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.

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.

OBJECTIVE: 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.

While guidelines recommend 150 ​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.

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.

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.

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.

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.

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.

OBJECTIVE: 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.
CONCLUSIONS: 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.