Acknowledgment
16.9 Tools Needed
Just a pen, a paper, and a tape measure are necessary.
References
Battevi, N. et al. (1999), Application of the synthetic exposure index in manual lifting of patients:
preliminary validation experience, Med. Lav., 90, 256–275.
Bordini, L. et al. (1999), Epidemiology of musculo-skeletal alterations due to biomechanical overload of the spine in manual lifting of patients, Med. Lav., 90, 103–116.
Colombini, D. et al. (1999a), Acute low back pain caused by manual lifting of patients in hospital ward:
prevalence and incidence data, Med. Lav., 90, 229–243.
Colombini, D. et al. (1999b), Preliminary epidemiological data on clinical symptoms in health care workers with tasks involving manual lifting of patients in hospital wards, Med. Lav., 90, 201–228.
Dehlin, O. (1976), Back symptoms in nursing aides in a geriatric hospital, Scand. J. Rehab. Med., 8, 47–52.
Gagnon, M. (1986), Evaluation of forces on the lumbo-sacral joint and assessment of work and energy transfers in nursing aides lifting patient, Ergonomics, 29, 407–421.
Garg, A. (1991), A biomechanical and ergonomics evaluation of patient transferring tasks: bed to wheel- chair and wheelchair to bed, Ergonomics, 34, 289–312.
Magora, A. (1970), Investigation of the relation between low back pain and occupation work history, Ind. Med. Surg., 39, 31–37.
Menoni, O. et al. (1999), Manual handling of patients in hospital and one particular kind of consequent diseases: acute and/or chronic spine alterations, Medicina Lavoro, 90, 99–436.
NIOSH (1981), Work Practices Guide for Manual Lifting, technical report 81-122, National Institute for Occupational Safety and Health, U.S. Department of Health and Human Services, Washington, D.C.
Stobbe, T.J. et al. (1988), Incidence of low back injuries among nursing personnel as a function of patient lifting frequency, J. Saf. Res., 19, 21–28.
Takala, E.P. (1987), The handling of patients on geriatric wards, Appl. Ergonomics, 18, 17–22.
Ulin, S.S. and Chaffin, D.B. (1997), A biomechanical analysis of methods used for transferring totally dependent patients, SCI Nurs., 14, 19–27.
Winkelmolen, G., Landeweerd, J.A., and Drost, M.R. (1994), An evaluation of patient lifting techniques, Ergonomics, 37, 921–932.
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Psychophysiological Methods
References ... 17-4 Various methods of measuring physiology used in the medical field are increasingly being borrowed for human factors and ergonomics purposes to study operators in workplaces with respect to workload or, more specifically, mental workload. There are many reasons why the measurement of operators’ mental workload earns great interest these days, and will increasingly enjoy this status in the near future. First, the nature of work has largely changed, or at least been extended, from physical (e.g., measured by muscle force exertion, addressed in this section) to cognitive (e.g., measured in brain activity, also covered in this section), a trend that has not reached ceiling yet. Second, accidents in workplaces of all sorts are numerous and costly, and they are seemingly ineradicably and in fact largely attributable to the victims themselves, human beings. Third, human errors related to mental workload, in the sense of inadequate information processing, are the major causes of the majority of accidents (Smiley and Brookhuis, 1987).
While both low and high mental workload (e.g., as reflected in heart rate parameters [Smiley and Brookhuis, 1987]) are undoubtedly basic conditions for the occurrence of errors, an exact relationship between mental workload and accident causation is not easily established, let alone measured in practice.
De Waard and Brookhuis (1997) discriminated between underload and overload, the former leading to reduced alertness and lowered attention (e.g., reflected in eye parameters), the latter to distraction, diverted attention, and insufficient time for adequate information processing. Both factors have been studied in relationship to operator state; however, the coupling to error occurrence is not via a direct link (see also Brookhuis et al., 2002). Criteria for when operator state is below a certain threshold, leading to erroneous behavior, should be established. Only then can accidents and mental workload (high or low) be related, in conjunction with the origins such as information overload (e.g., measured by blood pressure or galvanic skin response [Brookhuis et al., 2002]), fatigue (e.g., reflected in the electroenceph- alogram [Brookhuis et al., 2002]), or even a factor such as alcohol (e.g., measured with a breathalyzer [Brookhuis et al., 2002]). The operator’s working environment and the work itself will only gain in complexity, at least for the time being, with the rapid growth in complex electronic applications for control and management. And last but not least, aging plays a role in the interest of the measurement of operators’ mental workload these days, and will increasingly do so in the near future (the “gray wave”).
As long as 30 years ago, Kahneman (1973) defined mental workload as directly related to the proportion of the mental capacity an operator expends on task performance. The measurement of mental workload is the specification of that proportion (O’Donnell and Eggemeier, 1986; De Waard and Brookhuis, 1997) in terms of the costs of the cognitive processing, which is also referred to as mental effort (Mulder, 1980).
