Associations between perceived neighborhood environment and physical activity among breast cancer patients engaged in a physical activity program concomitant to cancer treatment: cross-sectional and longitudinal analyses in the DISCO trial (DiscoSpace)

Associations between perceived neighborhood environment and physical activity among breast cancer patients engaged in a physical activity program concomitant to cancer treatment: cross-sectional and longitudinal analyses in the DISCO trial (DiscoSpace) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations between perceived neighborhood environment and physical activity among breast cancer patients engaged in a physical activity program concomitant to cancer treatment: cross-sectional and longitudinal analyses in the DISCO trial (DiscoSpace) Margaux Langlois, Lény Grassot, Baptiste Fournier, Olivier Trédan, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8094974/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Mar, 2026 Read the published version in International Journal of Behavioral Nutrition and Physical Activity → Version 1 posted 9 You are reading this latest preprint version Abstract Background Despite the proven benefits of physical activity during breast cancer treatment, many women reduce their practice after diagnosis. A better understanding of how the neighborhood environment influences physical activity behavior could help optimize strategies for physical activity in breast cancer patients undergoing cancer treatment. Objective This study examined associations between perceived neighborhood environment and physical activity among breast cancer patients undergoing treatment and engaged in a physical activity program. Methods Participants were 313 breast cancer patients enrolled in the DISCO physical activity intervention trial (NCT03529383). In the present observational analysis (DiscoSpace), cross-sectional (at baseline) and longitudinal (during intervention) associations between perceived neighborhood environment and physical activity were investigated. The perceived neighborhood environment was assessed using the ALPHA questionnaire, physical activity and physical functioning were evaluated through the Recent Physical Activity Questionnaire (self-reported physical activity) and the 6-Minute Walk Test (to measure the 6-Minute Walk Distance (6MWD)). Associations were estimated through mixed linear regression models. Results Better perceived cycling and walking infrastructures and network were associated with higher self-reported physical activity at baseline (total infrastructures: β = 0.226, 95% CI (0.063;0.388); network: β = 0.161, 95% CI (0.008;0.314)). Perceived distance to local facilities was inversely associated with 6MWD at baseline (β=-11.363, 95% CI (-20.607;-2.118)). A perception of densely populated neighborhoods (β=-0.306, 95% CI (-0.494;-0.117)) was associated with a lesser increase in self-reported physical activity during the intervention, after adjustment for trial arm. These associations varied according to women’s socioeconomic status and municipality class. Conclusion The perceived neighborhood environment and socioeconomic characteristics of women with breast cancer should be given greater consideration for developing effective programs to promote physical activity in this population. breast cancer cancer treatment cross-sectional study longitudinal study perceived neighborhood environment physical activity BACKGROUND Breast cancer accounts for more than one-third of new female cancer cases worldwide, representing 2.3 million incident cancers in 2022( 1 ). In France as in other high-income countries, breast cancer incidence is increasing while mortality has declined since the 1990s( 2 ), resulting in a growing population of breast cancer patients and survivors. This population faces specific challenges during and after cancer treatments, including issues related to sequalae, comorbidities, cancer fatigue and quality of life( 3 ), which highlights the need for comprehensive, tailored strategies to address these issues. Physical activity during and after cancer treatment is associated with improved cancer- and non-cancer-related outcomes( 4 ). A positive association has been suggested with specific and overall survival in breast cancer patients( 5 ). Moreover, engagement in physical activity practice has been consistently associated with reduction in cancer-related fatigue and improvements in physical fitness and in the perceived quality of life in patients undergoing cancer treatment( 6 – 8 ). Several organizations have published guidelines on physical activity, emphasizing its importance in improving quality of life and reducing fatigue, especially during active cancer treatment with curative intent( 4 , 9 , 10 ). Despite this, many women reduce their practice of physical activity following breast cancer diagnosis( 11 , 12 ). While multiple studies have investigated barriers to engaging in physical activity during and after cancer treatment( 13 ), the specific role of the living environment on physical activity in cancer patients has received little attention. The role of the neighborhood environment on physical activity is increasingly recognized in the general population( 14 – 16 ). Notably, the effectiveness of physical activity interventions appears to be related to this environment, as reported in a systematic review( 17 ). Still, there is a growing need to understand the environmental correlates of physical activity in specific populations( 18 ), including in breast cancer patients, a population who remain underrepresented in these studies. Among breast cancer patients, two American studies have identified a negative association between the lack of facilities, or open space, and self-reported physical activity ( 19 , 20 ). In Europe, two qualitative studies also reported that limited access to recreational facilities was a significant environmental barrier to physical activity in this group( 21 , 22 ). Further research is warranted in the European context, as urban environments in Europe differ significantly from those on other continents, particularly in terms of housing density and land use mix( 23 ). In addition, while objective methods, such as spatial databases (e.g., GIS), are commonly used to assess neighborhood environmental characteristics in European studies on physical activity( 14 ), discrepancies between objectively measured (objective) and individually experienced (subjective) environmental features have been noted( 24 – 26 ). These differences highlight how objective measures may fail to capture the lived experience and the specific needs of individuals, potentially leading to inconsistent results( 27 , 28 ). In this context, the present study aimed to examine the relationship between the perceived neighborhood environment and physical activity before treatment initiation (baseline) and change in physical activity over 6 months, among breast cancer patients engaged in a physical activity program concomitant to cancer treatment. By combining self-reported physical activity data and functional capacity measurement, this study provides a comprehensive approach to understanding how perceptions of the neighborhood environment influence physical activity in women undergoing breast cancer treatment. MATERIALS AND METHODS Data collection The current work is an ancillary study (DiscoSpace) to the phase III interventional multicenter randomized trial DISCO (NCT03529383)( 29 ). This controlled trial, started in 2018 and ended in 2022, was promoted by the Léon Bérard Comprehensive Cancer Center (CLB), located in Lyon, France. This trial was designed to evaluate the efficacy of a 6-months adapted physical activity program using a connected device with activity trackers and a patient therapeutic education program among women with localized breast cancer. Study rationale, methods for recruitment, enrollment, data collection, and participant characteristics of the DISCO trial have been described in details elsewhere( 29 ). Briefly, with a 2×2 factorial design (1:1:1:1 ratio), 436 women diagnosed with primary localized invasive breast carcinoma and eligible for adjuvant chemotherapy, hormonotherapy, immunotherapy and/or radiotherapy, were randomized into one of the four arms: (A) individualized, semi-supervised exercise program physical activity program carried out autonomously with a connected device, (B) therapeutic patient education sessions on physical activity, (C) both interventions, (D) control group receiving usual care. All women in the trial were informed about international recommendations on physical activity by a certified sports instructor and a booklet. For each participant, data were collected at baseline, with follow-up assessments at 6 months and 12 months. For the DiscoSpace study initiated in 2021, an additional self-administered questionnaire (ALPHA, Assessing Levels of PHysical Activity and Fitness at population level( 23 )) was sent to participants of the DISCO trial to assess their perception of the neighborhood environment at their place of residence at baseline. Depending on each patient's status in the DISCO trial at the time of DiscoSpace initiation, the ALPHA questionnaire was administered either at inclusion in DISCO (n = 59 patients), during the intervention (n = 69 patients), or after completion of the intervention (n = 185 patients). The administration of the ALPHA questionnaire for the DiscoSpace study was approved by a French ethics committee (17th November 2020). The informed consent form signed by all patients randomized in the DISCO trial mentions the possibility of re-use the study data for other research purposes. Study population Of the 436 participants enrolled in the DISCO trial, all 409 patients followed at the CLB were invited to participate in the DiscoSpace study and received the French version of the ALPHA environmental questionnaire. Measurements Perceived neighborhood environment The standardized, self-administrated ALPHA questionnaire is a validated tool designed to measure the perceptions of the neighborhood environment (defined as "the area within approximately one kilometer or half a mile of your home or that you could walk to in 10–15 minutes”( 23 )) in relation to physical activity. Participants completed the questionnaire regarding their residence at baseline in the DISCO trial. The 49 items of the long version of the ALPHA questionnaire were summed into 15 environmental scores according to the rules of the authors' manual( 30 ): residential density (/315), distance to local facilities (/40), cycling infrastructure (/10), walking infrastructure (/10), total infrastructure (/20), maintenance of infrastructure (/15), safety from crime (/15), safety from traffic (/15), total safety (/30), esthetics (/15), pleasure (/20), connectivity (/15), cycling and walking network (/20), home environment (/6) and work/study environment (/10). Higher scores generally indicating a better perception of the neighborhood environment, except for residential density and distance to facilities, where higher scores reflect greater perceived residential density and longer perceived walking distances. Home and work environment scores were not analyzed as neighborhood determinants, as they did not provide relevant information about the residential environment. Self-reported physical activity and functional capacity Self-reported physical activity was assessed using the validated and standardized Recent Physical Activity Questionnaire (RPAQ)( 31 ), in which participants described their activities over the past four weeks across different domains: commuting, occupation, leisure time, and domestic life. Only moderate-to-vigorous activities (≥ 3 Metabolic Equivalent Tasks) were considered, and scores were summed in hours per week. Data were collected at baseline and at 6-month follow-up, i.e. at the end of the intervention. The 6-Minute Walk Distance (6MWD), which reflects an individual’s functional exercise capacity or walking ability, was objectively measured using the validated 6-Minute Walk Test (6MWT)( 32 ). The test records the maximum distance walked in six minutes along a flat 30-meter corridor. Assessments were conducted at baseline and 6-months by a certified professional. Covariates The trial arm corresponds to the randomization group in the DISCO trial (A/B/C/D). Clinical data, including age (years), menopausal status (pre/post), comorbidities (past or present/none), time since diagnosis (months) and since the first surgery for breast cancer (months) were collected during baseline medical assessment. Anthropometric measurements were taken by a trained professional at each visit to calculate body mass index (BMI, in kg/m 2 ). Self-reported variables included educational level (≤ baccalaureate/1 to 3 years post-baccalaureate/≥ 4 years post baccalaureate), employment status after diagnosis (active/on medical leave or disabled/retired), health (score calculated from the EQ-5D-5L questionnaire( 33 )), quality of life (score calculated from the EORTC QLQ-C30 questionnaire( 34 )), and living with a partner (yes/no). Social deprivation (yes/no) was assessed using an individual deprivation index called the “Evaluation of Deprivation and Inequalities in Health Examination Centers” (EPICES)( 35 ). A cut-off score of 30, identified in a validation study of the EPICES index as the lower limit of the 4th quintile, was used to define social deprivation( 36 ): women with an EPICES score above this threshold were classified as deprived, while those below were considered non-deprived. The municipality class of the residence at baseline (collected through the ALPHA questionnaire) was categorized using the 3-level grid developed by the French National Institute of Statistics and Economic Studies( 37 ): rural municipality, large urban center, or intermediate-density municipality ( Additional File 1 ). The COVID-19 pandemic trial status was determined according to the intervention period with respect to the first French lockdown, which began on March 17, 2020 (before/during or after). Statistical analysis Normality of the distribution for quantitative variables was assessed graphically. Data were described using means and standard deviations (± SD) for normally distributed quantitative variables, medians and interquartile ranges (IQR) for non-normally distributed quantitative variables, and frequencies (percentages) for categorical variables. For descriptive purposes, we defined a change in BMI as a 5% change over 6 months. As suggested by previous studies, we defined a change in health as a change of 8.6 points( 38 ) and a change in quality of life as a change of 5 points( 34 ). To examine the associations between perceived neighborhood environmental characteristics (predictors) and self-reported physical activity or 6MWD (dependent variables) at baseline and change over 6 months, we used linear mixed models. The basic model was run separately for each environmental score, and included: environmental score, physical activity (self-reported physical activity or 6MWD), intervention time point (baseline or 6 months), an interaction between visit and perceived neighborhood environment score, and a random intercept to account for clustering at the individual level. In this model, the first term represents the association between perceived neighborhood environment and baseline physical activity (cross-sectional association), while the interaction term indicates the association between perceived neighborhood environment and the change in physical activity over 6 months (longitudinal association). The use of a random intercept was assessed using a likelihood ratio test to ensure the improvement of each model. Physical activity variables were modeled continuously, and square root transformation was used for self-reported physical activity to better approximate a normal distribution of residuals. For comparability of results, all environmental scores were standardized by dividing individual scores by the mean standard deviation. Standardized coefficients represent the change in the dependent variable associated with a one SD change in the predictor variable. All models were adjusted for a priori-defined confounders selected by a Directed Acyclic Graph ( Additional File 2 ). Fixed (time-invariant) confounders included age, social deprivation, educational level, employment status after diagnosis, comorbidities, living with a partner, trial arm, municipality class, perceived home environment, and the COVID-19 pandemic trial status. Time-variant confounders were measured at baseline and at 6 months, and included BMI, quality of life, and health status. Among all analyses, the one estimating the effect of residential density score on physical activity were not adjusted for municipality class because of strong correlations between these variables. Standardized regression coefficients were used to express the beta with the corresponding 95% confidence interval (CI). Confounders, as well as other suspected modifiers, were investigated as potential effect modifiers using likelihood-ratio tests. Beforehand, time-variant variables were fixed at baseline, and quantitative variables were analyzed in subgroups using the WHO categorization for BMI( 39 ) (< 25/≥25 kg/m²) or medians as cut-off (for age (51 years), quality of life at baseline (75/100), health status at baseline (70/100), time since diagnosis (3 months), time since the first breast cancer surgery (1 month)). For each association and each considered modifier, we first tested a two-way ANOVA interaction between the effect modifier and the explanatory variable (to model the effect of the modifier on the association between perceived neighborhood environment and physical activity at baseline), and then tested an ANOVA three-way interaction between the effect modifier, the explanatory variable, and the time-point (to model the effect of the modifier on the association over time). Statistical significance for the interaction term was set at p < 0.05. Results were stratified based on significant interactions. Three sensitivity analyses were conducted to evaluate the robustness of our findings. The first adjustment accounted for the timing of the ALPHA questionnaire completion (before, during or after the DISCO trial intervention). Second, extreme values of self-reported physical activity (beyond the 99th percentile) were excluded to minimize the influence of outliers, resulting in the removal of two participants. The third sensitivity analysis stratified participants by cancer treatment subgroup (chemotherapy, immunotherapy, radiotherapy, hormonotherapy). Although cancer treatment was a potential confounder, the administration of multiple therapies during the 6-month follow-up period precluded its inclusion in the main adjustments. Multiple Imputation by Chained Equations was applied to covariates with missing