Association of green spaces on mental health and well-being in urban Bengaluru: A cross-sectional study

Association of green spaces on mental health and well-being in urban Bengaluru: A cross-sectional study | 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 Association of green spaces on mental health and well-being in urban Bengaluru: A cross-sectional study Deepika K, Chitra Venkateswaran, Aditya Singh, Tejaswini B D, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7183081/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Rapid urbanisation in Bengaluru has reduced green spaces, potentially impacting mental health. Urban green spaces promote psychological well-being by reducing stress, enhancing mood, and fostering social connections. This study examined the relationship between green spaces and mental health among residents of Bengaluru. Methods A cross-sectional study was conducted among 330 adults using stratified random sampling across nine strata based on income and park availability. Data were collected through questionnaires capturing park proximity, visit frequency, and mental health indicators. Satellite imagery assessed green space via NDVI (Normalized Difference Vegetation Index). Mental health was measured using the WHO-5 Well-being Index and K6 Psychological Distress Scale. Data were analysed using descriptive statistics, Spearman’s correlation, and multivariable regression in R software (version 4.5.0). Results Among participants, 79% had green spaces nearby, but only 19% visited daily. Living farther away (> 2 km) was associated with significantly lower well-being scores (β = − 5.58, p = 0.004). Infrequent visits (never vs. weekly) were associated with poorer well-being (β = − 16.02, p = 0.0003). No significant association emerged between proximity or visit frequency and psychological distress (K6). Higher income (β = − 1.97, p = 0.01) and a college education (vs. high school: β = 1.40, p = 0.04) were associated with psychological distress. Areas away from major roads had higher NDVI values. Frequent and closer access to green spaces was associated with improved mental well-being. Conclusion Closer proximity to and frequent use of green spaces are linked to better mental well-being. Urban planning in Bengaluru should prioritise equitable access to quality green spaces to promote public mental health. Green spaces Mental Health Urban Bengaluru Figures Figure 1 Key Messages • Closer proximity to green spaces and more frequent visits to green space are significantly associated with better mental well-being among urban adults in Bengaluru, India. • Promoting accessible, well-maintained green spaces should be an integral part of urban planning to support mental health and well-being in rapidly developing Indian cities. Background Rapid urbanisation has emerged as a major driver of environmental and social change in India’s metropolitan cities, including Bengaluru. The expansion of built environments often occurs at the expense of natural spaces such as parks, gardens, and community green areas, which play an important role in supporting the physical and mental health of urban populations 1 , 2 . With increasing urban density, residents frequently experience reduced opportunities for contact with nature, leading to heightened exposure to noise, air pollution, and stress, all of which can adversely affect psychological well-being 3 , 4 . Globally, there is growing recognition of the protective role of urban green spaces in promoting mental health 5 , 6 . Evidence suggests that access to and use of green spaces are associated with a range of benefits, including stress reduction, improved mood, enhanced social cohesion, and opportunities for physical activity 7 , 3 , 8 . Studies conducted in high-income countries consistently demonstrate that proximity to green environments can mitigate the negative psychological impacts of urban living 9 , 10 , 11 , 12 . However, the extent to which such associations hold true in the context of rapidly developing Indian cities remains underexplored 2 , 13 , 14 . In India, the loss of urban greenery has been accompanied by a rising burden of common mental health problems, including anxiety, depression, and psychological distress 15 , 16 . While national programmes such as the Smart Cities Mission and the National Urban Health Mission emphasise sustainable urban development and community well-being, there is limited empirical research on how the availability and utilisation of urban green spaces affect mental health outcomes among urban residents 17 . Local evidence is particularly scarce for cities like Bengaluru, which have experienced rapid land-use change and an uneven distribution of public parks and recreational spaces 2 , 18 . This study assessed the association between green spaces and the mental health and well-being of residents in urban Bengaluru. Materials and Methods Study Design and Setting This community-based cross-sectional study was conducted in selected urban wards of Bengaluru, Karnataka, India, between March and June 2025, following approval from the Institutional Ethics Committee. The study was designed to investigate the relationship between exposure to green spaces and mental health and well-being among adult residents. Data Source Data for this study were collected primarily through direct surveys using a structured, pre-tested questionnaire. The questionnaire was administered either face-to-face by the investigator or provided as a self-administered form, depending on participant convenience. It was available in both English and Kannada, with the Kannada translation carried out by language experts. The questionnaire included detailed sections covering socio-demographic information, mental health measures: the WHO-5 Well-Being Index, a widely used five-item tool with good validity and reliability for assessing subjective psychological well-being 19 , and the Kessler K6 Psychological Distress Scale, a six-item tool validated for screening non-specific psychological distress in community settings 20 .These tools were chosen due to their brevity, international comparability, and ease of administration in field surveys. Green space-related variables captured included access, proximity, visit frequency, and perceived quality. To complement self-reported data, objective greenness around each participant’s residence was assessed using the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 satellite imagery. Sentinel-2 data were accessed freely from the USGS Earth Explorer platform ( https://earthexplorer.usgs.gov/ ). NDVI was calculated and analysed using QGIS software version 3.42.3. Study Population Adults aged 18 years and above, residing in the selected wards (Appendix 1) of Bengaluru for at least six months, and able to understand English or Kannada were eligible to participate. Participants who were unable to provide informed consent were excluded from the study. To ensure representation across different socio-economic and environmental contexts, a stratified random sampling method was adopted. First, wards were categorised based on predominant income level into high, middle, and low-income groups. Separately, wards were also categorised based on park availability into high, medium, and low park availability categories (Fig. 1). By combining these two classifications, nine distinct strata were formed, representing each combination of income level and park availability. Participants were then recruited using equal allocation, with 37 individuals sampled from each stratum, resulting in a final sample of 330 individuals. Within each stratum, participants were selected using a field-based random walk approach. Due to the absence of comprehensive household lists, households were visited randomly within the selected wards by following pre-defined routes and approaching every nth house where feasible. If a household did not have an eligible participant or if participation was refused, the next household along the route was approached, continuing this process until the target of 37 participants per stratum was achieved. Outcome Variables The primary outcome variable was mental well-being, assessed using the WHO-5 Well-Being Index, a validated five-item tool that measures subjective psychological well-being. 