Physical Metrics Research Articles

Monitoring Activity and Gait in Children (MAGIC) using digital health technologies

BackgroundDigital health technologies (DHTs) can collect gait and physical activity in adults, but limited studies have validated these in children. This study compared gait and physical activity metrics collected using DHTs to those collected by reference comparators during in-clinic sessions, to collect a normative accelerometry dataset, and to evaluate participants’ comfort and their compliance in wearing the DHTs at-home.MethodsThe MAGIC (Monitoring Activity and Gait in Children) study was an analytical validation study which enrolled 40, generally healthy participants aged 3–17 years. Gait and physical activity were collected using DHTs in a clinical setting and continuously at-home.ResultsOverall good to excellent agreement was observed between gait metrics extracted with a gait algorithm from a lumbar-worn DHT compared to ground truth reference systems. Majority of participants either “agreed” or “strongly agreed” that wrist and lumbar DHTs were comfortable to wear at home, respectively, with 86% (wrist-worn DHT) and 68% (lumbar-worn DHT) wear-time compliance. Significant differences across age groups were observed in multiple gait and activity metrics obtained at home.ConclusionsOur findings suggest that gait and physical activity data can be collected from DHTs in pediatric populations with high reliability and wear compliance, in-clinic and in home environments.Trial registrationClinicalTrials.gov: NCT04823650ImpactDigital health technologies (DHTs) have been used to collect gait and physical activity in adult populations, but limited studies have validated these metrics in children.The MAGIC study comprehensively validates the performance and feasibility of DHT-measured gait and physical activity in the pediatric population.Our findings suggest that reliable gait and physical activity data can be collected from DHTs in pediatric populations, with both high accuracy and wear compliance both in-clinic and in home environments.The identified across-age-group differences in gait and activity measurements highlighted their potential clinical value.

PRODEC for human systems integration of increasingly autonomous systems

AbstractThe purpose of the PRODEC scenario‐based design method is the incremental cross‐fertilization and refinement of procedural scenarios and declarative configurations. It uses virtual prototypes of the developed systems (mainly life‐critical systems) to conduct human‐in‐the‐loop simulations (HITLSs). Based on human systems integration (HSI) principles and criteria, as well as expertise and experience in the domain at stake, this HSI approach grounded in virtual environments requires a clear definition of physical and cognitive tangibility metrics to assess the distance between virtual and tangible (grasp) dimensions of the system being developed when it is put to work. PRODEC considers human and machine systems described in terms of structures and functions incrementally designed using procedural scenarios (i.e., stories) in task and activity networks, which provide life to declarative configurations (i.e., system's functions and structures). This active modeling and simulation process enables the discovery of emergent structures and functions of the system being developed when it is virtually operated. PRODEC's use is illustrated in an example. We discuss the use of PRODEC and its results as to how they can be used with digital twins.

Investigating the association between the lunar cycle and sleep, physiological, cognitive, and physical performance in children with Down syndrome.

Children with Down syndrome (DS) offer a compelling context within the fieldof human biology for examining potential lunar influences. While the exact mechanisms governing lunar effects are still under investigation, a growing body of scientific inquiry suggests possible connections between lunar phases and physiological, physical, and cognitive parameters. This investigation holds promise for uncovering the intricate interplay between lunar cycles (LCs) and the unique biology of children with DS. This study investigated the potential influence of the LC on physiological, physical, and cognitive parameters in children with DS, focusing on sleep patterns, physical performance, and cognitive abilities. Seventeen children with DS participated in this study. Sleep data, physical performance metrics, and cognitive test results were collected throughout the LC, including the new moon (NM), first quarter, full moon (FM), and third quarter. Statistical analyses were conducted to assess the differences in these parameters across lunar phases. Significant differences were observed in sleep patterns, with reduced total sleep time (P < 0.01) and sleep efficiency (P < 0.001) during the FM phase. Heart rates (HRs) before (P < 0.001) and after (P < 0.01) exercise also displayed pronounced changes during LC. Additionally, the reaction time (RT) exhibited a significant difference (P < 0.01) across the lunar phases. However, physical performance metrics, including squat jump (SJ), sprint, and 6-minute walk distance (6MWD), did not show significant variations. This study suggests that LC may have a moderating effect on sleep patterns, HR, and cognitive performance in children with DS. These findings have practical implications for caregivers and educators and highlight the importance of considering lunar-associated variations in planning schedules and interventions for children with DS.

