Smartphone GPS-Based Exposure to Greenspace and Walkability and Accelerometer-Assessed Physical Activity During Pregnancy and Early Postpartum-Evidence from the MADRES Cohort.

A growing number of studies have associated walkability and greenspace exposure with greater physical activity (PA) in women during pregnancy. However, most studies have focused on examining women's residential environments and neglected exposure in locations outside the home neighborhood. Using 350 person-days (N = 55 participants) of smartphone global positioning system (GPS) location and accelerometer data collected during the first and third trimesters and 4-6 months postpartum from 55 Hispanic pregnant women from the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) study, we examined the day-level effect of women's exposure to walkability and greenspace on their PA outcomes during pregnancy and in the early postpartum period. Moderate-to-vigorous physical activity [MVPA] minutes per day was assessed using accelerometers. Walkability and greenspace were measured using geographic information systems (GIS) within women's daily activity spaces (i.e., places visited and routes taken) recorded using a smartphone GPS and weighted by time spent. We used a generalized linear mixed-effects model to estimate the effects of daily GPS-derived environmental exposures on day-level MVPA minutes. Results showed that women engaged in 23% more MVPA minutes on days when they had some versus no exposure to parks and open spaces in activity spaces (b = 1.23; 95%CI: 1.02-1.48). In addition, protective effects of daily greenspace and walkability exposure on MVPA were stronger in the first and third trimesters, among first-time mothers, and among women who had high pre-pregnancy body mass index (BMI) and lived in least-safe neighborhoods. Our results suggest that daily greenspace and walkability exposure are important for women's PA and associated health outcomes during pregnancy and early postpartum.

Device-measured physical activity and cardiac structure by magnetic resonance.

BACKGROUND AND AIMS: Although extreme cardiac adaptions mirroring phenotypes of cardiomyopathy have been observed in endurance athletes, adaptions to high levels of physical activity within the wider population are under-explored. Therefore, in this study, associations between device-measured physical activity and clinically relevant cardiac magnetic resonance volumetric indices were investigated. METHODS: Individuals without known cardiovascular disease or hypertension were included from the UK Biobank. Cardiac magnetic resonance data were collected between 2015 and 2019, and measures of end-diastolic chamber volume, left ventricular (LV) wall thickness, and LV ejection fraction were extracted. Moderate-to-vigorous-intensity physical activity (MVPA), vigorous-intensity physical activity (VPA), and total physical activity were assessed via wrist-worn accelerometers. RESULTS: A total of 5977 women (median age and MVPA: 62 years and 46.8 min/day, respectively) and 4134 men (64 years and 49.8 min/day, respectively) were included. Each additional 10 min/day of MVPA was associated with a 0.70 [95% confidence interval (CI): 0.62, 0.79] mL/m2 higher indexed LV end-diastolic volume (LVEDVi) in women and a 1.08 (95% CI: 0.95, 1.20) mL/m2 higher LVEDVi in men. However, even within the top decile of MVPA, LVEDVi values remained within the normal ranges [79.1 (95% CI: 78.3, 80.0) mL/m2 in women and 91.4 (95% CI: 90.1, 92.7) mL/m2 in men]. Associations with MVPA were also observed for the right ventricle and the left/right atria, with an inverse association observed for LV ejection fraction. Associations of MVPA with maximum or average LV wall thickness were not clinically meaningful. Results for total physical activity and VPA mirrored those for MVPA. CONCLUSIONS: High levels of device-measured physical activity were associated with cardiac remodelling within normal ranges.

Leveraging feature selection for enhanced fall risk prediction in elderly using gait analysis.

There is no effective fall risk screening tool for the elderly that can be integrated into clinical practice. Developing a system that can be easily used in primary care services is a current need. Current studies focus on the use of multiple sensors or activities to achieve higher accuracy. However, multiple sensors and activities reduce the availability of these systems. This study aims to develop a system to perform fall prediction for the elderly by using signals recorded from a single sensor during a short-term activity. A total of 168 features in the time and frequency domains were created using acceleration signals obtained from 71 elderly people. The features were weighted based on the ReliefF algorithm, and the artificial neural networks model was developed using the most important features. The best classification result was obtained using the 17 most important features of those weighted for K = 20 nearest neighbors. The highest accuracy was 82.2% (82.9% Sensitivity, 81.6% Specificity). The partially high accuracy obtained in our study shows that falling can be detected early with a sensor and a simple activity by determining the right features and can be easily applied in the assessment of the elderly during routine follow-ups.

