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The use of wearable technology is steadily increasing. In orthopedic trauma surgery, where the musculoskeletal system is directly affected, focus has been directed towards assessing aspects of physical functioning, activity behavior, and mobility/disability. This includes sensors and algorithms to monitor real-world walking speed, daily step counts, ground reaction forces, or range of motion. Several specific reviews have focused on this domain. In other medical fields, wearable sensors and algorithms to monitor digital biometrics have been used with a focus on domain-specific health aspects such as heart rate, sleep, blood oxygen saturation, or fall risk. This review explores the most common clinical and research use cases of wearable sensors in other medical domains and, from it, derives suggestions for the meaningful transfer and application in an orthopedic trauma context.

Background and Objectives: Outcome data from wearable devices are increasingly used in both research and clinics. Traditionally, a dedicated device is chosen for a given study or clinical application to collect outcome data as soon as the patient is included in a study or undergoes a procedure. The current study introduces a new measurement strategy, whereby patients’ own devices are utilized, allowing for both a pre-injury baseline measure and ability to show achievable results. Materials and Methods: Patients with a pre-existing musculoskeletal injury of the upper and lower extremity were included in this exploratory, proof-of-concept study. They were followed up for a minimum of 6 weeks after injury, and their wearable outcome data (from a smartphone and/or a body-worn sensor) were continuously acquired during this period. A descriptive analysis of the screening characteristics and the observed and achievable outcome patterns was performed. Results: A total of 432 patients was continuously screened for the study, and their screening was analyzed. The highest success rate for successful inclusion was in younger patients. Forty-eight patients were included in the analysis. The most prevalent outcome was step count. Three distinctive activity data patterns were observed: patients recovering, patients with slow or no recovery, and patients needing additional measures to determine treatment outcomes. Conclusions: Measuring outcomes in trauma patients with the Bring Your Own Device (BYOD) strategy is feasible. With this approach, patients were able to provide continuous activity data without any dedicated equipment given to them. The measurement technique is especially suited to particular patient groups. Our study’s screening log and inclusion characteristics can help inform future studies wishing to employ the BYOD design.

There is an unmet need for improved, clinically relevant methods to longitudinally quantify bone healing during fracture care. Here we develop a smart bone plate to wirelessly monitor healing utilizing electrical impedance spectroscopy (EIS) to provide real-time data on tissue composition within the fracture callus. To validate our technology, we created a 1-mm rabbit tibial defect and fixed the bone with a standard veterinary plate modified with a custom-designed housing that included two impedance sensors capable of wireless transmission. Impedance magnitude and phase measurements were transmitted every 48 h for up to 10 weeks. Bone healing was assessed by X-ray, µCT, and histology. Our results indicated the sensors successfully incorporated into the fracture callus and did not impede repair. Electrical impedance, resistance, and reactance increased steadily from weeks 3 to 7—corresponding to the transition from hematoma to cartilage to bone within the fracture gap—then plateaued as the bone began to consolidate. These three electrical readings significantly correlated with traditional measurements of bone healing and successfully distinguished between union and not-healed fractures, with the strongest relationship found with impedance magnitude. These results suggest that our EIS smart bone plate can provide continuous and highly sensitive quantitative tissue measurements throughout the course of fracture healing to better guide personalized clinical care.

Agricultural greenhouses have been identified as a niche application for organic photovoltaic (OPV) integration, leveraging key performance characteristics of OPV technology, including semi-transparency, light weight, and mechanical flexibility. For optimal electrical design and performance assessment of greenhouse-integrated OPV systems, knowledge of the solar irradiance incident on OPV module surfaces is essential. Many greenhouse designs feature roof curvature. For flexible OPV modules deployed on curved greenhouse roofs, this results in a non-homogenous distribution of solar radiation across the module surfaces, which affects electrical output. Conventional modeling methods for estimating solar irradiance on a PV surface assume planarity, and therefore they are insufficient to evaluate OPV (and other flexible PV) installations on curved greenhouse structures. In this study, practical methods to estimate incident solar irradiance on curved surfaces were developed and then applied in an outdoor performance evaluation of large-area, roll-to-roll printed OPV arrays (3.4 m2 active area) installed on a gothic-arch greenhouse roof in Tucson, Arizona between October–February. The outdoor performance of six OPV arrays was assessed using the curved-surface modeling tools primarily considering the effect of irradiance on electrical behavior. The OPV arrays had an overall power conversion efficiency (PCE) of 1.82%, with lower PCE in the afternoon periods compared to morning and midday periods. The OPV arrays experienced an average 32.6% loss in normalized PCE over the course of the measurement period. Based on these results, we conclude that the higher performing OPV devices that are more robust in outdoor conditions coupled with accurate performance monitoring strategies are needed to prove the case for agrivoltaic OPV greenhouses.

