Accuracy of an estimated core temperature algorithm for agricultural workers.
Ontology highlight
ABSTRACT:
SUBMITTER: Egbert J
PROVIDER: S-EPMC9346099 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
Accuracy of an estimated core temperature algorithm for agricultural workers.
Egbert Jared J Krenz Jennifer J Sampson Paul D PD Jung Jihoon J Calkins Miriam M Zhang Kai K Palmández Pablo P Faestel Paul P Spector June T JT
Archives of environmental & occupational health 20220203 10
There is a substantial burden of occupational health effects from heat exposure. We sought to assess the accuracy of estimated core body temperature (CBT<sub>est</sub>) derived from an algorithm that uses sequential heart rate and initializing CBT,<sup>1</sup> compared with gastrointestinal temperature measured using more invasive ingestible sensors (CBT<sub>gi</sub>), among outdoor agricultural workers. We analyzed CBT<sub>est</sub> and CBT<sub>gi</sub> data from Washington State, USA, pear and ...[more]
Similar Datasets
Project description:Monitoring and measuring core body temperature is important to prevent or minimize physiological strain and cognitive dysfunction for workers such as first responders (e.g., firefighters) and military personnel. The purpose of this study is to compare estimated core body temperature (Tco-est), determined by heart rate (HR) data from a wearable chest strap physiology monitor, to standard rectal thermometry (Tre) under different conditions. Tco-est and Tre measurements were obtained in thermoneutral and heat stress conditions (high temperature and relative humidity) during four different experiments including treadmill exercise, cycling exercise, passive heat stress, and treadmill exercise while wearing personal protective equipment (PPE). Overall, the mean Tco-est did not differ significantly from Tre across the four conditions. During exercise at low-moderate work rates under heat stress conditions, Tco-est was consistently higher than Tre at all-time points. Tco-est underestimated temperature compared to Tre at rest in heat stress conditions and at a low work rate under heat stress while wearing PPE. The mean differences between the two measurements ranged from -0.1 ± 0.4 to 0.3 ± 0.4°C and Tco-est correlated well with HR (r = 0.795 - 0.849) and mean body temperature (r = 0.637 - 0.861). These results indicate that, the comparison of Tco-est to Tre may result in over- or underestimation which could possibly lead to heat-related illness during monitoring in certain conditions. Modifications to the current algorithm should be considered to address such issues.
| S-EPMC9903159 | biostudies-literature
Project description:Non-invasive core body temperature (CBT) measurements using temperature and heat-flux have become popular in health, sports, work safety, and general well-being applications. This research aimed to evaluate two commonly used sensor designs: those that combine heat flux and temperature sensors, and those with four temperature sensors. We used analytical methods, particularly uncertainty analysis calculus and Monte Carlo simulations, to analyse measurement accuracy, which depends on the accuracy of the temperature and flux sensors, mechanical construction parameters (such as heat transfer coefficient), ambient air temperature, and CBT values. The results show the relationship between the accuracy of each measurement method variant and various sensor parameters, indicating their suitability for different scenarios. All measurement variants showed unstable behaviour around the point where ambient temperature equals CBT. The ratio of the heat transfer coefficients of the dual-heat flux (DHF) sensor's channels impacts the CBT estimation uncertainty. An analysis of the individual components of uncertainty in CBT estimates reveals that the accuracy of temperature sensors significantly impacts the overall uncertainty of the CBT measurement. We also calculated the theoretical limits of measurement uncertainty, which varied depending on the method variant and could be as low as 0.05 °C.
| S-EPMC10974837 | biostudies-literature
Project description:PurposeCareful perioperative temperature management is important because it influences clinical outcomes. In pediatric patients, the esophageal temperature is the most accurate indicator of core temperature. However, it requires probe insertion into the body cavity, which is mildly invasive. Therefore, a non-invasive easily and continuously temperature monitor system is ideal. This study aimed to assess the accuracy of Temple Touch Pro™ (TTP), a non-invasive temperature monitoring using the heat flux technique, compared with esophageal (Tesoph) and rectal (Trect) temperature measurements in pediatric patients, especially in infants and toddlers.MethodsThis single-center prospective observational study included 40 pediatric patients (< 3 years old) who underwent elective non-cardiac surgery. The accuracy of TTP was analyzed using Bland-Altman analysis and compared with Tesoph or Trect temperature measurements. The error was within ± 0.5 °C and was considered clinically acceptable.ResultsThe bias ± precision between TTP and Tesoph was 0.09 ± 0.28 °C, and 95% limits of agreement were - 0.48 to 0.65 °C (error within ± 0.5 °C: 94.0%). The bias ± precision between TTP and Trect was 0.41 ± 0.38 °C and 95% limits of agreement were - 0.35 to 1.17 °C (error within ± 0.5 °C: 68.5%). In infants, bias ± precision with 95% limits of agreement were 0.10 ± 0.30 °C with - 0.50 to 0.69 °C (TTP vs. Tesoph) and 0.35 ± 0.29 °C with - 0.23 to 0.92 °C (TTP vs. Trect).ConclusionCore temperature measurements using TTP in infants and toddlers were more accurate with Tesoph than with Trect. In the future, non-invasive TTP temperature monitoring will help perioperative temperature management in pediatric patients.
