Walkable neighborhoods = active kids? Exploring the relationship between a physical activity intervention for youth and walkability

Walkable neighborhoods = active kids? 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Exploring the relationship between a physical activity intervention for youth and walkability Laura Eipel, Paula Teich, Fabian Arntz, Daniel Scheller, Christoph Mall, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7365397/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2025 Read the published version in BMC Public Health → Version 1 posted 9 You are reading this latest preprint version Abstract Children and adolescents often do not meet the WHO´s physical activity (PA) recommendations. As many of them live in urban areas, these are important spaces for PA-promotion. Objective measures such as the Walkability Index are often used to assess urban spaces in terms of their PA friendliness. However, it is unclear whether such parameters can predict PA behavior of children and adolescents. This study examines the relationships between the Walkability Index and data of the intervention “Kreuz & Quer” (K&Q), promoting PA. K&Q collected data from 9,852 children and adolescents in urban neighborhoods. Activity – measured by interactions with K&Q checkpoints – acted as the dependent variable in a linear mixed models approach. Walkability served as a fixed factor and district, season of year and intervention day as random effects. Results indicate a significant positive correlation between a high Walkability Index and PA levels in children and adolescents. Some of the observed variance can be explained by the random effects. There is still unexplained variance, suggesting the need to consider additional influences to explain youth PA behavior. These may include qualitative explanations to provide a holistic picture. Subjective perspectives can help create environments that are structurally conducive to walking, thereby promoting PA. Walkability Index physical activity intervention children and adolescents urban design and planning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction 1.1 Physical (in)activity in children and adolescents Physical activity (PA) is related to multiple health benefits. Regular movement can increase physical, mental, and social health (Dimitri et al., 2020 ; Mitra et al., 2020 ; Pojednic et al., 2022 ). It can strengthen the cardiovascular system and the development of the musculoskeletal system (Chaput et al., 2020 ; Rhodes et al., 2020 ). In addition, PA helps to prevent medical conditions, such as overweight and obesity, or cancer (Baobeid et al., 2021 ; Dhuli et al., 2022 ; Warburton et al., 2006 ). A sedentary lifestyle, on the contrary, has a negative impact on health (Robert-Koch-Institut, 2023; Ubiali et al., 2021 ). It is estimated that 80% of adolescents across the globe are categorized as inactive (Robert-Koch-Institut, 2023; van Sluijs et al., 2021 ). In Germany, only 22,4% of girls and 29,4% of boys aged three to 17 reach the recommendations of the World Health Organization (WHO) of 60 minutes of PA per day. Especially childhood though should be regarded as an important stage of life to provide a basis for a health-conscious behavior in later life (Finger et al., 2018 ; Neil-Sztramko et al., 2021 ; Rhodes et al., 2020 ; Robert-Koch-Institut, 2023). The number of children and adolescents who reach the WHO recommendations should certainly be higher than it currently is. Individual PA behavior can be seen as a product of multiple interacting factors. In children and adolescents, those factors encompass demographic, individual, social, and environmental variables (Patnode et al., 2010 ). These environmental variables, or physical infrastructures such as parks, buildings and recreational facilities can either enable or hinder PA and encompass “settings such as the neighborhood or schools based on site-specific physical infrastructures” (Kelso et al., 2021 ). These settings provide spaces for both individual PA and social interaction(Kelso et al., 2021 ). For instance, PA is positively influenced by a residential environment that provides sufficient opportunities for PA (Smith et al., 2017 ). One such opportunity is that of active travel, a factor which has the potential to influence positive changes in PA and sedentary behavior (SB) in daily life (Kleszczewska et al., 2020 ). However, in terms of different modes of transport within a city, the car is the dominant mode of transport as stated in the German mobility profitability report (Nobis & Kuhnimhof, 2018 ). Children up to the age of nine are heavily dependent on their parents for mobility and are driven half the time dedicated to travel, which means that children are strongly socialized by this mode of transport (Nobis & Kuhnimhof, 2018 ). A study byHillman et al. ( 1990 ) is still influential in urban planning, childhood studies, and public health. It revealed a dramatic decline in the number of children permitted to travel without adult supervision (Independent Mobility [IM]). In 1971, 80% of seven- to eight-year-olds walked to school alone; by 1990, this had dropped to just 9%. The main reason parents limited their children’s mobility was fear of road traffic.Schoeppe et al. ( 2014 ) found an ongoing decline in IM due to parental concerns about road safety, stranger danger, or longer travel distances to schools and recreational facilities.Han et al. ( 2022 ) note, that despite an increasing awareness of the benefits of PA and IM, more parents are limiting children’s freedom. These restrictions manifest in the establishment of boundaries, time limits and spatial constraints (Carver et al., 2008 ; Valentine, 1997 ). For instance, it is not permissible for children to walk or cycle without adult supervision. Furthermore, access to parks or streets in the vicinity is restricted, and children are instead encouraged to travel by car rather than walking (Carver et al., 2008 ; Valentine, 1997 ). The authors posit that this creates a vicious cycle: a decline in the number of children in public space leads to less perceived safety, which further reduces autonomy. Consequently, children lose opportunities to develop spatial awareness, decision-making skills, and confidence. The authors warn that this shift is conducive to greater car dependency, reduced physical activity, and weaker community engagement (Hillman et al., 1990 ; Schoeppe et al., 2014 ). Active travel by foot or bike coul help to build a sense of independence, self-confidence, and concern for the environment, and plays an important role in improving the health of children and adolescents (Kleszczewska et al., 2020 ). There are many approaches to the promotion of PA, of which active travel in the urban built environment is one of the most promising. 1.2 Promotion of physical activity Research findings underscore a complex interplay of factors influencing children´s and adolescents’ engagement in PA. Hu et al. ( 2021 ) categorize influencing factors into the levels intrapersonal, interpersonal, organizational, community and public policy and thereby refer to the social ecological model (SEM) proposed by McLeroy et al. ( 1988 ). A key factor that can be categorized at the organizational level and seen as an environmental approach is the built environment. Public health research provides strong evidence that the built environment can influence overall health by promoting PA in younger populations (Krist et al., 2017 ; Yang et al., 2021 ). In the context of the built environment, PA pertains to various factors, including the extent of movement during walking and active travel within a neighborhood (Fathi et al., 2020 ; Fina et al., 2022 ; Scheller & Bachner, 2024 ). Associations have been observed between neighborhood design and walking and cycling for transportation (Frank et al., 2006 ). Studies show that an increase in walking can be achieved through mixed-use residential, commercial, office, entertainment, and other land use and that “the street network pattern can influence the choice of travel routes and modes of transportation” (Jensen et al., 2017 ; Wang et al., 2023 ). As mentioned above, walking can positively influence PA and SB, but also promotes several peripheral benefits for society, i.e. facilitating social interaction between people from different neighborhoods and contributing to the creation of a more pleasant and safer urban environment (Fathi et al., 2020 ; Freitas et al., 2024 ; Kleszczewska et al., 2020 ). Hence, urban planning should focus on promoting walking as an important factor of PA for improving public health. The finding's consistency across cities indicates the potential value of engaging the urban planning, transportation, and parks sectors in efforts to mitigate the health consequences of global physical inactivity (Sallis et al., 2016 ). This raises the question of how health- and walk-promoting urban design can best be achieved, particularly with the specific target group of children and adolescents in focus. In this regard, schools, their surroundings, and the immediate living environment of children and adolescents may be a particularly promising settings for interventions to promote PA (Jago et al., 2023 ; Pfeifer & Rütten, 2017 ). Kelso et al. ( 2021 ) also identified the neighborhood, school, and recreational environments as the settings most frequently used for PA in children and adolescents. Studies reported higher PA values for children and adolescents on streets, roads and pavements rather than in locations with green spaces, which could be due to more time being spent in built environments (Kelso et al., 2021 ). Additionally, participation in programs aimed at promoting PA seems to be strongly influenced by social interactions, such as those with peers and friends, referring to the interpersonal level (Hu et al., 2021 ). According to this, play, especially for children, represents a relevant factor in getting involved in neighborhood PA. In this regard, gamified scenarios appear to be beneficial for this target group (Gkintoni et al., 2024 ; Mazeas et al., 2022 ). In these cases, traditional button presses in sedentary video games are replaced with gross motor movements, thereby rendering PA enjoyable, engaging and rewarding (Gkintoni et al., 2024 ; Spring et al., 2025 ). The utilization of gamified scenarios, when designed with sufficient intensity and frequency, has been demonstrated to enhance participation, reduce levels of sedentariness, and promote positive physiological parameters such as heart rate (Ramírez-Granizo et al., 2020 ). Despite the limited scope, interventions employing this approach have already shown lasting changes in PA behavior, even long after the intervention has concluded (Harris, 2018 , 2019 ). To effectively support such socially and play-driven engagement in PA, the surrounding environment must also offer opportunities for active participation. In this regard, recent research has increasingly incorporated the concept of Walkability , a term that reflects how urban planning and pedestrian-friendly design can facilitate and encourage daily walking behavior (Baobeid et al., 2021 ). 1.3 Walkability and the Walkability Index Walkability as an umbrella term describes various environmental factors, including design and planning focusing on promoting walking and recreational activities, thus incorporating social factors like interactions between resident children or safety aspects. Walkability plays a role in urban planning as well as in public health, linking those fields (Paglione et al., 2024 ). Definitions of Walkability vary according to the respective field of research (Banger et al., 2024 ). In the original sense of urban design and planning, Walkability refers to the ease with which people can walk in an area. It focuses on the built environment and includes a “set of capacities of any given neighborhood that is embodied in urban morphology in three main ways”(Dovey & Pafka, 2020 ) including density, functional mix and access networks. These three elements are attributable to the foundational “3-Ds” density, diversity and design , a framework proposed by Cervero and Kockelman ( 1997 ). These measures can be used to design walkable, transit-oriented neighborhoods, which in turn can increase levels of primarily purpose-related walking or cycling to work or other places, resulting in positive outcomes for PA and public health (Dovey & Pafka, 2020 ; Westenhöfer et al., 2023 ). From a public health point of view, a walkable area can be defined as a PA-supporting area (Banger et al., 2024 ). It includes “everything made or maintained by people with characteristics that can promote increases in physical activity” (Duncan et al., 2014 ). With the adoption of this concept by researchers and practitioners in PA and public health, its scope expanded to include walking for both transportation and recreation, as well as other forms of PA such as biking (Kerr et al., 2014 ). Consequently, Walkability now extends beyond its literal sense of walking alone. Importantly, the influence of built-environment factors varies depending on the manner of walking. Whilst an enhancement in street connectivity, destination accessibility and transit provision is conducive to utilitarian walking, elements such as street greenness, which enhance the aesthetic appeal, safety and comfort, have a more significant influence on recreational walking (Bandara et al., 2025 ; Yu et al., 2024 ). Today, the term Walkability has a role to play in areas that go beyond urban planning and design, such as public health, climate change and social equality. In this sense, high Walkability can have a major impact on increasing residents´ PA and wellbeing, improving air quality, promoting social inclusion and providing pleasurable leisure spaces (Ibrahim et al., 2024 ; Venerandi et al., 2024 ). One way to assess Walkability in a city is to measure a so-called Walkability Index which can be used for planning a health promoting and sustainable mobility in the city (Frank et al., 2010 ; Heudobler et al., 2024 ; Sedlmeir, 2022 ; Tran, 2018 ; Yang et al., 2021 ). There are a number of approaches to this issue, each of which results in a slightly different index. However, the majority of these approaches are, in addition to more diverse dimensions, fundamentally based on the following parameters: urban density, land use mix, street connectivity and distance to facilities (Fonseca et al., 2022 ; Venerandi et al., 2024 ). These parameters are measured using Geographical Information Systems (GIS) and aggregated into a single Walkability Index (Frank et al., 2010 ). The index can be used in statistical studies for a variety of questions, linking health, environmental elements, and Walkability . In addition to various versions of the Walkability Index , there are also other measures with similar aims, such as the Walkscore (Hall & Ram, 2018). This is based on similar parameters, but so far there are no usable versions that have been specifically adapted to certain cultural areas. For example, the Walkscore is based on assumptions that relate to North American cities, which cannot be easily transferred to European cities (Hall & Ram, 2018). Next to the built environment, qualitative aspects such as socio-cultural offers, the accessibility of green space and safety aspects play a decisive role according to PA behavior, especially for children and adolescents (Ibrahim et al., 2024 ; Jennings & Bamkole, 2019 ; Yang et al., 2021 ). However, the GIS-based Walkability Index is not able to depict these qualitative aspects as there are particularly high costs of conducting direct field research on subjective Walkability (Telega et al., 2021 ). Concluding, the index is based on the narrow incorporation of the parameters of urban density, land use mix, street connectivity, and distance to facilities according to the field of urban planning and does not consider subjective facts and perceptions of different target groups (Rodrigue et al., 2022 ; van der Vlugt et al., 2024 ). This particularly limits research on children´s and adolescents’ environments and raises the question of whether classic urban design and planning measures, such as the objective Walkability Index , can be used in public health research to explain issues such as child and adolescent mobility, and even to inform decisions on PA promotion. 