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Perceived exertion can be lower when exercising in field versus indoors

  • Karin Sofia Elisabeth Olsson,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing

    Affiliation The Research Unit for Movement, Health and Environment, Department of Physical Activity and Health, The Swedish School of Sport and Health Sciences, GIH, Stockholm, Sweden

  • Ruggero Ceci,

    Roles Formal analysis, Investigation, Writing – original draft, Writing – review & editing

    Affiliations The Research Unit for Movement, Health and Environment, Department of Physical Activity and Health, The Swedish School of Sport and Health Sciences, GIH, Stockholm, Sweden, The Unit for Road Safety, Planning Department, Swedish Transport Administration, Solna, Sweden

  • Lina Wahlgren,

    Roles Data curation, Investigation, Project administration, Writing – review & editing

    Affiliation The Research Unit for Movement, Health and Environment, Department of Physical Activity and Health, The Swedish School of Sport and Health Sciences, GIH, Stockholm, Sweden

  • Hans Rosdahl,

    Roles Data curation, Methodology, Validation, Writing – review & editing

    Affiliation The Research Unit for Movement, Health and Environment, Department of Physiology, Nutrition and Biomechanics, The Swedish School of Sport and Health Sciences, GIH, Stockholm, Sweden

  • Peter Schantz

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

    Affiliations The Research Unit for Movement, Health and Environment, Department of Physical Activity and Health, The Swedish School of Sport and Health Sciences, GIH, Stockholm, Sweden, Department of Public Health and Clinical Medicine, Section of Sustainable Health, Umeå University, Umeå, Sweden



Studies indicate that the rated perceived exertion (RPE) during physical exercise can be lower in field environments than indoors. The environmental conditions of those studies are explored. Furthermore, we study if the same phenomenon is valid when cycling indoors versus in cycle commuting environments with high levels of stimuli from both traffic and suburban-urban elements.


Twenty commuter cyclists underwent measurements of heart rate (HR) and oxygen uptake (O2) and RPE assessments for breathing and legs, respectively, while cycling in both laboratory and field conditions. A validated mobile metabolic system was used in the field to measure O2. Three submaximal cycle ergometer workloads in the laboratory were used to establish linear regression equations between RPE and % of HR reserve (%HRR) and %O2max, separately. Based on these equations, RPE from the laboratory was predicted and compared with RPE levels at the participants’ individual cycle commutes at equal intensities. The same approach was used to predict field intensities and for comparisons with corresponding measured intensities at equal RPE levels.


The predicted RPE levels based on the laboratory cycling were significantly higher than the RPE levels in cycle commuting at equal intensities (67% of HRR; 65% of O2max). For breathing, the mean RPE levels were; 14.0–14.2 in the laboratory and 12.6 in the field. The corresponding levels for legs were; 14.0–14.2 and 11.5. The range of predicted field intensities in terms of %HRR and %O2max was 46–56%, which corresponded to median differences of 19–30% compared to the measured intensities in field at equal RPE.


The cycle commuters perceived a lower exertion during their cycle commutes compared to ergometer cycling in a laboratory at equal exercise intensities. This may be due to a higher degree of external stimuli in field, although influences from other possible causes cannot be ruled out.


Assessment of perceived exertion during physical activities was introduced in the research already in the early 1960s by Gunnar Borg, the creator of the scale for rating the perceived exertion (RPE) [1]. That scale is constructed to have a linear relationship with heart rate (HR) at group level in the intensity range between 60–200 beats · min-1 (corresponding to 6–20 in the scale) in aerobic activities involving large muscle groups such as cycling and running. Over the years, the scale has been developed with new verbal expressions and anchors, but the relations to both absolute and relative HR levels remain [2].

In the early work of Gunnar Borg, the concept of an “exertion gestalt” was formulated. It relates to how a multitude of sensations underlying the perception of exertion, e.g. muscle work, breathing, chemical substances in the blood etc., can be integrated to a perceived whole or gestalt [3]. Reviews have later addressed the origin of physiological exertion to explain the sensory cues underlying it, and its role in regulating exercise performance [e.g. 4, 5]. Both reviews argue that it is unclear how any specific sensory cue or single physiological variable can explain the perception of effort or how exertion is rated according to the RPE scale. However, in the literature underlying this assumption it is concluded that; “effort perception involves the integration of multiple afferent signals from a variety of perceptual cues” [4] from the body. On the other hand, there are indications that at least afferent feedback from the working muscles to the brain may not play a role in generating the perception of effort [6]. In line with this, a contrasting theory to the peripheral origin of effort has been developed; it suggests that the perception of exertion might originate from a copy of the central motor command, a so-called “corollary discharge” [cf. 6].

Adding to this complexity, Pennebaker and Lightner [7] suggested that the internal body signals, such as sensations of fatigue and discomfort, is in constant competition with the perception of sensory input from the environment. In their study, participants were asked to produce “comfortable pace” while running an equal distance in two different environments: a cross-country path in a wooded area, and a monotonous lap course on a flat field. The cross-country jogging, which required more focus on the external environment, elicited higher speeds (on average 10%) at comparable levels of fatigue symptoms.

To further these matters, the former RPE research group at Stockholm University, investigated a group of middle-aged men who were instructed to, in a randomized order run, (i) on a treadmill facing a wall without windows in a laboratory, and (ii) on a broad and curved outdoor recreation trail which was predominantly surrounded by greenery, had a plain surface, and was located along a lake. The field setting did not involve any ordinary traffic environment (for illustrations, see S1 Appendix) [8]. In both test conditions, participants produced three running bouts at a self-selected pace corresponding to 11, 13, and 15 on the RPE scale [1]. At the same levels of RPE, considerably higher running speeds were noted outdoors compared to in the laboratory (mean speed: 11.7 km · h-1 vs indoors 7.1 km · h-1, i.e. a 66% difference), and were matched with clearly higher physiological responses (HR and blood lactate). The RPE levels were approximately 2 units lower during running in field, when equal levels of speeds and physiological measures were evaluated. Furthermore, a high reliability regarding these results was obtained between two test sessions, separated by a month, during which the participants exercised on their own according to a prescribed program [8].

Later, Mieras and colleagues [9] determined psychological and physiological responses to laboratory and outdoor cycling with recreationally trained males. In the laboratory setting, an electronically braked cycle trainer ergometer was facing a wall with no windows. The outdoor cycling was “completed along a relatively flat, out, and back course on a paved recreation trail (Keystone Trail, Omaha, NE, USA)” [9]. It follows a creek, with the surroundings being a mix of green and built-up settings for mostly commercial and retail purposes. It did not involve any ordinary traffic environments (for illustrations, see S2 Appendix). Significantly higher levels of power output (about 30%), cycling speed and HR were noted in the outdoor cycling as compared to the laboratory at similar levels of RPE.

Given the three studies mentioned that examine exercise responses in different settings [79], it seems that an environmental effect exists in continuous running and cycling. Thus, it could be hypothesized that various forms of physical activities produce different levels of perceived exertion depending on the environment in which they are performed. Exercising in an environment rich in visual stimulation and other sensory input may be perceived as less strenuous than an environment with poor (low) stimulation such as a laboratory or running on a monotonous field. It is, on the other hand, possible that these findings are coupled to specific effects of green environments with trees, such as reduction of stress [10], or other positive psychological effects [cf. 11]. Another possibility is that there is a connection to different hedonic valuing of a preferred versus a not preferred green setting [7], or a natural versus a, normally less preferred, synthetic setting [cf. 11].