values. Under the Missing at random and Missing Completely At Random data assumptions( 40 ), 10 imputed data sets were generated, each consisting of 10 iterations. All the analyses were conducted using R, 4.4.0 version (notably lme4, mice and mitml packages). The type I error rate was set at 0.05. RESULTS Population characteristics Of the 409 patients of the DISCO trial followed at the CLB included in the DiscoSpace study, 314 completed the ALPHA environmental questionnaire (response rate 76.8%). One patient was subsequently excluded due to missing 6MWD measures at baseline ( Flowchart available in Additional File 3 ). Baseline characteristics of the DiscoSpace study participants ( n = 313) are shown in Table 1 . The mean age at enrollment was 52.0 years old (± 10.3 years), approximately one-third (32.9%) had not completed higher education, and nearly one-half (47.9%) were on medical leave or disabled. Overall, 14.1% of the women were in a situation of social deprivation. Patients were evenly distributed among the different types of municipalities (29.7%, 33.2%, and 37.1% lived in rural areas, intermediate density municipalities and in large urban centers, respectively). Baseline characteristics of respondents and non-respondents (n = 95) were broadly comparable, except for social deprivation, which was more prevalent among non-respondents (21.1%). However, interpretation is limited by the high proportion of missing data among non-respondents, with 64.2% missing data on education and 63.2% on employment ( Additional File 4 ). Table 1 – Baseline characteristics of the 313 participants of the DiscoSpace study, France, 2018–2022 (n = 313) SOCIODEMOGRAPHICS Age (years) , mean ± SD 52.0 ± 10.3 Educational level , n (%) ≤ baccalaureate 103 (32.9) 1 to 3 years post-baccalaureate 85 (27.2) ≥ 4 years post baccalaureate 92 (29.4) Missing 33 (10.5) Employment after diagnosis , n (%) Active 68 (21.7) On medical leave or disabled 150 (47.9) Retired 55 (17.6) Missing 40 (12.8) Social deprivation a , n (%) Deprived 44 (14.1) Non-deprived 262 (83.7) Missing 7 (2.2) Living with a partner , n (%) Yes 245 (78.3) No 66 (21.1) Missing 2 (0.6) HEALTH AND BEHAVIOUR Time since diagnosis (months) , median (IQR) 3.0 (2.5–4.0) Missing, n (%) 2 (0.6) Time since first surgery (months) , median (IQR) 1.0 (1.0–2.0) BMI (kg/m²) , mean ± SD 25.6 ± 5.1 Missing, n (%) 1 (0.3) BMI category , n (%) Normal weight 164 (52.4) Overweight 95 (30.4) Obesity 53 (16.9) Missing 1 (0.3) Quality of life (/100) b , median (IQR) 75.0 (58.3–83.3) Missing, n (%) 17 (5.4) Health status (/100) c , median (IQR) 70.0 (60.0–80.0) Missing, n (%) 2 (0.6) Menopausal status , n (%) Premenopausal or perimenopausal 166 (53.0) Postmenopausal 145 (46.3) Missing 2 (0.6) Comorbidities , n (%) Past or present 211 (67.4) None 102 (32.6) Municipality class d , n (%) Rural municipality 93 (29.7) Intermediate-density municipality 104 (33.2) Large urban center 116 (37.1) Abbreviations: IQR Inter-Quartile Range; SD Standard Deviation ; a Assessed using the Evaluation of Deprivation and Inequalities in Health Examination Centers (EPICES) index, with a cut-off score of 30 to define precarity ; b Score calculated from the EORTC QLQ-C30 questionnaire ; c Score calculated from the EQ-5D-5L questionnaire ; d Based on patients addresses at baseline, using the 3-level grid developed by the French National Institute of Statistics and Economic Studies (INSEE) The characteristics of the 313 patients during the 6-month follow-up are shown in Table 2 . Two thirds of patients were enrolled during or after the first national COVID-19 pandemic lockdown in France (66.1%). The vast majority of the patients received radiotherapy (90.7%) and/or a hormone therapy (81.2%), approximately 57.5% received chemotherapy, and 14.1% received immunotherapy. Most participants were enrolled after the DISCO intervention (59.1%), while 22.0% were included during and 18.9% before. Table 2 – Intervention-related characteristics of the 313 participants of the DiscoSpace study, France, 2018–2022 Characteristics Total (n = 313) INTERVENTION SPECIFICITIES Trial arm, n (%) (A) Individualized, semi-supervised exercise program physical activity program carried out autonomously with a connected device 79 (25.2) (B) Therapeutic patient education sessions on physical activity 76 (24.3) (C) Both interventions 77 (24.6) (D) Control group receiving usual care 81 (25.9) COVID-19 pandemic trial status, n (%) Before the first national lockdown 106 (33.9) During or after the first national lockdown 207 (66.1) Received therapies during the intervention (yes) , n (%) Radiotherapy 284 (90.7) Hormonotherapy 254 (81.2) Chemotherapy 180 (57.5) Immunotherapy 44 (14.1) OBSERVED CHANGES DURING INTERVENTION Change in BMI a , n (%) Weight gain 32 (10.2) None 172 (55.0) Weight loss 27 (8.6) Missing 82 (26.2) Change in health status b , n (%) Improvement 114 (36.5) None 113 (36.1) Deterioration 58 (18.5) Missing 28 (8.9) Change in quality of life c , n (%) Improvement 96 (30.7) None 60 (19.2) Deterioration 104 (33.2) Missing 53 (16.9) Abbreviations: IQR Inter-Quartile Range; SD : Standard Deviation ; a Defined as a 5% increase or decrease over the 6 months ; b Defined as a change of ± 8.6 units over the 6 months ; c Defined as a change of ± 5 units over the 6 months Data on physical activity are shown in Table 3 . At the time of randomization, the median amount of self-reported physical activity was 4.1 hours per week (IQR: 1.3–8.2), which increased to 8.7 hours per week (IQR: 4.9–14.6) by the end of the intervention, for a median increase of + 4.6 hours, and a relative increase of + 112.2%. The mean 6MWD was 575.8 meters (± 77.4 m) at baseline, and 596.2 meters (± 83.3 m) at 6 months, representing a median increase of + 20.4 m, and a relative increase of + 3.5%. Table 3 – Change in physical activity over the 6-months, DiscoSpace study, France, 2018–2022 (n = 313) Baseline, Median (IQR) / Mean ± SD 6 months, Median (IQR) / Mean ± SD Absolute difference Relative difference Missing data at 6-months, n (%) Self-reported physical activity (hour/week) a 4.1 (1.3–8.2) 8.7 (4.9–14.6) + 4.6 + 112.2% 19 (6.07) 6MWD (meters/6min) b 575.8 ± 77.4 596.2 ± 83.3 + 20.4 + 3.5% 102 (32.6) Abbreviations: 6MWD 6-Minute Walk Distance ; IQR Inter-Quartile Range ; SD : Standard Deviation; a Self-reported physical activity was calculated from the Recent Physical Activity Questionnaire (RPAQ) ; b 6MWD was measured by the 6-Minute Walk Test (6MWT) The scores obtained from the ALPHA questionnaire and their description are presented in Additional File 5 . The infrastructure maintenance score was excluded from all the analyses due to the large number of missing values (38.3%). Other scores had minimal missing data, with residential density and distance to local facilities having the highest rates of missing responses (10.5% and 10.9%, respectively). Cross-sectional associations In the overall model, an increase of one standardized unit of perceived infrastructure scores (cycling: β = 0.210, 95% CI (0.048;0.372); walking: β = 0.170, 95% CI (0.016;0.324); total infrastructure: β = 0.226, 95% CI (0.063;0.388), per 1SD) and walking and cycling network score (β = 0.161, 95% CI (0.008;0.314), per 1SD) predicted higher levels of self-reported physical activity at baseline (Table 4 ). Moreover, an increase of one standardized unit of the perceived distance to facilities was associated with lower 6MWD at baseline (-11 meters; β=-11.363, 95% CI (-20.607;-2.118), per 1SD). No association was observed for all other environmental scores and 6MWD at baseline. Table 4 – Associations between perceived neighborhood environment and physical activity, DiscoSpace study, France, 2018–2022 (n = 313) Physical Activity Outcome Perceived neighborhood environment a Self-reported physical activity b 6MWD c β d 95% CI p-value β d 95% CI p-value Residential density Cross-sectional e 0.047 (-0.105 ; 0.199) 0.542 0.189 (-8.377 ; 8.755) 0.965 Longitudinal f -0.306 (-0.494 ; -0.117) 0.002 2.307 (-6.163 ; 10.776) 0.593 Distance to local facilities Cross-sectional e -0.071 (-0.242 ; 0.100) 0.413 -11.363 (-20.607 ; -2.118) 0.016 Longitudinal f 0.181 (-0.014 ; 0.377) 0.069 0.160 (-8.379 ; 8.700) 0.971 Cycling infrastructures Cross-sectional e 0.210 (0.048 ; 0.372) 0.011 4.509 (-4.081 ; 13.098) 0.303 Longitudinal f -0.151 (-0.340 ; 0.038) 0.117 0.058 (-7.725 ; 7.841) 0.988 Walking infrastructures Cross-sectional e 0.170 (0.016 ; 0.324) 0.031 1.685 (-6.376 ; 9.747) 0.681 Longitudinal f -0.159 (-0.348 ; 0.030) 0.099 0.815 (-7.023 ; 8.653) 0.838 Total infrastructures Cross-sectional e 0.226 (0.063 ; 0.388) 0.007 3.837 (-4.806 ; 12.479) 0.384 Longitudinal f -0.175 (-0.363 ; 0.013) 0.068 0.988 (-7.338 ; 8.208) 0.912 Safety from crime Cross-sectional e -0.076 (-0.235 ; 0.083) 0.347 -0.979 (-9.288 ; 7.330) 0.817 Longitudinal f 0.113 (-0.075 ; 0.301) 0.240 -3.964 (-11.802 ; 3.874) 0.321 Safety from traffic Cross-sectional e -0.009 (-0.155 ; 0.138) 0.906 5.344 (-2.126 ; 12.815) 0.160 Longitudinal f 0.138 (-0.049 ; 0.326) 0.148 -6.353 (-14.044 ; 1.338) 0.105 Total safety Cross-sectional e -0.040 (-0.193 ; 0.114) 0.610 3.224 (-4.727 ; 11.175) 0.426 Longitudinal f 0.147 (-0.041 ; 0.335) 0.125 -6.114 (-13.901 ; 1.673) 0.124 Esthetics Cross-sectional e 0.017 (-0.139 ; 0.173) 0.828 4.879 (-4.252 ; 12.010) 0.349 Longitudinal f 0.152 (-0.037 ; 0.340) 0.114 -7.150 (-15.223 ; 0.923) 0.082 Pleasure Cross-sectional e 0.032 (-0.125 ; 0.188) 0.692 6.207 (-1.959 ; 14.373) 0.136 Longitudinal f 0.174 (-0.015 ; 0.364) 0.071 -6.862 (-15.052 ; 1.328) 0.100 Connectivity Cross-sectional e 0.120 (-0.030 ; 0.269) 0.118 4.457 (-3.252 ; 12.166) 0.257 Longitudinal f -0.049 (-0.241 ; 0.144) 0.620 -3.482 (-11.676 ; 4.713) 0.404 Walking and cycling network Cross-sectional e 0.161 (0.008 ; 0.314) 0.039 2.630 (-5.290 ; 10.551) 0.514 Longitudinal f -0.140 (-0.331 ; 0.050) 0.148 -3.191 (-11.301 ; 4.919) 0.440 Values in bold are statistically significant (P < 0.05) ; a Environmental scores were calculated from the ALPHA questionnaire (for Assessing Levels of PHysical Activity and Fitness at population level) ; b Self-reported physical activity was calculated from the Recent Physical Activity Questionnaire (RPAQ). The average difference in the outcome self-reported physical activity is expressed by the square root ; c 6MWD was measured by the 6-Minute Walk Test (6MWT). The average difference in the outcome 6MWD is expressed without transformation ; d The β indicate the overall longitudinal difference in the outcome score using linear mixed models per 1 SD of perceived built environment score after a standardized Z-score transformation. Analyses were adjusted on: age, social deprivation, educational level, employment status after diagnosis, comorbidities, living with a partner, trial arm, municipality class (except for Residential density score analyses), perceived home environment, COVID-19 pandemic trial status, longitudinal BMI, longitudinal quality of life, and longitudinal health status ; e The cross-sectional association of perceived neighborhood environment and physical activity is estimated by the environmental perception score term ; f The longitudinal association of perceived neighborhood environment and physical activity is estimated by the interaction term between the intervention visit and the environmental perception score. Longitudinal associations Overall, an increase in physical activity from baseline to 6-month follow-up was observed after adjustments for trial arm (mean effect of time on self-reported physical activity: β > 0.900, mean effect of time on 6MWD: β > 23.228; p < 0.001) ( Additional File 6 ). There was a negative association between perceived residential density and the change in self-reported physical activity (β=-0.306, 95% CI (-0.494;-0.117), per 1SD) (Table 4 ). In contrast, no significant association was found between the perceived neighborhood environment and the change in 6MWD from baseline to 6 months. Stratification by social deprivation After stratification by social deprivation, positive associations between baseline self-reported physical activity and perceived infrastructures remained only among non-deprived patients (with β = 0.209, 95% CI (0.038;0.379) for cycling; β = 0.204, 95% CI (0.037;0.371) for walking; β = 0.240, 95% CI (0.068;0.413) for total infrastructures, per 1SD); although interactions were not statistically significant for walking and total infrastructure scores (Table 5 ). Additionally, inverse associations between perceived residential density and self-reported physical activity change remained only among non-deprived patients (β=-0.400, 95% CI (-0.602;-0.195), per 1SD; p-value for interaction = 0.031). Moreover, among non-deprived women, positive associations were observed (per 1SD increase) between change in self-reported physical activity and distance to local facilities (β = 0.298, 95% CI (0.085;0.511)), and perceived infrastructures (β=-0.242, 95% CI (-0.442;-0.041) for cycling; β=-0.283, 95% CI (-0.487;-0.079) for walking; β=-0.292, 95% CI (-0.492;-0.092) for total infrastructures) (p-values for interaction ≤ 0.05). A positive association between self-reported physical activity change and perceived safety form traffic appeared among deprived women (β = 0.632, 95% CI (0.196;1.068), per 1SD; p-value for interaction = 0.016), but not among non-deprived women (β = 0.034, 95% CI (-0.175;0.243), per 1SD) (Table 5 ). An inverse association between the perceived safety from crime and 6MWD at baseline was observed among deprived women only (β=-23.860, 95% CI (-41.601;-6.118), per 1SD). Significant positive associations between baseline 6MWD and connectivity (β = 24.060, 95% CI (3.533;44.587), per 1SD), as well as cycling and walking network (β = 19.466, 95% CI (1.004;37.928), per 1SD) were observed among patients experiencing social deprivation but not among others (Table 5 ). Table 5 – Associations between perceived neighborhood environment and physical activity, stratified by social deprivation, DiscoSpace study, France, 2018–2022 (n = 306) Self-reported physical activity b Perceived neighborhood environment score a Non-deprived (n = 262) Deprived (n = 44) p-int β c 95% CI p-value β c 95% CI p-value Residential density Cross-sectional e 0.083 (-0.079 ; 0.246) 0.314 -0.154 (-0.555 ; 0.246) 0.449 0.724 Longitudinal f -0.400 (-0.602 ; -0.195) < 0.001 0.206 (-0.316 ; 0.729) 0.438 0.031 Distance to local facilities Cross-sectional e -0.100 (-0.286 ; 0.087) 0.293 0.087 (-0.276 ; 0.451) 0.637 0.444 Longitudinal f 0.298 (0.085 ; 0.511) 0.006 -0.187 (-0.582 ; 0.208) 0.352 0.011 Cycling infrastructures Cross-sectional e 0.209 (0.038 ; 0.379) 0.017 0.219 (-0.189 ; 0.626) 0.292 0.044 Longitudinal f -0.242 (-0.442 ; -0.041) 0.018 0.446 (-0.086 ; 0.977) 0.100 0.019 Walking infrastructures Cross-sectional e 0.204 (0.037 ; 0.371) 0.017 0.005 (-0.373 ; 0.383) 0.981 0.319 Longitudinal f -0.283 (-0.487 ; -0.079) 0.007 0.457 (-0.029 ; 0.943) 0.065 0.006 Total infrastructures Cross-sectional e 0.240 (0.068 ; 0.413) 0.006 0.135 (-0.263 ; 0.533) 0.506 0.085 Longitudinal f -0.292 (-0.492 ; -0.092) 0.004 0.502 (-0.009 ; 1.013) 0.054 0.005 Safety from traffic Cross-sectional e 0.041 (-0.120 ; 0.202) 0.619 0.258 (-0.600 ; 0.083) 0.138 0.969 Longitudinal f 0.034 (-0.175 ; 0.243) 0.750 0.632 (0.196 ; 1.068) 0.005 0.016 6MWD c Perceived neighborhood environment score Non-deprived (n = 262) Deprived (n = 44) p-int β c 95% CI p-value β c 95% CI p-value Safety from crime Cross-sectional e 4.210 (-4.758 ; 13.179) 0.357 -23.860 (-41.601 ; -6.118) 0.008 0.008 Longitudinal f -6.774 (-15.568 ; 2.020) 0.131 7.476 (-11.620 ; 26.572) 0.442 0.171 Total safety Cross-sectional e 7.304 (-1.279 ; 15.887) 0.095 -16.271 (-34.291 ; 1.748) 0.077 0.033 Longitudinal f -8.648 (-17.382 ; 0.085) 0.052 4.418 (-16.138 ; 24.974) 0.673 0.230 Connectivity Cross-sectional e 1.439 (-6.802 ; 9.681) 0.732 24.060 (3.533 ; 44.587) 0.022 0.048 Longitudinal f -2.275 (-11.219 ; 6.669) 0.618 -11.285 (-32.979 ; 10.409) 0.307 0.447 Walking and cycling network Cross-sectional e -0.733 (-9.325 ; 7.860) 0.867 19.466 (1.004 ; 37.928) 0.039 0.049 Longitudinal f -2.099 (-11.117 ; 6.919) 0.648 -8.964 (-28.567 ; 10.640) 0.369 0.529 Values in bold are statistically significant (P < 0.05) ; Social deprivation score was assessed using an individual deprivation index called the “Evaluation of Deprivation and Inequalities in Health Examination Centers” (EPICES) and a cut-off score of 30 was used to define social deprivation (yes/no) ; a Environmental scores were calculated from the ALPHA questionnaire (for Assessing Levels of PHysical Activity and Fitness at population level) ; b Self-reported physical activity was calculated from the Recent Physical Activity Questionnaire (RPAQ). The average difference in the outcome self-reported physical activity is expressed by the square root ; c 6MWD was measured by the 6-Minute Walk Test (6MWT). The average difference in the outcome 6MWD is expressed without transformation ; d The β indicate the overall longitudinal difference in the outcome score using linear mixed models per 1 SD of perceived built environment score after a standardized Z-score transformation. Analyses were adjusted on: age, social deprivation, educational level, employment status after diagnosis, comorbidities, living with a partner, trial arm, municipality class (except for Residential density score analyses), perceived home environment, COVID-19 pandemic trial status, longitudinal BMI, longitudinal quality of life, and longitudinal health status ; e The cross-sectional association of perceived neighborhood environment and physical activity is estimated by the environmental perception score term ; f The longitudinal association of perceived neighborhood environment and physical activity is estimated by the interaction term between the intervention visit and the environmental perception score. Stratification by municipality class After stratification by municipality class, associations between self-reported physical activity at baseline and perceived infrastructure remained only among rural municipalities (β = 0.512, 95% CI (0.202;0.822) for cycling; β = 0.448 95% CI (0.155;0.740) for total infrastructures, per 1SD) (Table 6 ). Table 6 – Associations between perceived neighborhood environment and physical activity, stratified by municipality class, DiscoSpace study, France, 2018–2022 (n = 313) Self-reported physical activity b Perceived neighborhood environment a Rural municipality (n = 93) Intermediate-density municipality (n = 104) Large urban center (n = 116) p-int β c 95% CI p-value β c 95% CI p-value β c 95% CI p-value Distance to local facilities Cross-sectional d -0.219 (-0.500 ; 0.062) 0.126 -0.002 (-0.303 ; 0.299) 0.990 0.271 (-0.121 ; 0.664) 0.175 0.122 Longitudinal e 0.175 (-0.189 ; 0.539) 0.346 0.427 (0.037 ; 0.818) 0.032 -0.565 (-1.066 ; -0.065) 0.027 0.008 Cycling infrastructures Cross-sectional d 0.512 (0.202 ; 