1.4 Problem and hypothesis Evidence shows a correlation between highly walkable areas and higher levels of active transportation among resident children and adolescents (Ubiali et al., 2021 ). However, other existing studies have produced mixed results, with some revealing a link between a greater Walkability and a decrease in PA (Bird et al., 2022 ). A systematic review found that eight out of 13 studies reported a positive association between higher Walkability , resulting from a diverse land use mix and street connectivity, and more active lifestyles. However, five studies did not support this (Yang et al., 2021 ). Laxer and Janssen ( 2013 ) concluded in their article, that Walkability as well as road and park space density, are associated with youth inactivity, a finding supported by Janssen and King ( 2015 ). They stated that walkable neighborhood designs may impede PA in children, adolescents, and neighborhoods, as Walkability was not associated with active travel to school and was negatively associated with free play. These mixed findings reveal Walkability to be a nuanced concept. While some neighborhoods can be categorized as highly walkable due to high street connectivity, they also tend to have more traffic, air pollution and crime as a result of being denser areas with more destinations and people. Bucksch et al. ( 2021 ) examined differences between rural and urban areas: Indices for Walkability do not seem to be associated with objectively measured PA in either area. Despite higher indices for walkable areas in urban environments, children and adolescents from rural areas walked more for transportation purposes (Bucksch et al., 2021 ). These findings show that a higher Walkability Index does not equate to more favorable PA or health scores. What accounts for this heterogeneity is mainly incongruities in methodology regarding Walkability , but also in defining and measuring PA-related behavior (Yang et al., 2021 ). The involvement of children and adolescents into planning and evaluating processes is a scarce practice and it is difficult to transfer results of studies with adult samples to children and adolescents (Davison & Lawson, 2006 ; Freitas et al., 2024 ; Scheller & Bachner, 2024 ). They are less autonomous in their decision making and their mobility is mainly limited to their individual neighborhood. Thus, their immediate environment might have a greater effect on their PA compared to adults (Scheller & Bachner, 2024 ). Objective measures of Walkability such as the Walkability Index are mostly constructed from an adult perspective and do not take into account the subjective perceptions of children and adolescents. As a result, no final conclusion can be drawn at this time as to whether the objectively measured Walkability Index is coherently related to PA in children and adolescents (Yang et al. 2021 ). This paper therefore examines the relationship between objective Walkability and the activity of children and adolescents in the context of a large-scale intervention in the urban area of the city of Munich in Germany. It is to be expected that this study will provide an answer to the following question: Is there a statistically significant and practically relevant correlation between the Walkability Index of specific urban locations and the levels of PA of children and adolescents carried out there? 2 Method As noted above, it is unclear whether classical urban design and planning measures can be used in public health research to explain issues such as the PA of children and adolescents, or even to inform PA promotion interventions. The “Kreuz & Quer” (“Criss-Cross”) (K&Q) intervention is a project combining measures of urban planning and public health research, implemented by the mobility department of the City of Munich and the XXXUniversity XY in seven city districts (Ellinger et al., 2025 ). 2.1 Data composition The key variables for investigating the research question are Activity (indicator for PA) and Walkability (measured by the Walkability Index ). Activity data was collected as part of the K&Q intervention, the Walkability Index was provided by the department for climate protection and environment of the city of Munich (Sedlmeir, 2022 ). In addition, the city district from which the data originated ( District ), the time of year at which it was collected ( Season ) and the day of the respective wave of K&Q from which the data originated ( Day ) have been documented as additional variables. Data sources for Activity and Walkability are described in more detail below. 2.1.1 Activity : Physical activity data of the intervention The K&Q project was initiated in 2019 by the mobility counsellor of the city of Munich. The intervention was carried out in three waves per year for four to six weeks in different city districts each. It is a gamified urban intervention that uses a digital scavenger hunt-like format, encouraging resident children and adolescents to explore their neighborhoods by locating and scanning physical checkpoints, in form of electronic boxes, to collect points. The participants can collect points as individuals or teams, such as school teams. In each chosen neighborhood, several different boxes are installed on accessible objects on the streets, for example streetlamps, for a certain period of time. The electronic boxes are regularly distributed in the seven neighborhoods at distances of about 500 meters. Figure 1 visualizes the box locations in each district on a map of Munich. The mean area size of neighborhoods where the intervention took place is 12,7 km 2 (± 6,7). Figure 1. Munich; the red dots represent the box locations, the red lines represent the district borders Participants receive a physical game card that can be scanned at the respective boxes. Each scan is rewarded with points. Participants can walk, bike or scooter from one box to another to scan their cards for collecting points and kilometers over the entire period of the campaign. Higher points are awarded for scanning more different boxes without a major break. The intervention incorporates gamification elements to keep participants motivated. These elements include daily ranking updates on the homepage, instant audio feedback after scanning a box, or the chance to win attractions such as a bouncy castle, a photo box or face painting for the final award ceremony. Participants are recruited through information campaigns at the schools via posters and flyers by the mobility council of the City of Munich. The legal guardians of underage participants had to give written informed consent to the use of their children's data in order to obtain a game card for their children. This paper analyzes data from seven districts in the City of Munich, which are numbered from one to seven in this paper. For the purposes of this analysis, each scan at one of these electronic boxes is counted as one Activity , which is the indicator for PA. The more often participants have visited a particular box, the higher the Activity of that box. For further interpretation of the data, the number of participants and the quantity of electronic game boxes were documented as well. It is important to understand that in this case Activity is not to be equated with an exact intensity or duration of PA, as in other PA studies. In this study, the variable Activity is indicative of PA, as the children are observed to be actively travelling from one box to another. Activity also serves to indicate the level of attractiveness of a given box location for children and adolescents to move there with a potentially free choice of other boxes. 2.1.2 Walkability : An index for Munich The Walkability Index is a specific measure for planning walkable environments and can be calculated for every city with available data (Frank et al., 2010 ). The Walkability Index employed in this study utilizes the pivotal factors of population density, connectivity, and entropy. The administration of the City of Munich used data sets from the Statistical Office, the Service for Geodata and Open Street Map in order to calculate specific Walkability Index scores for the whole city of Munich. Those calculations are based on the work of Frank et al. ( 2010 ). The Floor Area Ratio (FAR), as incorporated in the original Walkability Index for North American cities by Frank et al. ( 2010 ), was omitted in this study due to constrained data availability and considerable contextual variations. In many European cities, reliable and consistent FAR data is not available at neighborhood level. Furthermore, population density can function as a practical proxy for built intensity in compact European urban settings (Krehl et al., 2016 ). This approach aligns with recent European studies, which similarly exclude FAR and instead prioritize components such as population density, land use mix, and street connectivity (Lam et al., 2022 ; Patel et al., 2025 ). This approach is intended to ensure methodological consistency and comparability across regions. Munich is divided into 25 city districts. Each of these districts is further subdivided into smaller neighborhood units for statistical and administrative purposes. The Walkability Index is calculated on the smallest-scale level in so called “Stadtbezirksviertel” (SBV). The amount of SBVs (475 in total for Munich) per district varies depending on the size and structure of the district. The Walkability Index is calculated from three elements: population density (population per km 2 , standardized to z-score), connectivity (number of crossroads per km 2 , standardized to z-score) and entropy (measure for a balance of the distribution of living space, industry, culture, and administration in a neighborhood, standardized to z-score). All standardized z-scores from the three elements are combined to the final Walkability Index for each SBV (Sedlmeir, 2022 ). This results in scores from − 9 to 13.1, whereupon higher values indicate a higher Walkability . Based on this small-scale measurement of the Walkability Index for the SBVs, every box of K&Q can be assigned to their own individual Walkability Index score. Walkability is an indicator of the attractiveness of walking, which makes it interesting to see whether the two indicators of Activity and Walkability are correlated and whether a higher attractiveness of walking ( Walkability ) leads to more scans of the corresponding box ( Activity ). 2.2 Data preparation and analysis To test the relationship between Activity and Walkability , a linear mixed model (LMM) approach was implemented for the main analysis. Fixed effects correspond to the relation between the main predictor variables and the dependent variable ( Activity ). In this case, Walkability was defined as fixed effect (Pinheiro & Bates, 2004 ). The effects, which are not argued to be the correlation that is studied but could have an influence on the results, are referred to as random effects. In this case, District , Season and Day were therefore included as random effects with regard to their correlations with Activity . In summary, this means that primarily the relationship between the dependent variable ( Activity ) and the fixed effect ( Walkability ) was examined, as well as the relationships with the random effects were also considered ( District, Season, Day ). The initial visual inspection of the collected data was able to rule out heteroscedasticity. Based on the qualitative evaluation of the QQ plot, however, a positive skewness and thus a violation of the normal distribution had to be assumed. A log transformation was therefore carried out, which significantly improved the model quality. The comparison of different model variants attested the best fit to the more complex model described above (Activity ~ Walkability + (1 | District) + (1 | Season) + (1 | Day) in comparison with simpler model variants or model variants with interaction effects measured by AIC and BIC indicators (Pinheiro & Bates, 2004 ). Due to the model and variable structure, multicollinearity was not tested. The preparation of the data and the statistical analyses were conducted with the software R (latest version 4.4.2, 2024) and the editor R-Studio. 3 Results 3.1 Descriptive results To further describe the districts included for the intervention the respective population size under 18 years and the number of households with children was calculated with the use of an interactive map from the monitoring of the social department of the City of Munich. Table 1 provides a comprehensive overview of the numbers for each district, categorized according to the year in which the respective round of K&Q took place. Table 1 Characteristics of the seven districts including the respective year the intervention took place, the underaged population and the number of households with children. District Year Population < 18 Households with children 1 2 3 4 5 6 7 Mean 2019 2022 2022 2022 2023 2023 2023 3135 9362 9969 8750 10174 13432 7675 8928,1 1856 6046 6114 5244 6056 8192 4580 5441,1 Table 2 Descriptive characteristic values (sums and means) for the central variables. District Season Participants N (mean age ± sd) Days Boxes N Activity sum Walkability mean (range) 1 Fall 961 (8,5 ± 2,1) 45 43 56099 1.04 (-3; 4.36) 2 Spring 1946 (8,4 ± 2) 47 51 173997 3.63 (0.67; 8.34) 3 Summer 1071 (8,3 ± 1,9) 29 50 51937 0.23 (-3; 3.08) 4 Fall 876 (7,9 ± 2) 47 38 56164 3.14 (0.67; 8.34 5 Spring 1827 42 38 114556 1.14 (-2.4; 3.51) 6 Summer 1778 36 40 121929 1.04 (-1.9; 4.51) 7 Fall 1487 47 40 51033 2.82 (-1; 4.85) Mean 1407.4 41.8 42.9 Sum 9852 293 300 Table 2 provides an overview of the main variables for all seven districts, including the season, number of participants with mean age and standard deviation, duration of each intervention wave, number of boxes, summed number of activity, and the mean value for the Walkability Index with range. For a descriptive evaluation of the success of K&Q, see also Ellinger et al. ( 2025 ). The intervention waves were conducted in seven of the 25 existing districts of Munich. The age of the participants was documented with the information being provided by participants who had completed the registration process in a satisfactory manner (58% of all participants). The mean age for all participants was 9.7 years. The single interventions in the neighborhoods lasted from 29 to 47 days (mean = 41.8) and a total of 300 boxes existed during the whole period. The highest number for Activity can be seen in district 2, with a total of 173,997 box scans. Table 2 points out that the neighborhoods in districts 2 and 4 have the highest mean value of Walkability , the lowest mean value is assigned to district 3. According to the present data the mean Walkability indices for the respective neighborhoods range from − 3 (district 1) to 8.34 (district 2). 3.2 Statistical results 3.2.1 Relationship between Activity and Walkability The inferential statistical analysis using a LMM provides both indications of the extent to which Activity and Walkability are associated with each other, as well as how large the proportion of additional variables is in explaining the calculated variance. Table 3 Fixed effect: Statistical key figures on the relationship between Walkability (fixed effect) and the dependent variable of Activity . Estimate Standard Error p-value Walkability 0.0142 0.00134 < 0.001 As shown in Table 3 , Activity and Walkability show a highly significant correlation with a p-value of < 0.001. Due to the very low standard error, the estimate of 0.0142 can also be certified as highly reliable. The Hexbinplot in Fig. 2 illustrates the positive correlation between Activity and Walkability . Based on this data, an accumulation of Walkability Indices in the range between 0 and 4.5 can be observed. Areas with very low Walkability Indices also display very low Activity scores. 3.2.2 Random effects of the model explaining the variance Aside from this statistically significant relation between Activity and Walkability , additional factors contribute to the explanation of the observed variance of the data. Table 4 serves as an overview for the explained variance of the random effects District, Season and Day in the previously analyzed model. Table 4 Random effects: Statistical key figures on the proportion of the variance of the model explained by the variables District , Season and Day (random effects), as well as the variance not explained by the model ( Residual ). Variance District (Intercept) 0.008707 Season (Intercept) 0.003888 Day (Intercept) 0.005699 Residual 0.059434 There is a certain difference in the Activity scores that can be explained based on the respective District in which the respective wave of K&Q took place (proportion of the explanation of the total variance of the model: 0.008). As shown in Fig. 3, districts 3, 4 and 7 stand out with a comparatively higher level of Activity based on the average values. The comparatively smallest effect can be attributed to the Season factor (0.003), whereby those rounds of K&Q that took place in spring and summer generated a higher level of Activity (see Fig. 4). The Day factor explains 0.005 of the variance. As illustrated in Fig. 5 , a negative correlation can be assumed here, meaning that the longer the respective wave already lasts, the lower the Activity on that day. The comparably extremely high value of variance explanation for Residual in comparison to these factors implies that the model in this form cannot explain a total of 0.059 of the variance. 