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Volume 3 (2020): Issue 2 (Jun 2020)

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Evaluating the Performance of Sensor-Based Bout Detection Algorithms: The Transition Pairing Method

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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Accelerometer-Assessed Prolonged Sitting During Work and Leisure Time and Associations With Age, Body Mass Index, and Health: A Cross-Sectional Study

Inger Mechlenburg, Marianne Tjur, and Kristian Overgaard

Background: High levels of sitting may have a negative impact on health. The aim of this study was to examine how sitting time varies between work and leisure time and to identify parameters associated with overall sitting time and prolonged sitting. Methods: In a total of 189 persons ≥18 years randomly selected from the Danish Civil Registration System, sitting time was monitored with an accelerometer-based sensor mounted at the mid-thigh. Moreover, participants completed a questionnaire including data on demographics, work schedule, and general health. Data were processed using a custom built algorithm. Overall sitting was parametrized as mean % of time spent sitting and prolonged sitting as s (periods exceeding 30 minutes). Results: During working hours, the mean overall sitting time (49.2%) was significantly lower than during leisure time on both working days (60.6%, p < .0001) and on days off work (58.9%, p < .0001). For men, prolonged sitting was positively associated with age, while corresponding associations were negative among female participants (p = .01). Body mass index (BMI) increased by 0.06 kg/m2 for every % increase in prolonged sitting (p = .005). The odds ratio of reporting poor health was 1.05 for every % increase in overall sitting during leisure time on workdays (p = .005). Conclusions: Overall sitting time varies between work and leisure time. Prolonged sitting is positively associated with age for men and with BMI for both men and women.

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The Contribution of Commuting to Total Daily Moderate-to-Vigorous Physical Activity

Abolanle R. Gbadamosi, Alexandra M. Clarke-Cornwell, Paul A. Sindall, and Malcolm H. Granat

Background: Actively commuting to and from work can increase moderate-to-vigorous physical activity (MVPA) and increase adherence to physical activity (PA) guidelines; however, there is a lack of evidence on the contribution of mixed-mode commutes and continuous stepping bouts to PA. Many commuting studies employ the use of self-reported PA measures. This study objectively determined the contribution of MVPA during commuting to total MVPA, using cadence to define MVPA, and explored how the length of stepping bouts affects adherence to PA guidelines. Methods: Twenty-seven university staff wore an activPAL activity monitor for seven days and kept an activity diary. The activPALquantified MVPA and bouts duration and the activity diary collected information about commute times and the modes of commute. Twenty-three participants with at least four days of data were included in the final analysis. Results: The median total time per day spent in MVPA was 49.6 (IQR: 27.4–75.8) minutes and 31% of the total time was accumulated during commuting (median = 15.2 minutes; IQR: 4.11–26.9). Walking and mixed-mode commuters spent more time in MVPA (37.6 and 26.9 minutes, respectively), compared to car commuters (5.8 minutes). Seventeen out of the 23 participants achieved more than 30 minutes of MVPA per day, with five achieving this in their commute alone. A significant positive association was found between commute time spent in MVPA and total MVPA (p < .001). Conclusion: Commuting can be a major contributor to total MVPA, with the mode of commute having a significant role in the level of this contribution to total MVPA.

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Non-Wear Time and Presentation of Compositional 24-Hour Time-Use Analyses Influence Conclusions About Sleep and Body Mass Index in Children

Jillian J. Haszard, Kim Meredith-Jones, Victoria Farmer, Sheila Williams, Barbara Galland, and Rachael Taylor