0.822) 0.001 0.040 (-0.236 ; 0.316) 0.775 -0.028 (-0.345 ; 0.289) 0.862 0.302 Longitudinal e -0.568 (-0.993 ; -0.143) 0.009 -0.057 (-0.410 ; 0.295) 0.750 0.416 (0.008 ; 0.825) 0.046 0.004 Total infrastructures Cross-sectional d 0.448 (0.155 ; 0.740) 0.003 0.073 (-0.199 ; 0.346) 0.598 0.013 (-0.331 ; 0.357) 0.940 0.407 Longitudinal e -0.525 (-0.912 ; -0.139) 0.008 -0.090 (-0.438 ; 0.258) 0.611 0.440 (-0.001 ; 0.882) 0.050 0.005 Connectivity Cross-sectional d 0.127 (-0.123 ; 0.376) 0.319 0.208 (-0.038 ; 0.454) 0.098 -0.144 (-0.450 ; 0.162) 0.356 0.707 Longitudinal e 0.067 (-0.265 ; 0.398) 0.693 -0.357 (-0.682 ; -0.032) 0.031 0.535 (0.126 ; 0.943) 0.010 0.003 Walking and cycling network Cross-sectional d 0.221 (-0.049 ; 0.490) 0.108 0.185 (-0.064 ; 0.434) 0.146 -0.056 (-0.360 ; 0.248) 0.718 0.234 Longitudinal e -0.037 (-0.392 ; 0.318) 0.839 -0.455 (-0780 ; -0.130) 0.006 0.493 (0.083 ; 0.903) 0.018 0.002 Values in bold are statistically significant (P < 0.05) ; The municipality class of the residence at baseline was categorized using the 3-level grid developed by the French National Institute of Statistics and Economic Studies (INSEE) ; a Environmental scores were calculated from the ALPHA questionnaire (for Assessing Levels of PHysical Activity and Fitness at population level) ; b Self-reported physical activity was calculated from the Recent Physical Activity Questionnaire (RPAQ). The average difference in the outcome self-reported physical activity is expressed by the square root ; c The β indicate the overall longitudinal difference in the outcome score using linear mixed models per 1 SD of perceived built environment score after a standardized Z-score transformation. Analyses were adjusted on: age, social deprivation, educational level, employment status after diagnosis, comorbidities, living with a partner, trial arm, municipality class (except for Residential density score analyses), perceived home environment, COVID-19 pandemic trial status, longitudinal BMI, longitudinal quality of life, and longitudinal health status ; d The cross-sectional association of perceived neighborhood environment and physical activity is estimated by the environmental perception score term ; e The longitudinal association of perceived neighborhood environment and physical activity is estimated by the interaction term between the intervention visit and the environmental perception score. After stratification, significant associations were observed: perceived shorter distances to local facilities (β=-0.565, 95% CI (-1.066;-0.065), per 1SD), better connectivity (β = 0.535, 95% CI (0.126;0.943), per 1SD), and a better cycling and walking network (β = 0.493, 95% CI (0.083;0.903), per 1SD), were associated with a greater increase in self-reported physical activity over 6-months in large urban centers, which contrasts with the direction of associations observed in intermediate-density municipalities (respectively: β = 0.427, 95% CI (0.037;0.818); β=-0.357, 95% CI (-0.682;-0.032); β = -0.455, 95% CI (-0780;-0.130), per 1SD). Moreover, perceived cycling infrastructure was associated with a smaller increase at 6-months in self-reported physical activity in rural municipalities (β=-0.568, 95% CI (-0.993;-0.143), per 1SD), but with a greater increase in large urban centers (β = 0.416, 95% CI (0.008;0.825), per 1SD) (Table 6 ). Additional modifiers Regarding other modifiers, for self-reported physical activity, significant interactions were observed at baseline for employment status after diagnosis, quality of life, and trial arm; and in longitudinal analyses for BMI and health status. For 6MWD, significant interactions were observed in cross-sectional analyses for age, employment status after diagnosis, living with a partner, and trial arm; and in longitudinal analyses for time since first breast cancer surgery, quality of life, living with a partner, and trial arm ( Additional File 7 ). However, most of the time, confidence intervals overlap between subgroups and/or subgroup associations were not significant. Sensitivity analyses The sensitivity analysis adjusted for the ALPHA questionnaire completion period did not show any significant differences in the associations studied ( Additional File 8 ) . After removing extreme values of self-reported physical activity, direction and strength of observed associations remained unchanged ( Additional File 9 ). Generally, the associations observed in the main analysis were consistent after stratification by treatment subgroups, although some of them disappeared in certain subgroups ( Additional File 10 ). DISCUSSION To the best of our knowledge, this study is the first to perform a quantitative analysis of the effect of perceived neighborhood environment on physical activity in women with breast cancer. The perception of developed walking and cycling infrastructures and network was associated with higher self-reported physical activity at baseline. Furthermore, patients who reported being distant from local facilities exhibited lower initial 6MWD. While there was a general increase in self-reported physical activity over the six-month study period after adjustments for trial arm, this increase was more modest in neighborhoods perceived to be densely populated. Moreover, among women experiencing social deprivation, a heightened sense of traffic safety was associated with a more substantial increase in self-reported physical activity over the 6-month study period. Among non-deprived women, perceived better infrastructures and higher residential density were associated with smaller increases in self-reported physical activity over the study period. The associations between the perception of the neighborhood environment (distance to local facilities, walking and biking infrastructures and network) and changes in self-reported physical activity further varied according to the size of the municipality of residence. Residential density Greater perceived residential density was associated with smaller improvements in self-reported physical activity during the intervention, after adjusting for trial arms, despite no association being observed with baseline self-reported activity levels. The literature on this topic is inconsistent: two cross-sectional studies, one among Spanish patients with chronic obstructive pulmonary disease( 41 ) and the other among Chinese adults from the general population( 42 ), reported that higher residential density was associated with increased sedentary behavior. Conversely, an Australian study found that urban densification was linked to greater engagement in active behaviors( 43 ). In densely populated areas, increased air pollution—as shown in American and Chinese studies( 44 – 46 )—may discourage outdoor activities, potentially limiting improvements in overall activity levels. Additionally, higher traffic volumes( 47 ) and noise annoyance( 48 ), as reported in European longitudinal studies, might further reduce participation in physical activity. Distance to facilities In the present study, patients who perceived a greater distance to reach neighborhood facilities (e.g., shops, restaurants, public transport, leisure facilities, etc.) had a lower 6MWD at baseline. This supports the hypothesis that perceiving facilities as distant may encourage motorized transport over active travel, thereby reducing walking capacity. This finding is consistent with a systematic review( 49 ) showing that convenient distance to multiple destinations is associated with favorable cardiorespiratory fitness. Our findings may also suggest that patients with lower functional capacity may perceive facilities as being farther away. For example, an American study reported that elderly and individuals with chronic pain or reduced mobility tend to perceive distances as farther compared to pain-free or higher-capacity individuals( 50 ). Our results showed that increased perceived distance to access facilities was associated with greater increases in physical activity during intervention among patients in intermediate-sized municipalities, and the opposite association was observed among patients in large urban centers. In smaller communities, the perception of distance may be seen as a challenge to overcome, encouraging patients to be more active to access available resources. In contrast, in urban centers, where facilities are closer and more accessible, patients may not feel the same need to engage in physical activity. Given the established tendency of urban populations to utilize the proximity of destinations for their physical activity( 51 ), an alternative hypothesis suggests that they have already reached a state of maximum potential in this regard. In contrast, patients residing in smaller municipalities may be compelled to explore novel strategies for enhancing exercise, such as the utilization of active transportation to reach their destinations. Walkability and Bikeability Consistent with previous studies conducted on adult populations with or without a cancer history( 16 , 52 – 54 ), our findings showed that overall walkability and bikeability–as proxied in our research by perceptions of infrastructure (such as bike lanes, sidewalks and pedestrian zones for shopping) and street connectivity (including intersections and shortcuts by foot or bike)–were associated with higher levels of self-reported physical activity at baseline. However, walkability and bikeability, often used to summarize the built environment, lack consistent definitions( 55 , 56 ) and fail to capture specific environmental characteristics. Indeed, walking and cycling network, walking and cycling infrastructures, residential density, or safety may be differently perceived and associated with physical activity pattern, as shown in our analysis. Walking and cycling network For walking and cycling network, differences were observed between deprived and non-deprived patients. Deprived patients who perceived streets to be more interconnected—referring to perceived connectivity as well as cycling and walking networks—exhibited higher baseline 6MWD. This observation suggests that individuals, even those from deprived backgrounds, may benefit from well-designed environments, potentially mitigating socioeconomic inequalities in physical functioning. However, when examining self-reported physical activity, no significant associations were observed between environmental characteristics and baseline physical activity among deprived women. Previous Canadian studies have reported mixed results, with some studies showing positive associations( 57 ) and others negative associations( 58 ) between street connectivity and physical activity in adults of low socioeconomic status. These discrepancies may stem from variations in the measurement of socioeconomic status (often determined by household income and/or educational level) or the specific cultural and geographic context. Walking and cycling infrastructures In the present study, after adjustment for intervention arm, the availability of walking and cycling infrastructure was associated with smaller improvements in self-reported physical activity over the 6-month study period, but only among non-deprived women. This contrasts with previous studies showing a positive association between neighborhood amenities( 59 , 60 ), access to public transport( 61 ), and physical activity among women wishing to be more active. However, none of the aforementioned studies focused on European settings nor breast cancer patients. A Canadian study on prostate cancer patients( 62 ) found that the neighborhood environment was not associated with physical activity patterns in a behavior change intervention. This study did not distinguish between deprived patients and others, which might explain the differences with our results. Furthermore, the present study suggests a negative association between the perception of cycling infrastructure and improvements in physical activity in rural municipalities, and a positive association in urban areas, where infrastructures are more likely to be integrated into the daily environment. In rural areas, as highlighted by other research( 63 ), the perception of infrastructure is often related to factors such as accessibility or safety, which may limit its benefit on physical activity. These findings underscore the importance of considering the surrounding environmental context which can influence the effectiveness of existing infrastructures in promoting physical activity. Indeed, recent studies( 25 ) demonstrated how neighborhood perceptions can shed light on specific concerns of different groups regarding social and neighborhood contexts, and how enhancing the neighborhood environment alone may not be enough to increase physical activity. Safety In our population of breast cancer patients, deprived women who perceived their surroundings as safe regarding crime exhibited lower baseline 6MWD, even when accounting for health statuses and BMI. This result contradicts studies conducted in general adult populations( 16 , 64 ), which have established a positive association between walking and personal safety, especially when it was based on subjective measures. While the difference in results could be partly explained by the relatively small number of deprived women in our sample, future studies focusing specifically on a sample of deprived breast cancer patients may provide deeper insights into this association. Indeed, the persistence of other socioeconomic barriers, such as limited financial resources, restricted access to healthcare, or greater family and work-related responsibilities, has the potential to impact the use of the environment for physical activity( 65 ). Our work also revealed that, although no association was observed in the overall population, among deprived women, a higher perception of traffic safety was linked to greater improvements in self-reported physical activity over time. Previous studies( 68 ) have shown mixed evidence regarding the impact of neighborhood environment characteristics on physical activity change in adults, depending on the population and type of physical activity program involved. This variability underscores the necessity to consider perceived neighborhood environment in futures studies by accounting for the potential influence of socioeconomic status on these relationships. Strengths and limitations The DISCO trial provided accurate and longitudinal measurements of physical activity and clinical factors. Another strength is the use of a validated tool assessed environmental perceptions in a European context( 69 ). However, the present study is not without limitations. Although social deprivation, educational level, and municipality class varied within the sample, the generalizability of our findings is limited by the recruitment from a single cancer center in the Auvergne-Rhône-Alpes French region, with potential underrepresentation of deprived patients( 70 ) and selection bias due to voluntary participation in a physical activity intervention( 71 ). However, the absence of differences in associations between trial arms suggests a limited risk of bias related to the use of DISCO study data. Moreover, most of the patients completed the ALPHA environmental questionnaire months after the 6-month physical activity assessment, which limits causal inferences despite adjustments for the time period for completing the ALPHA questionnaire. Finally, self-reported physical activity (RPAQ) may also be overestimated due to social desirability bias. However, parallel analyses on objective 6-Minute Walk Distance (6MWD) strengthened the findings and showed that perceived environmental characteristics were differentially associated with self-reported activity and 6MWD, highlighting the value of analyzing both dimensions( 72 ). CONCLUSION The presence of adequate infrastructures and the development of a comprehensive cycling and walking network were positively associated with baseline self-reported physical activity in this population of breast cancer patients engaged in physical activity. Furthermore, the associations between the perceived neighborhood environment and improvements in both self-reported physical activity and functional capacity (6MWD) varied according to individual socioeconomic status (social deprivation) and municipality class, after adjusting for the physical activity program followed during the DISCO trial. These findings underscore the necessity to further consider both individual socioeconomic characteristics and perceived environmental characteristics when designing tailored physical activity interventions for breast cancer patients. Abbreviations 6MWD: 6-Minute Walk Distance 6MWT: 6-Minute Walk Test ALPHA: Assessing Levels of PHysical Activity and Fitness at population level BMI: Body Mass Index CLB: Léon Bérard Comprehensive Cancer Center CI: Confidence Interval DAG: Directed Acyclic Graph DISCO: DISpositif COnnecté EPICES: Evaluation of Deprivation and Inequalities in Health Examination Centers IQR: Inter-Quartile Range RPAQ: Recent Physical Activity Questionnaire SD: Standard Deviation WHO: World Health Organization Declarations Ethics approval and consent to participate The French ethics committee allowed the administration of the ALPHA questionnaire (17th November 2020). The DISCO trial protocol was approved by a French ethics committee (Comité de Protection des Personnes Est I, ID RCB 2017-A03360-53, 1st February 2018) and its database was reported to the French National Commission for Data Protection and Liberties (ref. MR-001 no. 2016177, 13th December 2016). The trial is registered on http://www.clinicaltrials.gov (NCT number: NCT03529383, 17th May 2018). The informed consent form signed by all patients randomized in the DISCO trial mentions the possibility of re-use the study data for other research purposes. Consent for publication Not applicable. Availability of data and materials The datasets supporting the conclusions of this article are available from the corresponding author on reasonable request. For general data sharing inquiries, contact Béatrice Fervers ( [email protected] ). Competing interests The authors declare that they have no competing interests. Funding The DiscoSpace study was financially supported by the French National Cancer Institute (l’Institut National du Cancer, INCa) (grant no. 2020-085). The DISCO study (NCT03529383) was financially supported by the ARC French Foundation for Cancer Research (ARC-France) and the French National Cancer Institute (l’Institut National du Cancer, INCa), the Cancer cluster of the Lyon Auvergne Rhône Alpes region (Cancéropôle Lyon Auvergne-Rhône-Alpes, CLARA), the Fundation for Medical Research (FMR), the Auvergne Rhône Alpes regional Health Agency (ARS-ARA), and the French life insurance company AG2R-LA-MONDIALE. The research was designed, conducted, analyzed, and interpreted by the authors entirely independently of these funding sources. The funder had no role in study design, data acquisition and analysis, decision to publish, or preparation of the manuscript. Authors’ contributions BFe, OP, BFo, LG, and DP contributed to the design and funding of the DiscoSpace study, with assistance of OT. ML conducted the data analysis under supervision of MH with assistance of OP. ML, MH, DP, BFe were responsible for drafting the manuscript. OP, CD, and AM handled administrative tasks. CD was responsible for the ALPHA environmental questionnaire data collection. LG built the ALPHA environmental scores. ML, CD, AM, LG and AS were involved in data management. All authors provided advice on the study design, analysis, critical interpretation of the results and review of the first draft. All authors read and approved the final manuscript. Acknowledgments The authors wish to thank all participants to the DISCO trial, as well as clinicians and all personnel involved in their recruitment and follow-up in the DISCO trial. 