wake-wear data ( n = 31) from healthy adults, and nonwear data from sensors left at various locations in a home ( n = 20), we developed an algorithm to detect nonwear, sleep-wear, and wake-wear for "idle sleep mode" (ISM) filtered data collected in the 2011-2014 National Health and Nutrition Examination Survey. The algorithm was then extended to process original raw data collected from devices without ISM filtering. Both algorithms were further validated using a polysomnography-based sleep and wake-wear data set ( n = 22) and diary-based wake-wear and nonwear labels from healthy adults ( n = 23). Classification performance (F1 scores) was compared with four alternative approaches. RESULTS: The F1 score of the ISM-based algorithm on the training data set using leave-one-subject-out cross-validation was 0.95 ± 0.13. Validation on the two independent data sets yielded F1 scores of 0.84 ± 0.60 for the data set with sleep-wear and wake-wear and 0.94 ± 0.04 for the data set with wake-wear and nonwear. The F1 score when using original, raw data was 0.96 ± 0.08 for the training data sets and 0.86 ± 0.18 and 0.97 ± 0.04 for the two independent validation data sets. The algorithm performed comparably or better than the alternative approaches on the data sets. CONCLUSIONS: A novel machine-learning algorithm was designed to recognize wake-wear, sleep-wear, and nonwear in 24-h wrist-worn accelerometer data that are applicable for ISM-filtered data or original raw data.

Effects of an "Active-Workstation" Cluster RCT on Daily Waking Physical Behaviors.

PURPOSE: This study aimed to evaluate the effects of sit-to-stand and treadmill desks on sedentary behavior during a 12-month, cluster-randomized multicomponent intervention with an intent-to-treat design in overweight office workers. METHODS: Sixty-six office workers were cluster-randomized into a control (n = 21; 8 clusters), sit-to-stand desk (n = 23; 9 clusters), or treadmill desk (n = 22; 7 clusters) group. Participants wore an activPAL™ accelerometer for 7 d at baseline, month 3, month 6, and month 12 and received periodic feedback on their physical behaviors. The primary outcome was total daily sedentary time. Exploratory outcomes included total daily and workplace sedentary, standing and stepping time, and the number of total daily and workplace sedentary, standing, and stepping bouts. Intervention effects were analyzed using random-intercept mixed linear models accounting for repeated measures and clustering effects. RESULTS: Total daily sedentary time did not significantly differ between or within groups after 12 months. Month 3 gains were observed in total daily and workplace standing time in both intervention groups (sit-to-stand desk: mean Δ ± SD, 1.03 ± 1.9 h·d-1 and 1.10 ± 1.87 h at work; treadmill desk: mean Δ ± SD, 1.23 ± 2.25 h·d-1 and 1.44 ± 2.54 h at work). At month 3, the treadmill desk users stepped more at the workplace than the control group (mean Δ ± SD, 0.69 ± 0.87 h). Month 6 gains in total daily stepping were observed within the sit-to-stand desk group (mean Δ ± SD, 0.82 ± 1.62 h·d-1), and month 3 gains in stepping at the workplace were observed for the treadmill desk group (mean Δ ± SD, 0.77 ± 0.83 h). These trends were sustained through month 12 in only the sit-to-stand desk group. CONCLUSIONS: Active-workstation interventions may cause short-term improvements in daily standing and stepping. Treadmill desk users engaged in fewer sedentary bouts, but sit-to-stand desks resulted in more frequent transitions to upright physical behaviors.

Signaligner Pro: A Tool to Explore and Annotate Multi-day Raw Accelerometer Data.

Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces. Many of the machine-learning-based activity recognition algorithms require multi-person, multi-day, carefully annotated training data with precisely marked start and end times of the activities of interest. To date, there is a dearth of usable tools that enable researchers to conveniently visualize and annotate multiple days of high-sampling-rate raw accelerometer data. Thus, we developed Signaligner Pro, an interactive tool to enable researchers to conveniently explore and annotate multi-day high-sampling rate raw accelerometer data. The tool visualizes high-sampling-rate raw data and time-stamped annotations generated by existing activity recognition algorithms and human annotators; the annotations can then be directly modified by the researchers to create their own, improved, annotated datasets. In this paper, we describe the tool's features and implementation that facilitate convenient exploration and annotation of multi-day data and demonstrate its use in generating activity annotations.

Posture and Physical Activity Detection: Impact of Number of Sensors and Feature Type.