Background: Vasopressors are frequently utilized for blood pressure stabilization in patients with cardiogenic shock (CS), although with a questionable benefit. Obtaining central venous access is time consuming and may be associated with serious complications. Hence, we thought to evaluate whether the administration of vasopressors through a peripheral venous catheter (PVC) is a safe and effective alternative for the management of patients with CS presenting to the intensive cardiovascular care unit (ICCU). Methods: A prospective single-center study was conducted to compare the safety and outcomes of vasopressors administered via a PVC vs. a central venous catheter (CVC) in patients presenting with CS over a 12-month period. Results: A total of 1100 patients were included; of them, 139 (12.6%) required a vasopressor treatment due to shock, with 108 (78%) treated via a PVC and 31 (22%) treated via a CVC according to the discretion of the treating physician. The duration of the vasopressor administration was shorter in the PVC group compared with the CVC group (2.5 days vs. 4.2 days, respectively, p < 0.05). Phlebitis and the extravasation of vasopressors occurred at similar rates in the PVC and CVC groups (5.7% vs. 3.3%, respectively, p = 0.33; 0.9% vs. 3.3%, respectively, p = 0.17). Nevertheless, the bleeding rate was higher in the CVC group compared with the PVC group (3% vs. 0%, p = 0.03). Conclusions: The administration of vasopressor infusions via PVC for the management of patients with CS is feasible and safe in patients with cardiogenic shock. Further studies are needed to establish this method of treatment.

Background: Acutely ill patients treated with blood transfusion (BT) have unfavorable prognoses. Nevertheless, data regarding outcomes in patients treated with BT admitted into a contemporary tertiary care medical center intensive cardiac care unit (ICCU) are limited. The current study aimed to assess the mortality rate and outcomes of patients treated with BT in a modern ICCU. Methods: Prospective single center study where we evaluated mortality, in the short and long term, of patients treated with BT between the period of January 2020 and December 2021 in an ICCU. Outcomes: A total of 2132 consecutive patients were admitted to the ICCU during the study period and were followed-up for up to 2 years. In total, 108 (5%) patients were treated with BT (BT-group) during their admission, with 305 packed cell units. The mean age was 73.8 ± 14 years in the BT-group vs. 66.6 ± 16 years in the non-BT (NBT) group, p < 0.0001. Females were more likely to receive BT as compared with males (48.1% vs. 29.5%, respectively, p < 0.0001). The crude mortality rate was 29.6% in the BT-group and 9.2% in the NBT-group, p < 0.0001. Multivariate Cox analysis found that even one unit of BT was independently associated with more than two-fold the mortality rate [HR = 2.19 95% CI (1.47–3.62)] as compared with the NBT-group, p < 0.0001]. Receiver operating characteristic (ROC) curve was plotted for multivariable analysis and showed area under curve (AUC) of 0.8 [95% CI (0.760–0.852)]. Conclusions: BT continues to be a potent and independent predictor for both short- and long-term mortality even in a contemporary ICCU, despite the advanced technology, equipment and delivery of care. Further considerations for refining the strategy of BT administration in ICCU patients and guidelines for different subsets of high-risk patients may be warranted.

Since leaf temperature (LT) is not a trivial measurement, deep-neural networks (DNN) and machine learning (ML) models were evaluated in this study as tools for estimating foliage temperature. Two DNN methods were used. The first DNN used convolutional layers, while the second DNN was based on fully-connected layers and was trained by cross-validation techniques. The machine learning used the K-nearest neighbors (KNN) method for LT estimation. All models used the meteorological and microclimatic parameters (hereafter referred to as features) of the examined greenhouses to determine the average foliage temperature. The models were trained on 75% of the collected data and tested on the remaining 25%. RMS and absolute error were used to evaluate the performance of the different models compared to the LT values measured by a thermal camera. In addition, after finding the correlation of each feature to the leaf temperature, the models were trained based on the high-correlated features only. The machine learning model was superior to DNN when all available features were used and when only high-correlated features were used, resulting in errors of 0.7 °C and 0.8 °C, respectively.

