Influence of blistering lesions on foot functionality in hikers.

BACKGROUND: Friction blisters are formed by abrasion from frictional forces on the upper layer of the epidermis and can make physical activity an uncomfortable experience. To our knowledge, no previous studies have considered how these injuries affect the functionality of the foot. For this reason, the main aim of this study was to evaluate foot function in hikers, with or without blisters. MATERIAL AND METHODS: This case-control study examined 298 hikers who walked the Camino de Santiago long-distance trail (in northern Spain); 207 had one or more blistering foot lesions and 91 had no blisters. Sociodemographic and clinical variables were collected, and the number of blisters and their locations on the foot were recorded. All participants self-completed the Foot Function Index (FFI) questionnaire, in their native language. RESULTS: Pain and disability were significantly greater among the hikers with blisters (pain p=<0.001; disability p = 0.015). However, there were no significant differences in the limitation of physical activity between those with blisters (case group) and the control group (p = 0.144). Neither was there any correlation between the number of blisters and pain, disability or limitation of activity. However, the location of the lesion did influence foot functionality. Blisters on the metatarsal heads were more limiting and caused greater pain (right foot p = 0.009; left foot p = 0.017), greater disability (right foot p = 0.005; left foot p = 0.005), greater limitation of activity (on right foot p = 0.012) and more loss of foot functionality (right foot p = 0.002; left foot p = 0.007). CONCLUSION: The hikers with blisters experienced reduced foot functionality in terms of pain and disability. The number of blisters was not related to foot functionality. Blisters located on the metatarsal heads caused the greatest increase in pain, disability and limitation of activity.

Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data.

We are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID-19 pandemic to create a decision support system for policy-makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14-day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14-day CI in scenarios with and without trend changes, reaching 0.93 [Formula: see text], 4.16 RMSE and 1.08 MAE.