To further understand these issues, this study compares ergometer cycling in a laboratory with commuter cycling in suburban-urban environments. Both these settings are predominantly built environments, but the commuter cycling requires attention to many traffic and urban elements, which is not the case in the laboratory. Our hypothesis is that external stimuli per se are important in masking internal cues from physical exercise, and that will lead to lower RPE levels at given exercise intensities in the suburban-urban environments compared to the laboratory.

For that purpose, 20 cycle commuters performed cycling sessions with measurements of HR and oxygen uptake (O2) as well as assessments of RPE according to Borg’s 6–20 scale [1] in both laboratory and field conditions. The laboratory session included submaximal workloads and a maximal test. In the field condition, the commuters rode their own bikes along their ordinary commuting route at a self-selected intensity, corresponding to their own normal commuting. Based on the laboratory cycling, it was possible to predict RPE levels at equal exercise intensities as in the cycle commuting in field in terms of percentages of the maximal oxygen uptake (%O2max) and heart rate reserve (%HRR). In that way, the RPE levels rated in the field could be compared with the RPE levels predicted from the laboratory cycling. Furthermore, in a similar manner it was possible to predict which intensities that the laboratory exercise corresponded to in the field. A principle scheme of these analytical approaches is shown in Fig 1.


The present study is a part of a greater multidisciplinary research project, Physically Active Commuting in Greater Stockholm (PACS), at the Swedish School of Sport and Health Sciences, GIH, in Stockholm, Sweden. An approval to conduct this study was obtained from the Ethics Committee North of the Karolinska Institute at the Karolinska Hospital (Dnr. 03–637), Stockholm, Sweden.


The recruitment of participants for the entire project (PACS) was initiated in 2004, and included several stages which are described in detail by Stigell and Schantz [12]. The following overall inclusion criteria were used: being at least 20 years old, living in the County of Stockholm (excluding the municipality of Norrtälje) and walking or cycling the whole way, any distance, to one’s work or place of study at least once a year. A questionnaire was used to select participants, including e.g. sex, age and commuting characteristics such as modality, duration and distance. The commuting distances were measured with a criterion method based on routes drawn in maps by each respondent [13].

The present sample was selected from the single mode cyclist category, i.e. those commuters who only cycled and never walked to work. No electrically assisted bicycles were included. Further specific criteria for the present sample was that the participants had ages and commuting distances close to the overall project’s median values of the male and female single mode cyclists [12]. In addition, they would also have rated their daily occupations as light or very light physically. Based on this information, ten male and ten female habitual commuter cyclists, who fulfilled the criteria, were chosen for participation. All had responded to a health declaration and certified themselves healthy for participation (individuals with high blood pressure or on medication that could affect normal HR were excluded). Prior to participation, the commuters also signed a consent of participation after receiving and reading a written information about the study procedures, and their rights as participants. This was in line with standard ethical requirements. When analysing the data, the identity of the individuals coupled to them was not disclosed to the researchers. Characteristics of the selected participants and their commuting behaviours are described in Table 1.

Table 1. Characteristics of the participants and their commuting behaviour (mean ± standard deviation (SD)).

Study design and standardization

The present study included two or three repeated test occasions with submaximal and maximal ergometer cycling in an exercise laboratory with a room temperature of 20°C. Another test occasion took place in the field while each participant performed their normal daily cycle commute. Measurements of HR and O2 as well as assessments of RPE according to Borg’s 6–20 scale [1] were performed in all tests. In accordance with Ekblom and Goldbarg [14], participants were instructed to distinguish between their central perceived exertion for breathing (named breathing) and their local perceived exertion in the leg muscles (named legs). The reason for this division is that RPE for a given oxygen uptake is higher when it is executed by a small versus a large muscle group, whereas the central RPE, referred to as breathing, can be similar [14].

The first test of cycle ergometer exercise in the laboratory was carried out with a purpose of familiarization regarding all aspects of the testing, and the laboratory environment. Thereafter, six participants performed a second test occasion in the laboratory before their field tests, while the 14 remaining participants performed both a second and a third test occasion in the laboratory before their field tests. The reason for this divergence was technical problems with the mobile metabolic system used, which had to be solved and the equipment re-evaluated. This delayed 14 participants’ field tests by 9 to 12 months, and therefore these participants performed an extra third test in the laboratory. Due to the addition of an extra laboratory test in 14 cases, it became possible to control the stability of the RPE assessments over time by comparing laboratory tests 2 and 3. The values collected from all participants’ last test occasion in the laboratory have been used as references and compared against their field tests. The mean time between the reference laboratory tests and the field tests was 13 ± 9 days (mean ± SD). A schematic illustration of the study procedure is shown in Fig 2.

Prior to all test occasions, the participants were instructed to follow the same standard guidelines. These were: 1) not to engage in any vigorous exercise for 24 hours beforehand, 2) not to cycle to the laboratory, 3) to refrain from eating, drinking, smoking and taking snuff for at least one hour before the test, 4) to not eat a large meal within three hours before the test, 5) to avoid stress, and 6) to cancel the test if they had fever, an infection or a cold.


Metabolic systems.

During the laboratory tests, a stationary metabolic system (SMS), Oxycon Pro® (Carefusion GmbH, Hoechberg, Germany) was used in the mixing chamber mode for all metabolic measurements. The software used was JLAB 4.53. In the field environments, a mobile metabolic system (MMS), Oxycon Mobile, version 5.10, (CareFusion GmbH, Hoechberg, Germany) was used while measuring gas exchange variables and ventilation breath by breath. Both metabolic systems were switched on at a minimum of 30 minutes before data collection and calibrated immediately before and after each test using the automated procedures in accordance with the manufacturer’s recommendations. A high precision gas of 15.00% O2 and 6.00% CO2 (accuracy: O2 ± 0.04% and CO2 ± 0.1%; Air Liquid AB, Kungsängen, Sweden) was used for calibrating the gas analyzers in both the SMS and MMS. The calibrations were always performed in the same environmental settings as the two different measurement conditions.

A facemask with non-rebreathing air inlet valves (Combitox, Dräger Safety, Lübeck, Germany) was used in both the laboratory and field conditions. All measured metabolic variables were saved in averages of 15 seconds. Both the SMS and the MMS were carefully controlled as well as validated prior to and during the present study. This included checks with a metabolic calibrator to ensure that the two systems were interchangeable. For all methodological studies, as well as more detailed descriptions of these systems, see Rosdahl and colleagues [15], Salier Eriksson and colleagues [16], and Schantz and colleagues [17].

Heart rate monitor.

In both the laboratory and field conditions, HR was recorded using a Polar Wearlink 31 transmitter (Polar Electro Oy, Kempele, Finland). The HR values were displayed and saved in averages of 15 seconds and stored in the SMS or the MMS. For safety reasons, HR values were also stored in averages of 15 seconds in the Polar Electro S610i HR monitor watch (Polar Electro Oy, Kempele, Finland) during the field tests. However, the HR values were always used from the MMS, except for three periods (1–6.25 minutes), one in each of three participants, when values were missing due to technical problems. In these cases, the HR values from the watch substituted the missing values. To confirm that this was appropriate, all individuals’ watch HR values were compared with the corresponding HR values from the MMS. No significant differences were detected in this comparison.

