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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.

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.

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.

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.

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.