Living in a 20-min neighborhood and brain health and resilience in older adults: The Healthy Brain Initiative

Abstract

The 20-min neighborhood (20 MN) concept, aimed at fostering livable communities, has garnered increasing attention among urban planners and policy makers. 20 MNs provide most daily needs (e.g., grocery, parks) within a 20-min walk of home. Among 352 older adults in the Healthy Brain Initiative (HBI), we examined whether 20 MN measures were associated with resilience against future cognitive impairment/dementia measured via the Resilience Index (RI), as well as with structural magnetic resonance imaging measures (i.e., hippocampal volume, white matter hyperintensity (WMH) volume). We calculated density of destinations within a 20-min walk of home across seven domains (i.e., social destinations, dining places, shopping/retail, grocery/supermarkets, healthcare facilities, parks, and transit availability) and created a dichotomous 20 MN variable (≥1 destination within a 20-min walk in all seven domains, yes versus no). In multivariable linear regression analyses, greater density of parks, greater density of grocery/supermarkets, and living in a 20 MN were associated lower WMH volumes. Our study suggests brain health benefits for older adults with greater densities of grocery and park destinations within a 20-min walk of home, as well as those living in 20 MNs. Future studies corroborating our findings for beneficial associations between 20 MNs and brain health would provide significant policy implications for dementia prevention.

1. Introduction

The 20-min neighborhood (20 MN) is an urban planning concept that aims to create self-sufficient communities where residents can meet most of their daily needs (e.g., grocery, parks, health care) within a 20-min walk, cycle, or public transport ride from their homes. Aimed at fostering livable communities, 20 MNs have garnered increasing attention among urban planners and policy makers (Allam et al., 2022; Kamruzzaman, 2022; Oostenbach et al., 2023a; Thornton et al., 2022). This model emphasizes walkability, access to essential services, and the promotion of local living, reducing reliance on cars, and enhancing both environmental sustainability and social cohesion. The idea gained momentum, particularly during the COVID-19 pandemic, as cities world-wide sought to rethink urban spaces to improve quality of life, public health, and resilience.

Cities around the world have adopted variations of the “X-minute neighborhood” model (e.g., 15-min, 20-min, or 30-min neighborhood), to promote walkability, local access to services, and reduced car dependency. For example, Melbourne, Australia, and Liverpool, England, have implemented the 20 MN concept, with Melbourne showing increased walking in higher-density areas and Liverpool emphasizing equity in local resources (Contardo Ayala et al., 2022). Similar adaptations seen in Paris (15-min neighborhood), New York, Milan, and Montreal (30-min neighborhoods) aimed to reorganize their urban systems, reduce car dependency, and create pedestrian-friendly spaces, with mixed success depending on each city’s local infrastructure and planning context (Birkenfeld, 2024; Gaglione et al., 2022). Despite growing enthusiasm, challenges remain in operationalizing the 20 MN concept and ensuring equitable implementation (AlWaer and Cooper, 2023). Differences in zoning, land use, and transit infrastructure can affect the feasibility of 20 MNs, particularly in car-dependent regions (Lu and Diab, 2023). Ultimately, the success of 20 MNs hinges on aligning urban planning policies with local contexts, establishing adequate infrastructure, and addressing socioeconomic disparities to ensure that these neighborhoods benefit all residents (Gower A, 2022).

Given the emphasis on livability and access to daily needs, 20 MNs may hold promise for supporting older adults’ health and mobility. Older adults frequently experience reduced life space and mobility, spending more time near their homes and neighborhoods with age (Al Snih et al., 2012; Huisingh et al., 2016). Older age is the biggest risk factor for developing Alzheimer’s disease and related dementias, with the percent of individuals affected by dementia increasing from around 5 % at ages 71–79 years to approximately 37.4 % among ≥90-year-olds (Plassman et al., 2007). Coupled with this, the U.S. population of older adults is expected increase substantially in the next few decades, from 58 million in 2022 to 82 million by 2050, with an expected corresponding rise in the prevalence of dementia (Alzheimer’s Association, 2024; Population Reference Bureau, 2024).