19 The secondary outcome was psychological distress, measured with the Kessler K6 scale 20 , which captures non-specific symptoms of distress. Both indices generate continuous scores and have demonstrated good reliability and validity in similar community settings. Main Exposure Variables The primary exposure of interest was access to green space, assessed through both objective and subjective measures. Objective exposure was measured by calculating NDVI values within concentric buffers of 100 meters, 300 meters, and 1000 meters around each participant’s residence to reflect immediate and neighbourhood-level greenness. Subjective exposure included self-reported proximity to the nearest green space, categorised as less than 500 meters, 500 to 1000 meters, one to two kilometres, or more than two kilometres, and the frequency of visits to these spaces, categorised as daily, weekly, monthly, rarely, or never. Predictor Variables Key predictor variables included participant age, gender, education level (school, college, or university), occupation status (employed, unemployed/retired, or other), and monthly household income in rupees. Contextual factors, such as perceived safety of green spaces, were also collected to account for possible environmental and social confounders that might influence mental health independently of green space exposure. Statistical Analysis All statistical analyses for this study were conducted using R version 4.5.0 (R Foundation for Statistical Computing, Vienna, Austria) 21 . Descriptive statistics were used to summarise the socio-demographic characteristics of the participants, levels of green space access and utilisation, and mental health outcomes. Categorical variables such as gender, education level, occupation, income categories, proximity to green spaces, and visit frequency were summarised as frequencies and percentages. Continuous variables, including age, WHO-5 Well-Being Index scores, Kessler K6 Psychological Distress scores, and NDVI values, were summarised using means and standard deviations. To examine the distribution of continuous variables, normality was assessed using the Shapiro-Wilk test. Associations between categorical socio-demographic variables and green space visit frequency were initially explored using chi-square tests or Fisher’s exact test, where cell counts were low. Correlations between continuous exposure measures (e.g., NDVI) and outcome variables (WHO-5 and K6 scores) were assessed using Spearman’s rank correlation coefficients due to the non-parametric nature of some variables. The independent effect of green space exposure on mental health outcomes was investigated while adjusting for potential confounders. Multivariable linear regression models were fitted separately for the WHO-5 Well-Being Index and Kessler K6 Psychological Distress Scale as outcome variables. The main exposure variables included objective greenness (NDVI within 100 m, 300 m, and 1000 m buffers) and subjective measures such as self-reported proximity to the nearest green space and frequency of visits. Covariates included in the models were age, gender, education level, occupation, household income, and distance to the nearest major road. These variables were chosen based on theoretical relevance and findings from bivariate analyses. All statistical tests were two-tailed, and a p-value of less than 0.05 was considered statistically significant. The Variance Inflation Factor (VIF) analysis showed that all predictor variables in the model had VIF values close to 1, ranging from 1.01 to 1.20. This indicates that there is no evidence of multicollinearity among the independent variables. Therefore, all variables can be retained in the regression model, as their inclusion does not cause instability or distortion of the regression estimates. Where appropriate, adjusted R-squared values were provided to indicate the proportion of variance explained by each model. Ethical Considerations The study protocol was reviewed and approved by the Institutional Ethics Committee of M.S. Ramaiah University of Applied Sciences, Bengaluru (EC-25/24-PG-FLAHS). Written informed consent was obtained from all participants before data collection. The study adhered to the ethical guidelines outlined by the Indian Council of Medical Research for research involving human participants. The research adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies to ensure methodological rigour and transparent reporting 22 . Results The mean age of the participants was 37.6 years (SD = 12.15), with a range of 18 to 75 years. The gender distribution was similar, with 171 females (51.8%) and 159 males (48.2%). In terms of educational background, 51.8% had completed university education, 27.6% had a pre-university education, and 19.1% had completed school-level education. Monthly income distribution was as follows: 33.9% reported earning ₹10,000–₹30,000, 33.3% earned ₹30,000–₹50,000, 15.5% earned less than ₹10,000, and 14.9% earned more than ₹50,000, Regarding occupation, 54.5% were employed, 28.7% were unemployed, 3.6% were retired, and 11.5% were students (Table 1 ). A significant majority of participants (79.1%) reported having access to green spaces near their homes, while 13.9% reported no access and 6.7% were unsure. The average self-reported distance to the nearest green space was 2.47 kilometers (SD = 1.11). Public parks were the most commonly accessed type of green space, used by 92.1% of participants. Table 1 Sociodemographic characteristics of study participants n = 330 Sociodemographic variables Category n(%) Age (Mean/SD) 37.56 (12.03) Gender Male 159(48.2%) Female 171 (51.8%) Education School 63(19.1%) Preuniversity 91(27.6%) College 171(51.8%) Monthly Income Less than ₹10000 48(14.5%) ₹10000- ₹30000 112(33.9%) ₹30000- ₹50000 103(31.2%) More than ₹50000 46(13.9%) Occupation Employed 180(54.5%) Unemployed 95(28.7%) Retired 12(3.6%) Student 38(11.5%) In terms of frequency of visits, 19.1% reported visiting green spaces daily, 30.0% weekly, 20.0% monthly, 22.7% rarely, and 7.0% never. The mean frequency score (where 1 = daily, 5 = never) was 2.65 (SD = 1.25). Duration of visits varied: 54.2% of participants spent 30 minutes to 1 hour per visit, 27.6% spent less than 30 minutes, 12.4% spent 1–2 hours, and 5.2% spent more than 2 hours. Participants reported engaging in various activities in green spaces, including walking or jogging (n = 63) and sitting or relaxing (n = 53). In the past month, 39.1% visited green spaces sometimes to reduce stress, and 34.2% did so fairly often. Regarding mood improvement, 34.2% reported feeling better fairly often, while 13% felt better very often after visiting green spaces. Mental health status was assessed using the WHO-5 Well-Being Index and the K6 Psychological Distress Scale. The mean WHO-5 score was 71.28 (SD = 17.15), and 89.4% of participants scored within the range indicative of good well-being. The mean K6 score was 5.94 (SD = 3.86), with 96.1% of participants falling below the threshold for severe psychological distress. A significant negative correlation was observed between WHO-5 and K6 scores (Spearman’s ρ = -0.59, p < 0.001), indicating that higher well-being was associated with lower psychological distress. Spearman’s correlation analysis revealed a moderate positive relationship between green space density (measured by NDVI) and distance to major roads. Specifically, NDVI within 100 meters of participants’ residences showed the strongest correlation (ρ = 0.42, p < 0.001), followed by NDVI within 300 meters (ρ = 0.33, p < 0.001) and 1000 meters (ρ = 0.30, p < 0.001). This indicates that areas farther from major roads tend to have greater green space coverage (Supplementary Table 1) Multivariable regression models examining the association between green space (NDVI_300m) and WHO-5 well-being components found no significant relationships after adjusting for confounders. Although NDVI_300m showed a positive coefficient for the cheerful component (β = 1.246, p = 0.330) and other well-being measures, these associations did not reach statistical significance, and model explanatory power was low (R² ranging from 2.3–9.6%)(Table 2 ). Similarly, no statistically significant associations were observed between NDVI_300m and K6 psychological distress components, except for a marginally significant positive association with feeling hopeless (β = 2.070, p = 0.051). The highest model fit was noted for feelings of worthlessness (R² = 12.4%), though NDVI_300m was not significantly associated with this outcome (p = 0.207). In contrast, socio-demographic factors, particularly income and gender, demonstrated stronger associations with mental health components. Middle-income status was significantly associated with higher nervousness (β = 0.445, p = 0.012), and high-income status was associated with lower feelings of worthlessness (β = −0.671, p = 0.004). Male participants reported significantly lower nervousness (β = −0.336, p = 0.040) but higher feelings of worthlessness (β = 0.406, p = 0.011)(Table 3 ). Overall, the findings suggest that while proximity to green space is moderately related to distance from major roads, green space availability as measured by NDVI had limited explanatory power for mental health outcomes in this sample. Table 2 Odds Ratios of Multivariable linear regression analysis of associations between greenness index (NDVI_300m) with the WHO-5 well-being index Predictor WHO_17a (Cheerful) WHO_17b (Calm) WHO_17c (Active) WHO_17d (Fresh) WHO_17e (Interest) NDVI_300m 1.246 p = 0.330 −0.962 p = 0.483 1.166 p = 0.369 1.143 p = 0.410 0.586 p = 0.708 Income (Middle) −0.303 p = 0.122 −0.284 p = 0.169 −0.246 p = 0.216 −0.122 p = 0.559 0.083 p = 0.727 Income (High) 0.260 p = 0.328 0.149 p = 0.594 −0.034 p = 0.900 0.238 p = 0.412 0.496 p = 0.128 Age 0.0105 p = 0.143 0.0015 p = 0.843 −0.00002 p = 0.998 −0.00097 p = 0.899 0.0099 p = 0.259 Gender 0.394 p = 0.030 0.116 p = 0.541 0.251 p = 0.171 −0.152 p = 0.433 0.177 p = 0.422 Distance to major road −3.47e-06 p = 0.815 1.05e-05 p = 0.500 −7.63e-06 p = 0.611 6.93e-06 p = 0.665 1.13e-06 p = 0.950 Model R 2 (%) 9.6 2.9 3.3 2.3 3.4 Table 3 Odds Ratios of Multivariable linear