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Assessing bone's response to physical activity interventions is challenging. This randomized controlled trial investigates if changes in bone turnover markers can offer an early evaluation of a physical activity intervention's effectiveness in improving bone mineral density (BMD) in premenopausal women. Participants in the intervention group (n = 27, with 24 completing the trial) were instructed to walk at least 10,000 steps every day on a brisk walk and to execute 60 jumps daily, each surpassing 4g of acceleration, using an accelerometer-based wearable device. Meanwhile, the control group (n = 26, with 18 completing the trial) continued with their usual lifestyle. Bone turnover markers, comprising of C-terminal telopeptide of Type I collagen, procollagen Type 1 N-terminal propeptide, and total osteocalcin (carboxylated and undercarboxylated) were measured at baseline and midway through the intervention (3 months). Dual-energy X-ray absorptiometry scans of the hip and lumbar spine were conducted at baseline and the end of the intervention (6 months) to estimate BMD. Analysis of covariance exhibited significant differences between groups in procollagen Type 1 N-terminal propeptide (−6.74 μg/L, p =.023) and C-terminal telopeptide of Type I collagen (−83 ng/L, p =.043) after 3 months, and in femoral neck BMD (+0.024 g/cm2, p =.016), total hip BMD (+0.036 g/cm2, p =.004), and lumbar spine BMD (+0.026 g/cm2, p =.020) after 6 months. A significant correlation (r = −.73; p <.001) was detected between reductions in C-terminal telopeptide of Type I collagen and increases in femoral neck BMD. In conclusion, this intervention improved BMD in premenopausal women, with bone turnover markers potentially useful for early intervention assessment, though further research is needed. [ABSTRACT FROM AUTHOR]

This study examined the bivariate association between fatigue and depression symptoms and physical activity behavior in persons with multiple sclerosis (MS). The sample of adults with MS completed the Fatigue Severity Scale (FSS) and the Hospital Anxiety and Depression Scale (HADS) and wore a waist mounted accelerometer during waking hours for 7 days. We categorized participants as having elevated fatigue and depression based on cut-points for the FSS (i.e., 4+ as indicative of severe fatigue) and the HADS (i.e., 8+ as indicative of elevated depressive symptoms). We used a two-way multivariate analysis of variance (MANOVA) to examine the contribution of fatigue and depression to volume and pattern of sedentary, light (LPA) and moderate-to-vigorous physical activity (MVPA). Results indicated no bivariate association between fatigue and depression and measures of physical activity behavior. The MANOVA indicated there was a significant association between fatigue and MVPA (F = 2.30, p = 0.032) and steps/day (F = 13.6, p < 0.001), independent of depression symptoms. There was no association between depression symptoms and physical activity behavior. This study demonstrated an interrelation between fatigue symptoms and MVPA and steps/day in MS, independent of depression symptoms, and this should be considered in the future design and delivery of physical activity interventions in MS. Fatigue and depression are prevalent and burdensome symptoms of multiple sclerosis (MS). These symptoms can collectively worsen psychological and functional outcomes in MS. Fatigue symptoms may impact ambulatory physical activity to a greater degree than depression symptom status in persons with MS. Fatigue is an important consideration when designing behavior change interventions targeted at promoting physical activity in persons with MS. [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellites are equipped with high-precision three-axis accelerometers to measure all non-gravitational accelerations acting on the satellites. Precise knowledge of these perturbations is a major prerequisite for successful Gravity Field Recovery (GFR). Unfortunately, after only one month in orbit the accelerometer on one of the two satellites produced decreasingly accurate measurements. Due to this, the GRACE-D accelerometer data has to be replaced by artificial data for the use in the GFR process. In most existing approaches, which are called transplants, this data is constructed from GRACE-C measurements. We are developing a GRACE-D accelerometer product that relies mainly on modelled acceleration data instead of a full transplant. By using physical models to generate acceleration data, we are able to better understand the accelerometer signal and its characteristics, which is beneficial for subsequent processes involving accelerometer data. We present the evaluation of our modelled data in terms of comparison to the official Science Data System (SDS) accelerometer data. For the first GRACE-FO satellite real accelerometer is used as a reference, but for the second satellite the main comparisons are done with respect to the ACH1B transplant data product. To get information about environmental changes and long-term effects data sets covering one year and nearly the whole mission duration are presented. In terms of gravity field solutions the performance of purely modelled accelerometer data for GRACE-D and a minimalistic transplant approach of estimated density values is compared to transplant products of other processing centres for the year 2019 as well as one month of 2023. Through this, the modelled solution can act as a cross-reference to the transplant, contributing to an improved implementation of the external perturbation characteristics and the influence of higher solar activity on our models and the subsequent gravity field solutions can be determined. Comparison of the artificial acceleration data to the real ACT1B data for GRACE-C showed that models related to the atmospheric drag are the limiting factors in our high-precision environment modelling approach. Nevertheless, it was still possible to generate monthly gravity field solutions with a combination of ACT1B data for GRACE-C and artificial data for GRACE-D that showed hydrological signals. Application of the transplant that substitutes the limiting modelled thermospheric density with estimated values obtained at GRACE-C positions reduces the residuals between artificial and real acceleration data significantly. Estimation of gravity fields showed that the improvements due to the transplant directly transfer into the gravity field solutions. Our minimalistic and physically motivated transplant is shown to be a valuable alternative to the transplant data from TUG and JPL. The GROOPS software is utilized to generate monthly gravity field solution to guarantee comparability of the presented solutions for the geodesy community. The presented results not only show the quality of our transplant product but also enabled us to identify potential areas of improvement in the data generation process and our in–house gravity field recovery tool. The ZARM GRACE-D transplant product, all radiation accelerations and estimated density is available for download at: https://www.zarm.uni-bremen.de/zarm_daten/. [ABSTRACT FROM AUTHOR]

Introduction: The Zio® XT continuous ambulatory electrocardiographic monitor (Zio) contains an accelerometer that can help quantify an individual's physical activity in the free-living environment. Purpose: (1) To estimate activity cut-points to estimate daily time spent performing very light, light, or moderate to vigorous physical activity (VLIPA, LIPA, MVPA) for the Zio accelerometer. (2) To describe how Zio's 24-h wear protocol affects estimates of daily MVPA relative to a waist-worn accelerometer's waking-wear protocol. Methods: Three hundred eighty one participants from the Atherosclerosis Risk in Communities (ARIC) study wore a waist-mounted ActiGraph GT3X (except while sleeping or water-based activities) and a Zio (24-h) simultaneously for three to seven days. For each person-minute, physical activity was summarized as the Mean Amplitude Deviation (MAD) for the Zio and Vector Magnitude Counts (VMC) for the GT3X. Cut points previously used in ARIC were mapped from GT3X VMC to Zio MAD using a conditional two-sample quantile–quantile approach. Results: Evenson VMC cut-points for the GT3X (VLIPA≥76, LIPA≥903, MVPA≥2075 counts/min) were mapped to MAD for Zio (9.04, 28.2, and 58.1 mili-g). Daily hours spent in each intensity category were each strongly correlated (ICC > 0.7) between summaries produced by Zio and GT3X when restricting estimates to overlapping wear time. Zio and GT3X-estimated MVPA had high agreement (ICC = 0.77) when using device-specific wear time, but Zio measured one hour more of daily LIPA and VLIPA (95% CI = 0.83, 1.07 hrs/day). Conclusions: We recommend the use of our cut-points for clinical research with Zio accelerometry in populations of older adults. [ABSTRACT FROM AUTHOR]

Accelerometers, devices that measure body movements, have become valuable tools for studying the fragmentation of rest-activity patterns, a core circadian rhythm dimension, using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probability (TP), and self-similarity parameter (named α ). However, their use remains mainly empirical. Therefore, we investigated the mathematical properties and interpretability of rest-activity fragmentation metrics by providing mathematical proofs for the ranges of IS and IV, proposing maximum likelihood and Bayesian estimators for TP, introducing the activity balance index (ABI) metric, a transformation of α , and describing distributions of these metrics in real-life setting. Analysis of accelerometer data from 2,859 individuals (age=60-83 years, 21.1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α . Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0.261 (95% CI -0.302, -0.220) to 0.228 (0.18, 0.268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical use in a large dataset. [ABSTRACT FROM AUTHOR]

