Walking is the most natural and common form of physical activity (18), and it can be practiced at all ages, from childhood to old age. Above all, it can be carried out any time of the year, both outdoors and indoors. For such reasons, it must be considered the most accessible physical activity for the maintenance of wellness. Furthermore, it maintains and improves aerobic fitness (12), prevents cardiovascular and metabolic pathologies (as reported by the American College Sports Medicine guidelines), and can be considered a therapy for several pathologies (3,13,17,34). Moreover, it has been reported that in exercise-conditioned individuals, a vigorous activity can increase the risk of acute myocardial infarction and sudden cardiac death, and therefore, a moderate-intensity physical activity, such as brisk walking every day, has been suggested (31).
To be able to walk safely, certain aspects, such as environment and terrain, must be taken into consideration. Such factors oblige the subject to make use of specific equipment, that is, when on mountain tracks trekking boots (TBs) are required. Various studies have correlated different physiological parameters to both equipment and environmental factors (10,15,16). More specifically, it is shown how walking on rough terrain requires a greater metabolic effort per meter than walking on smooth terrain. Other authors have shown how, when walking at near optimal speed (∼1.03 m·s−1) on the treadmill, the intake of oxygen is 6% greater when wearing boots as opposed to when wearing sneakers.
Also, to perform walking safely one must reach an appropriate workload or fitness level. Indeed, Böhm and Hösl (5) showed an increased cocontraction especially on knee joint muscles and manifestations of early fatigue during walking or hiking when wearing boots. However, Ponchia et al. (26) have shown how in a population that occasionally went to the mountains when on holiday, between those who did and those who did not perform regular physical activity, cardiovascular incidents occurred in the latter. In such subjects, the risk seems to be related to lack of physical fitness but not to altitude and other typical aspects of the mountains, such as temperature and type of terrain (26).
Hence, sedentary subjects who foresee undertaking outdoor walking or hiking without incurring problems are advised to undergo indoor training and conditioning with specific workloads.
The aim of this study was to define treadmill workloads that best reflect walking energy expenditure (EE) at different speeds on mountain paths to plan adequate training programs for safe mountaineering activity.
Experimental Approach to the Problem
The subjects enrolled were college students. All performed recreational physical activities but had poor mountaineering experience. After a physiological profile evaluation by means of the maximal exhaustion test, walking EE was assessed at several speeds on a leveled natural path using boots and on a treadmill varying the inclination using sneakers. Net energy cost, as obtained from EE, was used to define treadmill load that better reflects the outdoor condition.
After research protocol approval by the local Ethics Committee, 14 volunteers who were young men with a mean age of 23.9 ± 2.9 years, height 1.75 ± 0.04 m, body mass 72.9 ± 6.3 kg, and body mass index 23.7 ± 2.3 kg·m−2 were recruited. All the participants signed an informed consent in which the experimental protocol was reported in detail. They were found to be in apparently good health, and all practiced regular physical activity at a noncompetitive level.
In every phase of the experimental protocol, oxygen consumption (V[Combining Dot Above]O2), carbon dioxide output (V[Combining Dot Above]CO2), pulmonary ventilation (V[Combining Dot Above]E), and respiratory exchange ratio (RER) were assessed using a telemetric gas analyzer (K4b2, Cosmed, Rome, Italy). This instrumentation was judged valid for the measurement of gas exchange during exercise (20). Before each set of data acquisition, the gas analyzer was calibrated with a 3-L syringe for air volume and with a standard gas mixture (5% CO2, 16% O2, and 79% N2) for O2 and CO2 measurements. Pulmonary ventilation and gas exchange values were automatically corrected following National Institute of Standards and Technology guidelines. Cardiac activity was recorded by a heart rate (HR) monitor (Polar Vantage, NV, Oulu, Finland). The HR and metabolic values were acquired and stored on a PC using the management software of the above-mentioned telemetric gas analyzer and collected every 15 seconds. Acoustic tracks, characterized by an accurate temporal cadence, were generated for specific speeds and for a defined distance between 2 reference points using a custom-made software (C++ language, Borland, TX, USA). The acoustic tracks were then recorded on a commercial portable sound player device and used as acoustic signals in outdoors (described in “Environmental Condition”). Indoors different gradients (ranging between 0 and +4%, with steps of 1%) and speeds (0.28, 0.56, 0.84, 1.11, and 1.39 m·s−1) were imposed on a motorized treadmill (Runrace, Technogym, Bologna, Italy).
