Article

Energy Expenditure Comparison Between Walking and Running in Average Fitness Individuals

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Abstract

Increased energy expenditure (EE) is a key component in maintaining a healthy body mass. Walking and running are 2 common aerobic activities that increase EE above resting values. The purpose of this study was to compare the EE of individuals with average fitness during a walk and run for 1600 meters at 86 m·min(-1) and 160 m·min(-1), respectively. In addition, EE after the walk and run was compared. Fifteen females and 15 males (21.90 ± 2.52 y; 168.89 ± 11.20 cm; 71.01 ± 17.30 kg; 41.51 ± 6.31 ml(-1)·kg(-1)·min(-1)) volunteered to participate. Each participant completed a VO2max test. In addition, oxygen consumption was measured at rest for 10 minutes before exercise, during the walk and run, and after the walk and run for 30 minutes of recovery. EE during exercise was 372.54 ± 78.16 kilojoules for the walk and 471.03 ± 100.67 kilojoules for the run. Total EE including excess postexercise EE was 463.34 ± 80.38 kilojoules and 664.00 ± 149.66 kilojoules for the walk and run, respectively. Postexercise EE returned to resting values 10 minutes after the walk and 15 minutes after the run. Walking and running are both acceptable activities that increase EE above rest and can be performed without the expense of a health club membership and meet adequate kilojoule expenditure according to American College of Sports Medicine guidelines.

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... Running is any gait with a flight phase, in which the hip goes down and then up when one leg is in contact with the ground, as if the body bounces on a springy leg [9]. These common activities in which most people are able to participate without expensive equipment, the expense of a health club or gym membership, can increase energy expenditure (EE) above rest [35]. Establishing levels of EE for walking and running in people would assist fitness professionals in the design of PA programs based on walking and running. ...
... The findings of most studies examining EE of walking versus running suggest that during running individuals expend a greater number of calories than walking the same distance [3,10,25,35], while a few other studies concluded that EE yielded similar absolute caloric values during walking or running at preferred pace [16]. Furthermore, running results in higher EE than walking the same distance regardless of gender, and men have higher EE than women regardless of the exercise mode [25]. ...
... However, when EE data are expressed relative to mass or fat free mass (FFM), no differences are usually noted [10,15,16]. In addition, individuals with a greater body mass (BM) expend more energy during walking or running, regardless of gender and body composition [16,25,35]. ...
Article
This study aimed to determine the net energy expenditure (EENET) required for overground walking and running 1200 m in a sample of healthy adolescent boys and girls. A secondary purpose was to describe the effect of body composition on energy expenditure (EE) of walking versus running. Twenty healthy adolescents (9 boys, 11 girls) aged 15.85 ± 2.80 years performed 2 field tests in regular outdoor conditions: overground walking (1.64 ± 0.17 m/s) and submaximal running (3.13 ± 0.42 m/s), at a self-selected steady pace. EE was measured via indirect calorimetry. Paired sample t-tests were used to determine if there were differences between walking and running conditions and mean percentage differences were estimated for various physiological parameters. Differences in EENET between conditions were performed for both genders using a two (condition) by two (gender) analysis of variance repeated measures design, with fat free mass as a covariate. Speed increased by 90.43% between the 2 conditions, while the different components of EE increased by almost 20%. Running elicited a significantly greater EENET than walking for both genders; however, boys’ and girls’ EE did not differ significantly. When EENET was adjusted for fat free mass, there was a statistically significant condition × fat free mass effect. The findings in this study indicate that both adolescent boys and girls expend more energy during running than walking, without being affected by body composition. Body mass and fat free mass significantly correlated with EE only during running. In addition, the trained participants of the study optimized locomotion to minimize EE.
... Several studies have reported consistent findings in EE during running and walking exercises (3,6,7,9,12). Running results in a higher EE than walking the same distance regardless of gender, and men have a higher EE than women regardless of the exercise mode. ...
... The purpose of this study was to determine the effect of body composition on the EE of walking versus running 1-mile in men and women. In agreement with previous research (3)(4)(5)7,9,12), our data confirms that running 1-mile results in a higher EE than walking for both men (+36.5 kcal) and women (+24.9 kcal). ...
... Loftin and colleagues (9) reported mean 1-mile walk EE values of 103.1 kcal in 11 normal weight men and 81.1 kcal in 8 normal weight women. In addition, similar mean 1-mile walking EE values in men and women of 88.6 ± 13.9 kcal and 81.3 ± 4.2 kcal have also been previously reported in the literature, when converted from kJ (12). ...