4 Discussion The present study investigates the association between Walkability by using the Walkability Index of the City of Munich, and and indicator for children´s and adolescents´ Activity collected as part of an intervention to promote urban PA behavior. Influences of the respective District , Season and Day were examined, too. The results show that the Walkability Index is a significant positive predictor of Activity as an indicator for PA in this study. Random effects add value, but most of the variance remains unexplained. 4.1 Relationship of Walkability and Activity The results show a highly significant association between Activity and Walkability. It seems that factors resulting in a high Walkability Index also play a decisive role for the Activity of children and adolescents. According to the elements the index comprises (population density, entropy, connectivity), following aspects could explain the observed association. The extended concept of Walkability also includes social determinants, such as the number of physically active people in a neighborhood influencing the PA level of people living in this area (Wendel-Vos et al., 2007 ). Population density therefore could be relevant for children as a higher density means more people in one area which could be related to more playmates and social interaction. The fact that density is conducive to a higher level of social activity for the residents of the area is also explained in the article by Fina et al. ( 2022 ). Another possible explanation for higher PA due to higher population density is that more retail services and facilities in these areas increase the number of potential destinations within walking or cycling distance, encouraging PA (Zou et al., 2021 ). A higher density could also mean that more people, including children, live in the vicinity of the box locations, so that more people walk past the boxes and Activity increases. The second objective factor for Walkability is land use mix, which is represented by the entropy index. The element of entropy includes land use types as sport and recreation which indeed have a positive influence on PA behavior and enhance children ́s activity (Fina et al., 2022 ; Frohlich & Collins, 2023 ; Sedlmeir, 2022 ). The mixture of habitation, retail and recreation enables children to explore a diverse environment and to have more offers at their disposal. A high land use mix contributes to the livability of a neighborhood and a more appealing walking environment, promoting healthier lifestyles (Jia et al., 2020 ). Consequently, high entropy is indicative of a diverse range of amenities and services available within a given neighborhood. A higher connectivity is related with shorter distances being beneficial for children as they can walk or bike to school, friends, or sports more easily. It also means having more options to choose from and, ultimately, deciding to opt for a more attractive route. On the other hand, a higher street connectivity may be related to fewer cul-de-sacs and thus be high-traffic areas which hinder children from outdoor PA (Jia et al., 2021 ). In the context of the K&Q intervention, high street connectivity and traffic might discourage children from crossing roads. Even if they see a box on the other side of the road, they cannot reach it to scan it, resulting in a decrease in Activity . This is also linked to the presence of safety features such as pedestrian crossings or traffic lights (Nordbø et al., 2020 ). Those safety aspects are not included in the Walkability Index , but do play a decisive role for the PA behavior of children and adolescents and should be included in future research (Smith et al., 2022 ; Trapp et al., 2012 ). The extent to which greater connectivity is ultimately beneficial or detrimental remains equivocal. It has thus been demonstrated that Activity and Walkability are indeed associated. This could, in principle, be explained by a closer analysis of the elements that make up the index. This is despite the fact that it has been questioned whether such an objective index is at all suitable without the subjective perspective of children and adolescents. The Walkability Index for Munich is made up of three factors which all can have stronger or weaker influences on the respective district and the citizens living there. Despite the positive association, there are diverse additional factors that play a role when analyzing activity patterns completely. Access to recreational facilities, school environments, or parental supervision may be more decisive for youth PA than Walkability alone (Ding et al., 2011 ). Therefore, Walkability can serve as one but not the only explanatory approach concerning children´s PA behavior in respective neighborhoods, as is evident from the figures relating to the variance explained by the random effects. 4.2 Influencing factors of physical activity The included random effects Day, Season and District were able to explain a part of the variance. The variable Day showed a negative correlation, meaning that Activity decreases over time. The longer the intervention lasts, the less active participants become. This could be due to a better acceptance and more enthusiasm of the participants at the beginning of the intervention, as previous studies have shown that interest in interventions declines over time (Ryan et al., 2017 ). An examination of the results for the variable Season reveals that the majority of cumulative Activity occurs in the spring and subsequent summer, with a comparatively smaller amount occurring in the fall. Temperatures, precipitation as well as day length vary across seasons and might affect PA behavior (Kolle et al., 2009 ). Thus, weather conditions play a role as it could have been exceptionally hot in the summer months and worse conditions, like cold or rain, appeared in fall months, refraining children from being more active outside. Especially Spring and Summer are predestined to cause higher PA levels (Atkin et al., 2016 ; Garriga et al., 2021 ; Kolle et al., 2009 ). As the results show an influence of seasonal variations on PA, this should be considered in future public health research and the design of interventions to promote PA, as seasonality can be perceived as both a facilitator and a barrier (Garriga et al., 2021 ). Interventions could be adapted depending on the season with specific challenges, outdoor- and indoor-activity opportunities or weather adapted events. The variable District , which explains part of the variance in Activity , reflects a range of underlying contextual factors influencing PA. These may include geographical, social, and demographic characteristics. Smith et al. ( 2017 ) highlight that built environment features relevant to PA, such as access to green spaces, perceived safety, infrastructure for active transport, and land-use mix, are often unequally distributed across urban districts, particularly along socioeconomic lines. In addition, districts may vary in terms of population composition, cultural norms, availability of recreational facilities, and exposure to traffic or environmental stressors, all of which shape opportunities for and attitudes toward PA. Although intra-district variation exists, district-level clustering likely captures macro-level environmental and social disparities relevant to urban PA behavior. These contextual factors may explain variance not accounted for by objective indicators like the Walkability Index alone. As Gemmell et al. ( 2023 ) point out, neighborhood characteristics influencing outdoor play include playgrounds, sidewalks, destinations, green spaces, traffic levels, social safety, and cohesion. Since these factors can vary substantially between districts, differences in PA behavior are a plausible outcome. However, a great part of the model variance is not explained by the included fixed or random effects. It is possible that a proportion of the unexplained variance in the model can be attributed to the fact that the index developed by Frank et al. ( 2010 ) was not originally designed to assess Walkability for children and adolescents. It is noteworthy that the attributes may not be aligned with the specific requirements of the individual's walking needs (Buck et al., 2015 ; Lee et al., 2020 ). Furthermore, it suggests the presence of additional factors which may neither be displayed by objective indices nor theory-based models. One way of approaching these factors is to include the target group and their subjective view of corresponding interventions. As Wang et al. ( 2023 ) explain in their research, the impact of the built environment on an individual´s PA behavior cannot be fully described by using objective measurement tools. However, research demonstrates that both perceived and objective indicators of Walkability are useful to fully grasp influencing factors (Jensen et al., 2017 ). A positive example for a subjective approach can be named with the study “Walki-Muc” (Scheller & Bachner, 2024 ). The promising measure of the Neighborhood Environment Walkability Scale for Youth (NEWS-Y) questionnaire incorporates the subjective perception of children and adolescents regarding diverse environments. Current research highlights the adaptation of Walkability indices with target specific factors. Buck et al. ( 2015 ) reveal recreational facilities such as playgrounds and public open spaces as important features for PA. Based on that, the researchers developed moveability indices with strong effects on PA in school children. These examples highlight the importance of considering multi-level influences when planning, implementing, and evaluating PA in children and adolescents in their neighborhoods. 4.3 Practical implications in the context of urban planning Objective indices, such as the Walkability Index , have the capability to function as a guide in order to identify environments that may support or hinder PA. It is hard to imagine that areas with extremely low Walkability Indices are particularly attractive to children and adolescents and their PA behavior. However, if one aims to promote the PA of a specific population, additional data gathering methods as well as participative approaches should be applied on the development of interventions. The concept of co-creation, being one of many participative approaches, engages and empowers end-users and is proposed to increase behavior intervention adoption, adherence, and effectiveness (Sofro et al., 2023 ). Research suggests implementing the subjective perspective of vulnerable groups, in this case children, as they perceive their environment in a different way than adults do (Freitas et al., 2024 ; Scheller & Bachner, 2024 ; Ubiali et al., 2021 ). The Walkability Index is generally developed for adult mobility patterns, which may not fully capture how children and adolescents use their environment. When addressing children and adolescents, it could be useful to develop child-friendly Walkability- criteria consisting of safe, playful, and social factors and combining aspects such as traffic-calmed infrastructure, co-creation processes, and technological incentives. Consequently, the development of a Youth Walkability Index, grounded in the Walkability Index and adapted through qualitative and participatory methodologies, could prove more efficacious (Scheller & Bachner, 2024 ). Moreover, incorporating additional demographic factors, such as socio-economic status (SES), could facilitate an assessment of their influence on mobility choices and behaviors. Research has yielded inconsistent results regarding the mediating effect of demographics on the relationship between Walkability and PA (Andersen et al., 2022 ; D'Haese et al., 2014 ). Further research is necessary to provide more robust evidence on this matter. In exploring SES-moderated disparities in PA, these findings could be employed in urban planning to address inequalities (Aznar et al., 2024 ). 4.4 Strengths and limitations of this study The combinability of a Walkability Index officially surveyed in a city with millions of inhabitants, which requires a large number of parameters, often only available to the local authority, and a large-scale study on PA promotion with very large expenditure in terms of time and money, is scarce and offers unprecedented insights. The present study examined data from seven city districts, accounting for approximately 62,500 children and adolescents under the age of 18 living in the seven districts in question. Of these, 9,852 participated in the K&Q, representing a proportion of 15.7%. Key strengths include the use of sound data sources and analytical methods to support the findings, practical outcomes, and the ability to compare results to those of other studies and cities. Specifically, Walkability and an indicator for PA were measured objectively. This approach ensured that no recall bias, social desirability, or similar factors influenced the results, as is typical of similar studies. However, the Activity variable just provides insight into the temporal and spatial parameters of box scanning, including the specific times and locations at which scanning occurred. There is no information about the type of transport participants used. It can thus be concluded that Activity merely serves as an indicator for PA, and the incorporation of GPS tracking or accelerometer data would facilitate the acquisition of more comprehensive information regarding the nature and intensity of PA. Furthermore, large parts of the variance cannot be explained by the variables collected. Additional variables, such as the SES mentioned above, should therefore be included in future wherever possible. However, due to the practically unmanageable number of additional variables, a qualitative flanking would be particularly valuable. In this way, quantitative calculations could be combined with qualitative explanatory backgrounds to create a holistic picture. The combination of quantitative and qualitative elements is particularly advantageous for complex intervention planning and provides a better understanding of research problems (Palinkas et al., 2019 ). This mixed-methods design could increase the gain in knowledge and promote the inclusion of target groups (Kutzleben et al., 2023 ). Another limitation is that the results show correlations rather than cause-and-effect relationships. In order to create a longitudinal study that can provide a higher level of evidence for causal relationships, children’s PA levels and movement patterns could be measured before and during an intervention. This would provide additional interpretative approaches in this context, as well as additional variables to be collected. Also, the long-term effects of Walkability measures on PA behavior and public health remain open and should be considered in the future. 5 Conclusions The Walkability Index is a resource-saving, objective method that was applied in this study and proved to be associated with PA in children and adolescents. The index could thus be utilized in PA research for the purpose of intervention planning. Additionally, it can be employed in the field of urban planning, aiming at creating a PA-promoting environment for children and adolescents in an urban context. Differences in PA patterns between districts, seasons, and days are relatively small, but should be taken into account in future planning efforts. Despite that, a considerable amount of variance remains unclear, suggesting that important influencing factors have yet to be identified. Therefore, the unique needs and subjective perceptions of children and adolescents regarding Walkability could be given greater consideration in future urban planning processes to better understand their needs and reveal additional influencing factors. Integrating these perspectives can help create environments that are not only structurally conducive to walking but also perceived as safe and inviting by younger populations. Future urban planning strategies should embrace a more tailored, evidence-based approach that ensures sustainable, health-promoting, and inclusive cities for all. Declarations Ethical approval and consent to participate All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Approval (526/20 S-EB) was obtained from the ethics committee of the Technical University of Munich. Clinical trial number: not applicable. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests The authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding This publication was supported by the Technical University of Munich as part of the DEAL Agreement on Open Access Publishing. Authors' contributions The study was conducted by C.M. and J.S.-E., who also collected the data used. The analysis was performed by L.E. with substantial contributions from P.T. and F.A. The manuscript was written by L.E. and J.S.-E. with substantial contributions from D.S. All authors reviewed the manuscript. 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Paglione L, Donato MA, Cofone L, Sabato M, Appolloni L, D’Alessandro D. The Healthy City Reimagined: Walkability, Active Mobility, and the Challenges of Measurement and Evaluation. Urban Sci. 2024;8(4):157. https://doi.org/10.3390/urbansci8040157 . Palinkas LA, Mendon SJ, Hamilton AB. Innovations in Mixed Methods Evaluations. Annu Rev Public Health. 2019;40:423–42. https://doi.org/10.1146/annurev-publhealth-040218-044215 . Patel N, Nguyen H–H, van de Geest J, Wagtendonk A, Raju MJS, Dadvand P, de Hoogh K, Cirach M, Nieuwenhuijsen M, Lam TM, Lakerveld J. (2025). A Walk across Europe: Development of a high-resolution walkability index. https://doi.org/10.48550/ARXIV.2504.17897 Patnode CD, Lytle LA, Erickson DJ, Sirard JR, Barr-Anderson D, Story M. The relative influence of demographic, individual, social, and environmental factors on physical activity among boys and girls. Int J Behav Nutr Phys Act. 2010;7:79. https://doi.org/10.1186/1479-5868-7-79 . Pfeifer K, Rütten A. Nationale Empfehlungen für Bewegung und Bewegungsförderung [National Recommendations for Physical Activity and Physical Activity Promotion]. Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)). 2017;79(01):S2–3. https://doi.org/10.1055/s-0042-123346 . Pinheiro JC, Bates DM. Mixed-effects models in S and S-PLUS [Nachdr.]. Statistics and computing . Springer; 2004. Pojednic R, D'Arpino E, Halliday I, Bantham A. The Benefits of Physical Activity for People with Obesity, Independent of Weight Loss: A Systematic Review. Int J Environ Res Public Health. 2022;19(9). https://doi.org/10.3390/ijerph19094981 . Ramírez-Granizo IA, Ubago-Jiménez JL, González-Valero G, Puertas-Molero P, San Román-Mata S. The Effect of Physical Activity and the Use of Active Video Games: Exergames in Children and Adolescents: A Systematic Review. Int J Environ Res Public Health. 2020;17(12). https://doi.org/10.3390/ijerph17124243 . Rhodes RE, Guerrero MD, Vanderloo LM, Barbeau K, Birken CS, Chaput J–P, Faulkner G, Janssen I, Madigan S, Mâsse LC, McHugh T–L, Perdew M, Stone K, Shelley J, Spinks N, Tamminen KA, Tomasone JR, Ward H, Welsh F, Tremblay MS. Development of a consensus statement on the role of the family in the physical activity, sedentary, and sleep behaviours of children and youth. Int J Behav Nutr Phys Act. 2020;17(1):74. https://doi.org/10.1186/s12966-020-00973-0 . Robert-Koch-Institut. (2023, December 20). Themenschwerpunkt: Körperliche Aktivität: Gesundheitsmonitoring . https://www.rki.de/DE/Content/Gesundheitsmonitoring/Themen/Koerperl_Aktivitaet/koerperl_aktiv_node.html Rodrigue L, Daley J, Ravensbergen L, Manaugh K, Wasfi R, Butler G, El-Geneidy A. Factors influencing subjective walkability: Results from built environment audit data. J Transp Land Use. 2022;15(1):709–27. https://doi.org/10.5198/jtlu.2022.2234 . Ryan J, Edney S, Maher C. Engagement, compliance and retention with a gamified online social networking physical activity intervention. Translational Behav Med. 2017;7(4):702–8. https://doi.org/10.1007/s13142-017-0499-8 . Sallis JF, [James F], Cerin E, Conway TL, [Terry L], Adams MA, Frank LD, [Lawrence D], Pratt M, Salvo D, Schipperijn J, Smith G, Cain KL, Davey R, Kerr J, [Jacqueline], Lai P–C, Mitáš J, Reis R, Sarmiento OL, Schofield G, Troelsen J, van Dyck D, Owen N. Physical activity in relation to urban environments in 14 cities worldwide: A cross-sectional study. Lancet (London England). 2016;387(10034):2207–17. https://doi.org/10.1016/S0140-6736(15)01284-2 . Scheller DA, Bachner J. Subjective walkability perceived by children and adolescents living in urban environments: A study protocol for participatory methods and scale development in the WALKI-MUC project. PLoS ONE. 2024;19(3):e0299208. https://doi.org/10.1371/journal.pone.0299208 . Schoeppe S, Duncan MJ, Badland HM, Oliver M, Browne M. Associations between children's independent mobility and physical activity. BMC Public Health. 2014;14:91. https://doi.org/10.1186/1471-2458-14-91 . Sedlmeir G. (2022). Ein Walkability Index für München. Münchner Statistik(2 Quartalsheft), 28–35. Smith M, Hosking J, Woodward A, Witten K, MacMillan A, Field A, Baas P, Mackie H. Systematic literature review of built environment effects on physical activity and active transport - an update and new findings on health equity. Int J Behav Nutr Phys Act. 2017;14(1):158. https://doi.org/10.1186/s12966-017-0613-9 . Smith M, Mavoa S, Ikeda E, Hasanzadeh K, Zhao J, Rinne TE, Donnellan N, Kyttä M, Cui J. Associations between Children's Physical Activity and Neighborhood Environments Using GIS: A Secondary Analysis from a Systematic Scoping Review. Int J Environ Res Public Health. 2022;19(3). https://doi.org/10.3390/ijerph19031033 . Sofro ZM, Wibowo RA, Wasityastuti W, Kusumadewi AF, Utomo PS, Ekawati FM, Putri RE, Aldrin E, Fatmawati JS, Chang TC, Pratista MI, Agustiningsih D. Physical activity virtual intervention for improving mental health among university students during the COVID-19 pandemic: A Co-creation process and evaluation using the Behavior Change Wheel. Heliyon. 2023;9(8):e18915. https://doi.org/10.1016/j.heliyon.2023.e18915 . Spring FDH, Lærkholm G, Jensen RB, Kloppenborg JT. The Effect of Exergaming on BMI and Fitness in Children and Adolescents With Obesity: A Systematic Review. Acta Paediatr (Oslo Norway: 1992). 2025;114(7):1522–37. https://doi.org/10.1111/apa.70048 . Telega A, Telega I, Bieda A. Measuring Walkability with GIS—Methods Overview and New Approach Proposal. Sustainability. 2021;13(4):1883. https://doi.org/10.3390/su13041883 . Tran M–C. (2018). Walkability als ein Baustein gesundheitsförderlicher Stadtentwicklung und -gestaltung. In Baumgart, S., Höckler, A., Ritzinger, A., Rüdiger, A, editor, Planung für gesundheitsfördernde Städte (pp. 284–296). Trapp GSA, Giles-Corti B, Christian HE, Bulsara M, Timperio AF, McCormack GR, Villaneuva KP. Increasing children's physical activity: Individual, social, and environmental factors associated with walking to and from school. Health Educ Behavior: Official Publication Soc Public Health Educ. 2012;39(2):172–82. https://doi.org/10.1177/1090198111423272 . Ubiali A, Gori D, Rochira A, Raguzzoni G, Fantini MP. Measures of walkability in the pediatric population: A qualitative review of the literature. Annali Di Igiene: Med Preventiva E Di Comunita. 2021;33(1):67–85. https://doi.org/10.7416/ai.2021.2409 . Valentine G. Oh Yes I Can.Oh No You Can't: Children and Parents' Understandings of Kids' Competence to Negotiate Public Space Safely. Antipode. 1997;29(1):65–89. https://doi.org/10.1111/1467-8330.00035 . van der Vlugt A–L, Gerten C, Scheiner J. Regular Issue. Act Travel Stud. 2024;4(1). https://doi.org/10.16997/ats.1391 . van Sluijs EMF, Ekelund U, Crochemore-Silva I, Guthold R, Ha A, Lubans D, Oyeyemi AL, Ding D, Katzmarzyk PT. Physical activity behaviours in adolescence: Current evidence and opportunities for intervention. Lancet (London England). 2021;398(10298):429–42. https://doi.org/10.1016/S0140-6736(21)01259-9 . Venerandi A, Mellen H, Romice O, Porta S. Walkability Indices—The State of the Art and Future Directions: A Systematic Review. Sustainability. 2024;16(16):6730. https://doi.org/10.3390/su16166730 . Wang Y, Steenbergen B, van der Krabben E, Kooij H–J, Raaphorst K, Hoekman R. The Impact of the Built Environment and Social Environment on Physical Activity: A Scoping Review. Int J Environ Res Public Health. 2023;20(12). https://doi.org/10.3390/ijerph20126189 . Warburton DER, Nicol CW, Bredin SSD. Health benefits of physical activity: The evidence. CMAJ: Can Med Association J = J De L'association Medicale Canadienne. 2006;174(6):801–9. https://doi.org/10.1503/cmaj.051351 . Wendel-Vos W, Droomers M, Kremers S, Brug J, van Lenthe F. Potential environmental determinants of physical activity in adults: A systematic review. Obes Reviews: Official J Int Association Study Obes. 2007;8(5):425–40. https://doi.org/10.1111/j.1467-789X.2007.00370.x . Westenhöfer J, Nouri E, Reschke ML, Seebach F, Buchcik J. Walkability and urban built environments-a systematic review of health impact assessments (HIA). BMC Public Health. 2023;23(1):518. https://doi.org/10.1186/s12889-023-15394-4 . Yang S, Chen X, Wang L, [Lei], Wu T, Fei T, Xiao Q, Zhang G, Ning Y, Jia P. (2021). Walkability indices and childhood obesity: A review of epidemiologic evidence. Obesity Reviews: An Official Journal of the International Association for the Study of Obesity , 22 Suppl 1 (Suppl 1), e13096. https://doi.org/10.1111/obr.13096 Yu J, Zhang H, Dong X, Shen J. The impact of street greenery on active travel: A narrative systematic review. Front Public Health. 2024;12:1337804. https://doi.org/10.3389/fpubh.2024.1337804 . Zou Y, Ma Y, Wu Z, Liu Y, Xu M, Qiu G, Vos H, Jia P, Wang, L [Limin]. Neighbourhood residential density and childhood obesity. Obes Reviews: Official J Int Association Study Obes. 2021;22(Suppl 1). e13037.https://doi.org/10.1111/obr.13037 . Suppl 1 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7365397","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504356811,"identity":"6d45740b-3b1d-41e9-9dfa-826ff8fcffb2","order_by":0,"name":"Laura Eipel","email":"","orcid":"","institution":"Potsdam University","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Eipel","suffix":""},{"id":504356812,"identity":"9710898b-40f7-441b-be46-f950fc69fdb3","order_by":1,"name":"Paula Teich","email":"","orcid":"","institution":"Potsdam University","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"","lastName":"Teich","suffix":""},{"id":504356813,"identity":"9a53edaf-c7f6-4a23-82db-aaa1b7bde685","order_by":2,"name":"Fabian Arntz","email":"","orcid":"","institution":"Potsdam University","correspondingAuthor":false,"prefix":"","firstName":"Fabian","middleName":"","lastName":"Arntz","suffix":""},{"id":504356814,"identity":"f7699e8e-da24-42d3-aa3b-2dcde7c72ae6","order_by":3,"name":"Daniel Scheller","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Scheller","suffix":""},{"id":504356815,"identity":"4c7fc40e-dcce-458a-bd70-6d1d5f26bdb6","order_by":4,"name":"Christoph Mall","email":"","orcid":"","institution":"Planersocietät","correspondingAuthor":false,"prefix":"","firstName":"Christoph","middleName":"","lastName":"Mall","suffix":""},{"id":504356816,"identity":"f681722b-8611-4346-ae26-2ea791940f29","order_by":5,"name":"Jan Schmid-Ellinger","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACxgYQWWDBA+Z9AJMJxGgxkABrYZyRQIQWCDCQAFPMPMRoYW4/e4Dhg4GEjDn/4mOfbX8cjmZgTz6A32E9eQmMM4AOs5zxLHl2TsLh3AaeZ/itYWzIMWDmAWoxuHHGmBmsRSLHAL+W/jcGzH/AWs5/ZrYAa8n/gF/LDKAtoBAzON/DzMwAsQWvDqCWNwYHe8C2sBkz9qSl57bxPMPvMMP+HMMHPyps7A3OH37M8MPGOrefPfkBfi0NDAwHwCyJBIgIG35nMTDIw1n8BwipHQWjYBSMgpEKAPepQ3t+HygQAAAAAElFTkSuQmCC","orcid":"","institution":"Technical University of Munich","correspondingAuthor":true,"prefix":"","firstName":"Jan","middleName":"","lastName":"Schmid-Ellinger","suffix":""}],"badges":[],"createdAt":"2025-08-13 13:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7365397/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7365397/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-25317-0","type":"published","date":"2025-11-06T15:58:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90307524,"identity":"765afc28-41e6-47ec-b75d-fb60099d9e91","added_by":"auto","created_at":"2025-09-01 09:32:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1304529,"visible":true,"origin":"","legend":"\u003cp\u003eMunich; the red dots represent the box locations, the red lines represent the district borders\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7365397/v1/3820f57050653cd9ac04c23a.png"},{"id":90307520,"identity":"5c170d00-1a50-4905-967d-e5f86fd52edf","added_by":"auto","created_at":"2025-09-01 09:32:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14608,"visible":true,"origin":"","legend":"\u003cp\u003eHexbinplot showing the relationship between \u003cem\u003eActivity\u003c/em\u003e(cumulated for each box over the whole corresponding wave) and existing \u003cem\u003eWalkability\u003c/em\u003e index scores of the box locations (a darker color of the hexagon indicates a higher density, i.e. a concentration of correlating \u003cem\u003eActivity\u003c/em\u003e and \u003cem\u003eWalkability\u003c/em\u003e values in this area).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7365397/v1/fe1ae5b3ed803f696c69ad24.png"},{"id":90307522,"identity":"852737d9-20db-4d17-a840-5a562c3515a4","added_by":"auto","created_at":"2025-09-01 09:32:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61836,"visible":true,"origin":"","legend":"\u003cp\u003eViolin-boxplot comparing the cumulative \u003cem\u003eActivity\u003c/em\u003e per day and box in the different \u003cem\u003eDistricts\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7365397/v1/3fad5111cdecf18e33e74b96.png"},{"id":90307525,"identity":"0fb50907-7ca6-4cad-b312-dd47c605e2ce","added_by":"auto","created_at":"2025-09-01 09:32:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52032,"visible":true,"origin":"","legend":"\u003cp\u003eViolin-boxplot comparing the cumulative \u003cem\u003eActivity\u003c/em\u003e per day and box in the different \u003cem\u003eSeasons\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7365397/v1/43525af3e7010f9c4433ef91.png"},{"id":90309883,"identity":"7d1cad03-3e05-4167-81ca-b88f5ab3daf1","added_by":"auto","created_at":"2025-09-01 09:40:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":21338,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eActivity\u003c/em\u003e per day over time in the sum of all intervention waves (due to the different lengths of the waves, all were normalized to the maximum length; the daily data plotted on the x-axis are therefore to be understood as relative, with the first day of each wave on the left and each last day of each wave on the right). \u003cem\u003eActivity\u003c/em\u003e represents the number of scans at all boxes.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7365397/v1/7e30dc2373be703734562f13.png"},{"id":95564836,"identity":"c6868343-8f5f-42a2-8f65-43217e6b17d3","added_by":"auto","created_at":"2025-11-10 16:10:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2436045,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7365397/v1/4a772b06-a32b-4ba0-8edc-88e2f92cfb62.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Walkable neighborhoods = active kids? Exploring the relationship between a physical activity intervention for youth and walkability","fulltext":[{"header":"1 Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Physical (in)activity in children and adolescents\u003c/h2\u003e\u003cp\u003ePhysical activity (PA) is related to multiple health benefits. Regular movement can increase physical, mental, and social health (Dimitri et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mitra et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pojednic et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It can strengthen the cardiovascular system and the development of the musculoskeletal system (Chaput et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rhodes et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, PA helps to prevent medical conditions, such as overweight and obesity, or cancer (Baobeid et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dhuli et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Warburton et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). A sedentary lifestyle, on the contrary, has a negative impact on health (Robert-Koch-Institut, 2023; Ubiali et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is estimated that 80% of adolescents across the globe are categorized as inactive (Robert-Koch-Institut, 2023; van Sluijs et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Germany, only 22,4% of girls and 29,4% of boys aged three to 17 reach the recommendations of the World Health Organization (WHO) of 60 minutes of PA per day. Especially childhood though should be regarded as an important stage of life to provide a basis for a health-conscious behavior in later life (Finger et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Neil-Sztramko et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rhodes et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Robert-Koch-Institut, 2023). The number of children and adolescents who reach the WHO recommendations should certainly be higher than it currently is.