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Scott E. Crouter, Paul R. Hibbing, and Samuel R. LaMunion

The purpose of this study was to conduct a comprehensive evaluation of the ActiGraph GT3X+ (AG) and activPAL (AP) for assessing time spent in sedentary behaviors (SB) in youth using structured and free-living activities. Forty-four participants (M age, 12.7±0.8 yrs) completed up to eight structured activities and approximately 2 hrs of free-living activity while wearing an AG (right hip) and AP (right thigh). A Cosmed K4b2 was used for measured energy expenditure (METy; activity VO2 ÷ resting VO2). Direct observation was used during the structured activities. SB time was estimated using the inclinometer function of the AP and AG, and count thresholds with AG (<75 vector magnitude [VM] counts/10-s; <25 vertical axis [VA] counts/10-s; and <50, 100, 150, and 200 VA counts/min). For the structured activities, the AG inclinometer and AP correctly classified supine rest about 45% of the time, seated activities 54.6% and 65.1% of the time, respectively, and walking and running >96% of the time. For the free-living measurement, the VA <25 counts/10-s had the lowest RMSE (20.6 min), while the VM <75 counts/10-s had the lowest MAPE (69.2%). The AG inclinometer was within 0.2 minutes of measured time, but had the highest MAPE (107.1%). The AP was within 1.6 minutes of measured time, but had the highest RMSE (28.5 minutes). Compared to measured SB time, the VA <25 counts/10-s and VM <75 counts/10-s provided the most precise estimates of SB during free-living activity. Further refinement is needed to improve the AP and AG posture estimates.

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Paul R. Hibbing, Nicholas R. Lamoureux, Charles E. Matthews, and Gregory J. Welk

Physical behavior can be assessed using a range of competing methods. The Free-Living Activity Study for Health (FLASH) is an ongoing study that facilitates the comparison of such methods. The purpose of this report is to describe the FLASH, with a particular emphasis on a subsample of participants who have consented to have their deidentified data released in a shared repository. Participants in the FLASH wear seven physical activity monitors for a 24-hr period and then complete a detailed recall using the Activities Completed Over Time in 24-hr online assessment tool. The participants can optionally agree to be video recorded for 30–60 min, which allows for direct observation as a criterion indicator of their behavior during that period. As of version 0.1.0, the repository includes data from 38 participants, and the sample size will grow as data are collected, processed, and released in future versions. The repository makes it possible to combine sensor data (e.g., from ActiGraph and SenseWear) with minute-by-minute contextual data (from the Activities Completed Over Time in 24-hr recall system), which enables the FLASH to generate benchmark data for a wide range of future research. The repository itself provides an example of how a powerful open-source tool (GitHub) can be used to share data and code in a way that encourages communication and collaboration among a variety of scientists (e.g., algorithm developers and end users). The FLASH data set will provide long-term benefits to researchers interested in advancing the science of physical behavior monitoring.

Open access

Nicholas R. Lamoureux, Paul R. Hibbing, Charles Matthews, and Gregory J. Welk

Accelerometry-based monitors are commonly utilized to evaluate physical activity behavior, but the lack of contextual information limits the interpretability and value of the data. Integration of report-based with monitor-based data allows the complementary strengths of the two approaches to be used to triangulate information and to create a more complete picture of free-living physical behavior. This investigation utilizes data collected from the Free-Living Activity Study for Health to test the feasibility of annotating monitor data with contextual information from the Activities Completed Over Time in 24-hr (ACT24) previous-day recall. The evaluation includes data from 134 adults who completed the 24-hr free-living monitoring protocol and retrospective 24-hr recall. Analyses focused on the relative agreement of energy expenditure estimates between ACT24 and two monitor-based methods (ActiGraph and SenseWear Armband). Daily energy expenditure estimates from ACT24 were equivalent to the reference device-based estimate. Minute-level agreement of energy expenditure between ACT24 and device-based methods was moderate and was similar to the agreement between two different monitor-based methods. This minute-level agreement between ACT24 and device-based methods demonstrates the feasibility and utility of integrating self-report with accelerometer data to provide richer context on the monitored behaviors. This type of integration offers promise for advancing the assessment of physical behavior by aiding in data interpretation and providing opportunities to improve physical activity assessment methods under free-living conditions.