Article
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The purpose of this study was to examine the impact of body composition on energy expenditure (EE) of 164 young adults during a 1-mile walk and a 1-mile run on a treadmill. Segmental bioimpedance was used to measure body composition variables. The EE in men (108.3 ± 17.6 kcal) was greater than (P < 0.05) women (80.3 ± 10.6 kcal) during the 1-mile walk, and the difference increased in magnitude during the 1-mile run (144.9 ± 23.2 kcal vs. 105.1 ± 14.9 kcal, respectively). When EE was expressed per unit of body mass, men and women were similar. However, women had a higher EE per unit of fat-free mass (FFM). Regardless of gender, running 1-mile resulted in a greater EE than walking 1-mile. In addition, men expended more absolute calories than women due to a higher body mass. When EE was examined relative to FFM, women were found to be less economical than men, which was most likely due to carrying larger amounts of inactive adipose tissue.
... With respect to exercise intensity, there is some evidence that walking may be as effective as running for reducing cardiometabolic risk [5] at least when adjusting for energy expenditure (i.e., work). However, running is much more time efficient due to its higher physiological and biomechanical intensity [6]. This aspect is substantial, since lack of time was consistently reported as one of the central reasons for inactivity in Germany [7]. ...
... (5) With respect to exercise (intensity) compliance the authors did not analyze all the heart rate watches after the session but randomly selected 15-20 subjects per session (i.e., 50%) for this procedure. (6) In order to adequately focus on the effect of exercise intensity, unlike most other studies with healthy untrained persons, comparable work load for both groups was prescribed. However, the authors are aware that time effectiveness is an important benefit of HIIT protocols that largely account for its attractiveness [48]. ...
Article
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Aerobic exercise positively impacts cardiometabolic risk factors and diseases; however, the most effective exercise training strategies have yet to be identified. To determine the effect of high intensity (interval) training (HI(I)T) versus moderate intensity continuous exercise (MICE) training on cardiometabolic risk factors and cardiorespiratory fitness we conducted a 16-week crossover RCT with partial blinding. Eighty-one healthy untrained middle-aged males were randomly assigned to two study arms: (1) a HI(I)T-group and (2) a sedentary control/MICE-group that started their MICE protocol after their control status. HI(I)T focused on interval training (90 sec to 12 min >85-97.5% HRmax) intermitted by active recovery (1-3 min at 65-70% HRmax), while MICE consisted of continuous running at 65-75% HRmax. Both exercise groups progressively performed 2-4 running sessions/week of 35 to 90 min/session; however, protocols were adjusted to attain similar total work (i.e., isocaloric conditions). With respect to cardiometabolic risk factors and cardiorespiratory fitness both exercise groups demonstrated similar significant positive effects on MetS-Z-Score (HI(I)T: -2.06 ± 1.31, P = .001 versus MICE: -1.60 ± 1.77, P = .001) and (relative) VO2max (HI(I)T: 15.6 ± 9.3%, P = .001 versus MICE: 10.6 ± 9.6%, P = .001) compared with the sedentary control group. In conclusion, both exercise programs were comparably effective for improving cardiometabolic indices and cardiorespiratory fitness in untrained middle-aged males.
... This is because the pedestrian is then likely to overregulate their stride speed in comparison to overground walking (Dingwell & Cusumano, 2015) and be prevented from exercising fully adaptive stepping behaviour (Bocian, Burn, Macdonald, & Brownjohn, 2017). Furthermore, since the pedestrian is not moving relative to the environment, optic flow and motion parallax are in this case usually not preserved, while both are known to be used by walkers to stabilise their posture (Bardy, Warren, & Kay, 1996) and control spatial and temporal gait parameters (Wilkin, Cheryl, & Haddock, 2012), including speed (Patla, 1997). ...
... For US tests, the lowest variability is found at the range of walking speeds between 1.3 and 1.4 m/s, which corresponds to the average speeds observed for pedestrians walking on shopping floors and footbridges (Pachi & Ji, 2005). The human gait at this range of walking speeds is associated with minimum energy expenditure, increased stability and minimum control demands (Wilkin et al., 2012). ...