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Participant Bias in Community-Based Physical Activity Research: A Consistent Limitation? J Phys Act Health. 2023;21(2):109–12. Panter J, Griffin S, Ogilvie D. Correlates of reported and recorded time spent in physical activity in working adults: results from the commuting and health in Cambridge study. PLoS ONE. 2012;7(7):e42202. Additional Declarations No competing interests reported. Supplementary Files STROBEchecklistDiscoSpace.doc ADDITIONAL1.docx Additional Files S1. Municipality class variable. ADDITIONAL2.docx S2.Directed Acyclic Graph. ADDITIONAL3.docx S3.Flowchart for participant inclusion. ADDITIONAL4.docx S4.Descriptive characteristics of the non-respondents. ADDITIONAL5.docx S5.Perceived neighborhood environment scores at baseline. ADDITIONAL6.docx S6. Effect of time on physical activity. ADDITIONAL7.docx S7. Statistically significant interactions and corresponding stratified analyses. ADDITIONAL8.docx S8. Sensitivity analysis adjusted for the completion period of the ALPHA environmental questionnaire. ADDITIONAL9.docx S9. Sensitivity analysis excluding participants reporting self-reported physical activity above 99 th percentile. ADDITIONAL10.docx S10. Sensitivity analysis stratified by cancer treatment subgroups. Cite Share Download PDF Status: Published Journal Publication published 26 Mar, 2026 Read the published version in International Journal of Behavioral Nutrition and Physical Activity → Version 1 posted Editorial decision: Revision requested 29 Jan, 2026 Reviews received at journal 28 Jan, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviews received at journal 05 Jan, 2026 Reviewers agreed at journal 04 Dec, 2025 Reviewers invited by journal 01 Dec, 2025 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 12 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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16:27:11","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":96256,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistDiscoSpace.doc","url":"https://assets-eu.researchsquare.com/files/rs-8094974/v1/2c42aa9ed5427caf9fcddd53.doc"},{"id":97331131,"identity":"ea22cd00-50a8-40c4-bdc5-68df03ececa3","added_by":"auto","created_at":"2025-12-03 09:16:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional Files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS1.\u003c/strong\u003e Municipality class variable.\u003c/p\u003e","description":"","filename":"ADDITIONAL1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8094974/v1/1bbdda8b5389484902e836b8.docx"},{"id":97369711,"identity":"72056c71-d07c-4a43-ab9a-6c5302f26d68","added_by":"auto","created_at":"2025-12-03 16:25:36","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":280917,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS2.\u003c/strong\u003eDirected Acyclic Graph.\u003c/p\u003e","description":"","filename":"ADDITIONAL2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8094974/v1/4256d6cbc08b7412e9a2316b.docx"},{"id":97370213,"identity":"b50a4ddb-619c-4b30-be7e-f83ef792024d","added_by":"auto","created_at":"2025-12-03 16:26:55","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":127124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS3.\u003c/strong\u003eFlowchart for participant inclusion.\u003c/p\u003e","description":"","filename":"ADDITIONAL3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8094974/v1/2a6b7b76b572e888b849fadf.docx"},{"id":97331136,"identity":"dd5962dc-221d-4620-888e-062f8e43ca18","added_by":"auto","created_at":"2025-12-03 09:16:17","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":18905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS4.\u003c/strong\u003eDescriptive characteristics of the non-respondents.\u003c/p\u003e","description":"","filename":"ADDITIONAL4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8094974/v1/2e2fba9de0c2a729780bba84.docx"},{"id":97370226,"identity":"9825beb0-6043-489a-be43-f3430f5095a7","added_by":"auto","created_at":"2025-12-03 16:26:56","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":17940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS5.\u003c/strong\u003ePerceived neighborhood environment scores at baseline.\u003c/p\u003e","description":"","filename":"ADDITIONAL5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8094974/v1/8673f0a9951b04731d64feba.docx"},{"id":97369273,"identity":"54badd28-754a-46ea-bdbf-1c83a7206ef5","added_by":"auto","created_at":"2025-12-03 16:24:05","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":16670,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS6.\u003c/strong\u003e Effect of time on physical activity.\u003c/p\u003e","description":"","filename":"ADDITIONAL6.docx","url":"https://assets-eu.researchsquare.com/files/rs-8094974/v1/6cd265a7e9ac262abe5463bd.docx"},{"id":97331149,"identity":"14d842fa-2ba8-4743-8a85-1de85a13c80a","added_by":"auto","created_at":"2025-12-03 09:16:17","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":45646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS7.\u003c/strong\u003e Statistically significant interactions and corresponding stratified analyses.\u003c/p\u003e","description":"","filename":"ADDITIONAL7.docx","url":"https://assets-eu.researchsquare.com/files/rs-8094974/v1/de5afb326a4bae4c744dbdda.docx"},{"id":97331140,"identity":"100d4c9e-ee84-4afb-b1ee-379181eb3ffc","added_by":"auto","created_at":"2025-12-03 09:16:17","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":31856,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS8.\u003c/strong\u003e Sensitivity analysis adjusted for the completion period of the ALPHA environmental questionnaire.\u003c/p\u003e","description":"","filename":"ADDITIONAL8.docx","url":"https://assets-eu.researchsquare.com/files/rs-8094974/v1/630939d495d02946169aee93.docx"},{"id":97331151,"identity":"990504c6-6e00-447c-8155-097127918715","added_by":"auto","created_at":"2025-12-03 09:16:17","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":20978,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS9.\u003c/strong\u003e Sensitivity analysis excluding participants reporting self-reported physical activity above 99\u003csup\u003eth\u003c/sup\u003e percentile.\u003c/p\u003e","description":"","filename":"ADDITIONAL9.docx","url":"https://assets-eu.researchsquare.com/files/rs-8094974/v1/2d86998a5a811029be458907.docx"},{"id":97331143,"identity":"a4d3545d-0cf3-4937-9ffc-113929851f6b","added_by":"auto","created_at":"2025-12-03 09:16:17","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":48106,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS10.\u003c/strong\u003e Sensitivity analysis stratified by cancer treatment subgroups.\u003c/p\u003e","description":"","filename":"ADDITIONAL10.docx","url":"https://assets-eu.researchsquare.com/files/rs-8094974/v1/ef40d89aba1ea8ae939495ac.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between perceived neighborhood environment and physical activity among breast cancer patients engaged in a physical activity program concomitant to cancer treatment: cross-sectional and longitudinal analyses in the DISCO trial (DiscoSpace)","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eBreast cancer accounts for more than one-third of new female cancer cases worldwide, representing 2.3\u0026nbsp;million incident cancers in 2022(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In France as in other high-income countries, breast cancer incidence is increasing while mortality has declined since the 1990s(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), resulting in a growing population of breast cancer patients and survivors. This population faces specific challenges during and after cancer treatments, including issues related to sequalae, comorbidities, cancer fatigue and quality of life(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), which highlights the need for comprehensive, tailored strategies to address these issues.\u003c/p\u003e\u003cp\u003ePhysical activity during and after cancer treatment is associated with improved cancer- and non-cancer-related outcomes(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). A positive association has been suggested with specific and overall survival in breast cancer patients(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Moreover, engagement in physical activity practice has been consistently associated with reduction in cancer-related fatigue and improvements in physical fitness and in the perceived quality of life in patients undergoing cancer treatment(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Several organizations have published guidelines on physical activity, emphasizing its importance in improving quality of life and reducing fatigue, especially during active cancer treatment with curative intent(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Despite this, many women reduce their practice of physical activity following breast cancer diagnosis(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile multiple studies have investigated barriers to engaging in physical activity during and after cancer treatment(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), the specific role of the living environment on physical activity in cancer patients has received little attention. The role of the neighborhood environment on physical activity is increasingly recognized in the general population(\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Notably, the effectiveness of physical activity interventions appears to be related to this environment, as reported in a systematic review(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Still, there is a growing need to understand the environmental correlates of physical activity in specific populations(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), including in breast cancer patients, a population who remain underrepresented in these studies.\u003c/p\u003e\u003cp\u003eAmong breast cancer patients, two American studies have identified a negative association between the lack of facilities, or open space, and self-reported physical activity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In Europe, two qualitative studies also reported that limited access to recreational facilities was a significant environmental barrier to physical activity in this group(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Further research is warranted in the European context, as urban environments in Europe differ significantly from those on other continents, particularly in terms of housing density and land use mix(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In addition, while objective methods, such as spatial databases (e.g., GIS), are commonly used to assess neighborhood environmental characteristics in European studies on physical activity(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), discrepancies between objectively measured (objective) and individually experienced (subjective) environmental features have been noted(\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). These differences highlight how objective measures may fail to capture the lived experience and the specific needs of individuals, potentially leading to inconsistent results(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this context, the present study aimed to examine the relationship between the perceived neighborhood environment and physical activity before treatment initiation (baseline) and change in physical activity over 6 months, among breast cancer patients engaged in a physical activity program concomitant to cancer treatment. By combining self-reported physical activity data and functional capacity measurement, this study provides a comprehensive approach to understanding how perceptions of the neighborhood environment influence physical activity in women undergoing breast cancer treatment.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData collection\u003c/h2\u003e\u003cp\u003eThe current work is an ancillary study (DiscoSpace) to the phase III interventional multicenter randomized trial DISCO (NCT03529383)(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This controlled trial, started in 2018 and ended in 2022, was promoted by the L\u0026eacute;on B\u0026eacute;rard Comprehensive Cancer Center (CLB), located in Lyon, France. This trial was designed to evaluate the efficacy of a 6-months adapted physical activity program using a connected device with activity trackers and a patient therapeutic education program among women with localized breast cancer. Study rationale, methods for recruitment, enrollment, data collection, and participant characteristics of the DISCO trial have been described in details elsewhere(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Briefly, with a 2\u0026times;2 factorial design (1:1:1:1 ratio), 436 women diagnosed with primary localized invasive breast carcinoma and eligible for adjuvant chemotherapy, hormonotherapy, immunotherapy and/or radiotherapy, were randomized into one of the four arms: (A) individualized, semi-supervised exercise program physical activity program carried out autonomously with a connected device, (B) therapeutic patient education sessions on physical activity, (C) both interventions, (D) control group receiving usual care. All women in the trial were informed about international recommendations on physical activity by a certified sports instructor and a booklet. For each participant, data were collected at baseline, with follow-up assessments at 6 months and 12 months.\u003c/p\u003e\u003cp\u003eFor the DiscoSpace study initiated in 2021, an additional self-administered questionnaire (ALPHA, Assessing Levels of PHysical Activity and Fitness at population level(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)) was sent to participants of the DISCO trial to assess their perception of the neighborhood environment at their place of residence at baseline. Depending on each patient's status in the DISCO trial at the time of DiscoSpace initiation, the ALPHA questionnaire was administered either at inclusion in DISCO (n\u0026thinsp;=\u0026thinsp;59 patients), during the intervention (n\u0026thinsp;=\u0026thinsp;69 patients), or after completion of the intervention (n\u0026thinsp;=\u0026thinsp;185 patients).\u003c/p\u003e\u003cp\u003e The administration of the ALPHA questionnaire for the DiscoSpace study was approved by a French ethics committee (17th November 2020). The informed consent form signed by all patients randomized in the DISCO trial mentions the possibility of re-use the study data for other research purposes.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eOf the 436 participants enrolled in the DISCO trial, all 409 patients followed at the CLB were invited to participate in the DiscoSpace study and received the French version of the ALPHA environmental questionnaire.\u003c/p\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003ePerceived neighborhood environment\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe standardized, self-administrated ALPHA questionnaire is a validated tool designed to measure the perceptions of the neighborhood environment (defined as \"the area within approximately one kilometer or half a mile of your home or that you could walk to in 10\u0026ndash;15 minutes\u0026rdquo;(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)) in relation to physical activity. Participants completed the questionnaire regarding their residence at baseline in the DISCO trial.\u003c/p\u003e\u003cp\u003eThe 49 items of the long version of the ALPHA questionnaire were summed into 15 environmental scores according to the rules of the authors' manual(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e): residential density (/315), distance to local facilities (/40), cycling infrastructure (/10), walking infrastructure (/10), total infrastructure (/20), maintenance of infrastructure (/15), safety from crime (/15), safety from traffic (/15), total safety (/30), esthetics (/15), pleasure (/20), connectivity (/15), cycling and walking network (/20), home environment (/6) and work/study environment (/10). Higher scores generally indicating a better perception of the neighborhood environment, except for residential density and distance to facilities, where higher scores reflect greater perceived residential density and longer perceived walking distances. Home and work environment scores were not analyzed as neighborhood determinants, as they did not provide relevant information about the residential environment.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSelf-reported physical activity and functional capacity\u003c/h3\u003e\n\u003cp\u003eSelf-reported physical activity was assessed using the validated and standardized Recent Physical Activity Questionnaire (RPAQ)(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), in which participants described their activities over the past four weeks across different domains: commuting, occupation, leisure time, and domestic life. Only moderate-to-vigorous activities (\u0026ge;\u0026thinsp;3 Metabolic Equivalent Tasks) were considered, and scores were summed in hours per week. Data were collected at baseline and at 6-month follow-up, i.e. at the end of the intervention.\u003c/p\u003e\u003cp\u003eThe 6-Minute Walk Distance (6MWD), which reflects an individual\u0026rsquo;s functional exercise capacity or walking ability, was objectively measured using the validated 6-Minute Walk Test (6MWT)(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The test records the maximum distance walked in six minutes along a flat 30-meter corridor. Assessments were conducted at baseline and 6-months by a certified professional.