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Paul R. Hibbing, Samuel R. LaMunion, Haileab Hilafu, and Scott E. Crouter

Background: Bout detection algorithms are used to segment data from wearable sensors, but it is challenging to assess segmentation correctness. The purpose of this study was to present and demonstrate the Transition Pairing Method (TPM), a new method for evaluating the performance of bout detection algorithms. Methods: The TPM compares predicted transitions to a criterion measure in terms of number and timing. A true positive is defined as a predicted transition that corresponds with one criterion transition in a mutually exclusive pair. The pairs are established using an extended Gale-Shapley algorithm, and the user specifies a maximum allowable within-pair time lag, above which pairs cannot be formed. Unpaired predictions and criteria are false positives and false negatives, respectively. The demonstration used raw acceleration data from 88 youth who wore ActiGraph GT9X monitors (right hip and non-dominant wrist) during simulated free-living. Youth Sojourn bout detection algorithms were applied (one for each attachment site), and the TPM was used to compare predicted bout transitions to the criterion measure (direct observation). Performance metrics were calculated for each participant, and hip-versus-wrist means were compared using paired t-tests (α = 0.05). Results: When the maximum allowable lag was 1-s, both algorithms had recall <20% (2.4% difference from one another, p < .01) and precision <10% (1.4% difference from one another, p < .001). That is, >80% of criterion transitions were undetected, and >90% of predicted transitions were false positives. Conclusion: The TPM improves on conventional analyses by providing specific information about bout detection in a standardized way that applies to any bout detection algorithm.

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Elizabeth L. Stegemöller, Joshua R. Tatz, Alison Warnecke, Paul Hibbing, Brandon Bates, and Andrew Zaman

Auditory cues, including music, are commonly used in the treatment of persons with Parkinson’s disease. Yet, how music style and movement rate modulate movement performance in persons with Parkinson’s disease have been neglected and remain limited in healthy young populations. The purpose of this study was to determine how music style and movement rate influence movement performance in healthy young adults. Healthy participants were asked to perform repetitive finger movements at two pacing rates (70 and 140 beats per minute) for the following conditions: (a) a tone only, (b) activating music, and (c) relaxing music. Electromyography, movement kinematics, and variability were collected. Results revealed that the provision of music, regardless of style, reduced amplitude variability at both pacing rates. Intermovement interval was longer, and acceleration variability was reduced during both music conditions at the lower pacing rate only. These results may prove beneficial for designing therapeutic interventions for persons with Parkinson’s disease.

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Chelsea Steel, Katie Crist, Amanda Grimes, Carolina Bejarano, Adrian Ortega, Paul R. Hibbing, Jasper Schipperijn, and Jordan A. Carlson

Objective: To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in children and adults. Methods: Participants (N = 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit’s SmartTrack, and Garmin’s Move IQ. Trip- and day-level agreement were tested. Results: The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults. Conclusions: The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS’ pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research.

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Lindsay P. Toth, Susan Park, Whitney L. Pittman, Damla Sarisaltik, Paul R. Hibbing, Alvin L. Morton, Cary M. Springer, Scott E. Crouter, and David R. Bassett

Purpose: To examine the effect of brief, intermittent stepping bouts on step counts from 10 physical activity monitors (PAMs). Methods: Adults (N = 21; M ± SD, 26 ± 9.0 yr) wore four PAMs on the wrist (Garmin Vivofit 2, Fitbit Charge, Withings Pulse Ox, and ActiGraph wGT3X-BT [AG]), four on the hip (Yamax Digi-Walker SW-200 [YX], Fitbit Zip, Omron HJ-322U, and AG), and two on the ankle (StepWatch [SW] with default and modified settings). AG data were processed with and without the low frequency extension (AGL) and with the Moving Average Vector Magnitude algorithm. In Part 1 (five trials), walking bouts were varied (4–12 steps) and rest intervals were held constant (10 s). In Part 2 (six trials), walking bouts were held constant (4 steps) and rest intervals were varied (1–10 s). Percent of hand-counted steps and mean absolute percentage error were calculated. One sample t-test was used to compare percent of hand-counted steps to 100%. Results: In Parts 1 and 2, the SWdefault, SWmodified, YX, and AGLhip captured within 10% of hand-counted steps across nearly all conditions. In Part 1, estimates of most methods improved as the number of steps per bout increased. In Part 2, estimates of most methods decreased as the rest duration increased. Conclusion: Most methods required stepping bouts of >6–10 consecutive steps to record steps. Rest intervals of 1–2 seconds were sufficient to break up walking bouts in many methods. The requirement for several consecutive steps in some methods causes an underestimation of steps in brief, intermittent bouts.

