Study characteristics

Table 2 provides a detailed overview of all the study characteristics that were included. Among the 18 studies included, 4 were conducted in the United States [26, 45,46,47], 9 in Europe (i.e., United Kingdom [48], France [40, 49], the Netherlands [21], Norway [50], Italy [51], Czech Republic [52], Greece [27], and Germany [53]), and 4 in Asia (i.e., Israel [4] and Singapore [24, 54, 55]). The remaining study did not mention where the study was carried out [25].

Table 2 Overview of study characteristics

All studies were experimental, 9 of them used within-subjects, and 4 used a between-subjects study design. Four studies used a mixed-study design [24, 47, 51, 53].

Participants

Participants' sample sizes ranged from 4 [4] to 150 adults [24] with ages ranging from 18 to over 65 years. In terms of gender, 15 studies included both men and women, and 3 studies did not report gender information [46, 52, 55]. Overall, 78% (n = 14) of the study participants were either students, university employees or colleagues while the remaining studies did not provide information about participants ‘occupations. Only two studies included the general population [55] and specific target population (i.e., Land Transport Authority) [24]. No studies reported participants’ ethnicity.

Number of withdrawals, exclusions, lost to follow-up and reasons

Six studies reported participant withdrawals, exclusions, or failures to follow up during their experiments [21, 40, 47, 49, 54, 55], with only one study reporting exclusions due to symptoms of cybersickness caused by VE [40].

The main reasons for participant exclusion during the data analysis phase were incorrect eye-tracking calibration [49], non-qualified data driven from an electrocardiogram (ECG) [54], and a combination of technical issues and participants failing to adhere to the study instructions [47].

Risk of bias

The quality scores among the studies ranged from 42 to 96% (with 0% being the worst and 100% being the best), as presented in Table 2. Item 6 ‘If interventional and blinding of investigators was possible, was it reported?’, and item 7 ‘If interventional and blinding of subjects was possible, was it reported?’ were not applicable to these studies. Notably, item 12 ‘Controlled for confounding?’ appeared to be the most frequently missed among studies. Interestingly, studies had an average score of 73%, indicating an average good quality.

Synthesis of resultsGeographical environment attributes

Among the 18 studies included, 33% (N = 6) were carried out in a built environment [4, 21, 24, 40, 47, 55], 28% (N = 5) were conducted in nature [45, 48, 50, 52, 53], and 33% (N = 6) explored the social environment [25,26,27, 46, 49, 51]. In addition, one study compared nature with the built environment [54]. Geographical environment attributes can be categorized into static and dynamic. Static attributes remain constant over time, while dynamic attributes are non-stationary factors that might change or move in the VE (i.e., the presence of people, cyclists, cars, and their interactions). Table 3 references all the geographical environment attributes investigated.

Table 3 Geographical environment attributesWalking environmental correlates

Static attributes investigated in relation to walking comprise greenness/vegetation, blue environment, built elements, street inclinations, parked car, time of the day, and landmarks. Dynamic attributes include crowd density, soundscape, and car’s adaptive headlight systems (AHS).

Greenness was measured as the presence of greenery (vs. absence) in terms of trees along the street [4], grassy areas with trees [48], and spatial enclosures shaped by vegetation, including trees, bushes, and grass [45]. These green attributes were explored in relation to aesthetics [4, 21], stress [54], well-being, and perceived safety [45], and nature connectedness (i.e., one’s subjective sense of feeling connected to the natural world)[54].

Blue environment was investigated as a walk along a river in combination with built elements [50], and the presence of a shallow pond (vs. absence) to measure people’s movement alterations [48].

The impact of landmarks on perceived walking distance at various street inclinations was examined in relation with route decisions and spatial memory [40]. Additionally, pupil fixation on a parked car was investigated using eye tracking [4].

The influence of time of the day (daytime vs. nighttime) on the positive and negative affects experienced during the walk was investigated [53].

Social environment was studied in five distinct ways:

1) Observing individuals walking within a virtual crowd with varying densities (i.e., from 1.5 pedestrians per square meter to 24 in the VE) [25, 49].

2) Investigating impacts of crowd density (low: 1 pedestrian vs. high: 2.5 pedestrians per square meter), walking speed (low: 1.2 m/s vs. high: 3.8 m/s), and walking direction (straight vs. diagonal) on movement behaviors [26].

3) Assessing the impacts of tactile feedback (i.e., a sensory experience within a crowd), on movement behavior [27].

4) Investigating the effects of crowds with diverging motions and dividing the crowd into distinct subgroups, each with different proportions, influencing participants' path choices [46].

5) Exploring reachability and comfort distance judgements toward humans and objects while standing still (passive) or walking toward stimuli (active) [51].

Soundscape mimicking the presence of pedestrians, cyclists, and cars as well as their interactions, was investigated in various aspects including presence vs. absence [52, 53], auditory feedback (footstep sounds) [52], static vs. 3D sound [52], and music [52, 53].

Finally, presence or absence of a car's AHS was explored in terms of the color (white vs. red) and the timing of an icon projected onto the road. This icon was part of the dynamic attributes of the environment while participants crossed a road [47].

Cycling environmental correlates

Geographical environment attributes examined in relation to cycling behaviors included cycling path width and separation [21, 24, 55], greenness [21], and traffic volume [21, 24, 55]. Path separation conditions included sidewalk next to pedestrians, painted bicycle path on the sidewalk, painted bicycle path on the road, roadside next to vehicles, and segregated bicycle path [24, 55]. Furthermore, path width (wide vs. narrow) was investigated in combination with path separation (well-separated vs. poorly-separated) [21].

Presence (vs. absence) of greenness was explored in relation to aesthetics using a stated preference conjoint experiment [21]. Additionally, in terms of traffic volume, car traffic volumes (high vs. low) were assessed in relation to perceived levels of safety [24], and pedestrian and cyclist traffic volumes (high vs. low) were investigated in relation to enjoyment [21]. Moreover, cyclists' behaviors at street junctions were examined in relation to the presence (or absence) of car traffic [55].

VR measurements

Most experiments used HMD (N = 17), with only one study employing CAVE [47]. For detailed information regarding the different models of HMD or VR glasses, and CAVE setups, refer to Appendix 1: Table 4.

User’s natural interaction with virtual environment (VE)

User natural interaction with the VE refers to an individual's intuitive engagement with VE that simulates real-world interactions [56]. This is crucial for understanding the degree of realism and effectiveness of the virtual experience. Four interaction dimensions were introduced to describe participants' VR locomotion experiences:

1.

Immersion: how the technique (e.g., walking in the place) supports users’ attention in the virtual task and environment and alters their sense of space, time and self.

2.

Ease-of-use and mastering: how operating the technique (e.g., using a controller) can be learned and can enable efficient navigation.

3.

Competence and sense of effectiveness: how the technique can assist the users in accomplishing their goals and tasks.

4.

Psychophysical discomfort: if the technique causes fear, motion-sickness, and tiredness [57].

Overall, 67% (n = 12) of studies have reported on different aspects of users’ level of natural interaction with VR [4, 21, 24,25,26,27, 40, 50,