Walking is the most natural, accessible, and common daily lifestyle activity. Although walking is viewed as a simple skill, it is considered as a complex behavior resulting from a large number of components (e.g., nervous and sensory systems, muscles, bones, joints) interacting to produce a stable pattern (Diedrich and Warren, 1995). While most people can walk at speeds reaching up to about 9 km·h⁻¹, they naturally choose to walk at a typical speed (i.e., around 4.5 km·h⁻¹) known as the preferred walking speed (PWS) (Bohannon and Andrews, 2011). From a dynamic perspective, walking at PWS is a behavior known to possess properties of an attractor, in which any small change in the speed (i.e., control parameter) in either direction would result in loss of stability (Diedrich and Warren, 1995). According to self-optimization theories (Sparrow and Newell, 1998; Alexander, 2002), Humans naturally tend to adopt behaviors that minimize metabolic energy cost and other cost functions (e.g., mechanical, perceptual, cognitive). The relationship between walking speed and metabolic cost of transport has long been described as a U-shaped curve (Ralston, 1958; Zarrugh, Todd and Ralston, 1974; Margaria, 1976), indicating an optimum at PWS. Several studies have attempted to identify determinants of preferred behaviors or preferred intensities. Indeed, walking at PWS has been recognized to optimize not only energy cost, but also substrate utilization (Willis et al., 2005), biomechanical parameters (Saibene and Minetti, 2003), perceived exertion (Willis et al., 2005; Godsiff et al., 2018), cognitive function (Abernethy et al., 2002), and gait stability (Jordan et al., 2007). For instance, Willis et al. (2005) concluded that healthy young adults choose a PWS that minimizes carbohydrate oxidation rate. While there seems to be several competing factors being optimized at PWS (Fernández Menéndez et al., 2019), determinants of the spontaneous adoption of PWS are still a topic of debate.

From an evolutionary perspective, people are instinctively inclined to optimize their physical exertion when there is no need obtain food (Eaton and Eaton, 2003). In today’s modern societies, meeting minimal physical activity recommendations for health requires a cognitive effort (Peters et al., 2002), and a certain level of motivation that becomes even more challenging when exercise is recommended or performed at higher intensities (Perri et al., 2002; Parfitt and Hughes, 2009; Ekkekakis et al., 2011). From a behavioral perspective, exercising at one’s self-selected or preferred intensity may contribute to greater health benefits in the long-term (Ekkekakis et al., 2008; Williams, 2008). Walking is a major activity recommendation for health and disease prevention (Carnethon et al., 2009; Williams et al., 2014), therefore more focus is needed to better understand the multiple acute responses when walking at or around the preferred intensity as a primary mode of exercise, especially in sedentary individuals.

Specific physical activity guidelines or exercise recommendations usually relate to frequency, duration, and intensity (e.g., 40%–59% V ˙ O 2 reserve or RPE of 12–13) that might not be as accessible or as effective for changing behavior in inexperienced populations compared to just “moving” the way they prefer. For example, previous experimental studies have found that low-active, overweight adults volitionally completed more minutes per week of self-paced walking compared to walking regulated using prescribed heart rates (Williams et al., 2015).

Walking parameters have been analyzed at and around PWS, and studies have investigated how changes in walking speed affect gait and other acute responses. However, most studies have used a range of set absolute speeds for all participants (Willis et al., 2005; Fernández Menéndez et al., 2019), or expressed speed as a percentage of PWS (Jordan et al., 2007; Chung and Wang, 2010), or even defined speed as slow, self-selected and fast (Haight et al., 2014). The present study aims to examine metabolic, perceptual, spatiotemporal and gait stability parameters at an absolute range of speeds (3 km·h⁻¹) around each participant’s own PWS. We hypothesized that sedentary young adults choose to walk at speeds that optimize certain cost function (e.g., metabolic cost, perceived exertion, stability), while providing benefits in terms of fuel oxidation (Willis et al., 2005). Evidence of optimization at PWS is expected to be seen following one of two criteria. First, the minimum or maximum of a quadratic relationship between the studied variable and speed should coincide with PWS indicating an optimum. Second, with increasing speeds evidence of a deflection in values of the studied variable should happen at PWS. In addition to examining determinants of PWS, the present study further attempts to understand potential benefits of walking at or around the PWS, rather than at intensities relative to the maximum or reserve heart rate or oxygen consumption.