In recent years, researchers have focused more attention on understanding the factors that contribute to resilience against neurodegenerative disorders that cause dementia (Arida and Teixeira-Machado, 2020; Galvin et al., 2021; Joshi and Galvin, 2022; Masurkar et al., 2024). This increased attention on resilience led to the creation of the Resilience Index (RI), which is a measure of brain resilience that captures six lifestyle factors shown in prior studies to help maintain or improve brain health in older adults, including physical activity, cognitive reserve (higher educational and occupational attainment), cognitive and leisure activities, diet, mindfulness, and social interactions (Galvin et al., 2021). Physical activity is a key factor because of its ability to stimulate and preserve cognitive function and its role in promoting social engagement (Arida and Teixeira-Machado, 2020; Joshi and Galvin, 2022; Masurkar et al., 2024). It is also a modifiable risk factor that can be addressed through policy, urban planning, and outreach interventions.

Growing evidence suggests that health-promoting neighborhood environments benefit cognitive health and brain resilience in older age (Besser et al., 2017; Besser et al., 2021a; Besser et al., 2021b; Besser et al., 2024; Chen et al., 2022). Prior studies suggest that having more social and walking destinations in the residential neighborhood (i.e., greater walkability) is associated with better cognitive function and lower risk of developing dementia (Besser et al., 2018; Besser et al., 2021b; Finlay et al., 2021). For instance, a study of middle-to older-age adults in the Multi-Ethnic Study of Atherosclerosis found that greater walking destinations in the 1-mile surrounding the home was associated with a greater odds of maintaining/improving cognitive function over ~6 years (Besser et al., 2021b). Also, a mounting body of evidence suggests that living in neighborhoods with more parks and greenspace (e.g., tree cover, greenness) is associated with better cognitive function, lower dementia risk in older adults, and better brain resilience (Besser, 2021c; Besser et al., 2023a; X. Chen et al., 2022; Zagnoli et al., 2022). For example, using the Healthy Brain Initiative cohort, a previous study of 162 older adults found that greater access to greenspace and time spent in greenspace (both self-reported) were associated with greater brain resilience measured via the RI (Besser et al., 2024).

While more limited in number, studies have also demonstrated that health-promoting neighborhood environments are associated with better brain health measured via magnetic resonance imaging (MRI) (Besser et al., 2021d; Besser et al., 2023b; Cerin et al., 2017). MRI can capture biological changes in the brain that occur prior to overt clinical symptoms (i.e., cognitive decline and dementia), serving as biomarker measures of brain health and dementia risk. Prior studies have shown that individuals living in greener and more walkable neighborhoods have better MRI outcomes (Besser et al., 2021d; Besser et al., 2023b; Cerin et al., 2017). For instance, using data from the Australian Imaging, Biomarkers, and Lifestyle cohort, one study found that greater neighborhood walkability was cross-sectionally associated with greater volumes of the hippocampus, the area of the brain responsible for memory and learning that often declines first in AD (Cerin et al., 2017). Another study of older adults in the Cardiovascular Health Study found that white matter grade worsening (i.e., white matter damage) over time was more likely among those living in lower income neighborhoods with less greenspace compared to those living in higher income neighborhoods with more greenspace (Besser et al., 2023b). The mechanisms linking neighborhood environmental exposures to MRI measures are likely multifaceted. Numerous studies have demonstrated that individuals living in neighborhoods with more social and walking destinations and greenspace are more likely to participate in lifestyle behaviors including physical and social activity and have better mental health (Barnett et al., 2017; Deng et al., 2025; Hasselder et al., 2022; Portegijs et al., 2020). In turn, these factors have been associated with better brain health measured via MRI, including less white matter damage (e.g., fewer white matter hyperintensities) and less brain atrophy in regions such as the hippocampus (Chen et al., 2025; Murayama et al., 2024; Sheline et al., 1996; Smith et al., 2014). Thus, by fostering mental health and these lifestyle behaviors, health-promoting neighborhood environments may help maintain brain structure and function.

Preliminary, cross-sectional studies have found associations between 20 MNs and healthier diets, greater walking for transportation (active transportation), lower body mass index (BMI), and lower odds of cognitive impairment (Contardo Ayala et al., 2022; Oostenbach et al., 2023b; Yang et al., 2022; Zhang et al., 2023). Consequently, 20 MNs may be particularly beneficial in promoting neighborhood livability for the growing number of older adults, promoting healthy behaviors (e.g., physical activity, social engagement, healthful diets) that have been shown to maintain and improve brain resilience and cognitive health into older ages, thereby reducing risk of developing dementia (Livingston et al., 2024).