The aerobic fitness of each subject (peak oxygen uptake and ventilatory threshold) was evaluated using a maximal incremental test performed on a cycle ergometer (Ergoline, Ergoselect 100 K, Bitz, Germany). The test consisted of a 3-minute warm-up at a constant power of 40 W, followed by increments of 12 W every 20 seconds until exhaustion. At least 2 of the following 3 parameters were considered signs of exhaustion: peak oxygen uptake (V[Combining Dot Above]O2peak) reached a plateau, whereas power was increased further; RER was >1.1; 95% of the theoretical maximal HR was attained. Typically, ventilatory threshold (V[Combining Dot Above]O2thresh) was calculated using the V-Slope method (2) and confirmed by the ventilatory equivalent method (32).
All the measurements in outdoors were carried out in spring (April and May) and autumn (September and October), when the temperature was comfortable (21 ± 3° C) and air humidity (45 ± 9%) and atmospheric pressure (648 ± 2 mm Hg) were relatively constant. An appropriate area in Parco Nazionale d'Abruzzo (Valle di Forca D'Acero, district of San Donato Val Comino, central Italy) was selected. An ellipsoidal shaped route 100 m long, naturally leveled and at an altitude of about 1,300 m was tracked. The path showed a typical uneven surface, that is, an irregular compact surface characterized by nonuniformity with pebbles. At the route edges, colored markers were positioned every 10 m, matching the corresponding acoustic track used as signal to maintain a constant speed. The imposed speed was also verified by a Global Positioning System (eTrex Vista Color Xa, Garmin, Olathe, KS, USA).
Outdoors, the participants were required to wear long cotton trousers, T-shirt, mountain TBs, a portable digital audio player device with earphones, and measurement tools (i.e., gas analyzer and its accessories); the total weight of the equipment ranged between 2.8 and 3.2 kg. Indoors, the digital audio player was not used, and the participants were asked to wear running shoes (RSs) instead of TBs. In this condition, clothing and measurement tools reached a weight between 2.2 and 2.5 kg.
This study was carried out in 3 sessions: (a) evaluation of aerobic fitness of each participant to define the physiological profile; (b) outdoor measurements to assess the energy cost of walking on leveled natural path at several speeds wearing TB; (c) indoor measurements to assess the energy cost of walking on a treadmill at several speeds and gradients wearing RSs. The participants completed all the sessions in about 1 month. Four participants did not perform sessions 2 and 3 completely, and this was considered in the statistical analysis. To assess the energy cost, 5 different walking speeds (5 trials) were imposed on the participants: 0.28, 0.56, 0.84, 1.11, and 1.39 m·s−1. Every trial was between 250 and 600 m long to reach the steady state of physiological parameters, with a corresponding exercise time comprised between 7 and 15 minutes. Before each trial, the participants were required to quietly maintain an orthostatic posture for 5 minutes to allow measurements of oxygen uptake at rest (V[Combining Dot Above]O2rest) defined as the V[Combining Dot Above]O2 value when physiological parameters such as the HR and volume tidal were stable. Indoor, the imposed speeds of walking were randomized for each participant and spaced out by a recovery period of about 60 minutes, necessary for V[Combining Dot Above]O2 and HR to return to rest values. In a single day, 3 trials were acquired, whereas other trials necessary to complete the subject's measurements were carried out 2 days after. Outdoors, each subject performed 2 or 3 trials in a day, each separated by 60 minutes of rest, followed by a day of rest before the completion of the trials. The data collected in outdoors are identified as TB. Indoors, a week after outdoor measurements, the same walking speeds were randomly imposed on the treadmill at different gradients (0, 1, 2, 3, and 4%). These latter data are identified as RS(0), RS(1), RS(2), RS(3), and RS(4). All the indoor trials were completed in 4 weeks with rest time as defined in outdoor measurements.