Studies using wearable sensors to measure posture, physical activity (PA), and sedentary behavior typically use a single sensor worn on the ankle, thigh, wrist, or hip. Although the use of single sensors may be convenient, using multiple sensors is becoming more practical as sensors miniaturize. PURPOSE: We evaluated the effect of single-site versus multisite motion sensing at seven body locations (both ankles, wrists, hips, and dominant thigh) on the detection of physical behavior recognition using a machine learning algorithm. We also explored the effect of using orientation versus orientation-invariant features on performance. METHODS: Performance (F1 score) of PA and posture recognition was evaluated using leave-one-subject-out cross-validation on a 42-participant data set containing 22 physical activities with three postures (lying, sitting, and upright). RESULTS: Posture and PA recognition models using two sensors had higher F1 scores (posture, 0.89 ± 0.06; PA, 0.53 ± 0.08) than did models using a single sensor (posture, 0.78 ± 0.11; PA, 0.43 ± 0.03). Models using two nonwrist sensors for posture recognition (F1 score, 0.93 ± 0.03) outperformed two-sensor models including one or two wrist sensors (F1 score, 0.85 ± 0.06). However, two-sensor models for PA recognition with at least one wrist sensor (F1 score, 0.60 ± 0.05) outperformed other two-sensor models (F1 score, 0.47 ± 0.02). Both posture and PA recognition F1 scores improved with more sensors (up to seven; 0.99 for posture and 0.70 for PA), but with diminishing performance returns. Models performed best when including orientation-based features. CONCLUSIONS: Researchers measuring posture should consider multisite sensing using at least two nonwrist sensors, and researchers measuring PA should consider multisite sensing using at least one wrist sensor and one nonwrist sensor. Including orientation-based features improved both posture and PA recognition.

An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing.

BACKGROUND: Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep. PURPOSE: Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices. METHODS: We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2-5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20-100 Hz), and human data (N = 60) from an ActiGraph GT9X. RESULTS: During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors. CONCLUSIONS: Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.

Designing Videogames to Crowdsource Accelerometer Data Annotation for Activity Recognition Research.

Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces and interventions. However, developing valid algorithms that use accelerometer data to detect everyday activities often requires large amounts of training datasets, precisely labeled with the start and end times of the activities of interest. Acquiring annotated data is challenging and time-consuming. Applied games, such as human computation games (HCGs) have been used to annotate images, sounds, and videos to support advances in machine learning using the collective effort of "non-expert game players." However, their potential to annotate accelerometer data has not been formally explored. In this paper, we present two proof-of-concept, web-based HCGs aimed at enabling game players to annotate accelerometer data. Using results from pilot studies with Amazon Mechanical Turk players, we discuss key challenges, opportunities, and, more generally, the potential of using applied videogames for annotating raw accelerometer data to support activity recognition research.

Moving fast, thinking fast: The relations of physical activity levels and bouts to neuroelectric indices of inhibitory control in preadolescents.

BACKGROUND: Structured vigorous physical activity (VPA) can improve cognitive control in children, but studies relating daily physical activity (PA) to cognitive control have yielded conflicting findings. While objectively measured daily PA summarizes all occurrences of PA within a registered period, a minimum duration of continuous PA is required for registration of a PA bout. Because brief bouts of high-intensity PA can account for a large proportion of children's daily activity-related energy expenditure, this study assessed whether daily and bouted VPA were selectively related to cognitive control in preadolescents relative to other PA intensities. METHODS: A total of 75 children between the ages of 8 and 10 years (49% girls) wore an ActiGraph wGT3X+ on the hip for 7 days. The acceleration signal from the vertical axis was summarized over 1 s, 5 s, and 15 s epochs. Daily and bouted moderate PA, moderate-to-vigorous PA, and VPA were measured. PA bouts were expressed as the frequency and time spent in 2 different continuous PA bouts, one lasting ≥10 s and the other lasting ≥30 s at a given intensity. Inhibitory control was assessed using behavioral responses to a modified flanker task (mean reaction time (RTmean) and accuracy). Attentional resource allocation and cognitive processing speed were measured using the amplitude and latency of the P3 component of event-related brain potentials, respectively. Associations between PA, behavioral indices of inhibitory control, P3 amplitude, and latency were assessed using hierarchical regression models. RESULTS: Daily VPA was not related to RTmean or accuracy on either congruent or incongruent trials. In contrast, more time spent in VPA bouts lasting ≥30 s predicted shorter P3 latency across epochs and flanker congruencies (all ß ≤ -0.24, all p ≤ 0.04). The associations between shorter P3 latency and the time spent in moderate-to-vigorous PA bouts lasting ≥30 s were less consistent and largely limited to congruent trials (congruent: ß (-0.31, -0.34)). No significant associations were observed upon correction for false discovery rate. CONCLUSION: The pattern of uncorrected associations aligns with the dose-response literature and suggests that brief VPA bouts may yield the greatest benefits to cognitive processing speed in preadolescents. Future studies using measures of brain structure and function are needed to understand the mechanisms linking bouted VPA to neurocognitive function during childhood.

Differentiating Sitting and Lying Using a Thigh-Worn Accelerometer.