regression analysis of associations between greenness index(NDVI_300m) with K6 psychological distress Predictor K6_18a (Nervous) K6_18b (Hopeless) K6_18c (Restless) K6_18d (Depressed) K6_18e (Effort) K6_18f (Worthless) NDVI_300m 1.037 p = 0.369 + 2.070 p = 0.051 + 1.931 p = 0.110 + 1.228 p = 0.336 + 0.424 p = 0.729 + 1.411 p = 0.207 Income (Middle) + 0.445 p = 0.012 −0.190 p = 0.239 −0.056 p = 0.760 + 0.141 p = 0.468 −0.359 p = 0.056 −0.191 p = 0.263 Income (High) + 0.126 p = 0.597 −0.368 p = 0.094 −0.235 p = 0.347 −0.475 p = 0.073 −0.156 p = 0.539 −0.671 p = 0.004 Age + 0.006 p = 0.323 + 0.007 p = 0.206 + 0.003 p = 0.667 −0.006 p = 0.430 −0.005 p = 0.481 −0.006 p = 0.360 Gender (Male) −0.336 p = 0.040 −0.067 p = 0.654 −0.043 p = 0.798 + 0.016 p = 0.928 + 0.133 p = 0.440 + 0.406 p = 0.011 Distance to major Road −3.52e-06 p = 0.792 −1.45e-05 p = 0.239 −8.70e-06 p = 0.532 −2.47e-05 p = 0.096 −1.24e-05 p = 0.384 + 1.99e-05 p = 0.124 Model R² (%) 7.8 7.2 3.0 5.9 3.9 12.4 Fisher’s exact tests were conducted to examine associations between socio-demographic characteristics and green space visit frequency. The results indicated a significant association between age group and visit frequency (p = 0.002), suggesting that visit frequency varied meaningfully across different age categories. Specifically, certain age groups were more or less likely to visit green spaces frequently. There was no significant association between visit frequency and monthly income (p = 0.481) or gender (p = 0.549), indicating that visit patterns did not differ substantially by these factors in this sample. While associations with education (p = 0.098) and occupation (p = 0.061) did not reach statistical significance, both showed trends toward significance, implying that with a larger sample, these factors might demonstrate meaningful relationships with visit frequency. Overall, these findings highlight age as a key demographic factor influencing the use of green spaces in this urban population (Table 4 ). Table 4 Association between socio-demographic factors and green space visit frequency Socio-demographic factor Categories compared Fisher’s exact p-value Age 60 years 0.002 Gender Male, Female 0.54 Education School, pre-university, university 0.098 Occupation Employed, Unemployed/Retired, Student/Other 0.061 Monthly income ₹50,000 0.481 The multivariable regression analysis assessed the relationship between green space proximity, visit frequency, income, education, and mental health outcomes measured by WHO-5 well-being and K6 psychological distress scores (Supplementary Table 2) For WHO-5 well-being, participants living farther from green spaces reported significantly lower well-being scores compared to those residing within 500 meters of green spaces. Specifically, those living 1–2 km away had a mean reduction of 4.85 points (p = 0.035), and those living more than 2 km away had a reduction of 5.58 points (p = 0.0045). Similarly, lower visit frequency to green spaces was associated with poorer well-being. Participants who visited green spaces monthly (β = −9.42, p = 0.0038), rarely (β = −10.80, p = 0.0012), or never (β = −16.02, p = 0.0003) had significantly lower WHO-5 scores compared to those visiting weekly or more often. Educational status also showed an association, with those having a pre-university education reporting lower well-being (β = −7.44, p = 0.0108) compared to those with school-level education. Income did not show a significant association with well-being in this model. The model explained approximately 9.6% of the variance in WHO-5 scores (adjusted R² = 0.096). For K6 psychological distress, no significant associations were found with green space proximity or visit frequency. However, higher income (earning more than ₹50,000 per month) was associated with significantly lower distress scores (β = −1.97, p = 0.0101). Pre-university-level education was linked to higher distress (β = +1.40, p = 0.0388). The model explained about 5.1% of the variance in K6 scores (adjusted R² = 0.051). Discussion This study aimed to assess the association between green spaces and the mental health and well-being of residents in urban Bengaluru. The study found that living closer to green spaces and visiting them more frequently were associated with better mental well-being, as measured by the WHO-5 Well-being Index. However, no statistically significant association was observed between green space exposure and psychological distress measured by the K6 scale. Additionally, higher income and education levels were linked to better access to and utilisation of green spaces. These findings are consistent with global literature emphasizing the positive role of green spaces in supporting mental health. A systematic review concluded that access to green spaces was associated with reduced stress, depression, and anxiety 3 , 23 . Similarly, study found strong evidence linking green space exposure to improved sleep quality and reduced risk of psychiatric disorders 24 , 6 . Indian studies also support these associations. For instance, the study examined the shrinking green cover in Bengaluru and highlighted its adverse implications for residents’ health 2 . Environmental disturbances such as noise and overcrowding were significantly associated with psychological distress, highlighting that both the quantity and quality of green space exposure matter in mental health outcomes 25 , 26 . The findings of this study have important implications for urban public health practice and policy in Indian cities. Integrating green spaces as essential health-promoting infrastructure in urban planning can help address mental health burdens that often remain neglected in policy agendas. Municipal authorities and urban planners should prioritise equitable distribution and accessibility of green spaces, particularly in low-income and high-density areas, to reduce health disparities. Moreover, initiatives to improve the quality, safety, and usability of existing green spaces through community participation can encourage greater utilisation. At the policy level, incorporating mental health impact assessments into urban development projects and aligning them with national missions such as the Smart Cities Mission and National Urban Health Mission can institutionalise the role of green spaces in promoting population mental well-being. Such measures can strengthen preventive public health strategies, foster social cohesion, and improve the overall quality of urban life. However, this study has certain limitations that must be acknowledged. First, its cross-sectional design limits the ability to establish causal relationships between green space exposure and mental health outcomes. Second, data on green space use and perception were self-reported, which may be subject to recall bias or social desirability bias. Additionally, environmental exposures such as air quality or noise distribution are self-reported. Finally, the study lacked a qualitative component, which could have provided richer insights into the barriers to green space use and community perceptions. Despite its limitations, this study has several notable strengths. First, it employed validated mental health assessment tools (WHO-5 and K6), enhancing the reliability and comparability of mental health outcomes. Second, the study captured both subjective and objective dimensions of green space exposure, including proximity, frequency of use, and perceived environmental disturbances, offering a more holistic understanding of how green space access relates to well-being. Third, the study had a relatively large sample size, enabling more robust statistical analysis and the exploration of key predictors, such as socioeconomic status and environmental stressors. Finally, the research adds context-specific evidence from an Indian urban setting, addressing a significant gap in the literature where most studies on green space and mental health originate from high-income countries. The underutilisation of accessible green spaces, despite their availability, suggests potential barriers such as lack of time, safety concerns, or inadequate facilities. Addressing these barriers through community engagement, improved maintenance, and inclusive urban planning could enhance the utilization of green spaces and, consequently, public mental health. The study reinforces the importance of equitable access to quality green spaces in urban settings. Integrating green infrastructure into urban planning, especially in rapidly urbanizing cities like Bengaluru, is vital for promoting mental well-being and addressing health disparities. Conclusions This study found that socio-economic factors, living closer to green spaces and visiting them more often were linked to better mental well-being among urban Bengaluru residents. Our analysis showed that residents use green spaces sparingly regularly despite having access in urban Benglauru. Urban planning should focus not just on creating green spaces but also on making them safe, accessible, and encouraged to support mental health in rapidly growing cities. Declarations Ethics Approval: The study protocol was reviewed and approved by the Institutional Ethics Committee of M.S. Ramaiah University of Applied