Article
Walking is one of the fundamental forms of human gross motor activity in which spatiotemporal movement coordination can occur. While considerable body of evidence already exists on pedestrian movement coordination while walking in pairs, little is known about gait control while walking in more complex topological arrangements. To this end, this study provides some of the first evidence of spontaneous gait synchronisation while walking in a group. Nine subjects covered the total distance of 40 km at different speeds while assembled in a 3-by-3 formation. Two experimental protocols were applied in which the subjects were either not specifically asked to or specifically asked to synchronise their gait. To obtain results representative from the point of view of gait control, the movement coordination was quantified using the indirectly measured vertical component of ground reaction force, based on output from a network of wireless motion monitors. Bivariate phase difference analysis was conducted using wavelet transform, synchronisation strength measures derived from Shannon entropy, and circular statistics. A fundamental relationship describing the influence of the group walking speed on individuals’ pacing frequency was established, showing a positive correlation different from that previously reported for walking in solitude. A positive correlation was found between the average synchronisation strength within a group and group’s walking speed. The most persistent coordination patterns were identified for pedestrians walking front-to-back and side-by-side. Overall, the spontaneous gait synchronisation while walking in a group is relatively weak, well below the levels reported for walking in pairs.
... Previous studies comparing the energy expenditure in walking or running the same distance (4,9,11,12,19,22) have reported that running involves greater energy expenditure than walking. However, to our knowledge, no studies have compared the acute cardiopulmonary responses of walking and running the same distance at different speeds. ...
... Energy expenditure was higher while running than while during walking. This is a finding in keeping with other studies (4,9,11,12,19,22). Therefore, running 2 miles is more effective than walking 2 miles for improving body composition. ...
Article
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Walking below 6 km/h and running above 8 km/h are efficient effort intensities to maintain the economy of energy expenditure in the mechanical work. However, in the intermediate range (between the transition of the walking to running), the mechanical work against the energy expenditure still requires analysis. So, the purpose of the present study was to compare the energy expenditure between walking and running in the load work immediately below of the inversion point in the caloric expenditure. Ten young male subjects participated of the study (24.2 ± 2.04 years; 180.7 ± 3.8 cm; 79.5 ± 8.6 kg). The transition speed was determined by two cardiopulmonary sub-maximum tests, a walking and a running test, with the starting load of 5 Km/h being followed by an increase of 0.5 km/h per minute until the limit of 9 Km/h. The transition load was defined comparing the energy expenditure between the two forms of movement execution. Following this structure, the volunteers made two cardiopulmonary sub-maximum tests, a walking and a running, with the load immediately below the point of inversion in the energy expenditure (7.4 ± 0.32 km/h) during 30 minutes. The normality of the results was evaluated by the Shapiro-Wilk test, followed by the Test t student to the related samples. Values of caloric expenditure on walking (293.5 ±47.6 kcal) and running (309.4 ±23.7 kcal) are not different (P> 0.05). In conclusion, the data obtained, showed that independently of the locomotion way (walking or running) the energy expenditure was similar.
... It is important to determine the energy expenditure during the activities like walking and running to develop appropriate exercise prescriptions and to manage a healthy body weight. 12 In former studies, the REE and walking energy expenditure have been studied between the overweight/obese and normal individuals, and evaluated by using the different normalization methods. [13][14][15] However; we could not find any study in which the normalization data was used to compare the REE and walking energy expenditure of individuals at PWS and higher speeds with different body mass index (BMI) like underweight, normal, overweight and obese groups. ...