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs. [ABSTRACT FROM AUTHOR]

Utilizing an embedded system is crucial for exploring various fields of study, such as ground wave propagation, which interests earth and civil engineering scientists/students. The constructed system includes a data acquisition (DAQ) module and vibration sensors, which can be employed to study wave propagation. It is particularly beneficial for students lacking access to necessary instruments. The sensors utilized are MEMS accelerometers. Developed using LabVIEW programming software, a graphical interface controls the DAQ and visualizes the wave signals. The primary parameter of the DAQ is the sampling frequency, which is adjustable by the user from 1 kHz to 25 kHz. Consequently, when testing with distances ranging from 1 to 10 m between the sensor and the source, a sampling rate exceeding 10 kHz represents ground acceleration reliably. Another aspect addressed in subsequent studies using this system involves analyzing the results to characterize ground wave attributes. [ABSTRACT FROM AUTHOR]

Pelvic incidence (PI)–lumbar lordosis (LL) mismatch has a significant destabilizing effect on the center of gravity sway in the static standing position. However, the association between spinopelvic alignment and balance during gait in healthy volunteers is poorly understood. The degree of PI–LL mismatch and trunk anterior tilt in the static standing posture influences dynamic balance during gait. In this study, 131 healthy volunteers were divided into two groups: harmonious group (PI − LL ≤ 10°; n = 91) and unharmonious group (PI − LL > 10°; n = 40). A two-point accelerometer system was used for gait analysis; accelerometers were attached to the pelvis and upper trunk to measure acceleration in the forward–backward, right–left, and vertical directions so that sagittal (front–back) deviation width, coronal (right–left) width, and vertical width and their ratios were calculated. Measurements were compared between the two groups, and correlations between alignment and accelerometer data were examined. The harmonious group showed a negative correlation between pelvic sagittal width and PI – LL, pelvic tilt (PT), and sagittal vertical axis (SVA) (correlation coefficient ρ = −0.42, −0.38, and −0.4, respectively), and a positive correlation between sagittal ratio and PI − LL (ρ = 0.35). The unharmonious group showed a positive correlation between pelvic sagittal width and PI and PT (ρ = 0.43 and 0.33, respectively) and between sagittal ratio and SVA (ρ = 0.32). The unharmonious group showed a positive correlation between upper trunk sagittal width and PI − LL and PT (ρ = 0.38 and 0.36, respectively). The association between spinal alignment and gait parameters differs depending on the presence or absence of PI–LL mismatch. The degree of pelvic compensation and trunk anterior tilt during static standing were associated with unstable gait balance. • Subjects without PI–LL mismatch reduces pelvic movement during gait with compensation. • Poor compensated subjects with PI–LL mismatch increased upper trunk sway during gait. • Static standing spinal alignment associated with trunk sway during gait. [ABSTRACT FROM AUTHOR]

Objective: Compare the long-term effects of two different individualised physical activity interventions in hip or knee osteoarthritis patients. Design: Randomised, assessor-blinded, controlled trial. Setting: Primary care. Subjects: Patients with clinically verified hip or knee osteoarthritis, <150 min/week with moderate or vigorous physical activity, aged 40–74. Intervention: The advice group (n = 69) received a 1-h information and goalsetting session for individualised physical activity. The prescription group (n = 72) received information, goalsetting, individualised written prescription, self-monitoring, and four follow-ups. Main measures: Physical activity, physical function, pain and quality of life at baseline, 6, 12 and 24 months. Results: There were only minor differences in outcomes between the two groups. For self-reported physical activity, the advice group had improved from a mean of 102 (95% CI 74–130) minutes/week at baseline to 214 (95% CI 183–245) minutes/week at 24 months, while the prescription group had improved from 130 (95% CI 103–157) to 176 (95% CI 145–207) minutes/week (p = 0.01 between groups). Number of steps/day decreased by −514 (95% CI −567–462) steps from baseline to 24 months in the advice group, and the decrease in the prescription group was −852 (95% CI −900–804) steps (p = 0.415 between groups). Pain (HOOS/KOOS) in the advice group had improved by 7.9 points (95% CI 7.5–8.2) and in the prescription group by 14.7 points (95% CI 14.3–15.1) from baseline to 24 months (p = 0.024 between groups). Conclusions: There is no evidence that individualised physical activity on prescription differs from individualised advice in improving long-term effects in patients with hip or knee osteoarthritis. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to examine the associations of 24-h movement behaviors (moderate to vigorous physical activity [MVPA], light physical activity [LPA], sedentary behavior [SB], and sleep) with age-, sex-, and race-specific a real bone mineral density (aBMD) z-score parameters at clinical sites in young pediatric cancer survivors. Methods: This cross-sectional multicenter study was carried out within the iBoneFIT framework in which 116 young pediatric cancer survivors (12.1 ± 3.3 yr old; 42%female) were recruited. We obtained anthropometric and body composition data (i.e., body mass, stature, body mass index, and region-specific lean mass), time spent in movement behaviors over at least seven consecutive 24-h periods (wGT3x-BT accelerometer, ActiGraph), and aBMD z-score parameters (age-, sex-, and race-specific total at the body, total hip, femoral neck and lumbar spine). Survivors were classified according to somatic maturity (pre or peri/postpubertal depending on the estimated years from peak height velocity). The adjusted models' coefficients were used to predict the effect of reallocating time proportionally across behaviors on the outcomes. Results: In prepubertal young pediatric cancer survivors, reallocating time to MVPA from LPA, SB, and sleep was significantly associated with higher aBMD at total body (B = 1.765, P = 0.005), total hip (B = 1.709, P = 0.003), and lumbar spine (B = 2.093, P = 0.001). In peri/postpubertal survivors, reallocating time to LPA from MVPA, SB, and sleep was significantly associated with higher aBMD at all sites (B = 2.090 to 2.609, P = 0.003 to 0.038). Reallocating time to SB from MVPA or LPA was significantly associated with lower aBMD atmost sites in prepubertal and peri/postpubertal survivors, respectively. Finally, reallocating time to sleep from MVPA, LPA, and SB was significantly associated with lower aBMD at total body (B = -2.572, P = 0.036) and total hip (B = -3.371, P = 0.015). Conclusions: These findings suggest that every move counts and underline the benefits of increasing MVPA or LPA, when low MVPA levels are present, for bone regeneration after pediatric cancer treatment completion. [ABSTRACT FROM AUTHOR]

Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant interacted with the iPad. Five machine learning models, logistic regression (LR), support vector machine, neural net (NN), k-nearest neighbors (k-NN), and random forest (RF), were trained using 57 features generated from the sensor output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using F 1 score, accuracy, precision, and recall. Model performance was high, with F 1 scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with F 1 scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments. [ABSTRACT FROM AUTHOR]

Background: Assessments of arm motor function are usually based on clinical examinations or self-reported rating scales. Wrist-worn accelerometers can be a good complement to measure movement patterns after stroke. Currently there is limited knowledge of how accelerometry correlate to clinically used scales. The purpose of this study was therefore to evaluate the relationship between intermittent measurements of wrist-worn accelerometers and the patient's progression of arm motor function assessed by routine clinical outcome measures during a rehabilitation period. Methods: Patients enrolled in in-hospital rehabilitation following a stroke were invited. Included patients were asked to wear wrist accelerometers for 24 h at the start (T1) and end (T2) of their rehabilitation period. On both occasions arm motor function was assessed by the modified Motor Assessment Scale (M_MAS) and the Motor Activity Log (MAL). The recorded accelerometry was compared to M_MAS and MAL. Results: 20 patients were included, of which 18 completed all measurements and were therefore included in the final analysis. The resulting Spearman's rank correlation coefficient showed a strong positive correlation between measured wrist acceleration in the affected arm and M-MAS and MAL values at T1, 0.94 (p < 0.05) for M_MAS and 0.74 (p < 0.05) for the MAL values, and a slightly weaker positive correlation at T2, 0.57 (p < 0.05) for M_MAS and 0.46 − 0.45 (p = 0.06) for the MAL values. However, no correlation was seen for the difference between the two sessions. Conclusions: The results confirm that the wrist acceleration can differentiate between the affected and non-affected arm, and that there is a positive correlation between accelerometry and clinical measures. Many of the patients did not change their M-MAS or MAL scores during the rehabilitation period, which may explain why no correlation was seen for the difference between measurements during the rehabilitation period. Further studies should include continuous accelerometry throughout the rehabilitation period to reduce the impact of day-to-day variability. [ABSTRACT FROM AUTHOR]

Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) identifying the optimal window size for signal pre-processing, and (3) demonstrating the number of observations required to achieve the desired level of model accuracy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS collars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were apparent because of the head-down posture. Increasing the smoothing window size to 10 s improved classification accuracy (p < 0.05), but reducing the number of observations below 50% resulted in a decrease in accuracy for all behaviors (p < 0.05). In-pasture observation increased accuracy and precision (0.05 and 0.08 percent, respectively) compared with animal-borne collar video observations. [ABSTRACT FROM AUTHOR]

The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7–22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab and at-home protocols. We found strong agreement between GT1M and GT9X counts during the combined in-lab activities (mean bias = 2 counts) and between minutes per day with different intensities of activity (e.g., sedentary, light, moderate, and vigorous) classified using cutpoints (mean bias < 5 min/d at all intensities). We generated bin sizes that could be used to generate IGs from the count data (mean bias = −0.15; 95% LOA [−0.65, 0.34]) compared with the original IG. Therefore, the intensity gradient could be used to analyze count data. The count-based intensity gradient metric will be valuable for re-analyzing historical datasets collected using older accelerometer models, such as the GT1M. [ABSTRACT FROM AUTHOR]

Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the potential enhancement of HAR models through the fusion of biological signals with inertial signals. The classification of eight common low-, medium-, and high-intensity activities was assessed using machine learning (ML) algorithms, trained on accelerometer (ACC), blood volume pulse (BVP), and electrodermal activity (EDA) data obtained from a wrist-worn sensor. Two types of ML algorithms were employed: a random forest (RF) trained on features; and a pre-trained deep learning (DL) network (ResNet-18) trained on spectrogram images. Evaluation was conducted on both individual activities and more generalized activity groups, based on similar intensity. Results indicated that RF classifiers outperformed corresponding DL classifiers at both individual and grouped levels. However, the fusion of EDA and BVP signals with ACC data improved DL classifier performance compared to a baseline DL model with ACC-only data. The best performance was achieved by a classifier trained on a combination of ACC, EDA, and BVP images, yielding F1-scores of 69 and 87 for individual and grouped activity classifications, respectively. For DL models trained with additional biological signals, almost all individual activity classifications showed improvement (p-value < 0.05). In grouped activity classifications, DL model performance was enhanced for low- and medium-intensity activities. Exploring the classification of two specific activities, ascending/descending stairs and cycling, revealed significantly improved results using a DL model trained on combined ACC, BVP, and EDA spectrogram images (p-value < 0.05). [ABSTRACT FROM AUTHOR]

Thermosphere mass density and crosswind can be derived from accelerometer and GNSS tracking data. However, present datasets are often provided without comprehensive uncertainty specifications. We present a newly developed method that propagates measurement noise and errors in the satellite specification, thermosphere models, and radiation flux data to density observations to quantify their uncertainty. We focus specifically on density observations derived only from GNSS tracking data, which are limited in resolution along the orbit due to unavoidable smoothing. While the method can be applied to simulated and real data, making it useful for existing datasets and mission design, we demonstrated it using data from the GRACE B satellite. First, we compare the aerodynamic acceleration derived separately from the accelerometer and GNSS tracking data, highlighting the role of two significant noise sources: noise due to the differentiation of the positions and noise from the evaluation of the gravity vector at a noisy position. Averaging substantially reduces the noise in the aerodynamic acceleration as long as the differentiation noise dominates, which is the case at frequencies higher than the orbital frequency. Below, gravity vector evaluation noise becomes the dominating noise source, and consequently, averaging over longer periods leads to only marginal uncertainty reduction. Further, we investigate the uncertainty in the radiation pressure acceleration and demonstrate that averaging over one orbit substantially reduces the uncertainty in the along-track radiation pressure acceleration. We show that the uncertainty of density observations derived from the accelerometer data is about 4% of the density for data from 2003 when the GRACE B satellite was at 490 km altitude during high solar activity. In 2008, solar activity was very low, and the altitude was still 476 km, resulting in an uncertainty of 5%–20% because GNSS tracking noise and radiation pressure modeling errors play a much larger role as the aerodynamic acceleration becomes smaller. In the case of density observations derived only from GNSS tracking data, the uncertainty is about 5% in 2003 and 20%–50% in 2008 when averaging over one-third orbit. In 2008, GNSS tracking noise explains nearly all uncertainty in the density observation. Averaging over one orbit reduces the uncertainty to 4% and 5% in 2003 and 2008, respectively. [ABSTRACT FROM AUTHOR]

This cross-sectional study aimed to identify and validate cut-points for measuring physical activity using Axivity AX6 accelerometers positioned at the shank in older adults. Free-living physical activity was assessed in 35 adults aged 55 and older, where each participant wore a shank-mounted Axivity and a waist-mounted ActiGraph simultaneously for 72 hours. Optimized cut-points for each participant's Axivity data were determined using an optimization algorithm to align with ActiGraph results. To assess the validity between the physical activity assessments from the optimized Axivity cut-points, a leave-one-out cross-validation was conducted. Bland-Altman plots with 95% limits of agreement, intraclass correlation coefficients (ICC), and mean differences were used for comparing the systems. The results indicated good agreement between the two accelerometers when classifying sedentary behaviour (ICC = 0.85) and light physical activity (ICC = 0.80), and moderate agreement when classifying moderate physical activity (ICC = 0.67) and vigorous physical activity (ICC = 0.70). Upon removal of a significant outlier, the agreement was slightly improved for sedentary behaviour (ICC = 0.86) and light physical activity (ICC = 0.82), but substantially improved for moderate physical activity (ICC = 0.81) and vigorous physical activity (ICC = 0.96). Overall, the study successfully demonstrated the capability of the resultant cut-point model to accurately classify physical activity using Axivity AX6 sensors placed at the shank. [ABSTRACT FROM AUTHOR]

Spending time outdoors is associated with increased time spent in physical activity, lower chronic disease risk, and wellbeing. Many studies rely on self-reported measures, which are prone to recall bias. Other methods rely on features and functions only available in some GPS devices. Thus, a reliable and versatile method to objectively quantify time spent outdoors is needed. This study sought to develop a versatile method to classify indoor and outdoor (I/O) GPS data that can be widely applied using most types of GPS and accelerometer devices. To develop and test the method, five university students wore an accelerometer (ActiGraph wGT3X-BT) and a GPS device (Canmore GT-730FL-S) on an elastic belt at the right hip for two hours in June 2022 and logged their activity mode, setting, and start time via activity diaries. GPS trackers were set to collect data every 5 seconds. A rule-based point cluster-based method was developed to identify indoor, outdoor, and in-vehicle time. Point clusters were detected using an application called GPSAS_Destinations and classification were done in R using accelerometer lux, building footprint, and park location data. Classification results were compared with the submitted activity diaries for validation. A total of 7,006 points for all participants were used for I/O classification analyses. The overall I/O GPS classification accuracy rate was 89.58% (Kappa = 0.78), indicating good classification accuracy. This method provides reliable I/O clarification results and can be widely applied using most types of GPS and accelerometer devices. [ABSTRACT FROM AUTHOR]

This paper reports a MEMS accelerometer based on bulk SiC. The fabrication and packaging scheme were designed, an efficient signal processing circuit was built, and the output performance of the sensor was tested. The test results show that the resistivity of the piezoresistive material is 194.40 Ω μm, the sensor's output sensitivity is 0.21 mV/g, the linearity is 99.8%, and the resonance frequency is 1636 Hz. Finally, the accelerometer was applied to the two ground vibration tests of aero‐engine, and the vibration signal of the turbine components and the test bed was successfully extracted. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