Sciences, Bengaluru (EC-25/24-PG-FLAHS). Conflicts of interest : The authors declare no conflict of interest in this study. Clinical Trial Registration This study is an observational study and hence clinical trial registration was not conducted and the trial detailsa are not available. Funding: No funding was received to support the conduct of this study. Author Contribution Deepika K conceptualised the study, developed the methodology, conducted data collection and analysis, and drafted the manuscript. Dr. Chitra Venkateswaran provided subject-matter expertise on mental health and critically reviewed the manuscript. Dr. Aditya Singh guided the use of QGIS, assisted with data analysis, and reviewed the manuscript. Tejaswini B D guided the NDVI and spatial analysis components and contributed to data interpretation. Dr. Denny John provided overall research supervision, critical revision of the manuscript, and final approval. All authors reviewed and approved the final version. This manuscript is based on the MPH dissertation submitted by Deepika K under the supervision of Dr. Denny John, Dr. Chitra Venkateswaran, and Dr. Aditya Singh. Acknowledgement The authors acknowledge the respondents who participated in the study. Data Availability Data is available with corresponding author and can be provided on request. References Callaghan A, McCombe G, Harrold A, et al. The impact of green spaces on mental health in urban settings: a scoping review. J Mental Health. 2020;30:1–15. 10.1080/09638237.2020.1755027 . Nagendra H, Gopal D. Street trees in Bangalore: Density, diversity, composition and distribution. Urban Forestry Urban Green. 2010;9(2):129–37. 10.1016/j.ufug.2009.12.005 . Gascon M, Triguero-Mas M, Martínez D, et al. 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J Clin Epidemiol. 2008;61(4):344–9. 10.1016/j.jclinepi.2007.11.008 . Sarkar C, Webster C, Gallacher J. Residential greenness and prevalence of major depressive disorders: a cross-sectional, observational, associational study of 94 879 adult UK Biobank participants. Lancet Planet Health. 2018;2(4):e162–73. 10.1016/S2542-5196(18)30051-2 . Twohig-Bennett C, Jones A. The health benefits of the great outdoors: A systematic review and meta-analysis of greenspace exposure and health outcomes. Environ Res. 2018;166:628–37. 10.1016/j.envres.2018.06.030 . Dzhambov AM, Hartig T, Tilov B, Atanasova V, Makakova DR, Dimitrova DD. Residential greenspace is associated with mental health via intertwined capacity-building and capacity-restoring pathways. Environ Res. 2019;178:108708. 10.1016/j.envres.2019.108708 . Astell-Burt T, Mitchell R, Hartig T. The association between green space and mental health varies across the lifecourse. A longitudinal study. J Epidemiol Commun Health. 2014;68. 10.1136/jech-2013-203767 . Sagar R, Dandona R, Gururaj G, et al. The burden of mental disorders across the states of India: the Global Burden of Disease Study 1990–2017. Lancet Psychiatry. 2020;7(2):148–61. 10.1016/S2215-0366(19)30475-4 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1and2.docx STROBEChecklist.docx Appendix1.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7183081","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502024277,"identity":"ca079a7f-4539-4aea-a17c-66ee2fbc8483","order_by":0,"name":"Deepika K","email":"","orcid":"","institution":"M S Ramaiah University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Deepika","middleName":"","lastName":"K","suffix":""},{"id":502024278,"identity":"79dbd580-f479-4b09-bdbb-441aed7e93ec","order_by":1,"name":"Chitra Venkateswaran","email":"","orcid":"","institution":"Believers Church Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chitra","middleName":"","lastName":"Venkateswaran","suffix":""},{"id":502024280,"identity":"0f469156-c3ea-40b4-b3fb-2690df468d14","order_by":2,"name":"Aditya Singh","email":"","orcid":"","institution":"Banaras Hindu University","correspondingAuthor":false,"prefix":"","firstName":"Aditya","middleName":"","lastName":"Singh","suffix":""},{"id":502024281,"identity":"842921ee-12b9-401b-b952-9a57b202cbdb","order_by":3,"name":"Tejaswini B D","email":"","orcid":"","institution":"M S Ramaiah University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tejaswini","middleName":"B","lastName":"D","suffix":""},{"id":502024285,"identity":"2e85e022-a746-4d38-bdcf-24a3347811a0","order_by":4,"name":"Denny John","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDCCAwfYGBgMbOr72RuAPAMLYrUUpDHO7DkA0iJBjBYGoJYPhxk3zEgAcYnQwnfw8LHHBQaHmQ0kn1/d8KNAgoG/vTsBrxbJA8fSjWcYpLOZS+eU3ewBOkzizNkNeLUYHDhjJs1jYM1jOTsn7QYPUIuBRC5RWpglDG6eSbv5hwQtzgYGN9iP3SbKFqBf0oBa0hIke3LYbssYSPAQ9AvfjcPHpHn+2CTwsx9/dvPNHxs5/vZe/FoYJA7AWDwGYBK/chDgb4Cx2B8QVj0KRsEoGAUjEgAA2oNLIr7w3PAAAAAASUVORK5CYII=","orcid":"","institution":"M S Ramaiah University of Applied Sciences","correspondingAuthor":true,"prefix":"","firstName":"Denny","middleName":"","lastName":"John","suffix":""}],"badges":[],"createdAt":"2025-07-22 06:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7183081/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7183081/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89981835,"identity":"89f2c108-70b4-425e-b0c4-34d82bcdd7e7","added_by":"auto","created_at":"2025-08-27 06:26:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48085,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7183081/v1/94caa20256da0dfe716c8895.jpg"},{"id":104873915,"identity":"12a873d3-ebc6-4b09-a621-b420667ca2ef","added_by":"auto","created_at":"2026-03-18 08:28:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":807490,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7183081/v1/ee6eb0e8-0aac-4ca1-a8a6-f5502feb8799.pdf"},{"id":89983820,"identity":"25e02a23-62bc-4271-9206-0739849e4735","added_by":"auto","created_at":"2025-08-27 06:34:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16796,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1and2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7183081/v1/17970f3fbf2fed39696c3d82.docx"},{"id":89981844,"identity":"57e865c5-4266-481e-ab60-7d9c01e0b119","added_by":"auto","created_at":"2025-08-27 06:26:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21704,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEChecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-7183081/v1/301e94a2ddbbdd87a5ee60d8.docx"},{"id":89981834,"identity":"fc403e4a-21ec-4ced-93d5-2fd9f351e1ef","added_by":"auto","created_at":"2025-08-27 06:26:33","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15396,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7183081/v1/88c7b1ad4e55dcf402b3bc8a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of green spaces on mental health and well-being in urban Bengaluru: A cross-sectional study","fulltext":[{"header":"Key Messages","content":"\u003cp\u003e\u0026bull; \u0026nbsp;Closer proximity to green spaces and more frequent visits to green space are significantly associated with better mental well-being among urban adults in Bengaluru, India.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Promoting accessible, well-maintained green spaces should be an integral part of urban planning to support mental health and well-being in rapidly developing Indian cities.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eRapid urbanisation has emerged as a major driver of environmental and social change in India\u0026rsquo;s metropolitan cities, including Bengaluru. The expansion of built environments often occurs at the expense of natural spaces such as parks, gardens, and community green areas, which play an important role in supporting the physical and mental health of urban populations\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. With increasing urban density, residents frequently experience reduced opportunities for contact with nature, leading to heightened exposure to noise, air pollution, and stress, all of which can adversely affect psychological well-being\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGlobally, there is growing recognition of the protective role of urban green spaces in promoting mental health\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Evidence suggests that access to and use of green spaces are associated with a range of benefits, including stress reduction, improved mood, enhanced social cohesion, and opportunities for physical activity\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Studies conducted in high-income countries consistently demonstrate that proximity to green environments can mitigate the negative psychological impacts of urban living\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, the extent to which such associations hold true in the context of rapidly developing Indian cities remains underexplored\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn India, the loss of urban greenery has been accompanied by a rising burden of common mental health problems, including anxiety, depression, and psychological distress\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. While national programmes such as the Smart Cities Mission and the National Urban Health Mission emphasise sustainable urban development and community well-being, there is limited empirical research on how the availability and utilisation of urban green spaces affect mental health outcomes among urban residents\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Local evidence is particularly scarce for cities like Bengaluru, which have experienced rapid land-use change and an uneven distribution of public parks and recreational spaces\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This study assessed the association between green spaces and the mental health and well-being of residents in urban Bengaluru.