\u003c/p\u003e\u003cp\u003eIndividual PA behavior can be seen as a product of multiple interacting factors. In children and adolescents, those factors encompass demographic, individual, social, and environmental variables (Patnode et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These environmental variables, or physical infrastructures such as parks, buildings and recreational facilities can either enable or hinder PA and encompass \u0026ldquo;settings such as the neighborhood or schools based on site-specific physical infrastructures\u0026rdquo; (Kelso et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These settings provide spaces for both individual PA and social interaction(Kelso et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, PA is positively influenced by a residential environment that provides sufficient opportunities for PA (Smith et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). One such opportunity is that of active travel, a factor which has the potential to influence positive changes in PA and sedentary behavior (SB) in daily life (Kleszczewska et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, in terms of different modes of transport within a city, the car is the dominant mode of transport as stated in the German mobility profitability report (Nobis \u0026amp; Kuhnimhof, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Children up to the age of nine are heavily dependent on their parents for mobility and are driven half the time dedicated to travel, which means that children are strongly socialized by this mode of transport (Nobis \u0026amp; Kuhnimhof, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A study byHillman et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) is still influential in urban planning, childhood studies, and public health. It revealed a dramatic decline in the number of children permitted to travel without adult supervision (Independent Mobility [IM]). In 1971, 80% of seven- to eight-year-olds walked to school alone; by 1990, this had dropped to just 9%. The main reason parents limited their children\u0026rsquo;s mobility was fear of road traffic.Schoeppe et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found an ongoing decline in IM due to parental concerns about road safety, stranger danger, or longer travel distances to schools and recreational facilities.Han et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) note, that despite an increasing awareness of the benefits of PA and IM, more parents are limiting children\u0026rsquo;s freedom. These restrictions manifest in the establishment of boundaries, time limits and spatial constraints (Carver et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Valentine, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). For instance, it is not permissible for children to walk or cycle without adult supervision. Furthermore, access to parks or streets in the vicinity is restricted, and children are instead encouraged to travel by car rather than walking (Carver et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Valentine, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The authors posit that this creates a vicious cycle: a decline in the number of children in public space leads to less perceived safety, which further reduces autonomy. Consequently, children lose opportunities to develop spatial awareness, decision-making skills, and confidence. The authors warn that this shift is conducive to greater car dependency, reduced physical activity, and weaker community engagement (Hillman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Schoeppe et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eActive travel by foot or bike coul help to build a sense of independence, self-confidence, and concern for the environment, and plays an important role in improving the health of children and adolescents (Kleszczewska et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There are many approaches to the promotion of PA, of which active travel in the urban built environment is one of the most promising.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Promotion of physical activity\u003c/h2\u003e\u003cp\u003eResearch findings underscore a complex interplay of factors influencing children\u0026acute;s and adolescents\u0026rsquo; engagement in PA. Hu et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) categorize influencing factors into the levels intrapersonal, interpersonal, organizational, community and public policy and thereby refer to the social ecological model (SEM) proposed by McLeroy et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). A key factor that can be categorized at the organizational level and seen as an environmental approach is the built environment. Public health research provides strong evidence that the built environment can influence overall health by promoting PA in younger populations (Krist et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the context of the built environment, PA pertains to various factors, including the extent of movement during walking and active travel within a neighborhood (Fathi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fina et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Scheller \u0026amp; Bachner, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Associations have been observed between neighborhood design and walking and cycling for transportation (Frank et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Studies show that an increase in walking can be achieved through mixed-use residential, commercial, office, entertainment, and other land use and that \u0026ldquo;the street network pattern can influence the choice of travel routes and modes of transportation\u0026rdquo; (Jensen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As mentioned above, walking can positively influence PA and SB, but also promotes several peripheral benefits for society, i.e. facilitating social interaction between people from different neighborhoods and contributing to the creation of a more pleasant and safer urban environment (Fathi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Freitas et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kleszczewska et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, urban planning should focus on promoting walking as an important factor of PA for improving public health. The finding's consistency across cities indicates the potential value of engaging the urban planning, transportation, and parks sectors in efforts to mitigate the health consequences of global physical inactivity (Sallis et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis raises the question of how health- and walk-promoting urban design can best be achieved, particularly with the specific target group of children and adolescents in focus. In this regard, schools, their surroundings, and the immediate living environment of children and adolescents may be a particularly promising settings for interventions to promote PA (Jago et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pfeifer \u0026amp; R\u0026uuml;tten, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Kelso et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also identified the neighborhood, school, and recreational environments as the settings most frequently used for PA in children and adolescents. Studies reported higher PA values for children and adolescents on streets, roads and pavements rather than in locations with green spaces, which could be due to more time being spent in built environments (Kelso et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, participation in programs aimed at promoting PA seems to be strongly influenced by social interactions, such as those with peers and friends, referring to the interpersonal level (Hu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to this, play, especially for children, represents a relevant factor in getting involved in neighborhood PA. In this regard, gamified scenarios appear to be beneficial for this target group (Gkintoni et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mazeas et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In these cases, traditional button presses in sedentary video games are replaced with gross motor movements, thereby rendering PA enjoyable, engaging and rewarding (Gkintoni et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Spring et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The utilization of gamified scenarios, when designed with sufficient intensity and frequency, has been demonstrated to enhance participation, reduce levels of sedentariness, and promote positive physiological parameters such as heart rate (Ram\u0026iacute;rez-Granizo et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite the limited scope, interventions employing this approach have already shown lasting changes in PA behavior, even long after the intervention has concluded (Harris, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo effectively support such socially and play-driven engagement in PA, the surrounding environment must also offer opportunities for active participation. In this regard, recent research has increasingly incorporated the concept of \u003cem\u003eWalkability\u003c/em\u003e, a term that reflects how urban planning and pedestrian-friendly design can facilitate and encourage daily walking behavior (Baobeid et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Walkability and the Walkability Index\u003c/h2\u003e\u003cp\u003e\u003cem\u003eWalkability\u003c/em\u003e as an umbrella term describes various environmental factors, including design and planning focusing on promoting walking and recreational activities, thus incorporating social factors like interactions between resident children or safety aspects. \u003cem\u003eWalkability\u003c/em\u003e plays a role in urban planning as well as in public health, linking those fields (Paglione et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDefinitions of \u003cem\u003eWalkability\u003c/em\u003e vary according to the respective field of research (Banger et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the original sense of urban design and planning, \u003cem\u003eWalkability\u003c/em\u003e refers to the ease with which people can walk in an area. It focuses on the built environment and includes a \u0026ldquo;set of capacities of any given neighborhood that is embodied in urban morphology in three main ways\u0026rdquo;(Dovey \u0026amp; Pafka, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) including density, functional mix and access networks. These three elements are attributable to the foundational \u0026ldquo;3-Ds\u0026rdquo; \u003cem\u003edensity, diversity\u003c/em\u003e and \u003cem\u003edesign\u003c/em\u003e, a framework proposed by Cervero and Kockelman (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). These measures can be used to design walkable, transit-oriented neighborhoods, which in turn can increase levels of primarily purpose-related walking or cycling to work or other places, resulting in positive outcomes for PA and public health (Dovey \u0026amp; Pafka, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Westenh\u0026ouml;fer et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom a public health point of view, a walkable area can be defined as a PA-supporting area (Banger et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It includes \u0026ldquo;everything made or maintained by people with characteristics that can promote increases in physical activity\u0026rdquo; (Duncan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). With the adoption of this concept by researchers and practitioners in PA and public health, its scope expanded to include walking for both transportation and recreation, as well as other forms of PA such as biking (Kerr et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Consequently, \u003cem\u003eWalkability\u003c/em\u003e now extends beyond its literal sense of walking alone. Importantly, the influence of built-environment factors varies depending on the manner of walking. Whilst an enhancement in street connectivity, destination accessibility and transit provision is conducive to utilitarian walking, elements such as street greenness, which enhance the aesthetic appeal, safety and comfort, have a more significant influence on recreational walking (Bandara et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Today, the term \u003cem\u003eWalkability\u003c/em\u003e has a role to play in areas that go beyond urban planning and design, such as public health, climate change and social equality. In this sense, high \u003cem\u003eWalkability\u003c/em\u003e can have a major impact on increasing residents\u0026acute; PA and wellbeing, improving air quality, promoting social inclusion and providing pleasurable leisure spaces (Ibrahim et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Venerandi et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne way to assess \u003cem\u003eWalkability\u003c/em\u003e in a city is to measure a so-called \u003cem\u003eWalkability Index\u003c/em\u003e which can be used for planning a health promoting and sustainable mobility in the city (Frank et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Heudobler et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sedlmeir, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tran, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There are a number of approaches to this issue, each of which results in a slightly different index. However, the majority of these approaches are, in addition to more diverse dimensions, fundamentally based on the following parameters: urban density, land use mix, street connectivity and distance to facilities (Fonseca et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Venerandi et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These parameters are measured using Geographical Information Systems (GIS) and aggregated into a single \u003cem\u003eWalkability Index\u003c/em\u003e (Frank et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The index can be used in statistical studies for a variety of questions, linking health, environmental elements, and \u003cem\u003eWalkability\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eIn addition to various versions of the \u003cem\u003eWalkability Index\u003c/em\u003e, there are also other measures with similar aims, such as the \u003cem\u003eWalkscore\u003c/em\u003e (Hall \u0026amp; Ram, 2018). This is based on similar parameters, but so far there are no usable versions that have been specifically adapted to certain cultural areas. For example, the \u003cem\u003eWalkscore\u003c/em\u003e is based on assumptions that relate to North American cities, which cannot be easily transferred to European cities (Hall \u0026amp; Ram, 2018).\u003c/p\u003e\u003cp\u003eNext to the built environment, qualitative aspects such as socio-cultural offers, the accessibility of green space and safety aspects play a decisive role according to PA behavior, especially for children and adolescents (Ibrahim et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jennings \u0026amp; Bamkole, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the GIS-based \u003cem\u003eWalkability Index\u003c/em\u003e is not able to depict these qualitative aspects as there are particularly high costs of conducting direct field research on subjective \u003cem\u003eWalkability\u003c/em\u003e (Telega et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConcluding, the index is based on the narrow incorporation of the parameters of urban density, land use mix, street connectivity, and distance to facilities according to the field of urban planning and does not consider subjective facts and perceptions of different target groups (Rodrigue et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; van der Vlugt et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This particularly limits research on children\u0026acute;s and adolescents\u0026rsquo; environments and raises the question of whether classic urban design and planning measures, such as the objective \u003cem\u003eWalkability Index\u003c/em\u003e, can be used in public health research to explain issues such as child and adolescent mobility, and even to inform decisions on PA promotion.