Measured evapotranspiration (LE) of screenhouse banana plantations was utilized to derive and compare two types of machine-learning models: artificial neural network (ANN) and multiple linear regression (MLR). The measurements were conducted by eddy-covariance systems and meteorological sensors in two similar screenhouse banana plantations during two consecutive seasons, 2016 and 2017. Most of the study focused on the season of 2017, which includes a more extended data set (141 days) than 2016 (52 days). The results show that in most cases, the ANN model was superior to MLR. When trained and validated over the whole data set of 2017, the ANN and MLR models provided R2 of 0.92 and 0.89, RMSE of 37.5 and 45.1 W m−2 and MAE of 21 and 27.2 W m−2, respectively. Models could be derived using a training dataset as short as one month and still provide reliable estimations. Depending on the chosen calendar month for training, R2 of the ANN model varied in the range 0.81–0.89, while for the MLR model, it ranged 0.73–0.88. When trained using a data set as short as one week, there was some deterioration in model performance; the corresponding ranges of R2 for the ANN and MLR models were 0.37–0.89 and 0.37–0.71, respectively. As expected for a screenhouse decoupled environment, solar radiation (Rg) was the variable that most influenced LE; using Rg as the sole input variable, the ANN model resulted in R2, RMSE and MAE of 0.88 and 47 W m−2 and 25.6 W m−2, respectively, values that are not much worse than using all input variables (solar radiation, air temperature, air relative humidity and wind speed). Using Rg alone as the input to the MLR model only slightly deteriorated R2 (=0.88); however, RMSE (=124 W m−2) and MAE (=75.7 W m−2) were significantly larger compared to a model based on all input variables. To examine model performance in different seasons, models were trained using the data set of 2017 and validated in 2016, and vice versa. Results showed that training on the data of 2017 and validation in 2016 provided superior results than the opposite, presumably since the 2017 measurement season was longer and weather conditions were more diverse than in the 2016 data set. It is concluded that the ANN and MLR models are reasonable options for estimating evapotranspiration in a banana screenhouse.

Recognizing the growing interest in the application of organic photovoltaics (OPVs) with greenhouse crop production systems, in this study we used flexible, roll-to-roll printed, semi-transparent OPV arrays as a roof shade for a greenhouse hydroponic tomato production system during a spring and summer production season in the arid southwestern U.S. The wavelength-selective OPV arrays were installed in a contiguous area on a section of the greenhouse roof, decreasing the transmittance of all solar radiation wavelengths and photosynthetically active radiation (PAR) wavelengths (400–700 nm) to the OPV-shaded area by approximately 40% and 37%, respectively. Microclimate conditions and tomato crop growth and yield parameters were measured in both the OPV-shaded (‘OPV’) and non-OPV-shaded (‘Control’) sections of the greenhouse. The OPV shade stabilized the canopy temperature during midday periods with the highest solar radiation intensities, performing the function of a conventional shading method. Although delayed fruit development and ripening in the OPV section resulted in lower total yields compared to the Control section (24.6 kg m−2 and 27.7 kg m−2, respectively), after the fourth (of 10 total) harvests, the average weekly yield, fruit number, and fruit mass were not significantly different between the treatment (OPV-shaded) and control group. Light use efficiency (LUE), defined as the ratio of total fruit yield to accumulated PAR received by the plant canopy, was nearly twice as high as the Control section, with 21.4 g of fruit per mole of PAR for plants in the OPV-covered section compared to 10.1 g in the Control section. Overall, this study demonstrated that the use of semi-transparent OPVs as a seasonal shade element for greenhouse production in a high-light region is feasible. However, a higher transmission of PAR and greater OPV device efficiency and durability could make OPV shades more economically viable, providing a desirable solution for co-located greenhouse crop production and renewable energy generation in hot and high-light intensity regions.