Isotemporal substitution effect of 24-hour movement behavior on the mental health of Chinese preschool children.

The 24-h movement behavior of preschoolers comprises a spectrum of activities, including moderate-to-vigorous intensity physical activity (MVPA), light-intensity physical activity (LPA), screen-based sedentary behavior (SCSB), non-screen-based sedentary behavior (NSCSB), and sleep. While previous research has shed light on the link between movement behaviors and children's mental health, the specific impacts on the unique demographic of Chinese preschoolers remain underexplored. This study significantly contributes to the literature by exploring how 24-h movement behavior affects the mental health of preschoolers in a Chinese context. The study involved205 Chinese preschool children (117 boys and 88 girls) between the ages of 3 and 6 years wore accelerometers to measure their LPA, MVPA, and sedentary behavior (SB), while their parents reported the time spent on sleep and SCSB. The parents also completed the Strength and Difficulties Questionnaire to assess their children's mental health. The study used compositional regression and isotemporal substitution models to examine the relationship between the various components of 24-h movement behavior and mental health. The results showed that greater NCSSB compared to MVPA, LPA, sleep, and SCSB was associated with good prosocial behavior and lower scores on externalizing problems. This highlights the potential of NSCSB as a beneficial component in the daily routine of preschoolers for fostering mental well-being. Replacing 15 min of sleep and SCSB with 15 min of NSCSB was associated with a decrease of 0.24 and 0.15 units, respectively, in externalizing problems. Reallocating 15 min of sleep to NSCSB was linked to an increase of 0.11 units in prosocial behavior. There were no significant substitution effects between LPA and MVPA time with any other movement behavior on prosocial behavior and externalizing problems. Given the positive associations observed, further longitudinal studies are necessary to explore the link between 24-h movement behavior and mental health in preschool children.

[Analysis of inducement of benign paroxysmal positional vertigo].

Objective:To analyze the related factors that may affect the onset of benign paroxysmal positional vertigo（BPPV）. Methods:Fifty BPPV patients treated in Department of Otolaryngology Head and Neck Surgery, Shanxi Provincial People's Hospital from May to September 2023 were selected as the case group, and 50 healthy adults were selected as the control group. Relevant information was collected by means of questionnaire survey and medical history inquiry. The two groups were compared in terms of sleep time, night sleep duration, wake times, underlying diseases（hypertension, diabetes, coronary heart disease, etc.） and negative emotional impact. Results:The proportion of male and female in the case group was 16% and 84%, and that in the control group was 20% and 80%. The mean age of the case group was（54.66±13.39） years old, and the mean age of the control group was（54.42±12.55） years old, ranging from 27 to 80 years old. The sleeping time of the case group was significantly later than that of the healthy group, and the difference was statistically significant（P<0.05）. The night sleep duration of the case group was shorter than that of the healthy group, the difference was statistically significant（P<0.05）. There was no significant difference in awakening times between the case group and the healthy group（P>0.05）. There were more patients in the case group with underlying diseases（54%） and affected by negative emotions（70%） than in the healthy group, and the difference was statistically significant（P<0.05）. Conclusion:Late sleep time, short sleep duration at night, accompanied by underlying diseases and negative emotions can affect the onset of BPPV.

Replacing the Scalpel With a Computer Mouse: An Evaluation of Time Spent on Electronic Health Record for Plastic Surgery Residents and Its Impact on Resident Training.