Although 24-hour time-use data are increasingly being examined in relation to indices of health, consensus has yet to be reached about the best way to present estimates from compositional analyses. This analysis explored the impact of different presentations of results when assessing the relationship between 24-hour time-use and body mass index (BMI) z-score using compositional analysis of 5-day actigraphy data in 742 children. First it was found that reallocating non-wear time to day-time components only (sedentary behavior, light physical activity, and moderate-to-vigorous physical activity [MVPA]) before normalization to 24 hours provided stronger estimates with BMI z-score than simply removing non-wear time before normalization. Estimates for sleep time were substantially affected, where associations with BMI z-score nearly doubled (mean difference [95% CI] in BMI z-score for 10% longer sleep were −0.20 [−0.32, −0.08] compared to −0.11 [−0.23, 0.002]). Presenting estimates in terms of a greater number of minutes in a component, relative to all others, showed MVPA to be the strongest predictor of BMI z-score, while estimates in terms of the proportion of minutes showed sleep to be the strongest predictor. Both presentations have value. However, presentations in terms of one-to-one “substitutions” of time may need careful interpretation due to the uneven distribution of time in each component. In conclusion, when analyzing relationships between 24-hour time-use and health outcomes, non-wear time and presentation of estimates can impact final conclusions. As a result, the current understanding of the importance of sleep for child health may be underestimated.

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Step-Counting Validity of Wrist-Worn Activity Monitors During Activities With Fixed Upper Extremities

Rebekah Lynn, Rebekah Pfitzer, Rebecca R. Rogers, Christopher G. Ballmann, Tyler D. Williams, and Mallory R. Marshall

Little is known about validity of wrist-worn physical activity monitors during activities when an arm-swing is not present. The purpose of this study was to compare the step-counting validity of wrist-worn activity monitors (Fitbit Charge HR Series 2, ActiGraph GT9X Link, Apple Watch Series 4) during functional physical activities with fixed upper extremities. Tasks included treadmill walking at 3 mph and five free-living tasks (walking with a baby doll on the left hip and the right hip, holding groceries, and pushing a stroller while walking and while jogging). Device step counts were compared to hand-counted steps from GoPro video footage. Fitbit Charge had less error when compared to the left ActiGraph in both stroller walking and jogging, treadmill walking, and grocery walking tasks (p < .001 to .020). For grocery walking, walking with a baby on the right, and walking with a baby on the left, device percentage errors ranged from 0 (0.5%) to −7.6 (15.8%). For stroller jogging, stroller walking, and treadmill walking, device percentage errors ranged from −8.3 (7.3%) to −94.3 (17.9%). Tasks with the hands fixed to an item that also had contact with the floor (stroller and treadmill) had more error than when participants held an item that was not in contact with the floor (doll and groceries). Though wrist-worn, consumer-grade step-counting devices typically undercount steps in general, consumers should be aware that their devices may particularly undercount steps during activities with the hands fixed. This may be especially true with items in contact with the floor.

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Review of Validity and Reliability of Garmin Activity Trackers

Kelly R. Evenson and Camden L. Spade

Purpose: A systematic review to summarize the validity and reliability of steps, distance, energy expenditure, speed, elevation, heart rate, and sleep assessed by Garmin activity trackers. Methods: Searches included studies published through December 31, 2018. Correlation coefficients (CC) were assessed as low (<0.60), moderate (0.60 to <0.75), good (0.75 to <0.90), or excellent (≥0.90). Mean absolute percentage errors (MAPE) were assessed as acceptable at <5% in controlled conditions and <10% for free-living conditions. Results: Overall, 32 studies of adults documented validity. Four of these studies also documented reliability. The sample size ranged from 1–95 for validity and 4–31 for reliability testing. Step inter- and intra-reliability was good-to-excellent and speed intra-reliability was excellent. No other features were explored for reliability. Step validity, across 16 studies, generally indicated good-to-excellent CC and acceptable MAPE. Distance validity, tested in three studies, generally indicated poor CC and MAPE that exceeded acceptable limits, with both over and underestimation. Energy expenditure validity, across 12 studies, generally indicated wide variability in CC and MAPE that exceeded acceptable limits. Heart rate validity in five studies had low-to-excellent CC and all MAPE exceeded acceptable limits. Speed, elevation, and sleep validity were assessed in only one or two studies each; for sleep, the criterion relied on self-report rather than polysomnography. Conclusion: This systematic review of Garmin activity trackers among adults indicated higher validity of steps; few studies on speed, elevation, and sleep; and lower validity for distance, energy expenditure, and heart rate. Intra- and inter-device feature reliability needs further testing.