To investigate a smartphone-enabled quantitative evaluation of anticipatory postural adjustments (APA) during one-leg stance (OLS) movements among individuals with Parkinson's disease (PD). This cross-sectional study included 10 young controls, 10 older individuals, and 13 individuals with PD. A smartphone and accelerometer were attached to the participants' lower back (L5), and the movements of the lower back toward the stance side during OLS were measured. For acceleration, the time to the peak value in the stance direction (peak latency [PL]) and the amount of displacement to the peak value in the stance direction (peak magnitude [PM]) were analyzed as APA characteristics. Additionally, the measured PL was divided by the PM for each group to obtain the APA ratio (APAr) as a new index. Individuals with PD showed a delayed PL and decreased PM (vs. young controls: p =.002 for PL, p <.001 for PM) (vs. older individuals: p =.022 for PL, p =.001 for PM). The APAr clustered the young controls, older individuals, and individuals with PD. According to the receiver operating characteristic curve the APAr value was 0.95, and individuals in the PD group were identified (i.e. area under the curve: 0.98; sensitivity: 85.0%; specificity: 100%). Moreover the APAr was correlated with severity and balance ability in individuals with PD (p =.015 for NFOG-Q, p =.028 for UPDRS, p =.036 for TUG, p =.015 for Mini-BESTest, p =.018 for OLS time). This smartphone-based evaluation using the APAr index was reflective of disease severity and decreased balance ability among individuals with PD. The facilitation of this measurement can help clinicians and physiotherapists quantitatively evaluate the APA of individuals with PD at laboratories and hospitals as well as in home environments. [ABSTRACT FROM AUTHOR]

Background: There is a growing population of survivors of colorectal cancer (CRC). Fatigue and insomnia are common symptoms after CRC, negatively influencing health-related quality of life (HRQoL). Besides increasing physical activity and decreasing sedentary behavior, the timing and patterns of physical activity and rest over the 24-h day (i.e. diurnal rest-activity rhythms) could also play a role in alleviating these symptoms and improving HRQoL. We investigated longitudinal associations of the diurnal rest-activity rhythm (RAR) with fatigue, insomnia, and HRQoL in survivors of CRC. Methods: In a prospective cohort study among survivors of stage I-III CRC, 5 repeated measurements were performed from 6 weeks up to 5 years post-treatment. Parameters of RAR, including mesor, amplitude, acrophase, circadian quotient, dichotomy index, and 24-h autocorrelation coefficient, were assessed by a custom MATLAB program using data from tri-axial accelerometers worn on the upper thigh for 7 consecutive days. Fatigue, insomnia, and HRQoL were measured by validated questionnaires. Confounder-adjusted linear mixed models were applied to analyze longitudinal associations of RAR with fatigue, insomnia, and HRQoL from 6 weeks until 5 years post-treatment. Additionally, intra-individual and inter-individual associations over time were separated. Results: Data were available from 289 survivors of CRC. All RAR parameters except for 24-h autocorrelation increased from 6 weeks to 6 months post-treatment, after which they remained relatively stable. A higher mesor, amplitude, circadian quotient, dichotomy index, and 24-h autocorrelation were statistically significantly associated with less fatigue and better HRQoL over time. A higher amplitude and circadian quotient were associated with lower insomnia. Most of these associations appeared driven by both within-person changes over time and between-person differences in RAR parameters. No significant associations were observed for acrophase. Conclusions: In the first five years after CRC treatment, adhering to a generally more active (mesor) and consistent (24-h autocorrelation) RAR, with a pronounced peak activity (amplitude) and a marked difference between daytime and nighttime activity (dichotomy index) was found to be associated with lower fatigue, lower insomnia, and a better HRQoL. Future intervention studies are needed to investigate if restoring RAR among survivors of CRC could help to alleviate symptoms of fatigue and insomnia while enhancing their HRQoL. Trial registration: EnCoRe study NL6904 (https://www.onderzoekmetmensen.nl/). [ABSTRACT FROM AUTHOR]

Observing animals in the wild often poses extreme challenges, but animal‐borne accelerometers are increasingly revealing unobservable behaviours. Automated machine learning streamlines behaviour identification from the substantial datasets generated during multi‐animal, long‐term studies; however, the accuracy of such models depends on the qualities of the training data. We examined how data processing influenced the predictive accuracy of random forest (RF) models, leveraging the easily observed domestic cat (Felis catus) as a model organism for terrestrial mammalian behaviours. Nine indoor domestic cats were equipped with collar‐mounted tri‐axial accelerometers, and behaviours were recorded alongside video footage. From this calibrated data, eight datasets were derived with (i) additional descriptive variables, (ii) altered frequencies of acceleration data (40 Hz vs. a mean over 1 s) and (iii) standardised durations of different behaviours. These training datasets were used to generate RF models that were validated against calibrated cat behaviours before identifying the behaviours of five free‐ranging tag‐equipped cats. These predictions were compared to those identified manually to validate the accuracy of the RF models for free‐ranging animal behaviours. RF models accurately predicted the behaviours of indoor domestic cats (F‐measure up to 0.96) with discernible improvements observed with post‐data‐collection processing. Additional variables, standardised durations of behaviours and higher recording frequencies improved model accuracy. However, prediction accuracy varied with different behaviours, where high‐frequency models excelled in identifying fast‐paced behaviours (e.g. locomotion), whereas lower‐frequency models (1 Hz) more accurately identified slower, aperiodic behaviours such as grooming and feeding, particularly when examining free‐ranging cat behaviours. While RF modelling offered a robust means of behaviour identification from accelerometer data, field validations were important to validate model accuracy for free‐ranging individuals. Future studies may benefit from employing similar data processing methods that enhance RF behaviour identification accuracy, with extensive advantages for investigations into ecology, welfare and management of wild animals. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular (CV) morbidity after kidney transplantation (KTx) in childhood is of increasing importance. In light of a high prevalence of CV risk factors, protective measures such as physical activity (PA) come into focus. Our aim was to comprehensively assess PA in pediatric KTx recipients and evaluate its impact on CV health. Methods: Forty-eight patients were assessed for frequency, duration, intensity, and setting of PA using the "Motorik–Modul" PA questionnaire. Walking-based activity was measured by accelerometer in a subgroup (n = 23). CV risk factors and subclinical CV organ damage were determined. The impact of PA on CV parameters was analyzed using linear regression models. Results: Fifty-two percent of pediatric KTx recipients did not reach WHO recommended PA level; 54% did not engage in PA with vigorous intensity (VPA). Twenty-nine percent indicated an extremely inactive lifestyle (< 120 min/week of moderate to vigorous intensity PA, MVPA). Compared to the healthy German KiGGS cohort, KTx recipients specifically lacked engagement in sport activities (KTx: 129 min/week; 95%CI, 97–162 vs. KiGGS, 242 min/week; 95%CI, 230–253). VPA was associated with lower systolic blood pressure (p = 0.024) and resting heart rate (p = 0.005), MVPA with fewer components of the post-transplant metabolic syndrome (p = 0.037), and better left ventricular diastolic function (p = 0.006). Conclusions: A considerable lack of PA, especially VPA, exists in young KTx recipients. PA was positively associated with important parameters of CV health. While long-term CV protection through PA seems promising in pediatric KTx recipients, specific educational approaches are most likely needed to increase patients' engagement in sport activities. [ABSTRACT FROM AUTHOR]

Combining elastic full waveform inversion (FWI) with rock physics holds promise for expanding the application of FWI beyond seismic imaging to predicting and monitoring reservoir properties. Distributed acoustic sensing (DAS), a rapidly developing seismic acquisition technology, is being explored for its potential in supporting FWI applications. In this study, we implement a sequential inversion scheme that integrates elastic FWI and Bayesian rock physics inversion, using a vertical seismic profile (VSP) data set acquired with accelerometer and collocated DAS fibre at the Carbon Management Canada's Newell County Facility. Our aim is to establish a baseline model of porosity and lithology parameters to support later monitoring of CO2 storage. Key strategies include an effective source approach for addressing near-surface complications, a modelling strategy to simulate DAS data comparable to field data, and a Gaussian mixture approach to capture the bimodality of rock properties. We conduct FWI tests on accelerometer, DAS, and combined accelerometer-DAS data. While our inversion results accurately reproduce either data set, the elastic models inverted from accelerometer data outperform the other two in matching well logs and identifying the target reservoir. We attribute this outcome to the limited complementarity of DAS data with accelerometer data in our experiment, along with the limitations imposed by single-component measurements on DAS. The porosity and lithology models predicted from accelerometer-derived elastic models are reasonably accurate at the well location and exhibit geologically meaningful spatial distribution. [ABSTRACT FROM AUTHOR]