| S-EPMC11584424 | biostudies-literature
Project description:PurposeNon-invasive non-obtrusive continuous and real-time monitoring of core temperature (Tc) may enhance pacing strategies, the efficacy of heat mitigation measures, and early identification of athletes at risk for heat-related disorders. The Estimated Core Temperature (ECTemp™) algorithm uses sequential heart rate (HR) values to predict Tc. We examined the validity of ECTemp™ among elite athletes exercising in the heat.Methods101 elite athletes performed an exercise test in simulated hot and humid environmental conditions (ambient temperature: 31.6 ± 1.0°C, relative humidity: 74 ± 5%). Tc was continuously measured using a validated ingestible telemetric temperature capsule system. In addition, HR was continuously measured and used to compute the estimated core temperature (Tc-est) using the ECTemp™ algorithm.ResultsAthletes exercised for 44 ± 10 min and n = 5,025 readouts of Tc (range: 35.8-40.4°C), HR (range: 45-207 bpm), and Tc-est (range: 36.7-39.9°C) were collected. Tc-est demonstrated a small yet significant bias of 0.15 ± 0.29°C (p < 0.001) compared to Tc, with a limit of agreement of ±0.45°C and a root mean square error of 0.35 ± 0.18°C. Utilizing the ECTemp™ algorithm as a diagnostic test resulted in a fair to excellent sensitivity (73-96%) and specificity (72-93%) for Tc-est thresholds between 37.75 and 38.75°C, but a low to very-low sensitivity (50-0%) for Tc-est thresholds >39.0°C, due to a high prevalence of false-negative observations.ConclusionECTemp™ provides a valuable and representative indication of thermal strain in the low- to mid-range of Tc values observed during exercise in the heat. It may, therefore, be a useful non-invasive and non-obtrusive tool to inform athletes and coaches about the estimated core temperature during controlled hyperthermia heat acclimation protocols. However, the ECTemp™ algorithm, in its current form, should not solely be used to identify athletes at risk for heat-related disorders due to low sensitivity and high false-negative rate in the upper end of the Tc spectrum.
| S-EPMC9256956 | biostudies-literature
Project description:A popular method to estimate proximal/distal temperature (TPROX and TDIST) consists in calculating a weighted average of nine wireless sensors placed on pre-defined skin locations. Specifically, TPROX is derived from five sensors placed on the infra-clavicular and mid-thigh area (left and right) and abdomen, and TDIST from four sensors located on the hands and feet. In clinical practice, the loss/removal of one or more sensors is a common occurrence, but limited information is available on how this affects the accuracy of temperature estimates. The aim of this study was to determine the accuracy of temperature estimates in relation to number/position of sensors removed. Thirteen healthy subjects wore all nine sensors for 24 hours and reference TPROX and TDIST time-courses were calculated using all sensors. Then, all possible combinations of reduced subsets of sensors were simulated and suitable weights for each sensor calculated. The accuracy of TPROX and TDIST estimates resulting from the reduced subsets of sensors, compared to reference values, was assessed by the mean squared error, the mean absolute error (MAE), the cross-validation error and the 25th and 75th percentiles of the reconstruction error. Tables of the accuracy and sensor weights for all possible combinations of sensors are provided. For instance, in relation to TPROX, a subset of three sensors placed in any combination of three non-homologous areas (abdominal, right or left infra-clavicular, right or left mid-thigh) produced an error of 0.13°C MAE, while the loss/removal of the abdominal sensor resulted in an error of 0.25°C MAE, with the greater impact on the quality of the reconstruction. This information may help researchers/clinicians: i) evaluate the expected goodness of their TPROX and TDIST estimates based on the number of available sensors; ii) select the most appropriate subset of sensors, depending on goals and operational constraints.