In all the trials, the gross oxygen consumption (V[Combining Dot Above]O2gross) was defined as mean of the last 3 minutes. Net oxygen consumption (V[Combining Dot Above]O2net) was obtained as V[Combining Dot Above]O2gross − V[Combining Dot Above]O2rest. The energy uptake was calculated by multiplying V[Combining Dot Above]O2net per 20.9, caloric equivalent of O2 in a sole aerobic metabolism. The EE was the net energy uptake scaled by the total weight (body and outdoor or indoor equipment). Moreover, the energy cost per total weight and per unit distance (EC) was calculated as the ratio between EE and speed. The values of EC, plotted vs. speed, were interpolated by a best-fitting curve. The best-fitting curve showed a typical concave upward trend fitted by the polynomial second-order equation: EC (speed) = A × speed2 +B × speed + C.
This fitting curve exhibits a minimal value that corresponds to the optimal speed, that is, walking speed requiring the minimum EC. This minimum can be easily computed by means: speedoptimal = −(B) × 2× A − 1.
We used paired Student's t-test to compare the mean values of EC of RS and TB for different values of speed and slope. Evaluation of normality was performed using the Shapiro-Wilk test. Statistical significance was set using alpha = 0.05 in all the tests. Because multiple observations over time were performed for the same subjects, for different values of footwear, speed and slope multilevel mixed-effects linear regression was used to estimate regression coefficients.
The physiological profile of all the participants was evaluated from a maximal incremental test performed indoors within a week of trials. Average values of V[Combining Dot Above]O2peak and V[Combining Dot Above]O2thresh measured, respectively, 49.4 ± 5.7 and 34.5 ± 6.2 ml·kg−1·min−1, and V[Combining Dot Above]O2thresh expressed as the percentage of V[Combining Dot Above]O2peak resulted in being equal to 69.5 ± 5.2%. These values of V[Combining Dot Above]O2peak and V[Combining Dot Above]O2thresh are typical of a population characterized by an average-good aerobic capacity (3).
The individual RER values in all the trials both outdoors and indoors were consistently <1, indicative of an activity specifically involving aerobic metabolism. Additionally, the ratio of V[Combining Dot Above]O2 measured in each trial, and V[Combining Dot Above]O2peak can be used as a parameter to define the metabolism involved. In all experimental conditions, the ratio values were always <V[Combining Dot Above]O2thresh (Table 1) further supporting the exclusive use of aerobic metabolism during walking.
In Table 2, The values of V[Combining Dot Above]O2gross measured during walking on leveled terrain at several speeds both indoors and outdoors are reported. The V[Combining Dot Above]O2gross of walking on natural paths (TB) resulted in being consistently higher than that on the treadmill (RS) at all speeds, as detailed in Table 2, ranging in percentage between 1.89 and 18.14%.
As described in the Methods section (Outdoor and Indoor experimental protocol), before the beginning of each set of trials, each subject underwent oxygen uptake evaluation in the rest condition (V[Combining Dot Above]O2rest), and this value was used to compute the Energy Cost. No statistical differences were observed between values of V[Combining Dot Above]O2rest measured in each subject (p < 0.001). The average V[Combining Dot Above]O2rest of all the subjects measured 4.45 ± 0.03 ml·kg−1·min−1, corresponding at about 1.2 MET according to the literature (1).
Figure 1 shows the indoor EC best fit polynomial second-order curves of walking at different speeds and gradients on RS. All EC best fit curves presented a typical concave upward trend. The minimum of the best fit equations was calculated and found to correspond to optimal speeds ranging between 1.03 and 1.11 m·s−1 (Figure 1 on arrowheads).
Also, TB mean EC data were fitted by a second-order curve, and the relative optimal speed resulted in being 1.02 m·s−1.
To identify the treadmill gradient that most accurately reflects the EC required in outdoor conditions, TB data were statistically compared with RS data. Mean values and the SDs of EC according to the type of shoes, speed, and slope, and statistical results are shown in Table 3. Nonsignificant differences between RS and TB were found for 3% slope (at all speeds) and 2% slope (at all speeds with the exception of the highest speed). The mean EC at either higher or lower values of the slope markedly differed from that of TB.
The multilevel mixed-effects linear regression model allowed us to evaluate the relationship between shoes or slopes independently of speed. The beta coefficient for footwear and terrain, which is highly statistically significant, suggested that the EC using RSs is 0.90 J·kg−1·m−1 lower than that using TBs (about 17 cal·m−1 in a subject weighing 80 kg). On the other hand, the beta coefficient for the slope suggested that a 1% gradient increase corresponds to a mean EC increase of 0.32 J·kg−1·m−1.
