Effect of Traffic Separation and Nature Integration on Affect and Cerebral Oxygenation During Active Transport: An Immersive Virtual Reality and Multi-Study Design

Effect of Traffic Separation and Nature Integration on Affect and Cerebral Oxygenation During Active Transport: An Immersive Virtual Reality and Multi-Study Design – Article ID #1090 PCI Registered Reports – 20 November 2025 Dear Dr Fillon, Please find attached the revised version of our manuscript titled: “Effect of Traffic Separation and Nature Integration on Affect and Cerebral Oxygenation During Active Transport: An Immersive Virtual Reality and Multi-Study Design” for Stage 1 submission consideration in PCI Registered Reports. Please note that, according to the reviewers’ comments, the title has been changed from "Effect of Urban Design and Natural Environment on Affect and Cerebral Oxygenation During Active Transport: An Immersive Virtual Reality and Multi-Study Design" to "Effect of Traffic Separation and Nature Integration on Affect and Cerebral Oxygenation During Active Transport: An Immersive Virtual Reality and Multi-Study Design". We are thankful for the recommender and reviewers’ comments as they significantly contributed to improving the overall quality of the manuscript. Please find below our point-by-point responses. Comments from the recommender and the reviewers are in bold font, our responses are in regular font, quotes from the manuscript are in italic font, and each change made to the manuscript is in blue font. Best regards, The authors Recommender’s Comments to Authors: #1 — Dear authors, I am delighted to have received two thorough and high-quality reviews that provide valuable feedback on your work. I would like to join them in congratulating you on the quality of your paper as a first draft. Reply: We would like to thank Dr Fillon sincerely for their thoughtful and encouraging comments. #2 — In addition to their comments, I would like to make a few brief remarks of my own. First, you stated that you will use the median absolute deviation to check for outliers. Could you please clarify what threshold you intend to use for identifying a participant as an outlier? In the current manuscript, no decision is described regarding how outliers are defined. 1 Reply: Thank you for raising this point. The threshold intended for identifying outliers has been added to the Methods section. Specifically, we will use the performance R package (Lüdecke et al., 2021) default threshold to detect univariate and multivariate outliers. The package’s default threshold for classifying outliers is 1.959 (threshold = list("zscore" = 1.959)), which corresponds to the most extreme 2.5% (qnorm(0.975)) of observations. Pages 21, Lines 537–543: "Possible outliers will be checked for each statistical model instead of the original data (Leys et al., 2019), using the performance R package (Lüdecke et al., 2021). The median absolute deviation (MAD; Leys et al., 2013) will be used to identify univariate outliers, and the Mahalanobis-MCD distance (Leys et al., 2018) will be used to identify multivariate outliers. The threshold for classifying univariate outliers will be set as 1.959 (threshold = list["zscore" = 1.959]), corresponding to the most extreme 2.5% (qnorm[0.975]) of observations (Lüdecke et al., 2021)." #3 — The same applies to the TOST test: what equivalence margin do you intend to use? Reply: Thank you for raising this point. We have now specified the equivalence margins for the TOST tests based on the smallest effect size of interest (SESOI), following the recommendations of Lakens et al. (2018). The lower (∆L) and upper (∆U) bounds are symmetric around zero, defined as ∆L=−SESOI and ∆U= +SESOI. Page 22, Lines 561–573: "If no significant differences are found, TOSTs will be performed to test for equivalence (Lakens et al., 2018). The equivalence margins for the TOSTs will be based on the smallest effect size of interest (SESOI; Lakens et al., 2018). The lower (∆L) and upper (∆U) bounds will be set as symmetric around zero, defined as ∆L=−SESOI and ∆U= +SESOI. To determine the SESOI, the small telescope approach will be used, which sets the SESOI to the effect size that would have provided the reference study with 33% power (see Lakens, 2022; Simonsohn, 2015). Specifically, the following equivalence margins will be used: •For H1:∆L= -0.10 (f) or -0.20 (d) and ∆U= 0.10 (f) or 0.20 (d) (Batistatou et al., 2022). •For H2:∆L= -0.16 (f) or -0.32 (d) and ∆U= 0.16 (f) or 0.32 (d) (Focht, 2013) •For H3:∆L= -0.17 (f) or -0.34 (d) and ∆U= 0.17 (f) or 0.34 (d) (Geissler et al., 2021) #4 — Finally, I think that, based on the complexity of the task and the moment the participants will experience, it could be interesting (but not mandatory) to collect verbal feedback from them, particularly regarding the understanding they have of the hypotheses. Such feedback might help refine the task or even inspire new ideas. Reply: We thank the recommender for this valuable suggestion. We agree that including verbal feedback would help us to gain a deeper understanding of our results. Therefore, we have added a series of openended questions at the end of the protocol. Pages 17–18, Lines 426–440:"If the score is strictly above 4 (i.e. "slightly disagree"), any differences in the dependent variables across our conditions could be attributed to the perceived presence of these environmental features. 2 Following this, participants will respond to a series of open-ended questions to further explore their perceptions: "What differences did you notice between the environments?", "During the study, what did you understand about the purpose of this experiment?", "Between the odours, sound environment, and visual environment, which element had the most positive impact on your affective experience during the session? Why?", "Between the odours, sound environment, and visual environment, which element had the most negative impact on your affective experience during the session? Why?", "During the session, what elements in the environment did you focus on the most?" These questions will allow us to assess whether participants correctly interpreted the study’s hypotheses and to identify which multisensory elements most influenced their affective experiences." #5 — As a side note, if you have not yet decided whether you will include the treadmill model, please note that I will need to mention this in the Stage 1 IPA for transparency purposes. Reply: The model of the treadmill has been selected and added to the manuscript. Page 17, Lines 409–412: "Power, RPM, speed and total covered distance will be recorded using a Garmin®Edge 540 