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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.

Purpose: School recess provides a valuable opportunity for children's daily moderate- to vigorous-intensity physical activity (MVPA). This study aimed to quantify MVPA during school recess in a representative sample of Scottish children and examine whether recess MVPA varied by gender, socioeconomic status, season, urban/rural residency, and recess length. Method: Five-day accelerometry MVPA data were analyzed from 773 children (53.9% girls, 46.1% boys, 10- to 11-y-olds) from 471 schools. Binary logistic regression explored associations between meeting/not meeting the recommendation to spend 40% of recess time in MVPA and the aforementioned risk factors. Descriptive recess data were also analyzed. Results: Participants spent an average of 3.2 minutes (SD 2.1) in MVPA during recess. Girls engaged in 2.5 minutes (SD 1.7) of MVPA compared with 4.0 minutes (SD 2.2) for boys. Only 6% of children met the recess MVPA recommendation. The odds of girls (odds ratio 0.09; 95% confidence interval, 0.04–0.25) meeting the recommendation was lower (P <.001) compared with boys. No statistically significant differences were observed in meeting the recommendation for the other risk factors. Conclusion: Levels of MVPA during school recess are very low in Scottish children, and interventions aimed at increasing MVPA during recess are needed. [ABSTRACT FROM AUTHOR]

Purpose: There are important methodological considerations for translating wearable-based gait-monitoring data to field settings. This study investigated different devices' sampling rates, signal lengths, and testing frequencies for athlete monitoring using dynamical systems variables. Methods: Secondary analysis of previous wearables data (N = 10 runners) from a 5-week intensive training intervention investigated impacts of sampling rate (100–2000 Hz) and signal length (100–300 strides) on detection of gait changes caused by intensive training. Primary analysis of data from 13 separate runners during 1 week of field-based testing determined day-to-day stability of outcomes using single-session data and mean data from 2 sessions. Stride-interval long-range correlation coefficient α from detrended fluctuation analysis was the gait outcome variable. Results: Stride-interval α reduced at 100- and 200- versus 300- to 2000-Hz sampling rates (mean difference: −.02 to −.08; P ≤.045) and at 100- compared to 200- to 300-stride signal lengths (mean difference: −.05 to −.07; P <.010). Effects of intensive training were detected at 100, 200, and 400 to 2000 Hz (P ≤.043) but not 300 Hz (P =.069). Within-athlete α variability was lower using 2-session mean versus single-session data (smallest detectable change:.13 and.22, respectively). Conclusions: Detecting altered gait following intensive training was possible using 200 to 300 strides and a 100-Hz sampling rate, although 100 and 200 Hz underestimated α compared to higher rates. Using 2-session mean data lowers smallest detectable change values by nearly half compared to single-session data. Coaches, runners, and researchers can use these findings to integrate wearable-device gait monitoring into practice using dynamic systems variables. [ABSTRACT FROM AUTHOR]

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participants level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the bodys center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearsons r = -0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individuals stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Background: Little investigation of accelerometry assessed movement behaviors and physical inactivity was carried out in middle-aged and older adults in low-middle-income countries. Objective: Describe accelerometry-measured movement behaviors and prevalence of physical inactivity in middle-aged and older adults. Methods: We collected raw accelerometry data during the third visit (2017–2019) of ELSA-Brasil, a large-scale multicenter Brazilian cohort. Participants wore an ActiGraph wGT3X-BT on the waist for 24 hours for 7 days and documented sleep in a diary. Results: Nine thousand two hundred and seventy-nine participants had valid data (73.4% of the eligible cohort). Overall activity was higher for men (11.82mg; 95% confidence interval [CI], 11.7 to 11.93) than women (10.69mg; 95% CI, 10.6 to 10.77) and lower in older groups—women (−0.12mg/y; 95% CI, −0.13 to −0.11), men (−0.16mg/y; 95% CI, −0.17 to −0.14). Participants were more active from noon to midnight. Distribution of movement behaviors varied with sex and age, and sleep duration was longer in older individuals. Overall, 14.4% (95% CI, 13.7 to 15.1) were inactive, with inactivity being more frequent in women (16.4%; 95% CI, 15.4 to 17.4) than men (12.2%; 95% CI, 11 to 13). Higher rates were observed in the oldest. Retirement was associated with a higher prevalence of physical inactivity in both sexes. Conclusion: Women were less active than men. Older individuals showed a high prevalence of physical inactivity, probably related to transition into retirement. These findings strengthen evidence for public policies promoting physical activity by emphasizing the need to target women, older individuals, and those transitioning to retirement to improve and/or maintain physical activity levels throughout the course of their lives. [ABSTRACT FROM AUTHOR]