The recommendations for adults from the American College of Sports Medicine (ACSM) and the American Heart Association (12) are as follows: Regular physical activity reduces the risk of chronic disease and premature mortality; the majority of the health benefits can be reached by performing an aerobic physical activity with an EE ranging between 450 and 750 MET minutes in a week, among all moderate-intensity activities, walking at 1.3 m·s−1 for 30 minutes for 5 d·wk−1 should be preferred.
Hiking is widely practiced and is an activity that links up to ACSM's guidelines. However, Ponchia et al. (26) reported that subjects with low fitness levels were more susceptible to cardiovascular events. Thus, before taking up mountaineering activity, adequate conditioning physical training should be performed. The aim of this study was to define walking workloads at different speeds on mountain paths and to use these to plan indoor training programs.
Our findings showed that walking under all investigated experimental conditions both outdoors and indoors involved an aerobic metabolism because oxygen uptake was constantly below the anaerobic threshold. As expected, under all the conditions, V[Combining Dot Above]O2gross was related coherently to speed in agreement with the findings reported by other authors (4,6,7,9,19,21–24,29,33). The outdoor EE resulted in being systematically higher than those obtained on level treadmills at all speeds. Comparison of outdoor and indoor V[Combining Dot Above]O2gross data in the level condition showed statistically significant differences at 1.11 and 1.39 m·s−1 only. However no statistical differences were recognized at 0.28, 0.56, and 0.84 m·s−1 suggesting that the metabolic requirements were similar when walking at these speeds independently of environmental conditions. Interestingly, several articles have reported that the optimal walking speed is very close to 1.11 m·s−1 (6,29). To our knowledge, to date, no one has studied the combined effect of footwear and terrain on the EE during walking, although several studies have been carried out on the influence of a single parameter (footwear or terrain). In fact, Jones et al. (15,16) did evaluate the V[Combining Dot Above]O2 changes comparing different shoe types on the same surface and, more recently, Guillebastre et al. (11) have studied the influence on gait of an ankle-foot blockage; other articles studied the influence of several surfaces (14,25,28,30). For example, as far as shoes were concerned, a 6% of V[Combining Dot Above]O2gross percentage difference (ΔV[Combining Dot Above]O2gross) was estimated during treadmill walking on TB or on RS at 1.1 m·s−1 speed (15,16). Likewise, Soule and Goldman (30) studied the terrain effect when walking with RS. These authors reported a ΔV[Combining Dot Above]O2gross of 10% for an uneven road and 20% for light undergrowth compared with that of the treadmill. Interestingly, present ΔV[Combining Dot Above]O2gross findings have shown a 16.11% difference between outdoor vs. indoor (0% of gradient) at a speed of 1.1 m·s−1. This value is very close to the sum of the above-mentioned effects, assuming that the natural path corresponds to a uneven road.
The EE outdoors was constantly higher than on a level treadmill at each walking speed. To simulate the outdoor workload, the present authors decided to introduce several gradients on the treadmill. To identify the treadmill gradient that best reflects EE on outdoor activity at every investigated speed, the net EE per distance unit (EC) was employed. The EC represents the net metabolic cost per unit distance, so it is necessary to determine the net EE from V[Combining Dot Above]O2net scaling by body weight (BW).
Concerning net EE, Bastien et al. (4) showed how the relationship between EC and speed was extra load independent (in a range of 10–80% of the BW) when the net EE was computed as V[Combining Dot Above]O2net normalized by a total weight represented by BW plus extra load. Accordingly, to remove the bias caused by the weight of shoes, in our data on net EE was obtained by normalizing total weight (i.e., clothing, shoes, and measurement tools) considering that the subject's extra load was different under indoor and outdoor conditions. The Mean EC data were fitted with a best-fitting curve which, as expected, showed a quadratic relationship with the speeds at all test conditions (9,19,21,22). From these fitting curves, the minimum value, representing optimal speed, was computed. In agreement with the findings of other authors (4,8,9,19,27,35), optimal speeds ranged between 1.02 (using TBs) and 1.11 m·s−1.
