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The preferred walking speed is the speed at which humans or other animals choose to walk. In the absence of significant external factors, humans tend to walk at about 1.4 m/s (5.0 km/h; 3.1 mph). Although humans are capable of walking at speeds from nearly 0 m/s to upwards of 2.5 m/s (9.0 km/h; 5.6 mph), humans typically choose to use only a small range within these speeds. Individuals find exceptionally fast or slow speeds uncomfortable. Horses have also demonstrated normal, narrow distributions of preferred walking speed within a given gait, which suggests that the process of speed selection may follow similar patterns across species. Preferred walking speed has important clinical applications as an indicator of mobility and independence. For example, elderly people or those suffering from osteoarthritis prefer to walk more slowly. Improving (increasing) preferred walking speed has therefore been a significant clinical goal in these populations.
People have suggested mechanical, energetic, physiological and psychological factors as contributors to speed selection. Likely, individuals face a tradeoff between the numerous costs associated with different walking speeds and select a speed which minimizes these costs. For example, humans may trade off time to destination, which is minimized at fast walking speeds, and metabolic rate, muscle force or joint stress, which are minimized at slow walking speeds. Broadly, increasing value of time, motivation, or metabolic efficiency may cause people to walk more quickly. Conversely, aging, joint pain, instability, incline, metabolic rate and visual gain cause people to walk more slowly.
Value of timeEdit
Commonly, individuals place some value on their time. For walks of a fixed distance, time to destination can be minimized by walking more quickly. Unlike other possible determinants of preferred walking speed, which become less favorable at higher speeds, time to destination becomes more favorable (less time spent walking) with increasing speed. Economic theory therefore predicts that value-of-time is a key factor influencing preferred walking speed.
Levine and Norenzayan (1999) measured preferred walking speeds of urban pedestrians in 31 countries and found that walking speed is positively correlated with the country's per capita GDP and purchasing power parity, as well as with a measure of individualism in the country's society. It is plausible that affluence correlates with actual value considerations for time spent walking, and this may explain why people in affluent countries tend to walk more quickly.
This idea is broadly consistent with common intuition. Everyday situations often change the value of time. For example, when walking to catch a bus, arriving marginally after the bus has left may result in a relatively long wait. Here, the value of the one minute immediately before the bus has departed may be worth 30 minutes of time (the time saved not waiting for the next bus). The idea of hurrying to catch a bus has become almost a cliché. Supporting this idea, Darley and Bateson show that individuals who are “hurried” under experimental conditions are less likely to stop in response to a distraction and arrive at their destination sooner.
Energy minimization is widely considered a primary goal of the central nervous system. The rate at which a human expends metabolic energy while walking (gross metabolic rate) increases curvilinearly with increasing speed. However, humans also require a continuous basal metabolic rate to maintain normal function. The energetic cost of walking itself is therefore best understood by subtracting basal metabolic rate from gross metabolic rate, yielding net metabolic rate. In human walking, net metabolic rate also increases curvilinearly with speed. These measures of walking energetics are based on how much oxygen people consume per unit time. Many locomotion tasks, however, require walking a fixed distance rather than for a set time. Dividing gross metabolic rate by walking speed results in gross cost of transport. For human walking, gross cost of transport is U-shaped. Similarly, dividing net metabolic rate by walking speed yields a U-shaped net cost of transport. These curves reflect the cost of moving a given distance at a given speed and may better reflect the energetic cost associated with walking.
Ralston (1958) showed that humans tend to walk at or near the speed that minimizes gross cost of transport. He showed that gross cost of transport is minimized at about Template:Convert/m/sTemplate:Convert/test/Aon, which corresponded to the preferred speed of his subjects. Supporting this, Wickler et al. (2000) showed that the preferred speed of horses both uphill and on the level corresponds closely to the speed that minimizes their gross cost of transport. Among other gait costs that human walkers choose to minimize, this observation has led many to suggest that people minimize cost and maximize efficiency during locomotion. Because gross cost of transport includes velocity, gross cost of transport includes an inherent value of time. Subsequent research suggests that individuals may walk marginally faster than the speed that minimizes gross cost of transport under some experimental setups, although this may be due to how preferred walking speed was measured.
In contrast, other researchers have suggested that gross cost of transport may not represent the metabolic cost of walking. People must continue to expend their basal metabolic rate regardless of whether they are walking, suggesting that the metabolic cost of walking should not include basal metabolic rate. Some researchers have therefore used net metabolic rate instead of gross metabolic rate to characterize the cost of locomotion. Net cost of transport reaches a minimum at about Template:Convert/m/sTemplate:Convert/test/Aon. Healthy pedestrians walk faster than this in many situations.
Gross metabolic rate may also directly limit preferred walking speed. Aging is associated with reduced aerobic capacity (reduced VO2 max). Malatesta et al. (2004) suggests that walking speed in elderly individuals is limited by aerobic capacity; elderly individuals are unable to walk faster because they cannot sustain that level of activity. For example 80-year-old individuals are walking at 60% of their VO2 max even when walking at speeds significantly slower than those observed in younger individuals.
Biomechanical factors such as mechanical work, stability, and joint or muscle forces may also influence human walking speed. Walking faster requires additional external mechanical work per step. Similarly, swinging the legs relative to the center of mass requires some internal mechanical work. As faster walking is accomplished with both longer and faster steps, internal mechanical work also increases with increasing walking speed. Therefore, both internal and external mechanical work per step increases with increasing speed. Individuals may try to reduce either external or internal mechanical work by walking more slowly, or may select a speed at which mechanical energy recovery is at a maximum.