Deep learning-enabled probing of irradiation-induced defects in time-series micrographs

Modeling time-series data with convolutional neural networks (CNNs) requires building a model to learn in batches as opposed to training sequentially. Coupling CNNs with in situ or operando techniques opens the possibility of accurately segmenting dynamic reactions and mass transport phenomena to understand how materials behave under the conditions in which they are used. In this article, in situ ion irradiation transmission electron microscopy (TEM) images are used as inputs into the CNN to assess the defect generation rate, defect cluster density, and saturation of defects. We then use the output segmentation maps to correlate with conventional TEM micrographs to assess the model’s ability to detail nanoscale interactions. Next, we discuss the implications of preprocessing and hyperparameters on model variability, accuracy when expanded to other datasets, and the role of regularization when controlling model variance. Ultimately, we eliminate human bias when extrapolating physical metrics, speed up analysis time, decouple reactions that happen at 100 ms intervals, and deploy models that are both accurate and transferable to similar experiments.

Examining the Influence of Warm-Up Static and Dynamic Stretching, as well as Post-Activation Potentiation Effects, on the Acute Enhancement of Gymnastic Performance: A Systematic Review with Meta-Analysis.

The primary objective of this systematic review with meta-analysis is to methodically discern and compare the impact of diverse warm-up strategies, including both static and dynamic stretching, as well as post-activation potentiation techniques, on the immediate performance of gymnasts. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper evaluated studies that examined the gymnasts' performance after different warm-up strategies namely stretching (static [SS] or dynamic), vibration platforms (VP) or post-activation, in comparison to control conditions (e.g., mixed warm-up routines; no warm-up). The principal outcomes were centered on technical performance metrics (e.g., split, gymnastic jumps) and physical performance metrics (e.g., squat jump, countermovement jump, drop jump, balance, range of motion). Methodological assessments of the included studies were conducted using the Downs and Black Checklist. From the initial search across PubMed, Scopus, and the Web of Science databases, a total of 591 titles were retrieved, and 19 articles were ultimately incorporated in the analysis. The results revealed a non-significant differences (p > 0.05) between the SS condition and control conditions in squat jump performance, countermovement jump and gymnastic technical performance (e.g., split; split jump). Despite the difference in warm-up strategies and outcomes analyzed, the results suggest that there is no significant impairment of lower-limb power after SS. Additionally, technical elements dependent on flexibility appear to be enhanced by SS. Conversely, dynamic stretching and VP seem to be more effective for augmenting power-related and dynamic performance in gymnasts.

Chromosomal characterization mediated by karyomorphological analysis and differential banding pattern in fenugreek (Trigonella foenum-graecum L.): a neglected legume

Fenugreek or Trigonella foenum-graecum L. is a commercially important yet neglected crop of the family Fabaceae, with potent medicinal applications, and can treat several diseases as well. Conventional breeding studies for higher yields of commercial crops largely depend on chromosomal information of the particular species. Despite a number of cytological research being conducted on T. foenum-graecum, a complete characterization of its chromosomes has not been achieved due to the limitations of traditional karyotype analysis methods. A range of chromosomal markers are advantageous to characterize at full extent and identify individual chromosomes rather than relying on only physical metrics. Thus, in this study, in addition to giemsa staining, other approaches like fluorochrome and silver staining were used for the precise karyomorphological analysis of this species. Enzyme maceration and air drying (EMA) based fluorochrome banding with GC-specific stain Chromomycin A3 (CMA), and AT-specific stain 4’,6-diamidino-2-phenylindole (DAPI) applied for the first time for chromosome characterization. The results showed 2n = 16 chromosomes in metaphase cells, with karyotype formula of 2m+6sm. The unique banding pattern observed in the CMA/DAPI and AgNOR staining highlights the AT and GC-rich regions as well as the nucleolar organizer regions (NORs). All this crucial information can further assist in conducting breeding studies of more precision with simultaneously encouraging similar studies that need to be done in other unexploited species of importance.

