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Bidirectional Day-to-Day Associations of Reported Sleep Duration With Accelerometer Measured Physical Activity and Sedentary Time Among Dutch Adolescents: An Observational Study

Nathalie Berninger, Gregory Knell, Kelley Pettee Gabriel, Guy Plasqui, Rik Crutzen, and Gill Ten Hoor

Objectives: To examine the bidirectional association of sleep duration with proportions of time spent in physical behaviors among Dutch adolescents. Methods: Adolescents (n = 294, 11–15 years) completed sleep diaries and wore an accelerometer (ActiGraph) over 1 week. With linear mixed-effects models, the authors estimated the association of sleep categories (short, optimal, and long) with the following day’s proportion in physical behaviors. With generalized linear mixed models with binomial distribution, the authors estimated the association of physical behavior proportions on sleep categories. Physical behavior proportions were operationalized using percentages of wearing time and by applying a compositional approach. All analyses were stratified by gender accounting for differing developmental stages. Results: For males (number of observed days: 345, n = 83), short as compared with optimal sleep was associated with the following day’s proportion spent in sedentary (−2.57%, p = .03, 95% confidence interval [CI] [−4.95, −0.19]) and light-intensity activities (1.96%, p = .02, 95% CI [0.27, 3.65]), which was not significant in the compositional approach models. Among females (number of observed days: 427, n = 104), long sleep was associated with the proportions spent in moderate- to vigorous-intensity physical activity (1.69%, p < .001, 95% CI [0.75, 2.64]) and in sedentary behavior (−3.02%, p < .01, 95% CI [−5.09, −0.96]), which was replicated by the compositional approach models. None of the associations between daytime activity and sleep were significant (number of obs.: 844, n = 204). Conclusions: Results indicate partial associations between sleep and the following day’s physical behaviors, and no associations between physical behaviors and the following night’s sleep.

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Where to Place Which Sensor to Measure Sedentary Behavior? A Method Development and Comparison Among Various Sensor Placements and Signal Types

Roman P. Kuster, Daniel Baumgartner, Maria Hagströmer, and Wilhelmus J.A. Grooten

Background: Sedentary behavior (SB) is associated with several chronic diseases and office workers especially are at increased risk. SB is defined by a sitting or reclined body posture with an energy expenditure of ≤1.5 metabolic equivalents. However, current objective methods to measure SB are not consistent with its definition. There is no consensus on which sensor placement and type should be used. Aim: To compare the accuracy of newly developed artificial intelligence models for 15 sensor placements in combination with four signal types (accelerometer only/plus gyroscope and/or magnetometer) to detect posture and physical in-/activity during desk-based activities. Method: Signal features for the model development were extracted from sensor raw data of 30 office workers performing 10 desk-based tasks, each lasting 5 min. Direct observation (posture) and indirect calorimetry (in-/activity) served as the reference criteria. The best classification model for each sensor was identified and compared among the sensor placements, both using Friedman and post hoc Wilcoxon tests (p ≤ .05). Results: Posture was most accurately measured with a lower body sensor, while in-/activity was most accurately measured with an upper body or waist sensor. The inclusion of additional signal types improved the posture classification for some placements, while the acceleration signal already contained the relevant signal information for the in-/activity classification. Overall, the thigh accelerometer most accurately classified desk-based SB. Conclusion: This study favors, in line with previous work, the measurement of SB with a thigh-worn accelerometer and adds the information that this sensor is also accurate in measuring physical in-/activity while sitting and standing.

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Fast and Robust Algorithm for Detecting Body Posture Using Wrist-Worn Accelerometers

Marcin Straczkiewicz, Nancy W. Glynn, Vadim Zipunnikov, and Jaroslaw Harezlak

Background: The increasing popularity of wrist-worn accelerometers introduces novel challenges to the research on physical activity and sedentary behavior. Estimation of body posture is one such challenge. Methods: The authors proposed an approach called SedUp to differentiate between sedentary (sitting/lying) and standing postures. SedUp is based on the logistic regression classifier, using the wrist elevation and the motion variability extracted from raw accelerometry data collected on the axis parallel to the forearm. The authors developed and tested our method on data from N = 45 community-dwelling older adults. All subjects wore ActiGraph GT3X+ accelerometers on the left and right wrist, and activPAL was placed on the thigh in the free-living environment for 7 days. ActivPAL provided ground truth about body posture. The authors reported SedUp’s classification accuracy for each wrist separately. Results: Using the data from the left wrist, SedUp estimated the standing posture with median true positive rate = 0.83 and median true negative rate = 0.91. Using the data from the right wrist, SedUp estimated the standing posture with median true positive rate = 0.86 and median true negative rate = 0.93. Conclusions: SedUp provides accurate classification of body posture using wrist-worn accelerometers. The separate validation for each wrist allows for the application of SedUp in a wide spectrum of free-living studies.