Article
ABS TRACT Objective: The energy expenditure can be measured either during resting condition or performing a particular type of a physical activity. The purpose of this study is to evaluate the resting and walking energy expenditure at preferred walking speed (PWS) with different body mass index (BMI) and to determine the effect of normal-ization techniques to these data. Material and Methods: Four groups are formed as underweight, normal, overweight, and obese according to BMI of individuals. A total of 64 healthy young adults with no known gait disabilities were recruited. The gross resting energy expenditure (REE) was measured with indirect calorimeter method for 30 min and walking energy expenditure was measured during subjects' walk in their PWS on treadmill for 7 min. Results: The gross REE was significantly higher in obese subjects compared to underweight and normal subjects (p<0.0001). When REE was normalized to body weight, it was higher in underweight and normal groups than overweight and obese groups (p<0.0001). However, when REE was normalized to fat-free mass, it did not differ significantly between groups. The gross walking energy expenditure in PWS was higher in obese and overweight groups than underweight and normal groups (p<0.0001). Conclusion: In order to eliminate fat-mass effect on REE of obese individuals, REE normalized to fat-free mass should be used to acquire more accurate results. On the other hand, the fat-mass increment raises energy requirement while walking to retain the body balance. Thus, gross walking energy expenditure should be taken into consideration for the evaluating energy expenditure of walking. ÖZET Amaç: Enerji tüketimi, hem dinlenim hem de belli bir fiziksel aktivite sırasında ölçülebilmektedir. Bu çalışmanın amacı farklı beden kitle indeksi (BKİ)'ne sahip bireylerin dinlenim ve tercih edilen yü-rüme hızı (TEYH)'ndaki yürüme enerji tüketimlerini değerlendirmek ve normalizasyon yöntemlerinin bu veriler üzerindeki etkisini belirle-mektir. Gereç ve Yöntemler: Bireylerin BKİ'sine göre zayıf, normal, vücut ağırlığı fazla ve obez olmak üzere 4 grup oluşturulmuştur. Yü-rüme bozukluğu olmayan 64 sağlıklı genç birey çalışmaya dahil edil-miştir. Brüt dinlenim enerji tüketimi indirekt kalorimetre yöntemiyle 30 dakika ve yürüme enerji tüketimi bireylerin TEYH'de 7 dakika bo-yunca ölçülmüştür. Bulgular: Brüt dinlenim enerji tüketimi zayıf ve normal bireylerle kıyaslandığında obez bireylerde anlamlı olarak daha yüksektir (p<0,0001). Dinlenim enerji tüketimi vücut ağırlığı ile normalize edildiğinde zayıf ve normal gruplarda vücut ağırlığı fazla ve obez gruplara göre daha yüksek bulunmuştur (p<0,0001). Ancak din-lenim enerji tüketimi yağsız kütle ile normalize edildiğinde gruplar ara-sında anlamlı bir fark olmamıştır. TEYH'deki brüt yürüme enerji tüketimi vücut ağırlığı fazla ve obez gruplarda zayıf ve normal gruplara göre yüksek bulunmuştur (p<0,0001). Sonuç: Obez bireylerde dinlenim enerji tüketimi üzerinde yağ kütlesinin etkisini elimine etmek için din-lenim enerji tüketiminin yağsız kütle ile normalizasyonu daha doğru sonuçların elde edilmesini sağlayabilir. Diğer yandan, yağ kütlesinin artışı yürüme sırasında vücut dengesini korumak için enerji gereksini-mini artırmaktadır. Bu nedenle yürüme enerji tüketiminin değerlendi-rilmesinde brüt yürüme enerji tüketimi göz önünde bulundurulabilir. Anah tar Ke li me ler: Enerji tüketimi; vücut kompozisyonu; obezite
... Step count: Walking and running are two of the most common physical activities in daily life that consume energy [48]. In 2009, a study reported a positive relationship between the HR recovery level and the number of steps [49]. ...
Preprint
The technological advancement in wireless health monitoring through the direct contact of the skin allows the development of light-weight wrist-worn wearable devices to be equipped with different sensors such as photoplethysmography (PPG) sensors. However, the motion artifact (MA) is possible to occur during daily activities. In this study, we attempted to perform a post-calibration of the heart rate (HR) estimation during the three possible states of average daily activity (resting, \textcolor{red}{laying down}, and intense treadmill activity states) in 29 participants (130 minutes/person) on four popular wearable devices: Fitbit Charge HR, Apple Watch Series 4, TicWatch Pro, and Empatica E4. In comparison to the standard measurement (HR$_\text{ECG}$), HR provided by Fitbit Charge HR (HR$_\text{Fitbit}$) yielded the highest error of $3.26 \pm 0.34$ bpm in resting, $2.33 \pm 0.23$ bpm in \textcolor{red}{laying down}, $9.53 \pm 1.47$ bpm in intense treadmill activity states, and $5.02 \pm 0.64$ bpm in all states combined among the four chosen devices. Following our improving HR estimation model with rolling windows as feature (HR$_\text{R}$), the mean absolute error (MAE) was significantly reduced by $33.44\%$ in resting, $15.88\%$ in \textcolor{red}{laying down}, $9.55\%$ in intense treadmill activity states, and $18.73\%$ in all states combined. This demonstrates the feasibility of our proposed methods in order to correct and provide HR monitoring post-calibrated with high accuracy, raising further awareness of individual fitness in the daily application.