UNLABELLED: A triaxial accelerometer worn on the thigh can provide information on the angle of rotation of the thigh. These data may be used to estimate periods of lying versus sitting. PURPOSE: To develop and test a classification algorithm to identify sedentary events as either lying or sitting events using a thigh-worn, triaxial accelerometer. METHODS: Seven-day free-living activity from 14 sedentary workers was recorded using the activPAL3™ monitor. Participants recorded when they went to bed and when they got up in a diary. All "in-bed" sedentary events were assumed to be lying and all "not-in-bed" sedentary events as sitting. An algorithm computed the angle of rotation of the y-axis, which was used to detect orientation of the thigh. Crossing a rotational threshold in the transverse plane of ±65° was used to classify a sedentary event as lying. The classification accuracy of the algorithm was compared with self-reported classification from the diary. RESULTS: The algorithm classified 96.7% of the sedentary time "in bed" (sensitivity) as lying and 92.9% of the time "not in bed" as not lying (specificity). CONCLUSIONS: Triaxial accelerometer data recorded from a single site on the thigh can be used to classify sedentary events as sitting and lying. The automated method developed in this study will allow objective measurement of diurnal lying time and that while sleeping without relying on self-report. This will help advance the understanding of the impact of different types of sedentary behaviors on various health outcomes.

Validity of Activity Monitor Step Detection Is Related to Movement Patterns.

BACKGROUND: There is a need to examine step-counting accuracy of activity monitors during different types of movements. The purpose of this study was to compare activity monitor and manually counted steps during treadmill and simulated free-living activities and to compare the activity monitor steps to the StepWatch (SW) in a natural setting. METHODS: Fifteen participants performed laboratory-based treadmill (2.4, 4.8, 7.2 and 9.7 km/h) and simulated free-living activities (eg, cleaning room) while wearing an activPAL, Omron HJ720-ITC, Yamax Digi- Walker SW-200, 2 ActiGraph GT3Xs (1 in "low-frequency extension" [AGLFE] and 1 in "normal-frequency" mode), an ActiGraph 7164, and a SW. Participants also wore monitors for 1-day in their free-living environment. Linear mixed models identified differences between activity monitor steps and the criterion in the laboratory/free-living settings. RESULTS: Most monitors performed poorly during treadmill walking at 2.4 km/h. Cleaning a room had the largest errors of all simulated free-living activities. The accuracy was highest for forward/rhythmic movements for all monitors. In the free-living environment, the AGLFE had the largest discrepancy with the SW. CONCLUSION: This study highlights the need to verify step-counting accuracy of activity monitors with activities that include different movement types/directions. This is important to understand the origin of errors in step-counting during free-living conditions.

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.

Energy Expenditure for 70 Activities in Children and Adolescents.

BACKGROUND: Thirty-five percent of the activities assigned MET values in the Compendium of Energy Expenditures for Youth were obtained from direct measurement of energy expenditure (EE). The aim of this study was to provide directly measured EE for several different activities in youth. METHODS: Resting metabolic rate (RMR) of 178 youths (80 females, 98 males) was first measured. Participants then performed structured activity bouts while wearing a portable metabolic system to directly measure EE. Steady-state oxygen consumption data were used to compute activity METstandard (activity VO2/3.5) and METmeasured (activity VO2/measured RMR) for the different activities. RESULTS: Rates of EE were measured for 70 different activities and ranged from 1.9 to 12.0 METstandard and 1.5 to 10.0 METmeasured. CONCLUSION: This study provides directly measured energy cost values for 70 activities in children and adolescents. It contributes empirical data to support the expansion of the Compendium of Energy Expenditures for Youth.

Trends in Physical Activity and Sedentary Behaviors of United States Youth.

BACKGROUND: Increases in childhood and adolescent obesity are a growing concern in the United States (U.S.), and in most countries throughout the world. Declines in physical activity are often postulated to have contributed to the rise in obesity rates during the past 40 years. METHODS: We searched for studies of trends in physical activity and sedentary behaviors of U.S. youth, using nontraditional data sources. Literature searches were conducted for active commuting, physical education, high-school sports, and outdoor play. In addition, trends in sedentary behaviors were examined. RESULTS: Data from the Youth Risk Behavior Surveillance System (YRBSS) and other national surveys, as well as longitudinal studies in the transportation, education, electronic media, and recreation sectors showed evidence of changes in several indicators. Active commuting, high school physical education, and outdoor play (in 3- to 12-year-olds) declined over time, while sports participation in high school girls increased from 1971 to 2012. In addition, electronic entertainment and computer use increased during the first decade of the 21st century. CONCLUSIONS: Technological and societal changes have impacted the types of physical activities performed by U.S. youth. These data are helpful in understanding the factors associated with the rise in obesity, and in proposing potential solutions.