Despite the increasing number of studies on neighborhood environments and brain health, only four known, cross-sectional studies have examined associations between 20 MNs and health outcomes (Contardo Ayala et al., 2022; Oostenbach et al., 2023b; Yang et al., 2022; Zhang et al., 2023), with none focusing on potential impacts to older adults’ brain health and resilience. To address this gap in the literature, we used data on older adults in the Healthy Brain Initiative cohort (Besser et al., 2023c) to determine if greater densities of health-promoting destinations within a 20-min walk of home, representing seven domains (i.e., social, shopping, dining, grocery, health facilities, parks, transit availability), are associated with better brain health and resilience measured using structural magnetic resonance imaging measures (i.e., hippocampal and white matter hyperintensity volumes) and the Resilience Index. We then investigated if living in 20 MNs (having ≥1 destination from each of 7 domains) is associated with better brain health and resilience.

2. Methods

2.1. Sample

We used cross-sectional data on 352 participants from the Healthy Brain Initiative (HBI), a longitudinal cohort study that since March 2022 has been enrolling ≥50-year-olds who have no or mild cognitive impairment or mild dementia (Clinical Dementia Rating (Morris, 1993) ≤1), who have a study partner, and who are able to undergo magnetic resonance imaging (MRI). HBI participants live in South Florida, specifically Palm Beach, Broward, and Miami-Dade counties. As part of their annual visits, participants complete comprehensive surveys assessing information including but not limited to sociodemographics, residential address history, and comorbidities, and receive comprehensive clinical and cognitive assessments that inform clinical diagnosis (i. e., normal cognition, mild cognitive impairment, or dementia). Additional details about HBI and the annual assessments are provided elsewhere (Besser et al., 2023c). University of Miami’s IRB approved the study and participants and their study partners provided informed consent prior to participation. For this study, we restricted to HBI participants that had non-missing address locations to derive the neighborhood measures and non-missing data on the Resilience Index.

2.2. Resilience Index (RI)

The RI, which has been shown to discriminate individuals with cognitive impairment (AUC = 0.84), is derived for each participant with non-missing data on 6 scales: (1) the Cognitive Reserve Unit Scale (CRUS) (based on education and occupational attainment), (2) Social Engagement Scale (i.e., social activities, socialization, and engagement), (3) Quick Physical Activity Rating (QPAR) (covering 10 categories of physical activity), Cognitive and Leisure Activity Scale (CLAS) (capturing 15 categories of cognitively stimulating activities), (4) Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) Diet Score (covering 15 categories of food assessing adherence to MIND diet), and (6) Applied Mindfulness Process Scale (AMPS) (i.e., decentering, emotional regulation) (Galvin et al., 2021). RI scores range from 1 to 378 with higher scores indicating greater resilience against cognitive impairment/dementia.

2.3. Structural magnetic resonance imaging (MRI)

At their initial visits, HBI participants have brain MRI scans using a GE 3T 750W scanner. High-resolution 3D sagittal Magnetization-Prepared Rapid Gradient-Echo (MPRAGE) images with axial and coronal reconstructions quantify regional brain volumes (e.g., hippocampus) and axial 3DT2 FLAIR and 2D T2 brain images quantify white matter hyperintensities (WMH). Measures of hippocampal and WMH volume were chosen because they are established risk factors for dementia and cerebrovascular disease (Apostolova et al., 2012; Debette and Markus, 2010; Kandel et al., 2016). The hippocampus is responsible for memory and learning and is typically atrophied in Alzheimer’s disease (lower hippocampal volumes = atrophy). White matter hyperintensities are areas detectable on MRI indicative of white matter damage (e.g., axonal degeneration and demyelination) (greater WMH volumes = greater white matter damage). We employed a standard practice used among neuroimaging experts to account for participant differences in head size (Kandel et al., 2016; Whitwell et al., 2001), in which both measures were divided by intracranial volume (e.g., (WMH volume/intracranial volume) × 100 %). WMH volumes were log transformed prior to entry in the regression analyses due to skewness.

