Predicting Workers' Stress: Application of a High-Performance Algorithm Using Working-Style Characteristics.

BACKGROUND: Work characteristics, such as teleworking rate, have been studied in relation to stress. However, the use of work-related data to improve a high-performance stress prediction model that suits an individual's lifestyle has not been evaluated. OBJECTIVE: This study aims to develop a novel, high-performance algorithm to predict an employee's stress among a group of employees with similar working characteristics. METHODS: This prospective observational study evaluated participants' responses to web­based questionnaires, including attendance records and data collected using a wearable device. Data spanning 12 weeks (between January 17, 2022, and April 10, 2022) were collected from 194 Shionogi Group employees. Participants wore the Fitbit Charge 4 wearable device, which collected data on daily sleep, activity, and heart rate. Daily work shift data included details of working hours. Weekly questionnaire responses included the K6 questionnaire for depression/anxiety, a behavioral questionnaire, and the number of days lunch was missed. The proposed prediction model used a neighborhood cluster (N=20) with working-style characteristics similar to those of the prediction target person. Data from the previous week predicted stress levels the following week. Three models were compared by selecting appropriate training data: (1) single model, (2) proposed method 1, and (3) proposed method 2. Shapley Additive Explanations (SHAP) were calculated for the top 10 extracted features from the Extreme Gradient Boosting (XGBoost) model to evaluate the amount and contribution direction categorized by teleworking rates (mean): low: <0.2 (more than 4 days/week in office), middle: 0.2 to <0.6 (2 to 4 days/week in office), and high: ≥0.6 (less than 2 days/week in office). RESULTS: Data from 190 participants were used, with a teleworking rate ranging from 0% to 79%. The area under the curve (AUC) of the proposed method 2 was 0.84 (true positive vs false positive: 0.77 vs 0.26). Among participants with low teleworking rates, most features extracted were related to sleep, followed by activity and work. Among participants with high teleworking rates, most features were related to activity, followed by sleep and work. SHAP analysis showed that for participants with high teleworking rates, skipping lunch, working more/less than scheduled, higher fluctuations in heart rate, and lower mean sleep duration contributed to stress. In participants with low teleworking rates, coming too early or late to work (before/after 9 AM), a higher/lower than mean heart rate, lower fluctuations in heart rate, and burning more/fewer calories than normal contributed to stress. CONCLUSIONS: Forming a neighborhood cluster with similar working styles based on teleworking rates and using it as training data improved the prediction performance. The validity of the neighborhood cluster approach is indicated by differences in the contributing features and their contribution directions among teleworking levels. TRIAL REGISTRATION: UMIN UMIN000046394; https://www.umin.ac.jp/ctr/index.htm.

Real-World Effectiveness of Ensitrelvir in Reducing Severe Outcomes in Outpatients at High Risk for COVID-19.

INTRODUCTION: This study aimed to evaluate the effectiveness of ensitrelvir, an oral antiviral, in reducing hospitalization risk in outpatients at high-risk for severe COVID-19 during the Omicron era. METHODS: This was a retrospective study using a large Japanese health insurance claims database. It included high-risk outpatients for severe symptoms who received their first COVID-19 diagnosis between November 2022 and July 2023. The study included outpatients aged ≥ 18 years. The primary endpoint was all-cause hospitalization during the 4-week period from the date of outpatient diagnosis and medication, comparing the ensitrelvir group (n = 5177) and the no antiviral treatment group (n = 162,133). The risk ratio and risk difference were evaluated after adjusting patient background distribution by the inverse probability of treatment weight (IPTW) method. Secondary endpoints were incidence of respiratory and heart rate monitoring, oxygen therapy, ventilator use, intensive care admission, and all-cause death. RESULTS: The risk ratio for all-cause hospitalization between the ensitrelvir group (n = 167,385) and the no antiviral treatment group (n = 167,310) after IPTW adjustment was 0.629 [95% confidence interval (CI) 0.420, 0.943]. The risk difference was - 0.291 [95% CI - 0.494, - 0.088]. The incidence of both respiratory and heart rate monitoring and oxygen therapy was lower in the ensitrelvir group. Ventilator use, intensive care admission, and all-cause death were difficult to assess because of the limited events. CONCLUSIONS: The incidence of all-cause hospitalization was significantly lower in the ensitrelvir group than in the no antiviral treatment group, suggesting ensitrelvir is an effective treatment in patients at risk of severe COVID-19.

