Assessment of physical work demands of long-distance industrial gas delivery truck drivers.

AIM: The aim of this study was to assess the work-related physical demands of long-distance truck drivers employed by a large gas delivery company in Canada. METHODS: A total of 15 truck drivers participated in a data collection that included self-reporting assessments, field observations, and direct measurements to describe daily tasks organization, postural demands, physical workload, and force exertions. RESULTS: Truck drivers' work was characterized by long working days ranging from 9.9 to 15.1 h (mean = 11.4 h), with half (49%) of the total working time spent behind the wheel. The overall workload as measured by relative cardiac strain (18.7% RHR) was found excessive for the long term given the shift duration. Peaks of heart rate in excess of 30 beats per minute above the daily average occurred mainly while operating valves and handling heavy hoses during gas deliveries. The task of delivering gas at a client's site required a moderate work rate on average (8.3 mlO2/kg/min) requiring 24.4% or maximum work capacity on average. CONCLUSION: Based on multiple data sources, this study highlights the risks of over-exertion and of excessive physical fatigue in the truck drivers' work that are coherent with the high prevalence of self-reported musculoskeletal pain in this group of workers.

Assessment of physical work demand of short distance industrial gas delivery truck drivers.

AIM: This study assessed the work-related physical demands of short-distance truck drivers employed by a large gas delivery company in Canada. METHODS: A total of 19 truck drivers participated in the data collection, which included a combination of self-reports, field observations and direct measurements to report on the work shift task composition, postures, physical workload, and force exertions. RESULTS: Driving (mean of 43% of daily work shift) and delivering gas cylinders to customers (28%) were the main tasks of the truck drivers. Delivering gas cylinders measured as moderate level work and daily work duration was not excessive with respect to mean cardiac strain for most drivers. However, manual handling and force exertion activities were frequent and deemed unsafe most of the time with respect to existing guidelines on manual materials handling. CONCLUSION: This study documents physical risk factors that are consistent with musculoskeletal pain prevalence reported for short-distance truck drivers.

Comparison of nine heart rate-based models to predict work metabolism of Forest workers.

Predicted work metabolism (WM) from 9 heart rate (HR)-based models were compared to measured WM obtained during work in 39 forest workers. Using measured (i.e. raw) HR in these models can overestimate actual WM since the HR increase associated with body heat accumulation is non-metabolic. Hence, accuracy of WM prediction was assessed on all possible combinations of models using raw HR and corrected HR (thermal component removed) and with five different estimates of maximum work capacity (MWC) for the models that require it as an input. The 50 model combinations produced a wide range of WM estimates. Three models using individual calibration produced the lowest RMSE and narrowest LoA with corrected HR (rRMSE ≤13%; LoA [rBias <5% ± 25%]). One of the models that requires neither determination of the thermal component nor individual calibration performed very well (rRMSE = 18%; LoA [rBias = 1% ± 36%]). Practitioner Summary: These results provide a better understanding of the accuracy of various HR-based work metabolism (WM) estimation models. This information should prove particularly useful to ergonomics professionals wishing to select a method that provides accurate estimation of WM from HR measurements during work in varied thermal environments. Abbreviations: BMI: body mass index; HR: heart rate (beats per min); HR99: HR value exceeded during 99% of the duration of the HR recording period; HRcorr: heart rate without thermal pulses; HRraw: measured heart rate; HRres: heart rate reserve; HRrest: heart rate at rest; LoA: limits of agreement; Mrest: resting metabolism; MWC: maximum work capacity; RMSE: root mean square error; VO2: oxygen consumption; VO2 max: maximum oxygen consumption; WM: work metabolism.

Worker heat stress prevention and work metabolism estimation: comparing two assessment methods of the heart rate thermal component.