Association between "weekend warrior" physical activity and the incidence of neurodegenerative diseases.

While guidelines recommend 150 â€‹min of moderate to vigorous physical activity (MVPA) weekly to enhance health, it remains unclear whether concentrating these activities into 1-2 days of the week, "weekend warrior" (WW) pattern, has the same benefit for neurodegenerative diseases (NDDs). This study aimed to evaluate the associations of WW pattern and the risk of NDDs. This prospective study was conducted using accelerometer-based physical activity data for a full week from June 2013 to December 2015 in the UK Biobank. These individuals were categorized into distinct physical activity patterns, including the WW pattern (i.e., over 50% or 75% of recommended MVPA achieved over 1-2 days), regular pattern, and inactive pattern. Cox proportional hazards model was used to evaluate the association between physical activity patterns and outcomes. Compared to inactive group, WW pattern and regular pattern was similarly linked to a reduced risk of all-cause dementia (WW: Hazard Ratio [HR]: 0.68, 95% Confidence Interval [CI]: 0.56-0.84; regular: HR: 0.86, 95% CI: 0.67-1.1) and all-cause Parkinsonism (WW: HR: 0.47, 95% CI: 0.35-0.63; regular: HR: 0.69, 95% CI: 0.5-0.95). When the exercise threshold was increased to 75% of MVPA, both patterns still were associated with decreased risk of incident all-cause dementia (WW: HR: 0.61, 95% CI: 0.41-0.91; regular: HR: 0.76, 95% CI: 0.63-0.92) and all-cause Parkinsonism (WW: HR: 0.22, 95% CI: 0.10-0.47; regular: HR: 0.59, 95% CI: 0.46-0.75). Concentrating recommended physical activities into 1-2 days per week is associated with a lower incidence of NDDs.

Lower physical activity levels in youth with Cystic Fibrosis compared to healthy controls: A multicentre comparative study.

BACKGROUND: Regular physical activity (PA) offers significant health benefits on both short (i.e., emotional well-being) and long term (i.e., fewer hospitalizations) in Youth with Cystic Fibrosis (YwCF). Regardless, evidence on PA levels in YwCF compared to healthy controls (HC) is inconsistent. Additionally, PA is a multidimensional outcome influenced by several factors such as Quadriceps strength and functional performance. Therefore, we aimed to assess whether PA, Quadriceps strength and functional performance differ between YwCF and HC across different age groups (i.e., children and adolescents). METHODS: YwCF aged 6-17 from two Belgian CF centres and age- and sex-matched HC were recruited. PA was measured with an ActiGraph GT3X + BT during 7 consecutive days. Isometric Quadriceps strength was assessed with a Hand Held Dynamometer and functional performance with a sit-to stand test (STS) and standing long jump (SLJ). RESULTS: A total of 49 YwCF (44 % male; 11.3 ± 3.3 years) and 49 HC (48 % male; 11.9 ± 3.5 years) were included. On average days, YwCF performed 4 ± 6.4 min less light PA and 7.5 ± 6.7 min less moderate-to-vigorous PA compared to HC (p = 0.04; p = 0.01). The differences in moderate-to-vigorous PA seem more pronounced in children (6-11 years)(p = 0.04). Furthermore, YwCF had similar Quadriceps strength to HC but had lower scores on the STS and SLJ (p = 0.50, p = 0.08; p = 0.02). CONCLUSIONS: This study shows lower PA levels and functional performance for YwCF, indicating that there is an urgent need for interventions promoting PA in YwCF. PA promotion will become increasingly important in the post modulator area to prevent health risks associated with low PA.

A robust multimodal detection system: physical exercise monitoring in long-term care environments.