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This community-based cross-sectional study was conducted in selected urban wards of Bengaluru, Karnataka, India, between March and June 2025, following approval from the Institutional Ethics Committee. The study was designed to investigate the relationship between exposure to green spaces and mental health and well-being among adult residents.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Source\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData for this study were collected primarily through direct surveys using a structured, pre-tested questionnaire. The questionnaire was administered either face-to-face by the investigator or provided as a self-administered form, depending on participant convenience. It was available in both English and Kannada, with the Kannada translation carried out by language experts. The questionnaire included detailed sections covering socio-demographic information, mental health measures: the WHO-5 Well-Being Index, a widely used five-item tool with good validity and reliability for assessing subjective psychological well-being\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, and the Kessler K6 Psychological Distress Scale, a six-item tool validated for screening non-specific psychological distress in community settings\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.These tools were chosen due to their brevity, international comparability, and ease of administration in field surveys.\u003c/p\u003e\u003cp\u003eGreen space-related variables captured included access, proximity, visit frequency, and perceived quality. To complement self-reported data, objective greenness around each participant\u0026rsquo;s residence was assessed using the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 satellite imagery. Sentinel-2 data were accessed freely from the USGS Earth Explorer platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). NDVI was calculated and analysed using QGIS software version 3.42.3.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAdults aged 18 years and above, residing in the selected wards (Appendix 1) of Bengaluru for at least six months, and able to understand English or Kannada were eligible to participate. Participants who were unable to provide informed consent were excluded from the study. To ensure representation across different socio-economic and environmental contexts, a stratified random sampling method was adopted. First, wards were categorised based on predominant income level into high, middle, and low-income groups. Separately, wards were also categorised based on park availability into high, medium, and low park availability categories (Fig.\u0026nbsp;1). By combining these two classifications, nine distinct strata were formed, representing each combination of income level and park availability. Participants were then recruited using equal allocation, with 37 individuals sampled from each stratum, resulting in a final sample of 330 individuals. Within each stratum, participants were selected using a field-based random walk approach. Due to the absence of comprehensive household lists, households were visited randomly within the selected wards by following pre-defined routes and approaching every nth house where feasible. If a household did not have an eligible participant or if participation was refused, the next household along the route was approached, continuing this process until the target of 37 participants per stratum was achieved.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary outcome variable was mental well-being, assessed using the WHO-5 Well-Being Index, a validated five-item tool that measures subjective psychological well-being.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e The secondary outcome was psychological distress, measured with the Kessler K6 scale\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, which captures non-specific symptoms of distress. Both indices generate continuous scores and have demonstrated good reliability and validity in similar community settings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMain Exposure Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary exposure of interest was access to green space, assessed through both objective and subjective measures. Objective exposure was measured by calculating NDVI values within concentric buffers of 100 meters, 300 meters, and 1000 meters around each participant\u0026rsquo;s residence to reflect immediate and neighbourhood-level greenness. Subjective exposure included self-reported proximity to the nearest green space, categorised as less than 500 meters, 500 to 1000 meters, one to two kilometres, or more than two kilometres, and the frequency of visits to these spaces, categorised as daily, weekly, monthly, rarely, or never.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePredictor Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eKey predictor variables included participant age, gender, education level (school, college, or university), occupation status (employed, unemployed/retired, or other), and monthly household income in rupees. Contextual factors, such as perceived safety of green spaces, were also collected to account for possible environmental and social confounders that might influence mental health independently of green space exposure.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses for this study were conducted using R version 4.5.0 (R Foundation for Statistical Computing, Vienna, Austria)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Descriptive statistics were used to summarise the socio-demographic characteristics of the participants, levels of green space access and utilisation, and mental health outcomes. Categorical variables such as gender, education level, occupation, income categories, proximity to green spaces, and visit frequency were summarised as frequencies and percentages. Continuous variables, including age, WHO-5 Well-Being Index scores, Kessler K6 Psychological Distress scores, and NDVI values, were summarised using means and standard deviations.\u003c/p\u003e\u003cp\u003eTo examine the distribution of continuous variables, normality was assessed using the Shapiro-Wilk test. Associations between categorical socio-demographic variables and green space visit frequency were initially explored using chi-square tests or Fisher\u0026rsquo;s exact test, where cell counts were low. Correlations between continuous exposure measures (e.g., NDVI) and outcome variables (WHO-5 and K6 scores) were assessed using Spearman\u0026rsquo;s rank correlation coefficients due to the non-parametric nature of some variables.\u003c/p\u003e\u003cp\u003eThe independent effect of green space exposure on mental health outcomes was investigated while adjusting for potential confounders. Multivariable linear regression models were fitted separately for the WHO-5 Well-Being Index and Kessler K6 Psychological Distress Scale as outcome variables. The main exposure variables included objective greenness (NDVI within 100 m, 300 m, and 1000 m buffers) and subjective measures such as self-reported proximity to the nearest green space and frequency of visits. Covariates included in the models were age, gender, education level, occupation, household income, and distance to the nearest major road. These variables were chosen based on theoretical relevance and findings from bivariate analyses. All statistical tests were two-tailed, and a p-value of less than 0.05 was considered statistically significant. The Variance Inflation Factor (VIF) analysis showed that all predictor variables in the model had VIF values close to 1, ranging from 1.01 to 1.20. This indicates that there is no evidence of multicollinearity among the independent variables. Therefore, all variables can be retained in the regression model, as their inclusion does not cause instability or distortion of the regression estimates. Where appropriate, adjusted R-squared values were provided to indicate the proportion of variance explained by each model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthical Considerations\u003c/b\u003e\u003c/p\u003e\u003cp\u003e The study protocol was reviewed and approved by the Institutional Ethics Committee of M.S. Ramaiah University of Applied Sciences, Bengaluru (EC-25/24-PG-FLAHS). Written informed consent was obtained from all participants before data collection. The study adhered to the ethical guidelines outlined by the Indian Council of Medical Research for research involving human participants.