COVID-19 still poses a risk for patients with serious health conditions and weakened immune systems, who are more likely to develop severe illness. Several studies have indicated that some oral antiviral medications might be effective in preventing severe disease. This study aimed to evaluate if ensitrelvir, an oral antiviral medication, can help prevent hospitalization in outpatients who are at risk of developing severe symptoms from the Omicron variant of the SARS-CoV-2 virus. The hospitalization rates of patients who received ensitrelvir was compared with those who did not receive any antiviral treatment, using medical records from a large health insurance database in Japan focused on outpatients who were at risk of severe symptoms and were diagnosed with COVID-19 between November 2022 and July 2023. Respiratory and heart rate monitoring, oxygen therapy, ventilator use, intensive care admission, and all-cause death were also evaluated. The study found that patients who received ensitrelvir had a lower risk of being hospitalized compared to those who did not receive any antiviral treatment. The ensitrelvir group also had lower rates of respiratory and heart rate monitoring and oxygen therapy. However, it was challenging to assess the effects on ventilator use, intensive care admission, and all-cause death due to the small number of events in the population under evaluation. Based on these findings, ensitrelvir appears to be an effective treatment for reducing the risk of hospitalization in patients at risk of severe COVID-19.

Comparative Effectiveness of Baloxavir Marboxil and Oseltamivir Treatment in Reducing Household Transmission of Influenza: A Post Hoc Analysis of the BLOCKSTONE Trial.

BACKGROUND: The transmission of influenza virus in households, especially by children, is a major route of infection. Prior studies suggest that timely antiviral treatment of ill cases may reduce infection in household contacts. The aim of the study was to compare the effects of oseltamivir (OTV) and baloxavir marboxil (BXM) treatment of index cases on the secondary attack rate (SAR) of influenza within household. METHODS: A post hoc analysis was done in BLOCKSTONE trial-a placebo-controlled, double-blinded post-exposure prophylaxis of BXM. Data were derived from the laboratory-confirmed index cases' household contacts who received placebo in the trial and also from household members who did not participate in the trial but completed illness questionnaires. To assess the SAR of household members, multivariate analyses adjusted for factors including age, vaccination status, and household size were performed and compared between contacts of index cases treated with BXM or OTV. RESULTS: In total, 185 index cases (116 treated with BXM and 69 treated with OTV) and 410 household contacts (201 from trial, 209 by questionnaire) were included. The Poisson regression modeling showed that the SAR in household contacts of index cases treated with BXM and OTV was 10.8% and 18.5%, respectively; the adjusted relative reduction in SAR was 41.8% (95% confidence interval: 1.0%-65.7%, p = 0.0456) greater with BXM than OTV. Similar reductions were found in contacts from the trial and those included by questionnaire. CONCLUSION: BXM treatment of index cases appeared to result in a greater reduction in secondary household transmission than OTV treatment.

Virtual reality perceptual training can improve the temporal discrimination ability of swinging during softball batting.

Perception and action uncoupling in fastball sports anticipatory training is often criticized. Nevertheless, perception-only training offers distinct advantages, such as flexibility concerning time, place, and injury limitations. Therefore, the effectiveness of this training approach warrants evaluation. This study developed a virtual reality (VR) training system based on the idea that the two visual pathways in the brain are associated with visual information attributes, rather than perception or action. A key feature of this study's perception-only training was its presentation of not only the opponent's kinematics but also the ball's flight information (the attributes that guide hitting) to train the visual system necessary for real situations. Seventeen female softball batters were assigned to two groups: a training group (N = 9) and a control group (N = 8). Only the training group underwent the VR anticipatory skill training to discriminate the different ball speeds. Both groups completed a perception test and an action test on the VR during the pre- and post-training periods. The perception test assessed response accuracy (RA) in discriminating ball speed, and the action test assessed the temporal difference in swing onset (delta onset). Comparison of these two outcome variables between groups revealed that perception-only training improved both perceptual and action responses. This study demonstrated the effectiveness of perception-only training and emphasized the need for its thoughtful utilization and design.