Introduction: Falls are a major cause of accidents that can lead to serious injuries, especially among geriatric populations worldwide. Ensuring constant supervision in hospitals or smart environments while maintaining comfort and privacy is practically impossible. Therefore, fall detection has become a significant area of research, particularly with the use of multimodal sensors. The lack of efficient techniques for automatic fall detection hampers the creation of effective preventative tools capable of identifying falls during physical exercise in long-term care environments. The primary goal of this article is to examine the benefits of using multimodal sensors to enhance the precision of fall detection systems. Methods: The proposed paper combines time-frequency features of inertial sensors with skeleton-based modeling of depth sensors to extract features. These multimodal sensors are then integrated using a fusion technique. Optimization and a modified K-Ary classifier are subsequently applied to the resultant fused data. Results: The suggested model achieved an accuracy of 97.97% on the UP-Fall Detection dataset and 97.89% on the UR-Fall Detection dataset. Discussion: This indicates that the proposed model outperforms state-of-the-art classification results. Additionally, the proposed model can be utilized as an IoT-based solution, effectively promoting the development of tools to prevent fall-related injuries.

Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.

OBJECTIVE: Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairment (PFI) in older adults. This study aims to identify unique temporal patterns and develop novel digital biomarkers from wrist accelerometer data for predicting PFI and its subtypes using explainable artificial intelligence techniques. MATERIALS AND METHODS: Wrist accelerometer streaming data from 747 participants in the National Health and Aging Trends Study (NHATS) were used to calculate 231 PA features through time-series analysis techniques-Tsfresh. Predictive models for PFI and its subtypes (walking, balance, and extremity strength) were developed using 6 machine learning (ML) algorithms with hyperparameter optimization. The SHapley Additive exPlanations method was employed to interpret the ML models and rank the importance of input features. RESULTS: Temporal analysis revealed peak PA differences between PFI and healthy controls from 9:00 to 11:00 am. The best-performing model (Gradient boosting Tree) achieved an area under the curve score of 85.93%, accuracy of 81.52%, sensitivity of 77.03%, and specificity of 87.50% when combining wrist accelerometer streaming data (WAPAS) features with demographic data. DISCUSSION: The novel digital biomarkers, including change quantiles, Fourier transform (FFT) coefficients, and Aggregated (AGG) Linear Trend, outperformed traditional PA metrics in predicting PFI. These findings highlight the importance of capturing the multidimensional nature of PA patterns for PFI. CONCLUSION: This study investigates the potential of wrist accelerometer digital biomarkers in predicting PFI and its subtypes in older adults. Integrated PFI monitoring systems with digital biomarkers would improve the current state of remote PFI surveillance.

Longitudinal associations of participation in organized and unorganized sports in youth with physical activity in mid-adulthood: The Young Finns Study.

We investigated the longitudinal associations between sports participation patterns in youth and physical activity (PA) in adulthood. PA was self-reported triannually between ages 9-18 (n = 2550, 52% females) and measured by accelerometers in mid-adulthood (n = 1002, 61% females, aged 48 ± 4 years). Three latent classes of youth sports participation emerged for both genders: 1) "organized sports" (persistent high PA with regular sports club activities), 2) "unorganized sports" (persistent high PA without sports club activities and 3) "low activity" (low PA with decreasing sports involvement). These groups comprised 29%, 34% and 37% of males, and 23%, 27% and 50% of females, respectively. Youth "organized sports" was associated with higher adult PA in both males (+1166 steps/day, p = 0.012) and females (+15 min/day moderate-to-vigorous PA [MVPA], +1064 steps/day, +1066 leisure-time steps/day; p ≤ 0.005) compared to "low activity". In males, youth "organized sports" was associated with higher adult PA (+1103 steps/day, -26 min/day sedentary time and +133 counts/minute higher total PA, p ≤ 0.039) compared to "unorganized sports". In females, "unorganized sports" in youth was related to higher adult PA (+10 min/day MVPA, p = 0.034) when compared to "low activity". Sustained participation in youth organized sports, and for females, also in unorganized sports, is positively linked with adult PA.