\u003c/p\u003e\u003cp\u003eThe research adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies to ensure methodological rigour and transparent reporting\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean age of the participants was 37.6 years (SD\u0026thinsp;=\u0026thinsp;12.15), with a range of 18 to 75 years. The gender distribution was similar, with 171 females (51.8%) and 159 males (48.2%). In terms of educational background, 51.8% had completed university education, 27.6% had a pre-university education, and 19.1% had completed school-level education. Monthly income distribution was as follows: 33.9% reported earning ₹10,000\u0026ndash;₹30,000, 33.3% earned ₹30,000\u0026ndash;₹50,000, 15.5% earned less than ₹10,000, and 14.9% earned more than ₹50,000, Regarding occupation, 54.5% were employed, 28.7% were unemployed, 3.6% were retired, and 11.5% were students (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A significant majority of participants (79.1%) reported having access to green spaces near their homes, while 13.9% reported no access and 6.7% were unsure. The average self-reported distance to the nearest green space was 2.47 kilometers (SD\u0026thinsp;=\u0026thinsp;1.11). Public parks were the most commonly accessed type of green space, used by 92.1% of participants.\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\u003eSociodemographic characteristics of study participants n\u0026thinsp;=\u0026thinsp;330\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSociodemographic variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (Mean/SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.56 (12.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e159(48.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e171 (51.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSchool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63(19.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePreuniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91(27.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollege\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e171(51.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eMonthly Income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than ₹10000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48(14.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e₹10000- ₹30000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e112(33.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e₹30000- ₹50000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e103(31.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMore than ₹50000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46(13.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180(54.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95(28.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12(3.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38(11.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn terms of frequency of visits, 19.1% reported visiting green spaces daily, 30.0% weekly, 20.0% monthly, 22.7% rarely, and 7.0% never. The mean frequency score (where 1\u0026thinsp;=\u0026thinsp;daily, 5\u0026thinsp;=\u0026thinsp;never) was 2.65 (SD\u0026thinsp;=\u0026thinsp;1.25). Duration of visits varied: 54.2% of participants spent 30 minutes to 1 hour per visit, 27.6% spent less than 30 minutes, 12.4% spent 1\u0026ndash;2 hours, and 5.2% spent more than 2 hours.\u003c/p\u003e\u003cp\u003eParticipants reported engaging in various activities in green spaces, including walking or jogging (n\u0026thinsp;=\u0026thinsp;63) and sitting or relaxing (n\u0026thinsp;=\u0026thinsp;53). In the past month, 39.1% visited green spaces sometimes to reduce stress, and 34.2% did so fairly often. Regarding mood improvement, 34.2% reported feeling better fairly often, while 13% felt better very often after visiting green spaces.\u003c/p\u003e\u003cp\u003eMental health status was assessed using the WHO-5 Well-Being Index and the K6 Psychological Distress Scale. The mean WHO-5 score was 71.28 (SD\u0026thinsp;=\u0026thinsp;17.15), and 89.4% of participants scored within the range indicative of good well-being. The mean K6 score was 5.94 (SD\u0026thinsp;=\u0026thinsp;3.86), with 96.1% of participants falling below the threshold for severe psychological distress. A significant negative correlation was observed between WHO-5 and K6 scores (Spearman\u0026rsquo;s ρ = -0.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that higher well-being was associated with lower psychological distress.\u003c/p\u003e\u003cp\u003eSpearman\u0026rsquo;s correlation analysis revealed a moderate positive relationship between green space density (measured by NDVI) and distance to major roads. Specifically, NDVI within 100 meters of participants\u0026rsquo; residences showed the strongest correlation (ρ\u0026thinsp;=\u0026thinsp;0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by NDVI within 300 meters (ρ\u0026thinsp;=\u0026thinsp;0.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 1000 meters (ρ\u0026thinsp;=\u0026thinsp;0.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that areas farther from major roads tend to have greater green space coverage (Supplementary Table\u0026nbsp;1)\u003c/p\u003e\u003cp\u003eMultivariable regression models examining the association between green space (NDVI_300m) and WHO-5 well-being components found no significant relationships after adjusting for confounders. Although NDVI_300m showed a positive coefficient for the cheerful component (β\u0026thinsp;=\u0026thinsp;1.246, p\u0026thinsp;=\u0026thinsp;0.330) and other well-being measures, these associations did not reach statistical significance, and model explanatory power was low (R\u0026sup2; ranging from 2.3\u0026ndash;9.6%)(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, no statistically significant associations were observed between NDVI_300m and K6 psychological distress components, except for a marginally significant positive association with feeling hopeless (β\u0026thinsp;=\u0026thinsp;2.070, p\u0026thinsp;=\u0026thinsp;0.051). The highest model fit was noted for feelings of worthlessness (R\u0026sup2; = 12.4%), though NDVI_300m was not significantly associated with this outcome (p\u0026thinsp;=\u0026thinsp;0.207). In contrast, socio-demographic factors, particularly income and gender, demonstrated stronger associations with mental health components. Middle-income status was significantly associated with higher nervousness (β\u0026thinsp;=\u0026thinsp;0.445, p\u0026thinsp;=\u0026thinsp;0.012), and high-income status was associated with lower feelings of worthlessness (β = \u0026minus;0.671, p\u0026thinsp;=\u0026thinsp;0.004). Male participants reported significantly lower nervousness (β = \u0026minus;0.336, p\u0026thinsp;=\u0026thinsp;0.040) but higher feelings of worthlessness (β\u0026thinsp;=\u0026thinsp;0.406, p\u0026thinsp;=\u0026thinsp;0.011)(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, the findings suggest that while proximity to green space is moderately related to distance from major roads, green space availability as measured by NDVI had limited explanatory power for mental health outcomes in this sample.\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\u003eOdds Ratios of Multivariable linear regression analysis of associations between greenness index (NDVI_300m) with the WHO-5 well-being index\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWHO_17a\u003c/p\u003e\u003cp\u003e(Cheerful)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWHO_17b\u003c/p\u003e\u003cp\u003e(Calm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWHO_17c\u003c/p\u003e\u003cp\u003e(Active)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWHO_17d\u003c/p\u003e\u003cp\u003e(Fresh)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWHO_17e\u003c/p\u003e\u003cp\u003e(Interest)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI_300m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.246\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;0.962\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.166\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.143\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.708\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome (Middle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.303\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;0.284\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.246\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.122\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.727\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome (High)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.034\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0105\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0015\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.00002\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.00097\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0099\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.259\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.152\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.422\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to major