... Exercise Protocol. All subjects performed 30 minutes of rest in the dorsal decubitus position, before the initiation of each HIIT protocol, to guarantee a total steady state (37). In each test, a standard warm-up was performed for 3 minutes and consisted of running at 10% below of ventilatory threshold (VT 1 ), with subsequent passive resting of 2 minutes before the start of each HIIT protocol. ...
Article
Full-text available
Germano, MD, Sindorf, MAG, Crisp, AH, Braz, TV, Brigatto, FA, Nunes, AG, Verlengia, R, Moreno, MA, Aoki, MS, and Lopes, CR. Effect of different recoveries during HIIT sessions on metabolic and cardiorespiratory responses and sprint performance in healthy men. J Strength Cond Res XX(X): 000-000, 2019- The purpose of this study was to investigate how the type (passive and active) and duration (short and long) recovery between maximum sprints affect blood lactate concentration, O 2 consumed, the time spent at high percentages of V ̇ O 2 max, and performance. Participants were randomly assigned to 4 experimental sessions of high-intensity interval training exercise. Each session was performed with a type and duration of the recovery (short passive recovery-2 minutes, long passive recovery [LPR-8 minutes], short active recovery-2 minutes, and long active recovery [LAR-8 minutes]). There were no significant differences in blood lactate concentration between any of the recoveries during the exercise period (p > 0.05). The LAR presented a significantly lower blood lactate value during the postexercise period compared with LPR (p < 0.01). The LPR showed a higher O 2 volume consumed in detriment to the active protocols (p < 0.001). There were no significant differences in time spent at all percentages of V ̇ O 2 max between any of the recovery protocols (p > 0.05). The passive recoveries showed a significantly higher effort time compared with the active recoveries (p < 0.001). Different recovery does not affect blood lactate concentration during exercise. All the recoveries permitted reaching and time spent at high percentages of V ̇ O 2 max. Therefore, all the recoveries may be efficient to generate disturbances in the cardiorespiratory system.
... Step count: Walking and running are two of the most common physical activities in daily life that consume energy [49]. In 2009, a study reported a positive relationship between the HR recovery level and the number of steps [50]. ...
Preprint
Full-text available
The technological advancement in wireless health monitoring allows the development of light-weight wrist-worn wearable devices to be equipped with different sensors. Although the equipped photoplethysmography (PPG) sensors can measure the changes in the blood volume directly through the contact with skin, the motion artifact (MA) is possible to occur during an intense exercise. In this study, we propose a method of heart rate (HR)estimation and MA correction during the three possible states of average daily activity (resting, sleeping, and intense treadmill activity states) in 29 participants (130 minutes/person) on four popular wearable devices: Fitbit Charge HR, Apple Watch Series 4, TicWatch Pro, and E4. In comparison to the HR (HR_PPG) provided by Fitbit Charge HR with the highest error of 3.26 +/- 0.34% in resting, 2.33 +/- 0.23% in sleeping, 9.53 +/- 1.47% in intense treadmill activity states, and 5.02 +/- 0.64% in all states combined, our improving HR estimation model with rolling windows as feature reduced the MAE to 2.16 +/- 0.18% in resting, 1.97 +/- 0.23% in sleeping, 8.60 +/- 1.03% in intense treadmill activity states, and 4.03 +/- 0.30% in all states combined. Furthermore, three other HR estimation models, namely TROIKA, WPFV, and modified CorNET, using a dataset of 22participants (5 minutes/person) with our proposed rolling windows as feature were tested and found to display higher accuracy and lower the error. This demonstrates the feasibility of our proposed methods in order to correct and provide HR monitoring with high accuracy, raising further awareness of individual fitness in the daily application.
... To encourage people to exercise, physical activity aimed at increasing energy expenditure and improving health should be as diverse as possible. For this purpose, physiological response and energy expenditure during various physical activities such as walking or jogging [10], [26], cycling and rowing [11], exercise to music [6], playing active video games [22], and Nordic walking [13], and many others, were evaluated. One of the new products which can be used to increase physical activity and energy expenditure is the Torqway vehicle (Torqway, Poland), powered by the upper limbs. ...