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Physical Activity Tracking Wristbands for Use in Research With Older Adults: An Overview and Recommendations

Alanna Weisberg, Alexandre Monte Campelo, Tanzeel Bhaidani, and Larry Katz

Traditional physical activity tracking tools, such as self-report questionnaires, are inherently subjective and vulnerable to bias. Physical activity tracking technology, such as activity tracking wristbands, is becoming more reliable and readily available. As such, researchers are employing these objective measurement tools in both observational- and intervention-based studies. There remains a gap in the literature on how to properly select activity tracking wristbands for research, specifically for the older adult population. This paper outlines considerations for choosing the most appropriate wrist-worn wearable device for use in research with older adults. Device features, outcome measures, population, and methodological considerations are explored.

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Volume 3 (2020): Issue 3 (Sep 2020)

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Equivalency of Sleep Estimates: Comparison of Three Research-Grade Accelerometers

Tatiana Plekhanova, Alex V. Rowlands, Tom Yates, Andrew Hall, Emer M. Brady, Melanie Davies, Kamlesh Khunti, and Charlotte L. Edwardson

Introduction: This study examined the equivalency of sleep estimates from Axivity, GENEActiv, and ActiGraph accelerometers worn on the nondominant and dominant wrists and with and without using a sleep log to guide the algorithm. Methods: 47 young adults wore an Axivity, GENEActiv, and ActiGraph accelerometer continuously on both wrists for 4–7 days. Sleep time, sleep window, sleep efficiency, sleep onset, and wake time were produced using the open-source software (GGIR). For each outcome, agreement between accelerometer brands, dominant and nondominant wrists, and with and without use of a sleep log, was examined using pairwise 95% equivalence tests (±10% equivalence zone) and intraclass correlation coefficients (ICCs), with 95% confidence intervals and limits of agreement. Results: All sleep outcomes were within a 10% equivalence zone irrespective of brand, wrist, or use of a sleep log. ICCs were poor to good for sleep time (ICCs ≥ .66) and sleep window (ICCs ≥ .56). Most ICCs were good to excellent for sleep efficiency (ICCs ≥ .73), sleep onset (ICCs ≥ .88), and wake time (ICCs ≥ .87). There were low levels of mean bias; however, there were wide 95% limits of agreement for sleep time, sleep window, sleep onset, and wake time outcomes. Sleep time (up to 25 min) and sleep window (up to 29 min) outcomes were higher when use of the sleep log was not used. Conclusion: The present findings suggest that sleep outcomes from the Axivity, GENEActiv, and ActiGraph, when analyzed identically, are comparable across studies with different accelerometer brands and wear protocols at a group level. However, caution is advised when comparing studies that differ on sleep log availability.

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Validating Accelerometers for the Assessment of Body Position and Sedentary Behavior

Marco Giurgiu, Johannes B.J. Bussmann, Holger Hill, Bastian Anedda, Marcel Kronenwett, Elena D. Koch, Ulrich W. Ebner-Priemer, and Markus Reichert

There is growing evidence that sedentary behavior is a risk factor for somatic and mental health. However, there is still a lack of objective field methods, which can assess both components of sedentary behavior: the postural (sitting/lying) and the movement intensity part. The purpose of the study was to compare the validity of different accelerometers (ActivPAL [thigh], ActiGraph [hip], move [hip], and move [thigh]). 20 adults (10 females; age 25.68 ± 4.55 years) participated in a structured protocol with a series of full- and semistandardized sessions under laboratory conditions. Direct observation via video recording was used as a criterion measure of body positions (sitting/lying vs. nonsitting/lying). By combining direct observation with metabolic equivalent tables, protocol activities were also categorized as sedentary or nonsedentary. Cohen’s kappa was calculated as an overall validity measure to compare accelerometer and video recordings. Across all conditions, for the measurement of sitting/lying body positions, the ActivPAL ([thigh], ĸ = .85) and Move 4 ([thigh], ĸ = .97) showed almost perfect agreement, whereas the Move 4 ([hip], ĸ = .78) and ActiGraph ([hip], ĸ = .67) showed substantial agreement. For the sedentary behavior part, across all conditions, the ActivPAL ([thigh], ĸ = .90), Move 4 ([thigh], ĸ = .95) and Move 4 ([hip], ĸ = .84) revealed almost perfect agreement, whereas the ActiGraph ([hip], ĸ = .69) showed substantial agreement. In particular, thigh-worn devices, namely the Move and the ActivPAL, achieved up to excellent validity in measuring sitting/lying body positions and sedentary behavior and are recommended for future studies.