2.4. Neighborhood measures

At the HBI visit, participants provide their residential address, which is geocoded to the latitude-longitude (lat-long) location. We used those lat-longs from the baseline HBI visit to establish 20-min neighborhood boundaries based on the area surrounding the home that an individual can walk, assuming the individual is walking on the road network/sidewalks at a walking speed of 3.1 miles/hour (average walk speed set within ArcGIS Pro). We chose a 20-min boundary because establishing 10- or 15-min walk neighborhoods in primarily car-dependent Florida regions is less feasible.

2.5. 20-Minute neighborhoods (20 MN)

We define a 20 MN as living in an area that has at least 1 of each of 7 destinations within a 20-min walk of the residence: (1) social destinations, (2) shopping destinations, (3) dining destinations, (4) grocery stores/supermarkets, (5) health facilities; (6) parks, and (7) bus or train stops/stations. We used 2022 Florida parcel data from the Florida Geographic Data Library’s (University of Florida, 2024) (FGDL) data catalog to calculate density of social destinations (churches, clubs, lodges, and union halls; drive-in theaters, enclosed theaters; night clubs, lounges; and bars, bowling, and rinks; golf courses, race tracks, forest, park, recreation), density of shopping destinations (i.e., one-story stores, department stores, regional shopping, community shopping, florists/- greenhouses), density of dining destinations (restaurants, cafeterias, drive-in restaurants), and density of grocery stores/supermarkets. Using the FGDL explorer database, we calculated the density of health facilities based on point location data on hospitals and health facilities. Park locations were obtained from two parks datasets (ESRI USA Parks (ESRI, 2024) and FGDL (University of Florida, 2024)) and park density was derived as the number of parks per 20-min walk boundary. The National Transit Map (NTM) defined a stop/platform as a location where passengers board or disembark from a transit vehicle (U.S. Department of Transportation, 2024). Data are frequently updated using the General Transit Feed Specification (GTFS) Schedule, and the stop data used for this study was obtained and updated July 2024. For each participant, we used these density measures to derive the 20 MN measure (i.e., having ≥1 from each of the 7 destination categories within a 20-min walk, yes versus no).

2.6. Analyses

Descriptive statistics (e.g., means and percentages) were calculated to characterize the demographics, cognitive status, health characteristics, MRI, and neighborhood measures for the entire sample and stratified by whether they live in a 20 MN (yes versus no). Chi-square tests and t-tests were used to test for significant differences (α = 0.05) in these characteristics by the dichotomous 20 MN variable. We then conducted multivariable linear regression analyses to examine associations between the seven destination density measures and the brain health outcomes (i.e., RI, hippocampal and WMH volumes), as well as between the 20 MN measure and the brain health outcomes. The models included stepwise adjustment for covariates. Model 1 controlled for age, education (not included in RI models because is a component of RI), sex assigned at birth, self-reported racial group (White, Black, other), self-reported Hispanic ethnicity (yes versus no), living alone (yes versus no), no cognitive impairment (CDR = 0 versus 0.5 or 1), and area deprivation index (ADI) (University of Wisconsin School of Medicine and Public Health, 2015, Area Deprivation Index v2.0) (measures of neighborhood disadvantage; range: 1–100; higher scores indicating greater disadvantage). These covariates were chosen apriori based on prior studies indicating that they are associated with cognitive and MRI outcomes. Before including ADI in the models, we confirmed that it was not highly correlated with the 20-min walk variables to avoid multi-collinearity. Model 2 further adjusted for comorbidities (i.e., self-reported hypertension, hypercholesterolemia, heart disease, stroke, and diabetes; and obesity (≥30 kg/m²) calculated from height and weight measurements). Adjustment for comorbidities was done separately because although they may serve as confounders, they may also be mediators. We used the Benjamini-Hochberg procedure for calculating False Discovery Rate p-values to adjust for multiple comparisons (Benjamini and Hochberg, 1995).