Our findings demonstrate that the EC of walking on the level natural pathways while wearing TBs is greater than that shown for a horizontal treadmill while wearing RSs at all speeds investigated. A statistical model quantified this difference as about 1 J·kg−1·m−1 at all speeds, and considering that our findings were scaled by the total weight they cannot be attributed to different shoe weights. Thus, this difference may be attributed to biomechanical differences and stiffness and limitations (5,16). Indeed, in TBs, the hard boot shaft is present to prevent ankle injuries or falls when walking on an uneven road. The ankle's range of motion is decreased and the eccentric energy being absorbed by the ankle joint (5). To boot, compensatory cocontraction at the knee joint induces an increase in muscular activations (5).
Finally, we can affirm that, indoors, a treadmill gradient of 3% is the load that better reflects that required for natural level paths walking at speeds ranging between 0.28 and 1.39 m·s−1.
This would seem to suggest that walking with RSs on graded paths at 3% could be effective training as preparation for hiking in the mountains to lower the risk of untoward cardiovascular events.
An adequate physical fitness level is needed to ensure the ability of subjects to undertake hiking activity in safety. This may also be obtained with a conditioning training period indoors on a treadmill. This study shows that, at speeds ranging between 0.28 and 1.39 m·s−1, the EE required in hiking is higher than the one needed when using sneakers indoors at the same workload, that is, about 17 cal·m−1 in a subject weighing 80 kg. Thus, a gradient of 3% is the supplementary workload needed to set the treadmill to best reflect the energy demand for distance unit request by a level natural path.
Because outdoors the subject would be wearing TBs, one could advance a further hypothesis whereby this finding could be adapted accordingly by saying that for easy mountain tracks with an average gradient of 3%, indoor training should be modified 6% to compensate for the above-mentioned slope.
Consequently, indoor training using the above-mentioned criteria could be considered a valid method of preparation for hiking activity in all subjects.
The authors are grateful to Dr. Pamela Pallicca for assistance during outdoor measurements, Prof. Loriana Castellani for useful suggestions on manuscript revision, and Deborah Ballantyne for language revision. The authors have no conflict of interest in connection with this article. The corresponding author had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
1. Ainsworth BE, Haskell WL, Leon AS, Jacobs DR Jr, Montoye HJ, Sallis JF, Paffenbarger RS Jr. Compendium of physical activities: Classification of energy costs of human physical activities. Med Sci Sports Exerc 25: 71–80, 1993.
2. Amann M, Subudhi AW, Walker J, Eisenman P, Shultz B, Foster C. An evaluation of the predictive validity and reliability of ventilatory threshold. Med Sci Sports Exerc 36: 1716–1722, 2004.
3. American College of Sports Medicine. American College of Sports Medicine Guidelines for Exercise Testing and Prescription (7th ed.). Philadelphia, PA:Lippincott Williams & Wilkins, 2005.
4. Bastien GJ, Willems PA, Schepens B. Effect of load and speed on the energetic cost of human walking. Eur J Appl Physiol 94: 76–83, 2005.
5. Böhm H, Hösl M. Effect of boot shaft stiffness on stability joint energy and muscular co-contraction during walking on uneven surface. J Biomech 43: 2467–2472, 2010.
6. Bunc V, Dlohua R. Energy cost of treadmill walking. J Sports Med Phys Fitness 37: 103–109, 1997.
7. Cavagna GA, Kaneko M. Mechanical work and efficiency in level walking and running. J Physiol 268: 467–481, 1977.
8. Cotes JE, Meade F. The energy expenditure and mechanical energy demand in walking. Ergonomics 3: 96–119, 1960.
9. Di Prampero PE. The energy cost of human locomotion on land and in water. Int J Sports Med 7: 55–72, 1986.
10. Givoni B, Goldman RF. Predicting metabolic energy cost. J Appl Physiol 30: 429–433, 1971.
11. Guillebastre B, Calmels P, Rougier P. Effects of rigid and dynamic ankle-foot orthoses on normal gait. Foot Ankle Int 30: 51–56, 2009.
12. Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, Macera CA, Heath GW, Thompson PD, Bauman A. Physical activity and public health: Updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 39: 1423–1434, 2007.