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCovariates\u003c/h2\u003e\u003cp\u003eThe trial arm corresponds to the randomization group in the DISCO trial (A/B/C/D). Clinical data, including age (years), menopausal status (pre/post), comorbidities (past or present/none), time since diagnosis (months) and since the first surgery for breast cancer (months) were collected during baseline medical assessment. Anthropometric measurements were taken by a trained professional at each visit to calculate body mass index (BMI, in kg/m\u003csup\u003e2\u003c/sup\u003e). Self-reported variables included educational level (\u0026le;\u0026thinsp;baccalaureate/1 to 3 years post-baccalaureate/\u0026ge; 4 years post baccalaureate), employment status after diagnosis (active/on medical leave or disabled/retired), health (score calculated from the EQ-5D-5L questionnaire(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)), quality of life (score calculated from the EORTC QLQ-C30 questionnaire(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)), and living with a partner (yes/no). Social deprivation (yes/no) was assessed using an individual deprivation index called the \u0026ldquo;Evaluation of Deprivation and Inequalities in Health Examination Centers\u0026rdquo; (EPICES)(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). A cut-off score of 30, identified in a validation study of the EPICES index as the lower limit of the 4th quintile, was used to define social deprivation(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e): women with an EPICES score above this threshold were classified as deprived, while those below were considered non-deprived. The municipality class of the residence at baseline (collected through the ALPHA questionnaire) was categorized using the 3-level grid developed by the French National Institute of Statistics and Economic Studies(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e): rural municipality, large urban center, or intermediate-density municipality (\u003cb\u003eAdditional File 1\u003c/b\u003e). The COVID-19 pandemic trial status was determined according to the intervention period with respect to the first French lockdown, which began on March 17, 2020 (before/during or after).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eNormality of the distribution for quantitative variables was assessed graphically. Data were described using means and standard deviations (\u0026plusmn;\u0026thinsp;SD) for normally distributed quantitative variables, medians and interquartile ranges (IQR) for non-normally distributed quantitative variables, and frequencies (percentages) for categorical variables. For descriptive purposes, we defined a change in BMI as a 5% change over 6 months. As suggested by previous studies, we defined a change in health as a change of 8.6 points(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) and a change in quality of life as a change of 5 points(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo examine the associations between perceived neighborhood environmental characteristics (predictors) and self-reported physical activity or 6MWD (dependent variables) at baseline and change over 6 months, we used linear mixed models. The basic model was run separately for each environmental score, and included: environmental score, physical activity (self-reported physical activity or 6MWD), intervention time point (baseline or 6 months), an interaction between visit and perceived neighborhood environment score, and a random intercept to account for clustering at the individual level. In this model, the first term represents the association between perceived neighborhood environment and baseline physical activity (cross-sectional association), while the interaction term indicates the association between perceived neighborhood environment and the change in physical activity over 6 months (longitudinal association). The use of a random intercept was assessed using a likelihood ratio test to ensure the improvement of each model. Physical activity variables were modeled continuously, and square root transformation was used for self-reported physical activity to better approximate a normal distribution of residuals. For comparability of results, all environmental scores were standardized by dividing individual scores by the mean standard deviation. Standardized coefficients represent the change in the dependent variable associated with a one SD change in the predictor variable.\u003c/p\u003e\u003cp\u003eAll models were adjusted for a priori-defined confounders selected by a Directed Acyclic Graph (\u003cb\u003eAdditional File 2\u003c/b\u003e). Fixed (time-invariant) confounders included age, social deprivation, educational level, employment status after diagnosis, comorbidities, living with a partner, trial arm, municipality class, perceived home environment, and the COVID-19 pandemic trial status. Time-variant confounders were measured at baseline and at 6 months, and included BMI, quality of life, and health status. Among all analyses, the one estimating the effect of residential density score on physical activity were not adjusted for municipality class because of strong correlations between these variables. Standardized regression coefficients were used to express the beta with the corresponding 95% confidence interval (CI).\u003c/p\u003e\u003cp\u003eConfounders, as well as other suspected modifiers, were investigated as potential effect modifiers using likelihood-ratio tests. Beforehand, time-variant variables were fixed at baseline, and quantitative variables were analyzed in subgroups using the WHO categorization for BMI(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) (\u0026lt;\u0026thinsp;25/\u0026ge;25 kg/m\u0026sup2;) or medians as cut-off (for age (51 years), quality of life at baseline (75/100), health status at baseline (70/100), time since diagnosis (3 months), time since the first breast cancer surgery (1 month)). For each association and each considered modifier, we first tested a two-way ANOVA interaction between the effect modifier and the explanatory variable (to model the effect of the modifier on the association between perceived neighborhood environment and physical activity at baseline), and then tested an ANOVA three-way interaction between the effect modifier, the explanatory variable, and the time-point (to model the effect of the modifier on the association over time). Statistical significance for the interaction term was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Results were stratified based on significant interactions.\u003c/p\u003e\u003cp\u003eThree sensitivity analyses were conducted to evaluate the robustness of our findings. The first adjustment accounted for the timing of the ALPHA questionnaire completion (before, during or after the DISCO trial intervention). Second, extreme values of self-reported physical activity (beyond the 99th percentile) were excluded to minimize the influence of outliers, resulting in the removal of two participants. The third sensitivity analysis stratified participants by cancer treatment subgroup (chemotherapy, immunotherapy, radiotherapy, hormonotherapy). Although cancer treatment was a potential confounder, the administration of multiple therapies during the 6-month follow-up period precluded its inclusion in the main adjustments.\u003c/p\u003e\u003cp\u003eMultiple Imputation by Chained Equations was applied to covariates with missing values. Under the Missing at random and Missing Completely At Random data assumptions(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), 10 imputed data sets were generated, each consisting of 10 iterations.\u003c/p\u003e\u003cp\u003eAll the analyses were conducted using R, 4.4.0 version (notably lme4, mice and mitml packages). The type I error rate was set at 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePopulation characteristics\u003c/h2\u003e\u003cp\u003eOf the 409 patients of the DISCO trial followed at the CLB included in the DiscoSpace study, 314 completed the ALPHA environmental questionnaire (response rate 76.8%). One patient was subsequently excluded due to missing 6MWD measures at baseline (\u003cb\u003eFlowchart available in Additional File 3\u003c/b\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eBaseline characteristics of the DiscoSpace study participants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;313) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age at enrollment was 52.0 years old (\u0026plusmn;\u0026thinsp;10.3 years), approximately one-third (32.9%) had not completed higher education, and nearly one-half (47.9%) were on medical leave or disabled. Overall, 14.1% of the women were in a situation of social deprivation. Patients were evenly distributed among the different types of municipalities (29.7%, 33.2%, and 37.1% lived in rural areas, intermediate density municipalities and in large urban centers, respectively). Baseline characteristics of respondents and non-respondents (n\u0026thinsp;=\u0026thinsp;95) were broadly comparable, except for social deprivation, which was more prevalent among non-respondents (21.1%). However, interpretation is limited by the high proportion of missing data among non-respondents, with 64.2% missing data on education and 63.2% on employment (\u003cb\u003eAdditional File 4\u003c/b\u003e).\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\u003e\u0026ndash; Baseline characteristics of the 313 participants of the DiscoSpace study, France, 2018\u0026ndash;2022 (n\u0026thinsp;=\u0026thinsp;313)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOCIODEMOGRAPHICS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e, \u003cb\u003emean\u003c/b\u003e\u0026thinsp;\u003cem\u003e\u0026plusmn;\u003c/em\u003e\u0026thinsp;\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.0\u0026nbsp;\u0026plusmn; 10.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le; baccalaureate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103 (32.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 to 3 years post-baccalaureate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (27.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge; 4 years post baccalaureate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (29.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (10.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEmployment after diagnosis\u003c/b\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (21.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOn medical leave or disabled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150 (47.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (17.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (12.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocial deprivation\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeprived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (14.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-deprived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e262 (83.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (2.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLiving with a partner\u003c/b\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e245 (78.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (21.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHEALTH AND BEHAVIOUR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime since diagnosis (months)\u003c/b\u003e, \u003cb\u003emedian (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.0 (2.5\u0026ndash;4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMissing, n (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime since first surgery (months)\u003c/b\u003e, \u003cb\u003emedian (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (1.0\u0026ndash;2.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e, \u003cb\u003emean\u003c/b\u003e\u0026thinsp;\u003cem\u003e\u0026plusmn;\u003c/em\u003e\u0026thinsp;\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMissing, n (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI category\u003c/b\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164 (52.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95 (30.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (16.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMissing\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQuality of life (/100)\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003emedian (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.0 (58.3\u0026ndash;83.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMissing, n (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (5.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth status (/100)\u003c/b\u003e \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003emedian (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.0 (60.0\u0026ndash;80.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMissing, n (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMenopausal status\u003c/b\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremenopausal or perimenopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166 (53.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostmenopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145 (46.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePast or present\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e211 (67.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102 (32.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMunicipality class\u003c/b\u003e \u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural municipality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (29.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate-density municipality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104 (33.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLarge urban center\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (37.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations: \u003cem\u003eIQR\u003c/em\u003e Inter-Quartile Range; \u003cem\u003eSD\u003c/em\u003e Standard Deviation\u0026nbsp;; \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Assessed using the Evaluation of Deprivation and Inequalities in Health Examination Centers (EPICES) index, with a cut-off score of 30 to define precarity\u0026nbsp;; \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Score calculated from the EORTC QLQ-C30 questionnaire\u0026nbsp;; \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e Score calculated from the EQ-5D-5L questionnaire\u0026nbsp;; \u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e Based on patients addresses at baseline, using the 3-level grid developed by the French National Institute of Statistics and Economic Studies (INSEE)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe characteristics of the 313 patients during the 6-month follow-up are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Two thirds of patients were enrolled during or after the first national COVID-19 pandemic lockdown in France (66.1%). The vast majority of the patients received radiotherapy (90.7%) and/or a hormone therapy (81.2%), approximately 57.5% received chemotherapy, and 14.1% received immunotherapy. Most participants were enrolled after the DISCO intervention (59.1%), while 22.0% were included during and 18.9% before.\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\u003e\u0026nbsp;\u0026ndash; Intervention-related characteristics of the 313 participants of the DiscoSpace study, France, 2018\u0026ndash;2022\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;313)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINTERVENTION SPECIFICITIES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrial arm, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(A) Individualized, semi-supervised exercise program physical activity program carried out autonomously with a connected device\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79 (25.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(B) Therapeutic patient education sessions on physical activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76 (24.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(C) Both interventions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77 (24.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(D) Control group receiving usual care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81 (25.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCOVID-19 pandemic trial status, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBefore the first national lockdown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106 (33.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuring or after the first national lockdown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e207 (66.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReceived therapies during the intervention (yes)\u003c/b\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e284 (90.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHormonotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e254 (81.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e180 (57.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmunotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44 (14.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOBSERVED CHANGES DURING INTERVENTION\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChange in BMI\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight gain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32 (10.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e172 (55.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27 (8.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82 (26.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChange in health status\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImprovement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114 (36.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e113 (36.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeterioration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58 (18.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28 (8.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChange in quality of life\u003c/b\u003e \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImprovement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96 (30.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60 (19.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeterioration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104 (33.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53 (16.