The present study investigated metabolic, perceptual, spatiotemporal and stability parameters when walking at a 3 km·h⁻¹ range around the individual preferred walking speed (PWS). Main findings indicate that sedentary young adults do not select their PWS based on principles of minimization of metabolic cost (Ralston, 1958; Zarrugh, Todd and Ralston, 1974; Margaria, 1976) or based on perceived exertion (Willis et al., 2005; Fernández Menéndez et al., 2019). Participants preferred to walk at a speed that optimizes fuel oxidation (Willis et al., 2005), mechanical efficiency and gait stability, which confirms in part our hypothesis. Indeed, it seems that several competing factors are optimized at PWS (Fernández Menéndez et al., 2019). However, present results could not confirm the well-established U-shaped relationship between metabolic cost of transport and speed (Ralston, 1958; Zarrugh, Todd and Ralston, 1974; Margaria, 1976), which is in line with a more recent study (Willis et al., 2005). Our results support those demonstrating that PWS does not occur at a minimum energy cost (Godsiff et al., 2018). The absolute speed range considered in the current study (i.e., 0.5 km·h⁻¹ increments from PWS-1.5 to PWS+1.5 km·h⁻¹) differed according to each participant’s own PWS, which was not the case in previous studies examining the relationship between metabolic and perceptual responses to a set absolute speed continuum for all participants (Willis et al., 2005). Although this might make comparisons harder, factors being optimized at PWS are more easily identified when individual PWS is considered.

The average PWS of the present sample was 4.1 (±0.58) km·h⁻¹ which is in accordance with previous studies in Western (Bohannon and Andrews, 2011; Bohannon and Wang, 2019) and Arab populations (Majed et al., 2020; 2022). A first result indicated that walking speed significantly affected all studied metabolic, perceptual, spatiotemporal and stability parameters (Tables 2, 3). Participants were sensitive to the smallest change in speed (i.e., 0.5 km·h⁻¹) around PWS, which was also revealed by significant changes in the subjective perception of effort (i.e., RPE). Increasing speeds around PWS was linked to an increase in relative oxygen consumption, energy expenditure, metabolic cost of transport, heart rate, and RPE (Figure 1). Interestingly, certain parameters presented significant speed-related changes only below PWS or above PWS. For instance, values of mechanical efficiency and fat oxidation rate seem to stabilize close to PWS after initially increasing with speed. Moreover, a clear decline in %fat oxidation and a rise in carbohydrate oxidation rate are seen at speeds above PWS only, indicating a possible shift of substrate contribution to energy production (Willis et al., 2005). These important observations indicating a deflection point at or close to PWS in mechanical efficiency and substrate oxidation could be interpreted as potential determinants of PWS.

Findings indicate that participants choose to walk at a speed that not only optimizes mechanical efficiency, but also minimizes reliance on carbohydrate to potentially avoid fatigue (Willis et al., 2005). However, it remains unclear whether walking at PWS corresponds to the range of maximal rate of fat oxidation (Fat_max). Previous reports have indicated that the maximal rate of fat oxidation is expected to occur at low or moderate intensities around approximately 36%–65% V ˙ O_2max (Achten and Jeukendrup, 2004). In a recent study examining changes in the rate of fat oxidation during walking in sedentary young men (29.3 ± 0.7 years), authors reported maximal fat oxidation rates occurring at an average speed of 4.35 km·h⁻¹ and corresponding to an intensity of 57.2% HR_max (Özdemir et al., 2019). Assuming an age-predicted maximal heart rate (i.e., 220–age) of 197.5 beats·min⁻¹ for the present sample, it is possible that their PWS corresponded closely to predicted intensities (i.e., approximately 53% HR_max). This speculation cannot be confirmed from the present data given that no maximal testing was performed to accurately determine maximal capacity. Fat oxidation could be a desired process for weight loss or maintenance, and walking at PWS could present advantages in that sense. While protocols of 3-min stages have been proven to be sufficient for determining maximal fat oxidation rates (Achten and Jeukendrup, 2004), walking at PWS for longer periods reveals contradictory findings as to the sustainability of maximum fat oxidation state. For example, Özdemir et al. (2019) found that the maximal rate of fat oxidation during walking decreases in the first 16 min of exercise, however, earlier studies have demonstrated an increased contribution of fat to energy production with longer exercise durations (Klein et al., 1994; Ravussin et al., 1986). In an effort to make exercise recommendations for sedentary individuals, duration needs to be considered given that the present study only reports short-term acute responses to walking.

The examination of spatiotemporal and gait stability parameters offers further insights into determinants of PWS. In previous work, PWS was shown to correspond to a typical spatiotemporal organization of gait in which a specific combination of stride frequency (SF) and stride length (SL) is adopted (Saibene and Minetti, 2003). Additionally, SF seems to play an important role in selection of PWS, as the U-shaped curve found between SF and metabolic cost of transport indicates an optimum at the preferred SF in both adults and children (Holt, Hamill and Andres, 1991; Jeng, Liao, Lai and Hou, 1997). Optimal temporal characteristics related to stride frequency were successfully mathematically predicted based on the resonant period of a force-driven harmonic oscillator (Holt, Hamill and Andres, 1990). According to these authors, walking at the preferred SF requires less muscle forces to maintain gait and potentially less mechanical work by body segments. Our results do not show an optimum in SL or SF around PWS per se, as both parameters increased significantly at each speed increment. Nevertheless, it is important to note that the organization of spatiotemporal gait parameters presented significantly higher variability (i.e., coefficient of variation, CoV) at speeds below PWS. Variability in stride characteristics is considered an indirect measure of gait stability (Hamacher et al., 2011; Fischer et al., 2021). This indicates that walking slower than PWS was less stable, and stability increased with speed until PWS where no further changes were seen in most variables. Importantly, CoV of the support phase duration presented as the most appropriate factor in the search of a PWS determinant, as its quadratic relationship with speed indicates an optimum exactly at PWS (Figure 3). As noted by Jordan et al. (2007), walking at PWS presents an enhanced stability and reproducibility compared to walking slower or faster, and thus behaves like a stable attractor. Any change in walking speed from PWS would result in the loss of dynamic stability. The low CoV values found in this study (i.e., approximately 3%) correspond to those found in healthy adults (Beauchet et al., 2009; Fischer et al., 2021) and reflect the general stability of this well-learned and repetitive motor skill. Interestingly, when comparing factors influencing PWS, Fischer et al. (2021) reported significantly higher CoV of contact time (i.e., support phase duration) in patients with lung disease as compared to healthy control group. This brings further support to our finding on the importance of contact time in gait that is directly linked to stability and risk of falls (Fischer et al., 2021).