Cycle ergometer.

A mechanically braked pendulum cycle ergometer (828E Monark Exercise AB, Vansbro, Sweden) was used for performing the cycle exercise in the laboratory. Before each test, the scale was zeroed while each participant sat on the saddle with his or her feet resting on the frame between the pedals. The saddle height was adjusted so that one knee of the participant was slightly bent when the foot was on the pedal in its lowest position. A digital metronome (DM70 Seiko S-Yard Co. Ltd, Tokyo, Japan) was used to instruct the participants to maintain correct cycling cadence. The workload was controlled every minute by checking the cadence and the braking force as indicated on the pendulum position.


Laboratory tests.

All the repeated tests in the laboratory kept the same standard procedures of measurements. Firstly, a few measurements at rest were carried out before the cycle ergometer exercise. This included measurements of body weight and height as well as resting HR. For determining the resting HR, the participants rested quietly in supine position on a treatment table for ten minutes. The values from the last five minutes were averaged and determined as the resting HR.

The submaximal cycle ergometer exercise consisted of three different workloads; 50, 100 and 150 watt (W) for the females, and 100, 150 and 200 W for the males. The cycling cadence was kept to 50 revolutions per minute (rpm) in accordance with Åstrand [18, p. 19]. For each workload, the participants cycled until steady state HR was attained for two consecutive minutes (total work time approximately six minutes), after which the resistance was increased. Steady state HR was defined as the average HR value of one minute, based on the 30 seconds before and the 30 seconds after each full minute, were 2–3 beats · min-1 at two consecutive minutes. The third workload was increased to only 125 W or 175 W for the females and males, respectively, if, after the second workload, the participant’s HR was higher than 150 beats · min-1 and their rated RPE exceeded 15 [1].

Between the second and third workloads, the participants sustained pedalling for one minute at a self-chosen low cadence, and with a low resistance of 5 Newton (N). They were then instructed to resume the cadence of 50 rpm while the investigator gradually increased the resistance until, after one minute, the third workload was achieved (resistance was increased to 50 W during the first 15 seconds, to 100 W the next 15 seconds and successively to the final workload during the last 30 seconds). During all submaximal workloads, the participants sat in an upright position with their hands laying on the handlebars. The RPE assessments for breathing and legs, respectively, took place during the latter part of the final minute of each workload. After this phase of submaximal exercise, and before the maximal exercise, a period of two minutes followed by continued cycling with a self-chosen low cadence at a resistance of 5 N.

The maximal exercise was carried out with a cycling cadence of 80 rpm, as this cadence has been shown to be associated with the longest time to exhaustion at maximal efforts [19]. During the first three minutes, the workloads were set to 60, 100, and 120 or 140 W for each minute. The latter alternatives depended on which third workload the participants had performed during the submaximal exercise. Thus, 120 W was selected if the third level had been 125 W or 175 W for the females and males, respectively, whereas 140 W was used if it had been 150 W or 200 W. After these initial three minutes, the resistance increased by 20 W every minute until voluntary exhaustion occurred and ended the test. The RPE assessments for breathing and legs, respectively, were made immediately after this completion. To ensure that the participants achieved their O2max, at least two of the following criteria were met by each participant: 1) a plateau in O2 despite increasing exercise intensity (defined as a O2 increment of < 150 mL · min-1), 2) a respiratory exchange ratio of ≥ 1.1, and 3) a RPE of ≥ 17 [1, 20, 21]. During all cycle ergometer exercise, the participants looked into a shield with a blue-grey colour without any pattern. Thus, the visual stimulation was at a low level.

Field tests.

The participants pedaled their own bicycles either to or from their workplace choosing themselves which direction and time suited them best. They were instructed to cycle at their ordinary commuting intensities for the entire route. Characteristics of the commuters’ bicycles, such as weight and number of gears, can be found in Schantz and colleagues [22]. The tests were carried out between June and November. 18 of the tests were performed during the morning rush hours, while the remaining two were conducted during the evening rush hours. The cycle commutes took place in the inner urban and suburban areas of Greater Stockholm, Sweden. This mixture of areas meant that the commuters experienced a variety of route environments, in terms of e.g. buildings, greenery, topography and traffic. The study setting for the field tests, in overall terms, is illuminated in S3 Appendix. For a more detailed description of these areas, see Wahlgren and Schantz [23].

The participants were met at their designated start address by one of the investigators who transported the measurement equipment. Prior to the commutes, the MMS was placed in a custom-made backpack on the participants. A GPS was also placed in the backpack to track the road. The starting time of the cycle commutes was synchronized with a second investigator waiting at the final address. When the commuters arrived at their destinations, the total trip time was noted (means were; 29 minutes for men and 23 for women). The participants were then immediately asked to rate RPE for breathing and legs, respectively, of the overall commute. They also stated the amount of stops they made at traffic lights as well as other stops [22]. Levels of ambient conditions in terms of temperature, relative humidity and wind speed for each individual cycle commute were obtained from the website of the Stockholm-Uppsala Air Quality Management Association [24], see Table 2.

Table 2. Ambient conditions during the cycle commuting in field (mean ± SD).

Assessments of the cycle route environments.

The Active Commuting Route Environment Scale (ACRES) was used for assessments of the participants’ perceptions and appraisals of their route environments. For most of the participants, this was done a year before the field tests. ACRES has been characterized by considerable criterion-related validity and reasonable test-retest reproducibility [23, 25]. In this study, two items from ACRES were used for overall assessments of the routes cycled to work. The first was whether the route environments inhibit or stimulate the cycling. This item was formulated as follows: “Do you think that, on the whole, the environment you cycle in stimulates/inhibits your commuting?”. The second item was whether the route environment was experienced as unsafe or safe for reasons of traffic. This was formulated as follows: “How unsafe/safe do you feel in traffic as a cyclist along your route?”. The items’ response scales ranged between 1 and 15, with adjectival opposites labelled as “inhibits a lot” and “stimulates a lot”, respectively, “very unsafe” and “very safe”. Along the scale, the position number 8 represented a neutral position labelled as neither nor e.g. inhibiting or stimulating. The questionnaire instructions included a drawn map that separated the areas into; inner urban and suburban–rural areas [25]. Participants were asked to differentiate their commuting experiences between these two areas, see the ratings in Table 3.

Table 3. Route environment ratings in the inner urban and suburban–rural areas of Greater Stockholm, Sweden (mean ± SD).

Analytical approach

The individual levels of RPE, HR and O2, obtained from the reference laboratory test, have been applied as a basis for the comparisons with the rated RPE values in field. Moreover, analyses of the stability of the RPE assessments over time in laboratory conditions have also been conducted by comparing individual predictions from test 2 and test 3 (based on those 14 participants who performed an extra third laboratory test) (see Fig 2).

At first, paired HR and O2 values from the last two consecutive minutes at steady state in each of the three submaximal cycle ergometer workloads, at laboratory tests 2 and 3, were averaged for each individual and used for further analyses. The maximal values from the reference laboratory test were also used and determined by averaging the highest consecutive paired values of HR and O2 during one minute. These maximal values, as well as the resting HR values from the reference laboratory test, were used in all calculations of individual exercise intensities in terms of %HRmax, %HRR and %O2max. These relative intensities were used to describe the three submaximal workloads at the reference laboratory test as well as the mean levels of exercise intensities during the cycle commutes in field.