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations: \u003cem\u003eIQR\u003c/em\u003e Inter-Quartile Range; \u003cem\u003eSD\u003c/em\u003e: Standard Deviation\u0026nbsp;; \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Defined as a 5% increase or decrease over the 6 months\u0026nbsp;; \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Defined as a change of \u0026plusmn;\u0026thinsp;8.6 units over the 6 months\u0026nbsp;; \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e Defined as a change of \u0026plusmn;\u0026thinsp;5 units over the 6 months\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData on physical activity are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. At the time of randomization, the median amount of self-reported physical activity was 4.1 hours per week (IQR: 1.3\u0026ndash;8.2), which increased to 8.7 hours per week (IQR: 4.9\u0026ndash;14.6) by the end of the intervention, for a median increase of +\u0026thinsp;4.6 hours, and a relative increase of +\u0026thinsp;112.2%. The mean 6MWD was 575.8 meters (\u0026plusmn;\u0026thinsp;77.4 m) at baseline, and 596.2 meters (\u0026plusmn;\u0026thinsp;83.3 m) at 6 months, representing a median increase of +\u0026thinsp;20.4 m, and a relative increase of +\u0026thinsp;3.5%.\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\u003e\u0026nbsp;\u0026ndash; Change in physical activity over the 6-months, DiscoSpace study, France, 2018\u0026ndash;2022 (n\u0026thinsp;=\u0026thinsp;313)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eBaseline,\u003c/p\u003e\u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 months,\u003c/p\u003e\u003cp\u003eMedian (IQR) / Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAbsolute\u003c/p\u003e\u003cp\u003edifference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRelative\u003c/p\u003e\u003cp\u003edifference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMissing data at 6-months, n (%)\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\u003eSelf-reported physical activity (hour/week)\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.1 (1.3\u0026ndash;8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.7 (4.9\u0026ndash;14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;112.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19 (6.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6MWD (meters/6min)\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e575.8\u0026thinsp;\u0026plusmn;\u0026thinsp;77.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e596.2\u0026thinsp;\u0026plusmn;\u0026thinsp;83.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;3.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e102 (32.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: \u003cem\u003e6MWD\u003c/em\u003e 6-Minute Walk Distance ; \u003cem\u003eIQR\u003c/em\u003e Inter-Quartile Range\u0026nbsp;; \u003cem\u003eSD\u003c/em\u003e: Standard Deviation; \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Self-reported physical activity was calculated from the Recent Physical Activity Questionnaire (RPAQ) ; \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e 6MWD was measured by the 6-Minute Walk Test (6MWT)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe scores obtained from the ALPHA questionnaire and their description are presented in \u003cb\u003eAdditional File 5\u003c/b\u003e. The infrastructure maintenance score was excluded from all the analyses due to the large number of missing values (38.3%). Other scores had minimal missing data, with residential density and distance to local facilities having the highest rates of missing responses (10.5% and 10.9%, respectively).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCross-sectional associations\u003c/h2\u003e\u003cp\u003eIn the overall model, an increase of one standardized unit of perceived infrastructure scores (cycling: β\u0026thinsp;=\u0026thinsp;0.210, 95% CI (0.048;0.372); walking: β\u0026thinsp;=\u0026thinsp;0.170, 95% CI (0.016;0.324); total infrastructure: β\u0026thinsp;=\u0026thinsp;0.226, 95% CI (0.063;0.388), per 1SD) and walking and cycling network score (β\u0026thinsp;=\u0026thinsp;0.161, 95% CI (0.008;0.314), per 1SD) predicted higher levels of self-reported physical activity at baseline (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Moreover, an increase of one standardized unit of the perceived distance to facilities was associated with lower 6MWD at baseline (-11 meters; β=-11.363, 95% CI (-20.607;-2.118), per 1SD). No association was observed for all other environmental scores and 6MWD at baseline.\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\u003e\u0026ndash; Associations between perceived neighborhood environment and physical activity, DiscoSpace study, France, 2018\u0026ndash;2022 (n\u0026thinsp;=\u0026thinsp;313)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e\u003cp\u003ePhysical Activity Outcome\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePerceived neighborhood environment \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSelf-reported physical activity \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e6MWD \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eβ \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\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\u003eResidential density\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.105\u0026nbsp;; 0.199)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-8.377\u0026nbsp;; 8.755)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.306\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(-0.494\u0026nbsp;; -0.117)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-6.163\u0026nbsp;; 10.776)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance to local facilities\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.242\u0026nbsp;; 0.100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-11.363\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e(-20.607\u0026nbsp;; -2.118)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.014\u0026nbsp;; 0.377)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-8.379\u0026nbsp;; 8.700)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCycling infrastructures\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.210\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(0.048\u0026nbsp;; 0.372)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-4.081\u0026nbsp;; 13.098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.340\u0026nbsp;; 0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-7.725\u0026nbsp;; 7.841)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWalking infrastructures\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.170\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(0.016\u0026nbsp;; 0.324)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-6.376\u0026nbsp;; 9.747)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.348\u0026nbsp;; 0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-7.023\u0026nbsp;; 8.653)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal infrastructures\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.226\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(0.063\u0026nbsp;; 0.388)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-4.806\u0026nbsp;; 12.479)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.363\u0026nbsp;; 0.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-7.338\u0026nbsp;; 8.208)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSafety from crime\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.235\u0026nbsp;; 0.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-9.288\u0026nbsp;; 7.330)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.075\u0026nbsp;; 0.301)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-3.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-11.802\u0026nbsp;; 3.874)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSafety from traffic\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.155\u0026nbsp;; 0.138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-2.126\u0026nbsp;; 12.815)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.049\u0026nbsp;; 0.326)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-6.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-14.044\u0026nbsp;; 1.338)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal safety\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.193\u0026nbsp;; 0.114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-4.727\u0026nbsp;; 11.175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.041\u0026nbsp;; 0.335)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-6.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-13.901\u0026nbsp;; 1.673)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEsthetics\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.139\u0026nbsp;; 0.173)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-4.252\u0026nbsp;; 12.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.037\u0026nbsp;; 0.340)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-7.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-15.223\u0026nbsp;; 0.923)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePleasure\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.125\u0026nbsp;; 0.188)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-1.959\u0026nbsp;; 14.373)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.015\u0026nbsp;; 0.364)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-6.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-15.052\u0026nbsp;; 1.328)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConnectivity\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.030\u0026nbsp;; 0.269)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-3.252\u0026nbsp;; 12.166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.241\u0026nbsp;; 0.144)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-3.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-11.676\u0026nbsp;; 4.713)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWalking and cycling network\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.161\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(0.008\u0026nbsp;; 0.314)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-5.290\u0026nbsp;; 10.551)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.331\u0026nbsp;; 0.050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-3.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e(-11.301\u0026nbsp;; 4.919)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eValues in bold are statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) ; \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Environmental scores were calculated from the ALPHA questionnaire (for Assessing Levels of PHysical Activity and Fitness at population level) ; \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Self-reported physical activity was calculated from the Recent Physical Activity Questionnaire (RPAQ). The average difference in the outcome self-reported physical activity is expressed by the square root ; \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e 6MWD was measured by the 6-Minute Walk Test (6MWT). The average difference in the outcome 6MWD is expressed without transformation ; \u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e The β indicate the overall longitudinal difference in the outcome score using linear mixed models per 1 SD of perceived built environment score after a standardized Z-score transformation. Analyses were adjusted on: age, social deprivation, educational level, employment status after diagnosis, comorbidities, living with a partner, trial arm, municipality class (except for Residential density score analyses), perceived home environment, COVID-19 pandemic trial status, longitudinal BMI, longitudinal quality of life, and longitudinal health status ; \u003csup\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sup\u003e The cross-sectional association of perceived neighborhood environment and physical activity is estimated by the environmental perception score term ; \u003csup\u003ef\u003c/sup\u003e The longitudinal association of perceived neighborhood environment and physical activity is estimated by the interaction term between the intervention visit and the environmental perception score.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLongitudinal associations\u003c/h2\u003e\u003cp\u003eOverall, an increase in physical activity from baseline to 6-month follow-up was observed after adjustments for trial arm (mean effect of time on self-reported physical activity: β\u0026thinsp;\u0026gt;\u0026thinsp;0.900, mean effect of time on 6MWD: β\u0026thinsp;\u0026gt;\u0026thinsp;23.228; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eAdditional File 6\u003c/b\u003e). There was a negative association between perceived residential density and the change in self-reported physical activity (β=-0.306, 95% CI (-0.494;-0.117), per 1SD) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, no significant association was found between the perceived neighborhood environment and the change in 6MWD from baseline to 6 months.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStratification by social deprivation\u003c/h2\u003e\u003cp\u003eAfter stratification by social deprivation, positive associations between baseline self-reported physical activity and perceived infrastructures remained only among non-deprived patients (with β\u0026thinsp;=\u0026thinsp;0.209, 95% CI (0.038;0.379) for cycling; β\u0026thinsp;=\u0026thinsp;0.204, 95% CI (0.037;0.371) for walking; β\u0026thinsp;=\u0026thinsp;0.240, 95% CI (0.068;0.413) for total infrastructures, per 1SD); although interactions were not statistically significant for walking and total infrastructure scores (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, inverse associations between perceived residential density and self-reported physical activity change remained only among non-deprived patients (β=-0.400, 95% CI (-0.602;-0.195), per 1SD; p-value for interaction\u0026thinsp;=\u0026thinsp;0.031). Moreover, among non-deprived women, positive associations were observed (per 1SD increase) between change in self-reported physical activity and distance to local facilities (β\u0026thinsp;=\u0026thinsp;0.298, 95% CI (0.085;0.511)), and perceived infrastructures (β=-0.242, 95% CI (-0.442;-0.041) for cycling; β=-0.283, 95% CI (-0.487;-0.079) for walking; β=-0.292, 95% CI (-0.492;-0.092) for total infrastructures) (p-values for interaction\u0026thinsp;\u0026le;\u0026thinsp;0.05). A positive association between self-reported physical activity change and perceived safety form traffic appeared among deprived women (β\u0026thinsp;=\u0026thinsp;0.632, 95% CI (0.196;1.068), per 1SD; p-value for interaction\u0026thinsp;=\u0026thinsp;0.016), but not among non-deprived women (β\u0026thinsp;=\u0026thinsp;0.034, 95% CI (-0.175;0.243), per 1SD) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAn inverse association between the perceived safety from crime and 6MWD at baseline was observed among deprived women only (β=-23.860, 95% CI (-41.601;-6.118), per 1SD). Significant positive associations between baseline 6MWD and connectivity (β\u0026thinsp;=\u0026thinsp;24.060, 95% CI (3.533;44.587), per 1SD), as well as cycling and walking network (β\u0026thinsp;=\u0026thinsp;19.466, 95% CI (1.004;37.928), per 1SD) were observed among patients experiencing social deprivation but not among others (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026ndash; Associations between perceived neighborhood environment and physical activity, stratified by social deprivation, DiscoSpace study, France, 2018\u0026ndash;2022 (n\u0026thinsp;=\u0026thinsp;306)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e\u003cp\u003eSelf-reported physical activity \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePerceived neighborhood environment score \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNon-deprived (n\u0026thinsp;=\u0026thinsp;262)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eDeprived (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-int\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eβ \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidential density\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.079\u0026nbsp;; 0.246)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.555\u0026nbsp;; 0.246)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.724\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.400\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(-0.602\u0026nbsp;; -0.195)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.316\u0026nbsp;; 0.729)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistance to local facilities\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.286\u0026nbsp;; 0.087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.276\u0026nbsp;; 0.451)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.298\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(0.085\u0026nbsp;; 0.511)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.582\u0026nbsp;; 0.208)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCycling infrastructures\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.209\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(0.038\u0026nbsp;; 0.379)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.189\u0026nbsp;; 0.626)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.242\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(-0.442\u0026nbsp;; -0.041)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.086\u0026nbsp;; 0.977)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWalking infrastructures\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.204\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(0.037\u0026nbsp;; 0.371)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.373\u0026nbsp;; 0.383)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.283\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(-0.487\u0026nbsp;; -0.079)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.029\u0026nbsp;; 0.943)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal infrastructures\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.240\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(0.068\u0026nbsp;; 0.413)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.263\u0026nbsp;; 0.533)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.292\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(-0.492\u0026nbsp;; -0.092)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.009\u0026nbsp;; 1.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSafety from traffic\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.120\u0026nbsp;; 0.202)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.600\u0026nbsp;; 0.