device (Study 1). In Study 2, speed and total distance covered will be recorded using the treadmill software (pluto®lt sport OEM, COSMED)." #6 — Please respond to all comments in a “response to reviewers” document detailing each corresponding change in your manuscript. I am looking forward to receiving a new version of the manuscript. All the best, Adrien Reply: We appreciate the constructive suggestions you provided. You will find answers to all the reviewers’ comments below. Reviewer 1 #1 — The authors have produced a very clear manuscript and present a feasible study. The protocol has already been pre-tested and the power analyses have been carried out accurately. In my opinion, the major limitation of the study lies in the ’colorful condition’. No previous evidence suggests that colors could influence the affective experiences or haemodynamic responses in dlPFC during walking or biking. Reply: We would like to thank Dr Bernard for their helpful comments. We also appreciate their suggestion regarding the "colourful condition", which helped us to substantially improve our protocol. #2 — Introduction l72-74 This sentence should be revised. Indeed, a reduction of transport related GHG emissions is effective if car drivers shift for active transports (ATs). Some previous studies suggested that AT promotion is not necessary related with a car use reduction (see Bernard et al. 2021 for review). 3 Reply: Thank you for raising this point. The sentence has been amended to emphasise the importance of shifting from private car use to active transport. Page 5, Lines 72–78: "In particular, the increase in greenhouse gas (GHG) emissions from human activities has severe negative impacts on planet warm, water availability and food production, biodiversity and ecosystems, health and well-being, and urban infrastructure (IPCC, 2023; Nations, 2015; Ripple et al., 2021; Steffen et al., 2015). At the individual level, shifting from the use of private car to active transport—such as walking or cycling—represents one of the most effective strategies for reducing GHG emissions in urban environments (Bernard et al., 2021; Chevance et al., 2023)." #3 — l85-93 This paragraph underscores the current limits of interventions based on socio-cognitive model to promote physical activity. However, authors should present the interests and limits of these models for the AT promotion. E.g., + Semenescu_2020_30 Years of soft interventions to reduce car use – A systematic review and meta-analysis + Arnott 2014 Efficacy of behavioural interventions for transport behaviour change: systematic review, meta-analysis and intervention coding. + Javaid 2020 Determinants of low-carbon transport mode adoption-systematic review of reviews Reply: We thank the reviewer for providing such useful references. As recommended, the paragraph has been reframed in the context of promoting active transport. Page 5–6, Lines 89–104: "The socio-cognitive approach remains the dominant framework to improve engagement and maintenance of physical activity behaviour (Rhodes et al., 2019). This approach posits that peoples’ choices are influenced by a deliberate and rational assessment of the potential advantages and disadvantages of intended actions, as well as the likelihood of achieving these advantages (Rhodes et al., 2019). In the context of active transport, systematic reviews and meta-analyses have revealed inconsistent effects of socio-cognitive interventions in reducing car use and increasing active transport. For example, Arnott et al. (2014) found no evidence that such interventions reduce car use or increase active transport. In contrast, Semenescu et al. (2020) reported a 7% reduction in car use when interventions targeted knowledge and awareness, capability and self-efficacy, and social, cultural and moral norms. Finally, an umbrella review further highlighted that socio-cognitive factors (e.g., social norms, attitudes) are more strongly associated with intentions to adopt low-carbon travel modes than with actual behaviour change (Javaid et al., 2020). Therefore, while targeting socio-cognitive factors appears to be a promising approach to promoting active transport use, it is not sufficient on its own." #4.1 — l123 Authors present potential extrinsic/environmental variables associated with PA related behaviors. They should present the environmental specific factors explaining biking and walking, respectively. For instance, walkability/bikeability, perceived safety, light, weather are a set of major environmental associated with AT (Javaid et al 2020). 4 #4.2 — l127 Author could present more recent findings comparing outdoor/indoor PA effects. For instance, Peddie et al https://doi.org/10.1080/17437199.2024.2383758the found a larger effect of outdoor PA on affective experiences. Reply: We thank the reviewer for highlighting this issue and providing these useful references. The paragraph has been updated to include more recent findings comparing the effects of outdoor and indoor physical activity and the environmental factors that influence the adoption of active transport. Pages 7–8, Lines 138–152: "It has been argued that environmental factors play a critical role in enhancing the affective experience of physical activity and promoting the adoption of active transport (Fessler et al., 2024; Javaid et al., 2020; Jones & Zenko, 2023). For instance, a narrative review by Bourke et al. (2021b) showed that outdoor physical activity elicits more positive self-reported affective valence than indoor activity, particularly when conducted in natural environments. This finding is supported by a recent meta-analysis indicating that enjoyment levels are significantly higher during outdoor exercise when compared to indoor exercise (Peddie et al., 2024). In the context of active transport, the major environmental factors associated with its usage include walkability, bikeability, perceived safety and weather (Javaid et al., 2020; Mengiste et al., 2025). For example, the separation of motorised and non-motorised transport modes through infrastructure such as bike lanes and pavement has been demonstrated to improve perceived safety and pleasure during active travel (Cambra & Moura, 2020; Timmons et al., 2024). Additionally, the integration of natural elements, such as trees and green spaces, in urban environments can further improve pleasantness (Basu et al., 2022). #6 — Theoretical foundations of data collection, primary/secondary outcomes, and VR use are well-explained. Regarding the ’colorful condition’, I am not convinced that it is useful in the current protocol. Indeed, authors present only 2 or 3 previous studies