Purpose Physical distancing policies in the state of New South Wales (Australia) were implemented on March 23, 2020, because of the COVID-19 pandemic. This study investigated changes in physical activity, dietary behaviors, and well-being during the early period of this policy. Methods A cohort of young people aged 13–19 years from Sydney (N = 582) were prospectively followed for 22 weeks (November 18, 2019, to April 19, 2020). Daily, weekly, and monthly trajectories of diet, physical activity, sedentary behavior, well-being, and psychological distress were collected via smartphone, using a series of ecological momentary assessments and smartphone sensors. Differences in health and well-being outcomes were compared pre- and post-implementation of physical distancing guidelines. Results After the implementation of physical distancing measures in NSW, there were significant decreases in physical activity (odds ratio [OR] = .53, 95% confidence interval [CI] = .34–.83), increases in social media and Internet use (OR = 1.86, 95% CI = 1.15–3.00), and increased screen time based on participants' smartphone screen state. Physical distancing measures were also associated with being alone in the previous hour (OR = 2.09, 95% CI: 1.33–3.28), decreases in happiness (OR = .38, 95% CI = .18–.82), and fast food consumption (OR = .46, 95% CI = .29–.73). Conclusions Physical distancing and social restrictions had a contemporaneous impact on health and well-being outcomes associated with chronic disease among young people. As the pandemic evolves, it will be important to consider how to mitigate against any longer term health impacts of physical distancing restrictions.

BackgroundCurrent critical care prognostic models are predominantly developed in high-income countries (HICs) and may not be feasible in intensive care units (ICUs) in lower- and middle-income countries (LMICs). Existing prognostic models cannot be applied without validation in LMICs as the different disease profiles, resource availability, and heterogeneity of the population may limit the transferability of such scores. A major shortcoming in using such models in LMICs is the unavailability of required measurements. This study proposes a simplified critical care prognostic model for use at the time of ICU admission.MethodsThis was a prospective study of 3855 patients admitted to 21 ICUs from Bangladesh, India, Nepal, and Sri Lanka who were aged 16 years and over and followed to ICU discharge. Variables captured included patient age, admission characteristics, clinical assessments, laboratory investigations, and treatment measures.Multivariate logistic regression was used to develop three models for ICU mortality prediction: model 1 with clinical, laboratory, and treatment variables; model 2 with clinical and laboratory variables; and model 3, a purely clinical model.Internal validation based on bootstrapping (1000 samples) was used to calculate discrimination (area under the receiver operating characteristic curve (AUC)) and calibration (Hosmer-Lemeshow C-Statistic; higher values indicate poorer calibration). Comparison was made with the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II models.ResultsModel 1 recorded the respiratory rate, systolic blood pressure, Glasgow Coma Scale (GCS), blood urea, haemoglobin, mechanical ventilation, and vasopressor use on ICU admission. Model 2, named TropICS (Tropical Intensive Care Score), included emergency surgery, respiratory rate, systolic blood pressure, GCS, blood urea, and haemoglobin. Model 3 included respiratory rate, emergency surgery, and GCS. AUC was 0.818 (95% confidence interval (CI) 0.800–0.835) for model 1, 0.767 (0.741–0.792) for TropICS, and 0.725 (0.688–0.762) for model 3. The Hosmer-Lemeshow C-Statistic p values were less than 0.05 for models 1 and 3 and 0.18 for TropICS. In comparison, when APACHE II and SAPS II were applied to the same dataset, AUC was 0.707 (0.688–0.726) and 0.714 (0.695–0.732) and the C-Statistic was 124.84 (p < 0.001) and 1692.14 (p < 0.001), respectively.ConclusionThis paper proposes TropICS as the first multinational critical care prognostic model developed in a non-HIC setting.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-017-1843-6) contains supplementary material, which is available to authorized users.

ObjectiveThis study describes the availability of core parameters for Early Warning Scores (EWS), evaluates the ability of selected EWS to identify patients at risk of death or other adverse outcome and describes the burden of triggering that front-line staff would experience if implemented.DesignLongitudinal observational cohort study.SettingDistrict General Hospital Monaragala.ParticipantsAll adult (age >17 years) admitted patients.Main outcome measuresExisting physiological parameters, adverse outcomes and survival status at hospital discharge were extracted daily from existing paper records for all patients over an 8-month period.Statistical analysisDiscrimination for selected aggregate weighted track and trigger systems (AWTTS) was assessed by the area under the receiver operating characteristic (AUROC) curve.Performance of EWS are further evaluated at time points during admission and across diagnostic groups. The burden of trigger to correctly identify patients who died was evaluated using positive predictive value (PPV).ResultsOf the 16 386 patients included, 502 (3.06%) had one or more adverse outcomes (cardiac arrests, unplanned intensive care unit admissions and transfers). Availability of physiological parameters on admission ranged from 90.97% (95% CI 90.52% to 91.40%) for heart rate to 23.94% (95% CI 23.29% to 24.60%) for oxygen saturation. Ability to discriminate death on admission was less than 0.81 (AUROC) for all selected EWS. Performance of the best performing of the EWS varied depending on admission diagnosis, and was diminished at 24 hours prior to event. PPV was low (10.44%).ConclusionThere is limited observation reporting in this setting. Indiscriminate application of EWS to all patients admitted to wards in this setting may result in an unnecessary burden of monitoring and may detract from clinician care of sicker patients. Physiological parameters in combination with diagnosis may have a place when applied on admission to help identify patients for whom increased vital sign monitoring may not be beneficial. Further research is required to understand the priorities and cues that influence monitoring of ward patients.Trial registration numberNCT02523456.

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