Purpose: Guided by the model of domain learning theory, this study was intended to determine the extent to which integrating a 10-min physical activity (PA) break in elementary school mathematics lessons would enhance the learning of mathematics content. Methods: A sample of 22 intact classes from a random sample of eight schools in Taiwan were randomly assigned to an experimental condition with PA breaks (n = 11) and a control condition (n = 11) for 12 weeks. Students' (N = 420) mathematics knowledge was assessed using the standardized aptitude tests accompanying the curriculum, which encompassed five knowledge domains. Results: Analysis on class means showed that adding the PA breaks did not benefit mathematics learning, which lends support to the model of domain learning theory. Discussion/Conclusion: The findings seem to question the theoretical foundation for adding PA breaks to classroom-taught content, including mathematics. Although interdisciplinary or cross-disciplinary instruction and learning may be necessary to advance students' knowledge, including PA in classroom-taught subjects needs extensive research. [ABSTRACT FROM AUTHOR]

Background: To examine longitudinal changes in accelerometer-measured moderate to vigorous physical activity (MVPA) and associated factors of changes in MVPA among community-dwelling older Japanese men and women over 2 years of follow-up. Methods: In total, 601 participants (72.2 [5.4] y, 40.6% men) were included. MVPA was assessed at baseline (2011) and follow-up (2013) using triaxial accelerometers. Sex-stratified multiple linear regression models were used to identify associated factors of changes in MVPA. Results: On average, a significant decrease in MVPA over 2 years was observed only in women (P <.001). Higher baseline MVPA levels and older age were significantly associated with a decrease in MVPA over 2 years in both men and women. Men who were currently drinking (vs no) and had faster maximum gait speed showed statistically significant increases in MVPA. Women who had very poor/poor economic status (vs fair/good) and were socially isolated (vs no) showed statistically significant increases in MVPA over 2 years, while those who had fear of falling (vs no) and poor/fair self-rated health (vs good/very good) showed statistically significant decreases in MVPA over 2 years. Conclusions: Our findings showed different associated factors of changes in MVPA by sex, suggesting the importance of accounting for sex differences in terms of developing specific intervention strategies for promoting MVPA among older men and women. [ABSTRACT FROM AUTHOR]

Background: This study adds to the current literature by using a novel device-based method to explore where preschool children are physically active outside of home and childcare settings. This study combined accelerometry with geospatial data to explore the influence of the environment on preschool children's physical activity by objectively identifying the locations where preschool children engage in moderate to vigorous physical activity (MVPA) within and outside of their neighborhood. Methods: Accelerometry, Global Positioning System, and Geographic Information Systems data from 168 preschool children aged 2–5 years were processed in ArcGIS Pro to identify locations (per 25 × 25-m fishnet cell) with high MVPA counts. Locations with high MVPA counts were defined as those with the top 20% of MVPA counts per fishnet cell. The land use for high MVPA count location was determined for 3 domains: <500 m from home, 500 to 1600 m from home, and >1600 m from home. Results: Locations with high MVPA counts <500 m from home were playgrounds (66.6%), schools (16.7%), and parks (16.7%). Locations with high MVPA counts 500 to 1600 m from home included playgrounds (33.3%), nonhome residential (29.6%), childcare centers (11.1%), and parks (3.7%). Locations with high MVPA counts >1600 m from home included nonhome residential (54.7%), sports and recreation centers (11.1%), playgrounds (6.8%), and parks (5.3%). Conclusions: Our findings highlight that local parks and playgrounds provide physical activity opportunities for preschool children, yet beyond the local neighborhood, the homes of others are important locations for preschool children to accumulate MVPA. These findings can be used to inform the design of current and future neighborhood places to better accommodate preschool children's MVPA. [ABSTRACT FROM AUTHOR]

Field-based tibial acceleration measurements are increasingly common but sampling frequencies vary between accelerometers. The purpose of this study was to determine the minimum sampling frequency needed for reliable and accurate measurement of peak axial and resultant tibial acceleration during running in the field. Tibial acceleration was measured at 7161 Hz in 19 healthy runners on concrete and grass. Acceleration data were down sampled to approximate previously used sampling frequencies. Peak axial and resultant tibial acceleration were calculated for each sampling frequency. The within-session reliability and accuracy of peak axial and resultant tibial accelerations were evaluated using intraclass correlation coefficients, mean differences, and 95% limits of agreements. Intraclass correlation coefficients greater than.9 indicated excellent within-session reliability for both peak axial and resultant tibial acceleration measured while running on concrete and grass. Peak axial and resultant tibial accelerations were 0.5 to 1.4 g lower and minimal detectable differences were up to 0.6 g higher at 102 Hz compared with higher sampling frequencies. We recommend a minimum sampling frequency of 199 Hz for accurate and reliable measurements of peak axial and resultant tibial acceleration in the field. [ABSTRACT FROM AUTHOR]