The heart rate thermal component ( ΔHRT ) can increase with body heat accumulation and lead to work metabolism (WM) overestimation. We used two methods (VOGT and KAMP) to assess ΔHRT of 35 forest workers throughout their work shifts, then compared ΔHRT at work and at rest using limits of agreement (LoA). Next, for a subsample of 20 forest workers, we produced corrected WM estimates from ΔHRT and compared them to measured WM. Although both methods produced significantly different ΔHRT time-related profiles, they yielded comparable average thermal cardiac reactivity (VOGT: 24.8 bpm °C-1; KAMP: 24.5 bpm °C-1), average ΔHRT (LoA: 0.7 ± 11.2 bpm) and average WM estimates (LoA: 0.2 ± 3.4 ml O2 kg-1min-1 for VOGT, and 0.0 ± 5.4 ml O2 kg-1min-1 for KAMP). Both methods are suitable to assess heat stress through ΔHRT and improve WM estimation. Practitioner summary: We compared two methods for assessing the heart rate thermal component ( ΔHRT ), which is needed to produce a corrected HR profile for estimating work metabolism (WM). Both methods yielded similar ΔHRT estimates that allowed accurate estimations of heat stress and WM at the group level, but they were imprecise at the individual level. Abbreviations: AIC: akaike information criterion; bpm: beats per minute; CI: confidence intervals; CV: coefficient of variation in %; CV drift: cardiovascular drift; ΔHRT: the heart rate thermal component in bpm; ΔHRT: the heart rate thermal component in bpm; ΔΔHRT: variation in the heart rate thermal component in bpm; ΔTC: variation in core body temperature in °C; HR: heart rate in bpm; HRmax: maximal heart rate in bpm; Icl: cloting insulation in clo; KAMP: Kampmann et al. (2001) method to determe ΔHRT; LoA: Limits of Agreement; PMV-PPD: the Predicted Mean Vote and Predicted Percentage Dissatisfied; PHS: Predicted Heat Strain model; RCM: random coefficients model; SD: standard deviation; TC: core body temperature in °C; TCR: thermal cardiac reactivity in bpm °C-1; τΔHRT: rate of change in the heart rate thermal component in bpm min-1; τTC: rate of change in core body temperature in °C min-1; tα,n-1: Student's t statistic with level of confidence alpha and n-1 degrees of freedom; TWL: Thermal Work Limit model; V̇O2 : oxygen consumption in ml O2 kg-1 min-1; V̇O2 max: maximal oxygen consumption in ml O2 kg-1 min-1; VOGT: Vogt et al. (1973) method to determine ΔHRT; WBGT: Wet-Bulb Globe Temperature in °C; WM: work metabolism.

Risk factors associated with self-reported musculoskeletal pain among short and long distance industrial gas delivery truck drivers.

AIM: This study investigated and compared the associations between self-reported exposures to individual as well as work-related physical and psychosocial risk factors for musculoskeletal (MS) disorders and the prevalence of MS symptoms in different body areas among short- (P&D) and long-distance (Bulk delivery) truck drivers working for the same large gas delivery company in Canada. METHODS: 123 truck drivers nationwide participated in this questionnaire-based cross-sectional study. Univariate and multivariate logistic regression analyses were performed. RESULTS: 43.1% of drivers reported MS pain in at least one body area over the past 12 months and 26.8% over the past 7 days. Bulk drivers had a significantly higher prevalence of MS pain than P&D drivers for both periods. When P&D and Bulk drivers were pooled together, belonging to the Bulk subgroup emerged as the strongest factor for low back pain (OR = 8.45, p = 0.002), for shoulder pain (OR = 3.70, p = 0.027) and for MS pain in any body area (OR = 4.05, p = 0.006). In Bulk drivers "High effort-reward imbalance" was strongly associated with MS pain in any body area (OR = 6.47, p = 0.01), with shoulder pain (OR = 4.95, p = 0.016), and with low back pain (OR = 4.51, p = 0.02). In P&D drivers MS pain in any body area was strongly associated with "Working with hands above shoulders" (OR = 6.58, p = 0.009) and "Whole-body vibration" (OR = 5.48, p = 0.018), while shoulder pain was strongly associated with "Hand-arm vibration" (OR = 7.27, p = 0.041). CONCLUSIONS: Prevalence of MS pain was higher among industrial gas delivery truck drivers than in the general Quebec male worker population, and higher for Bulk drivers compared to P&D drivers. MS pain in Bulk drivers was mainly associated with psychosocial risk factors and lifestyle; MS pain in P&D drivers was mainly associated with physical risk factors.

Removing the thermal component from heart rate provides an accurate VO2 estimation in forest work.