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Problem and hypothesis\u003c/h2\u003e\u003cp\u003eEvidence shows a correlation between highly walkable areas and higher levels of active transportation among resident children and adolescents (Ubiali et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, other existing studies have produced mixed results, with some revealing a link between a greater \u003cem\u003eWalkability\u003c/em\u003e and a decrease in PA (Bird et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A systematic review found that eight out of 13 studies reported a positive association between higher \u003cem\u003eWalkability\u003c/em\u003e, resulting from a diverse land use mix and street connectivity, and more active lifestyles. However, five studies did not support this (Yang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Laxer and Janssen (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) concluded in their article, that \u003cem\u003eWalkability\u003c/em\u003e as well as road and park space density, are associated with youth inactivity, a finding supported by Janssen and King (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). They stated that walkable neighborhood designs may impede PA in children, adolescents, and neighborhoods, as \u003cem\u003eWalkability\u003c/em\u003e was not associated with active travel to school and was negatively associated with free play. These mixed findings reveal \u003cem\u003eWalkability\u003c/em\u003e to be a nuanced concept. While some neighborhoods can be categorized as highly walkable due to high street connectivity, they also tend to have more traffic, air pollution and crime as a result of being denser areas with more destinations and people.\u003c/p\u003e\u003cp\u003eBucksch et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined differences between rural and urban areas: Indices for \u003cem\u003eWalkability\u003c/em\u003e do not seem to be associated with objectively measured PA in either area. Despite higher indices for walkable areas in urban environments, children and adolescents from rural areas walked more for transportation purposes (Bucksch et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings show that a higher \u003cem\u003eWalkability Index\u003c/em\u003e does not equate to more favorable PA or health scores. What accounts for this heterogeneity is mainly incongruities in methodology regarding \u003cem\u003eWalkability\u003c/em\u003e, but also in defining and measuring PA-related behavior (Yang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe involvement of children and adolescents into planning and evaluating processes is a scarce practice and it is difficult to transfer results of studies with adult samples to children and adolescents (Davison \u0026amp; Lawson, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Freitas et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Scheller \u0026amp; Bachner, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). They are less autonomous in their decision making and their mobility is mainly limited to their individual neighborhood. Thus, their immediate environment might have a greater effect on their PA compared to adults (Scheller \u0026amp; Bachner, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Objective measures of \u003cem\u003eWalkability\u003c/em\u003e such as the \u003cem\u003eWalkability Index\u003c/em\u003e are mostly constructed from an adult perspective and do not take into account the subjective perceptions of children and adolescents. As a result, no final conclusion can be drawn at this time as to whether the objectively measured \u003cem\u003eWalkability Index\u003c/em\u003e is coherently related to PA in children and adolescents (Yang et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis paper therefore examines the relationship between objective \u003cem\u003eWalkability\u003c/em\u003e and the activity of children and adolescents in the context of a large-scale intervention in the urban area of the city of Munich in Germany. It is to be expected that this study will provide an answer to the following question: Is there a statistically significant and practically relevant correlation between the \u003cem\u003eWalkability Index\u003c/em\u003e of specific urban locations and the levels of PA of children and adolescents carried out there?\u003c/p\u003e\u003c/div\u003e"},{"header":"2 Method","content":"\u003cp\u003eAs noted above, it is unclear whether classical urban design and planning measures can be used in public health research to explain issues such as the PA of children and adolescents, or even to inform PA promotion interventions. The \u0026ldquo;Kreuz \u0026amp; Quer\u0026rdquo; (\u0026ldquo;Criss-Cross\u0026rdquo;) (K\u0026amp;Q) intervention is a project combining measures of urban planning and public health research, implemented by the mobility department of the City of Munich and the XXXUniversity XY in seven city districts (Ellinger et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data composition\u003c/h2\u003e\u003cp\u003eThe key variables for investigating the research question are \u003cem\u003eActivity\u003c/em\u003e (indicator for PA) and \u003cem\u003eWalkability\u003c/em\u003e (measured by the \u003cem\u003eWalkability Index\u003c/em\u003e). \u003cem\u003eActivity\u003c/em\u003e data was collected as part of the K\u0026amp;Q intervention, the \u003cem\u003eWalkability Index\u003c/em\u003e was provided by the department for climate protection and environment of the city of Munich (Sedlmeir, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, the city district from which the data originated (\u003cem\u003eDistrict\u003c/em\u003e), the time of year at which it was collected (\u003cem\u003eSeason\u003c/em\u003e) and the day of the respective wave of K\u0026amp;Q from which the data originated (\u003cem\u003eDay\u003c/em\u003e) have been documented as additional variables. Data sources for \u003cem\u003eActivity\u003c/em\u003e and \u003cem\u003eWalkability\u003c/em\u003e are described in more detail below.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1 \u003cem\u003eActivity\u003c/em\u003e: Physical activity data of the intervention\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe K\u0026amp;Q project was initiated in 2019 by the mobility counsellor of the city of Munich. The intervention was carried out in three waves per year for four to six weeks in different city districts each. It is a gamified urban intervention that uses a digital scavenger hunt-like format, encouraging resident children and adolescents to explore their neighborhoods by locating and scanning physical checkpoints, in form of electronic boxes, to collect points. The participants can collect points as individuals or teams, such as school teams. In each chosen neighborhood, several different boxes are installed on accessible objects on the streets, for example streetlamps, for a certain period of time. The electronic boxes are regularly distributed in the seven neighborhoods at distances of about 500 meters. Figure\u0026nbsp;1 visualizes the box locations in each district on a map of Munich. The mean area size of neighborhoods where the intervention took place is 12,7 km\u003csup\u003e2\u003c/sup\u003e (\u0026plusmn;\u0026thinsp;6,7).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;1.\u003c/b\u003e Munich; the red dots represent the box locations, the red lines represent the district borders\u003c/p\u003e\u003cp\u003eParticipants receive a physical game card that can be scanned at the respective boxes. Each scan is rewarded with points. Participants can walk, bike or scooter from one box to another to scan their cards for collecting points and kilometers over the entire period of the campaign. Higher points are awarded for scanning more different boxes without a major break. The intervention incorporates gamification elements to keep participants motivated. These elements include daily ranking updates on the homepage, instant audio feedback after scanning a box, or the chance to win attractions such as a bouncy castle, a photo box or face painting for the final award ceremony. Participants are recruited through information campaigns at the schools via posters and flyers by the mobility council of the City of Munich. The legal guardians of underage participants had to give written informed consent to the use of their children's data in order to obtain a game card for their children.\u003c/p\u003e\u003cp\u003eThis paper analyzes data from seven districts in the City of Munich, which are numbered from one to seven in this paper. For the purposes of this analysis, each scan at one of these electronic boxes is counted as one \u003cem\u003eActivity\u003c/em\u003e, which is the indicator for PA. The more often participants have visited a particular box, the higher the \u003cem\u003eActivity\u003c/em\u003e of that box. For further interpretation of the data, the number of participants and the quantity of electronic game boxes were documented as well. It is important to understand that in this case \u003cem\u003eActivity\u003c/em\u003e is not to be equated with an exact intensity or duration of PA, as in other PA studies. In this study, the variable \u003cem\u003eActivity\u003c/em\u003e is indicative of PA, as the children are observed to be actively travelling from one box to another. \u003cem\u003eActivity\u003c/em\u003e also serves to indicate the level of attractiveness of a given box location for children and adolescents to move there with a potentially free choice of other boxes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2 \u003cem\u003eWalkability\u003c/em\u003e: An index for Munich\u003c/h2\u003e\u003cp\u003eThe \u003cem\u003eWalkability Index\u003c/em\u003e is a specific measure for planning walkable environments and can be calculated for every city with available data (Frank et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The \u003cem\u003eWalkability Index\u003c/em\u003e employed in this study utilizes the pivotal factors of population density, connectivity, and entropy. The administration of the City of Munich used data sets from the Statistical Office, the Service for Geodata and Open Street Map in order to calculate specific \u003cem\u003eWalkability Index\u003c/em\u003e scores for the whole city of Munich. Those calculations are based on the work of Frank et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The Floor Area Ratio (FAR), as incorporated in the original \u003cem\u003eWalkability Index\u003c/em\u003e for North American cities by Frank et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), was omitted in this study due to constrained data availability and considerable contextual variations. In many European cities, reliable and consistent FAR data is not available at neighborhood level. Furthermore, population density can function as a practical proxy for built intensity in compact European urban settings (Krehl et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This approach aligns with recent European studies, which similarly exclude FAR and instead prioritize components such as population density, land use mix, and street connectivity (Lam et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This approach is intended to ensure methodological consistency and comparability across regions.\u003c/p\u003e\u003cp\u003eMunich is divided into 25 city districts. Each of these districts is further subdivided into smaller neighborhood units for statistical and administrative purposes. The \u003cem\u003eWalkability Index\u003c/em\u003e is calculated on the smallest-scale level in so called \u0026ldquo;Stadtbezirksviertel\u0026rdquo; (SBV). The amount of SBVs (475 in total for Munich) per district varies depending on the size and structure of the district. The \u003cem\u003eWalkability Index\u003c/em\u003e is calculated from three elements: \u003cem\u003epopulation density\u003c/em\u003e (population per km\u003csup\u003e2\u003c/sup\u003e, standardized to z-score), \u003cem\u003econnectivity\u003c/em\u003e (number of crossroads per km\u003csup\u003e2\u003c/sup\u003e, standardized to z-score) and \u003cem\u003eentropy\u003c/em\u003e (measure for a balance of the distribution of living space, industry, culture, and administration in a neighborhood, standardized to z-score). All standardized z-scores from the three elements are combined to the final \u003cem\u003eWalkability Index\u003c/em\u003e for each SBV (Sedlmeir, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This results in scores from \u0026minus;\u0026thinsp;9 to 13.1, whereupon higher values indicate a higher \u003cem\u003eWalkability\u003c/em\u003e. Based on this small-scale measurement of the \u003cem\u003eWalkability Index\u003c/em\u003e for the SBVs, every box of K\u0026amp;Q can be assigned to their own individual \u003cem\u003eWalkability Index\u003c/em\u003e score.\u003c/p\u003e\u003cp\u003e\u003cem\u003eWalkability\u003c/em\u003e is an indicator of the attractiveness of walking, which makes it interesting to see whether the two indicators of \u003cem\u003eActivity\u003c/em\u003e and \u003cem\u003eWalkability\u003c/em\u003e are correlated and whether a higher attractiveness of walking (\u003cem\u003eWalkability\u003c/em\u003e) leads to more scans of the corresponding box (\u003cem\u003eActivity\u003c/em\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data preparation and analysis\u003c/h2\u003e\u003cp\u003eTo test the relationship between \u003cem\u003eActivity\u003c/em\u003e and \u003cem\u003eWalkability\u003c/em\u003e, a linear mixed model (LMM) approach was implemented for the main analysis. Fixed effects correspond to the relation between the main predictor variables and the dependent variable (\u003cem\u003eActivity\u003c/em\u003e). In this case, \u003cem\u003eWalkability\u003c/em\u003e was defined as fixed effect (Pinheiro \u0026amp; Bates, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The effects, which are not argued to be the correlation that is studied but could have an influence on the results, are referred to as random effects. In this case, \u003cem\u003eDistrict\u003c/em\u003e, \u003cem\u003eSeason\u003c/em\u003e and \u003cem\u003eDay\u003c/em\u003e were therefore included as random effects with regard to their correlations with \u003cem\u003eActivity\u003c/em\u003e. In summary, this means that primarily the relationship between the dependent variable (\u003cem\u003eActivity\u003c/em\u003e) and the fixed effect (\u003cem\u003eWalkability\u003c/em\u003e) was examined, as well as the relationships with the random effects were also considered (\u003cem\u003eDistrict, Season, Day\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eThe initial visual inspection of the collected data was able to rule out heteroscedasticity. Based on the qualitative evaluation of the QQ plot, however, a positive skewness and thus a violation of the normal distribution had to be assumed. A log transformation was therefore carried out, which significantly improved the model quality. The comparison of different model variants attested the best fit to the more complex model described above (Activity\u0026thinsp;~\u0026thinsp;Walkability + (1 | District) + (1 | Season) + (1 | Day) in comparison with simpler model variants or model variants with interaction effects measured by AIC and BIC indicators (Pinheiro \u0026amp; Bates, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Due to the model and variable structure, multicollinearity was not tested. The preparation of the data and the statistical analyses were conducted with the software R (latest version 4.4.2, 2024) and the editor R-Studio.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Descriptive results\u003c/h2\u003e\u003cp\u003eTo further describe the districts included for the intervention the respective population size under 18 years and the number of households with children was calculated with the use of an interactive map from the monitoring of the social department of the City of Munich. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a comprehensive overview of the numbers for each district, categorized according to the year in which the respective round of K\u0026amp;Q took place.