For the comparisons of RPE predictions between test 2 and test 3, the absolute HR and O2 values from the three submaximal cycle ergometer workloads in these tests were calculated into exercise intensities in terms of %HRR and %O2max. These relative intensities were used given possible interindividual differences in training status and maximal HR levels [cf. 2]. Linear regression equations were calculated based on these three intensity levels, and their corresponding RPE levels for breathing and legs, respectively. Thus, this led to a total of eight regression equations for each participant: %HRR-RPE breathing, %HRR-RPE legs, %O2max-RPE breathing, and %O2max-RPE legs, for test 2 and test 3, respectively. Thereafter, the three individual levels of exercise intensities for %HRR and %O2max, respectively, from the reference laboratory test were used in their corresponding regression equation to predict RPE values for each test occasion. Thus, the same levels of intensities were used in the regression equations for both test 2 and test 3. The individual levels of absolute RPE differences between test 2 and test 3 were calculated for all comparisons.

Regarding the laboratory and field comparisons, the RPE levels and exercise intensities for %HRR and %O2max, respectively, from the three submaximal cycle ergometer workloads at the reference laboratory test were used to establish four linear regression equations for each individual: %HRR-RPE breathing, %HRR-RPE legs, %O2max-RPE breathing, and %O2max-RPE legs. The individual mean exercise intensities of the cycle commutes were then used in the corresponding regression equations to predict RPE values representing the overall cycle commutes in field. The absolute differences between the predicted and rated RPE were calculated for all individuals’ comparisons. In the same way, the average relative commuting intensities for each individual were predicted by inserting the corresponding rated RPE values of the overall cycle commutes, into each of the four above-mentioned regression equations. The individual absolute and relative differences between the measured and predicted commuting intensities were calculated.

Statistical analyses

The Statistical Package for the Social Sciences (SPSS, 27.0, Chicago, IL, USA) was used to perform the statistical analyses. Illustrations were created with GraphPad Prism® 8.0 software package (GraphPad Software Inc., San Diego, CA, USA). An alpha level of 0.05 was used to determine statistical significance. In cases when the same rated RPE or measured intensity levels in the field were compared twice with different predictions of RPE or intensity, a Bonferroni correction for multiple adjustments was applied to reduce the Type I error probability [26, p. 377]. To keep the chosen alpha level (0.05) consistent across all comparisons, the P-values obtained were multiplied by two instead of lowering the alpha value. Values are reported as mean ± SD, unless otherwise stated.

The normality of distribution of all absolute and relative differences were evaluated with the Shapiro-Wilk test. Because the absolute differences between the predicted and rated RPE levels in the field were not normally distributed in two of four cases, all of these differences were analysed using both the parametric one-sample T-test and the non-parametric one-sample Wilcoxon signed rank test. However, since the two different tests generated similar significance levels, only the P-values from the one-sample T-test are reported. In the cases with absolute and relative differences between measured and predicted commuting intensities, the differences were not normally distributed in five of eight cases. Therefore, the one-sample Wilcoxon signed rank test was used in all these cases. The relative intensity differences were expressed as median values, instead of means, due to the occurrence of outliers. Confidence intervals (CI) of 95% were calculated for all predicted and rated RPE levels, as well as for the measured and predicted commuting intensities. Linear regressions were used to predict RPE values.


Exercise characteristics in the laboratory

RPE and exercise intensities, in terms of absolute and relative levels of HR and O2, from the submaximal and maximal cycle ergometer exercise at the reference laboratory test, are given in Table 4. For the males, the three submaximal work rates induced mean levels of RPE ranging between 10.3–14.2 (breathing) and 10.2–14.3 (legs). The corresponding levels for the females varied between 8.9–14.6 (breathing) and 9.1–14.8 (legs) (Table 4).

Table 4. RPE, HR and O2 during submaximal and maximal cycle ergometer exercise in the laboratory (mean ± SD).

Stability controls of RPE levels in the laboratory

Stability controls of RPE over time are presented in Table 5 (n = 14). Comparisons are made between cycle ergometer test 2 and test 3 for predicted RPE levels based on the regression equations for %HRR-RPE breathing, %HRR-RPE legs, %O2max-RPE breathing, and %O2max-RPE leg. No significant RPE differences were found. The range of all absolute differences was -0.7 to 0.2 RPE units (Table 5).

Table 5. Comparisons of predicted RPE levels between test 2 and test 3 in the laboratory for males and females together (n = 14) (mean ± SD and (95% CI)).

Exercise characteristics in the field

Descriptive characteristics of the cycle commutes, such as duration, distance, speed, and cycling environment are reported in Table 6 for males and females, respectively. The overall RPE levels as well as the average exercise intensities, in terms of both absolute and relative HR and O2 levels, of these cycle commutes are given in Table 7. For the males, the average RPE was 12.8 for breathing and 11.5 for legs. The corresponding levels for the females were 12.4 (breathing) and 11.5 (legs) (Table 7).

Table 6. Characteristics of the cycle commuting in field (mean ± SD).

Table 7. RPE, HR and O2 of the overall cycle commuting in field (mean ± SD).

Comparisons of RPE levels between laboratory and field conditions

All participants’ levels of RPE and exercise intensities of the three submaximal cycle ergometer work rates at the reference laboratory test (cf. Table 4), as well as the corresponding levels from the cycle commutes in field (cf. Table 7), are illustrated in Fig 3.

Fig 3. Levels of RPE and exercise intensities of the three submaximal cycle ergometer work rates at the reference laboratory test as well as the corresponding levels from the cycle commutes in field.

Based on all participants’ individual values (n = 20), and presented as mean ± SD. A) RPE breathing and %HRR, B) RPE legs and %HRR, C) RPE breathing and %O2max, and D) RPE legs and %O2max.

Comparisons of the predicted RPE levels in field, based on the cycle ergometer exercise at the reference laboratory test, and the rated RPE values of the overall cycle commutes in field are presented in Table 8 for all participants. In all comparisons, the laboratory based predicted RPE levels were significantly higher than the RPE rated in field. Based on linear regression equations established between %HRR and RPE, the absolute mean differences were: 1.6 RPE units (P < 0.01) for breathing and 2.7 (P < 0.001) for legs. The corresponding RPE predictions based on %O2max yielded mean differences of: 1.4 RPE units (P < 0.05) for breathing and 2.5 (P < 0.001) for legs (Table 8).

Table 8. Comparisons between laboratory based predicted and rated RPE levels for the overall cycle commuting in field for all participants (n = 20) (mean ± SD and (95% CI)).

Comparisons of intensity levels between laboratory and field conditions

Comparisons of the measured relative intensities in the field in terms of %O2max and %HRR and the corresponding predicted intensities, based on the relationships between intensities and RPE levels obtained during the cycle ergometer exercise at the reference laboratory test, are presented in Table 9 for all participants. In both absolute and relative terms, all comparisons between the measured and predicted intensities, yielded significantly higher measured intensities compared to the predicted levels. For instance, the median values of all relative differences ranged between 19 and 30% (range of P-values: < 0.001–0.025).