showing a potential effect on affective experiences. There is no previous evidence that this condition will have an potential effect on AT related behaviors. Furthermore, authors did not expect (or test) a higher effect of natural VS colorful condition on their primary outcomes. It could be more appropriated to examine a more AT ’friendly’ condition such as presence of cars, bikelane... Reply: We thank the reviewer for their comment and suggestion. Although a few studies have examined the potential impact of colourful bike lanes and pavements on associated emotional experiences (Gu et al., 2024; Vera-Villarroel et al., 2016), we agree that adding colour may be less effective in promoting active transport than other well-studied strategies, such as separating motorised and non-motorised traffic (Javaid et al., 2020). Therefore, the experimental conditions were modified to represent conditions that are more "active transport friendly": (1) almost-inexistent separation between motorised and non-motorised traffic (NS condition); (2) separation between motorised and non-motorised traffic (S condition); and (3) separation between motorised and non-motorised traffic with natural features (SN condition). The NS condition will be conceptualised as a painted bike path (Study 1) or painted pavement (Study 2), located 1.5 metres from the road and at the same level, with no explicit separation from the road (i.e., white continuous line separating cars from the participant). The S condition will be conceptualised as a separated cycle lane (Study 1) or separated pavement (Study 2), 3 metres from the road and elevated above it. The SN condition will be the same as the S condition but with trees and bushes providing separation between cars and the bike lane (Study 1) or pavement (Study 2). A schematic example of the environment is provided in Figure 1. 5 Figure 1: Illustration of 3D modular blocks for the virtual environments. Provided by Raftel Studio (https://raftel-studio.com/fr/projects). As separation of motorised and non-motorised traffic is often associated with perceived safety in the context of active transport (see Javaid et al., 2020), this variable will be measured after each session using the following item: "How safe did you feel when cycling or walking in this environment?" (derived from Olsson & Elldér, 2023) (see Supplementary Material 2, Table S1). The effect of the conditions on perceived safety will be tested in exploratory analyses. The above-mentioned changes led to amendments of the title, abstract, objective, hypotheses, and power analysis, as follows. Title: “Effect of Traffic Separation and Nature Integration on Affect and Cerebral Oxygenation During Active Transport: An Immersive Virtual Reality and Multi-Study Design” Abstract: "The proposed programmatic registered report aims to investigate whether integrating traffic separation and nature integration into urban concrete environments during active transport sessions (i.e., cycling [Study 1] or walking [Study 2]) would improve the affective experience and result in changes to cerebral oxygenation. A minimum of 36 adults will take part in three 15-min, moderate-intensity cycling 6 (N=18; Study 1) or walking (N = 18; Study 2) sessions. These sessions will involve three different virtual environments using the Meta Quest 3 headset: (a) an urban environment without traffic separation; (b) an urban environment with traffic separation; and (c) an urban environment with traffic separation and natural features. Abstract: "It is hypothesised that the separation of motorised and non-motorised traffic in an urban concrete environment will lead to higher affective valence (H1) and remembered pleasure (H2), as well as lower cerebral oxygenation of the dlPFC (H3). We expect this effect to be magnified by the inclusion of natural natural features." Page 10, Lines 231–235: "The aim of the proposed programmatic registered report is to investigate the effect of motorised and non-motorised traffic separation and the addition of natural features on affective (i.e., affective valence and remembered pleasure) and neurophysiological (i.e., cerebral oxygenation) outcomes during multisensorial VR active transport sessions (see Table 1)." Page 11, Lines 238–244: "For both studies, we hypothesise that a clear separation of motorised and non-motorised traffic in an urban concrete environment will lead to higher affective valence (H1) and remembered pleasure (H2), as well as lower cerebral oxygenation of the dlPFC (H3). We expect these effects to be amplified by the inclusion of natural features, as previous research suggests that such elements provide restorative benefits that may counterbalance any increased perceptual complexity from additional visual stimuli (Bolouki, 2023; Geissler et al., 2021; Neale et al., 2020)." Page 12, Lines 271–276: "Eligible participants will take part in three 15-minute, moderate-intensity cycling (Study 1) or walking (Study 2) sessions during a single laboratory visit, totalling 45 minutes. These sessions will involve three different virtual environments: a non-separated traffic (NS) condition; (b) a separated traffic (S) condition; and (c) a separated traffic with natural features (SN) condition." Pages 13–14, Lines 302–326: "The calculation was based on the smallest effect sizes reported in prior studies involving adults (Batistatou et al., 2022; Cambra & Moura, 2020; Focht, 2013; Geissler et al., 2021), using an alpha level of .05 and a desired power of .90. To formally model the correction for publication bias and use a more conservative effect size estimate, we performed a safeguard power analysis (Lakens, 2022; Perugini et al., 2014) using the lower bound of the 60% two-sided confidence interval around the effect size estimate (see R script for details of calculation, https://doi.org/10.5281/ zenodo.17658559). For H1, a minimum of 15 (f = 0.43; Cambra & Moura, 2020) and 12 participants (f = 0.48; Batistatou et al., 2022) will be required to detect an effect of the S and SN conditions on affective valence, respectively. For H2, a minimum of 15 participants will be required to detect an effect of both conditions on remembered pleasure (f = 0.43; Cambra & Moura, 2020; Focht, 2013). As our design is fully counterbalanced across three conditions (six possible sequences), the sample size should be a multiple of six. Thus, 18 participants will be required for H1and H2. For H3, a minimum of six participants will be required to detect an effect of both conditions on cerebral oxygenation in the dlPFC (f = 1.93; Geissler et al., 2021). Similar results were found for all hypotheses using the SIMR R package method (see Supplementary Material 1). Due to the limited number of studies exploring this research question, the extant literature does not clearly specify any differences between cycling and walking with regard to our variables of interest and study design. Consequently, the effect sizes utilised for sample calculation in both studies will be identical. It should be noted that, even when a safeguard power analysis 7 is performed, using the effect size from previous studies can introduce biases. Therefore, we aim to recruit as many participants as possible within our time and resource constrains (Lakens, 2022). Any excluded participant will be replaced to ensure a minimum of n = 18 per study." 