BACKGROUND: Following the integration of the electronic health record (EHR) into the healthcare system, concern has grown regarding EHR use on physician well-being. For surgical residents, time spent on the EHR increases the burden of a demanding, hourly restricted schedule and detracts from time spent honing surgical skills. To better characterize these burdens, we sought to describe EHR utilization patterns for plastic surgery residents. METHODS: Integrated plastic surgery resident EHR utilization from March 2019 to March 2020 was extracted via Cerner Analytics at a tertiary academic medical center. Time spent in the EHR on-duty (0600-1759) and off-duty (1800-0559) in the form of chart review, orders, documentation, and patient discovery was analyzed. Statistical analysis was performed in the form of independent t tests and Analysis of Variance (ANOVA). RESULTS: Twelve plastic surgery residents spent a daily average of 94 ± 84 minutes on the EHR, one-third of which was spent off-duty. Juniors (postgraduate years 1-3) spent 123 ± 99 minutes versus seniors (postgraduate years 4-6) who spent 61 ± 49 minutes (P < 0.01). Seniors spent 19% of time on the EHR off-duty, compared with 37% for juniors (P < 0.01). Chart review comprised the majority (42%) of EHR usage, followed by patient discovery (22%), orders (14%), documentation (12%), other (6%), and messaging (1%). Seniors spent more time on patient discovery (25% vs 21%, P < 0.001), while juniors spent more time performing chart review (48% vs 36%, P = 0.19). CONCLUSION: Integrated plastic surgery residents average 1.5 hours on the EHR daily. Junior residents spend 1 hour more per day on the EHR, including more time off-duty and more time performing chart review. These added hours may play a role in duty hour violations and detract from obtaining operative skill sets.

Blood donation projections using hierarchical time series forecasting: the case of Zimbabwe's national blood bank.

BACKGROUND: The discrepancy between blood supply and demand requires accurate forecasts of the blood supply at any blood bank. Accurate blood donation forecasting gives blood managers empirical evidence in blood inventory management. The study aims to model and predict blood donations in Zimbabwe using hierarchical time series. The modelling technique allows one to identify, say, a declining donor category, and in that way, the method offers feasible and targeted solutions for blood managers to work on. METHODS: The monthly blood donation data covering the period 2007 to 2018, collected from the National Blood Service Zimbabwe (NBSZ) was used. The data was disaggregated by gender and blood groups types within each gender category. The model validation involved utilising actual blood donation data from 2019 and 2020. The model's performance was evaluated through the Mean Absolute Percentage Error (MAPE), uncovering expected and notable discrepancies during the Covid-19 pandemic period only. RESULTS: Blood group O had the highest monthly yield mean of 1507.85 and 1230.03 blood units for male and female donors, respectively. The top-down forecasting proportions (TDFP) under ARIMA, with a MAPE value of 11.30, was selected as the best approach and the model was then used to forecast future blood donations. The blood donation predictions for 2019 had a MAPE value of 14.80, suggesting alignment with previous years' donations. However, starting in April 2020, the Covid-19 pandemic disrupted blood collection, leading to a significant decrease in blood donation and hence a decrease in model accuracy. CONCLUSIONS: The gradual decrease in future blood donations exhibited by the predictions calls for blood authorities in Zimbabwe to develop interventions that encourage blood donor retention and regular donations. The impact of the Covid-19 pandemic distorted the blood donation patterns such that the developed model did not capture the significant drop in blood donations during the pandemic period. Other shocks such as, a surge in global pandemics and other disasters, will inevitably affect the blood donation system. Thus, forecasting future blood collections with a high degree of accuracy requires robust mathematical models which factor in, the impact of various shocks to the system, on short notice.

Effect of mental fatigue on hand force production capacities.

Mental fatigue is common in society, but its effects on force production capacities remain unclear. This study aimed to investigate the impact of mental fatigue on maximal force production, rate of force development-scaling factor (RFD-SF), and force steadiness during handgrip contractions. Fourteen participants performed two randomized sessions, during which they either carried out a cognitively demanding task (i.e., a visual attention task) or a cognitively nondemanding task (i.e., documentary watching for 62 min). The mental fatigue was evaluated subjectively and objectively (performances and electroencephalography). Maximal voluntary contraction (MVC) force, RFD-SF, and force steadiness (i.e., force coefficient of variation at submaximal intensities; 25, 50, and 75% of MVC) were recorded before and after both tasks. The feeling of mental fatigue was much higher after completing the cognitively demanding task than after documentary watching (p < .001). During the cognitively demanding task, mental fatigue was evidenced by increased errors, missed trials, and decreased N100 amplitude over time. While no effect was reported on force steadiness, both tasks induced a decrease in MVC (p = .040), a force RFD-SF lower slope (p = .011), and a reduction in the coefficient of determination (p = .011). Nevertheless, these effects were not explicitly linked to mental fatigue since they appeared both after the mentally fatiguing task and after watching the documentary. The study highlights the importance of considering cognitive engagement and mental load when optimizing motor performance to mitigate adverse effects and improve force production capacities.