Table 9. Comparisons between laboratory based predicted and measured average intensity levels for cycle commuting in field for all participants (n = 20) (mean ± SD and (95% CI)).


To our knowledge, no other study has compared cycle exercise induced RPE levels in a laboratory setting versus a cycling commuting route environment at equal exercise intensities in terms of %HRR and %O2max. The cycle exercise in the laboratory was performed on an ergometer cycle, whereas the cycling in field was undertaken using each participant’s own bicycle. The main findings were that the predicted RPE levels from the ergometer cycling in the laboratory (means: 14.0–14.2) were significantly higher in all cases compared to the rated RPE levels in the field (means: 11.5–12.6) at the same measured intensities in field. These overestimates amounted to averages of 1.4–1.6 RPE units for breathing, and 2.5–2.7 RPE units for legs. When instead comparing the predicted field intensities with the corresponding measured field intensities at equal RPE levels, it was found that the measured intensities were significantly higher in all cases. This resulted in relative differences, expressed as medians, ranging between 19 and 30%.

These results are in line with three previous studies undertaken in different field conditions [79]. To facilitate an overview of all four studies, relevant data has been compiled in S4 Appendix. Overall, these studies indicate that exercise performed in environments with high levels of visual stimuli is perceived as less exerting compared to exercising in environments with lower levels of stimulation, such as in a laboratory or running on a monotonous field. One explanation could be that the visual stimuli act to some extent through a passive fascination. Alternatively, the stimuli from the external environment during commuter cycling could demand a focused attention to analyze the traffic conditions. Thus, although the cause for the findings cannot be determined, our hypothesis that external stimuli per se are important for masking internal cues from physical exercise is supported by the lower RPE levels when cycling in the suburban-urban environments compared to the laboratory.

The two conditions compared in the present study are, however, different in both methodological and environmental aspects. Therefore, potential moderating factors of the RPE levels need to be scrutinized in relation to the present results.

Potential moderators of RPE

The submaximal cycle ergometer exercise in the laboratory included three constant workloads. During these, the participants were instructed to cycle with a fixed cadence of 50 rpm for approximately 6 minutes per workload. This contrasts with the cycling commuting in field, during which the participants pedalled their own bikes along their ordinary routes at a self-selected intensity and cadence, corresponding to their own normal commuting.

Cycling cadence.

With regard to the cycling cadence, even if this variable was not monitored in the field, it can be assumed that varying cadences have been used in the different measurement conditions. Most likely, the participants preferred to use an average higher cadence in the field compared to the fixed level of 50 rpm in the laboratory [27]. Hansen and colleagues [27] studied a range of cadences around 60–90 rpm at two different cycle ergometer workloads covering our average commuting intensity (65% of O2max; Table 7). Based on their results, the relationship between RPE and O2 does not appear to be affected by using different cadences. This is supported by Kounalakis and colleagues [28], who found no significant differences in RPE or HR at the same absolute O2 values when comparing cadences of 40 and 80 rpm during 90 minutes of cycle ergometer exercise. On the contrary, an early study [29] noted that a cycling cadence of 40 rpm compared to 60 and 80 rpm generated higher RPE levels despite no differences in O2. These findings were most evident at the higher workloads (corresponding to approximately 45 and 64% of O2max). Given this discrepancy in findings, it cannot be ruled out that a use of varying cadences has contributed to the present deviations in RPE between the laboratory and field conditions.

Interestingly, numerically higher RPE mean differences between the two measurement conditions were noted in the rating for legs (2.5–2.7 RPE units) compared to breathing (1.4–1.6 RPE units). Possibly, these different results may be due to that varying cadences have been used. According to the two-factor categorization of perceived exertion [14], it is likely that the participants perceived that the local muscular strain in their legs affected them more differently, than their central breathing exertion did, when using varying cadences. Nevertheless, the fact that RPE for breathing differs significantly between the two conditions indicates that there may be more moderating factors affecting the present RPE results than the cycling cadence.

Exercise duration.

The commuting durations were about 29 and 23 minutes for males and females, respectively (Table 6). These are slightly longer time periods than the total exercise duration for the submaximal cycle ergometer exercise in the laboratory (about 18 minutes). Due to these time variations, it is possible that the present RPE differences between the laboratory and the field environment have been underestimated, as RPE has been shown to rise with increasing exercise duration of ergometer cycling and treadmill walking at work rates corresponding to 60% of O2max [30, 31].

This presumed underestimation of RPE difference between the two conditions, is further supported by data from 1661 commuter cyclists of both sexes, and with varying ages. Multiple regression analyses showed that Borg’s 6–20 RPE scale increased with 0.58 RPE units per 10 minutes at a given cycling speed when taking speed, duration, age and sex into account (Peter Schantz, personal communication 2022-03-22). Furthermore, given that longer exercise durations are related to higher cycling speeds in the above mentioned large sample of cyclists [32], it can be anticipated that they who cycle longer durations have higher O2max. Thereby, they can sustain a given cycling speed at a lower % of O2max, and at a lower RPE as well. Thus, it is possible that the value from the multiple regression analyses stated above represents an additional form of underestimation of the duration effect on the present RPE results.

Constant versus variable exercise.

Given the naturalistic setting of the field tests in suburban-urban commuting environments, including varying topography and different traffic situations such as queuing, turning and red lights, etc., it is clear that the field cycling involved distinctly more variable and intermittent exercise compared to the constant submaximal cycle ergometer workloads in the laboratory. The question is whether this affects the relation between RPE and the overall workload responses in terms of %HRR and %O2max. Based on previous studies, it appears that the overall workload during a given exercise duration, regardless of whether the intensity levels are constant or varying intermittently, is the important determinant of RPE in this respect [33, 34]. In such a case, the RPE responses in the laboratory and field conditions are comparable at equal mean exercise intensities. However, more studies of these issues are warranted.

Instructions and protocols.

The fact that the participants self-regulated their commuting cycling in field (regarding e.g. levels of cycling gears, cadence, and exercise intensity) versus that they followed a protocol in the laboratory, is also a methodological difference possibly affecting RPE. This is because a high degree of autonomy during high-intensity interval training as well as during submaximal exercise sessions has been associated with significantly lower RPE levels compared to conventional and prescribed exercise protocols [35, 36]. Thus, it is possible that also the present results can be due to differences in exercise autonomy.

Moreover, the participants rated RPE during the last minute of each of the submaximal workloads in the laboratory, whereas in the field setting, they made their RPE assessments for the total commute immediately thereafter. Since RPE assessments for longer exercise durations have been shown to reflect the exertion rate close to the end of the exercise rather than the total exercise period [37], the present relations between RPE and exercise intensities in the field may be questioned. Given this, we have compared mean values of %HRR and %O2max, respectively, between the entire cycle commutes and the last 5-min periods for all participants in this study (see S5 Appendix). These analyses were based on a data set where the transition periods at both the start and the end of the cycle commutes have been excluded, i.e. the transitions from resting level to exercise intensity as well as from exercise intensity back to resting level. The comparisons showed that there were no differences in terms of % of O2max between the entire cycle commutes and the last 5-min periods. On the other hand, % of HRR was slightly lower for the entire commutes compared to the last 5-min periods (averages: 68.9% versus 71.5%; P-value = 0.003; S5 Appendix). Thus, in line with Kilpatrick and colleagues [37], the present RPE ratings in the field may correspond to a somewhat higher mean level of %HRR than the one stated in this study. However, this would have led to an increased difference between the laboratory based predicted RPE and the rated RPE in the field, and therefore, it cannot explain the present results.