Discrete-coordinate crypto-Hermitian quantum system controlled by time-dependent Robin boundary conditions

A family of exactly solvable quantum square wells with discrete coordinates and with certain non-stationary Hermiticity-violating Robin boundary conditions is proposed and studied. Manifest non-Hermiticity of the model in conventional Hilbert space Hfriendly is required to coexist with the unitarity of system in another, ad hoc Hilbert space Hphysical . Thus, quantum mechanics in its non-Hermitian interaction picture (NIP) representation is to be used. We must construct the time-dependent states (say, ψ(t)) as well as the time-dependent observables (say, Λ(t)). Their evolution in time is generated by the operators denoted, here, by the respective symbols G(t) (a Schrödinger-equation generator) and Σ(t) (a Heisenberg-equation generator, a.k.a. quantum Coriolis force). The unitarity of evolution in Hphysical is then guaranteed by the reality of spectrum of the energy observable alias Hamiltonian H(t) = G(t) + Σ(t). The applicability of these ideas is illustrated via an N by N matrix model. At N = 2, closed formulae are presented not only for the measurable instantaneous energy spectrum but also for all of the eligible time-dependent physical inner-product metrics Θ(N=2)(t), for the related Dyson maps Ω(N=2)(t), for the Coriolis force Σ(N=2)(t) as well as, in the very ultimate step of the construction, for the truly nontrivial Schrödinger-equation generator G (N=2)(t).

Physical fitness metrics and their relationship to locomotor activity profiles among female international field hockey players across an Olympic cycle

ObjectivesThe study explored the correlations between physical fitness metrics and match locomotor activity profiles. Furthermore exploring the transformations of both fitness tests as well as match locomotor activities over an Olympic cycle. DesignObservational descriptive study. MethodsFitness testing (n = 24) and match locomotor activity (n = 102) profiles were collected. A Pearson's product-moment correlation and 95 % confidence intervals were calculated to determine relationships between metrics for each year. A mixed-effects model was used for repeated measures to identify differences in the physical fitness tests and match locomotor activity profiles between years. ResultsThe YoYo intermittent reocvery testest level 1 changed significantly (p < 0.0001) and accounted for 29 % of the variance in average speed (p < 0.001, rrm (59) = 0.54) Upper and lower body muscular strength, also improved, evidenced by the 3RM bench press (p < 0.0001), and a 28 % (2017–2019) increase in 3RM squat (p < 0.0001). Also pull-ups increased from 4.2 ± 2.8 pull-ups (2017) to 10.0 ± 3.1 pull-ups (2020) (p < 0.0001) and. countermovement jump height increased over the study duration (p < 0.0001). Match total distances remained unchanged, while average speed increased significantly (p < 0.0001). High-speed running distance (p < 0.0003) and sprint counts (p < 0.0001), showed significant improvements over the study period. ConclusionsThe study underscores noteworthy improvements in physical attributes and performance metrics over an Olympic cycle. Contributing valuable insights for enhancing athletic performance in hockey players competing in the Olympics.

Characterizing planar SERS substrates: unraveling the link between physical characteristics and performance metrics

Surface-enhanced Raman spectroscopy (SERS) is a powerful optical sensing technique used in various applications, including medicine, microbiology, and environmental analysis. Planar SERS substrates are of particular interest due to their ease of integration in lab-on-chips and better reproducibility compared to colloidal SERS. The performance of these SERS substrates is quantified using metrics such as enhancement factor, sensitivity, and reproducibility. However, there is yet to be a consensus on how to practically compare and interpret such metrics in publications and experiments. These performance metrics are strongly influenced by the nanostructures’ material, architecture, element sizes, as well as the circumstances surrounding the experiments. Understanding the effect of these characteristics on the SERS substrates’ performance could not only enable a better performance but also direct their development for different applications. Thus, we prepared a planar SERS-substrate characterization methodology to explore the correlation between the nanostructures’ physical characteristics and the performance metrics through coordinate-transformed spectroscopic measurements over structure-characterized areas. Seven commercial SERS substrates, with various surface architectures fabricated using different fabrication technologies, were studied using this benchmarking methodology. The results demonstrated how this methodology can indicate a SERS substrate’s suitability for a specific application, thus, guiding the substrate’s further adaptations or development.