Increasing step rate has been proposed to reduce overuse injury risk in runners by reducing the magnitude of musculoskeletal loading. Although estimating structure-specific loads is challenging, during level running, changes in a runner's vertical ground reaction force (vGRF) may provide insight to changes in their musculoskeletal loading. Further, vGRF can be approximated using force sensing insoles. Purpose: The primary goal of this study was to examine changes in peak insole force and cumulative weighted peak force (CWPF)/km with increased step rate in collegiate runners. The secondary goal was to determine whether sacral acceleration correlates with insole force when increasing step rate. Methods: Twelve collegiate distance runners ran 1000 m outdoors at 3.83 m⋅s-1 at preferred and 10% increased step rates while insole force and sacral acceleration were recorded. Cumulative weighted peak force/km was calculated from insole force based on cumulative damage models. The effects of step rate on peak insole force and CWPF⋅km-1 were tested using paired t tests or Wilcoxon tests. Correlation coefficients between peak axial (approximately vertical) sacral acceleration times body mass and peak insole force were calculated on cohort and individual levels. Results: Peak insole force and CWPF⋅km-1 decreased (P < 0.001) with increased step rate. Peak axial sacral acceleration did not correlate with peak insole force on the cohort level (r = 0.3 5, P = 0.109) but did within individuals (mean, r = 0.69-0.78; P < 0.05). Conclusions: Increasing step rate may reduce peak vGRF and CWPF⋅km-1 in collegiate runners. Therefore, clinicians should consider step rate interventions to reduce peak and cumulative vGRF in this population. Individual-specific calibrations may be required to assess changes in peak vGRF in response to increasing step rate using wearable accelerometers. [ABSTRACT FROM AUTHOR]

Purpose: Hip and knee arthroplasty aims to reduce joint pain and increase functional mobility in patients with osteoarthritis; however, the degree to which arthroplasty is associated with higher physical activity is unclear. The current study sought to assess the association of hip and knee arthroplasty with objectively measured physical activity. Methods: This cross-sectional study analyzed wrist-worn accelerometer data collected in 2013-2016 from UK Biobank participants (aged 43-78 yr). Multivariable linear regression was performed to assess step count, cadence, overall acceleration, and activity behaviors between nonarthritic controls, end-stage arthritic, and postoperative cohorts, controlling for demographic and behavioral confounders. From a cohort of 94,707 participants with valid accelerometer wear time and complete self-reported data, electronic health records were used to identify 3506 participants having undergone primary or revision hip or knee arthroplasty and 68,389 nonarthritic controls. Results: End-stage hip or knee arthritis was associated with taking 1129 fewer steps per day (95% confidence interval (CI), 811-1447; P < 0.001) and having 5.8 fewer minutes per day (95% CI, 3.0-8.7; P < 0.001) of moderate-to-vigorous activity compared with nonarthritic controls. Unilateral primary hip and knee arthroplasties were associated with 877 (95% CI, 284-1471; P = 0.004) and 893 (95% CI, 232-1554; P = 0.008) more steps than end-stage osteoarthritic participants, respectively. Postoperative unilateral hip arthroplasty participants demonstrated levels of moderate-to-vigorous physical activity and daily step count equivalent to nonarthritic controls. No difference in physical activity was observed between any cohorts in terms of overall acceleration, or time spent in daily light activity, sedentary behavior, or sleep. Conclusions: Hip and knee arthroplasties are associated with higher levels of physical activity compared with participants with end-stage arthritis. Unilateral hip arthroplasty patients, in particular, demonstrate equivalence to nonarthritic peers at more than 1 yr after surgery. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to examine head acceleration event (HAE) propensity and incidence during elite-level men's and women's rugby union matches. Methods: Instrumented mouthguards (iMGs) were fitted in 92 male and 72 female players from nine elite-level clubs and three international teams. Data were collected during 406 player matches (239 male, 167 female) using iMGs and video analysis. Incidence was calculated as the number of HAEs per player hour and propensity as the proportion of contact events resulting in an HAE at a range of linear and angular thresholds. Results: HAE incidence above 10 g was 22.7 and 13.2 per hour in men's forwards and backs and 11.8 and 7.2 per hour in women's forwards and backs, respectively. Propensity varied by contact event, with 35.6% and 35.4% of men's tackles and carries and 23.1% and 19.6% of women's tackles and carries producing HAEs above 1.0 krad/s2. Tackles produced significantly more HAEs than carries, and incidence was greater in forwards compared with backs for both sexes and in men compared with women. Women's forwards were 1.6 times more likely to experience a medium-magnitude HAE from a carry than women's backs. Propensity was similar from tackles and carries, and between positional groups, while significantly higher in men than women. The initial collision stage of the tackle had a higher propensity than other stages. Conclusion: This study quantifies HAE exposures in elite rugby union players using iMGs. Most contact events in rugby union resulted in lower-magnitude HAEs, while higher-magnitude HAEs were comparatively rare. An HAE above 40 g occurred once every 60–100 min in men and 200–300 min in women. Future research on mechanisms for HAEs may inform strategies aimed at reducing HAEs. [ABSTRACT FROM AUTHOR]

Introduction: Age‐related decline in physical functioning has significant implications for health in later life but declines begin earlier in midlife. Physical activity (PA) volume is associated with physical function, but the importance of the pattern in which PA is accumulated is unclear. This study investigates associations between patterns of PA accumulation, including the composition, variation, and temporal distribution of upright and stepping events, with physical function in midlife. Methods: Participants (n = 4378) from the 1970 British Cohort Study wore an activPAL3 accelerometer on the thigh for 7 consecutive days. Exposure measures included a suite of metrics describing the frequency, duration, and composition of upright events, as well as the duration and volume (total steps) of stepping events. In addition, patterns of accumulation of upright and sedentary events were examined including how fragmented/transient they were (upright‐to‐sedentary transition probability [USTP]) and their burstiness (the tendency for events to be clustered together followed by longer interevent times). Physical function outcomes included grip strength (GS), balance, and SF‐36 physical functioning subscale (SF‐36pf). Cross‐sectional analyses included multivariable linear regression models to assess associations, adjusting for covariates including overall PA volume (mean daily step count). Results: Higher upright event burstiness was associated with higher GS, and higher USTP was associated with lower GS. Duration and step volume of stepping events were positively associated with SF‐36pf in females. Step‐weighted cadence was positively associated with SF‐36pf and balance. Contradictory findings were also present (e.g., more transient stepping events were associated with better GS) particularly for GS in males. Inconsistencies between sexes were observed across some associations. Conclusion: Our study reveals that diverse patterns of PA accumulation exhibit distinct associations with various measures of physical function in midlife, irrespective of the overall volume. Contradictory findings and inconsistency between sexes warrant further investigation. Patterns of PA accumulation, in addition to volume, should be considered in future PA research. Longitudinal studies are required to determine whether a given volume of activity accumulated in different patterns, impacts associations between PA and health outcomes. [ABSTRACT FROM AUTHOR]