Identifying prey capture events of a free-ranging marine predator using bio-logger data and deep learning.

Marine predators are integral to the functioning of marine ecosystems, and their consumption requirements should be integrated into ecosystem-based management policies. However, estimating prey consumption in diving marine predators requires innovative methods as predator-prey interactions are rarely observable. We developed a novel method, validated by animal-borne video, that uses tri-axial acceleration and depth data to quantify prey capture rates in chinstrap penguins (Pygoscelis antarctica). These penguins are important consumers of Antarctic krill (Euphausia superba), a commercially harvested crustacean central to the Southern Ocean food web. We collected a large data set (n = 41 individuals) comprising overlapping video, accelerometer and depth data from foraging penguins. Prey captures were manually identified in videos, and those observations were used in supervised training of two deep learning neural networks (convolutional neural network (CNN) and V-Net). Although the CNN and V-Net architectures and input data pipelines differed, both trained models were able to predict prey captures from new acceleration and depth data (linear regression slope of predictions against video-observed prey captures = 1.13; R 2 ≈ 0.86). Our results illustrate that deep learning algorithms offer a means to process the large quantities of data generated by contemporary bio-logging sensors to robustly estimate prey capture events in diving marine predators.

Concurrent validity between self-reported International Physical Activity Questionnaire Short Form and Fibion accelerometer data among young adults in the UAE.

Self-reported physical activity questionnaires (e.g., International Physical Activity Questionnaire, IPAQ) are a cost-effective, time-saving, and accessible method to assess sedentary behaviour and physical activity. There are conflicting findings regarding the validity of self-reported questionnaires in comparison to accelerometer-measured data in a free-living environment. This study aimed to investigate the concurrent validity between self-reported Arabic-English IPAQ short form (IPAQ-SF) and Fibion (Fibion Inc., Jyväskylä, Finland) accelerometer-measured sedentary and physical activity time among young adults. One hundred and one young healthy adults (mean age 20.8 ± 2.4 years) filled in the IPAQ short form (IPAQ-SF) and wore the Fibion device on the anterior thigh for ≥ 600 min per day for 4-7 days. Concurrent validity between the IPAQ-SF and Fibion accelerometer for sitting, walking, moderate activity, and vigorous activity time was assessed using the Spearman correlation coefficient ( ρ ) and Bland-Altman plots. Significant weak associations between IPAQ-SF and Fibion measurements were found for total activity time ( ρ = 0.4; P < 0.001) and for the duration of walking ( ρ = 0.3; P = 0.01), moderate ( ρ = 0.2; P = 0.02), and vigorous-intensity activities ( ρ = 0.4; P < 0.001). However, ρ was not significant ( ρ = - 0.2; P = 0.09) for sitting time. In addition, all the plots of the measured variables showed a proportional bias. A low association and agreement were found between self-reported IPAQ-SF scores and Fibion accelerometer measurements among young adults in the UAE. Adult sedentary and physical activity measurements should be obtained objectively with accelerometers rather than being limited to self-reported measures.

The relationship between accelerometer-based physical activity, sedentary behavior, and seven common geriatric syndromes: a two-sample Mendelian randomization study.

Introduction: To investigate the causal associations between accelerometer-based physical activity (PA), sedentary behavior (SB), and seven common geriatric syndromes (GSs) (frailty, falls, delirium, urinary incontinence, dysphagia, hearing loss, and visual impairment) by Mendelian randomization (MR) analysis. Methods: Instrumental variables from a genome-wide association study were used for MR analysis. The exposure factors were three PA phenotypes (average acceleration, overall activity, and moderate-intensity activity) and one SB phenotype (SB). The outcome variables were seven common GSs. The inverse variance weighted (IVW) method was utilized for the primary MR analysis. Additionally, sensitivity, pleiotropy, and heterogeneity analyses were subsequently conducted to assess the robustness of the present study's findings. Results: According to the primary MR results obtained using the IVW method, genetically predicted PA (average acceleration) decreased the risk of two GSs (frailty, p = 0.01; dysphagia, p = 0.03). Similarly, overall activity decreased the risk of two GSs (frailty, p = 0.01; delirium, p = 0.03), and moderate-intensity activity reduced the risk of three GSs (urinary incontinence, p = 0.04; hearing loss, p = 0.02; visual impairment, p = 0.01). Furthermore, SB was causally correlated with a greater risk for three GSs (frailty, p = 0.03; fall, p = 0.01; dysphagia, p = 0.04). Conclusion: This study provided evidence that accelerometer-based PA may be causally associated with a lower risk of GSs, while SB may increase the risk of GSs.