Gender differences seen in some physiological and metabolic variables (i.e., EE, ME, HR and V ˙ E) were not related to differences in speeds as both male and female groups had similar PWS values. It is safe to assume that anthropometric differences might have affected values that were not normalized to body mass (e.g., EE, V ˙ E). The gender-differences observed in heart rate could also be potentially due to a difference in stroke volume or aerobic capacity ( V ˙ O 2 _max), however this could not be verified in the present study.

In sum, Figure 3 offers a visual summary of findings with the best fit functions of all studied variables and indicates the calculated optimum for quadratic models. Most of the studied variables were best fitted with a quadratic model and the predicted optimum corresponded to PWS or was non-significantly different in some variables that were considered here as potential determinants of PWS. Namely, mechanical efficiency, fat and carbohydrate oxidation and stability parameters met the criteria, and it is believed that participants chose their PWS based on these factors. A strength of the present study was its sample size (N = 34) as compared to other similar studies that examined 11 (Jordan et al., 2007), 12 (Willis et al., 2005), 14 (Godsiff et al., 2018), or 23 participants (Fernández Menéndez et al., 2019). In addition, the simultaneous examination of metabolic, perceptual, spatiotemporal and stability parameters at an absolute speed range around the individual PWS is also innovative as it brings a new perspective to studying the relationship between studied variables and speed. To our knowledge, no previous studies have done that, and the closest studies have examined different speeds presented as a percentage of PWS which makes it difficult to relate to actual magnitude of change in speed when examining relationships with intensity. Moreover, this also makes it harder to recommend a walking exercise on treadmill. Findings allow us to predict for a sedentary young adult an average of 110 kcal spent in a 30-min walk at PWS. The best fit equations (Table 4) could be used to predict a further rise of 11 kcal for a 30-min walk at PWS+0.5 km·h⁻¹ or an additional 43 kcal at PWS+1.5 km·h⁻¹. The proportion of this energy expended coming from fat oxidation can also be predicted using the equations.

One weakness of the present study is that walking was performed on a treadmill rather than overground, and in a controlled laboratory environment. While most studies have used a treadmill to acquire larger number of gait cycles or due to other limitations (e.g., equipment), walking on a treadmill seems to be more stable and less variable (Dingwell et al., 2001). However, both treadmill and overground locomotion do not seem to present mechanical differences (van Ingen Schenau, 1980). Walking in a natural outdoor environment rather than a controlled laboratory one also confers additional psychological benefits that were not accounted for in the present study (Focht, 2009). Indeed, in an outdoor environment and as compared to a laboratory environment, preferred walking speed is expected to be higher and ratings of perceived exertion lower (Focht, 2009). Another weakness of the current study relates to the duration of speed trials that only allow prediction of short-term acute responses to walking. Even though a steady state was reached at each trial (given the rather low-intensity nature of all trials), potential changes in various responses with time are to be investigated in future studies. Furthermore, similar future study designs should include maximal exercise testing to account for maximal aerobic capacity and anaerobic thresholds, which could provide a better understanding of the relative effort and intensity reached at PWS, compared to lower and higher speeds. This would help to obtain insights into whether walking at PWS occurs within general physical activity intensity recommendations. Indeed, de Moura et al. (2011) have verified that most normal weight individuals adopt a PWS that falls into the “moderate” intensity classification and was judged adequate to elicit health benefits according to ACSM recommendations.

In sum, the present findings support benefits of walking at the preferred walking speed for sedentary young adults, as opposed to walking at a slower or faster prescribed intensity. The choice of the preferred walking speed seems to be determined by factors being optimized at that intensity and related to mechanical efficiency, stability of gait and fuel oxidation. Therefore, walking at one’s preferred intensity could be an effective strategy for young sedentary adults to become active. Given the increased fat oxidation at the preferred intensity, walking at PWS could also serve as a safe exercise for weight management or disease prevention. Future studies could focus on examining responses to preferred walking for different portions of healthy and clinical populations. Finally, it is recommended that physical activity guidelines focus less on prescribing intensities and more on encouraging self-determined active behaviors and intensities, not just for their long-term benefits on adherence and overall health, but also for their potential acute benefits even when performed in short bouts.