Measurement of vibration without having contact with the target is crucial for many applications. However, real time vibration signals are frequently mixed with various levels of dynamic noises. In this research, a non-contact type vibration measurement system based on laser triangulation method is proposed by using a PSD (Position sensitive detector) and here a single axis Brüel and Kjaer piezoelectric accelerometer is used to validate the experimental results. The primary goal of this measurement system is to develop a Gabor Transform based signal denoising technique, in order to eliminate noise from the real-time vibration data. Furthermore, PSD platform vibration, which might cause unanticipated inaccuracies in the detected vibration data, has also been eliminated by utilizing a self-vibration compensation approach using an ADXL-345 three-axis accelerometer. The self-vibration compensated vibration measurement technique has been designed using an NI (national instruments) cRIO (CompactRIO) 9074 real-time FPGA (field programmable gate array) based embedded controller. Consequently, a Gabor Transform and Expansion-based Signal Processing Technique is designed using NI cRIO-9074 real time platform to reduce external noise from the self-vibration compensated vibration data. It has been observed that the implementation of Gabor Transform has improved the performance of the system in term of enhancing the SNR (Signal-to-noise ratio). [ABSTRACT FROM AUTHOR]

Gravity field solutions determined from the Gravity Recovery and Climate Experiment Follow‐on (GRACE‐FO) are affected by the unexpected performance degradation of one accelerometer onboard, which is circumvented by the accelerometer data transplant (ACT) technique. Three operational ACT products are presently available: JPL‐ACT and JPL‐ACH from NASA Jet Propulsion Laboratory (JPL) and TUG‐ACT from Graz University of Technology (TUG). Considering the heterogeneous qualities of individual ACT products, we propose to combine them to improve the gravity field models after a comprehensive assessment of individual products using a consistent Level‐1B data processing. The combination is carried out on the normal equation of gravity field solutions. Spectral, temporal, and spatial evaluations over the period October 2018 to December 2022 demonstrate that the combined solution significantly reduces the noise relative to solutions using individual ACT products, and the average noise reduction rates over JPL‐ACT range from 5% to 12% with varying post‐processing filters and C20 and C30 coefficient treatments, which are double those of the second‐best JPL‐ACH. Moreover, the C20 estimate exhibits significant improvement in the combined solution with its deviation from the satellite laser ranging (SLR) solution showing a root mean square (RMS) value of 7.6 × 10−11, compared to similar RMS values of 15.9 × 10−11, 12.1 × 10−11, and 14.9 × 10−11 for JPL‐ACT, JPL‐ACH, and TUG‐ACT, respectively. This improvement helps greatly reduce the amplitude of the 161‐day spurious signal over polar regions. For C30, the JPL‐ACT gives degraded estimates with an RMS of differences to SLR of 6.2 × 10−11, while the differences are greatly reduced to 2.6 × 10−11, 2.7 × 10−11 and 3.6 × 10−11 for the combined solution, JPL‐ACH and TUG‐ACT, respectively, approaching the formal error of SLR estimates. Plain Language Summary: The Gravity Recovery and Climate Experiment Follow‐on (GRACE‐FO) mission continues to provide global measurements of the Earth's gravity field to study large‐scale mass redistribution in the Earth system. Monthly snapshots of the Earth's mass changes are routinely provided in terms of gravity field models based on GRACE‐FO observation data. However, one accelerometer onboard, used for measuring non‐gravitational forces, does not provide the expected high‐precision data, which requires the so‐called accelerometer data transplant (ACT) technique to copy measurements from the other normally functioning accelerometer. There are currently three operational ACT products but of non‐uniform qualities, and, in this study, we combine them to make the best use of each one. Evaluations show that our combined solution achieves consistently lower noise levels than other solutions derived from single ACT products over different months. Moreover, the combined solution offers a significantly improved estimation of C20, the coefficient reflecting the Earth's dynamic oblateness, as validated by the more reliable SLR technique, which is beneficial to the study of mass changes at the largest spatial scale, such as the global mean ocean mass. Key Points: The combination of different ACT products achieves an improved time series of Gravity Recovery and Climate Experiment Follow‐on (GRACE‐FO) gravity field models with uniformly lower noise levelsDifferences in C20 estimates between the combined solution and satellite laser ranging are significantly reduced compared to other GRACE‐FO solutions derived from individual ACT productsBoth JPL‐ACH and TUG‐ACT provide significantly more reliable C30 estimates than the JPL‐ACT [ABSTRACT FROM AUTHOR]

This study introduces a novel approach for fault classification in bearing components utilizing raw accelerometer data. By employing various neural network models, including deep learning architectures, we bypass the traditional preprocessing and feature-extraction stages, streamlining the classification process. Utilizing the Case Western Reserve University (CWRU) bearing dataset, our methodology demonstrates remarkable accuracy, particularly in deep learning networks such as the three variant convolutional neural networks (CNNs), achieving above 98% accuracy across various loading levels, establishing a new benchmark in fault-detection efficiency. Notably, data exploration through principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) provided valuable insights into feature relationships and patterns, aiding in effective fault detection. This research not only proves the efficacy of neural network classifiers in handling raw data but also opens avenues for more straightforward yet effective diagnostic methods in machinery health monitoring. These findings suggest significant potential for real-world applications, offering a faster yet reliable alternative to conventional fault-classification techniques. [ABSTRACT FROM AUTHOR]

Background: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). Methods: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. Results: At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized β ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. Conclusion: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. Trial registration: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145; International Registered Report Identifier (IRRID): DERR1-10.2196/28684 [ABSTRACT FROM AUTHOR]

Shoe-based wearable sensor systems are a growing research area in health monitoring, disease diagnosis, rehabilitation, and sports training. These systems—equipped with one or more sensors, either of the same or different types—capture information related to foot movement or pressure maps beneath the foot. This captured information offers an overview of the subject's overall movement, known as the human gait. Beyond sensing, these systems also provide a platform for hosting ambient energy harvesters. They hold the potential to harvest energy from foot movements and operate related low-power devices sustainably. This article proposes two types of strategies (Strategy 1 and Strategy 2) for an energy-autonomous shoe-based system. Strategy 1 uses an accelerometer as a sensor for gait acquisition, which reflects the classical choice. Strategy 2 uses a piezoelectric element for the same, which opens up a new perspective in its implementation. In both strategies, the piezoelectric elements are used to harvest energy from foot activities and operate the system. The article presents a fair comparison between both strategies in terms of power consumption, accuracy, and the extent to which piezoelectric energy harvesters can contribute to overall power management. Moreover, Strategy 2, which uses piezoelectric elements for simultaneous sensing and energy harvesting, is a power-optimized method for an energy-autonomous shoe system. [ABSTRACT FROM AUTHOR]

Background: Actigraphy is often used to measure sleep in pediatric populations, despite little confirmatory evidence of the accuracy of existing sleep/wake algorithms. The aim of this study was to determine the performance of 11 sleep algorithms in relation to overnight polysomnography in children and adolescents. Methods: One hundred thirty-seven participants aged 8–16 years wore two Actigraph wGT3X-BT (wrist, waist) and three Axivity AX3 (wrist, back, thigh) accelerometers over 24-h. Gold standard measures of sleep were obtained using polysomnography (PSG; Embletta MPRPG, ST + Proxy and TX Proxy) in the home environment, overnight. Epoch by epoch comparisons of the Sadeh (two algorithms), Cole-Kripke (three algorithms), Tudor-Locke (four algorithms), Count-Scaled (CS), and HDCZA algorithms were undertaken. Mean differences from PSG values were calculated for various sleep outcomes. Results: Overall, sensitivities were high (mean ± SD: 91.8%, ± 5.6%) and specificities moderate (63.8% ± 13.8%), with the HDCZA algorithm performing the best overall in terms of specificity (87.5% ± 1.3%) and accuracy (86.4% ± 0.9%). Sleep outcome measures were more accurately measured by devices worn at the wrist than the hip, thigh or lower back, with the exception of sleep efficiency where the reverse was true. The CS algorithm provided consistently accurate measures of sleep onset: the mean (95%CI) difference at the wrist with Axivity was 2 min (-6; -14,) and the offset was 10 min (5, -19). Several algorithms provided accurate measures of sleep quantity at the wrist, showing differences with PSG of just 1–18 min a night for sleep period time and 5–22 min for total sleep time. Accuracy was generally higher for sleep efficiency than for frequency of night wakings or wake after sleep onset. The CS algorithm was more accurate at assessing sleep period time, with narrower 95% limits of agreement compared to the HDCZA (CS:-165 to 172 min; HDCZA: -212 to 250 min). Conclusion: Although the performance of existing count-based sleep algorithms varies markedly, wrist-worn devices provide more accurate measures of most sleep measures compared to other sites. Overall, the HDZCA algorithm showed the greatest accuracy, although the most appropriate algorithm depends on the sleep measure of focus. [ABSTRACT FROM AUTHOR]

