Publications by authors named "Diego Arguello"

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.

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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.

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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.

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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.

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Unlabelled: 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.

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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.

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(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.

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Article Synopsis
  • The study tested how well the GT9X proximity sensor detects wear time in both controlled lab settings and real-life situations with 52 participants.
  • In the lab, the GT9X showed a high sensitivity of 93% for detecting wear but only 49% specificity for non-wear, meaning it struggled to confirm when the device was not being worn.
  • The results in free-living settings revealed lower sensitivity (72% and 84% for wrist and hip sensors, respectively) but better specificity (90% and 92%), indicating that the sensor's accuracy may require additional methods for reliable wear-time detection.
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