Predicting adverse events after thoracic endovascular aortic repair for patients with type B aortic dissection.

The potential of adverse events (AEs) after thoracic endovascular aortic repair (TEVAR) in patients with type B aortic dissection (TBAD) has been reported. To avoid the occurrence of AEs, it is important to recognize high-risk population for prevention in advance. The data of 261 patients with TBAD who received TEVAR between June 2017 and June 2021 at our medical center were retrospectively reviewed. After the implementation of exclusion criteria, 172 patients were finally included, and after 2.8 years (range from 1 day to 5.8 years) of follow up, they were divided into AEs (n = 41) and non-AEs (n = 131) groups. We identified the predictors of AEs, and a prediction model was constructed to calculate the specific risk of postoperative AEs at 1, 2, and 3 years, and to stratify patients into high-risk (n = 78) and low-risk (n = 94) group. The prediction model included seven predictors: Age > 75 years, Lower extremity malperfusion (LEM), NT-proBNP > 330 pg/ml, None distal tear, the ratio between the diameter of the ascending aorta and descending aorta (A/D ratio) > 1.2, the ratio of the area of the false lumen to the total aorta (FL ratio) > 64%, and acute TEVAR, which exhibited excellent predictive accuracy performance and discriminatory ability with C statistic of 82.3% (95% CI 77.3-89.2%). The prediction model was contributed to identify high-risk patients of postoperative AEs, which may serve to achievement of personalized treatment and follow-up plans for patients.

Umbilical cord milking and delayed cord clamping for the prevention of neonatal hypoglycaemia: a systematic review and meta-analysis.

BACKGROUND: Placental management strategies such as umbilical cord milking and delayed cord clamping may provide a range of benefits for the newborn. The aim of this review was to assess the effectiveness of umbilical cord milking and delayed cord clamping for the prevention of neonatal hypoglycaemia. METHODS: Three databases and five clinical trial registries were systematically reviewed to identify randomised controlled trials comparing umbilical cord milking or delayed cord clamping with control in term and preterm infants. The primary outcome was neonatal hypoglycaemia (study defined). Two independent reviewers conducted screening, data extraction and quality assessment. Quality of the included studies was assessed using the Cochrane Risk of Bias tool (RoB-2). Certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Meta-analysis using a random effect model was done using Review Manager 5.4. The review was registered prospectively on PROSPERO (CRD42022356553). RESULTS: Data from 71 studies and 14 268 infants were included in this review; 22 (2 537 infants) compared umbilical cord milking with control, and 50 studies (11 731 infants) compared delayed with early cord clamping. For umbilical cord milking there were no data on neonatal hypoglycaemia, and no differences between groups for any of the secondary outcomes. We found no evidence that delayed cord clamping reduced the incidence of hypoglycaemia (6 studies, 444 infants, RR = 0.87, CI: 0.58 to 1.30, p = 0.49, I2 = 0%). Delayed cord clamping was associated with a 27% reduction in neonatal mortality (15 studies, 3 041 infants, RR = 0.73, CI: 0.55 to 0.98, p = 0.03, I2 = 0%). We found no evidence for the effect of delayed cord clamping for any of the other outcomes. The certainty of evidence was low for all outcomes. CONCLUSION: We found no data for the effectiveness of umbilical cord milking on neonatal hypoglycaemia, and no evidence that delayed cord clamping reduced the incidence of hypoglycaemia, but the certainty of the evidence was low.

Effectiveness of workplace interventions with digital elements to reduce sedentary behaviours in office employees: a systematic review and meta-analysis.

