Iranian Journal of Ergonomics

---

Introduction: Driver fatigue is one of the major causes of accidents in roads. It is suggested that driver fatigue and drowsiness accounted for more than 30% of road accidents. Therefore, it is important to use features for real-time detection of driver mental fatigue to minimize transportation fatalities. The purpose of this study was to explore the EEG alpha power variations in sleep deprived drivers on a car driving simulator.

Materials and Methods: The present descriptive-analytical study was achieved on nineteen healthy male car drivers. After taking informed written consent, the subjects were requested to stay awake 18 hrs before the experiments and refrain from caffeinated drinks or any other stimulant as well as cigarette smoking for 12 hrs prior to the experiments. The drivers sleep patterns were studied through sleep diary for one week before the experiment. The participants performed a simulated driving task in a 110 Km monotonous route at the fixed speed of 90 km/hr. The subjective self-assessment of fatigue was performed in every 10 minute interval during the driving using Karolinska Sleepiness Scale (KSS). At the same time, video recordings from the drivers face and their behaviors were achieved in lateral and front views and rated by two trained observers. Continuous EEG and EOG records were taken with 16 channels during driving. After filtering and artifact removal, power spectrum density and fast Fourier transform (FFT) were used to determine the absolute and relative alpha powers in the initial and final 10 minutes of driving. To analyze the data, descriptive statistics, Pearson and Spearman coefficients and paired-sample T test were employed to describe and compare the variables.

Results: The findings showed a significant increase in KSS scores in the final 10 minutes of driving (p<0.001). Similar results were obtained concerning video rating scores. Meanwhile, there was a significant increase in the absolute alpha power during the final section of driving (p=0.006).

Conclusion: Driver mental fatigue is considered as one of the major implications for road safety. This study suggests that alpha brain wave rhythm can be a good indicator for early prediction of driver fatigue.

Normal 0 false false false EN-US X-NONE FA

---

This before-after clinical trial investigated the effect of daylight on morning and night melatonin, subjective general health using GHQ questionnaire, sleepiness and alertness on elderly who lived in nursing houses. Nineteen nursing home residents participated voluntarily. They exposed to daylight from 9 to 10 A.M. and from 4 to 5 P.M. for 6 weeks. General health of all participants was evaluated using general health questionnaire (GHQ) as well. Also we used of KSS and VAS questionnaire foe assessment of sleepiness and alertness. Baseline morning and night melatonin levels were 25.39 (±19.75)pg/ml, and 40.3 (±21.55)pg/ml respectively. Daylight exposure significantly affected morning melatonin at 7:00 A.M. but had no significant effect on night melatonin at 8:00 P.M. Mean score of general health changed 36.31 to 29.89 (P=0.003). Karolinska sleepiness scale (KSS) and Visual Analogue Scale (VAS) showed increase sleepiness and decrease alertness from 3:00 to 7:00 A.M. Sleepiness decreased and alertness increased during 1:00 P.M and 20:00 P.M. Overall day light exposure may delay sleep phase and circadian rhythm problems in elderly people. Anxiety and insomnia could be improved with daylight exposure. Our findings suggest that elders should be exposed to scheduled daylight in morning and evening for prevention and improvement of mental disorders. Adequate light should be provided for elder’s homes and nursing house. We have proposed to further study with a larger sample size be done to assess effect of daylight on GHQ' subscales.

---

Introduction: Sleep disorder is very common among employees and is considered as one of the crucial health problems. Sleep disorders have a negative impact on job performance. The present study was regarding the relationship of sleep quality, sleepiness and sickness absence.

Methods: This cross-sectional study was conducted on 400 employees of a gas company through random sampling. The study data were collected using the Pittsburg Sleep Quality Index (PSQI) and the Karolinska Sleepiness Scale (KSS). Mann-Whitney U test and Spearman’s correlation coefficient were applied for data analysis.

Results: While 43.2% of the participants enjoyed satisfactory sleep quality, 56.8% had unsatisfactory sleep quality. Statistical tests indicated a significant difference in sickness absence of the personnel with good and poor sleep qualities (P = 0.0001). Spearman’s correlation coefficient (ρ = 0.777) indicated a statistically significant correlation between sleepiness index and sickness absence (P = 0.0001).

Conclusions: The results showed a strong positive correlation in sleep quality, sleepiness index of the personnel and sickness absence; i.e. along with decline in sleep quality and incline in sleepiness, the personnel’s sickness absence increased.