Cardiometabolic disease risk in relation to objectively measured physical activity, sedentary behaviour in South African adults with knee and hip osteoarthritis.

PURPOSE: This study aimed to investigate the relationship between cardiometabolic disease risk and time spent in device-measured activity behaviours in a cohort of people with advanced osteoarthritis (OA) awaiting joint replacement surgery. MATERIALS AND METHODS: Cardiometabolic risk biomarkers were assessed in people with OA (n = 96; hip n = 38, knee n = 58; mean (SD) age = 64.3 (9.8) years; 71% female). Physical activity (PA) and sedentary behaviour (SB) were measured by accelerometer over seven days (24 h/day). RESULTS: There were similar patterns of PA and SB between the hip and knee OA participants except for total number of steps (hip = 3365 (2926) vs knee 4344 (2836) steps/day; p = 0.018) and total stepping time (hip = 50.8 (38.2) vs knee = 67.2 (38.5) min/day; p = 0.005). Each additional cardiometabolic risk factor accumulated was associated with a 26.3 min/day increase in sedentary behaviour (p = 0.032; 95% CI: 2.3, 50.2), a 26.3 min/day decrease in upright time (p = 0.032; -50.2, -2.3) and a 23.6 min/day decrease in standing time (p = 0.032; -45.1, -2.1). CONCLUSIONS: In people with hip or knee OA, increased cardiometabolic disease risk was associated with more sitting and less upright and standing time. Findings support targeting reductions in sedentary behaviour for improvements in cardiometabolic health in people with osteoarthritis.IMPLICATIONS FOR REHABILITATIONKnee and hip osteoarthritis is a condition which is associated with an increased risk of cardiometabolic disease but also due to the low levels of physical activity and high levels of sedentary behaviour.Offsetting sedentary behaviour with light physical activity offers a feasible interventional target to reduce the risk of cardiometabolic disease in people with hip and knee osteoarthritis.

An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.

BACKGROUND: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors-based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning-based FDSs using manual feature extraction, and deep learning (DL)-based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. OBJECTIVE: This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. METHODS: Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. RESULTS: The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%). CONCLUSIONS: This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.

Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study.

BACKGROUND: Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual's functional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessment of physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional evaluation based on subjective observation has limitations in precision and objectivity. OBJECTIVE: The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and noninvasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals. METHODS: To monitor movements, wearable inertial sensors were developed, which include accelerometers and triaxial gyroscopes. The developed sensors were used to create a proprietary database with 6 movements related to the shoulder and 3 related to the back. We registered 53,165 activity records in the database (consisting of accelerometer and gyroscope measurements), which were reduced to 52,600 after processing to remove null or abnormal values. Finally, 4 deep learning (DL) models were created by combining various processing layers to explore different approaches in ADL recognition. RESULTS: The results revealed high performance of the 4 proposed models, with levels of accuracy, precision, recall, and F1-score ranging between 95% and 97% for all classes and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs. CONCLUSIONS: The DL models implemented have demonstrated solid performance, indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization-being noninvasive and practically imperceptible to the user-which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions.

Identification of footstrike pattern using accelerometry and machine learning.

Recent reports have suggested that there may be a relationship between footstrike pattern and overuse injury incidence and type. With the recent increase in wearable sensors, it is important to identify paradigms where the footstrike pattern can be detected in real-time from minimal data. Machine learning was used to classify tibial acceleration data into three distinct footstrike patterns: rearfoot, midfoot, or forefoot. Tibial accelerometry data were collected during treadmill running from 58 participants who each ran with rearfoot, midfoot, and forefoot strike patterns. These data were used as inputs into an artificial neural network classifier. Models were created by using three distinct acceleration data sets, using the first 100%, 75%, and 40% of stance phase. All models were able to predict the footstrike pattern with up to 89.9% average accuracy. The highest error was associated with the identification of the midfoot versus forefoot strike pattern. This technique required no pre-selection of features or filtering of the data and may be easily incorporated into a wearable device to aid with real-time footstrike pattern detection.