BACKGROUND: Digital interventions are potential tools for reducing and limiting occupational sedentary behaviour (SB) in sedentary desk-based jobs. Given the harmful effects of sitting too much and sitting for too long while working, the aim of this systematic review and meta-analysis was to examine the effectiveness of workplace interventions, that incorporated digital elements, to reduce the time spent in SB in office workers. METHODS: Randomised control trials that evaluated the implementation of workplace interventions that incorporated digital elements for breaking and limiting SB among desk-based jobs were identified by literature searches in six electronic databases (PubMed, Web of Science, Scopus, CINAHL, PsycINFO and PEDro) published up to 2023. Studies were included if total and/or occupational SB were assessed. Only studies that reported pre- and postintervention mean differences and standard deviations or standard errors for both intervention arms were used for the meta-analysis. The meta-analysis was conducted using Review Manager 5 (RevMan 5; Cochrane Collaboration, Oxford, UK). Risk of bias was assessed using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields QUALSYST tool. RESULTS: Nineteen studies were included in the systematic review. The most employed digital elements were information delivery and mediated organisational support and social influences. Multicomponent, information, and counselling interventions measuring total and/or occupational/nonoccupational SB time by self-report or via device-based measures were reported. Multicomponent interventions were the most represented. Eleven studies were included in the meta-analysis, which presented a reduction of 29.9 (95% CI: -45.2, -14.5) min/8 h workday in SB (overall effect: Z = 3.81). CONCLUSIONS: Multicomponent interventions, using a wide range of digital features, have demonstrated effectiveness in reducing time spent in SB at the workplace among desk-based employees. However, due to hybrid work (i.e., work in the office and home) being a customary mode of work for many employees, it is important for future studies to assess the feasibility and effectiveness of these interventions in the evolving work landscape. TRIAL REGISTRATION: The review protocol was registered in the Prospero database (CRD42022377366).

Temporal validation of a Generalized Additive2 Model (GA2M) to assess the risk of Chronic Kidney Disease (CKD).

OBJECTIVE: To assess the temporal validity of a model predicting the risk of Chronic Kidney Disease (CKD) using Generalized Additive2 Models (GA2M). MATERIALS: We adopted the Italian Health Search Database (HSD) with which the original algorithm was developed and validated by comparing different machine learnings models. METHODS: We selected all patients aged >=15 being active in HSD in 2019. They were followed up until December 2022 so being updated with three years of data collection. Those with prior diagnosis of CKD were excluded. A GA2M-based algorithm for CKD prediction was applied to this cohort in order to compare observed and predicted risk. Area Under Curve (AUC) and Average Precision (AP) were calculated. RESULTS: We obtained an AUC and AP equal to 88% and 30%, respectively. DISCUSSION: The prediction accuracy of the algorithm was largely consistent with that obtained in our prior work which was based on a different time-window for data collection. We therefore underlined and demonstrated the relevance of temporal validation for this prediction tool. CONCLUSION: The GA2M confirmed its high accuracy in prediction of CKD. As such, the respective patient- and population-based informatic tools might be implemented in primary care.

Trial- vs. cycle-level detrending in the analysis of cyclical biomechanical data.

Biomechanical time series may contain low-frequency trends due to factors like electromechanical drift, attentional drift and fatigue. Existing detrending procedures are predominantly conducted at the trial level, removing trends that exist over finite, adjacent time windows, but this fails to consider what we term 'cycle-level trends': trends that occur in cyclical movements like gait and that vary across the movement cycle, for example: positive and negative drifts in early and late gait phases, respectively. The purposes of this study were to describe cycle-level detrending and to investigate the frequencies with which cycle-level trends (i) exist, and (ii) statistically affect results. Anterioposterior ground reaction forces (GRF) from the 41-subject, 8-speed, open treadmill walking dataset of Fukuchi (2018) were analyzed. Of a total of 552 analyzed trials, significant cycle-level trends were found approximately three times more frequently (21.1%) than significant trial-level trends (7.4%). In statistical comparisons of adjacent walking speeds (i.e., speed 1 vs. 2, 2 vs. 3, etc.) just 3.3% of trials exhibited cycle-level trends that changed the null hypothesis rejection decision. However 17.6% of trials exhibited cycle-level trends that qualitatively changed the stance phase regions identified as significant. Although these results are preliminary and derived from just one dataset, results suggest that cycle-level trends can contribute to analysis bias, and therefore that cycle-level trends should be considered and/or removed where possible. Software implementing the proposed cycle-level detrending is available at https://github.com/0todd0000/detrend1d.

A national multi centre pre-hospital ECPR stepped wedge study; design and rationale of the ON-SCENE study.