Article
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Purpose: One of the new products which can be used to increase physical activity and energy expenditure is the Torqway vehicle, powered by the upper limbs. The aim of this study was to 1) assess the usefulness and repeatability of the Torqway vehicle for physical exercise, 2) compare energy expenditure and physiological responses during walking on a treadmill and during physical effort while moving on the Torqway at a constant speed. Methods: The participants (11 men, aged 20.2±1.3) performed the incremental test and submaximal exercises (walking on the treadmill and moving on the Torqway vehicle at the same speed). Results: Energy expenditure during the exercise on the Torqway was significantly higher (p=0.001) than during the walking performed at the same speed. The intensity of the exercise performed on the Torqway expressed as %VO2max and %HRmax was significantly (p<0.001) higher than during walking (respectively: 35.0±6.0 vs. 29.4±7.4% VO2max and 65.1±7.3 vs. 47.2±7.4% HRmax). Conclusions: Exercise on the Torqway vehicle allows for the intensification of the exercise at a low movement speed, comparable to walking. Moving on the Torqway vehicle could be an effective alternative activity for physical fitness and exercise rehabilitation programs.
... It is important to determine the energy expenditure during the activities like walking and running to develop appropriate exercise prescriptions and to manage a healthy body weight. 12 In former studies, the REE and walking energy expenditure have been studied between the overweight/obese and normal individuals, and evaluated by using the different normalization methods. [13][14][15] However; we could not find any study in which the normalization data was used to compare the REE and walking energy expenditure of individuals at PWS and higher speeds with different body mass index (BMI) like underweight, normal, overweight and obese groups. ...
... Participants with a BMI less than 18.5 were more likely to sleep 9 hours or more. Being physically active [32,33] or being over- weight or obese (Ravussin et al., 1982;Leibel et al., 1995;Javed et al., 2010) is known to increase metabolic rate. Experimental studies of sleep restriction and energy ex- penditure have shown that in both animals and humans, partial sleep deprivation increases energy expenditure [34][35][36]. ...
Article
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This study aimed to investigate associations between: (a) psychological distress, self-perceived health status and sleep duration among a large representative general population sample; and (b) patterns of sleep duration, physical activity and Body Mass Index among a subgroup of participants who self-reported as being in good health with low psychological distress. Data collected from six waves of the Nation Health Interview Surveys (NHISs) was employed. The results indicated that both psychological distress and self-perceived health status were strong predictors of sleep duration. Participants with high serious psychological distress scores reported sleeping 7 - 8 hours less often than those in low or moderate psychological distress and were also most likely to sleep for less than 6 hours or 9 or more hours. Similar patterns were observed for sleep duration by self-reported health status. Subgroup analysis including only participants in self-reported excellent or very good physical health with low mental distress scores showed that participants who engaged in higher frequencies of vigorous and strengthening exercises were more likely to sleep less than six hours, and participants with a BMI of 25 or higher were also more likely to sleep less than six hours.
... The requirements included healthy subjects between 18 and 50 years old, with no illness, chronical diseases, injures or spine operation in the last 6 months. It was asked not to ingest alcohol or caffeine 24 hours before the test [40], [41]. The tests took place in the "Prevention and Performance Lab" of TUM. ...
Article
For several years, the detection of gait has been popularly implemented using wearable sensors, especially in the sports and medical areas. They are unobtrusive devices which allow to monitor individuals without the need of any ambulatory technology. Despite the fact, the optimal location of the sensor remains uncertain and dependent on the type of measurement. Ear-worn sensors provide a tactical position, robust against movement, that might be significant for gait classification. The purpose of this paper is to demonstrate the accuracy and reliability of in-ear accelerometer sensor to perform gait classification, between the activities walking and running. The data was collected from fourteen participants using an in-ear sensor called ‘Cosinussº One’, which contains a three-dimensional accelerometer sensor. The main characteristics between these two activities were detected using 17 time domain features, as for instance the maximums and standard deviations of the 3-axes, and 3 different window sizes were evaluated: 3.75s, 2s and 1s. Support vector machine (SVM) and k-nearest neighbors (KNN) classifiers were implemented and later compared. The total number of features was reduced to 6 for SVM and 12 for KNN preserving the same results. An accuracy over 99% for both classifiers was achieved, outperforming most of the previous studies.
