This study aimed to assess cardiovascular strain during sawmilling operations in terms of physical workload, based on heart rate changes. We measured resting and working heart rates and calculated cardiovascular load (%CVL), cardiovascular strain (%CVS) and relative heart rate (%RHR) in 35 sawmill workers. Based on heart rate only, the work in sawmills was classified as very heavy and extremely heavy. Similarly, a high-level category was recorded for %CVL and a very high range for %CVS. Thus, the workload in sawmill operations is usually very high and can lead to physiological strain of the workers. There is a need to redesign the work content of this occupation to prevent excessive strain in the workers, as this will increase their productivity and reduce their health risk.cardiovascular strain cardiovascular load workload energy expenditure sawmill worker

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PurposeThe purpose of this paper is to identify the factors responsible for road traffic accidents among commercial commuter drivers in Lagos, Nigeria.Design/methodology/approachA total of 188 commercial drivers, driving 18‐seater mass transit buses in inter‐city highways were studied. Their accident records for the previous three years collected from Police records, Federal Road Safety Commission records, self‐administered questionnaires and observation of driving habits of the drivers. Their eye status were measured and data also collected from the Driver Union's Insurance Agency. A total of 25 human factor variables, identified as possible contributors to road traffic accidents were studied.FindingsSix variables were found as possible significant contributors to road traffic accidents. They are old age, overtaking, overspeeding, religious affiliation, bad brake conditions and use of bad tyres. When the variables were subjected to two‐way interactions, 44 variable combinations were significant (p < 0.05) with R2 of 0.994 and residual error of 1.165.Research limitations/implicationsAccidents are not usually caused by single variables but by a combination of variables.Originality/valueApart from variables commonly found in the literature as possible causes of road traffic accidents, there may be some factors that may be peculiar to some countries like religious affiliation, belief in use of charms to protect drivers from accident and eye problems that are not identified because of lack of regular tests among others.

Purpose – The purpose of this study was to obtain some anthropometric dimensions of students in secondary schools necessary for the design of school furniture and use them to establish the models that best determine the relationships among the anthropometric dimensions and standing height. Design/methodology/approach – A total of 480 students aged 10 to 18 years (n = 480, SD = ± 2.3 years) were randomly selected from eight public and eight private secondary schools in Ibadan, South West Nigeria. All the dimensions were subjected to curve estimation using Statistical Products and Services Solution 16.0 Statistical Package. The models with p < 0.005 and highest coefficient of determinations were selected as the best models to predict the relationships among the standing height and other variables. Findings – The study proposes 11 models using standing height to estimate necessary anthropometric dimensions for the design of school furniture. The results of the study show that all anthropometric dimensions correlate more non-linearly with standing height than linearly. Originality/value – The proposed models will have wide applications for the estimation of anthropometric data necessary for the design and construction of school furniture for use in secondary schools in South Western Nigeria.

The main aim of the study was to propose a model for predicting the peak expiratory flow rate (PEFR) of Nigerian workers in a cement factory. Sixty randomly selected non-smoker and healthy workers (30 in production sections, 30 in the administrative section of the factory) participated in the study. Their physical characteristics and PEFR were measured. Multiple correlations using SPSS version 16.0 were performed on the data. The values of PEFR, using the obtained model, were compared with the measured values using a two-tailed t test. There were positive correlations among age, height and PEFR. A prediction equation for PEFR based on age, height, weight and years of exposure (experience) was obtained with R² = .843 (p < 0.001). The developed model will be useful for the management in determining PEFR of workers in the cement industry for possible medical attention.