3. Results

The sample (n = 352) was on average 69 ± 10 years old, 71 % were women, 22 % were Hispanic, and 16 % were Black, 79 % were White, and 5 % identified as another racial group/multi-racial (Table 1). Thirty-one percent lived alone (versus with others), 68 % were cognitively normal (versus mild cognitive impairment), and the mean RI score was 173.7 (SD = 31.4). Mean hippocampal volume was 7.17 cm³ (SD = 0.91) for females and 7.58 cm³ (SD = 0.98) for males, and mean WMH volume was 5.06 cm³ (SD = 7.62) for females and 5.14 cm³ (SD = 8.15) for males. Fifteen percent (n = 54) of participants lived in a 20 MN, with a greater percentage of Black participants (27 %) living in 20 MN compared to White participants (13 %). Participants living in 20 MN less often had normal cognition (48 %) compared to those not living in 20 MN (72 %). Obesity was more prevalent for individuals living in 20 MN (45 %) compared to those who did not (30 %).

Table 1.

Participant characteristics.

Characteristic	Total sample	Lives in 20-min neighborhood		p-valuea
		Yes	No
Sample, n	352	54	298	-
Age (years), mean (SD)	68.5 (9.5)	68.7 (8.7)	68.4 (9.6)	0.82
Woman (versus man), n (%)	250 (71.0 %)	41 (75.9%)	209 (70.1 %)	0.39
Education, n (%)
High school or less	44 (12.5%)	10 (18.5%)	34 (11.4%)	0.40
Some college	56 (15.9%)	10 (18.5%)	46 (15.4%)
College degree	118 (33.5 %)	17 (31.5%)	101 (33.9 %)
Graduate degree	134 (38.1 %)	17 (31.5%)	117 (39.3 %)
Racial groupb, n (%)
White	278 (79.2 %)	35 (66.0%)	243 (82.7 %)	0.02
Black	56 (16.0%)	15 (28.3%)	41 (14.0%)
Other	17 (4.8%)	3 (5.7 %)	10 (3.4%)
Hispanic ethnicityb, n (%)	76 (21.7%)	14 (25.9%)	62 (20.9%)	0.41
Lives alone, n (%)	110 (31.4 %)	22 (40.7%)	88 (29.7%)	0.11
Normal cognition (CDR = 0 vs. 0.5 or 1), n (%)	237 (68.3 %)	26 (48.2%)	211 (72.0 %)	0.0005
Hypertensionb, n (%)	140 (41.4 %)	22 (41.5%)	118 (41.4 %)	0.99
Hypercholesterolemiab, n (%)	187 (55.3 %)	32 (60.4%)	155 (54.4 %)	0.42
Heart diseaseb, n (%)	44 (13.0%)	5 (9.4 %)	39 (13.7%)	0.40
Strokeb, n (%)	18 (5.3%)	5 (9.4 %)	13 (4.6%)	0.15
Diabetesb, n (%)	45 (13.3%)	6 (11.3%)	39 (13.7%)	0.64
Obesity (≥30 kg/m2), n (%)	107 (31.9 %)	23 (45.1%)	84 (29.5%)	0.03
Resilience index, mean (SD)	173.7 (31.4)	169.6 (33.9)	174.5 (31.0)	0.29
Hippocampal volume (cm3), mean (SD)
Males	7.58 (0.98)	7.68 (0.94)	7.57 (1.00)	0.77
Females	7.17 (0.91)	7.34 (0.82)	7.13 (0.92)	0.26
WMH volume (cm3), mean (SD)
Males	5.14 (8.15)	5.07 (7.70)	5.15 (8.27)	0.98
Females	5.06 (7.62)	5.30 (6.60)	5.02 (7.83)	0.86
Total ICV (cm3), mean (SD)
Males	1476.2 (119.6)	1497.5 (88.2)	1473.5 (123.2)	0.60
Females	1319.1 (116.8)	1336.3 (108.5)	1315.5 (118.5)	0.38

SD = standard deviation; MCI = mild cognitive impairment; WMH = white matter hyperintensity; ICV = intracranial volume.

^aTested group differences using chi-square test for categorical variables, t-test for continuous variables.

^bSelf-reported.

For the overall sample, mean ADI was 40.7 (SD = 26.2) and on average, participants had ≥1 of each destination within a 20-min walk of home, except for grocery/supermarkets (mean = 0.4, SD = 0.8) (Table 2). Individuals living in 20 MNs had significantly higher ADI scores (i.e., more disadvantaged) and had higher densities of each destination type within a 20-min walk, except health facilities. Fig. 1 displays example 20 MNs for the South Florida area where HBI participants reside.