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.175 ; 0.243)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.632\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e(0.196 ; 1.068)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003e6MWD\u003c/b\u003e \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ePerceived neighborhood environment score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNon-deprived (n\u0026thinsp;=\u0026thinsp;262)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003eDeprived (n\u0026thinsp;=\u0026thinsp;44)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ep-int\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eβ\u003c/b\u003e \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eβ\u003c/b\u003e \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSafety from crime\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-4.758\u0026nbsp;; 13.179)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-23.860\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e(-41.601\u0026nbsp;; -6.118)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-15.568 ; 2.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-11.620 ; 26.572)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal safety\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.279 ; 15.887)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-16.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-34.291 ; 1.748)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-17.382 ; 0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-16.138 ; 24.974)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConnectivity\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-6.802 ; 9.681)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e24.060\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e(3.533 ; 44.587)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-11.219 ; 6.669)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-11.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-32.979 ; 10.409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.447\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWalking and cycling network\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-9.325 ; 7.860)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e19.466\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e(1.004 ; 37.928)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-11.117 ; 6.919)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-28.567 ; 10.640)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eValues in bold are statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) ; Social deprivation score was assessed using an individual deprivation index called the \u0026ldquo;Evaluation of Deprivation and Inequalities in Health Examination Centers\u0026rdquo; (EPICES) and a cut-off score of 30 was used to define social deprivation (yes/no) ; \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Environmental scores were calculated from the ALPHA questionnaire (for Assessing Levels of PHysical Activity and Fitness at population level) ; \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Self-reported physical activity was calculated from the Recent Physical Activity Questionnaire (RPAQ). The average difference in the outcome self-reported physical activity is expressed by the square root ; \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e 6MWD was measured by the 6-Minute Walk Test (6MWT). The average difference in the outcome 6MWD is expressed without transformation ; \u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e The β indicate the overall longitudinal difference in the outcome score using linear mixed models per 1 SD of perceived built environment score after a standardized Z-score transformation. Analyses were adjusted on: age, social deprivation, educational level, employment status after diagnosis, comorbidities, living with a partner, trial arm, municipality class (except for Residential density score analyses), perceived home environment, COVID-19 pandemic trial status, longitudinal BMI, longitudinal quality of life, and longitudinal health status ; \u003csup\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sup\u003e The cross-sectional association of perceived neighborhood environment and physical activity is estimated by the environmental perception score term ; \u003csup\u003e\u003cb\u003ef\u003c/b\u003e\u003c/sup\u003e The longitudinal association of perceived neighborhood environment and physical activity is estimated by the interaction term between the intervention visit and the environmental perception score.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStratification by municipality class\u003c/h2\u003e\u003cp\u003eAfter stratification by municipality class, associations between self-reported physical activity at baseline and perceived infrastructure remained only among rural municipalities (β\u0026thinsp;=\u0026thinsp;0.512, 95% CI (0.202;0.822) for cycling; β\u0026thinsp;=\u0026thinsp;0.448 95% CI (0.155;0.740) for total infrastructures, per 1SD) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026ndash; Associations between perceived neighborhood environment and physical activity, stratified by municipality class, DiscoSpace study, France, 2018\u0026ndash;2022 (n\u0026thinsp;=\u0026thinsp;313)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"12\" nameend=\"c13\" namest=\"c2\"\u003e\u003cp\u003eSelf-reported physical activity \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePerceived neighborhood environment \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eRural municipality\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eIntermediate-density municipality (n\u0026thinsp;=\u0026thinsp;104)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eLarge urban center (n\u0026thinsp;=\u0026thinsp;116)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-int\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eβ \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eβ \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\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\u003eDistance to local facilities\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.500\u0026nbsp;; 0.062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.303\u0026nbsp;; 0.299)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(-0.121\u0026nbsp;; 0.664)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.189\u0026nbsp;; 0.539)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.427\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e(0.037\u0026nbsp;; 0.818)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-0.565\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e(-1.066\u0026nbsp;; -0.065)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCycling infrastructures\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.512\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(0.202\u0026nbsp;; 0.822)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.236\u0026nbsp;; 0.316)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(-0.345\u0026nbsp;; 0.289)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.568\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(-0.993\u0026nbsp;; -0.143)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.410\u0026nbsp;; 0.295)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.416\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e(0.008\u0026nbsp;; 0.825)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal infrastructures\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.448\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(0.155\u0026nbsp;; 0.740)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.199\u0026nbsp;; 0.346)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(-0.331\u0026nbsp;; 0.357)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.525\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(-0.912\u0026nbsp;; -0.139)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.438\u0026nbsp;; 0.258)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(-0.001\u0026nbsp;; 0.882)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConnectivity\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.123\u0026nbsp;; 0.376)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.038\u0026nbsp;; 0.454)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(-0.450\u0026nbsp;; 0.162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.265\u0026nbsp;; 0.398)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-0.357\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e(-0.682\u0026nbsp;; -0.032)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.535\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e(0.126\u0026nbsp;; 0.943)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWalking and cycling network\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-sectional \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.049\u0026nbsp;; 0.490)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(-0.064\u0026nbsp;; 0.434)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(-0.360\u0026nbsp;; 0.248)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.392\u0026nbsp;; 0.318)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-0.455\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e(-0780\u0026nbsp;; -0.130)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.493\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e(0.083\u0026nbsp;; 0.903)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eValues in bold are statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) ; The municipality class of the residence at baseline was categorized using the 3-level grid developed by the French National Institute of Statistics and Economic Studies (INSEE) ; \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Environmental scores were calculated from the ALPHA questionnaire (for Assessing Levels of PHysical Activity and Fitness at population level) ; \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Self-reported physical activity was calculated from the Recent Physical Activity Questionnaire (RPAQ). The average difference in the outcome self-reported physical activity is expressed by the square root ; \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e The β indicate the overall longitudinal difference in the outcome score using linear mixed models per 1 SD of perceived built environment score after a standardized Z-score transformation. Analyses were adjusted on: age, social deprivation, educational level, employment status after diagnosis, comorbidities, living with a partner, trial arm, municipality class (except for Residential density score analyses), perceived home environment, COVID-19 pandemic trial status, longitudinal BMI, longitudinal quality of life, and longitudinal health status ; \u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e The cross-sectional association of perceived neighborhood environment and physical activity is estimated by the environmental perception score term ; \u003csup\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sup\u003e The longitudinal association of perceived neighborhood environment and physical activity is estimated by the interaction term between the intervention visit and the environmental perception score.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter stratification, significant associations were observed: perceived shorter distances to local facilities (β=-0.565, 95% CI (-1.066;-0.065), per 1SD), better connectivity (β\u0026thinsp;=\u0026thinsp;0.535, 95% CI (0.126;0.943), per 1SD), and a better cycling and walking network (β\u0026thinsp;=\u0026thinsp;0.493, 95% CI (0.083;0.903), per 1SD), were associated with a greater increase in self-reported physical activity over 6-months in large urban centers, which contrasts with the direction of associations observed in intermediate-density municipalities (respectively: β\u0026thinsp;=\u0026thinsp;0.427, 95% CI (0.037;0.818); β=-0.357, 95% CI (-0.682;-0.032); β = -0.455, 95% CI (-0780;-0.130), per 1SD). Moreover, perceived cycling infrastructure was associated with a smaller increase at 6-months in self-reported physical activity in rural municipalities (β=-0.568, 95% CI (-0.993;-0.143), per 1SD), but with a greater increase in large urban centers (β\u0026thinsp;=\u0026thinsp;0.416, 95% CI (0.008;0.825), per 1SD) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eAdditional modifiers\u003c/h2\u003e\u003cp\u003eRegarding other modifiers, for self-reported physical activity, significant interactions were observed at baseline for employment status after diagnosis, quality of life, and trial arm; and in longitudinal analyses for BMI and health status. For 6MWD, significant interactions were observed in cross-sectional analyses for age, employment status after diagnosis, living with a partner, and trial arm; and in longitudinal analyses for time since first breast cancer surgery, quality of life, living with a partner, and trial arm (\u003cb\u003eAdditional File 7\u003c/b\u003e). However, most of the time, confidence intervals overlap between subgroups and/or subgroup associations were not significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity analyses\u003c/h2\u003e\u003cp\u003eThe sensitivity analysis adjusted for the ALPHA questionnaire completion period did not show any significant differences in the associations studied (\u003cb\u003eAdditional File 8\u003c/b\u003e\u003cem\u003e)\u003c/em\u003e. After removing extreme values of self-reported physical activity, direction and strength of observed associations remained unchanged (\u003cb\u003eAdditional File 9\u003c/b\u003e). Generally, the associations observed in the main analysis were consistent after stratification by treatment subgroups, although some of them disappeared in certain subgroups (\u003cb\u003eAdditional File 10\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTo the best of our knowledge, this study is the first to perform a quantitative analysis of the effect of perceived neighborhood environment on physical activity in women with breast cancer. The perception of developed walking and cycling infrastructures and network was associated with higher self-reported physical activity at baseline. Furthermore, patients who reported being distant from local facilities exhibited lower initial 6MWD. While there was a general increase in self-reported physical activity over the six-month study period after adjustments for trial arm, this increase was more modest in neighborhoods perceived to be densely populated. Moreover, among women experiencing social deprivation, a heightened sense of traffic safety was associated with a more substantial increase in self-reported physical activity over the 6-month study period. Among non-deprived women, perceived better infrastructures and higher residential density were associated with smaller increases in self-reported physical activity over the study period. The associations between the perception of the neighborhood environment (distance to local facilities, walking and biking infrastructures and network) and changes in self-reported physical activity further varied according to the size of the municipality of residence.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eResidential density\u003c/h2\u003e\u003cp\u003eGreater perceived residential density was associated with smaller improvements in self-reported physical activity during the intervention, after adjusting for trial arms, despite no association being observed with baseline self-reported activity levels. The literature on this topic is inconsistent: two cross-sectional studies, one among Spanish patients with chronic obstructive pulmonary disease(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) and the other among Chinese adults from the general population(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), reported that higher residential density was associated with increased sedentary behavior. Conversely, an Australian study found that urban densification was linked to greater engagement in active behaviors(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). In densely populated areas, increased air pollution\u0026mdash;as shown in American and Chinese studies(\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u0026mdash;may discourage outdoor activities, potentially limiting improvements in overall activity levels. Additionally, higher traffic volumes(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) and noise annoyance(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), as reported in European longitudinal studies, might further reduce participation in physical activity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eDistance to facilities\u003c/h2\u003e\u003cp\u003eIn the present study, patients who perceived a greater distance to reach neighborhood facilities (e.g., shops, restaurants, public transport, leisure facilities, etc.) had a lower 6MWD at baseline. This supports the hypothesis that perceiving facilities as distant may encourage motorized transport over active travel, thereby reducing walking capacity. This finding is consistent with a systematic review(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) showing that convenient distance to multiple destinations is associated with favorable cardiorespiratory fitness. Our findings may also suggest that patients with lower functional capacity may perceive facilities as being farther away. For example, an American study reported that elderly and individuals with chronic pain or reduced mobility tend to perceive distances as farther compared to pain-free or higher-capacity individuals(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur results showed that increased perceived distance to access facilities was associated with greater increases in physical activity during intervention among patients in intermediate-sized municipalities, and the opposite association was observed among patients in large urban centers. In smaller communities, the perception of distance may be seen as a challenge to overcome, encouraging patients to be more active to access available resources. In contrast, in urban centers, where facilities are closer and more accessible, patients may not feel the same need to engage in physical activity. Given the established tendency of urban populations to utilize the proximity of destinations for their physical activity(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), an alternative hypothesis suggests that they have already reached a state of maximum potential in this regard. In contrast, patients residing in smaller municipalities may be compelled to explore novel strategies for enhancing exercise, such as the utilization of active transportation to reach their destinations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eWalkability and Bikeability\u003c/h2\u003e\u003cp\u003eConsistent with previous studies conducted on adult populations with or without a cancer history(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), our findings showed that overall walkability and bikeability\u0026ndash;as proxied in our research by perceptions of infrastructure (such as bike lanes, sidewalks and pedestrian zones for shopping) and street connectivity (including intersections and shortcuts by foot or bike)\u0026ndash;were associated with higher levels of self-reported physical activity at baseline. However, walkability and bikeability, often used to summarize the built environment, lack consistent definitions(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e) and fail to capture specific environmental characteristics. Indeed, walking and cycling network, walking and cycling infrastructures, residential density, or safety may be differently perceived and associated with physical activity pattern, as shown in our analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eWalking and cycling network\u003c/h2\u003e\u003cp\u003eFor walking and cycling network, differences were observed between deprived and non-deprived patients. Deprived patients who perceived streets to be more interconnected\u0026mdash;referring to perceived connectivity as well as cycling and walking networks\u0026mdash;exhibited higher baseline 6MWD. This observation suggests that individuals, even those from deprived backgrounds, may benefit from well-designed environments, potentially mitigating socioeconomic inequalities in physical functioning. However, when examining self-reported physical activity, no significant associations were observed between environmental characteristics and baseline physical activity among deprived women. Previous Canadian studies have reported mixed results, with some studies showing positive associations(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) and others negative associations(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) between street connectivity and physical activity in adults of low socioeconomic status. These discrepancies may stem from variations in the measurement of socioeconomic status (often determined by household income and/or educational level) or the specific cultural and geographic context.