Inertial measurement units (IMUs) attached to the distal tibia are a validated method of measuring lower-extremity impact accelerations, called tibial accelerations (TAs), in runners. However, no studies have investigated the effects of small errors in IMU placement, which would be expected in real-world, autonomous use of IMUs. The purpose of this study was to evaluate the effect of a small proximal shift in IMU location on mean TAs and relationships between TAs and ground reaction force loading rates. IMUs were strapped to 18 injury-free runners at a specified standard location (∼1 cm proximal to medial malleolus) and 2 cm proximal to the standard location. TAs and ground reaction forces were measured while participants ran at self-selected and 10% slower/faster speeds. Mean TA was lower at the standard versus proximal IMU location in the faster running condition (P =.026), but similar in the slower (P =.643) and self-selected conditions (P =.654). Mean TAs measured at the standard IMU explained more variation in ground reaction force loading rates (r2 =.79−.90; P <.001) compared with those measured at the proximal IMU (r2 =.65−.72; P <.001). These results suggest that careful attention should be given to IMU placement when measuring TAs during running. [ABSTRACT FROM AUTHOR]

The purpose of this study was to measure heart rate, activity intensity, and steps in recreational singles and doubles pickleball players. We collected data in 22 singles and 31 doubles players (62.1 ± 9.7 years of age) using Garmin Fenix 5 watches (Garmin International, Inc.) and ActiGraph GT3X+ (ActiGraph LLC) accelerometers. Mean heart rates during singles and doubles were 111.6 ± 13.5 and 111.5 ± 16.2 beats/min (70.3% and 71.2% of predicted maximum heart rate), respectively. Over 70% of singles and doubles playing time was categorized in moderate to vigorous heart rate zones whereas 80.5% of singles time and 50.4% of doubles time were moderate based on Freedson accelerometer cut-points. Steps per hour were higher in singles versus doubles (3,322 ± 493 vs. 2,791 ± 359), t(51) = 4.540, p <.001. Singles and doubles pickleball are moderate- to vigorous-intensity activities that can contribute substantially toward older adults meeting physical activity guidelines. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To (1) develop and evaluate a machine learning model incorporating gait and physical activity to predict medial tibiofemoral cartilage worsening over 2 years in individuals without advanced knee osteoarthritis and (2) identify influential predictors in the model and quantify their effect on cartilage worsening. DESIGN: An ensemble machine learning model was developed to predict worsened cartilage MRI Osteoarthritis Knee Score at follow-up from gait, physical activity, clinical and demographic data from the Multicenter Osteoarthritis Study. Model performance was evaluated in repeated cross-validations. The top 10 predictors of the outcome across 100 held-out test sets were identified by a variable importance measure. Their effect on the outcome was quantified by g-computation. RESULTS: Of 947 legs in the analysis, 14% experienced medial cartilage worsening at follow-up. The median (2.5-97.5th percentile) area under the receiver operating characteristic curve across the 100 held-out test sets was 0.73 (0.65-0.79). Baseline cartilage damage, higher Kellgren-Lawrence grade, greater pain during walking, higher lateral ground reaction force impulse, greater time spent lying and lower vertical ground reaction force unloading rate were associated with greater risk of cartilage worsening. Similar results were found for the subset of knees with baseline cartilage damage. CONCLUSIONS: A machine learning approach incorporating gait, physical activity and clinical/demographic features showed good performance for predicting cartilage worsening over 2 years. While identifying potential intervention targets from the model is challenging, lateral ground reaction force impulse, time spent lying and vertical ground reaction force unloading rate should be investigated further as potential early intervention targets to reduce medial tibiofemoral cartilage worsening.

Purpose: Although physical activity (PA) has been consistently associated with breast cancer, existing evidence is limited to self-reported physical activity, which is prone to dilution bias. Therefore, this aims to examine the associations of device-measured PA domains with breast cancer risk and whether it differs by menopausal status. Methods: Prospective cohort study. Data from 48,286 women from the UK Biobank cohort were analyzed. A wrist triaxial accelerometer was used to collect physical activity data for light, moderate, vigorous, moderate to vigorous, and total PA. Cox proportional models were performed to examine the association between PA domains, menopausal status, and breast cancer risk. Results: Eight hundred thirty-six breast cancer cases were diagnosed during a median of 5.4 years (interquartile range: 4.7–5.9). For total PA, those in the most active quartile had a 26% lower risk of breast cancer (Hazard ratio [HR]: 0.74; 95% confidence interval [CI], 0.61–0.91) compared with those least active. Similar results were observed for light PA (HR: 0.79; 95% CI, 0.64–0.96), and moderate to vigorous PA (HR: 0.78; 95% CI, 0.64–0.96). However, moderate PA (HR: 0.73; 95% CI, 0.44–1.19) and vigorous PA (HR: 0.77; 95% CI, 0.56–1.05) was nonsignificant. No evidence of interaction between PA domains and menopause status was found (P >.10). Conclusion: High levels of PA are associated with a lower risk of breast cancer with similar magnitude of associations observed across different intensity domains. [ABSTRACT FROM AUTHOR]