Stability may be another factor influencing speed selection. Hunter et al. (2010) showed that individuals use energetically suboptimal gaits when walking downhill. He suggests that people may instead be choosing gait parameters that maximize stability while walking downhill. This suggests that under adverse conditions such as down hills, gait patterns may favor stability over speed.
Individual joint and muscle biomechanics also directly affect walking speed. Norris showed that elderly individuals walked faster when their ankle extensors were augmented by an external pneumatic muscle. Muscle force, specifically in the gastrocnemius and/or soleus, may limit walking speed in certain populations and lead to slower preferred speeds. Similarly, patients with ankle osteoarthritis walked faster after a complete ankle replacement than before. This suggests that reducing joint reaction forces or joint pain may factor into speed selection.
- Main article: Visual flow
The rate at which the environment flows past the eyes seems to be a mechanism for regulating walking speed. In virtual environments, the gain in visual flow can be decoupled from a person’s actual walking speed, much as one might experience when walking on a conveyor belt. There, the environment flows past an individual more quickly than their walking speed would predict (higher than normal visual gain). At higher than normal visual gains, individuals prefer to walk more slowly, while at lower than normal visual gains, individuals prefer to walk more quickly. This behavior is consistent with returning the visually observed speed back toward the preferred speed and suggests that vision is used correctively to maintain walking speed at a value that is perceived to be optimal. Moreover, the dynamics of this visual influence on preferred walking speed are rapid - when visual gains are changed suddenly, individuals adjust their speed within a few seconds. The timing and direction of these responses strongly indicate that a rapid predictive process informed by visual feedback helps select preferred speed, perhaps to complement a slower optimization process that directly senses metabolic rate and iteratively adapts gait to minimize it.
- ↑ 1.0 1.1 Browning, R. C., Baker, E. A., Herron, J. A. and Kram, R. (2006). Effects of obesity and sex on the energetic cost and preferred speed of walking. Journal of Applied Physiology 100 (2): 390–398.
- ↑ 2.0 2.1 Mohler, B. J., Thompson, W. B., Creem-Regehr, S. H., Pick, H. L., Jr, Warren, W. H., Jr. (2007). Visual flow influences gait transition speed and preferred walking speed. Experimental Brain Research 181 (2): 221–228.
- ↑ 3.0 3.1 Levine, R. V. and Norenzayan, A. (1999). The Pace of Life in 31 Countries. Journal of Cross-Cultural Psychology 30 (2): 178–205.
- ↑ Minetti, A. E. (2000). "The three modes of terrestrial locomotion" Biomechanics and Biology of Movement, 67–78, Human Kinetics.
- ↑ Hoyt, D. F. and Taylor, C. R. (1981). Gait and the energetics of locomotion in horses. Nature 292 (5820): 239–240.
- ↑ Darley, J. M. and Batson, C. D. (1973). "From Jerusalem to Jericho": A study of situational and dispositional variables in helping behavior. Journal of Personality and Social Psychology 27 (1): 100–108.
- ↑ 7.0 7.1 Alexander, McNeill R. (2002). Energetics and optimization of human walking and running: The 2000 Raymond Pearl memorial lecture. American Journal of Human Biology 14 (5): 641–648.
- ↑ Ralston, H. (1958). Energy–speed relation and optimal speed during level walking. Int. Z. angew. Physiol. einschl. Arbeitphysiol. 17: 277–283.
- ↑ Wickler, S. J., Hoyt, D. F., Cogger, E. A. and Hirschbein, M. H. (2000). Preferred speed and cost of transport: the effect of incline. Journal of Experimental Biology 203 (14): 2195–2200.
- ↑ Snaterse, M., Ton, R., Kuo, A. D. and Donelan, J. M. (2011). Distinct fast and slow processes contribute to the selection of preferred step frequency during human walking. Journal of Applied Physiology 110 (6): 1682–1690.
- ↑ Malatesta, D., Simar, D., Dauvilliers, Y., Candau, R., Saad, H., Préfaut, C. and Caillaud, C. (2004). Aerobic determinants of the decline in preferred walking speed in healthy, active 65- and 80-year-olds. European Journal of Physiology 447 (6): 915–921.
- ↑ Donelan, J. M., Kram, R. and Kuo, A. D. (2002). Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking. Journal of Experimental Biology 205 (23): 3717–3727.
- ↑ Doke, J., Donelan, J. M. and Kuo, A. D. (2005). Mechanics and energetics of swinging the human leg. Journal of Experimental Biology 208 (3): 439–445.
- ↑ Alexander, R. (1991). Energy-saving mechanisms in walking and running. Journal of Experimental Biology 160 (1): 55–69.
- ↑ Hunter, L. C., Hendrix, E. C. and Dean, J. C. (2010). The cost of walking downhill: Is the preferred gait energetically optimal?. Journal of Biomechanics 43 (10): 1910–1915.
- ↑ Norris, J. A., Granata, K. P., Mitros, M. R., Byrne, E. M. and Marsh, A. P. (2007). Effect of augmented plantarflexion power on preferred walking speed and economy in young and older adults. Gait & Posture 25 (4): 620–627.
- ↑ O’Connor, S. M., Donelan, J. M. (2012). Fast visual prediction and slow optimization of preferred walking speed. Journal of Neurophysiology 107 (9): 2549–59.
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