Article
Knowledge of measured energy expenditure (EE) during walking and running is important for exercise prescription. Further, research on the EE comparison and EE predicted equation during walking or running among different ethnicities is limited. The purpose of the current study was to compare EE to walk or run a mile in Caucasian, African American and Asian adults and to develop a regression equation to predict EE to walk or run a mile. Two hundred and twenty-four participants were included (71 Caucasians, 68 African Americans and 85 Asians) with three groups (normal weight walking, overweight walking and running). EE was measured via indirect calorimetry. Analysis of variance was used to compare EE across groups. Multiple regression analysis was employed for EE prediction, and the prediction equation was cross-validated. A significant EE difference was found between walking and running among three ethnicities. The prediction equation was: EE = 0.978 Body Weight – 4.571 Gender (male = 1; female = 2) + 3.524 Ethnicities (Caucasians = 1, African Americans = 2, Asians = 3) + 32.447 (standard error of estimate = 12.5 kcal·mile⁻¹). The equation was valid through cross-validation, so it is recommended to apply for calculating EE during walking or running one mile among Caucasians, African Americans and Asians.
Article
Circuit resistance training (CT) constitutes a high-intensity interval program commonly used to target weight loss; however, the loads and exercise patterns that maximize energy expenditure (EE) remain undetermined. We examined differences in EE among CT protocols using varying loads and contraction speeds in recreationally-trained males and females. Seven males (21.1 ± 0.5y) and eight females (20.0 ± 0.9y) performed three randomized CT protocols incorporating three circuits using heavy-load (80%1RM) explosive (HLEC), heavy-load, controlled (2s) (HLCC), and moderate-load (50%1RM) explosive contractions (MLEC). Expired air was collected continuously before, during, and after exercise. Blood lactate was collected at rest, immediately post-exercise, and five min post-exercise. No significant differences were detected for resting EE; however, there was a significant difference among conditions during exercise (p=.034, ηp2=.229). Post-hoc analysis revealed that MLEC produced significantly higher EE than HLCC, but not HLEC (p=.023). There was a significant difference among conditions for rate of EE during exercise (p=.003, ηp2=.361). Post-hoc analysis revealed that HLEC produced a significantly higher EE rate than HLCC (p=.012) or MLEC (p=.001). A condition x sex interaction was seen for blood lactate changes (ηp2=.249; p=.024). Females produced significantly greater change for MLEC than HLEC (p=.011), while males showed no significant differences. Our results favor CT using MLEC for a higher EE during a full workout; however, the EErate was highest when using HLEC.
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The aim of this study was to compare the acute cardiopulmonary responses of men to walking and running the same distance at different speeds on a treadmill. Sixteen trained young men participated. All the volunteers underwent one maximal and two submaximal cardiopulmonary exercise tests. The submaximal tests covered a distance of two miles each, one walking at 3.0 miles-h-1 for 40 min and the other 6.0 miles-h-1 running for 20 min. The following variables were higher while running: oxygen uptake, carbon dioxide output, heart rate, oxygen pulse, pulmonary ventilation, and energy expenditure. No significant differences were found between running and walking for ventilatory equivalents for oxygen or carbon dioxide. The percentage of oxygen uptake reserve was 58.9 ± 5.2% for running and 18.5 ± 2.1% for walking. The findings indicate that running 2 miles is more efficient than walking for improving cardiorespiratory fitness in trained young men because running is associated with greater cardiopulmonary responses.
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Background: Walking and running are main human locomotor activities during daily living, and well known to strongly predict health impairment and mortality. Hence, the main aim of this study was to assess the ability of a commercial and a custom made software for determining number of steps and step frequency during walking and running with an accelerometer in a semi-standardized setting. Methods: 20 subjects (6 males and 14 females) equipped with the Actigraph GT3X+ tri-axial accelerometer at the thigh and the hip carried out a protocol of three walking speeds and three running speeds. The validity of the accelerometer ability to count steps and estimate step frequency was determined by comparing data from ActiLife 5 and custom made software (Acti4) with observations from video recordings from the different activity speeds. Results: No significant differences in number of steps or step frequencies were found between the video observations and Acti4 measures in any walking and running speeds. The ActiLife 5 software recorded a significantly lower number of steps and step frequencies compared to the video observations in the three walking speeds and in the fastest running speed. Pearson’s correlations and Bland-Altman plots indicated large to very large correlations and a high degree of agreement between the video observations and both the custom made Acti4 software and commercially available ActiLife software at all speeds of walking and running. Conclusion: The custom made Acti4 software showed valid for estimating steps and step frequency at slow, moderate and fast speeds of walking and running. Combined with the ability to detect activity type, the Acti4 software provides a valid objective method for measurements of number of steps and step frequencies.
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