This paper examines the within-day, bidirectional associations between physical activity and self-reported pain among older adults. Older adults (N = 104; range: 60–98 years) participated in a 10-day Ecological Momentary Assessment (EMA) study. Participants received six EMA prompts/day with a single item assessing pain. Participants wore an activPAL monitor measuring step counts. At the within-person level, on occasions when participants took more steps than usual in the 30 min before the EMA prompt, they were more likely to experience pain at the prompt ( β ^ 02 = 0.0003 , p <.03). At the between-person level, greater step counts in the 30 min before the EMA prompt, on average, were associated with less pain on occasions when pain was experienced ( β ^ 01 = − 0.0005 , p <.04). Pain was not related to subsequent stepping. Bidirectional associations between physical activity and pain were not documented, but physical activity did appear to be related to subsequent pain. [ABSTRACT FROM AUTHOR]

Different starch-to-protein ratios were compared among neutered and spayed domiciled cats. Male and female obese and non-obese cats were fed kibble diets ad libitum for 4 months high in starch (HS (38 % crude protein (CP)): starch 32 %, protein 38 %; DM basis) or high in protein (HP (55 % CP): starch 19 %, protein 55 %) but similar in energy and fat in a crossover design. Physical activity was evaluated using an accelerometer, and body composition (BC), energy expenditure (EE) and water turnover (WT) using the doubly labelled water method. Results were compared in a 2 diet × 2 sex × 2 body condition factorial arrangement. Cats fed the HS (38 % CP) diet maintained a constant body weight, but lean mass (LM) tended to be reduced in female obese but to be increased in male non-obese (P < 0·08) and increased in female non-obese cats (P = 0·01). The HP (55 % CP) diet induced an increase in cat body weight and LM (P < 0·05) without altering BC proportion. EE tended to be higher in males (351 (se 8) kJ/kg0·67/d) than females (330 (se 8) kJ/kg0·67/d; P = 0·06), was unaffected by diet or BC, decreased as age increased (R 2 0·44; P < 0·01) and increased as physical activity increased (R 2 0·58; P < 0 · 01). WT was higher for the HP (55 % CP) diet (P < 0 · 01) and increased with EE (R 2 0·65; P < 0 · 01). The HS (38 % CP) diet favoured body weight control during 4 months of ad libitum feeding. Caution is necessary to balance protein in diets of female obese cats over 5 years, as they may have low energy and food intake, with LM loss. [ABSTRACT FROM AUTHOR]

Background: Previous research indicates the start of primary school (4-5-year-old) as an essential period for the development of children's physical activity (PA) patterns, as from this point, the age-related decline of PA is most often observed. During this period, young children are exposed to a wider variety of environmental- and social contexts and therefore their PA is influenced by more diverse factors. However, in order to understand children's daily PA patterns and identify relevant opportunities for PA promotion, it is important to further unravel in which (social) contexts throughout the day, PA of young children takes place. Methods: We included a cross-national sample of 21 primary schools from the Startvaardig study. In total, 248 children provided valid accelerometer and global positioning (GPS) data. Geospatial analyses were conducted to quantify PA in (social) environments based on their school and home. Transport-related PA was evaluated using GPS speed-algorithms. PA was analysed at different environments, time-periods and for week- and weekend days separately. Results: Children accumulated an average of 60 min of moderate-to-vigorous PA (MVPA), both during week- and weekend days. Schools contributed to approximately half of daily MVPA during weekdays. During weekends, environments within 100 m from home were important, as well as locations outside the home-school neighbourhood. Pedestrian trips contributed to almost half of the daily MVPA. Conclusions: We identified several social contexts relevant for children's daily MVPA. Schools have the potential to significantly contribute to young children's PA patterns and are therefore encouraged to systematically evaluate and implement parts of the school-system that stimulate PA and potentially also learning processes. Pedestrian trips also have substantial contribution to daily MVPA of young children, which highlights the importance of daily active transport in school- and parental routines. [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]