8 Table 1: Study Design Question Hypothesis Sampling plan Analysis plan Rationale for deciding the sensitivity of the test for confirming or disconfirming the hypothesis Interpretation given different outcomes Theory that could be shown wrong by the outcomes Could the separation of motorised and non-motorised traffic, with and without natural elements, into virtual urban environments made of concrete lead to a higher affective valence during active transport sessions? Mean affective valence score NS < S<SN(H1). Minimum of n= 18 for each study (f = 0.41; α= .05; 1-β= .90). Total N = as many participants as possible within our time and resource constraints. LMMs with associated contrasts across conditions. Conditions will be specified as fixed factor and participants as random factor. Potential covariates are age, gender and BMI. TOSTs if non-significant differences (∆L = -0.20 (d), ∆U = 0.20 (d). A perceived difference score of ≥4 out of 9 between the environments. The hypothesis will be accepted if the statistical test is significant (p< .050) and the perceived difference score between the conditions ≥4. Failure to confirm H1 would call into question the claim that the separation of motorised and non-motorised traffic, with or without natural elements, improves affective valence during an active transport session in an urban environment. Could the separation of motorised and non-motorised traffic, with and without natural elements, into urban concrete virtual environments during active transport sessions lead to people remembering sessions more pleasurably? Remembered pleasure score NS <S<SN(H2) n= 18 for each study (f = 0.43; α= .05; 1-β= .90). Total N = as many participants as possible within our time and resource constraints. LMMs with associated contrasts across conditions. Conditions will be specified as fixed factor and participants as random factor. Potential covariates are age, gender and BMI. TOSTs if non-significant differences (∆L = -0.20 (d), ∆U = 0.20 (d). A perceived difference score of ≥4 out of 9 between the environments. The hypothesis will be accepted if the statistical test is significant (p< .050) and the perceived difference score between the conditions ≥4. Failure to confirm H2 would call into question the claim that the separation of motorised and non-motorised traffic, with or without natural elements, improves remembered pleasure of an active transport session in an urban environment. Continued on next page 9 where Z=X−m s,Xis the original image, mis the original mean (luminance), sis the original standard deviation (contrast), Sis the target standard deviation, and Mis the target mean. Target values will be calculated as the average mean and standard deviation across all conditions, ensuring consistent visual properties without introducing artificial noise or distortion. Spatial frequency analysis will be performed using 2D Fast Fourier Transform (FFT) to decompose images into frequency components. Frequency spectra will be analysed in three bands (low, medium, high) corresponding to coarse (0-20 cycles/image), medium (20-40 cycles/image), and fine (> 40 cycles/image) visual details respectively. Dominant frequencies will be identified as the peak energy components in each spectrum, providing a quantitative measure of the most prevalent spatial patterns in each condition. To statistically compare low-level visual properties across conditions, a one-way ANOVA will be performed for each property, followed by Tukey’s HSD post hoc tests if p< 0.05. The associated Python and R codes are available on Zenodo (https://doi.org/10.5281/zenodo.17658559). An example can be found in Figure 3 and Supplementary Material 3. Pages 17–18, Lines 430–440: "Following this, participants will respond to a series of open-ended questions to further explore their perceptions: "What differences did you notice between the environments?", "During the study, what did you understand about the purpose of this experiment?", "Between the odours, sound environment, and visual environment, which element had the most positive impact on your affective experience during the session? Why?", "Between the odours, sound environment, and visual environment, which element had the most negative impact on your affective experience during the session? Why?", "During the session, what elements in the environment did you focus on the most?" These questions will allow us to assess whether participants correctly interpreted the study’s hypotheses and to identify which multisensory elements most influenced their affective experiences." Pages 18–19, Lines 441–470: "Virtual Environments Check The VR environments’ low-level visual properties (i.e., luminance, contrast, spatial frequencies) will be checked and compared across conditions, as these could potentially influence perceptual load independently of the targeted manipulation.To this end, all VR environment screenshots will be analysed in Python (v3.13.7) using a standardised pipeline. Representative screenshots from each condition will first be converted to greyscale and resized to 512×512 pixels to control for dimensional confounds. Following SHINE toolbox guidelines (Willenbockel et al., 2010), luminance will be quantified as mean pixel intensity and contrast as pixel intensity standard deviation. Images will be normalised using a linear transformation approach, following Equation 1. E=Z·S+M(1) where Z=X−m s,Xis the original image, mis the original mean (luminance), sis the original standard deviation (contrast), Sis the target standard deviation, and Mis the target mean. Target values will be calculated as the average mean and standard deviation across all conditions, ensuring consistent visual properties without introducing artificial noise or distortion. Spatial frequency analysis will be performed using 2D Fast Fourier Transform (FFT) to decompose images into frequency components. Frequency spectra will be analysed in three