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Susan Park, Lindsay P. Toth, Paul R. Hibbing, Cary M. Springer, Andrew S. Kaplan, Mckenzie D. Feyerabend, Scott E. Crouter, and David R. Bassett

It has become common to wear physical activity monitors on the wrist to estimate steps per day, but few studies have considered step differences between monitors worn on the dominant and non-dominant wrists. Purpose: The purpose of this study was to compare four step counting methods on the dominant versus non-dominant wrist using the Fitbit Charge (FC) and ActiGraph GT9X (GT9X) across all waking hours of one day. Methods: Twelve participants simultaneously wore two monitors (FC and GT9X) on each wrist during all waking hours for an entire day. GT9X data were analyzed with three step counting methods: ActiLife algorithm with default filter (AG-noLFE), ActiLife algorithm with low-frequency extension (AG-LFE), and the Moving Average Vector Magnitude (AG-MAVM) algorithm. A 2-way repeated measures ANOVA (method × wrist) was used to compare step counts. Results: There was a significant main effect for wrist placement (F(1,11) = 11.81, p = .006), with the dominant wrist estimating an average of 1,253 more steps than the non-dominant wrist. Steps differed between the dominant and non-dominant wrist for three of the step methods: AG-noLFE (1,327 steps), AG-LFE (2,247 steps), AG-MAVM (825 steps), and approached statistical significance for FC (613 steps). No significant method x wrist placement interaction was found (F(3,9) = 2.62, p = .115). Conclusion: Findings suggest that for step counting algorithms, it may be important to consider the placement of wrist-worn monitors since the dominant wrist location tended to yield greater step estimates. Alternatively, standardizing the placement of wrist-worn monitors could help to reduce the differences in daily step counts across studies.

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Samantha F. Ehrlich, Amanda J. Casteel, Scott E. Crouter, Paul R. Hibbing, Monique M. Hedderson, Susan D. Brown, Maren Galarce, Dawn P. Coe, David R. Bassett, and Assiamira Ferrara

Background: This study sought to compare three sensor-based wear-time estimation methods to conventional diaries for ActiGraph wGT3X-BT accelerometers worn on the non-dominant wrist in early pregnancy. Methods: Pregnant women (n = 108) wore ActiGraph wGT3X-BT accelerometers for seven days and recorded their device on and off times in a diary (criterion). Average daily wear-time estimates from the Troiano and Choi algorithms and the wGT3X-BT accelerometer wear sensor were compared against the diary. The Hibbing 2-regression model was used to estimate time spent in activity (during periods of device wear) for each method. Wear-time and time spent in activity were compared with multiple repeated measures ANOVAs. Bland Altman plots assessed agreement between methods. Results: Compared to the diary (825.5 minutes [795.1, 856.0]), the Choi (843.0 [95% CI: 812.6, 873.5]) and Troiano (839.1 [808.7, 869.6]) algorithms slightly overestimated wear-time, whereas the sensor (774.4 [743.9, 804.9]) underestimated it, although only the sensor differed significantly from the diary (p < .0001). Upon adjustment for average daily wear-time, there were no statistically significant differences between the wear-time methods in regards to minutes per day of moderate-to-vigorous physical activity (MVPA), vigorous physical activity, and moderate physical activity. Bland Altman plots indicated the Troiano and Choi algorithms were similar to the diary and within ≤0.5% of each other for wear-time and MVPA. Conclusions: The Choi or Troiano algorithms offer a valid and efficient alternative to diaries for the estimation of daily wear-time in larger-scale studies of MVPA during pregnancy, and reduce burden for study participants and research staff.

Open access

Gregory J. Welk, Pedro F. Saint-Maurice, Philip M. Dixon, Paul R. Hibbing, Yang Bai, Gabriella M. McLoughlin, and Michael Pereira da Silva

A balance between the feasibility and validity of measures is an important consideration for physical activity (PA) research—particularly in school-based research with youth. The present study extends previously tested calibration methods to develop and test new equations for an online version of the youth activity profile (YAP) tool, a self-report tool designed for school applications. Data were collected across different regions and seasons to develop more robust, generalizable equations. The study involved a total of 717 youth from 33 schools (374 elementary [ages 9–11 years], 224 middle [ages 11–14 years], and 119 high school [ages 14–18 years]) in two different states in the United States. Participants wore a Sensewear monitor for a full week and then completed the online YAP at school to report PA and sedentary behaviors in school and at home. Accelerometer data were processed using an R-based segmentation program to compute PA and sedentary behavior levels. Quantile regression models were used with half of the sample to develop item-specific YAP calibration equations, and these were cross validated with the remaining half of the sample. Computed values of mean absolute percentage error ranged from 15 to 25% with slightly lower error observed for the middle school sample. The new equations had improved precision compared with the previous versions when tested on the same sample. The online version of the YAP provides an efficient and effective way to capture school level estimates of PA and sedentary behaviors in youth.