Background: Accelerometers are commonly used for the assessment of PA; however, these devices have not been validated in people with dystonia who experience movement limitations. To properly understand movement behaviors and deliver accurate exercise prescription in this population, the validity of these devices must be tested. Objective: This study aimed to validate step count and postural transitions detected by the activPAL accelerometer (AP) against direct observation (DO) during two functional assessments: the 30-s sit-to-stand (30STS) and 6-min usual-pace walk tests. Methods: A total of 11 participants with cervical dystonia (CD) (male/female n = 5/6; mean age = 61 years; BMI = 24 kg/m²) performed the 6-min usual pace walking and 30STS while wearing the activPAL. A trained observer counted steps and observed the number of sit-to-stands. Results: The average step count detected with AP and DO was 651.8 (218-758) and 654.5 (287-798) respectively. The average transitions detected were 11 (4-16) and 12 (4-17) respectively. Both methods showed good agreement and there was a statistically significant and strong correlation between the two methods, i.e., transitions (r = 0.983, p = 0.0001), and step counts (r = 0.9841, p = 0.0001). Conclusion: There is a good agreement between activPAL and direct observation for step counts and transitions between sitting and standing in people living with CD. [ABSTRACT FROM AUTHOR]

Automatic detection of foot lift is one of the most important events of Functional Electrical Stimulation (FES). The FES system is used for the correction of Foot Drop (FD). FD is a condition where a person is unable to lift their foot from the ground due to complications that may arise after a stroke or spinal cord injury. It is crucial to accurately detect the patient’s foot lift event when correcting FD through FES as the pulse should only be applied when the person lifts their foot. The FES system applies the electrical pulse based on the input of the foot-lift detection sensor. A conventional FES system employs a sensor that is affixed on the heel to detect the lifting of the foot, but the connecting cables make the patient uncomfortable. To address this problem, IMU (Inertial Measurement Unit)-based sensors have been used, but they have some disadvantages, such as false triggering, low accuracy, and calibration. In this paper, we have presented an algorithm for detecting foot-lift events with high accuracy using a single IMU sensor through the application of deep learning techniques. We have recorded data from 10 healthy people and 10 foot drop patients. We have implemented Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), and Convolutional Neural Network (CNN) models on these data and compared the results of these three models. The proposed algorithm aims to improve the precision of stimulation in the FES system. [ABSTRACT FROM AUTHOR]

Present wearable sensors are able to measure the frequency of vital signs such as respiration rate and heartbeat rate, but unable to measure those quantitatively, e.g. respiratory volume and heartbeat strength. Meanwhile, airflow at mouth contains both the respiration and the heartbeat quantitative signals. In this study, we propose a calibration method for a wearable vital sensor attached on the chest using the airflow at the mouth for quantitative respiration monitoring. An artificial ventilator and an experimental animal were introduced as test benches prior to human clinical experiment. As a proof of concept, a micro‐electro mechanical systems (MEMS) airflow sensor and a wearable accelerometer were implemented to both the test benches. The velocity output of the piston motion measured by the wearable sensor conformed with the airflow rate output, and the obtained results indicated that the wearable sensor can monitor not only the frequency of vital signs but also quantitative magnitudes such as the respiratory volume. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [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]

The issue of the transport of goods is well-known, yet, in practice, there are often cases of damaged shipments due to improper storage and inappropriately chosen transport technology. Many cases are due to ignorance of the basic characteristics of the cargo and, consequently, its transport characteristics. Vehicle dynamics is crucial to the design of proper cargo securing; therefore, this article provides the values of longitudinal and lateral acceleration of a 16.5 m semi-trailer vehicle combination for test routes of length of 10,827 km on highways and other roads in Slovakia, Austria, and Germany from the monitoring of goods. The horizontal acceleration of 0.2 g is considered as the minimum stability of the load unit that should withstand transport. A load unit with a stability from 0.2 g to 0.3 g could be considered as the weakest load unit. The test results show that even the weakest load units such as these can be damaged in transports, as semi-trailer vehicle combinations still reach longitudinal ax1000 and lateral ay1000 accelerations between 0.2 g and 0.3 g relatively frequently. Acceleration events higher than 0.3 g occur very rarely, at 1.4 event/1000 km for roads, but only 0.1 event/1000 km for highways from our test transports. We have demonstrated through our research that it is necessary for the load units to have a minimum stability of 0.2 g. We can conclude that load units with a stability of less than 0.2 g are completely unacceptable for transport without additional securing because we obtained 70.3 acceleration events per 1000 km in the interval from 0.1 g to 0.2 g on highways but 1148.1 events per 1000 km on other roads. There is a big difference between the number of acceleration events per 1000 km on roads and highways for all acceleration intervals, which means that there is a substantially lower probability of damaging the weak load units on highways than on other roads. [ABSTRACT FROM AUTHOR]