Environmental aspects.

The individual cycle commuting routes were in the inner urban and/or suburban areas of Greater Stockholm. Thereby, the participants experienced a variety of visual and auditory stimuli from the environment in terms of e.g. buildings, greenery, topography, traffic [cf. 23], and weather conditions. According to the ratings with the ACRES [25], the participants found that these environments ranged on average between 9.1–11.6 units in terms of inhibits or stimulates cycling (1 inhibits a lot; 8 being neutral; 15 stimulates a lot) (Table 3). These somewhat stimulating route environments should be seen in relation to the simple and standardized exercise laboratory environment, without the possibility of stimuli from outside. Thus, it can be concluded that the cycling commutes, compared to the laboratory exercise, had; (1) an overall higher level of external stimuli e.g. through traffic, (2) more natural components, including e.g. greenery and possibly water, and (3) more varying ambient conditions. So, how could these environmental differences have affected the present RPE results?

Already Pennebaker and Lightner [7] noted that symptoms of fatigue during exercise can be coupled to the degree of external stimuli. In their first experiment, they found that subjects who heard distracting street sounds via headphones during treadmill exercise reported less fatigue symptoms than subjects hearing an amplification of their own breathing. These findings were confirmed by their second experiment, in which subjects jogged a similar distance in two different environments, a cross-country path, and a monotonous lap course on a field. Although the cross-country jogging required more focus on the external environment than the lap course, higher speeds were demonstrated at comparable levels of fatigue symptoms. The findings of the present study can be related to Pennebaker and Lightner [7] in various ways. First of all, their effect of auditory stimuli can be related to the traffic situations that exposed the cycle commuters to varying ambient sounds. Traffic noise has been shown to adversely affect both cyclists and pedestrians [3840], and Miedema [41] has shown that such forms of auditory impact can create high levels of annoyance, which might influence the perception of exertion.

Furthermore, the traffic situations in the present study demanded certain levels of visual and integrative attention. These distractions may have diminished the responsiveness to the internal sensations from the physical exercise, and thus led to a lower perceived exertion in the field compared to the laboratory environment [cf. 7].

Similarly, results by Ceci and Hassmén [8] and Mieras and colleagues [9] indicate that an increased level of external stimuli can reduce the perceived exertion, compared to less stimuli, at equal exercise intensities (cf. S4 Appendix). Both studies compared physiological and psychological responses to exercise in two different environments, a laboratory setting (with low external stimuli) and a field setting (with high external stimuli). The field trials in Ceci and Hassmén [8] took place in a green and blue recreational area, and the cycling in Mieras and colleagues [9] (cf. S2 Appendix) took place on a trail along a creek that in itself was a green and blue setting, while the overall framing setting for the trail was a mixture of primarily built up and green settings. Therefore, it is possible that the nature per se had an impact on the reduction of perceived exertion. The effect of soft fascination from nature and greenery on human wellbeing and restoration points in this direction [cf. 10, 11, 42]. In the present RPE assessments in field, the effect of greenery cannot, however, be isolated from the other commuting route environments, making it difficult to evaluate any potential effect of greenery on RPE. Therefore, further studies are needed to investigate this matter.

It is no doubt that the study by Ceci and Hassmén [8] involve the most green and beautiful external environment of the three studies using RPE measurements (cf. S1S4 Appendices). At the same time, it is the study showing the greatest difference in physical performance. Whereas Mieras and colleagues [9] had 30% higher power outputs in field, the present study showed 19–30% higher relative levels of %HRR and %O2max at given RPE levels, the average running speed Ceci and Hassmén [8] was 66% higher outdoors compared to indoors at given RPE levels (cf. S4 Appendix). This difference stimulates to a hypothesis that greenery and aesthetics can possibly have a specific and greater role in this context, a matter that deserves future studies.

Another potential cause for the present higher RPE levels in the laboratory compared to the field setting could be due to that the participants experienced the cycling commuting somewhat unsafe, which demanded directed attention. The participants’ ACRES ratings for unsafety-safety traffic of the overall route environments ranged on average between 5.6–10.9 (1 very unsafe; 8 neutral; 15 very safe) (Table 3). Consequently, a traffic-related stress could have caused an elevated HR relative to the workload and O2 demand. This in turn could have led to the rated RPE levels in the field being compared with the laboratory based predicted RPE levels at a too high %HRR intensity. Contradictory to this, however, the relationships between %O2max and RPE also showed significant RPE differences between the two conditions. Moreover, this theory is further refuted by the fact that relationships between HR and O2 established during ergometer cycling in the laboratory have been shown to be valid for estimating intensity spectrums of O2 based on HR measurements during cycle commuting in field for the same group of participants as in the current study [43].

Finally, the environmental differences in ambient conditions are also worth mentioning. In this respect, the average temperature during the cycle commuting was 10 and 12°C for males and females (Table 2), respectively, while in the laboratory it was kept around 20°C. Regarding this rather narrow temperature range, there is, to our knowledge, no study that clearly points in the direction of the present RPE results. On the contrary, Maw and colleagues [44] showed that when cool (8°C) and natural (24°C) temperature conditions were compared during constant ergometer cycling at the same absolute HR level corresponding to the currently used cycle commuting average (around 136 beats · min-1; Table 7), the mean RPE difference between the two conditions was less than 0.5 RPE units.

Applications and external validity

It can be beneficial from a health-promoting perspective that physical activities performed with the same exercise intensity are perceived as less strenuous outdoors than indoors. This perceived lower degree of exertion may lead to less physically active and sedentary individuals becoming more active and gain health effects.

At the same time, the findings in this study should be considered when prescribing exercise in a medical context. This is because classifications of exercise intensity by e.g. the American College of Sports Medicine (ACSM) [45] indicate that the currently used average commuting intensity (about 65% of O2max; Table 7) corresponds to the lower part of the vigorous exercise intensity domain. According to the ACSM classification, this would correspond to an RPE level of approximately 14, which agrees very well with the laboratory based predicted RPE levels in this study (Table 8). However, the present rated RPE levels in the field (11.5–12.6; Table 8) fall between light to moderate exercise intensities according to this classification [45]. Supporting these findings on cycle commuting is a recent study of walking commuting [46] in which correspondingly lower RPE values were noted during field walking compared to what would be expected based on the relations between RPE and levels of %HRR and %O2max stated by ACSM [cf. 45]. Given this, an RPE instructing exercise prescription that has been based on a relationship between perceived exertion and exercise intensity indoors risks leading to an elevated intensity level when applied outdoors. This risk should be especially considered when exercise is instructed for individuals with impaired health, e.g. due to heart disease [cf. 8].

With both lower and higher exercise intensities than applied in this study, the balance between internal and external cues may very well differ. It is, for example, reasonable to assume that increasing the relative exercise intensities might lead to that the internal cues gradually dominate more and more over the external cues. Thereby the masking effect of external cues might decrease to a point where there are no effects left by them. This is supported by the findings of Ceci and Hassmén [8], in which the relative differences in running speeds decreased with higher RPE levels used to produce the running speeds (see S4 Appendix). Therefore, we do not believe that the present RPE differences observed between the different environmental conditions might be the same at both lower and higher exercise intensities. Indeed, we suggest that this issue is further studied.