| S-EPMC5493382 | biostudies-literature
Project description:BackgroundAdverse health effects among agricultural workers due to chronic heat exposure have been characterized in the literature as not only due to high ambient temperatures but also due to intensive manual labor in hot and humid conditions. The aim of this study was to use biomonitoring equipment to examine the effectiveness of selected cooling devices at preventing agricultural workers from exceeding the core body temperature threshold of 38.0°C (Tc38) and attenuating heat-related illness symptoms.MethodsA convenience sample of 84 agricultural workers in Florida was randomized to one of four groups: (a) no intervention, clothing as usual; (b) cooling bandana; (c) cooling vest; and (d) both the cooling bandana and cooling vest. Biomonitoring equipment worn by the participants included core body temperature monitor and an accelerometer to capture physical activity.FindingsA total of 78 agricultural workers completed one intervention workday trial. Compared with the control group, the bandana group had lower odds of exceeding Tc38 (odds ratio [OR] = 0.7, 90% confidence interval [CI] = [0.2, 3.2]) and the vest group had higher odds of exceeding Tc38 (OR = 1.8, 90% CI = [0.4, 7.9]). The simultaneous use of cooling vest and bandana showed an effect little different from the control group (OR = 1.3, 90% CI = [0.3, 5.6]).Conclusion/application to practiceThis is the first field-based study to examine cooling intervention among agricultural workers in the United States using biomonitoring equipment. This study found that using a bandana while working in a hot agricultural environment has the potential to be protective against exceeding the recommended Tc38 threshold.
| S-EPMC8693251 | biostudies-literature
Project description:BackgroundAgricultural work can expose workers to potentially hazardous agents including known and suspected carcinogens. This study aimed to evaluate cancer incidence in male and female agricultural workers in an international consortium, AGRICOH, relative to their respective general populations.MethodsThe analysis included eight cohorts that were linked to their respective cancer registries: France (AGRICAN: n = 128,101), the US (AHS: n = 51,165, MESA: n = 2,177), Norway (CNAP: n = 43,834), Australia (2 cohorts combined, Australian Pesticide Exposed Workers: n = 12,215 and Victorian Grain Farmers: n = 919), Republic of Korea (KMCC: n = 8,432), and Denmark (SUS: n = 1,899). For various cancer sites and all cancers combined, standardized incidence ratios (SIR) and 95% confidence intervals (CIs) were calculated for each cohort using national or regional rates as reference rates and were combined by random-effects meta-analysis.ResultsDuring nearly 2,800,000 person-years, a total of 23,188 cancers were observed. Elevated risks were observed for melanoma of the skin (number of cohorts = 3, meta-SIR = 1.18, CI: 1.01-1.38) and multiple myeloma (n = 4, meta-SIR = 1.27, CI: 1.04-1.54) in women and prostate cancer (n = 6, meta-SIR = 1.06, CI: 1.01-1.12), compared to the general population. In contrast, a deficit was observed for the incidence of several cancers, including cancers of the bladder, breast (female), colorectum, esophagus, larynx, lung, and pancreas and all cancers combined (n = 7, meta-SIR for all cancers combined = 0.83, 95% CI: 0.77-0.90). The direction of risk was largely consistent across cohorts although we observed large between-cohort variations in SIR for cancers of the liver and lung in men and women, and stomach, colorectum, and skin in men.ConclusionThe results suggest that agricultural workers have a lower risk of various cancers and an elevated risk of prostate cancer, multiple myeloma (female), and melanoma of skin (female) compared to the general population. Those differences and the between-cohort variations may be due to underlying differences in risk factors and warrant further investigation of agricultural exposures.