Heart rate (HR) was monitored continuously in 41 forest workers performing brushcutting or tree planting work. 10-min seated rest periods were imposed during the workday to estimate the HR thermal component (ΔHRT) per Vogt et al. (1970, 1973). VO2 was measured using a portable gas analyzer during a morning submaximal step-test conducted at the work site, during a work bout over the course of the day (range: 9-74 min), and during an ensuing 10-min rest pause taken at the worksite. The VO2 estimated, from measured HR and from corrected HR (thermal component removed), were compared to VO2 measured during work and rest. Varied levels of HR thermal component (ΔHRTavg range: 0-38 bpm) originating from a wide range of ambient thermal conditions, thermal clothing insulation worn, and physical load exerted during work were observed. Using raw HR significantly overestimated measured work VO2 by 30% on average (range: 1%-64%). 74% of VO2 prediction error variance was explained by the HR thermal component. VO2 estimated from corrected HR, was not statistically different from measured VO2. Work VO2 can be estimated accurately in the presence of thermal stress using Vogt et al.'s method, which can be implemented easily by the practitioner with inexpensive instruments.

Classifying work rate from heart rate measurements using an adaptive neuro-fuzzy inference system.

In a new approach based on adaptive neuro-fuzzy inference systems (ANFIS), field heart rate (HR) measurements were used to classify work rate into four categories: very light, light, moderate, and heavy. Inter-participant variability (physiological and physical differences) was considered. Twenty-eight participants performed Meyer and Flenghi's step-test and a maximal treadmill test, during which heart rate and oxygen consumption (VO2) were measured. Results indicated that heart rate monitoring (HR, HRmax, and HRrest) and body weight are significant variables for classifying work rate. The ANFIS classifier showed superior sensitivity, specificity, and accuracy compared to current practice using established work rate categories based on percent heart rate reserve (%HRR). The ANFIS classifier showed an overall 29.6% difference in classification accuracy and a good balance between sensitivity (90.7%) and specificity (95.2%) on average. With its ease of implementation and variable measurement, the ANFIS classifier shows potential for widespread use by practitioners for work rate assessment.

Prediction of work metabolism from heart rate measurements in forest work: some practical methodological issues.

Individual heart rate (HR) to workload relationships were determined using 93 submaximal step-tests administered to 26 healthy participants attending physical activities in a university training centre (laboratory study) and 41 experienced forest workers (field study). Predicted maximum aerobic capacity (MAC) was compared to measured MAC from a maximal treadmill test (laboratory study) to test the effect of two age-predicted maximum HR Equations (220-age and 207-0.7 × age) and two clothing insulation levels (0.4 and 0.91 clo) during the step-test. Work metabolism (WM) estimated from forest work HR was compared against concurrent work V̇O2 measurements while taking into account the HR thermal component. Results show that MAC and WM can be accurately predicted from work HR measurements and simple regression models developed in this study (1% group mean prediction bias and up to 25% expected prediction bias for a single individual). Clothing insulation had no impact on predicted MAC nor age-predicted maximum HR equations. Practitioner summary: This study sheds light on four practical methodological issues faced by practitioners regarding the use of HR methodology to assess WM in actual work environments. More specifically, the effect of wearing work clothes and the use of two different maximum HR prediction equations on the ability of a submaximal step-test to assess MAC are examined, as well as the accuracy of using an individual's step-test HR to workload relationship to predict WM from HR data collected during actual work in the presence of thermal stress.

Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate.

This paper presents a new model based on adaptive neuro-fuzzy inference systems (ANFIS) to predict oxygen consumption (V˙O2) from easily measured variables. The ANFIS prediction model consists of three ANFIS modules for estimating the Flex-HR parameters. Each module was developed based on clustering a training set of data samples relevant to that module and then the ANFIS prediction model was tested against a validation data set. Fifty-eight participants performed the Meyer and Flenghi step-test, during which heart rate (HR) and V˙O2 were measured. Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated V˙O2 in the overall HR range, and separately in different HR ranges. The ANFIS prediction model (MAE = 3 ml kg(-1) min(-1)) demonstrated better performance than Rennie et al.'s (MAE = 7 ml kg(-1) min(-1)) and Keytel et al.'s (MAE = 6 ml kg(-1) min(-1)) models, and comparable performance with the standard Flex-HR method (MAE = 2.3 ml kg(-1) min(-1)) throughout the HR range. The ANFIS model thus provides practitioners with a practical, cost- and time-efficient method for V˙O2 estimation without the need for individual calibration.

Estimating oxygen consumption from heart rate using adaptive neuro-fuzzy inference system and analytical approaches.

In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field.