bands (low, medium, high) corresponding to coarse (0-20 cycles/image), medium (20-40 cycles/image), and fine (> 40 cycles/image) visual details respectively. Dominant frequencies will be identified as the peak energy components in each spectrum, providing a quantitative 16 measure of the most prevalent spatial patterns in each condition. Finally, a log-log plot will be generated to describe the rotational average of the spectra (i.e., the energy at each spatial frequency, in cycles per image, averaged across all orientations). This will provide a scale-invariant representation of the spatial frequency distribution that allows direct comparison of the relative power at different spatial scales across conditions (Willenbockel et al., 2010) (see Figure 3 and Supplementary Material 3). To statistically compare low-level visual properties across conditions, a one-way ANOVA will be performed for each property, with associated Tukey’s HSD post-hoc tests when p < 0.05. The associated Python and R codes are available on Zenodo (https://doi.org/10.5281/zenodo.17658559)." Figure 3: Log-Log Plot of Spatial Frequency Spectra across conditions. The plot shows the rotational average of the spectra (energy at each spatial frequency, in cycles per image, averaged across all orientations) for each condition. U = urban environment; UC = urban environment with colourful design; UN = urban environment with natural features #5 — A final consideration concerns the addition of extra elements within the urban environment in the experimental design. While these features are intended to reduce cognitive effort, the inclusion of additional visual components (such as trees, textures, and colours) could also increase perceptual complexity and thereby engage greater attentional resources. I also wonder whether, in previous studies, nature and colour effects were implemented as distinct environments rather than as modifications within an urban context. Reply: We appreciate the reviewer’s thoughtful consideration regarding the potential impact of additional visual elements in our urban environment. As previously reported, the "colour" condition has been 17 removed from our protocol, but we maintain the integration of natural elements within the urban context. Though these natural features could theoretically increase perceptual complexity, existing literature suggests that their restorative properties likely outweigh this effect. While most studies examine nature and urban environments as distinct conditions (e.g., Bolouki, 2023; Geissler et al., 2021), some have directly compared urban settings with and without natural elements (Basu et al., 2022; Neale et al., 2020). For example, Neale et al. (2020) demonstrated significant variations in neural activity as participants transitioned between busy urban and green urban settings, with reduced low beta activity (i.e., 13–19 Hz) in green spaces (associated with attention). In addition, they found no significant differences in beta activity between urban green and urban quiet settings. Furthermore, the negative correlation between dlPFC activation and positive affective valence (Jones & Ekkekakis, 2019), the latter of which is enhanced by nature exposure (Bourke et al., 2021a), provides additional support for our hypothesis (H3). Taken together, these findings suggest that the restorative and calming effects of nature may counterbalance a potential increased attentional effort from added stimuli. Further justification for our hypothesis has been added to the introduction. Page 11, Lines 238–144: "For both studies, we hypothesise that the clear separation of motorised and non-motorised traffic in an urban concrete environment will lead to higher affective valence (H1) and remembered pleasure (H2), as well as lower cerebral oxygenation of the dlPFC (H3). We expect these effects to be amplified by the inclusion of natural features, as previous research suggests that such elements provide restorative benefits that may counterbalance any increased perceptual complexity from additional visual stimuli (Bolouki, 2023; Geissler et al., 2021; Neale et al., 2020)." #6 — Theoretical framework: The theoretical framework is overall well articulated, progressing from socio-cognitive models to affectivism and finally to the Theory of Effort Minimisation. However, at present, the text seems to juxtapose several theoretical perspectives without clearly specifying which one serves as the primary conceptual backbone of the study. It could be interesting to move more quickly toward the Effort Minimisation Theory, including its affective component, which appears to be the central theoretical framework on which the study is built. Reply: We thank the reviewer for their constructive comment regarding our theoretical framework. We understand the concern about juxtaposing multiple theoretical perspectives without clear prioritisation. However, we believe that these frameworks complement each other rather than competing with each other. The Affect and Health Behaviour Framework (AHBF) provides the primary conceptual backbone of our study design, offering a holistic approach that incorporates affective processes, environmental factors, and socio-cognitive variables. This framework enables us to examine comprehensively the multiple influences on active transport behaviour. We then supplement this with the Theory of Effort Minimisation in Physical Activity (TEMPA), which introduces the dimension of effort, aligning with our specific focus on cognitive effort (as measured by dlPFC activation) and perceived exertion. Together, these frameworks enable us to examine both the broader context of affective influences on behaviour (through the AHBF) and the specific mechanisms of effort (through the TEMPA). This complementary relationship has therefore been clarified. 