BACKGROUND: The likelihood of return of spontaneous circulation with conventional advanced life support is known to have an exponential decline and therefore neurological outcome after 20 min in patients with a cardiac arrest is poor. Initiation of venoarterial ExtraCorporeal Membrane Oxygenation (ECMO) during resuscitation might improve outcomes if used in time and in a selected patient category. However, previous studies have failed to significantly reduce the time from cardiac arrest to ECMO flow to less than 60 min. We hypothesize that the initiation of Extracorporeal Cardiopulmonary Resuscitation (ECPR) by a Helicopter Emergency Medical Services System (HEMS) will reduce the low flow time and improve outcomes in refractory Out of Hospital Cardiac Arrest (OHCA) patients. METHODS: The ON-SCENE study will use a non-randomised stepped wedge design to implement ECPR in patients with witnessed OHCA between the ages of 18-50 years old, with an initial presentation of shockable rhythm or pulseless electrical activity with a high suspicion of pulmonary embolism, lasting more than 20, but less than 45 min. Patients will be treated by the ambulance crew and HEMS with prehospital ECPR capabilities and will be compared with treatment by ambulance crew and HEMS without prehospital ECPR capabilities. The primary outcome measure will be survival at hospital discharge. The secondary outcome measure will be good neurological outcome defined as a cerebral performance categories scale score of 1 or 2 at 6 and 12 months. DISCUSSION: The ON-SCENE study focuses on initiating ECPR at the scene of OHCA using HEMS. The current in-hospital ECPR for OHCA obstacles encompassing low survival rates in refractory arrests, extended low-flow durations during transportation, and the critical time sensitivity of initiating ECPR, which could potentially be addressed through the implementation of the HEMS system. When successful, implementing on-scene ECPR could significantly enhance survival rates and minimize neurological impairment. TRIAL REGISTRATION: Clinicaltyrials.gov under NCT04620070, registration date 3 November 2020.

Advancing e-commerce user purchase prediction: Integration of time-series attention with event-based timestamp encoding and Graph Neural Network-Enhanced user profiling.

In recent years, the global e-commerce landscape has witnessed rapid growth, with sales reaching a new peak in the past year and expected to rise further in the coming years. Amid this e-commerce boom, accurately predicting user purchase behavior has become crucial for commercial success. We introduce a novel framework integrating three innovative approaches to enhance the prediction model's effectiveness. First, we integrate an event-based timestamp encoding within a time-series attention model, effectively capturing the dynamic and temporal aspects of user behavior. This aspect is often neglected in traditional user purchase prediction methods, leading to suboptimal accuracy. Second, we incorporate Graph Neural Networks (GNNs) to analyze user behavior. By modeling users and their actions as nodes and edges within a graph structure, we capture complex relationships and patterns in user behavior more effectively than current models, offering a nuanced and comprehensive analysis. Lastly, our framework transcends traditional learning strategies by implementing advanced meta-learning techniques. This enables the model to autonomously adjust learning parameters, including the learning rate, in response to new and evolving data environments, thereby significantly enhancing its adaptability and learning efficiency. Through extensive experiments on diverse real-world e-commerce datasets, our model demonstrates superior performance, particularly in accuracy and adaptability in large-scale data scenarios. This study not only overcomes the existing challenges in analyzing e-commerce user behavior but also sets a foundation for future exploration in this dynamic field. We believe our contributions provide significant insights and tools for e-commerce platforms to better understand and cater to their users, ultimately driving sales and improving user experiences.

Efficacy of Linezolid in the management of pneumonic COVID-19 patients. Bioinformatics-based clinical study.

INTRODUCTION: At the beginning in July 2023, there has been a significant increase in daily hospital admissions attributed to the new variant of COVID-19. Aim of this study is to explore the clinical benefits and outcomes of using linezolid in the management of pneumonic COVID-19 patients. METHODOLOGY: The study included 230 patients with SARS-CoV-2 infection confirmed by RT-PCR. Group 1: 118 patients were managed with Linazolid alongside steroids. Group 2: (control group) patients treated according to the Protocol for Egyptian COVID-19 management outlines and WHO guidelines (112 patients). Each patient group was categorized into 3 age groups: 20-40 years, 41-65 years, and over 65 years. Patients were carefully followed up until recovery or mortality. A docking analysis was carried out to investigate the potential of linezolid to act as an Mpro inhibitor. RESULTS: Group 1's average recovery time was 15.1 days in contrast to 18.7 days for Group 2 (control). There were no deaths reported. In silico investigations revealed that Linezolid was able to achieve a binding mode comparable to that of the co-crystalized inhibitor. CONCLUSIONS: Linazolid is considered an effective antiviral weapon against SARS-COV-2. It could be used in the management plan of pneumonic individuals due to SARS-COV-2 infection. We recommend using it to combat the current wave caused by Omicron EG-5 Variant.