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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]

Classifying behavior by tracking acceleration has received increased interest lately. Here, we evaluated the performance of three commercial activity trackers in differentiating seven dog behaviors. Adult companion dogs (N = 70) performed still (lying, sitting, standing) and dynamic (walking, sniffing, trotting, playing) tasks, while wearing ActiGraph GT9X Link, Kaunila and FitBark devices placed on the neck collar and ActiGraph GT9X Link placed on the back. Each task was performed for 3 min within a session and repeated in two sessions; the behaviors were confirmed from video recordings. Activity scores of devices were calculated as median values for behavioral differentiation, and as minute-based values for inter-device correlations and cutoff analysis. Measurements of all devices correlated with each other, and median activity scores of all devices − unaffected by dog age, weight or sex − differentiated the still from dynamic behaviors. Dynamic behaviors were also differentiated from each other, with exception of walking vs. sniffing by back-placed ActiGraph GT9X and Kaunila. The definition of cutoffs between behaviors varied from moderate to high accuracy; defined cutoffs for standing and walking were the least accurate. The classification performance of the cutoffs had an accuracy of 80% in all the devices; thus, they performed reasonably well in classifying these behaviors. [ABSTRACT FROM AUTHOR]

Satellites in Earth's orbit are exposed to Earth radiation, consisting of reflected solar and emitted thermal radiation, thereby exerting a non‐conservative force that causes acceleration and affects the orbits. Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO) mission aiming to retrieve the Earth's gravity potential is critically dependent on accounting for this force and all other non‐gravitational forces. There are both diurnal and seasonal variations in the Earth's radiation pressure, of which the seasonal variability can be represented by climatology. Nevertheless, the daily variations in the Earth's radiation pressure, due to the transient changes in the weather; for example, clouds and their properties, are not accounted for in the orbit perturbations studies. We show here that the top‐of‐atmosphere radiation fluxes computed with a numerical weather prediction (NWP) model explain most of the measured short‐term variations in the radial acceleration of the GRACE‐FO satellite. Our physics‐based modeling corrects a hitherto unexplained lack of power spectral density in the measured accelerations. For example, we can accurately model the accelerations associated with a tropical storm in the Indian Ocean in December 2020, which would not be possible when using climatological data. Our results demonstrate that using a global numerical weather prediction model significantly improves the simulation of non‐gravitational effects in the satellites' orbits. In the 7‐day data set, OpenIFS‐simulated acceleration exhibited higher accuracy than climatological‐data‐simulated acceleration (2.5 compared to 2.6 nms−2) and an improved precision (2.6 compared to 3.0 nms−2). This advancement contributes to a more precise orbit determination across various applications in Earth sciences. Plain Language Summary: Perturbing forces affect the satellites in Earth's orbit. A gravity field retrieval satellite mission is sensitive to the impacts that have a non‐gravitational origin, such as Earth radiation pressure. Thus, modeling this source of perturbation is crucial to have an accurate gravity field time series. In this article, we demonstrate the effectiveness of using a physics‐based numerical weather model to simulate Earth's radiation pressure. Our analysis supports the idea that this approach can improve the accuracy of non‐gravitational force estimation in various satellite missions. Key Points: We computed the top‐of‐atmosphere radiation fluxes from a weather prediction model named OpenIFSThese fluxes were used to quantify the measured short‐term variations in the radial acceleration of the Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO) satelliteWe proved that storms that OpenIFS can predict, exert high perturbation in GRACE‐FO orbit [ABSTRACT FROM AUTHOR]

Background: Gait speed is often used to estimate the walking ability in daily life in people after stroke. While measuring gait with inertial measurement units (IMUs) during clinical assessment yields additional information, it remains unclear if this information can improve the estimation of the walking ability in daily life beyond gait speed. Objective: We evaluated the additive value of IMU-based gait features over a simple gait-speed measurement in the estimation of walking ability in people after stroke. Methods: Longitudinal data during clinical stroke rehabilitation were collected. The assessment consisted of two parts and was administered every three weeks. In the first part, participants walked for two minutes (2MWT) on a fourteen-meter path with three IMUs attached to low back and feet, from which multiple gait features, including gait speed, were calculated. The dimensionality of the corresponding gait features was reduced with a principal component analysis. In the second part, gait was measured for two consecutive days using one ankle-mounted IMU. Next, three measures of walking ability in daily life were calculated, including the number of steps per day, and the average and maximal gait speed. A gait-speed-only Linear Mixed Model was used to estimate the association between gait speed and each of the three measures of walking ability. Next, the principal components (PC), derived from the 2MWT, were added to the gait-speed-only model to evaluate if they were confounders or effect modifiers. Results: Eighty-one participants were measured during rehabilitation, resulting in 198 2MWTs and 135 corresponding walking-performance measurements. 106 Gait features were reduced to nine PCs with 85.1% explained variance. The linear mixed models demonstrated that gait speed was weakly associated with the average and maximum gait speed in daily life and moderately associated with the number of steps per day. The PCs did not considerably improve the outcomes in comparison to the gait speed only models. Conclusions: Gait in people after stroke assessed in a clinical setting with IMUs differs from their walking ability in daily life. More research is needed to determine whether these discrepancies also occur in non-laboratory settings, and to identify additional non-gait factors that influence walking ability in daily life. [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]