Physical and mental health in adolescence: novel insights from a transdiagnostic examination of FitBit data in the ABCD study

Adolescence is among the most vulnerable period for the emergence of serious mental illnesses. Addressing this vulnerability has generated interest in identifying markers of risk for symptoms and opportunities for early intervention. Physical fitness has been linked to psychopathology and may be a useful risk marker and target for early intervention. New wearable technology has made assessing fitness behavior more practical while avoiding recall and self-report bias. Still, questions remain regarding the clinical utility of physical fitness metrics for mental health, both transdiagnostically and along specific symptom dimensions. The current study includes 5007 adolescents (ages 10–13) who participated in the Adolescent Brain Cognitive Development (ABCD) study and additional sub-study that collected fitness data from wearable technology and clinical symptom measures. Physical fitness metrics included resting heart rate (RHR- an index of cardiovascular health), time spent sedentary (associated with increased inflammation and cardiovascular disease), and time spent in moderate physical activity (associated with increased neurogenesis, neuroplasticity, and healthy neurodevelopment). Self-report clinical symptoms included measures of psychosis-like experiences (PLE), internalizing symptoms, and externalizing symptoms. Increased RHR- lower cardiovascular fitness- related only to greater internalizing symptoms (t = 3.63). More sedentary behavior related to elevated PLE severity (t = 5.49). More moderate activity related to lower PLE (t = −2.69) and internalizing (t = −6.29) symptom severity. Wearable technology fitness metrics linked physical health to specific mental health dimensions, which emphasizes the utility of detailed digital health data as a marker for risk and the need for precision in targeting physical health behaviors to benefit symptoms of psychopathology.

Association of physical activity metrics with glucose variability in people with type 1 diabetes: A cross‐sectional study

AbstractThis study aims to investigate the association of physical activity metrics with measures of glucose variability in people with type 1 diabetes. From August 2019 to January 2022, people with type 1 diabetes, attending clinics or participating in ongoing research at the Dasman Diabetes Institute in Kuwait, were invited to participate in the study. Physical activity was measured over a 7‐day period using a wrist‐worn accelerometer, and glucose variability data were measured by continuous glucose monitoring (CGM) of the same period. Three hundred and eleven participants were recruited (age 33 (10) years, BMI 27(5) kg/m2 and n = 311 (169 female and 142 male)). Overall physical activity levels were not associated with any measure of glucose variability. The intensity gradient, which measures the distribution of physical activity intensity, was negatively associated with mean glucose (−1.01(−0.28, −1.74) and p = 0.007), CONGA (−1.00(−0.28, −1.72) and p = 0.007), J‐index (−11.7(−2.23, 21.2) and p = 0.016), HBGI (−2.73(−0.44, −5.02) and p = 0.020), GRADE (−2.27(−0.59, −3.95), p = 0.009) and GRADE – euglycaemia (−4.26(−0.46, −8.06) and p = 0–029) and the M‐value (−4.41 (−0.05, −8.77) and p = 0.049). Overall physical activity remains important, but it may be worth recommending people with type 1 diabetes to spend proportionately more of their day doing moderate to higher intensity physical activity, although this remains to be confirmed in an appropriately designed trial.

Evaluation and Optimization of Sense of Security during the Day and Night in Campus Public Spaces Based on Physical Environment and Psychological Perception

The safety of public spaces on university campuses directly affects the mental and physical well-being of both faculty and students, significantly contributing to the stability and growth of the institution. Therefore, evaluating and optimizing the sense of security in these public areas is paramount. This study adopts a human-centric approach and selects five physical environmental metrics to construct a comprehensive safety perception model encompassing both day and night, using the analytic hierarchy process. By leveraging evaluation outcomes, a maximum coverage location-allocation model constrained by a facility service radius is employed to optimize the layout of the public amenities on campus, supported by empirical research conducted at the Wuchang Campus of the Wuhan Institute of Technology. The results indicate that a notable decrease in public areas provides a low sense of security to users and that an increase in public areas provides users with a high sense of security. The results demonstrate a considerable enhancement in both the daytime and nighttime sense of security due to the implemented optimization measures. This research contributes to a deeper understanding of the constituent elements shaping the sense of security in campus public spaces, offering concrete strategies for their evaluation and enhancement, thereby fostering a more scientifically grounded layout of campus public facilities and advancing the creation of a harmonious and secure campus environment.

Predicting radiation pneumonitis in lung cancer: a EUD-based machine learning approach for volumetric modulated arc therapy patients.