18 Page 7, Lines 123–137: "To further extend these frameworks, the Theory of Effort Minimisation in Physical Activity (Cheval & Boisgontier, 2021; Cheval et al., 2024) posits that humans have a natural evolutionary tendency to minimise physical effort in order to conserve energy resources. This tendency to minimise effort is an automatic incentive that can override the intention to be physically active (Cheval & Boisgontier, 2021; Maltagliati et al., 2025b). According to this model, positive affective experiences associated with physical activity could help reduce the perceived effort involved and thus facilitate engagement with and maintenance of this practice throughout life (Maltagliati et al., 2025a). For example, a person who enjoys cycling to work will perceive less effort and be more inclined to repeat the experience, compared to someone who dislikes cycling. Thus, improving the affective experience of active transport is warranted. Together, these frameworks offer a comprehensive model for understanding physical activity behaviours. While the AHBF takes a holistic view of how affective, cognitive and environmental factors influence decisions about physical activity, the TEMPA goes further by specifically addressing the role of effort in behaviour regulation." #7 — Method It might also strengthen the paper to briefly critically review previous empirical studies, their methodological limitations, and how the present design overcomes these shortcomings. Additionally, if prior literature has produced mixed results regarding affective responses or prefrontal activation in natural vs urban environments, this could be highlighted to justify the need for the present study. Reply: We thank the reviewer for their comment. We have added a paragraph providing a brief critical review of empirical studies – including affective and neurobiological responses – before our objectives and hypotheses. Pages 9–10, Lines 206–229: "Taken together, these results suggest that separating motorised and non-motorised transport modes has the potential to make active transport more enjoyable and less cognitively demanding. Furthermore, the addition of natural features may magnify these effects. However, previous empirical studies suffer from limitations that hinder definitive conclusions. First, most studies treat natural and urban environments as separate conditions (e.g., nature vs. city), rather than examining how integrating natural elements into existing urban settings might influence affective and neurobiological responses during active transport (e.g., Bolouki, 2023; Geissler et al., 2021; Peddie et al., 2024). In addition, no study have formally tested the effect of traffic separation on dlPFC activity. Consequently, the potential impact of combining traffic separation and natural features within the same urban environment on affective and neurobiological responses remains unclear. Second, prior EEG and fNIRS studies have primarily focused on walking (e.g., Bolouki, 2023; Neale et al., 2020), leaving a gap in understanding the neurobiological correlates of cognitive effort during other active transport mode (e.g., cycling). Third, while traffic separation appears to enhance pleasure in pedestrian contexts (Cambra & Moura, 2020), its effect on cyclists remains ambiguous. For example, although Javaid et al. (2020)’s systematic review suggests that separated bicycle infrastructure decrease perceived stress and produce more positive feeling, Xing et al. (2018) found that perceptions of bicycle infrastructure (e.g., bike lanes) are not related to bicycling affect (i.e., liking of bicycling). Thus, the effect of traffic separation on affective experiences during cycling remains unclear. In summary, further research is needed to investigate how the simultaneous implementation of traffic separation and natural elements in urban environments influences affective and cognitive responses during active transport." 19 #8 — The claim that exposure to nature leads to “more efficient cognitive processing and lower dlPFC activation” could be further unpacked. It would be useful to specify what is meant by “more efficient.” Does reduced dlPFC activity reflect lower cognitive effort, more automatic processing, or a redistribution of cognitive resources? It is important to note that lower neural activation does not always imply greater efficiency, it could also reflect reduced engagement. Adding a short conceptual clarification would greatly help non-expert readers understand the interpretation of dlPFC oxygenation in this context. Reply: We thank the reviewer for their constructive comment. We agree that the term "more efficient" can be ambiguous. In our protocol, the primary purpose of measuring dlPFC activation is to provide a neurobiological correlate of cognitive effort. This has been clarified in the text. Page 8, Lines 153–169: "Mechanistic explanations suggest that green environments may trigger our innate tendency to respond positively to nature, thereby increasing our sense of safety and reducing the cognitive effort required to overcome potential threats (García-Martín et al., 2025; Kaplan, 1992; Staats et al., 2003). This is supported by neurobiological evidence showing lower beta activity during walks in urban green spaces compared to busy urban areas, which may be attributed to lower demands on attention and vigilance (Neale et al., 2020). Furthermore, converging evidence from electroencephalography (EEG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS) studies have demonstrated that exposure to nature tends to induce lower activation of the dorsolateral prefrontal cortex (dlPFC), a brain region involved in attentional control, effort processing, and cognitive effort (Causse et al., 2017; Gerber et al., 2025), when compared to urban environments (Bolouki, 2023; Geissler et al., 2021). For instance, Geissler et al. (2021) found that driving in the countryside resulted in lower dlPFC oxygenation than urban driving, indicating reduced mental workload — a component of cognitive effort. While these findings provide important preliminary evidence, further research is needed to examine these effects specifically in active transport contexts." #9 — Conclusion: Overall, this is an excellent and carefully crafted registered report that combines theoretical depth, methodological rigor, and a clear commitment to open science. The integration of affective, cognitive, and neurophysiological measures within an ecologically valid VR paradigm is particularly impressive. The study is likely to make a valuable contribution to the literature. With minor clarifications regarding theoretical positioning and experimental control, the paper would reach an even higher level of conceptual clarity and robustness. In its current form, it already represents a high-quality, innovative, and promising contribution to the field!! Dylan Naceur PhD Student, Laboratoire de Psychologie Sociale et Cognitive (LAPSCO, CNRS UMR 6024) Université Clermont Auvergne, France 20 Reply: We sincerely thank the reviewer for their thoughtful and constructive evaluation of our registered report. We greatly appreciate the recognition of our study’s theoretical depth, methodological rigour, and commitment to open science. The reviewer’s suggestions for enhancing theoretical positioning and experimental control are valuable and will help us further strengthen the conceptual clarity and robustness of our work. References Arnott, B., Rehackova, L., Errington, L., Sniehotta, F. F., Roberts, J., & Araujo-Soares, V. (2014). Efficacy of behavioural interventions for transport behaviour change: Systematic review, meta-analysis and intervention coding. International journal of behavioral nutrition and physical activity, 11(1), 133. https://doi.org/10.1186/s12966-014-0133-9 Barton, K. (2018). MuMIn: Multi-model inference. r package version 1.42.1. https://CRAN.Rproject.org/package=MuMIn. Basu, N., Oviedo-Trespalacios, O., King, M., Kamruzzaman, M., & Haque, M. M. (2022). The influence of the built environment on pedestrians’ perceptions of attractiveness, safety and security. Transportation research part F: traffic psychology and behaviour, 87, 203–218. https: //doi.org/10.1016/j.trf.2022.03.006 Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. 