To describe physical functioning after severe COVID-19-infection. An explanatory sequential mixed method design was used. Thirty-nine participants performed tests and answered questionnaires measuring physical functioning six months after hospitalisation due to COVID-19. Thirty of these participants participated in semi-structured interviews with questions regarding how they perceived their physical functioning and recovery from COVID-19 at 12 months post-hospitalisation. At six months, physical functioning measured via chair stand test and hip-worn accelerometers was lower than normal reference values. There was a reduction in breathing muscle strength. Participants estimated their functional status during different activities as lower compared to those before COVID-19-infection, measured with a patient-specific functional scale. At one year after infection, there were descriptions of a rough recovery process and remaining symptoms. Patients recovering from severe COVID-19 seem to have reduced physical functioning and activity levels, and they perceive their recovery to be slow and difficult. They experienced a lack of clinical support and contradictory advice regarding rehabilitation. Coaching in returning to physical functioning after the infection needs to be better co-ordinated and there is a need for guidelines for health professionals to avoid patients receiving contradictory advice. Coronavirus infection disease-19 (COVID-19) can have a great impact on a person's physical functioning. In the early course of the COVID-19 pandemic there were not any clear guidelines regarding rehabilitation for this group of patients. As some people with COVID-19 may have impairments in their physical functioning up to one year after leaving hospital there is a need to provide rehabilitation. [ABSTRACT FROM AUTHOR]

Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process. [ABSTRACT FROM AUTHOR]

To improve the GNSS receiver's accuracy, continuity, and stability in measuring the height of agricultural implements, this study proposed a variable-parameter Kalman filter (VPKF) algorithm based on GNSS and accelerometer to estimate the height of the implements optimally. The VPKF was verified, and its accuracy was evaluated by parallel rail platform and field tests. From the parallel rail test results, when the GNSS receiver was in real-time kinematic (RTK) positioning and the time delay of differential correction data (TDDCD) was less than or equal to 4 s, the root mean square error (RMSE) of the VPKF estimation was 9.82 mm. The RMSE of the GNSS measurement was 18.85 mm. When the GNSS receiver lost differential correction data within 28 s, the absolute error of VPKF was less than 30 mm, and the RMSE was 16.93 mm. The field test results showed that when the GNSS receiver was in RTK positioning and the TDDCD was less than or equal to 4 s, the RMSE of VPKF estimation was 13.43 mm, and the GNSS measurement was 14.56 mm. When the GNSS receiver lost differential correction data within 28 s, the RMSE of the VPKF estimate was 15.22 mm. These results show that VPKF can optimally estimate implement height with better accuracy. Overall, the VPKF can obtain a more accurate, continuous, and stable height of the implement, and increase the application scenarios of the GNSS receiver to measure the implement height. [ABSTRACT FROM AUTHOR]

The use of assistive devices and the presence of caregivers in the living space of older people using daycare rehabilitation facilities may increase their physical activity. However, the exact relationship between these factors and physical activity has not been well established. We aimed to examine whether assistive devices and caregivers were life-space factors associated with light-intensity physical activity (LPA) and moderate to vigorous-intensity physical activity (MVPA) among daycare rehabilitation facility users. Forty-seven daycare rehabilitation facility users were recruited (mean age: 76.5 ± 8.6 years; men: 16; women: 31). LPA and MVPA were measured using accelerometers and averaged over 7 days. Life-space assessment (LSA) and the Geriatric Depression Scale score were assessed. The LSA includes composite life-space, maximal life-space (LS-M), life-space using equipment (LS-E), and independent life-space. Grip strength and maximum gait speed were measured two times, and the best results were used. Multiple regression analysis showed that LPA was significantly associated with sex (β = 0.773), grip strength (β = 0.434), and LS-M (β = 0.325), whereas MVPA was significantly associated with age (β = −0.421) and LS-E (β = 0.455). A close association was found between LS-M and LS-E and the intensity of physical activity in older people who used daycare rehabilitation facilities. [ABSTRACT FROM AUTHOR]

Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10 − 3 comparing the DigiPen's computed Δ t (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR = 60 Δ t ) from the pen accurately correlate with the reference ECG signals. [ABSTRACT FROM AUTHOR]

This paper illustrates a novel and cost-effective wireless monitoring system specifically developed for operational modal analysis of bridges. The system employs battery-powered wireless sensors based on MEMS accelerometers that dynamically balance power consumption with high processing features and a low-power, low-cost Wi-Fi module that ensures operation for at least five years. The paper focuses on the system's characteristics, stressing the challenges of wireless communication, such as data preprocessing, synchronization, system lifetime, and simple configurability, achieved through the integration of a user-friendly, web-based graphical user interface. The system's performance is validated by a lateral excitation test of a model structure, employing dynamic identification techniques, further verified through FEM modeling. Later, a system composed of 30 sensors was installed on a concrete arch bridge for continuous OMA to assess its behavior. Furthermore, emphasizing its versatility and effectiveness, displacement is estimated by employing conventional and an alternative strategy based on the Kalman filter. [ABSTRACT FROM AUTHOR]

Sleep hygiene behaviours are recommendations given to both clinical and non-clinical populations with a focus on modifying behaviours to maximise sleep outcomes. However, methodological issues present in sleep hygiene research make it difficult to conclusively determine the impact of each behaviour. This study aimed to address these issues by adopting a two-week, repeated measures design which incorporated objective sleep measures and used linear mixed effect modelling to assess the daily association of a wide range of sleep hygiene behaviours on sleep in a non-clinical, university sample. Between-persons effects revealed that bedtime and frequency of daytime napping, alcohol use, and social media use were negatively related to sleep duration while waketime and frequency of too much water consumption were positively related to sleep duration. Within-person effects revealed that later than usual bedtime, earlier than usual waketime, no sunlight exposure, poor ventilation, having an unpleasant conversation before bed were negatively associated with sleep duration whereas using alcohol to deliberately help full asleep was positively related to sleep duration. In contrast, disproportionately more behaviours were not significantly related to either sleep outcome, only some of which could be explained by individual differences, which suggests that more research is needed to determine the conditions under which these behaviours affect sleep, if at all. [ABSTRACT FROM AUTHOR]

The reduction of non‐gravitational accelerations through an onboard accelerometer is a vital part of accurately estimating gravity fields for Gravity Recovery and Climate Experiment (GRACE) Follow‐On (GRACE‐FO). Current accelerometer quality, specifically a defect on the GRACE‐D accelerometer, necessitates the use of extensive calibration techniques to optimally utilize the accelerometer measurements. This paper describes these techniques and correction algorithms as they are used for the official GRACE‐FO accelerometer products, which rest on the information present in the partially functional GRACE‐D accelerometer. First, transplanted data from GRACE‐C is used to remove common effects between the spacecraft. Then, GRACE‐D data is utilized to compute a correction to the transplanted data to account for solar radiation pressure (SRP) and albedo using a parity argument and to account for non‐common drag and thruster leaks in the attitude control system. The SRP and albedo term predominantly improves C3,0 while the drag and leaky thruster term improves the gravity field across the entire spectrum. Finally, some comments on alternative data usage, initial modifications to the algorithms to account for other spacecraft operational modes, and future outlook are provided. Key Points: GRACE‐D accelerometer data, while noisy, is calibrated to recover useable signals and optimize science dataUpdated accelerometer calibrations improve gravity field recovery [ABSTRACT FROM AUTHOR]