13. Iwane M, Arita M, Tomimoto S, Satani O, Matsumoto M, Miyashita K, Nishio I. Walking 10,000 steps/day or more reduces blood pressure and sympathetic nerve activity in mild essential hypertension. Hypertens Res 23: 573–580, 2000.
14. Jones AM, Doust JH. A 1% treadmill grade most accurately reflects the energetic cost of outdoor running. J Sports Sci 14: 321–327, 1996.
15. Jones BH, Knapic JJ, William LD, Toner MM. The energy cost of women walking and running in shoes and boots. Ergonomics 29: 439–443, 1986.
16. Jones BH, Toner MM, William LD, Knapic JJ. The energy cost and heart rate of trained and untrained subjects walking and running in shoes and boots. Ergonomics 27: 895–902, 1984.
17. Leon AS, Casal D, Jacob D Jr. Effects of 2000 kcal per week of walking and stair climbing on physical fitness and risk factors for coronary heart disease. J Cardiopulm Rehabil 3: 183–192, 1996.
18. Marchetti M, Cappozzo A, Felici F, Figura F. Biomechanics of race-walking. Athletic Studi 6:19–42, 1982.
19. Margaria R. About physiology of energy consumption during walking and running at different speed and ground inclination. Atti Acc Naz Lincei 7: 299–368, 1938.
20. Meyer T, Davison RC, Kindermann W. Ambulatory gas exchanges measurement-current status and future options. Int J Sports Med 26: 19–27, 2005.
21. Minetti AE, Moia C, Roi GS, Susta D, Ferretti G. Energy cost of walking and running at extreme uphill and downhill slopes. J Appl Physiol 93: 1039–1046, 2002.
22. Minetti EA. Optimum gradient of mountain paths. J Appl Physiol 79: 1698–1703, 1995.
23. Minetti EA, Ardigò LP, Saibene F. Mechanical determinants of gradient walking energetics in man. J Physiol 472: 725–735, 1993.
24. Minetti EA, Ardigò LP, Saibene F. The transition between walking and running in humans: Metabolic and mechanical aspects at different gradients. Acta Physiol Scand 150: 315–323, 1994.
25. Passmore R, Durnin JV. Human energy expenditure. Physiol Rev 35: 801–840, 1955.
26. Ponchia A, Biasin R, Tempesta T, Thiene M, Volta SD. Cardiovascular risk during physical activity in the mountains. J Cardiovasc Med (Hagerstown) 2: 129–135, 2006.
27. Ralston HJ. Energy-speed relation and optimal speed
during level walking. Int Z Angew Physiol 17: 277–283, 1958.
28. Ralston HJ. Comparison of energy expenditure during treadmill walking and floor walking. J Appl Physiol 15:1156, 1960.
29. Saibene F, Minetti AE. Biomechanical and physiological aspects of legged locomotion in humans. Eur J Appl Physiol 88: 297–316, 2003.
30. Soule RG, Goldman RF. Terrain
coefficients for energy cost prediction. J Appl Physiol 32: 706–708, 1972.
31. Thompson PD, Franklin BA, Balady GJ, Corrado D, Estes NA 3rd, Fulton JE, Gordon NF, Haskell WL, Links MS, Maron BJ, Mittleman MA, Pelliccia A, Wenger NK, Willich SN, Costa F. American Heart Association Council of Nutrition, Physical Activity and Metabolism, American Heart Association Council on Clinical Cardiology, and American College of Sport Medicine. Exercise and acute cardiovascular events placing the risks into perspective: a scientific statement from the American Heart Association Council on Nutrition, Physical Activity, and Metabolism and the Council on Clinical Cardiology. Circulation 115: 2358–2368, 2007.
32. Wasserman K, Hansen JE, Sue DY, Whipp BJ, Casaburi R. Principles of Exercise Testing and Interpretation. Philadelphia, PA: Lea & Febiger. pp. 62–64, 1987.
33. Willems PA, Cavagna GA, Heglund NC. External, internal and total work in human locomotion. J Exp Biol 198: 379–393, 1995.
34. Williams PT. Reduced diabetic, hypertensive, and cholesterol medication use with walking. Med Sci Sports Exerc 40: 433–443, 2008.
35. Zarrugh MY, Todd FN, Ralston HJ. Optimization of energy expenditure during level walking. Eur J Appl Physiol Occup Physiol 33: 293–306, 1974.