The evolution of energy requirements of smartphones based on user behaviour and implications of the COVID-19 era

Smartphones have evolved to become frequent companions to humans. The common problem shared by Android users of smartphones was, and continues to be, about saving their batteries and preventing the need to use any recharging tools. A significant number of studies have been performed in the general field of "saving energy in smartphones". During a state of global lockdown, the use of smartphone devices has skyrocketed, and many governments have implemented location-tracking applications for their citizens as means of ensuring that the imposed governmental restrictions are being adhered to. Since smartphones are battery-powered, the opportunity to conserve electricity and ensure that the handset does not have to be charged so much or that it does not die and impede location-tracking during this period of crisis is of vital significance, impacting not only the reliability of tracking, but also the usability of the mobile itself. While there are methods to reduce the battery’s drain from mobile app use, they are not fully utilized by users. Simultaneously, the following the manuscript demonstrates the growing prevalence of mobile applications in daily lives, as well as the disproportionally increasing phone functionality, which results in the creation of a dependency towards smartphone use and the need of energy to recharge and operate theses smartphones.

Energy requirements for critically ill patients with COVID-19.

Early reports suggested that predictive equations significantly underestimate the energy requirements of critically ill patients with coronavirus disease 2019 (COVID-19) based on the results of indirect calorimetry (IC) measurements. IC is the gold standard for measuring energy expenditure in critically ill patients. However, IC is not available in many institutions. If predictive equations significantly underestimate energy requirements in severe COVID-19, this increases the risk of underfeeding and malnutrition, which is associated with poorer clinical outcomes. As such, the purpose of this narrative review is to summarize and synthesize evidence comparing measured resting energy expenditure via IC with predicted resting energy expenditure determined via commonly used predictive equations in adult critically ill patients with COVID-19. Five articles met the inclusion criteria for this review. Their results suggest that many critically ill patients with COVID-19 are in a hypermetabolic state, which is underestimated by commonly used predictive equations in the intensive care unit (ICU) setting. In nonobese patients, energy expenditure appears to progressively increase over the course of ICU admission, peaking at week 3. The metabolic response pattern in patients with obesity is unclear because of conflicting findings. Based on limited evidence published thus far, the most accurate predictive equations appear to be the Penn State equations; however, they still had poor individual accuracy overall, which increases the risk of underfeeding or overfeeding and, as such, renders the equations an unsuitable alternative to IC.