2015, 67(1), 48. https://doi.org/10.18637/jss.v067.i01 Batistatou, A., Vandeville, F., & Delevoye-Turrell, Y. N. (2022). Virtual reality to evaluate the impact of colorful interventions and nature elements on spontaneous walking, gaze, and emotion. Frontiers in Virtual Reality, 3. https://doi.org/10.3389/frvir.2022.819597 Bernard, P., Chevance, G., Kingsbury, C., Baillot, A., Romain, A.-J., Molinier, V., Gadais, T., & Dancause, K. N. (2021). Climate change, physical activity and sport: A systematic review. Sports medicine, 51(5), 1041–1059. https://doi.org/10.1007/s40279-021-01439-4 Bolouki, A. (2023). Neurobiological effects of urban built and natural environment on mental health: Systematic review. Reviews on Environmental Health, 38(1), 169–179. https://doi.org/10. 1515/reveh-2021-0137 Bourke, M., Hilland, T. A., & Craike, M. (2021a). A systematic review of the within-person association between physical activity and affect in children’s and adolescents’ daily lives. Psychology of Sport and Exercise, 52, e101825. https://doi.org/10.1016/j.psychsport.2020.101825 Bourke, M., Hilland, T. A., & Craike, M. (2021b). Variance in the valenced response during moderate-to-vigorous physical activity: A review of cognitive and contextual mechanisms. International Review of Sport and Exercise Psychology, 14(1), 154–185. https://doi.org/10. 1080/1750984X.2020.1780626 Cambra, P., & Moura, F. (2020). How does walkability change relate to walking behavior change? effects of a street improvement in pedestrian volumes and walking experience. Journal of Transport & Health, 16, 100797. https://doi.org/10.1016/j.jth.2019.100797 Causse, M., Chua, Z., Peysakhovich, V., Del Campo, N., & Matton, N. (2017). Mental workload and neural efficiency quantified in the prefrontal cortex using fnirs. Scientific reports, 7(1), 5222. https://doi.org/0.1038/s41598-017-05378-x Cheval, B., & Boisgontier, M. P. (2021). The theory of effort minimization in physical activity. Exercise and Sport Sciences Reviews, 49(3), 168–178. https : / / doi . org / 10 . 1249 / JES . 0000000000000252 21 Cheval, B., Zou, L., Maltagliati, S., Fessler, L., Owen, N., Falck, R. S., Yu, Q., Zhang, Z., & Dupuy, O. (2024). Intention–behaviour gap in physical activity: Unravelling the critical role of the automatic tendency towards effort minimisation. British journal of sports medicine, e108144. https://doi.org/10.1136/bjsports-2024-108144 Chevance, G., Fresán, U., Hekler, E., Edmondson, D., Lloyd, S. J., Ballester, J., Litt, J., Cvijanovic, I., Araújo-Soares, V., & Bernard, P. (2023). Thinking health-related behaviors in a climate change context: A narrative review. Annals of Behavioral Medicine, 57(3), 193–204. https: //doi.org/10.1093/abm/kaac039 Denissen, J. J., Butalid, L., Penke, L., & Van Aken, M. A. (2008). The effects of weather on daily mood: A multilevel approach. Emotion, 8(5), 662. https://doi.org/10.1037/a0013497 Fessler, L., Maltagliati, S., Cheval, B., & Sarrazin, P. (2024). Chapitre 9. S’engager dans une activité physique régulière : le rôle des expériences affectives positives. In Psychologie positive des activités physiques et sportives (pp. 202–219). Dunod. Focht, B. C. (2013). Affective responses to 10-minute and 30-minute walks in sedentary, overweight women: Relationships with theory-based correlates of walking for exercise. Psychology of Sport and Exercise, 14(5), 759–766. https://doi.org/10.1016/j.psychsport.2013.04.003 García-Martín, M., Kolecka, N., Hunziker, M., Graz, L., Dopico, J., Schäffer, B., Wunderli, J. M., & Tobias, S. (2025). The role of greenness and road traffic noise for psychological restoration in everyday environments. a participatory mapping approach. Landscape and Urban Planning, 259, 105339. https://doi.org/10.1016/j.landurbplan.2025.105339 Geissler, C. F., Schneider, J., & Frings, C. (2021). Shedding light on the prefrontal correlates of mental workload in simulated driving: A functional near-infrared spectroscopy study. Scientific reports, 11(1), 705. https://doi.org/10.1038/s41598-020-80477-w Gerber, M., Cheval, B., Cody, R., Colledge, F., Hohberg, V., Klimentidis, Y. C., Lang, C., Looser, V. N., Ludyga, S., Stults-Kolehmainen, M., & Faude, O. (2025). Psycho-physiological foundations of human physical activity behavior and motivation: Theories, systems, mechanisms, evolution, and genetics. Physiological Reviews. https://doi.org/10.1152/physrev.00021.2024 Gu, L., Dimitrov-Discher, A., Knöll, M., & Roe, J. (2024). Cool colors promote a restorative sidewalk experience: A study on effects of color and pattern design of ground murals on mood states and perceived restorativeness using 2d street view images. Environment and Planning B: Urban Analytics and City Science, 0(0). https://doi.org/10.1177/23998083241272100 Hall, P. A., Zehr, C. E., Ng, M., & Zanna, M. P. (2012). Implementation intentions for physical activity in supportive and unsupportive environmental conditions: An experimental examination of intention–behavior consistency. Journal of Experimental Social Psychology, 48(1), 432–436. https://doi.org/10.1016/j.jesp.2011.09.004 IPCC, I. P. o. C. C. (2023). Climate Change 2023 Synthesis Report (tech. rep.). https://www.ipcc. ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_FullVolume.pdf Javaid, A., Creutzig, F., & Bamberg, S. (2020). Determinants of low-carbon transport mode adoption: Systematic review of reviews. Environmental Research Letters, 15(10), 103002. https: //doi.org/10.1088/1748-9326/aba032 Jones, L., & Ekkekakis, P. (2019). Affect and prefrontal hemodynamics during exercise under immersive audiovisual stimulation: Improving the experience of exercise for overweight adults. Journal of Sport and Health Science, 8(4), 325–338. https://doi.org/10.1016/j.jshs.2019.03. 