Prospective Validation of 2B-Cool : Integrating Wearables and Individualized Predictive Analytics to Reduce Heat Injuries.

INTRODUCTION: An uncontrollably rising core body temperature (T C ) is an indicator of an impending exertional heat illness. However, measuring T C invasively in field settings is challenging. By contrast, wearable sensors combined with machine-learning algorithms can continuously monitor T C nonintrusively. Here, we prospectively validated 2B-Cool , a hardware/software system that automatically learns how individuals respond to heat stress and provides individualized estimates of T C , 20-min ahead predictions, and early warning of a rising T C . METHODS: We performed a crossover heat stress study in an environmental chamber, involving 11 men and 11 women (mean ± SD age = 20 ± 2 yr) who performed three bouts of varying physical activities on a treadmill over a 7.5-h trial, each under four different clothing and environmental conditions. Subjects wore the 2B-Cool system, consisting of a smartwatch, which collected vital signs, and a paired smartphone, which housed machine-learning algorithms and used the vital sign data to make individualized real-time forecasts. Subjects also wore a chest strap heart rate sensor and a rectal probe for comparison purposes. RESULTS: We observed very good agreement between the 2B-Cool forecasts and the measured T C , with a mean bias of 0.16°C for T C estimates and nearly 75% of measurements falling within the 95% prediction intervals of ±0.62°C for the 20-min predictions. The early-warning system results for a 38.50°C threshold yielded a 98% sensitivity, an 81% specificity, a prediction horizon of 35 min, and a false alarm rate of 0.12 events per hour. We observed no sex differences in the measured or predicted peak T C . CONCLUSION: 2B-Cool provides early warning of a rising T C with a sufficient lead time to enable clinical interventions and to help reduce the risk of exertional heat illness.

Wearables Detect Malaria Early in a Controlled Human-Infection Study.

OBJECTIVE: Observational studies on the use of commercially available wearable devices for infection detection lack the rigor of controlled clinical studies, where time of exposure and onset of infection are exactly known. Towards that end, we carried out a feasibility study using a commercial smartwatch for monitoring heart rate, skin temperature, and body acceleration on subjects as they underwent a controlled human malaria infection (CHMI) challenge. METHODS: Ten subjects underwent CHMI and were asked to wear the smartwatch for at least 12 hours/day from 2 weeks pre-challenge to 4 weeks post-challenge. Using these data, we developed 2B-Healthy, a Bayesian-based infection-prediction algorithm that estimates a probability of infection. We also collected data from eight control subjects for 4 weeks to assess the false-positive rate of 2B-Healthy. RESULTS: Nine of 10 CHMI subjects developed parasitemia, with an average time to parasitemia of 12 days. 2B-Healthy detected infection in seven of nine subjects (78% sensitivity), where in six subjects it detected infection 6 days before parasitemia (on average). In the eight control subjects, we obtained a false-positive rate of 6%/week. CONCLUSION: The 2B-Healthy algorithm was able to reliably detect infection prior to the onset of symptoms using data collected from a commercial smartwatch in a controlled human infection study. SIGNIFICANCE: Our findings demonstrate the feasibility of wearables as a screening tool to provide early warning of infection and support further research on the use of the 2B-Healthy algorithm as the basis for a wearable infection-detection platform.

A 3-D virtual human thermoregulatory model to predict whole-body and organ-specific heat-stress responses.

OBJECTIVE: This study aimed at assessing the risks associated with human exposure to heat-stress conditions by predicting organ- and tissue-level heat-stress responses under different exertional activities, environmental conditions, and clothing. METHODS: In this study, we developed an anatomically detailed three-dimensional thermoregulatory finite element model of a 50th percentile U.S. male, to predict the spatiotemporal temperature distribution throughout the body. The model accounts for the major heat transfer and thermoregulatory mechanisms, and circadian-rhythm effects. We validated our model by comparing its temperature predictions of various organs (brain, liver, stomach, bladder, and esophagus), and muscles (vastus medialis and triceps brachii) under normal resting conditions (errors between 0.0 and 0.5 °C), and of rectum under different heat-stress conditions (errors between 0.1 and 0.3 °C), with experimental measurements from multiple studies. RESULTS: Our simulations showed that the rise in the rectal temperature was primarily driven by the activity level (~ 94%) and, to a much lesser extent, environmental conditions or clothing considered in our study. The peak temperature in the heart, liver, and kidney were consistently higher than in the rectum (by ~ 0.6 °C), and the entire heart and liver recorded higher temperatures than in the rectum, indicating that these organs may be more susceptible to heat injury. CONCLUSION: Our model can help assess the impact of exertional and environmental heat stressors at the organ level and, in the future, evaluate the efficacy of different whole-body or localized cooling strategies in preserving organ integrity.

Osteoinduction by a collagen mineral composite combined with isologous bone marrow in a subcutaneous rat model.

In a subcutaneous implant rat model, Collagraft alone (n=8), Collagraft plus isologous bone marrow (n=8), and marrow alone (n=8) were evaluated. Twelve rats were euthanized at 11 days and 12 at 21 days. Explants were evaluated histologically for evidence of bone formation, and osteogenic activity was determined by an assay for alkaline phosphatase. Histological bone formation was absent in all groups at 11 days. At 21 days, in the Collagraft alone and bone marrow alone groups, no bone induction was noted. In contrast, 21-day specimens from the Collagraft plus bone marrow group showed newly formed bone. Alkaline phosphatase activity was negligible (<0.05 units/mg) in all 11-day specimens. Activity in Collagraft plus bone marrow specimens at 21 days (0.38 units/mg) was significantly higher than any other group (P<.001 for all comparisons). This study demonstrates that Collagraft plus bone marrow is an osteoinductive matrix.