High-dimensional multisubject time series transition matrix inference with application to brain connectivity analysis.

Brain-effective connectivity analysis quantifies directed influence of one neural element or region over another, and it is of great scientific interest to understand how effective connectivity pattern is affected by variations of subject conditions. Vector autoregression (VAR) is a useful tool for this type of problems. However, there is a paucity of solutions when there is measurement error, when there are multiple subjects, and when the focus is the inference of the transition matrix. In this article, we study the problem of transition matrix inference under the high-dimensional VAR model with measurement error and multiple subjects. We propose a simultaneous testing procedure, with three key components: a modified expectation-maximization (EM) algorithm, a test statistic based on the tensor regression of a bias-corrected estimator of the lagged auto-covariance given the covariates, and a properly thresholded simultaneous test. We establish the uniform consistency for the estimators of our modified EM, and show that the subsequent test achieves both a consistent false discovery control, and its power approaches one asymptotically. We demonstrate the efficacy of our method through both simulations and a brain connectivity study of task-evoked functional magnetic resonance imaging.

Does radiofrequency ablation procedural data improve the accuracy of identifying atrial fibrillation recurrence?

Radiofrequency ablation (RFA) using the CARTO 3D mapping system is a common approach for pulmonary vein isolation to treat atrial fibrillation (AF). Linkage between CARTO procedural data and patients' electronical health records (EHR) provides an opportunity to identify the ablation-related parameters that would predict AF recurrence. The objective of this study is to assess the incremental accuracy of RFA procedural data to predict post-ablation AF recurrence using machine learning model. Procedural data generated during RFA procedure were downloaded from CARTONET and linked to deidentified Mercy Health EHR data. Data were divided into train (70%) and test (30%) data for model development and validation. Automate machine learning (AutoML) was used to predict 1 year AF recurrence, defined as a composite of repeat ablation, electrical cardioversion, and AF hospitalization. At first, AutoML model only included Patients' demographic and clinical characteristics. Second, an AutoML model with procedural variables and demographical/clinical variables was developed. Area under receiver operating characteristic curve (AUROC) and net reclassification improvement (NRI) were used to compare model performances using test data. Among 306 patients, 67 (21.9%) patients experienced 1-year AF recurrence. AUROC increased from 0.66 to 0.78 after adding procedural data in the AutoML model based on test data. For patients with AF recurrence, NRI was 32% for model with procedural data. Nine of 10 important predictive features were CARTO procedural data. From CARTO procedural data, patients with lower contact force in right inferior site, long ablation duration, and low number of left inferior and right roof lesions had a higher risk of AF recurrence. Patients with persistent AF were more likely to have AF recurrence. The machine learning model with procedural data better predicted 1-year AF recurrence than the model without procedural data. The model could be used for identification of patients with high risk of AF recurrence post ablation.

Attribute latencies causally shape intertemporal decisions.

Intertemporal choices - decisions that play out over time - pervade our life. Thus, how people make intertemporal choices is a fundamental question. Here, we investigate the role of attribute latency (the time between when people start to process different attributes) in shaping intertemporal preferences using five experiments with choices between smaller-sooner and larger-later rewards. In the first experiment, we identify attribute latencies using mouse-trajectories and find that they predict individual differences in choices, response times, and changes across time constraints. In the other four experiments we test the causal link from attribute latencies to choice, staggering the display of the attributes. This changes attribute latencies and intertemporal preferences. Displaying the amount information first makes people more patient, while displaying time information first does the opposite. These findings highlight the importance of intra-choice dynamics in shaping intertemporal choices and suggest that manipulating attribute latency may be a useful technique for nudging.