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eWalking and cycling infrastructures\u003c/h2\u003e\u003cp\u003eIn the present study, after adjustment for intervention arm, the availability of walking and cycling infrastructure was associated with smaller improvements in self-reported physical activity over the 6-month study period, but only among non-deprived women. This contrasts with previous studies showing a positive association between neighborhood amenities(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), access to public transport(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e), and physical activity among women wishing to be more active. However, none of the aforementioned studies focused on European settings nor breast cancer patients. A Canadian study on prostate cancer patients(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e) found that the neighborhood environment was not associated with physical activity patterns in a behavior change intervention. This study did not distinguish between deprived patients and others, which might explain the differences with our results.\u003c/p\u003e\u003cp\u003eFurthermore, the present study suggests a negative association between the perception of cycling infrastructure and improvements in physical activity in rural municipalities, and a positive association in urban areas, where infrastructures are more likely to be integrated into the daily environment. In rural areas, as highlighted by other research(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e), the perception of infrastructure is often related to factors such as accessibility or safety, which may limit its benefit on physical activity. These findings underscore the importance of considering the surrounding environmental context which can influence the effectiveness of existing infrastructures in promoting physical activity. Indeed, recent studies(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) demonstrated how neighborhood perceptions can shed light on specific concerns of different groups regarding social and neighborhood contexts, and how enhancing the neighborhood environment alone may not be enough to increase physical activity.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eSafety\u003c/h2\u003e\u003cp\u003eIn our population of breast cancer patients, deprived women who perceived their surroundings as safe regarding crime exhibited lower baseline 6MWD, even when accounting for health statuses and BMI. This result contradicts studies conducted in general adult populations(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e), which have established a positive association between walking and personal safety, especially when it was based on subjective measures. While the difference in results could be partly explained by the relatively small number of deprived women in our sample, future studies focusing specifically on a sample of deprived breast cancer patients may provide deeper insights into this association. Indeed, the persistence of other socioeconomic barriers, such as limited financial resources, restricted access to healthcare, or greater family and work-related responsibilities, has the potential to impact the use of the environment for physical activity(\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur work also revealed that, although no association was observed in the overall population, among deprived women, a higher perception of traffic safety was linked to greater improvements in self-reported physical activity over time. Previous studies(\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) have shown mixed evidence regarding the impact of neighborhood environment characteristics on physical activity change in adults, depending on the population and type of physical activity program involved. This variability underscores the necessity to consider perceived neighborhood environment in futures studies by accounting for the potential influence of socioeconomic status on these relationships.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThe DISCO trial provided accurate and longitudinal measurements of physical activity and clinical factors. Another strength is the use of a validated tool assessed environmental perceptions in a European context(\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, the present study is not without limitations. Although social deprivation, educational level, and municipality class varied within the sample, the generalizability of our findings is limited by the recruitment from a single cancer center in the Auvergne-Rh\u0026ocirc;ne-Alpes French region, with potential underrepresentation of deprived patients(\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e) and selection bias due to voluntary participation in a physical activity intervention(\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). However, the absence of differences in associations between trial arms suggests a limited risk of bias related to the use of DISCO study data. Moreover, most of the patients completed the ALPHA environmental questionnaire months after the 6-month physical activity assessment, which limits causal inferences despite adjustments for the time period for completing the ALPHA questionnaire. Finally, self-reported physical activity (RPAQ) may also be overestimated due to social desirability bias. However, parallel analyses on objective 6-Minute Walk Distance (6MWD) strengthened the findings and showed that perceived environmental characteristics were differentially associated with self-reported activity and 6MWD, highlighting the value of analyzing both dimensions(\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe presence of adequate infrastructures and the development of a comprehensive cycling and walking network were positively associated with baseline self-reported physical activity in this population of breast cancer patients engaged in physical activity. Furthermore, the associations between the perceived neighborhood environment and improvements in both self-reported physical activity and functional capacity (6MWD) varied according to individual socioeconomic status (social deprivation) and municipality class, after adjusting for the physical activity program followed during the DISCO trial. These findings underscore the necessity to further consider both individual socioeconomic characteristics and perceived environmental characteristics when designing tailored physical activity interventions for breast cancer patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e6MWD:\u003c/em\u003e\u003c/strong\u003e 6-Minute Walk Distance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e6MWT:\u003c/em\u003e\u003c/strong\u003e 6-Minute Walk Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eALPHA:\u003c/em\u003e\u003c/strong\u003e Assessing Levels of PHysical Activity and Fitness at population level\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBMI:\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBody Mass Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCLB:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eL\u0026eacute;on B\u0026eacute;rard Comprehensive Cancer Center\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCI:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eConfidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDAG:\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDirected Acyclic Graph\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDISCO:\u003c/em\u003e\u003c/strong\u003e DISpositif COnnect\u0026eacute;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEPICES:\u003c/em\u003e\u003c/strong\u003e Evaluation of Deprivation and Inequalities in Health Examination Centers\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIQR:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eInter-Quartile Range\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRPAQ:\u003c/em\u003e\u003c/strong\u003e Recent Physical Activity Questionnaire\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD:\u003c/em\u003e\u003c/strong\u003e Standard Deviation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWHO:\u003c/em\u003e\u003c/strong\u003e World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe French ethics committee allowed the administration of the ALPHA questionnaire (17th November 2020). The DISCO trial protocol was approved by a French ethics committee (Comit\u0026eacute; de Protection des Personnes Est I, ID RCB 2017-A03360-53, 1st February 2018) and its database was reported to the French National Commission for Data Protection and Liberties (ref. MR-001 no. 2016177, 13th December 2016). The trial is registered on http://www.clinicaltrials.gov (NCT number: NCT03529383, 17th May 2018). The informed consent form signed by all patients randomized in the DISCO trial mentions the possibility of re-use the study data for other research purposes.\u0026nbsp;\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 supporting the conclusions of this article are available from the corresponding author on reasonable request. For general data sharing inquiries, contact B\u0026eacute;atrice Fervers ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DiscoSpace study was financially supported by the French National Cancer Institute (l\u0026rsquo;Institut National du Cancer, INCa) (grant no. 2020-085).\u0026nbsp;The DISCO study (NCT03529383) was financially supported by the ARC French Foundation for Cancer Research (ARC-France) and the French National Cancer Institute (l\u0026rsquo;Institut National du Cancer, INCa), the Cancer cluster of the Lyon Auvergne Rh\u0026ocirc;ne Alpes region (Canc\u0026eacute;rop\u0026ocirc;le Lyon Auvergne-Rh\u0026ocirc;ne-Alpes, CLARA), the Fundation for Medical Research (FMR), the Auvergne Rh\u0026ocirc;ne Alpes regional Health Agency (ARS-ARA), and\u0026nbsp;the French life insurance company AG2R-LA-MONDIALE. The research was designed, conducted, analyzed, and interpreted by the authors entirely independently of these funding sources.\u0026nbsp;The funder had no role in study design, data acquisition and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBFe, OP, BFo, LG, and DP contributed to the design and funding of the DiscoSpace study, with assistance of OT. ML conducted the data analysis under supervision of MH with assistance of OP. ML, MH, DP, BFe were responsible for drafting the manuscript. OP, CD, and AM handled administrative tasks.\u0026nbsp;CD was responsible for the ALPHA environmental questionnaire data collection. LG built the ALPHA environmental scores. ML, CD, AM, LG and AS were involved in data management. All authors provided advice on the study design, analysis, critical interpretation of the results and review of the first draft. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank all participants to the DISCO trial, as well as clinicians and all personnel involved in their recruitment and follow-up in the DISCO trial. The authors also wish to acknowledge contributions from: The Department of Biostatistics - Clinical Research Unit of the L\u0026eacute;on B\u0026eacute;rard Cancer Center for their contribution to the DISCO trial, in particular Marina Touillaud for DISCO trial conception and C\u0026eacute;cile Dalban for her help with DISCO trial data used in the present study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. 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PLoS ONE. 2012;7(7):e42202.\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":"international-journal-of-behavioral-nutrition-and-physical-activity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbn","sideBox":"Learn more about [International Journal of Behavioral Nutrition and Physical Activity](http://ijbnpa.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ijbn/default.aspx","title":"International Journal of Behavioral Nutrition and Physical Activity","twitterHandle":"@IJBNPA","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, cancer treatment, cross-sectional study, longitudinal study, perceived neighborhood environment, physical activity","lastPublishedDoi":"10.21203/rs.3.rs-8094974/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8094974/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite the proven benefits of physical activity during breast cancer treatment, many women reduce their practice after diagnosis. A better understanding of how the neighborhood environment influences physical activity behavior could help optimize strategies for physical activity in breast cancer patients undergoing cancer treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examined associations between perceived neighborhood environment and physical activity among breast cancer patients undergoing treatment and engaged in a physical activity program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were 313 breast cancer patients enrolled in the DISCO physical activity intervention trial (NCT03529383). In the present observational analysis (DiscoSpace), cross-sectional (at baseline) and longitudinal (during intervention) associations between perceived neighborhood environment and physical activity were investigated. The perceived neighborhood environment was assessed using the ALPHA questionnaire, physical activity and physical functioning were evaluated through the Recent Physical Activity Questionnaire (self-reported physical activity) and the 6-Minute Walk Test (to measure the 6-Minute Walk Distance (6MWD)). Associations were estimated through mixed linear regression models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetter perceived cycling and walking infrastructures and network were associated with higher self-reported physical activity at baseline (total infrastructures: β = 0.226, 95% CI (0.063;0.388); network: β = 0.161, 95% CI (0.008;0.314)). Perceived distance to local facilities was inversely associated with 6MWD at baseline (β=-11.363, 95% CI (-20.607;-2.118)). A perception of densely populated neighborhoods (β=-0.306, 95% CI (-0.494;-0.117)) was associated with a lesser increase in self-reported physical activity during the intervention, after adjustment for trial arm. These associations varied according to women’s socioeconomic status and municipality class.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe perceived neighborhood environment and socioeconomic characteristics of women with breast cancer should be given greater consideration for developing effective programs to promote physical activity in this population.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Associations between perceived neighborhood environment and physical activity among breast cancer patients engaged in a physical activity program concomitant to cancer treatment: cross-sectional and longitudinal analyses in the DISCO trial (DiscoSpace)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 09:16:12","doi":"10.21203/rs.3.rs-8094974/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-30T03:06:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T18:30:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80954753770814926620630240964912365966","date":"2026-01-28T18:28:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T13:56:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183799040427281867396634650574798278253","date":"2025-12-04T13:30:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T20:58:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T10:16:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T10:15:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Behavioral Nutrition and Physical Activity","date":"2025-11-12T09:54:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-behavioral-nutrition-and-physical-activity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbn","sideBox":"Learn more about [International Journal of Behavioral Nutrition and Physical Activity](http://ijbnpa.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ijbn/default.aspx","title":"International Journal of Behavioral Nutrition and Physical Activity","twitterHandle":"@IJBNPA","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"367516fb-ea24-4df7-ba12-18bf34650488","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:27:49+00:00","versionOfRecord":{"articleIdentity":"rs-8094974","link":"https://doi.org/10.1186/s12966-026-01909-w","journal":{"identity":"international-journal-of-behavioral-nutrition-and-physical-activity","isVorOnly":false,"title":"International Journal of Behavioral Nutrition and Physical Activity"},"publishedOn":"2026-03-26 16:09:35","publishedOnDateReadable":"March 26th, 2026"},"versionCreatedAt":"2025-12-03 09:16:12","video":"","vorDoi":"10.1186/s12966-026-01909-w","vorDoiUrl":"https://doi.org/10.1186/s12966-026-01909-w","workflowStages":[]},"version":"v1","identity":"rs-8094974","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8094974","identity":"rs-8094974","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}