Association of accelerometer-derived physical activity with all-cause and cause-specific mortality among individuals with cardiovascular diseases: A prospective cohort study.

AIMS: To investigate the association of accelerometer-measured intensity-specific physical activity (PA) with all-cause and cause-specific mortality among individuals with cardiovascular disease (CVD). METHODS: In this prospective cohort study, 8,024 individuals with pre-existing CVD (mean age: 66.6 years, female: 34.1%) from the UK Biobank had their PA measured using wrist-worn accelerometers over a 7-day period in 2013-2015. All-cause, cancer, and CVD mortality was ascertained from death registries. Cox regression modelling and restricted cubic splines were used to assess the associations. Population-attributable fractions (PAFs) were used to estimate the proportion of preventable deaths if more PA were undertaken. RESULTS: During an average of 6.8 years of follow-up, 691 deaths (273 from cancer and 219 from CVD) were recorded. An inverse non-linear association was found between PA duration and all-cause mortality risk, irrespective of PA intensity. The hazard ratio (HR) of all-cause mortality plateaued at 1800 minutes/week for light-intensity PA (LPA), 320 minutes/week for moderate-intensity PA (MPA) and 15 minutes/week for vigorous-intensity PA (VPA). The highest quartile of PA associated lower risks for all-cause mortality, with HRs of 0.63 (95% confidence interval [CI]: 0.51-0.79), 0.42 (0.33-0.54) and 0.47 (0.37-0.60) for LPA, MPA, and VPA, respectively. Similar associations were observed for cancer and CVD mortality. Additionally, the highest PAF were noted for VPA, followed by MPA. CONCLUSION: We found an inverse non-linear association between all intensities of PA (LPA, MPA, VPA, and MVPA) and mortality risk in CVD patients using accelerometer-derived data, but with larger magnitude of the associations than that in previous studies based on self-reported PA.

This study investigated the associations of accelerometer-derived intensity-specific physical activity (PA) with the risks of all-cause and cause-specific mortality among individuals with cardiovascular disease (CVD). L-shaped dose-response relationships between the duration of PA and all-cause mortality were observed across all levels of PA intensities. The risk reduction for mortality exhibited a sharp decline from 0 to 1800 minutes/week of light-intensity PA, followed by reaching a plateau. Notably, the inflection points for moderate-intensity PA and vigorous-intensity PA were found at 320 and 15 minutes per week, respectively. The population-attributable fraction analysis indicated that a significant number of deaths could potentially be prevented if individuals with CVD engaged in more vigorous physical activities.

Associations of 24-hour movement behaviors with depressive symptoms in rural-dwelling older adults: a compositional data analysis.

BACKGROUND: We aimed to explore the association of sleep duration with depressive symptoms among rural-dwelling older adults in China, and to estimate the impact of substituting sleep with sedentary behavior (SB) and physical activity (PA) on the association with depressive symptoms. METHODS: This population-based cross-sectional study included 2001 rural-dwelling older adults (age ≥ 60 years, 59.2% female). Sleep duration was assessed using the Pittsburgh Sleep Quality Index. We used accelerometers to assess SB and PA, and the 15-item Geriatric Depression Scale to assess depressive symptoms. Data were analyzed using restricted cubic splines, compositional logistic regression, and isotemporal substitution models. RESULTS: Restricted cubic spline curves showed a U-shaped association between daily sleep duration and the likelihood of depressive symptoms (P-nonlinear < 0.001). Among older adults with sleep duration < 7 h/day, reallocating 60 min/day spent on SB and PA to sleep were associated with multivariable-adjusted odds ratio (OR) of 0.81 (95% confidence interval [CI] = 0.78-0.84) and 0.79 (0.76-0.82), respectively, for depressive symptoms. Among older adults with sleep duration ≥ 7 h/day, reallocating 60 min/day spent in sleep to SB and PA, and reallocating 60 min/day spent on SB to PA were associated with multivariable-adjusted OR of 0.78 (0.74-0.84), 0.73 (0.69-0.78), and 0.94 (0.92-0.96), respectively, for depressive symptoms. CONCLUSIONS: Our study reveals a U-shaped association of sleep duration with depressive symptoms in rural older adults and further shows that replacing SB and PA with sleep or vice versa is associated with reduced likelihoods of depressive symptoms depending on sleep duration.