This study aims to develop an optimal machine learning model that uses lung equivalent uniform dose (lung EUD to predict radiation pneumonitis (RP) occurrence in lung cancer patients treated with volumetric modulated arc therapy (VMAT). We analyzed a cohort of 77 patients diagnosed with locally advanced squamous cell lung cancer (LASCLC) receiving concurrent chemoradiotherapy with VMAT. Patients were categorized based on the onset of grade II or higher radiation pneumonitis (RP 2+). Dose volume histogram data, extracted from the treatment planning system, were used to compute the lung EUD values for both groups using a specialized numerical analysis code. We identified the parameter α, representing the most significant relative difference in lung EUD between the two groups. The predictive potential of variables for RP2+, including physical dose metrics, lung EUD, normal tissue complication probability (NTCP) from the Lyman-Kutcher-Burman (LKB) model, and lung EUD-calibrated NTCP for affected and whole lung, underwent both univariate and multivariate analyses. Relevant variables were then employed as inputs for machine learning models: multiple logistic regression (MLR), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). Each model's performance was gauged using the area under the curve (AUC), determining the best-performing model. The optimal α-value for lung EUD was 0.3, maximizing the relative lung EUD difference between the RP 2+ and non-RP 2+ groups. A strong correlation coefficient of 0.929 (P< 0.01) was observed between lung EUD (α = 0.3) and physical dose metrics. When examining predictive capabilities, lung EUD-based NTCP for the affected lung (AUC: 0.862) and whole lung (AUC: 0.815) surpassed LKB-based NTCP for the respective lungs. The decision tree (DT) model using lung EUD-based predictors emerged as the superior model, achieving an AUC of 0.98 in both training and validation datasets. The likelihood of developing RP 2+ has shown a significant correlation with the advancements in RT technology. From traditional 3-D conformal RT, lung cancer treatment methodologies have transitioned to sophisticated techniques like static IMRT. Accurately deriving such a dose-effect relationship through NTCP modeling of RP incidence is statistically challenging due to the increased number of degrees-of-freedom. To the best of our knowledge, many studies have not clarified the rationale behind setting the α-value to 0.99 or 1, despite the closely aligned calculated lung EUD and lung mean dose MLD. Perfect independence among variables is rarely achievable in real-world scenarios. Four prominent machine learning algorithms were used to devise our prediction models. The inclusion of lung EUD-based factors substantially enhanced their predictive performance for RP 2+. Our results advocate for the decision tree model with lung EUD-based predictors as the optimal prediction tool for VMAT-treated lung cancer patients. Which could replace conventional dosimetric parameters, potentially simplifying complex neural network structures in prediction models.

Differentiation of Physical Training for Athletes Aged 10-12 in Kickboxing: Potential and Research Perspectives

Martial arts hold a prominent position in the array of sports disciplines, characterized by an extensive range of techniques, severe performance standards in competitive settings, impactful actions, unpredictability of outcomes, and substantial physical contact. Within this framework, the implementation of a personalized approach to physical training for children in the 10-12 age group becomes essential. This strategy encompasses an inclusive evaluation of each young athlete's unique physiological, biomechanical, and psychological traits. Such a holistic approach not only facilitates their well-rounded development but also minimizes the likelihood of injuries.&#x0D; From an educational perspective, this technique covers not only the physical aspects of training, but also emphasizes the development of academic and social skills, using methods that stimulate interest, self-motivation and positive self-esteem in athletes. It is important to use personalized training strategies that take into account the different learning styles and needs of each child, which contributes to the better integration of kickboxing techniques and the development of the necessary skills. Special attention is paid to the development of training programs that meet the individual needs and capabilities of each child, ensuring harmonious physical, psychological and social development at this key stage of their life.&#x0D; In conducting a systematic review of contemporary scholarly literature addressing physical training, the efficacy of assorted methodological approaches in the differentiation of training across various sports disciplines, such as fencing, the military- sports complex all-around, sports games, volleyball, and rowing, has been substantiated. However, there is a notable deficiency in scholarly focus on exploring the application of these differentiated physical training approaches within martial arts, especially in terms of specific physical fitness metrics for athletes in these disciplines. This research gap highlights the imperative for additional investigation and the creation of refined training strategies tailored to martial arts practitioners. These strategies should be designed to expertly integrate the distinct physical and physiological traits of these athletes.

