ABSTRACT
OBJECTIVES: To identify prognostic models which estimate the risk of critical COVID-19 in hospitalized patients and to assess their validation properties. STUDY DESIGN AND SETTING: We conducted a systematic review in Medline (up to January 2021) of studies developing or updating a model that estimated the risk of critical COVID-19, defined as death, admission to intensive care unit, and/or use of mechanical ventilation during admission. Models were validated in two datasets with different backgrounds (HM [private Spanish hospital network], n = 1,753, and ICS [public Catalan health system], n = 1,104), by assessing discrimination (area under the curve [AUC]) and calibration (plots). RESULTS: We validated 18 prognostic models. Discrimination was good in nine of them (AUCs ≥ 80%) and higher in those predicting mortality (AUCs 65%-87%) than those predicting intensive care unit admission or a composite outcome (AUCs 53%-78%). Calibration was poor in all models providing outcome's probabilities and good in four models providing a point-based score. These four models used mortality as outcome and included age, oxygen saturation, and C-reactive protein among their predictors. CONCLUSION: The validity of models predicting critical COVID-19 by using only routinely collected predictors is variable. Four models showed good discrimination and calibration when externally validated and are recommended for their use.
ABSTRACT
BACKGROUND: The study of impact of lockdowns on individual health-related behaviors has produced divergent results. PURPOSE: To identify patterns of change in multiple health-related behaviors analyzed as a whole, and their individual determinants. METHODS: Between March and August 2020, we collected data on smoking, alcohol, physical activity, weight, and sleep in a population-based cohort from Catalonia who had available pre-pandemic data. We performed multiple correspondence and cluster analyses to identify patterns of change in health-related behaviors and built multivariable multinomial logistic regressions to identify determinants of behavioral change. RESULTS: In 10,032 participants (59% female, mean (SD) age 55 (8) years), 8,606 individuals (86%) modified their behavior during the lockdown. We identified five patterns of behavioral change that were heterogeneous and directed both towards worsening and improvement in diverse combinations. Patterns ranged from "global worsening" (2,063 participants, 21%) characterized by increases in smoking, alcohol consumption, and weight, and decreases in physical activity levels and sleep time, to "improvement" (2,548 participants, 25%) characterized by increases in physical activity levels, decreases in weight and alcohol consumption, and both increases and decreases in sleep time. Being female, of older age, teleworking, having a higher education level, assuming caregiving responsibilities, and being more exposed to pandemic news were associated with changing behavior (all p < .05), but did not discriminate between favorable or unfavorable changes. CONCLUSIONS: Most of the population experienced changes in health-related behavior during lockdowns. Determinants of behavior modification were not explicitly associated with the direction of changes but allowed the identification of older, teleworking, and highly educated women who assumed caregiving responsibilities at home as susceptible population groups more vulnerable to lockdowns.
ABSTRACT
Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person's time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research. • R tutorial to quantify resilience from physical activity time series. • Physical activity resilience is measured using an idiographic approach. • Physical activity resilience is operationalized as the AUC. • Growth models are fitted to step count time series to define the limits of the AUC. • Further indicators of resilience are provided to describe the phenomenon. [ FROM AUTHOR]
ABSTRACT
Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person’s time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research.