Exploring Associations Between Device-Based Occupational Sedentary Behavior and Need for Recovery in White Collar Workers: A Compositional Data-Analysis.

Objectives: White collar workers spend an increasing amount of time in occupational sedentary behavior (OSB) and are thereby at risk for adverse health outcomes. Nevertheless, the association between OSB and the need for recovery (NFR), an important indicator of wellbeing, is unknown and therefore examined. Methods: Baseline data from a cluster randomized controlled trial was used. A subgroup of 89 white collar workers wore a triaxial accelerometer for 7 days. NFR was measured using the Questionnaire on the Experience and Evaluation of Work. Compositional data analysis was applied to determine the composition of different OSB bouts (short, medium and long) and occupational physical activity (OPA) (light, moderate and vigorous and standing). Linear regression analyses were performed to explore the associations between occupational compositions and NFR. Results: Relatively more time spent in long OSB bouts was associated with a lower NFR (ß: -11.30, 95% CI: -20.2 to -2.4). Short and medium OSB bouts and OPA were not associated with NFR. Conclusion: Associations between OSB bouts, OPA and NFR hinted at contrasting trends, suggesting the need to consider different bout lengths of OSB in future studies.

Use of a poll-mounted accelerometer for quantification and characterisation of equine trigeminal-mediated headshaking.

BACKGROUND: Equine trigeminal-mediated (TGM) headshaking (HS) is a neuropathic facial pain syndrome characterised by varying intensity and frequencies of head movements and signs of nasal irritation. An accurate method for quantification and/or characterisation of HS severity is lacking. OBJECTIVES: To develop and validate an objective measure of TGMHS. STUDY DESIGN: Prospective case control study. METHODS: Horses presenting for investigation of HS were recruited alongside those presenting for forelimb lameness (LAME) and pre-purchase examination as well as healthy controls (CONTROL). Head movement data were collected for 5 min whilst trotting on the lunge using a tri-axial accelerometer, with a range of ±16 g and sampling rate of 800 Hz, attached to the bridle headpiece. Recordings were exported for processing. Peak detection was performed using minimum and maximum thresholds of -1 g and +1 g (corrected for gravity) and a minimum peak width of 10 samples. RESULTS: Fifty-six horses were included in the study; 18 TGMHS, 10 non-TGMHS, 12 LAME and 16 CONTROL. Characteristics and frequency of vertical (Z axis) head movements of TGMHS horses differed significantly from other horses. TGMHS horses had peaks with greater mean and maximum positive g-force (P < 0.005) and lower mean and minimum negative g-force (P < 0.001), greater frequency of peaks/min (P < 0.001) and over 12 times greater percentage of peaks >+2 g compared with other horses (P < 0.001). Receiver operator curve characteristics of percentage of peaks >+2 g (CI 0.72-0.95), percentage of peaks <-2 g (CI 0.66-0.92) and percentage of peaks <-2 g and >+2 g (CI 0.72-0.96) showed excellent discrimination of TGMHS horses from LAME, CONTROL and non-TGMHS horses. MAIN LIMITATIONS: Referral population of horses, small sample size and control horses were not evaluated for orthopaedic disease. CONCLUSIONS: Accelerometer data from trotting exercise on the lunge provides an objective measure of HS and can differentiate between TGMHS, non-TGMHS, normal head movements and those associated with forelimb lameness. Accelerometer use may aid HS diagnosis and monitoring of management strategies.