Mental effort is similar to what is commonly referred to as doing your best to achieve a certain target level, to even “trying hard” in case of a strong cognitive processing demand, reflected in several physio- logical measures. The concomitant changes in effort will not show easily in work performance measures because operators are inclined to cope actively with changes in task demands, for instance in traffic, as Karel A. Brookhuis
University of Groningen
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drivers do by adapting their driving behavior to control safety (Cnossen et al., 1997). However, the changes in effort will be apparent in self-reports of the drivers and, a fortiori, in the changes in certain physiological measures such as activity in certain brain regions as well as heart rate and heart rate variability (cf. De Waard, 1996).
Mulder (1986) discriminates between two types of mental effort, i.e., the mental effort devoted to the processing of information in controlled mode (computational effort) and the mental effort needed to apply when the operator’s energy state is affected (compensatory effort). Computational effort is exerted to keep task performance at an acceptable level, for instance, when task complexity level varies or secondary tasks are added to the primary task. In case of (ominous) overload, extra computational effort could forestall safety hazards. Compensatory effort takes care of performance decrement in case of, for instance, fatigue up to a certain level. Underload by boredom, affecting the operator’s capability to deal with the task demands, might be compensated as well. In case effort is exerted, be it computational or compensatory, both task difficulty and mental workload will be increased. Effort is a voluntary process under control by the operator, while mental workload is determined by the interaction of operator and task. As an alternative to exerting effort, the operator might decide to change the (sub)goals of the task.
Adapting driving behavior as a strategic solution is a well-known phenomenon. For example, overload because of an additional task, such as looking up telephone numbers while driving, is demonstrated to be reduced by lowering vehicle speed (see De Waard et al., 1998, 1999).
Basically there are three global categories of measurement distinguished in this field: measures of task performance, subjective reports, and physiological measures (see also Eggemeier and Wilson, 1991;
Wierwille and Eggemeier, 1993; Brookhuis, 1993). The first and the most widely used category of measures is based on techniques of direct registration of the operator’s capability to perform a task at an acceptable level, i.e., with respect to an acceptably low accident likelihood. Subjective reports of operator perfor- mance are of two kinds: observer reports, which are mostly given by experts, and self-reports by the operators. The value of observer reports is by virtue of strict protocols that limit variation as produced by personal interpretation; the value of self-reports is mainly by virtue of validation through multiple applications in controlled settings. Well-known examples of the latter are the NASA task-load index (NASA-TLX) (Hart and Staveland, 1988) and the rating scale of mental effort (RMSE) (Zijlstra, 1993).
Finally, physiological measures are the most natural type of workload index, since, by definition, work demands physiological activity. Both physical and mental workload have, for instance, a clear impact on heart rate and heart rate variability (Mulder, 1980, 1986, 1988, 1992; Brookhuis et al., 1991), on galvanic skin response (Boucsein, 1992), on blood pressure (Rau, 2001), and on respiration (Mulder, 1992;
Wientjes et al., 1998). Mental workload might increase heart rate and decrease heart rate variability at the same time (Mulder et al., Chapter 20, this volume). Other measures of major interest are event- related phenomena in the brain activity (Kramer, 1991; Noesselt et al., 2002) and environmental effects on certain muscles (Jessurun, 1997).
In this section, i.e., the next nine chapters of the handbook (Chapter 18 through Chapter 26), the methodology of measuring a number of relevant physiological parameters is elaborated. These include the cardiovascular parameters of heart rate and heart rate variability; the electrocortical parameters of frequency shifts in the electroencephalogram and event-related potentials; galvanic skin responses; blood pressure responses; respiration rate; magnetic reflections of activities in the brain; eyelid movements;
and muscle activity.
The section starts with an overview of a very old method, the measurement of electrical phenomena in the skin (Chapter 18). Measurement techniques include galvanic skin response (GSR), skin potential, peripheral autonomic surface potentials, etc., all with the aim of studying electrodermal activity. The latter can be regarded as a psychophysiological indicator of arousal, stress–strain processes, and emotion.
The measurement of electrodermal activity is used to investigate orienting responses and their habitua- tion, for studying autonomic conditioning, for determining the amount of information-processing capac- ity needed during a task, and for determining the arousal/stress level, especially in situations evoking negative emotions. It has also been used for measuring workload and mental strain, specifically emotional strain; increases in certain types of electrodermal activity indicate readiness for action.
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Chapter 19 addresses electromyography (EMG), i.e., muscle function through analysis of the electrical signals emanated during muscular contractions. EMG is commonly used in ergonomics and occupational health research because it is noninvasive, allowing convenient measures of physical effort during move- ments as well as physiological reactions caused by mentally controlled processes.