road\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;3.47e-06\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05e-05\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;7.63e-06\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.93e-06\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.13e-06\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.950\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel R\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOdds Ratios of Multivariable linear regression analysis of associations between greenness index(NDVI_300m) with K6 psychological distress\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eK6_18a\u003c/p\u003e\u003cp\u003e(Nervous)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK6_18b\u003c/p\u003e\u003cp\u003e(Hopeless)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eK6_18c\u003c/p\u003e\u003cp\u003e(Restless)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eK6_18d\u003c/p\u003e\u003cp\u003e(Depressed)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eK6_18e\u003c/p\u003e\u003cp\u003e(Effort)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eK6_18f\u003c/p\u003e\u003cp\u003e(Worthless)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI_300m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.037 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;2.070 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;1.931 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;1.228 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u0026thinsp;0.424 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u0026thinsp;1.411 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome (Middle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.445\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;0.190 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.056 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;0.141 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.359 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;0.191 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.263\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome (High)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.126 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;0.368 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.235 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.475 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.156 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;0.671\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.006 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;0.007 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;0.003 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;0.006 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;0.005 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;0.006 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.360\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (Male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.336\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;0.067 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.043 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;0.016 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u0026thinsp;0.133 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u0026thinsp;0.406\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to major Road\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;3.52e-06 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;1.45e-05 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;8.70e-06 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;2.47e-05 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;1.24e-05 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u0026thinsp;1.99e-05 \u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel R\u0026sup2; (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFisher\u0026rsquo;s exact tests were conducted to examine associations between socio-demographic characteristics and green space visit frequency. The results indicated a significant association between age group and visit frequency (p\u0026thinsp;=\u0026thinsp;0.002), suggesting that visit frequency varied meaningfully across different age categories. Specifically, certain age groups were more or less likely to visit green spaces frequently. There was no significant association between visit frequency and monthly income (p\u0026thinsp;=\u0026thinsp;0.481) or gender (p\u0026thinsp;=\u0026thinsp;0.549), indicating that visit patterns did not differ substantially by these factors in this sample. While associations with education (p\u0026thinsp;=\u0026thinsp;0.098) and occupation (p\u0026thinsp;=\u0026thinsp;0.061) did not reach statistical significance, both showed trends toward significance, implying that with a larger sample, these factors might demonstrate meaningful relationships with visit frequency. Overall, these findings highlight age as a key demographic factor influencing the use of green spaces in this urban population (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eAssociation between socio-demographic factors and green space visit frequency\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocio-demographic factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories compared\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFisher\u0026rsquo;s exact p-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;30 years, 30\u0026ndash;45 years, 45\u0026ndash;60 years, \u0026gt;\u0026thinsp;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale, Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSchool, pre-university, university\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployed, Unemployed/Retired, Student/Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt; ₹10,000; ₹10,000\u0026ndash;30,000; ₹30,000\u0026ndash;50,000; \u0026gt;₹50,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe multivariable regression analysis assessed the relationship between green space proximity, visit frequency, income, education, and mental health outcomes measured by WHO-5 well-being and K6 psychological distress scores (Supplementary Table\u0026nbsp;2)\u003c/p\u003e\u003cp\u003eFor WHO-5 well-being, participants living farther from green spaces reported significantly lower well-being scores compared to those residing within 500 meters of green spaces. Specifically, those living 1\u0026ndash;2 km away had a mean reduction of 4.85 points (p\u0026thinsp;=\u0026thinsp;0.035), and those living more than 2 km away had a reduction of 5.58 points (p\u0026thinsp;=\u0026thinsp;0.0045). Similarly, lower visit frequency to green spaces was associated with poorer well-being. Participants who visited green spaces monthly (β = \u0026minus;9.42, p\u0026thinsp;=\u0026thinsp;0.0038), rarely (β = \u0026minus;10.80, p\u0026thinsp;=\u0026thinsp;0.0012), or never (β = \u0026minus;16.02, p\u0026thinsp;=\u0026thinsp;0.0003) had significantly lower WHO-5 scores compared to those visiting weekly or more often. Educational status also showed an association, with those having a pre-university education reporting lower well-being (β = \u0026minus;7.44, p\u0026thinsp;=\u0026thinsp;0.0108) compared to those with school-level education. Income did not show a significant association with well-being in this model. The model explained approximately 9.6% of the variance in WHO-5 scores (adjusted R\u0026sup2; = 0.096).\u003c/p\u003e\u003cp\u003eFor K6 psychological distress, no significant associations were found with green space proximity or visit frequency. However, higher income (earning more than ₹50,000 per month) was associated with significantly lower distress scores (β = \u0026minus;1.97, p\u0026thinsp;=\u0026thinsp;0.0101). Pre-university-level education was linked to higher distress (β = +1.40, p\u0026thinsp;=\u0026thinsp;0.0388). The model explained about 5.1% of the variance in K6 scores (adjusted R\u0026sup2; = 0.051).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to assess the association between green spaces and the mental health and well-being of residents in urban Bengaluru. The study found that living closer to green spaces and visiting them more frequently were associated with better mental well-being, as measured by the WHO-5 Well-being Index. However, no statistically significant association was observed between green space exposure and psychological distress measured by the K6 scale. Additionally, higher income and education levels were linked to better access to and utilisation of green spaces.