003 22 Jones, L., & Zenko, Z. (2023). A systematic narrative review of extrinsic strategies to improve affective responses to exercise. Frontiers in Sports and Active Living, 5, 1–28. https://doi. org/10.3389/fspor.2023.1186986 Kaplan, S. (1992). The restorative environment: Nature and human experience. In The role of horticulture in human well-being and social development. Timber Press. Kuznetsova, A. (2016). ImerTest: Tests in linear mixed effects models. r package version 2.0-33. Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1). https://doi.org/10.1525/ collabra.33267 Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10.1177/2515245918770963 Lenth, R., & Lenth, M. R. (2025). Package ‘lsmeans’. The American Statistician, 34(4), 216–221. https://doi.org/10.1080/00031305.1980.10483031 Leys, C., Delacre, M., Mora, Y. L., Lakens, D., & Ley, C. (2019). How to classify, detect, and manage univariate and multivariate outliers, with emphasis on pre-registration. International Review of Social Psychology, 32(1), 5. https://doi.org/10.5334/irsp.289 Leys, C., Klein, O., Dominicy, Y., & Ley, C. (2018). Detecting multivariate outliers: Use a robust variant of the mahalanobis distance. Journal of Experimental Social Psychology, 74, 150– 156. https://doi.org/10.1016/j.jesp.2017.09.011 Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766. https://doi.org/10.1016/j.jesp.2013.03.013 Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P., & Makowski, D. (2021). Performance: An r package for assessment, comparison and testing of statistical models. Journal of Open Source Software, 6 (60), 1241–1252. https://doi.org/10.21105/joss.03139 Maltagliati, S., Fessler, L., Yu, Q., Zhang, Z., Chen, Y., Dupuy, O., Falck, R. S., Owen, N., Zou, L., & Cheval, B. (2025a). Effort minimization: A permanent, dynamic, and surmountable influence on physical activity. Journal of Sport and Health Science, 14, 100971. https://doi. org/10.1016/j.jshs.2024.100971 Maltagliati, S., Raichlen, D. A., Rhodes, R. E., & Cheval, B. (2025b). Closing the intentionbehaviour gap in physical activity: The moderating effect of individual differences in the valuation of physical effort [_eprint: https://bpspsychub.onlinelibrary.wiley.com/doi/pdf/10.1111/bjhp.12790]. British Journal of Health Psychology, 30(2), e12790. https://doi.org/https://doi.org/10. 1111/bjhp.12790 e12790 BJHP-2024-0209.R2. Mengiste, B. M., Alemayehu, Y. A., Mersha, G. T., Ali, A. S., Tadesse, Y. F., Dirar, T. M., & Bimir, M. N. (2025). Exploring the drivers of walkability: Implications for enhancing perception and policy to livable cities. City and Environment Interactions, 26, 100197. https://doi.org/10. 1016/j.cacint.2025.100197 Nations, U. (2015). United nations framework convention on climate change, paris agreement [Ed]. https://unfccc.int/sites/default/files/resource/parisagreement_publication.pdf Neale, C., Aspinall, P., Roe, J., Tilley, S., Mavros, P., Cinderby, S., Coyne, R., Thin, N., & Ward Thompson, C. (2020). The impact of walking in different urban environments on brain activity in older people. Cities & Health, 4(1), 94–106. https://doi.org/10.1080/23748834. 2019.1619893 23 Olsson, S. R., & Elldér, E. (2023). Are bicycle streets cyclist-friendly? micro-environmental factors for improving perceived safety when cycling in mixed traffic. Accident analysis & prevention, 184, 107007. https://doi.org/10.1016/j.aap.2023.107007 Parma, J. O., Bacelar, M. F. B., Cabral, D. A. R., Recker, R. S., Orsholits, D., Renaud, O., Sander, D., Krigolson, O. E., Miller, M. W., Cheval, B., & Boisgontier, M. P. (2023). Relationship between reward-related brain activity and opportunities to sit. Cortex, 167, 197–217. https: //doi.org/10.1016/j.cortex.2023.06.011 Peddie, L., Gosselin Boucher, V., Buckler, E. J., Noseworthy, M., Haight, B. L., Pratt, S., Injege, B., Koehle, M., Faulkner, G., & Puterman, E. (2024). Acute effects of outdoor versus indoor exercise: A systematic review and meta-analysis. Health Psychology Review, 18(4), 853–883. https://doi.org/10.1080/17437199.2024.2383758 Perugini, M., Gallucci, M., & Costantini, G. (2014). Safeguard power as a protection against imprecise power estimates. Perspectives on psychological science, 9(3), 319–332. https://doi. org/10.1177/1745691614528519 Rhodes, R. E., McEwan, D., & Rebar, A. L. (2019). Theories of physical activity behaviour change: A history and synthesis of approaches. Psychology of Sport and Exercise, 42, 100–109. https: //doi.org/10.1016/j.psychsport.2018.11.010 Ripple, W. J., Wolf, C., Newsome, T. M., Gregg, J. W., Lenton, T. M., Palomo, I., Eikelboom, J. A. J., Law, B. E., Huq, S., Duffy, P. B., & Rockström, J. (2021). World scientists’ warning of a climate emergency 2021. BioScience, 71 (9), 894–898. https://doi.org/10.1093/biosci/ biab079 Semenescu, A., Gavreliuc, A., & Sârbescu, P. (2020). 30 years of soft interventions to reduce car use–a systematic review and meta-analysis. Transportation research part D: transport and environment, 85, 102397. https://doi.org/10.1016/j.trd.2020.102397 Simonsohn, U. (2015). Small telescopes:detectability and the evaluation of replication results. Psychological Science, 26 (5), 559–569. https://doi.org/10.1177/0956797614567341 Staats, H., Kieviet, A., & Hartig, T. (2003). Where to recover from attentional fatigue: An expectancyvalue analysis of environmental preference. Journal of environmental psychology, 23(2), 147– 157. https://doi.org/10.1016/S0272-4944(02)00112-3 Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O., & Ludwig, C. (2015). The trajectory of the anthropocene: The great acceleration. The Anthropocene Review, 2(1), 81–98. https: //doi.org/10.1177/2053019614564785 Timmons, S., Andersson, Y., McGowan, F. P., & Lunn, P. D. (2024). Active travel infrastructure design and implementation: Insights from behavioral science. Wiley Interdisciplinary Reviews: Climate Change, 15(3), e878. https://doi.org/10.1002/wcc.878 Vera-Villarroel, P., Contreras, D., Lillo, S., Beyle, C., Segovia, A., Rojo, N., Moreno, S., & Oyarzo, F. (2016). Perception of safety and liking associated to the colour intervention of bike lanes: Contribution from the behavioural sciences to urban design and wellbeing. Plos one, 11(8), e0160399. https://doi.org/10.1371/journal.pone.0160399 Willenbockel, V., Sadr, J., Fiset, D., Horne, G. O., Gosselin, F., & Tanaka, J. W. (2010). Controlling low-level image properties: The shine toolbox. Behavior research methods, 42(3), 671–684. https://doi.org/10.3758/BRM.42.3.671 Xing, Y., Volker, J., & Handy, S. (2018). Why do people like bicycling? modeling affect toward bicycling. Transportation research part F: traffic psychology and behaviour, 56, 22–32. https: //doi.org/10.1016/j.trf.2018.03.018 24