Strengths and limitations

It is a strength that the participants in this study were selected to be representative of a larger group of active commuters [12], in terms of sex, age, commuting mode, and distance. Thus, the present results should be considered valid in other habitual middle-aged commuters who cycle in inner urban and suburban areas. Whether the same applies to other groups of individuals such as young adults, the elderly, and athletes needs further investigation.

The present results are strengthened by the fact that the RPE assessments in the laboratory were demonstrated to be stable over time. In analyses of the 14 participants who performed an extra, third, cycle ergometer test in the laboratory, no significant differences were noted in any of the RPE comparisons with the second laboratory test (range of all mean differences: -0.7 -0.2 RPE units) (Table 5). This is in line with the high reliability previously observed for RPE assessments in both laboratory and field conditions [8].

It is a strength that both “central” (breathing) and “local” (leg muscles) RPE were assessed. The fact that similar results were noted in these two variables strengthen the findings and indicates that a general phenomenon may underlie the differences in RPE between ergometer cycling in a laboratory and commuter cycling in urban and suburban commuting environments.

Valid and well controlled measurement equipment was used in both the laboratory and field conditions [1517]. This enabled that predicted RPE levels based on linear regression equations from ergometer cycling data in a laboratory could, for the first time, be compared with ratings of RPE from field cycling at equal exercise intensities. Simultaneously as this comparison is valuable, the fact that it makes use of two different forms of RPE assessments (ratings and predictions) is not optimal. Initially, a generalization is made when linear regressions are created between RPE and the average exercise intensities (%HRR and %O2max) from the three cycle ergometer workloads. Subsequently, when these regression equations are used to predict the laboratory based RPE levels, several potential moderating factors may have been incorporated into the comparisons with the rated RPE values in the field.

The fact that neither cycling cadence nor power output was measured during the cycle commutes in the field is a limitation. These variables had made it easier to understand the physical work performed in the field and its relation to the ergometer cycling in the laboratory.

Additionally, the interpretation of results is limited by the fact that the two conditions used different test protocols and instructions. For instance, it would have been more optimal for this study to first perform the cycle commuting test in field and then a test with continuous cycling in the laboratory with the same exercise duration and average relative intensity as during the field cycling.


The present study extends the previous research demonstrating that perceived exertion induced from aerobic exercise can be related to the degree of external stimuli from the environment. In this case, it was illuminated by cycle commuters who assessed significantly higher RPE when performing ergometer cycling in the laboratory versus cycling commuting in suburban-urban environments at equal exercise intensities. Although several moderating factors (such as cycling cadence and exercise character) may have influenced the results, they are in line with previous studies pointing to external cues being modifiers of RPE. The findings prompt further studies of these matters.

Supporting information

S1 Appendix. Environmental description of the study by Ceci & Hassmén 1991.


S2 Appendix. Environmental description of the study by Mieras et al. 2014.


S3 Appendix. Environmental description of the present study by Olsson et al. 2024.


S4 Appendix. A comparison of four studies related to physical activity, perceived exertion and environment.


S5 Appendix. Comparisons of average exercise intensities between the entire cycle commutes versus the last 5-minute periods of the cycle commutes.


S1 Checklist. PLOS ONE clinical studies checklist.



The authors are grateful to the volunteers for participating in the study, and for the technical assistance of Phoung Pihlträd, Jane Salier Eriksson, Cecilia Schantz-Eyre, Per Brink, Golam Sajid, Eva Minten and Erik Stigell. Drs James Pennebaker, Ruggero Ceci, Peter Hassmén and Dustin Slivka are thanked for sharing information about both the laboratory and environmental settings used in their studies. Landscape architect Dennis Bryers at Omaha Parks, Recreation & Public Property Department, Omaha, Nebraska, USA, is thanked for his generous assistance in sharing information and photos of Keystone Trail. Michael Schonlau, GIS Administrator for the Douglas County GIS Department, Nebraska, USA, and Hans-Olov Andersson, at The Land Survey, Gävle, Sweden, are thanked for assistance with aerial photos. Dr Magnus Strömgren is thanked for creating and letting us use a map. Finally, we express our gratitude to the reviewers for valuable comments.