In Chapter 20, heart rate is the central topic. Heart rate is derived from the electrocardiogram (ECG), which reflects the (electrical) activity of the heart. For the assessment of mental effort, the ECG itself is not of interest; rather, the time duration between heartbeats is the interesting information. During task performance, operators have to expend (mental) effort, which is usually reflected in increased heart rate and decreased heart rate variability when compared with resting situations. The general cardiovascular response pattern that is found in many mental-effort studies can be characterized by an increase of heart rate and blood pressure and a decrease of variability in heart rate and blood pressure in all frequency bands. This pattern is comparable with a defense reaction and is predominantly found in laboratory studies using short-lasting tasks requiring challenging mental operations in working memory.
Chapter 21 describes the technique for detecting hypovigilance, sleepiness, or even sleep, by ambulatory EEG (and EOG) recording, enabling the investigator to obtain a second-to-second measure of manifest sleepiness (and sleep). The measurement technique is relatively old, much used, reliable, and well accepted by the research community.
Chapter 22 describes mental chronometry using the event-related potential (ERP), which is derived as a transient series of voltage oscillations in the brain that can be recorded from the scalp in response to discrete stimuli and responses. Some ERP components, usually defined in terms of polarity and latency with respect to discrete stimuli or responses, have been found to reflect a number of distinct perceptual, cognitive, and motor processes, thereby proving useful in decomposing the processing requirements of complex tasks. ERPs are being used to study aspects of cognition that are relevant to human factors and ergonomics research, including such topics as vigilance, mental workload, fatigue, adaptive aiding, stres- sor effects on cognition, and automation.
Chapter 23 is a companion to Chapter 22. Both chapters describe the measurement of neural activity within the brain, but Chapter 23 focuses on the use of a noninvasive outside-the-head technique that uses magnetic fields to monitor neural activity (deep) within the brain. The corresponding recordings are known as a magnetoencephalogram (MEG), which is supplemented by a comparable technique called magnetic resonance imaging (MRI). In recent years, these techniques have gained considerable interest in neurophysiological applications because they enable the analysis of the substrate of specific cognitive processes. Recent attempts to employ these methods in the diagnosis of certain neurological diseases seem to be successful.
Chapter 24 describes techniques for evaluating workload by measuring ambulatory blood pressure.
This type of ambulant technique can assess interactions — behavior, emotion, and activation — with workload under real work conditions. Carryover effects of workload on activities, behavior, strain after work, and recovery effects during rest can also be measured. This implies an enhancement of the load–strain paradigm from short-term effects (fatigue, boredom, vigilance, etc.) to long-term effects of work (disturbed recovery processes after work, cardiovascular health diseases, diabetes mellitus, depres- sion, etc.).
Chapter 25 is about alertness monitoring. Certain measures of ocular psychophysiology have been identified for their potential to detect minute-to-minute changes in the drowsiness and hypovigilance that are associated with lapses of attention and diminishing alertness during performance. A measure of slow eyelid closure, referred to as percentage of closure (PERCLOS), correlated highly with lapses in visual vigilance performance. The technique is increasingly being used to monitor operators in their working environment (e.g., professional drivers).
Finally, Chapter 26 describes the measurement of respiration in applied research. Respiratory mea- surement is a potentially powerful asset, as it seems closely related to a variety of important functional psychological dimensions, including response requirements and appraisal patterns. Respiratory measures can also provide valuable supplementary information to alternative measures (subjective measures and
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other measures of operator workload) and in cases where the task environment is stressful or potentially hazardous.
The selection criteria for the chapters included in this section were: (a) nonintrusiveness and (b) proved effects in relation to mental work conditions. Measurement of most of the included physiological parameters is relatively easy, or at least feasible, in the working environment. However, some measures of brain activity in the working environment are difficult (event-related potentials in the electroenceph- alogram) or even impossible (magnetic phenomena within the cortex), at least for the time being.
Nevertheless, all of these measurement techniques are relevant within the context of this section.
References
Boucsein, W. (1992), Electrodermal Activity, Plenum Press, New York.
Brookhuis, K.A. (1993), The use of physiological measures to validate driver monitoring, in Driving Future Vehicles, Parkes, A.M. and Franzén, S., Eds., Taylor & Francis, London, pp. 365–377.
Brookhuis, K.A., De Vries, G., and De Waard, D. (1991), The effects of mobile telephoning on driving performance, Accident Anal. Prev., 23, 309–316.
Brookhuis, K.A., Van Winsum, W., Heijer, T., and Duynstee, L. (1999), Assessing behavioural effects of in- vehicle information systems, Transp. Hum. Factors, 1, 261–272.
Brookhuis, K.A., de Waard, D., and Fairclough, S.H. (2002), Criteria for driver impairment, Ergonomics, 45, 433–445.