| S-EPMC8484858 | biostudies-literature
Project description:BackgroundEnvironmental exposure assessments often require a study participant's residential location, but the positional accuracy of geocoding varies by method and the rural status of an address. We evaluated geocoding error in the Agricultural Health Study (AHS), a cohort of pesticide applicators and their spouses in Iowa and North Carolina, U.S.A.MethodsFor 5,064 AHS addresses in Iowa, we compared rooftop coordinates as a gold standard to two alternate locations: 1) E911 locations (intersection of the private and public road), and 2) geocodes generated by matching addresses to a commercial street database (NAVTEQ) or placed manually. Positional error (distance in meters (m) from the rooftop) was assessed overall and separately for addresses inside (non-rural) or outside town boundaries (rural). We estimated the sensitivity and specificity of proximity-based exposures (crops, animal feeding operations (AFOs)) and the attenuation in odds ratios (ORs) for a hypothetical nested case-control study. We also evaluated geocoding errors within two AHS subcohorts in Iowa and North Carolina by comparing them to GPS points taken at residences.ResultsNearly two-thirds of the addresses represented rural locations. Compared to the rooftop gold standard, E911 locations were more accurate overall than address-matched geocodes (median error 39 and 90 m, respectively). Rural addresses generally had greater error than non-rural addresses, although errors were smaller for E911 locations. For highly prevalent crops within 500 m (>97% of homes), sensitivity was >95% using both data sources; however, lower specificities with address-matched geocodes (more common for rural addresses) led to substantial attenuation of ORs (e.g., corn <500 m ORobs = 1.47 vs. ORtrue = 2.0). Error in the address-matched geocodes resulted in even greater ORobs attenuation for AFO exposures. Errors for North Carolina addresses were generally smaller than those in Iowa.ConclusionsGeocoding error can be minimized when known coordinates are available to test alternative data and methods. Our assessment suggests that where E911 locations are available, they offer an improvement upon address-matched geocodes for rural addresses. Exposure misclassification resulting from positional error is dependent on the geographic database, geocoding method, and the prevalence of exposure.
| S-EPMC4203975 | biostudies-literature
Project description:Collection of accurate and representative data from agricultural fields is required for efficient crop management. Since growers have limited available resources, there is a need for advanced methods to select representative points within a field in order to best satisfy sampling or sensing objectives. The main purpose of this work was to develop a data-driven method for selecting locations across an agricultural field given observations of some covariates at every point in the field. These chosen locations should be representative of the distribution of the covariates in the entire population and represent the spatial variability in the field. They can then be used to sample an unknown target feature whose sampling is expensive and cannot be realistically done at the population scale. An algorithm for determining these optimal sampling locations, namely the multifunctional matching (MFM) criterion, was based on matching of moments (functionals) between sample and population. The selected functionals in this study were standard deviation, mean, and Kendall's tau. An additional algorithm defined the minimal number of observations that could represent the population according to a desired level of accuracy. The MFM was applied to datasets from two agricultural plots: a vineyard and a peach orchard. The data from the plots included measured values of slope, topographic wetness index, normalized difference vegetation index, and apparent soil electrical conductivity. The MFM algorithm selected the number of sampling points according to a representation accuracy of 90% and determined the optimal location of these points. The algorithm was validated against values of vine or tree water status measured as crop water stress index (CWSI). Algorithm performance was then compared to two other sampling methods: the conditioned Latin hypercube sampling (cLHS) model and a uniform random sample with spatial constraints. Comparison among sampling methods was based on measures of similarity between the target variable population distribution and the distribution of the selected sample. MFM represented CWSI distribution better than the cLHS and the uniform random sampling, and the selected locations showed smaller deviations from the mean and standard deviation of the entire population. The MFM functioned better in the vineyard, where spatial variability was larger than in the orchard. In both plots, the spatial pattern of the selected samples captured the spatial variability of CWSI. MFM can be adjusted and applied using other moments/functionals and may be adopted by other disciplines, particularly in cases where small sample sizes are desired.
| S-EPMC8329933 | biostudies-literature
Project description:Cardiorespiratory fitness (CRF) is an independent predictor of morbidity and mortality. In Japan, annual physical exams are mandatory in workplace settings, and most healthcare settings have electronic medical records (EMRs). However, in both settings, CRF is not usually determined, thereby limiting the potential for epidemiological investigations using EMR data. To estimate CRF (mL/kg/min) using variables commonly recorded in EMRs. Participants were 5293 Japanese adults (11.7% women) who completed an annual physical exam at a large gas company in Tokyo, Japan, in 2004. The mean age was 48.3 ± 8.0 years. Estimated CRF (eCRF) was based on age, measured body mass index, resting heart rate, systolic and diastolic blood pressure, and smoking. Measured CRF was determined by a submaximal cycle ergometer graded exercise test. Regression models were used for males and females to calculate Pearson's correlation and regression coefficients. Cross-classification of measured CRF and eCRF was conducted using the lowest quintile, quartile, and tertile as the unfit categories. R's for eCRF were 0.61 (MD 4.41) for men and 0.64 (MD 4.22) for women. The overall accuracy level was reasonable and consistent across models, yet the unfit lower tertile model provided the best overall model when considering the positive predictive value and sensitivity. eCRF may provide a useful method for conducting investigations using data derived from EMRs or datasets devoid of CRF or physical activity measures.
| S-EPMC8656653 | biostudies-literature