Dose-response association between 24-hour total movement activity and testosterone deficiency in adult males.

Previous studies on the relationship between physical activity and testosterone are limited and controversial. Hence we investigated whether high level of physical activity is associated with a low risk of testosterone deficiency (TD). This cross-sectional analysis was conducted in a representative sample of US adult males who participated in the 2011-2014 cycle of the National Health and Nutrition Examination Survey (NHANES). We used the monitor independent movement summary (MIMS) to assess activity intensity, a novel physical activity metrics developed using raw data collected by accelerometers. Multivariable regression and smooth curve fitting was used to describe the relationships between physical activity and TD, and segmented regression model were used to analyze the threshold effect between them. Sensitivity analysis was conducted using interaction and stratified analysis. A U-shaped relationship between daily MIMS units and risk of TD was observed. The optimal value of daily MIMS units for the lowest risk of TD was 14.77 (×103), the risk of TD decreased by 5% in patients per unit increase of daily MIMS units when daily MIMS units <14.77 (×103) (adjusted OR = 0.95, 95%CI: 0.91, 0.99), but increased by 12% per unit increase of daily MIMS units when daily MIMS units ≥14.77 (×103) (adjusted OR = 1.12, 95%CI: 1.01, 1.23). In sensitivity analyses, the threshold effect was also similar according to baseline characteristics (P-interaction >0.05). In a nationally representative sample of US adult males, light to moderate intensity physical activity is associated with a lower odds of TD, while high-intensity physical activity is associated with a higher risk of TD.

Asymmetric Solvents Regulated Crystallization-Limited Electrolytes for All-Climate Lithium Metal Batteries.

Electrolytes that can keep liquid state are one of the most important physical metrics to ensure the ions transfer with stable operation of rechargeable lithium-based batteries at a wide temperature window. It is generally accepted that strong polar solvents with high melting points favor the safe operation of batteries above room temperatures but are susceptible to crystallization at low temperatures (≤-40 °C). Here, a crystallization limitation strategy was proposed to handle this issue. We demonstrate that, although the high melting points of ethylene sulfite (ES, -17 °C) and fluoroethylene carbonate (FEC, ≈23 °C), their mixtures can avoid crystallization at low temperatures, which can be attributed to low intermolecular interactions and altered molecular motion dynamics. A suitable ES/FEC ratio (10 % FEC) can balance the bulk and interface transport of ions, enabling LiNi0.8 Mn0.1 Co0.1 O2 ||lithium (NCM811||Li) full cells to deliver excellent temperature resilience and cycling stability over a wide temperature range from -50 °C to +70 °C. More than 66 % of the capacity retention was achieved at -50 °C compared to room temperature. The NCM811||Li pouch cells exhibit high cycling stability under realistic conditions (electrolyte weight to cathode capacity ratio (E/C)≤3.5 g Ah-1 , negative to positive electrode capacity ratio (N/P)≤1.09) at different temperatures.

Asymmetric Solvents Regulated Crystallization‐Limited Electrolytes for All‐Climate Lithium Metal Batteries

AbstractElectrolytes that can keep liquid state are one of the most important physical metrics to ensure the ions transfer with stable operation of rechargeable lithium‐based batteries at a wide temperature window. It is generally accepted that strong polar solvents with high melting points favor the safe operation of batteries above room temperatures but are susceptible to crystallization at low temperatures (≤−40 °C). Here, a crystallization limitation strategy was proposed to handle this issue. We demonstrate that, although the high melting points of ethylene sulfite (ES, −17 °C) and fluoroethylene carbonate (FEC, ≈23 °C), their mixtures can avoid crystallization at low temperatures, which can be attributed to low intermolecular interactions and altered molecular motion dynamics. A suitable ES/FEC ratio (10 % FEC) can balance the bulk and interface transport of ions, enabling LiNi0.8Mn0.1Co0.1O2||lithium (NCM811||Li) full cells to deliver excellent temperature resilience and cycling stability over a wide temperature range from −50 °C to +70 °C. More than 66 % of the capacity retention was achieved at −50 °C compared to room temperature. The NCM811||Li pouch cells exhibit high cycling stability under realistic conditions (electrolyte weight to cathode capacity ratio (E/C)≤3.5 g Ah−1, negative to positive electrode capacity ratio (N/P)≤1.09) at different temperatures.