Introduction: Frailty is conventionally diagnosed using clinical tests and self-reported assessments. However, digital health technologies (DHTs), such as wearable accelerometers, can capture physical activity and gait during daily life, enabling more objective assessments. In this study, we assess the feasibility of deploying DHTs in community-dwelling older individuals, and investigate the relationship between digital measurements of physical activity and gait in naturalistic environments and participants' frailty status, as measured by conventional assessments. Methods: Fried Frailty Score (FFS) was used to classify fifty healthy individuals as non-frail (FFS = 0, n/female = 21/11, mean ± SD age: 71.10 ± 3.59 years), pre-frail (FFS = 1–2, n/female = 23/9, age: 73.74 ± 5.52 years), or frail (FFS = 3+, n/female = 6/6, age: 70.70 ± 6.53 years). Participants wore wrist-worn and lumbar-worn GENEActiv accelerometers (Activinsights Ltd., Kimbolton, UK) during three in-laboratory visits, and at-home for 2 weeks, to measure physical activity and gait. After this period, they completed a comfort and usability questionnaire. Compliant days at-home were defined as follows: those with ≥18 h of wear time, for the wrist-worn accelerometer, and those with ≥1 detected walking bout, for the lumbar-worn accelerometer. For each at-home measurement, a group analysis was performed using a linear regression model followed by ANOVA, to investigate the effect of frailty on physical activity and gait. Correlation between at-home digital measurements and conventional in-laboratory assessments was also investigated. Results: Participants were highly compliant in wearing the accelerometers, as 94% indicated willingness to wear the wrist device, and 66% the lumbar device, for at least 1 week. Time spent in sedentary activity and time spent in moderate activity as measured from the wrist device, as well as average gait speed and its 95th percentile from the lumbar device were significantly different between frailty groups. Moderate correlations between digital measurements and self-reported physical activity were found. Conclusions: This work highlights the feasibility of deploying DHTs in studies involving older individuals. The potential of digital measurements in distinguishing frailty phenotypes, while unobtrusively collecting unbiased data, thus minimizing participants' travels to sites, will be further assessed in a follow-up study. [ABSTRACT FROM AUTHOR]

This study examined the concordance between accelerometry-measured and self-reported physical activity (PA) and sedentary time in adults with autism. Twenty-four participants wore an ActiGraph GT3X + accelerometer for seven consecutive days and completed the International Physical Activity Questionnaire-Short Form (IPAQ-SF) on the last day of their study participation. Bland-Altman plots assessed the magnitude of agreement between the two measures. Nearly 80% of the participants accumulated the recommended ≥ 150 min of moderate to vigorous PA (MVPA)/week, but were also sedentary for over nine hours/day according to accelerometry data. Findings showed that adults with autism tended to overreport MVPA (b = 1.606, p < 0.01) and underreport sedentary time (b = 1.161, p = 0.03) via the IPAQ-SF, as compared to objective measurements. [ABSTRACT FROM AUTHOR]

Introduction: Objective measurement of compliance is mandatory to evaluate therapeutic effects and clinical benefits of medical compression stockings. This Valitrack study evaluates the Accutrack sensor equipped with an accelerometer. The evaluation is done under controlled and free conditions and during washing. Results: Global accordance results are excellent: Sensitivity (98.6%) and specificity (95.2%) and 92% of patients are not disturbed by the sensors when putting on the stockings. Discussion: Thanks to its specificities one sensor per patient is sufficient. Its sensitivity distinguishes still and active positions. Only hand washing remains a false positive. The Accutrack sensor objectively measures compliance and will not be a bias because 96% of patients forget it. Conclusions: Objective compliance measurement with Accutrack allows us to evaluate the efficacity of compression, the reasons of non-compliance, and the patient's actual physical activity according to its accelerometer time-stamped. [ABSTRACT FROM AUTHOR]

There is a well‐established relationship between physical activity (PA) and physical fitness in children, being the latter an important marker for present and future health; however, there is still insufficient knowledge for the transition from the preschool age to early childhood. Therefore, this study in Swedish children aimed to investigate the estimated effect of meeting the aerobic component of the PA guidelines at 4 and/or 9 years of age on physical fitness measured at 9 years of age. PA was assessed using a wrist‐worn ActiGraph accelerometer and identical data processing in 217 healthy children in Sweden (114 boys and 103 girls). Physical fitness test included cardiorespiratory (20 m shuttle run test), motor (4 × 10 m shuttle run), and muscular fitness (hand grip strength and long jump). A linear mixed model was run, investigating the interaction between meeting the PA guidelines and time (either 4 or 9 years of age) and each fitness component (at 4 and 9). Interactions by sex were also checked. Meeting the PA guidelines consistently (at 4 and 9 years) was significantly associated to better performance in physical fitness parameters for motor fitness (−0.76 s, p < 0.001) and lower body muscular fitness (+4.6 cm; p < 0.001) at 9 years. There was an interaction between meeting the PA guidelines and time point, for cardiorespiratory fitness (+4.58 laps; p < 0.001). This study shows that meeting the PA guidelines at 4 and 9 years of age is associated to higher physical fitness at 9 years of age. [ABSTRACT FROM AUTHOR]

This study aimed to develop and validate machine learning models to predict intensities in children and adolescents with cystic fibrosis (CF) across different accelerometry brands and placements. Thirty-five children and adolescents with CF (11.6 ± 2.8 yrs; 15 girls) and 28 healthy youth (12.2 ± 2.7 yrs; 16 girls) performed six activities whilst wearing GENEActivs (both wrists) and ActiGraphs GT9X (both wrists and waist). Three supervised learning classifiers (K-Nearest Neighbour, Random Forest and eXtreme Gradient Boosted Decision Tree) were used to identify the input signal pattern for each PA type and intensity, with a 10-fold cross-validation utilized to assess the performance of the classifiers. ActiGraph GT9X on the dominant wrist and waist and GENEActiv on the dominant wrist failed to predict vigorous intensity PA activities. All other models, for activity type and intensities, exceeded 97% accuracy, with a sensitivity and specificity of greater than 95%, irrespective of accelerometer brand, placement or health condition. [ABSTRACT FROM AUTHOR]

This study purposed to identify the better accelerometer location for monitoring badminton sports training load by investigating the correlation between internal (HR, training impulse [TRIMP]) training load and external (acceleration, player load [PL]) training loads measured from 5 different body locations. Twelve college-level badminton athletes wore a HR transmitter belt and five accelerometers fixed on both hands, legs, and lower back while performing continuous three-minute training of four badminton skills including backhand serve (BS), net shot (NS), footwork training (FT), and jump smash (JS). Results showed PL at the five locations were significantly related to TRIMP (r = 0.570–0.843, p < 0.05), in which PL at lower back has highest Pearson (r = 0.843) and partial (r = 0.366) correlated to TRIMP (p < 0.05). Base on the stepwise multiple regression, PL at lower back and racket hand explained 88% of the variance of TRIMP (R2 = 0.879). In conclusion, lower back is an idea location for accelerometer to monitor overall external training load in badminton if considering one accelerometer location, while combining PL at the lower back and racket hand can predict 88% of the variation of the internal training load. [ABSTRACT FROM AUTHOR]

The article addresses some of the novel technologies being increasingly utilised within sports biomechanics and highlights key factors to be considered in best practice. Topics include the benefits of these technologies, categories of measurements that can be obtained with inertial measurement units (IMUs,) and considerations for fixation selecting and using IMUs.

The aim was to compare the differences in kinematics of elbow and wrist and shock transmission of the upper extremity in wheelchair tennis players and able-bodied players (in standing and sitting position) during backhand strokes. Fifteen wheelchair tennis and 15 able-bodied tennis players enrolled. Electromagnetic system and trial-axial accelerometers were used to measure the difference in the kinematic parameters of the upper extremity and the impact vibration transferred across the wrist joint. The results indicated that wheelchair players demonstrated unique elbow and wrist kinematics, especially shorter total swing time, greater elbow flexion at preparation, lower wrist extension acceleration before impact, and smaller racket vibration at impact. Comparing to able-bodied players in standing, wheelchair players and players in sitting demonstrated significantly greater elbow joint flexion/extension angle, angular velocity, angular acceleration during extension, and wrist joint flexion angle. Wheelchair players also differ significantly with the players in sitting regarding elbow joint angular velocity and acceleration, and wrist joint flexion velocity. These adaptations and adjustments can be attributed to the missing lower extremity function and deficient trunk kinetic chain. The differences between wheelchair tennis players and able-bodied players in sitting could represent the progress as the wheelchair players moving from novices to experts. [ABSTRACT FROM AUTHOR]