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Comparison of Sedentary Time Between Thigh-Worn and Wrist-Worn Accelerometers

Kristin Suorsa, Anna Pulakka, Tuija Leskinen, Jaana Pentti, Andreas Holtermann, Olli J. Heinonen, Juha Sunikka, Jussi Vahtera, and Sari Stenholm

Background: The accuracy of wrist-worn accelerometers in identifying sedentary time has been scarcely studied in free-living conditions. The aim of this study was to compare daily sedentary time estimates between a thigh-worn accelerometer, which measured sitting and lying postures, and a wrist-worn accelerometer, which measured low levels of movement. Methods: The study population consisted of 259 participants (M age = 62.8 years, SD = 0.9) from the Finnish Retirement and Aging Study (FIREA). Participants wore an Axivity AX3 accelerometer on their mid-thigh and an Actigraph wActiSleep-BT accelerometer on their non-dominant wrist simultaneously for a minimum of 4 days in free-living conditions. Two definitions to estimate daily sedentary time were used for data from the wrist-worn accelerometer: 1) the count cutpoint, ≤1853 counts per minute; and 2) the Euclidean Norm Minus One (ENMO) cutpoint, <30 mg. Results: Compared to the thigh-worn accelerometer, daily sedentary time estimate was 63 min (95% confidence interval [CI] = −53 to −73) lower by the count cutpoint and 50 min (95% CI = 34 to 67) lower by the ENMO cutpoint. The limits of agreement in daily sedentary time estimates between the thigh- and cutpoint methods for wrist-worn accelerometers were wide (the count cutpoint: −117 to 243, the ENMO cutpoint: −212 to 313 min). Conclusions: Currently established cutpoint-based methods to estimate sedentary time from wrist-worn accelerometers result in underestimation of daily sedentary time compared to posture-based estimates of thigh-worn accelerometers. Thus, sedentary time estimates obtained from wrist-worn accelerometers using currently available cutpoint-based methods should be interpreted with caution and future work is needed to improve their accuracy.

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Reliability and Criterion-Related Validity of the activPAL Accelerometer When Measuring Physical Activity and Sedentary Behavior in Adults With Lower Limb Absence

Sarah Deans, Alison Kirk, Anthony McGarry, and David Rowe

Introduction: Accurate measurement of physical behavior in adults with lower limb absence is essential to report true patterns of physical behavior and the effectiveness of interventions. The effect of placing accelerometers on prostheses may also affect the reliability and validity. Purpose: To assess reliability and criterion-related validity of the activPAL for measuring incidental and purposeful stepping, and reclining and stepping time in adults with unilateral lower limb absence. Methods: 15 adults with unilateral lower limb absence completed simulated lifestyle activities in a laboratory setting that were retrospectively scored via video analysis. Objective data were obtained simultaneously from two activPAL monitors placed on the sound and prosthetic side. Data were analyzed using one-way intraclass correlation coefficients (ICC), paired t-tests and Cohen’s d. Results: Reliability (prosthetic side vs. sound side) was poor for incidental steps (ICC = .05, d = 0.48) but acceptable for all other measures (ICC = .77–.88; d = .00–.18). Mean activPAL measures, although highly related to the criterion, underestimated, on average, stepping and time-related variables. Differences were large for all stepping variables (d = .38–.96). Conclusions: The activPAL is a reliable measurement tool in adults with lower limb absence when used in a laboratory setting. Placement of the monitor on the sound side limb is recommended for testing. The activPAL shows evidence of relative validity, but not absolute validity. Further evaluation is needed to assess whether similar evidence is found in free-living activity and sedentary contexts.

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Body-Worn Sensors Are a Valid Alternative to Forceplates for Measuring Balance in Children

Vincent Shieh, Ashwini Sansare, Minal Jain, Thomas Bulea, Martina Mancini, and Cris Zampieri

Aims: Clinical evaluation of balance has relied on forceplate systems as the gold standard for postural sway measures. Recently, systems based on wireless inertial sensors have been explored, mostly in the adult population, as an alternative given their practicality and lower cost. Our goal was to validate body-worn sensors against forceplate balance measures in typically developing children during tests of quiet stance. Methods: 18 participants (8 males) 7 to 17 years old performed a quiet stance test standing on a forceplate while wearing 3 inertial sensors. Three 30-second trials were performed under 4 conditions: firm surface with eyes open and closed, and foam surface with eyes open and closed. Sway area, path length, and sway velocity were calculated. Results: We found 20 significant and 8 non-significant correlations. Variables found to be significant were represented across all conditions, except for the foam eyes closed condition. Conclusions: These results support the validity of wearable sensors in measuring postural sway in children. Inertial sensors may represent a viable alternative to the gold standard forceplate to test static balance in children.