\u003c/p\u003e\u003cp\u003eThese findings are consistent with global literature emphasizing the positive role of green spaces in supporting mental health. A systematic review concluded that access to green spaces was associated with reduced stress, depression, and anxiety\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Similarly, study found strong evidence linking green space exposure to improved sleep quality and reduced risk of psychiatric disorders\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Indian studies also support these associations. For instance, the study examined the shrinking green cover in Bengaluru and highlighted its adverse implications for residents\u0026rsquo; health\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Environmental disturbances such as noise and overcrowding were significantly associated with psychological distress, highlighting that both the quantity and quality of green space exposure matter in mental health outcomes\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe findings of this study have important implications for urban public health practice and policy in Indian cities. Integrating green spaces as essential health-promoting infrastructure in urban planning can help address mental health burdens that often remain neglected in policy agendas. Municipal authorities and urban planners should prioritise equitable distribution and accessibility of green spaces, particularly in low-income and high-density areas, to reduce health disparities. Moreover, initiatives to improve the quality, safety, and usability of existing green spaces through community participation can encourage greater utilisation. At the policy level, incorporating mental health impact assessments into urban development projects and aligning them with national missions such as the Smart Cities Mission and National Urban Health Mission can institutionalise the role of green spaces in promoting population mental well-being. Such measures can strengthen preventive public health strategies, foster social cohesion, and improve the overall quality of urban life.\u003c/p\u003e\u003cp\u003eHowever, this study has certain limitations that must be acknowledged. First, its cross-sectional design limits the ability to establish causal relationships between green space exposure and mental health outcomes. Second, data on green space use and perception were self-reported, which may be subject to recall bias or social desirability bias. Additionally, environmental exposures such as air quality or noise distribution are self-reported. Finally, the study lacked a qualitative component, which could have provided richer insights into the barriers to green space use and community perceptions.\u003c/p\u003e\u003cp\u003eDespite its limitations, this study has several notable strengths. First, it employed validated mental health assessment tools (WHO-5 and K6), enhancing the reliability and comparability of mental health outcomes. Second, the study captured both subjective and objective dimensions of green space exposure, including proximity, frequency of use, and perceived environmental disturbances, offering a more holistic understanding of how green space access relates to well-being. Third, the study had a relatively large sample size, enabling more robust statistical analysis and the exploration of key predictors, such as socioeconomic status and environmental stressors. Finally, the research adds context-specific evidence from an Indian urban setting, addressing a significant gap in the literature where most studies on green space and mental health originate from high-income countries.\u003c/p\u003e\u003cp\u003eThe underutilisation of accessible green spaces, despite their availability, suggests potential barriers such as lack of time, safety concerns, or inadequate facilities. Addressing these barriers through community engagement, improved maintenance, and inclusive urban planning could enhance the utilization of green spaces and, consequently, public mental health. The study reinforces the importance of equitable access to quality green spaces in urban settings. Integrating green infrastructure into urban planning, especially in rapidly urbanizing cities like Bengaluru, is vital for promoting mental well-being and addressing health disparities.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study found that socio-economic factors, living closer to green spaces and visiting them more often were linked to better mental well-being among urban Bengaluru residents. Our analysis showed that residents use green spaces sparingly regularly despite having access in urban Benglauru. Urban planning should focus not just on creating green spaces but also on making them safe, accessible, and encouraged to support mental health in rapidly growing cities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics Approval:\u003c/h2\u003e\u003cp\u003eThe study protocol was reviewed and approved by the Institutional Ethics Committee of M.S. Ramaiah University of Applied Sciences, Bengaluru (EC-25/24-PG-FLAHS).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cb\u003eConflicts of interest\u003c/b\u003e:\u003c/strong\u003e\u003cp\u003eThe authors declare no conflict of interest in this study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e\u003cp\u003eThis study is an observational study and hence clinical trial registration was not conducted and the trial detailsa are not available.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNo funding was received to support the conduct of this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDeepika K conceptualised the study, developed the methodology, conducted data collection and analysis, and drafted the manuscript. Dr. Chitra Venkateswaran provided subject-matter expertise on mental health and critically reviewed the manuscript. Dr. Aditya Singh guided the use of QGIS, assisted with data analysis, and reviewed the manuscript. Tejaswini B D guided the NDVI and spatial analysis components and contributed to data interpretation. Dr. Denny John provided overall research supervision, critical revision of the manuscript, and final approval. All authors reviewed and approved the final version. This manuscript is based on the MPH dissertation submitted by Deepika K under the supervision of Dr. Denny John, Dr. Chitra Venkateswaran, and Dr. Aditya Singh.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the respondents who participated in the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is available with corresponding author and can be provided on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCallaghan A, McCombe G, Harrold A, et al. 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The burden of mental disorders across the states of India: the Global Burden of Disease Study 1990\u0026ndash;2017. Lancet Psychiatry. 2020;7(2):148\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2215-0366(19)30475-4\u003c/span\u003e\u003cspan address=\"10.1016/S2215-0366(19)30475-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Green spaces, Mental Health, Urban Bengaluru","lastPublishedDoi":"10.21203/rs.3.rs-7183081/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7183081/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eRapid urbanisation in Bengaluru has reduced green spaces, potentially impacting mental health. Urban green spaces promote psychological well-being by reducing stress, enhancing mood, and fostering social connections. This study examined the relationship between green spaces and mental health among residents of Bengaluru.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted among 330 adults using stratified random sampling across nine strata based on income and park availability. Data were collected through questionnaires capturing park proximity, visit frequency, and mental health indicators. Satellite imagery assessed green space via NDVI (Normalized Difference Vegetation Index). Mental health was measured using the WHO-5 Well-being Index and K6 Psychological Distress Scale. Data were analysed using descriptive statistics, Spearman\u0026rsquo;s correlation, and multivariable regression in R software (version 4.5.0).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong participants, 79% had green spaces nearby, but only 19% visited daily. Living farther away (\u0026gt;\u0026thinsp;2 km) was associated with significantly lower well-being scores (β = \u0026minus;\u0026thinsp;5.58, p\u0026thinsp;=\u0026thinsp;0.004). Infrequent visits (never vs. weekly) were associated with poorer well-being (β = \u0026minus;\u0026thinsp;16.02, p\u0026thinsp;=\u0026thinsp;0.0003). No significant association emerged between proximity or visit frequency and psychological distress (K6). Higher income (β = \u0026minus;\u0026thinsp;1.97, p\u0026thinsp;=\u0026thinsp;0.01) and a college education (vs. high school: β\u0026thinsp;=\u0026thinsp;1.40, p\u0026thinsp;=\u0026thinsp;0.04) were associated with psychological distress. Areas away from major roads had higher NDVI values. Frequent and closer access to green spaces was associated with improved mental well-being.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eCloser proximity to and frequent use of green spaces are linked to better mental well-being. Urban planning in Bengaluru should prioritise equitable access to quality green spaces to promote public mental health.\u003c/p\u003e","manuscriptTitle":"Association of green spaces on mental health and well-being in urban Bengaluru: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 06:26:29","doi":"10.21203/rs.3.rs-7183081/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5802487c-ba2c-4311-84dc-e999c53df8ab","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T08:25:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 06:26:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7183081","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7183081","identity":"rs-7183081","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}