Table 2.

Neighborhood characteristics.

Characteristic	Total sample	Lives in 20-min neighborhooda		p-valueb
		Yes	No
Sample, n	352	54	298
Area deprivation index (ADI)c, mean (SD)	40.7 (26.2)	53.6 (27.0)	38.6 (25.5)	0.0005
Num. social destinations within 20-min walk of home	10.7 (14.6)	26.8 (23.7)	7.8 (9.8)	<0.0001
Num. shopping destinations within 20-min walk of home	16.5 (38.5)	39.2 (29.0)	12.4 (38.6)	<0.0001
Num. dining destinations within 20-min walk of home	3.3 (5.5)	9.2 (6.8)	2.2 (4.5)	<0.0001
Num. grocery stores within 20-min walk of home	0.4 (0.8)	1.7 (0.9)	0.2 (0.5)	<0.0001
Num. health facilities within 20-min walk of home	8.5 (16.5)	12.6 (16.3)	7.8 (16.4)	0.05
Num. parks within 20-min walk of home	3.1 (4.1)	5.9 (4.4)	2.6 (3.9)	<0.0001
Num. transit stops within 20-min walk of home	19.5 (25.2)	41.7 (30.8)	15.5 (21.9)	<0.0001

Num = number of;

^aAt least one destination/place within 20-min walk of home from each of 7 domains: social destinations, shopping, grocery, health facilities, parks, transit stops;

^bTested group differences using t-test;

^cRange of 1–100 with higher scores meaning greater neighborhood disadvantage.

Fig. 1.

Example 20-min neighborhoods in South Florida.

In multivariable regression analyses controlling for demographics, cognitive status, and ADI, none of the 20-min walk variables were associated with RI (Table 3, Model 1). In the regression models that also controlled for comorbidities, individuals with a greater density of grocery stores/supermarkets within a 20-min walk of home had lower RI scores (estimate = −3.46, 95 % CI = −6.84, −0.08) but this was not significant after adjustment for multiple comparisons (Table 3, Model 2). No other associations with RI were observed, including no association between 20 MN and RI in Model 1 (Supplemental Table 1) or Model 2.

Table 3.

Adjusted associations between 20-min measures and Resilience Index.

Neighborhood measure type	Exposure (in separate models)	Model 1b	Model 2c
		Estimate (95 % CI)	Estimate (95 % CI)
Number of destinations within 20-min walk of home	Health care facilities	−0.01 (−0.21, 0.20)	−0.03 (−0.23, 0.17)
	Social destinations	0.02 (−0.28, 0.32)	0.03 (−0.26, 0.32)
	Shopping/retail	0.03 (−0.05, 0.12)	0.02 (−0.06, 0.10)
	Grocery/supermarkets	−3.11 (−7.56, 1.34)	−3.46 (−6.84, −0.08)d
	Dining places	0.28 (−0.36, 0.92)	0.15 (−0.43, 0.73)
	Parks	0.18 (−0.54, 0.91)	0.78 (−0.11, 1.67)
	Bus/train stops	0.02 (−0.16, 0.20)	0.01 (−0.15, 0.17)
20 MNa	Yes versus no	−1.17 (−10.04, 7.70)	2.93 (−3.36, 9.21)

Abbreviations: CI = confidence interval; 20 MN = 20-min neighborhood.

^aAt least one destination/place within 20-min walk of home from 7 domains: health care, social destination, shopping, grocery, dining, parks, transit stops.

^bModel 1 controls for age (years), sex, racial group (White, Black, Other), Hispanic ethnicity, living alone, cognitive status, and area deprivation index (i. e., neighborhood disadvantage);.

^cModel controls for covariates from Model 1 and additionally for and comorbidities (i.e., diabetes, hypertension, hypercholesterolemia, heart disease, stroke, obesity);.

^dp < 0.05 before adjusting for multiple comparisons; no longer significant after adjusting for multiple comparisons using False Discovery Rate.