Introduction Methods Consumer and Community Involvement Results Conclusion Physical activity is known to positively influence cognitive performance. For adults with mild cognitive impairment (MCI), the relationship between physical activity levels and cognitive performance is unknown. This cross‐sectional study aimed to determine if cognitive performance [as measured by the Montreal Cognitive Assessment (MoCA)] of people living in the community with MCI is associated with their physical activity levels or sedentary behaviour.ActivPAL™ accelerometers were used to objectively measure physical activity and sedentary behaviour for seven full days. Cognitive performance was measured using the MoCA.No involvement other than as research participantsEighty‐two participants from the Balance on the Brain randomised controlled trial were included. Most participants were retired (88%), with 33 (40%) reporting a fall in the last year. The median MoCA score was 24 (IQR 22–26). Participants achieved a mean of 6296 (±2420) steps per day and were sedentary for 10.6 (±2) hours per day. The only physical activity outcomes that had a fair, positive correlation were moderate‐ to vigorous‐intensity physical activity measures of total stepping time and total number of steps (with a cadence of ≥100 steps/min) with the orientation MoCA domain score (r(82) = 0.36, p ≤ 0.001 and r(82) = 0.37, p ≤ 0.001, respectively). Higher total sedentary time had a weak, positive correlation with better visuospatial/executive performance (r(82) = 0.23, p = 0.041). The orientation outcomes remained significant when analysed in an adjusted logistic regression model.This study found that performance in the MoCA orientation domain had a fair‐positive correlation with moderate‐intensity physical activity (i.e., stepping time and step count with a cadence of ≥100 steps/min) as measured by a thigh‐worn accelerometer for community‐dwelling older adults with MCI. When considering the relationship between cognitive domains and sedentary behaviour, consideration may be needed regarding whether cognitive enhancing activities (such as crosswords and other brain games) are being performed, which may confound this relationship. Further investigation is required to confirm these results. [ABSTRACT FROM AUTHOR]

The switched reluctance motor (SRM) is an electric motor that can function effectively in challenging operating conditions thanks to its sturdy construction and resilience to external factors. Despite somewhat weaker parameters in terms of energy and power density compared to other types of electric motors, the SRM is recommended for applications such as the military, mining, industry, and other locations where the reliability of vehicle drive is essential. Therefore, monitoring the motor's operating state and identifying the fault's condition while it is still in the beginning phase is crucial. The paper presents SRM diagnostic methods and the authors' research on the test stand. The examined faults were dynamic eccentricity and imbalance. Experiments were performed for various rotational speeds and loads. The analysis of the results consisted of the interpretation of the current and acceleration spectra acquired from proper sensors. The spectra bands are compared in terms of their amplitudes and frequency values. These results show the nonlinear characteristics of the motor's operation, and interpretation of these results allows for estimating the impact of a fault parameter on a motor's performance. [ABSTRACT FROM AUTHOR]

Greater impulse is a key performance indicator of success in short track speed skating. The main objective of this study was to develop a method to measure skating strokes using a single IMU. Eight elite or world-class speed skaters had one IMU placed against their skin on the lower back, and a camera setup was positioned to capture the test. A maximal speed trial was then executed by each participant, and the data were analysed to estimate agreement between the camera and IMU estimates of skate stroke events. Inter-evaluator reliability was assessed on a dataset of 22 athletes performing speed trials as well. The algorithm detected 100% of the strokes identified on the video capture system with a root mean square error of 0.06s. Bland-Altman analysis showed a bias of 0.03s between the two methods, which corresponds to the frame rate of the camera. The inter-evaluator reliability yielded an intra-class correlation of 1.00 (ICC3,1) from a dataset of 7089 strokes. This study provides an example of on-ice evaluation of speed skating strokes using a single IMU. This equipment is less expensive than that employed by previous authors and can be implemented in training situations with low invasiveness. [ABSTRACT FROM AUTHOR]

Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings. In this work, we explore the use of an alternative detection method, since audio can generate privacy and security concerns related to the use of always-on microphones. This study proposes the use of a non-contact tri-axial accelerometer for motion detection to differentiate between cough and noncough events/movements. A total of 43 time-domain features were extracted from the acquired tri-axial accelerometry signals. These features were evaluated and ranked for their importance using six methods with adjustable conditions, resulting in a total of 11 feature rankings. The ranking methods included model-based feature importance algorithms, first principal component, leave-one-out, permutation, and recursive features elimination (RFE). The ranking results were further used in the feature selection of the top 10, 20, and 30 for use in cough detection. A total of 68 classification models using a simple logistic regression classifier are reported, using two approaches for data splitting: subject-record-split and leave-one-subject-out (LOSO). The best-performing model out of the 34 using subject-record-split obtained an accuracy of 92.20%, sensitivity of 90.87%, specificity of 93.52%, and F1 score of 92.09% using only 20 features selected by the RFE method. The bestperforming model out of the 34 using LOSO obtained an accuracy of 89.57%, sensitivity of 85.71%, specificity of 93.43%, and F1 score of 88.72% using only 10 features selected by the RFE method. These results demonstrate the ability for future implementation of a motion-based wearable cough detector. [ABSTRACT FROM AUTHOR]

Evidence for the effectiveness of physical activity (PA) in the treatment of depression prevails for outpatients with mild and moderate symptom levels. For inpatient treatment of severe depression, evidence-based effectiveness exists only for structured and supervised group PA interventions. The Step Away from Depression (SAD) study investigated the effectiveness of an individual pedometer intervention (PI) combined with an activity diary added to inpatient treatment as usual (TAU). In this multicenter randomized controlled trial, 192 patients were randomized to TAU or TAU plus PI. The two primary outcomes at discharge were depression—blindly rated with the Montgomery–Åsberg Depression Rating Scale (MADRS)—and average number of daily steps measured by accelerometers. Secondary outcomes were self-rated depression and PA, anxiety, remission and response rates. Multivariate analysis of variance (MANOVA) revealed no significant difference between both groups for depression and daily steps. Mean MADRS scores at baseline were 29.5 (SD = 8.3) for PI + TAU and 28.8 (SD = 8.1) for TAU and 16.4 (SD = 10.3) and 17.2 (SD = 9.9) at discharge, respectively. Daily steps rose from 6285 (SD = 2321) for PI + TAU and 6182 (SD = 2290) for TAU to 7248 (SD = 2939) and 7325 (SD = 3357). No differences emerged between groups in secondary outcomes. For severely depressed inpatients, a PI without supervision or further psychological interventions is not effective. Monitoring, social reinforcement and motivational strategies should be incorporated in PA interventions for this population to reach effectiveness. [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]

Objective This study aimed to investigate the intensity of the physical work of workers with typical tasks in railway tunnel construction in order to provide support for scientifically monitoring workers' health. Methods From July to August 2022, 24 workers with different tasks(3 electric welders, 7 erectors, 4 waterproofers, and 10 steel bar workers)were recruited in tunnel construction site. Their physical activities were recorded for 24-hours for 3 - 5 consecutive days using the energy consumption meter on the dominant wrist. The intensity of physical work was estimated based on counts per minute, energy consumption, and metabolic equivalent. One -way ANOVA tests were used to compare differences in the intensity of physical work among different workers. Results Finally, 23 workers completed the investigation. Except for the maximum counts, significant statistical differences were detected in total counts, average counts, and counts per minute among the waterproofers, steel bar workers, electric welders, and erecters (P < 0.05). Particularly, the activity meter value of waterproofing workers was the largest (P < 0.05). The energy expenditure per unit time for the four types of work was (1 303.0 ± 78.5), (760.8 ± 24.5), (554.8 ± 91.4) and (521.9 ± 30.4) kcals (1 kcal = 4.184 kJ), and the differences between the groups were statistically significant (F = 5.72, P = 0.006); the metabolic equivalents were (2.89 ± 0.22), (2.57 ± 0.39), (1.81 ± 0.07) and (1.70 ± 0.27) METs, respectively. The differences between the groups were statistically significant (F = 6.99, P = 0.002). Conclusions The intensity of physical work varied greatly among workers with different tasks in railway tunnel construction. The intensity of physical work of the waterproofers was equivalent to Level Ⅲ(heavy intensity), while Level Ⅱ (medium intensity)was for the other workers. The manager in tunnel construction should pay attention to the differences in intensity of physical work among workers with different tasks, reasonably set labor quotas, and allocate human resources for protecting workers' physical and mental health. [ABSTRACT FROM AUTHOR]