The purpose of this study was to determine which thermometry technique is the most accurate for regular measurement of body temperature. We compared seven different commercially available thermometers with a gold standard medical-grade thermometer (Welch-Allyn): four digital infrared thermometers (Wellworks, Braun, Withings, MOBI), one digital sublingual thermometer (Braun), one zero heat flux thermometer (3M), and one infrared thermal imaging camera (FLIR One). Thirty young healthy adults participated in an experiment that altered core body temperature. After baseline measurements, participants placed their feet in a cold-water bath while consuming cold water for 30 min. Subsequently, feet were removed and covered with a blanket for 30 min. Throughout the session, temperature was recorded every 10 min with all devices. The Braun tympanic thermometer (left ear) had the best agreement with the gold standard (mean error: 0.044 °C). The FLIR One thermal imaging camera was the least accurate device (mean error: −0.522 °C). A sign test demonstrated that all thermometry devices were significantly different than the gold standard except for the Braun tympanic thermometer (left ear). Our study showed that not all temperature monitoring techniques are equal, and suggested that tympanic thermometers are the most accurate commercially available system for the regular measurement of body temperature.

The worldwide outbreak of the novel Coronavirus (COVID-19) has highlighted the need for a screening and monitoring system for infectious respiratory diseases in the acute and chronic phase. The purpose of this study was to examine the feasibility of using a wearable near-infrared spectroscopy (NIRS) sensor to collect respiratory signals and distinguish between normal and simulated pathological breathing. Twenty-one healthy adults participated in an experiment that examined five separate breathing conditions. Respiratory signals were collected with a continuous-wave NIRS sensor (PortaLite, Artinis Medical Systems) affixed over the sternal manubrium. Following a three-minute baseline, participants began five minutes of imposed difficult breathing using a respiratory trainer. After a five minute recovery period, participants began five minutes of imposed rapid and shallow breathing. The study concluded with five additional minutes of regular breathing. NIRS signals were analyzed using a machine learning model to distinguish between normal and simulated pathological breathing. Three features: breathing interval, breathing depth, and O2Hb signal amplitude were extracted from the NIRS data and, when used together, resulted in a weighted average accuracy of 0.87. This study demonstrated that a wearable NIRS sensor can monitor respiratory patterns continuously and non-invasively and we identified three respiratory features that can distinguish between normal and simulated pathological breathing.

Background:The anaerobic threshold (AT) is a point during intense exercise that can be used to predict muscular fatigue. Determining the AT non-invasively helps to adjust exercise intensity and prevent overuse injuries. Near-infrared spectroscopy (NIRS) is an optical technology that can provide real-time information about muscle oxidative metabolism. The objective of this pilot study was to investigate the relationship between NIRS parameters of muscle oxygenation and traditional measures of exercise monitoring, such as heart rate and relative body oxygen consumption (VO2).Methods: Healthy adults with moderate to high fitness levels participated in an incremental exercise protocol on a stationary bicycle. NIRS parameters were compared to ventilatory VO2 using a metabolic cart. Respiratory Exchange Ratio (RER) > 1.0 was used as a proxy for determining the AT. NIRS data were collected from the primary locomotor muscle (vastus lateralis -VL) and a control muscle (deltoid) using two wearable NIRS sensors. Heart rate data were collected by a wearable ECG sensor. Results:The NIRS data showed a significant decline in VL muscle oxygenated hemoglobin (O2Hb) concentration (p<0.05) at one exercise stage after the AT was identified. Muscle O2Hb did not show a significant decrease in the deltoid at the AT. Furthermore, there were no noticeable changes in heart rate at the AT. Conclusion:Our results indicate that a wearable NIRS sensor can predict the AT in exercising muscles and may provide a localized measure of muscular fatigue during exercise.