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the seven districts including the respective year the intervention took place, the underaged population and the number of households with children.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopulation\u0026thinsp;\u0026lt;\u0026thinsp;18\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHouseholds with children\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e6\u003c/p\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003cp\u003e2022\u003c/p\u003e\u003cp\u003e2022\u003c/p\u003e\u003cp\u003e2022\u003c/p\u003e\u003cp\u003e2023\u003c/p\u003e\u003cp\u003e2023\u003c/p\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3135\u003c/p\u003e\u003cp\u003e9362\u003c/p\u003e\u003cp\u003e9969\u003c/p\u003e\u003cp\u003e8750\u003c/p\u003e\u003cp\u003e10174\u003c/p\u003e\u003cp\u003e13432\u003c/p\u003e\u003cp\u003e7675\u003c/p\u003e\u003cp\u003e\u003cb\u003e8928,1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1856\u003c/p\u003e\u003cp\u003e6046\u003c/p\u003e\u003cp\u003e6114\u003c/p\u003e\u003cp\u003e5244\u003c/p\u003e\u003cp\u003e6056\u003c/p\u003e\u003cp\u003e8192\u003c/p\u003e\u003cp\u003e4580\u003c/p\u003e\u003cp\u003e\u003cb\u003e5441,1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive characteristic values (sums and means) for the central variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParticipants N\u003c/p\u003e\u003cp\u003e(mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;sd)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDays\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBoxes N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eActivity sum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWalkability mean (range)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e961 (8,5\u0026thinsp;\u0026plusmn;\u0026thinsp;2,1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e56099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.04 (-3; 4.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1946 (8,4\u0026thinsp;\u0026plusmn;\u0026thinsp;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e173997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.63 (0.67; 8.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1071 (8,3\u0026thinsp;\u0026plusmn;\u0026thinsp;1,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.23 (-3; 3.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e876 (7,9\u0026thinsp;\u0026plusmn;\u0026thinsp;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e56164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.14 (0.67; 8.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e114556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.14 (-2.4; 3.51)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e121929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.04 (-1.9; 4.51)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.82 (-1; 4.85)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1407.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e41.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e42.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSum\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides an overview of the main variables for all seven districts, including the season, number of participants with mean age and standard deviation, duration of each intervention wave, number of boxes, summed number of activity, and the mean value for the \u003cem\u003eWalkability Index\u003c/em\u003e with range. For a descriptive evaluation of the success of K\u0026amp;Q, see also Ellinger et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The intervention waves were conducted in seven of the 25 existing districts of Munich. The age of the participants was documented with the information being provided by participants who had completed the registration process in a satisfactory manner (58% of all participants). The mean age for all participants was 9.7 years. The single interventions in the neighborhoods lasted from 29 to 47 days (mean\u0026thinsp;=\u0026thinsp;41.8) and a total of 300 boxes existed during the whole period. The highest number for \u003cem\u003eActivity\u003c/em\u003e can be seen in district 2, with a total of 173,997 box scans. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e points out that the neighborhoods in districts 2 and 4 have the highest mean value of \u003cem\u003eWalkability\u003c/em\u003e, the lowest mean value is assigned to district 3. According to the present data the mean \u003cem\u003eWalkability\u003c/em\u003e indices for the respective neighborhoods range from \u0026minus;\u0026thinsp;3 (district 1) to 8.34 (district 2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Statistical results\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Relationship between \u003cem\u003eActivity\u003c/em\u003e and \u003cem\u003eWalkability\u003c/em\u003e\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe inferential statistical analysis using a LMM provides both indications of the extent to which \u003cem\u003eActivity\u003c/em\u003e and \u003cem\u003eWalkability\u003c/em\u003e are associated with each other, as well as how large the proportion of additional variables is in explaining the calculated variance.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFixed effect: Statistical key figures on the relationship between \u003cem\u003eWalkability\u003c/em\u003e (fixed effect) and the dependent variable of \u003cem\u003eActivity\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWalkability\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cem\u003eActivity\u003c/em\u003e and \u003cem\u003eWalkability\u003c/em\u003e show a highly significant correlation with a p-value of \u0026lt;\u0026thinsp;0.001. Due to the very low standard error, the estimate of 0.0142 can also be certified as highly reliable. The Hexbinplot in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the positive correlation between \u003cem\u003eActivity\u003c/em\u003e and \u003cem\u003eWalkability\u003c/em\u003e. Based on this data, an accumulation of \u003cem\u003eWalkability Indices\u003c/em\u003e in the range between 0 and 4.5 can be observed. Areas with very low \u003cem\u003eWalkability Indices\u003c/em\u003e also display very low \u003cem\u003eActivity\u003c/em\u003e scores.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Random effects of the model explaining the variance\u003c/h2\u003e\u003cp\u003eAside from this statistically significant relation between \u003cem\u003eActivity\u003c/em\u003e and \u003cem\u003eWalkability\u003c/em\u003e, additional factors contribute to the explanation of the observed variance of the data. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e serves as an overview for the explained variance of the random effects \u003cem\u003eDistrict, Season\u003c/em\u003e and \u003cem\u003eDay\u003c/em\u003e in the previously analyzed model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRandom effects: Statistical key figures on the proportion of the variance of the model explained by the variables \u003cem\u003eDistrict\u003c/em\u003e, \u003cem\u003eSeason\u003c/em\u003e and \u003cem\u003eDay\u003c/em\u003e (random effects), as well as the variance not explained by the model (\u003cem\u003eResidual\u003c/em\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistrict\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSeason\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDay\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidual\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.059434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThere is a certain difference in the \u003cem\u003eActivity\u003c/em\u003e scores that can be explained based on the respective \u003cem\u003eDistrict\u003c/em\u003e in which the respective wave of K\u0026amp;Q took place (proportion of the explanation of the total variance of the model: 0.008). As shown in Fig.\u0026nbsp;3, districts 3, 4 and 7 stand out with a comparatively higher level of \u003cem\u003eActivity\u003c/em\u003e based on the average values. The comparatively smallest effect can be attributed to the \u003cem\u003eSeason\u003c/em\u003e factor (0.003), whereby those rounds of K\u0026amp;Q that took place in spring and summer generated a higher level of \u003cem\u003eActivity\u003c/em\u003e (see Fig.\u0026nbsp;4). The \u003cem\u003eDay\u003c/em\u003e factor explains 0.005 of the variance. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e, a negative correlation can be assumed here, meaning that the longer the respective wave already lasts, the lower the \u003cem\u003eActivity\u003c/em\u003e on that day. The comparably extremely high value of variance explanation for \u003cem\u003eResidual\u003c/em\u003e in comparison to these factors implies that the model in this form cannot explain a total of 0.059 of the variance.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe present study investigates the association between \u003cem\u003eWalkability\u003c/em\u003e by using the \u003cem\u003eWalkability Index\u003c/em\u003e of the City of Munich, and and indicator for children\u0026acute;s and adolescents\u0026acute; \u003cem\u003eActivity\u003c/em\u003e collected as part of an intervention to promote urban PA behavior. Influences of the respective \u003cem\u003eDistrict\u003c/em\u003e, \u003cem\u003eSeason\u003c/em\u003e and \u003cem\u003eDay\u003c/em\u003e were examined, too. The results show that the \u003cem\u003eWalkability Index\u003c/em\u003e is a significant positive predictor of \u003cem\u003eActivity\u003c/em\u003e as an indicator for PA in this study. Random effects add value, but most of the variance remains unexplained.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Relationship of \u003cem\u003eWalkability\u003c/em\u003e and \u003cem\u003eActivity\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eThe results show a highly significant association between \u003cem\u003eActivity\u003c/em\u003e and \u003cem\u003eWalkability.\u003c/em\u003e It seems that factors resulting in a high \u003cem\u003eWalkability Index\u003c/em\u003e also play a decisive role for the \u003cem\u003eActivity\u003c/em\u003e of children and adolescents. According to the elements the index comprises (population density, entropy, connectivity), following aspects could explain the observed association.\u003c/p\u003e\u003cp\u003eThe extended concept of \u003cem\u003eWalkability\u003c/em\u003e also includes social determinants, such as the number of physically active people in a neighborhood influencing the PA level of people living in this area (Wendel-Vos et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Population density therefore could be relevant for children as a higher density means more people in one area which could be related to more playmates and social interaction. The fact that density is conducive to a higher level of social activity for the residents of the area is also explained in the article by Fina et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another possible explanation for higher PA due to higher population density is that more retail services and facilities in these areas increase the number of potential destinations within walking or cycling distance, encouraging PA (Zou et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A higher density could also mean that more people, including children, live in the vicinity of the box locations, so that more people walk past the boxes and \u003cem\u003eActivity\u003c/em\u003e increases.\u003c/p\u003e\u003cp\u003eThe second objective factor for \u003cem\u003eWalkability\u003c/em\u003e is land use mix, which is represented by the entropy index. The element of \u003cem\u003eentropy\u003c/em\u003e includes land use types as \u003cem\u003esport\u003c/em\u003e and \u003cem\u003erecreation\u003c/em\u003e which indeed have a positive influence on PA behavior and enhance children ́s activity (Fina et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Frohlich \u0026amp; Collins, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sedlmeir, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The mixture of \u003cem\u003ehabitation, retail\u003c/em\u003e and \u003cem\u003erecreation\u003c/em\u003e enables children to explore a diverse environment and to have more offers at their disposal. A high land use mix contributes to the livability of a neighborhood and a more appealing walking environment, promoting healthier lifestyles (Jia et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, high entropy is indicative of a diverse range of amenities and services available within a given neighborhood.\u003c/p\u003e\u003cp\u003eA higher \u003cem\u003econnectivity\u003c/em\u003e is related with shorter distances being beneficial for children as they can walk or bike to school, friends, or sports more easily. It also means having more options to choose from and, ultimately, deciding to opt for a more attractive route. On the other hand, a higher street connectivity may be related to fewer cul-de-sacs and thus be high-traffic areas which hinder children from outdoor PA (Jia et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the context of the K\u0026amp;Q intervention, high street connectivity and traffic might discourage children from crossing roads. Even if they see a box on the other side of the road, they cannot reach it to scan it, resulting in a decrease in \u003cem\u003eActivity\u003c/em\u003e. This is also linked to the presence of safety features such as pedestrian crossings or traffic lights (Nordb\u0026oslash; et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Those safety aspects are not included in the \u003cem\u003eWalkability Index\u003c/em\u003e, but do play a decisive role for the PA behavior of children and adolescents and should be included in future research (Smith et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Trapp et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The extent to which greater connectivity is ultimately beneficial or detrimental remains equivocal.\u003c/p\u003e\u003cp\u003eIt has thus been demonstrated that \u003cem\u003eActivity\u003c/em\u003e and \u003cem\u003eWalkability\u003c/em\u003e are indeed associated. This could, in principle, be explained by a closer analysis of the elements that make up the index. This is despite the fact that it has been questioned whether such an objective index is at all suitable without the subjective perspective of children and adolescents. The \u003cem\u003eWalkability Index\u003c/em\u003e for Munich is made up of three factors which all can have stronger or weaker influences on the respective district and the citizens living there. Despite the positive association, there are diverse additional factors that play a role when analyzing activity patterns completely. Access to recreational facilities, school environments, or parental supervision may be more decisive for youth PA than \u003cem\u003eWalkability\u003c/em\u003e alone (Ding et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, \u003cem\u003eWalkability\u003c/em\u003e can serve as one but not the only explanatory approach concerning children\u0026acute;s PA behavior in respective neighborhoods, as is evident from the figures relating to the variance explained by the random effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Influencing factors of physical activity\u003c/h2\u003e\u003cp\u003eThe included random effects \u003cem\u003eDay, Season\u003c/em\u003e and \u003cem\u003eDistrict\u003c/em\u003e were able to explain a part of the variance. The variable \u003cem\u003eDay\u003c/em\u003e showed a negative correlation, meaning that \u003cem\u003eActivity\u003c/em\u003e decreases over time. The longer the intervention lasts, the less active participants become. This could be due to a better acceptance and more enthusiasm of the participants at the beginning of the intervention, as previous studies have shown that interest in interventions declines over time (Ryan et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAn examination of the results for the variable \u003cem\u003eSeason\u003c/em\u003e reveals that the majority of cumulative \u003cem\u003eActivity\u003c/em\u003e occurs in the spring and subsequent summer, with a comparatively smaller amount occurring in the fall. Temperatures, precipitation as well as day length vary across seasons and might affect PA behavior (Kolle et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Thus, weather conditions play a role as it could have been exceptionally hot in the summer months and worse conditions, like cold or rain, appeared in fall months, refraining children from being more active outside. Especially Spring and Summer are predestined to cause higher PA levels (Atkin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Garriga et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kolle et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). As the results show an influence of seasonal variations on PA, this should be considered in future public health research and the design of interventions to promote PA, as seasonality can be perceived as both a facilitator and a barrier (Garriga et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Interventions could be adapted depending on the season with specific challenges, outdoor- and indoor-activity opportunities or weather adapted events.