Cnossen, F., Brookhuis, K.A., and Meijman, T. (1997), The effects of in-car information systems on mental workload: a driving simulator study, in Simulators and Traffic Psychology, Brookhuis, K.A., de Waard, D., and Weikert, C., Eds., Centre for Environmental and Traffic Psychology, Groningen, the Netherlands, pp. 151–163.
De Waard, D. (1996), The Measurement of Drivers' Mental Workload, Ph.D. thesis, Traffic Research Centre, University of Groningen, Haren, the Netherlands.
De Waard, D. and Brookhuis, K.A. (1997), On the measurement of driver mental workload, in Traffic and Transport Psychology, Rothengatter, J.A. and Carbonell Vaya, E., Eds., Pergamon, Amsterdam, pp.
161–171.
De Waard, D., Van der Hulst, M., and Brookhuis, K.A. (1998), The detection of driver inattention and breakdown, in Human Factors in Road Traffic II, Traffic Psychology and Engineering, Santos, J., Albuquerque, P., Pires da Costa, A., and Rodrigues, R., Eds., University of Minho, Braga, Portugal.
De Waard, D., Van Der Hulst, M., and Brookhuis, K.A. (1999), Elderly and young drivers’ reaction to an in-car enforcement and tutoring system, Appl. Ergonomics, 30, 147–157.
Eggemeier, F.T. and Wilson, G.F. (1991), Performance-based and subjective assessment of workload in multi- task environments, in Multiple-Task Performance, Damos, D.L., Ed., Taylor & Francis, London, pp.
207–216.
Hart, S.G. and Staveland, L.E. (1988), Development of NASA-TLX (task load index): results of experimental and theoretical research, in Human Mental Workload, Hancock, P.A. and Meshkati, N., Eds., North- Holland, Amsterdam.
Jessurun, M. (1997), Driving through a Road Environment, Ph.D. thesis, Traffic Research Centre, Uni- versity of Groningen, Haren, the Netherlands.
Kahneman, D. (1973), Attention and Effort, Prentice-Hall, Englewood Cliffs, NJ.
Kramer, A.F. (1991), Physiological metrics of mental workload: a review of recent progress, in Multiple- Task Performance, Damos, D.L., Ed., Taylor & Francis, London, pp. 279–328.
Mulder, G. (1980), The Heart of Mental Effort, Ph.D. thesis, University of Groningen, Groningen, the Netherlands.
Mulder, G. (1986), The concept and measurement of mental effort, in Energetics and Human Information Processing, Hockey, G.R.J., Gaillard, A.W.K., and Coles, M.G.H., Eds., Martinus Nijhoff Publishers, Dordrecht, the Netherlands, pp. 175–198.
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Mulder, L.J.M. (1988), Assessment of Cardiovascular Reactivity by Means of Spectral Analysis, Ph.D.
thesis, University of Groningen, Groningen, the Netherlands.
Mulder, L.J.M. (1992), Measurement and analysis methods of heart rate and respiration for use in applied environments, Biol. Psychol., 34, 205–236.
Noesselt, T., Hillyard, S.A., Woldorff, M.G., Schoenfeld, A., Hagner, T., Jäncke, L., Tempelmann, C., Hinrichs, H., Heinze, H.J. (2002), Delayed striate cortical activation during spatial attention, Neuron, 35, 575–687.
O’Donnell, R.D. and Eggemeier, F.T. (1986), Workload assessment methodology, in Handbook of Percep- tion and Human Performance, Boff, K.R., Kaufman, L., and Thomas, J.P., Eds., Vol. II, 42, Cognitive Processes and Performance, Wiley, New York, pp. 1–49.
Rau, R. (2001), Objective characteristics of jobs affect blood pressure at work, after work and at night, in Progress in Ambulatory Assessment, Fahrenberg, J. and Myrtek, M., Eds., Hogrefe and Huber, Seattle, WA, pp. 361–386.
Smiley, A. and Brookhuis, K.A. (1987), Alcohol, drugs and traffic safety, in Road Users and Traffic Safety, Rothengatter, J.A. and de Bruin, R.A., Eds., Van Gorcum, Assen, the Netherlands, pp. 83–105.
Wientjes, C.J.E., Grossman, P., and Gaillard, A.W.K. (1998), Influence of drive and timing mechanisms on breathing pattern and ventilation during mental task performance, Biol. Psychol., 49, 53–70.
Wierwille, W.W. and Eggemeier, F.T. (1993), Recommendation for mental workload measurement in a test and evaluation environment, Hum. Factors, 35, 263–281.
Zijlstra, F.R.H. (1993), Efficiency in Work Behavior: A Design Approach for Modern Tools, Ph.D. thesis, Delft University of Technology, Delft University Press, the Netherlands.
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