  1. 1. Borg G. Perceived exertion as an indicator of somatic stress. Scand J Rehabil Med. 1970;2(2):92–8. pmid:5523831
  2. 2. Borg G. Borg’s perceived exertion and pain scales. Champaign, IL: Human Kinetics Publishers; 1998.
  3. 3. Borg G. Physical Performance and Perceived Exertion. Lund: CWK Gleerup; 1962.
  4. 4. Hampson DB, St Clair Gibson A, Lambert MI, Noakes TD. The influence of sensory cues on the perception of exertion during exercise and central regulation of exercise performance. Sports Med. 2001;31(13):935–52. pmid:11708402
  5. 5. Mihevic PM. Sensory cues for perceived exertion: a review. Med Sci Sports Exerc. 1981;13(3):150–63. pmid:7019620
  6. 6. Bergevin M, Steele J, Payen de la Garanderie M, Feral-Basin C, Marcora SM, Rainville P, et al. Pharmacological Blockade of Muscle Afferents and Perception of Effort: A Systematic Review with Meta-analysis. Sports Med. 2023;53(2):415–35. pmid:36318384
  7. 7. Pennebaker JW, Lightner JM. Competition of internal and external information in an exercise setting. J Pers Soc Psychol. 1980;39(1):165–74. pmid:7411392
  8. 8. Ceci R, Hassmén P. Self-monitored exercise at three different RPE intensities in treadmill vs field running. Med Sci Sports Exerc. 1991;23(6):732–8. pmid:1886482
  9. 9. Mieras ME, Heesch MW, Slivka DR. Physiological and psychological responses to outdoor vs. laboratory cycling. J Strength Cond Res. 2014;28(8):2324–9. pmid:24476776
  10. 10. Ulrich RS, Simons RF, Losito BD, Fiorito E, Miles MA, Zelson M. Stress recovery during exposure to natural and urban environments. Journal of Environmental Psychology. 1991;11(3):201–30.
  11. 11. Ulrich RS. View through a window may influence recovery from surgery. Science. 1984;224(4647):420–1. pmid:6143402
  12. 12. Stigell E, Schantz P. Active Commuting Behaviors in a Nordic Metropolitan Setting in Relation to Modality, Gender, and Health Recommendations. Int J Environ Res Public Health. 2015;12(12):15626–48. pmid:26690193
  13. 13. Schantz P, Stigell E. A criterion method for measuring route distance in physically active commuting. Med Sci Sports Exerc. 2009;41(2):472–8. pmid:19151593
  14. 14. Ekblom B, Goldbarg AN. The influence of physical training and other factors on the subjective rating of perceived exertion. Acta Physiol Scand. 1971;83(3):399–406. pmid:5134177
  15. 15. Rosdahl H, Gullstrand L, Salier-Eriksson J, Johansson P, Schantz P. Evaluation of the Oxycon Mobile metabolic system against the Douglas bag method. Eur J Appl Physiol. 2010;109(2):159–71. pmid:20043228
  16. 16. Salier Eriksson J, Rosdahl H, Schantz P. Validity of the Oxycon Mobile metabolic system under field measuring conditions. Eur J Appl Physiol. 2012;112(1):345–55. pmid:21559947
  17. 17. Schantz P, Salier Eriksson J, Rosdahl H. An overview, description and synthesis of methodological issues in studying oxygen consumption during walking and cycling commuting using a portable metabolic system (Oxycon Mobile). 2018. Appendix I in: Jane Salier Eriksson. The heart rate method for estimating oxygen uptake in walking and cycle commuting. Evaluations based on reproducibility and validity studies of the heart rate method and a portable metabolic system. Doctoral Thesis 13: The Swedish School of Sport and Health Sciences, GIH, Stockholm, Sweden. Available from:
  18. 18. Åstrand P-O. Experimental studies of physical working capacity in relation to sex and age. Doctoral Thesis. Copenhagen: Ejnar Munksgaard; 1952. Available from:
  19. 19. Foss O, Hallén J. The most economical cadence increases with increasing workload. Eur J Appl Physiol. 2004;92(4–5):443–51. pmid:15232702
  20. 20. Howley ET, Bassett DR Jr., Welch HG. Criteria for maximal oxygen uptake: review and commentary. Med Sci Sports Exerc. 1995;27(9):1292–301. pmid:8531628
  21. 21. Midgley AW, McNaughton LR, Polman R, Marchant D. Criteria for determination of maximal oxygen uptake: a brief critique and recommendations for future research. Sports Med. 2007;37(12):1019–28. pmid:18027991
  22. 22. Schantz P, Salier Eriksson J, Rosdahl H. Perspectives on Exercise Intensity, Volume and Energy Expenditure in Habitual Cycle Commuting. Frontiers in Sports and Active Living. 2020;2(65). pmid:33345056
  23. 23. Wahlgren L, Schantz P. Bikeability and methodological issues using the active commuting route environment scale (ACRES) in a metropolitan setting. BMC Medical Research Methodology. 2011;11(1):6. pmid:21241470
  24. 24. Stockholm-Uppsala Air Quality Management Association (In Swedish: Stockholm och Uppsala Läns Luftvårdsförbund). SLB-analys. Stockholm, Sweden 2009 [Cited 2009 June 16]. Available from:
  25. 25. Wahlgren L, Stigell E, Schantz P. The Active Commuting Route Environment Scale (ACRES): Development and Evaluation. International Journal of Behavioral Nutrition and Physical Activity. 2010;7(58). pmid:20609250
  26. 26. Howell DC. Statistical Methods for Psychology. 7 ed. Belmont CA: Wadsworth; 2010.
  27. 27. Hansen EA, Andersen JL, Nielsen JS, Sjøgaard G. Muscle fibre type, efficiency, and mechanical optima affect freely chosen pedal rate during cycling. Acta Physiol Scand. 2002;176(3):185–94. pmid:12392498
  28. 28. Kounalakis SN, Keramidas ME, Nassis GP, Geladas ND. The role of muscle pump in the development of cardiovascular drift. Eur J Appl Physiol. 2008;103(1):99–107. pmid:18176813
  29. 29. Pandolf KB, Noble BJ. The effect of pedalling speed and resistance changes on perceived exertion for equivalent power outputs on the bicycle ergometer. Medicine and science in sports. 1973;5(2):132–6. pmid:4721008
  30. 30. Wingo JE, Salaga LJ, Newlin MK, Cureton KJ. Cardiovascular Drift and O2max During Cycling and Walking in a Temperate Environment. Aviat Space Environ Med. 2012;83(7):660–6.
  31. 31. Kounalakis SN, Geladas ND. Cardiovascular drift and cerebral and muscle tissue oxygenation during prolonged cycling at different pedalling cadences. Appl Physiol Nutr Metab. 2012;37(3):407–17. pmid:22509808
  32. 32. Schantz P. Distance, Duration, and Velocity in Cycle Commuting: Analyses of Relations and Determinants of Velocity. Int J Environ Res Public Health. 2017;14(10). pmid:28974051
  33. 33. Kang J, Chaloupka EC, Mastrangelo MA, Hoffman JR, Ratamess NA, O’Connor E. Metabolic and Perceptual Responses during Spinning Cycle Exercise. Med Sci Sports Exerc. 2005;37(5):853–9. pmid:15870641
  34. 34. Utter A, Nieman D, Dumke C, McAnulty S, Kang J, McAnulty L. Ratings of Perceived Exertion during Intermittent and Continuous Exercise. Perceptual and Motor Skills. 2007;104:1079–87. pmid:17879641
  35. 35. Hamlyn-Williams CC, Freeman P, Parfitt G. Acute affective responses to prescribed and self-selected exercise sessions in adolescent girls: an observational study. BMC Sports Sci Med Rehabil. 2014;6:35. pmid:25285215
  36. 36. Mastrofini GF, Collins RP, Rosado AP, Tauran RC, Fleming AR, Kilpatrick MW. The impact of variation and autonomy on psychological responses to high intensity interval training exercise. Psychology of Sport and Exercise. 2022;60:102142.
  37. 37. Kilpatrick MW, Robertson RJ, Powers JM, Mears JL, Ferrer NF. Comparisons of RPE before, during, and after self-regulated aerobic exercise. Med Sci Sports Exerc. 2009;41(3):682–7. pmid:19204580
  38. 38. Wahlgren L, Schantz P. Exploring bikeability in a suburban metropolitan area using the Active Commuting Route Environment Scale (ACRES). Int J Environ Res Public Health. 2014;11(8):8276–300. pmid:25153462
  39. 39. Andersson D, Wahlgren L, Olsson KSE, Schantz P. Pedestrians’ Perceptions of Motorized Traffic Variables in Relation to Appraisals of Urban Route Environments. Int J Environ Res Public Health. 2023;20(4).
  40. 40. Andersson D, Wahlgren L, Schantz P. Pedestrians’ perceptions of route environments in relation to deterring or facilitating walking. Front Public Health. 2023;10:1012222. pmid:37346457
  41. 41. Miedema H. Adverse effects of traffic noise. In: Gärling T, Steg L, editors. Threats from car traffic to the quality of urban life: problems, causes, and solutions. Amsterdam: Elsevier; 2007. p. 53–77.
  42. 42. Kaplan R, Kaplan S. The experience of nature: a psychological perspective. Cambridge: Cambridge Univ. Pr.; 1989.
  43. 43. Salier Eriksson J, Olsson KSE, Rosdahl H, Schantz P. Heart Rate Methods Can Be Valid for Estimating Intensity Spectrums of Oxygen Uptake in Field Exercise. Front Physiol. 2021;12:687566. pmid:34295264
  44. 44. Maw GJ, Boutcher SH, Taylor NA. Ratings of perceived exertion and affect in hot and cool environments. Eur J Appl Physiol Occup Physiol. 1993;67(2):174–9. pmid:8223525
  45. 45. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee IM, et al. American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc. 2011;43(7):1334–59. pmid:21694556
  46. 46. Schantz P, Olsson KSE, Salier Eriksson J, Rosdahl H. Perspectives on exercise intensity, volume, step characteristics and health outcomes in walking for transport. Front Public Health. 2022;10:911863. pmid:36339183