\u003c/p\u003e\u003cp\u003eThe variable \u003cem\u003eDistrict\u003c/em\u003e, which explains part of the variance in \u003cem\u003eActivity\u003c/em\u003e, reflects a range of underlying contextual factors influencing PA. These may include geographical, social, and demographic characteristics. Smith et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) highlight that built environment features relevant to PA, such as access to green spaces, perceived safety, infrastructure for active transport, and land-use mix, are often unequally distributed across urban districts, particularly along socioeconomic lines. In addition, districts may vary in terms of population composition, cultural norms, availability of recreational facilities, and exposure to traffic or environmental stressors, all of which shape opportunities for and attitudes toward PA. Although intra-district variation exists, district-level clustering likely captures macro-level environmental and social disparities relevant to urban PA behavior. These contextual factors may explain variance not accounted for by objective indicators like the \u003cem\u003eWalkability Index\u003c/em\u003e alone. As Gemmell et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) point out, neighborhood characteristics influencing outdoor play include playgrounds, sidewalks, destinations, green spaces, traffic levels, social safety, and cohesion. Since these factors can vary substantially between districts, differences in PA behavior are a plausible outcome.\u003c/p\u003e\u003cp\u003eHowever, a great part of the model variance is not explained by the included fixed or random effects. It is possible that a proportion of the unexplained variance in the model can be attributed to the fact that the index developed by Frank et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) was not originally designed to assess \u003cem\u003eWalkability\u003c/em\u003e for children and adolescents. It is noteworthy that the attributes may not be aligned with the specific requirements of the individual's walking needs (Buck et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, it suggests the presence of additional factors which may neither be displayed by objective indices nor theory-based models. One way of approaching these factors is to include the target group and their subjective view of corresponding interventions. As Wang et al. (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explain in their research, the impact of the built environment on an individual\u0026acute;s PA behavior cannot be fully described by using objective measurement tools. However, research demonstrates that both perceived and objective indicators of \u003cem\u003eWalkability\u003c/em\u003e are useful to fully grasp influencing factors (Jensen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A positive example for a subjective approach can be named with the study \u0026ldquo;Walki-Muc\u0026rdquo; (Scheller \u0026amp; Bachner, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The promising measure of the Neighborhood Environment Walkability Scale for Youth (NEWS-Y) questionnaire incorporates the subjective perception of children and adolescents regarding diverse environments. Current research highlights the adaptation of \u003cem\u003eWalkability\u003c/em\u003e indices with target specific factors. Buck et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reveal recreational facilities such as playgrounds and public open spaces as important features for PA. Based on that, the researchers developed moveability indices with strong effects on PA in school children. These examples highlight the importance of considering multi-level influences when planning, implementing, and evaluating PA in children and adolescents in their neighborhoods.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Practical implications in the context of urban planning\u003c/h2\u003e\u003cp\u003eObjective indices, such as the \u003cem\u003eWalkability Index\u003c/em\u003e, have the capability to function as a guide in order to identify environments that may support or hinder PA. It is hard to imagine that areas with extremely low \u003cem\u003eWalkability Indices\u003c/em\u003e are particularly attractive to children and adolescents and their PA behavior. However, if one aims to promote the PA of a specific population, additional data gathering methods as well as participative approaches should be applied on the development of interventions. The concept of co-creation, being one of many participative approaches, engages and empowers end-users and is proposed to increase behavior intervention adoption, adherence, and effectiveness (Sofro et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Research suggests implementing the subjective perspective of vulnerable groups, in this case children, as they perceive their environment in a different way than adults do (Freitas et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Scheller \u0026amp; Bachner, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ubiali et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The \u003cem\u003eWalkability Index\u003c/em\u003e is generally developed for adult mobility patterns, which may not fully capture how children and adolescents use their environment. When addressing children and adolescents, it could be useful to develop child-friendly \u003cem\u003eWalkability-\u003c/em\u003ecriteria consisting of safe, playful, and social factors and combining aspects such as traffic-calmed infrastructure, co-creation processes, and technological incentives. Consequently, the development of a Youth Walkability Index, grounded in the \u003cem\u003eWalkability Index\u003c/em\u003e and adapted through qualitative and participatory methodologies, could prove more efficacious (Scheller \u0026amp; Bachner, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, incorporating additional demographic factors, such as socio-economic status (SES), could facilitate an assessment of their influence on mobility choices and behaviors. Research has yielded inconsistent results regarding the mediating effect of demographics on the relationship between \u003cem\u003eWalkability\u003c/em\u003e and PA (Andersen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; D'Haese et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Further research is necessary to provide more robust evidence on this matter. In exploring SES-moderated disparities in PA, these findings could be employed in urban planning to address inequalities (Aznar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Strengths and limitations of this study\u003c/h2\u003e\u003cp\u003eThe combinability of a \u003cem\u003eWalkability Index\u003c/em\u003e officially surveyed in a city with millions of inhabitants, which requires a large number of parameters, often only available to the local authority, and a large-scale study on PA promotion with very large expenditure in terms of time and money, is scarce and offers unprecedented insights. The present study examined data from seven city districts, accounting for approximately 62,500 children and adolescents under the age of 18 living in the seven districts in question. Of these, 9,852 participated in the K\u0026amp;Q, representing a proportion of 15.7%. Key strengths include the use of sound data sources and analytical methods to support the findings, practical outcomes, and the ability to compare results to those of other studies and cities. Specifically, \u003cem\u003eWalkability\u003c/em\u003e and an indicator for PA were measured objectively. This approach ensured that no recall bias, social desirability, or similar factors influenced the results, as is typical of similar studies. However, the \u003cem\u003eActivity\u003c/em\u003e variable just provides insight into the temporal and spatial parameters of box scanning, including the specific times and locations at which scanning occurred. There is no information about the type of transport participants used. It can thus be concluded that \u003cem\u003eActivity\u003c/em\u003e merely serves as an indicator for PA, and the incorporation of GPS tracking or accelerometer data would facilitate the acquisition of more comprehensive information regarding the nature and intensity of PA.\u003c/p\u003e\u003cp\u003eFurthermore, large parts of the variance cannot be explained by the variables collected. Additional variables, such as the SES mentioned above, should therefore be included in future wherever possible. However, due to the practically unmanageable number of additional variables, a qualitative flanking would be particularly valuable. In this way, quantitative calculations could be combined with qualitative explanatory backgrounds to create a holistic picture. The combination of quantitative and qualitative elements is particularly advantageous for complex intervention planning and provides a better understanding of research problems (Palinkas et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This mixed-methods design could increase the gain in knowledge and promote the inclusion of target groups (Kutzleben et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother limitation is that the results show correlations rather than cause-and-effect relationships. In order to create a longitudinal study that can provide a higher level of evidence for causal relationships, children\u0026rsquo;s PA levels and movement patterns could be measured before and during an intervention. This would provide additional interpretative approaches in this context, as well as additional variables to be collected. Also, the long-term effects of \u003cem\u003eWalkability\u003c/em\u003e measures on PA behavior and public health remain open and should be considered in the future.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThe \u003cem\u003eWalkability Index\u003c/em\u003e is a resource-saving, objective method that was applied in this study and proved to be associated with PA in children and adolescents. The index could thus be utilized in PA research for the purpose of intervention planning. Additionally, it can be employed in the field of urban planning, aiming at creating a PA-promoting environment for children and adolescents in an urban context. Differences in PA patterns between districts, seasons, and days are relatively small, but should be taken into account in future planning efforts. Despite that, a considerable amount of variance remains unclear, suggesting that important influencing factors have yet to be identified. Therefore, the unique needs and subjective perceptions of children and adolescents regarding \u003cem\u003eWalkability\u003c/em\u003e could be given greater consideration in future urban planning processes to better understand their needs and reveal additional influencing factors. Integrating these perspectives can help create environments that are not only structurally conducive to walking but also perceived as safe and inviting by younger populations. Future urban planning strategies should embrace a more tailored, evidence-based approach that ensures sustainable, health-promoting, and inclusive cities for all.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Approval (526/20 S-EB) was obtained from the ethics committee of the Technical University of Munich. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis publication was supported by the Technical University of Munich as part of the DEAL Agreement on Open Access Publishing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted by C.M. and J.S.-E., who also collected the data used. The analysis was performed by L.E. with substantial contributions from P.T. and F.A. The manuscript was written by L.E. and J.S.-E. with substantial contributions from D.S. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndersen OK, Gebremariam MK, Kolle E, Tarp J. Socioeconomic position, built environment and physical activity among children and adolescents: A systematic review of mediating and moderating effects. 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Obes Reviews: Official J Int Association Study Obes. 2021;22(Suppl 1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ee13037.https://doi.org/10.1111/obr.13037\u003c/span\u003e\u003cspan address=\"e13037.10.1111/obr.13037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cem\u003eSuppl 1\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Walkability Index, physical activity, intervention, children and adolescents, urban design and planning","lastPublishedDoi":"10.21203/rs.3.rs-7365397/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7365397/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eChildren and adolescents often do not meet the WHO\u0026acute;s physical activity (PA) recommendations. As many of them live in urban areas, these are important spaces for PA-promotion. Objective measures such as the Walkability Index are often used to assess urban spaces in terms of their PA friendliness. However, it is unclear whether such parameters can predict PA behavior of children and adolescents. This study examines the relationships between the Walkability Index and data of the intervention \u0026ldquo;Kreuz \u0026amp; Quer\u0026rdquo; (K\u0026amp;Q), promoting PA. K\u0026amp;Q collected data from 9,852 children and adolescents in urban neighborhoods. Activity \u0026ndash; measured by interactions with K\u0026amp;Q checkpoints \u0026ndash; acted as the dependent variable in a linear mixed models approach. Walkability served as a fixed factor and district, season of year and intervention day as random effects. Results indicate a significant positive correlation between a high Walkability Index and PA levels in children and adolescents. Some of the observed variance can be explained by the random effects. There is still unexplained variance, suggesting the need to consider additional influences to explain youth PA behavior. These may include qualitative explanations to provide a holistic picture. Subjective perspectives can help create environments that are structurally conducive to walking, thereby promoting PA.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Walkable neighborhoods = active kids? Exploring the relationship between a physical activity intervention for youth and walkability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 09:32:47","doi":"10.21203/rs.3.rs-7365397/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-11T07:17:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-05T19:45:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-04T03:23:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91704077714050086935816094204174200608","date":"2025-08-21T14:11:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44951580909686873195424367103376925567","date":"2025-08-21T02:03:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-20T10:30:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-19T00:01:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-19T00:00:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-08-13T13:11:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad2d331f-b6da-4381-83f1-c9bef5122c30","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:09:37+00:00","versionOfRecord":{"articleIdentity":"rs-7365397","link":"https://doi.org/10.1186/s12889-025-25317-0","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-11-06 15:58:01","publishedOnDateReadable":"November 6th, 2025"},"versionCreatedAt":"2025-09-01 09:32:47","video":"","vorDoi":"10.1186/s12889-025-25317-0","vorDoiUrl":"https://doi.org/10.1186/s12889-025-25317-0","workflowStages":[]},"version":"v1","identity":"rs-7365397","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7365397","identity":"rs-7365397","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}