Comparability of 24-hr Activity Cycle Outputs From ActiGraph Counts Generated in ActiLife and RStudio

Data from ActiGraph accelerometers have long been imported into ActiLife software, where the company’s proprietary “activity counts” were generated in order to understand physical behavior metrics. In 2022, ActiGraph released an open-source method to generate activity counts from any raw, triaxial accelerometer data using Python, which has been translated into RStudio packages. However, it is unclear if outcomes are comparable when generated in ActiLife and RStudio. Therefore, the authors’ technical note systematically compared activity counts and related physical behavior metrics generated from ActiGraph accelerometer data using ActiLife or available packages in RStudio and provides example code to ease implementation of such analyses in RStudio. In addition to comparing triaxial activity counts, physical behavior outputs (sleep, sedentary behavior, light-intensity physical activity, and moderate- to vigorous-intensity physical activity) were compared using multiple nonwear algorithms, epochs, cut points, sleep scoring algorithms, and accelerometer placement sites. Activity counts and physical behavior outcomes were largely the same between ActiLife and the tested packages in RStudio. However, peculiarities in the application of nonwear algorithms to the first and last portions of a data file (that occurred on partial, first or last days of data collection), differences in rounding, and handling of counts values on the borderline of activity intensities resulted in small but inconsequential differences in some files. The hope is that researchers and both hardware and software manufacturers continue to push efforts toward transparency in data analysis and interpretation, which will enhance comparability across devices and studies and help to advance fields examining links between physical behavior and health.

Comparison of Sleep and Physical Activity Metrics From Wrist-Worn ActiGraph wGT3X-BT and GT9X Accelerometers During Free-Living in Adults

Background: ActiGraph accelerometers can monitor sleep and physical activity (PA) during free-living, but there is a need to confirm agreement in outcomes between different models. Methods: Sleep and PA metrics from two ActiGraphs were compared after participants (N = 30) wore a GT9X and wGT3X-BT on their nondominant wrist for 7 days during free-living. PA metrics including total steps, counts, average acceleration—Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation, intensity gradient, the minimum acceleration value of the most active 10 and 30 min (M10, M30), time spent in activity intensities from vector magnitude (VM) counts, and ENMO cut points and sleep metrics (sleep period time window, sleep duration, sleep onset, and waking time) were compared. Results: Excellent agreement was evident for average acceleration-Mean Amplitude Deviation, counts, total steps, M10, and light PA (VM counts) with good agreement evident from the remaining PA metrics apart from moderate–vigorous PA (VM counts) which demonstrated moderate agreement. Mean bias for all PA metrics were low, as were the limits of agreement for the intensity gradient, average acceleration-Mean Amplitude Deviation, and inactive time (ENMO and VM counts). The limits of agreement for all other PA metrics were &gt;10%. Excellent agreement, low mean bias, and narrow limits of agreement were evident for all sleep metrics. All sleep and PA metrics demonstrated equivalence (equivalence zone of ≤10%) apart from moderate–vigorous PA (ENMO) which needed an equivalence zone of 16%. Conclusions: Equivalent estimates of almost all PA and sleep metrics are provided from the GT9X and wGT3X-BT worn on the nondominant wrist.

The role of street network metrics in shaping distance distributions in a residential neighbourhood

Walkability studies often rely on physical proximity metrics for destination accessibility. However, some recent studies argue that configuration (topological) distance provides a more accurate reflection of the actual distances experienced by individuals. This study therefore aims to test the extent to which an association exists between network-metric proximity to several destinations and two main measures of space syntax – integration and choice – in a dendriform network structure such as Putrajaya city (Malaysia). Using GIS, multiple buffer-service areas were generated around each housing unit (N = 2,392). Interpolation and space syntax analyses were conducted to assess metric and topological distance at local and global levels for each buffer. The statistical analysis showed that distances to commercial areas, transportation and average distances to all destinations were strongly affected by the syntactical properties of the neighbourhood. Moreover, global measures were more powerful in detecting metric-distance changes compared to local measures. Aligned with natural movement theory, these results support the idea that the spatial properties of urban form have a significant impact on distance distributions within residential settings, specifically distances to commercial areas. Therefore, policies that promote mixed-use development, especially in areas with good transportation access, should consider the impact of syntactical accessibility of the network.