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Effect of Monitor Placement on the Daily Step Counts of Wrist and Hip Activity Monitors

Susan Park, Lindsay P. Toth, Scott E. Crouter, Cary M. Springer, Robert T. Marcotte, and David R. Bassett

Purpose: To examine the effect of activity monitor placement on daily step counts when monitors are worn at different positions on the wrist/forearm and the hip. Methods: Participants (N = 18) wore eight different models (four wrist and four hip models) across four days. Each day, one hip and one wrist model were selected, and four identical monitors of each model were worn on the right hip and the non-dominant wrist/forearm, respectively, during all waking hours. Step counts of each monitor were compared to the same model worn in the referent position (wrist: proximal to ulnar styloid process; hip: midline of thigh). Percent of referent steps and mean difference between observed and referent positions were computed. Significant differences in steps between positions for each method were determined using one-way repeated measures ANOVAs. For significant main effects, pairwise comparisons with Bonferroni corrections were used to determine which positions were significantly different. Results: All wrist methods showed a significant main effect for placement (p < .05) and alternate positions were 1–16% lower than the referent position. For hip methods, only the Omron HJ-325 differed across positions (p < .05), but differences were among non-referent positions and all were within ±2% of steps recorded by the referent position. Conclusions: Researchers should be aware that positions that deviate from the manufacturer’s recommended position at the wrist could influence step counts. Of all hip methods examined, the Omron had a significant placement effect which did not constitute a practical difference.

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Infant Leg Activity Intensity Before and After Naps

Ivan A. Trujillo-Priego, Judy Zhou, Inge F. Werner, Weiyang Deng, and Beth A. Smith

Wearable sensors are being used to measure intensity of infant physical activity across full days. The variability of infant activity intensity within and across days is important to study given the potential impact of physical activity on developmental trajectories. Using retrospective data, we analyzed the intensity of leg movements in 10 typically developing infants pre- and post-naptimes. Leg movement data were captured from 20 minutes before and after multiple events of naps across seven days for each infant. We hypothesized that leg movement intensity would be lower before a nap than after a nap potentially due to lower arousal and increased fatigue prior to attaining sleep. However, our results showed that leg movement intensity was not significantly different when comparing the 20-minute period pre- and post-naps (F(1,7) = 3.91, p = .089, η p 2 = 0.358 ). Our results are a first step in describing patterns of infant activity across days and highlights the need for further research regarding infant energy expenditure and physical activity.

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High-Tech Video Capture and Analysis for Counting Park Users

Richard R. Suminski, Gregory M. Dominick, Philip Saponaro, Elizabeth M. Orsega-Smith, Eric Plautz, and Matthew Saponaro

Today’s technology could contribute substantially to measuring physical activity. The current study evaluated traditional and novel approaches for assessing park use. The traditional approach involved a trained observer performing the System for Observing Play and Recreation in Communities (SOPARC) at 14 parks while wearing a point-of-view, wearable video device (WVD). The novel approach utilized computer vision to count park users in the WVD videos taken during in-person SOPARCs. Both approaches were compared to criterion counts from expert reviews of the WVD videos. In the 676 scans made during in-person SOPARCs, 293 individuals were observed while 341 were counted by experts in the corresponding WVD videos. When using scans/videos having individuals in them (84 scans/videos), intra-class correlations (ICC) indicated good-to-excellent reliability between in-person SOPARC and experts for counts of total women and men, within age groups (except seniors), of Blacks and Whites, and within intensity categories (ICCs > .87; p < 0.001). In a subsample of 42 scans/videos, 174 individuals were counted using computer vision and 213 by experts. When using 27 of the 42 WVD videos with individuals in them, ICCs indicated good reliability between computer vision and expert reviews (ICC = .83; p < 0.001). Bland-Altman analysis showed the concurrence of expert counts with both in-person SOPARC and computer vision counts decreased as the number of individuals in a scan/video increased. The results of this study support the use of a highly discrete method for obtaining point-of-view videos and the application of computer vision for automating the counting of park users in the videos.