HISTORIAL: La sacudida de cabeza (HS) en equinos mediada por el nervio trigémino (TGM), es un síndrome de dolor facial neuropático caracterizado por movimientos de cabeza de intensidad y frecuencia variables y por síntomas de irritación nasal. No existe un método preciso para cuantificar y/o caracterizar la gravedad de HS. OBJETIVOS: Desarrollar y validar una medida objetiva de TGMHS. DISEÑO DEL ESTUDIO: Estudio prospectivo de casos y controles. MÉTODOS: Caballos presentados para la investigación de HS fueron reclutados junto a aquellos presentados para cojera de mano (LAME) y examen de pre­compra como controles saludables (CONTROL). Datos de los movimiento de la cabeza fueron recolectados durante cinco minutos mientras trotaban a la cuerda usando un acelerómetro tri­axial, con un rango de ±16 g y frecuencia de muestreo de 800 Hz, ajustado a la testera de la cabezada. Las grabaciones se exportaron para ser procesadas. Detección de picos fue realizada usando umbrales mínimos y máximos de −1g y + 1g (corregidos para gravedad) y un muestreo mínimo de 10 picos de ancho. RESULTADOS: Cincuenta y seis caballos fueron incluidos en el estudio; 18 TGMHS, 10 no­TGMHS, 12 LAME y 16 CONTROL. Las características y la frecuencia de los movimientos de cabeza verticales (eje Z) de caballos TGMHS, difirieron significativamente de otros caballos. Los caballos TGMHS presentaron picos con una fuerza­g positiva con mayor promedio y máxima (P < 0.005) y una fuerza­g negativa con menor promedio y mínima (P < 0.001), con mayor frecuencia de picos/min (P < 0.001), y un porcentaje mas de doce veces mayor de picos >+2 g en comparación a los otros caballos (P < 0.001). Las características de las curvas del operador del receptor de los picos promedio >+2 g (CI 0.72­0.95), del porcentaje de picos <−2 g (CI 0.66­0.92), y del porcentaje de picos <−2 g y > +2 g (CI 0.72­0.96) mostraron una excelente discriminación de caballos TGMHS con respecto a caballos LAME, CONTROL y no­TGMHS. LIMITACIONES PRINCIPALES: Población de caballos derivados, numero de muestra pequeño, y los caballos control no fueron evaluados por enfermedades ortopédicas. CONCLUSIONES: Los datos de acelerómetros por trote a la cuerda, dan una medida objetiva de HS y permiten diferenciar entre TGMHS, no­TGMHS, movimientos normales de cabeza y aquellos asociados a cojera de mano. El uso de acelerómetros puede ayudar en el diagnostico de HS y monitorear las estrategias de manejo.

Jerk (d(acceleration)/dt) as an operative variable in pneumatic tube transport (PTT).

BACKGROUND: Jerk, the rate of change of acceleration (d(acceleration)/dt), is a known operative variable in public transportation safety, but this term has never appeared in the literature regarding pneumatic tube transport (PTT) and specimen integrity. We investigated profiles of acceleration and jerk for 2 PTT routes within our hospital system. METHODS: Acceleration data were collected for PTT for 2 routes (A, B) using an accelerometer. Acceleration vectors (a) were analyzed in terms of distributions of jerk (da/dt), and distributions of θ, the angle between successive acceleration vectors. RESULTS: Routes A and B had transit times of approximately 300 s. Acceleration vectors (a) ranged in magnitude from 0 to 8 g. For B, a > 1.2 g comprised 29.0% of results, compared to 13.5% of results for A (ratio = 2.1). Jerk ranged from 0 to 94 g/s. For B, jerk > 0.5 g/s comprised 71.9% of results, compared to 32.5% of results for A (ratio = 2.2). θ ranged from 0 to 180 degrees. For B, θ > 5 degrees comprised 59.3% of results, compared to 26.6% of results for A (ratio = 2.2). CONCLUSION: Differences in distribution in acceleration, jerk, and θ ran in parallel as variables for comparison between 2 PTT routes. Jerk and θ are likely to be operative variables in effects of PTT.