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.

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.

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.

Online preschool teacher training to promote physical activity in young children: A pilot cluster randomized controlled trial.

This study tested the Wellness Enhancing Physical Activity in Young Children (WE PLAY) program, a 4-week online preschool teacher training, on children's moderate-to-vigorous physical activity (MVPA). In this cluster RCT, six Head Start preschools were randomized to an intervention and comparison group. Children's MVPA was measured using accelerometers at pre- and posttest. The magnitude of the difference in MVPA between groups at posttest was small, but in the expected direction: Δ min/hour = 1.60, 95% CI [-0.97, 4.18], p = .22, Cohen's d = 0.32. We observed a pre/post within group increase in average minutes per hour of MVPA in school with a medium effect size for the intervention group: Δ mean min/hour = 2.09, 95% CI [0.51, 3.67], p = .0096, Cohen's d = 0.42. An increase was not seen for the comparison group: Δ mean min/hour = 0.44, 95% CI [-0.70, 1.59], p = .45, Cohen's d = 0.07. WE PLAY children in 6 hr/day programs gained 63 min of MVPA per week in school, providing preliminary evidence of the benefits of WE PLAY on children's physical activity levels. WE PLAY deserves further testing with larger groups of children and teachers. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

"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.

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.