| Autor: |
Ioannou, Leonidas G., Ciuha, Urša, Fisher, Jason T., Tsoutsoubi, Lydia, Tobita, Kunihito, Bonell, Ana, Cotter, James D., Kenny, Glen P., Flouris, Andreas D., Mekjavic, Igor B. |
| Předmět: |
|
| Zdroj: |
Applied Sciences (2076-3417); Aug2023, Vol. 13 Issue 15, p8561, 18p |
| Abstrakt: |
Background: Prevention plays a key role in ensuring health and safety and is particularly important in scenarios when life is threatened. Adverse thermal conditions are experienced by billions of people daily, affecting the human capacity for thermoregulation and increasing the risks of life-threatening accidents, diseases, and fatalities. The aim of this study was to develop and validate a new, freely accessible method that will ultimately allow health, as well as exercise and labour organizations, to predict and potentially mitigate the physiological strain experienced by people who exercise or work in thermally stressful environmental conditions. Methods: First, we used concurrent technological advances and thermophysiological modelling to (i) develop a mobile phone application that predicts the physiological heat strain experienced by individuals conducting physical activity in adverse environmental conditions, and (ii) provide them with individualized heat mitigation strategies. Second, to examine the construct validity of the newly developed mobile phone application, core body temperature was recorded using gastrointestinal thermometry in 37 healthy soldiers during different activities. These data were used to examine the predictive capacity of our application in pre-classifying individuals with an increased risk of experiencing elevated physiological heat strain during work based on the guidelines (core body temperature ≥ 38 °C) of the World Health Organization. Results: The core body temperature predictions made by the mobile phone application were positively related (r = 0.57, p < 0.05) with the actual physiological measurements taken by our participants (mean absolute error: 0.28 °C). More importantly, our application correctly predicted 93% of occurrences of elevated physiological heat strain and 90% of those that were not (overall accuracy: 92%). Conclusions: Mobile phone applications integrating thermophysiological models can predict the physiological heat strain experienced by an individual, but it remains to be studied whether the suggested heat mitigation strategies can reduce or prevent adverse impacts. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