In multivariable regression analyses focused on hippocampal volume (Table 4), participants with a greater density of grocery/supermarkets within a 20-min walk of home had greater hippocampal volumes in both the partially (estimate = 0.009, 95 % CI = 0.001, 0.017) (Model 1) and fully adjusted models (estimate = 0.007, 95 % CI = 0.000, 0.015), but the associations were no longer significant after adjustment for multiple comparisons (Model 2). In addition, those living in a 20 MN were not associated with hippocampal volume in Model 1 (Supplemental Table 2) but had greater hippocampal volumes in the fully adjusted model (estimate = 0.018, 95 % CI = 0.001, 0.034), although the association was no longer significant after adjustment for multiple comparisons (Model 2). No other associations were observed with hippocampal volume.

Table 4.

Adjusted associations between 20-min measures and hippocampal volume.

Neighborhood measure type	Exposure (in separate models)	Model 1b	Model 2c
		Estimate (95 % CI)	Estimate (95 % CI)
Number of destinations within 20-min walk of home	Health care facilities	0.000 (−0.000, 0.001)	0.000 (−0.000, 0.001)
	Social destinations	0.000 (−0.000, 0.001)	0.000 (−0.000, 0.001)
	Shopping/retail	0.000 (−0.000, 0.001)	0.000 (−0.000, 0.001)
	Grocery/supermarkets	0.009 (0.000, 0.017)d	0.007 (0.000, 0.015)d
	Dining places	0.001 (−0.001, 0.002)	0.001 (−0.000, 0.002)
	Parks	−0.000 (−0.002, 0.001)	−0.000 (−0.002, 0.001)
	Bus/train stops	−0.000 (−0.000, 0.000)	−0.000 (−0.000, 0.000)
20 MNa	Yes versus no	0.016 (−0.005, 0.037)	0.018 (0.001, 0.034)d

Abbreviations: CI = confidence interval; 20 MN = 20-min neighborhood.

^aAt least one destination/place within 20-min walk of home from 7 domains: health care, social destination, shopping, grocery, dining, parks, transit stops.

^bModel 1 controls for age (years), education (years), sex, racial group (White, Black, Other), Hispanic ethnicity, living alone, cognitive status, and area deprivation index (i.e., neighborhood disadvantage).

^cModel 2 controls for covariates from Model 1 and additionally for comorbidities (i.e., diabetes, hypertension, hypercholesterolemia, heart disease, stroke, obesity).

^dp < 0.05 before adjusting for multiple comparisons; no longer significant after adjusting for multiple comparisons using False Discovery Rate.

In multivariable regression analyses focused on WMH volume (Table 5), participants with a greater density of grocery/supermarkets within a 20-min walk of home had lower WMH volumes in the partially (estimate = −0.438, 95 % CI = −0.781, −0.096) (Model 1) and fully adjusted models (estimate = −0.327, 95 % CI = −0.578, −0.077) (Model 2). The fully adjusted grocery/food store-WMH association was significant after accounting for multiple comparisons. Participants with a greater density of dining places had lower WMH volumes in the partially (estimate = −0.032, 95 % CI = −0.062, −0.002) (Model 1) and fully adjusted model (estimate = −0.024, 95 % CI = −0.045, −0.002) (Model 2), but neither association was significant after adjustment for multiple comparisons. Those with greater density of parks had lower WMH volumes in the fully adjusted model (estimate = −0.060, 95 % CI = −0.094, −0.026), and this was significant after adjusting for multiple comparisons (Model 2). Lastly, individuals living in 20 MN had a borderline association (p = 0.06) with lower WMH volumes in Model 1 (Supplemental Table 3), which became significant in the fully adjusted model (estimate = −0.656, 95 % CI = —1.188, −0.124) after accounting for multiple comparisons (Model 2). No other associations were observed.

Table 5.

Adjusted associations between 20-min measures and white matter hyperintensity volume.

Neighborhood measure type	Exposure (in separate models)	Model 1b	Model 2c
		Estimate (95 % CI)	Estimate (95 % CI)
Number of destinations with 20-min walk of home	Health care facilities	0.001 (−0.006, 0.008)	0.004 (−0.007, 0.015)
	Social destinations	−0.008 (−0.019, 0.003)	−0.006 (−0.015, 0.004)
	Shopping/retail	−0.004 (−0.008, 0.000)	−0.004 (−0.009, 0.000)
	Grocery/supermarkets	−0.438 (−0.781, −0.096)d	−0.327 (−0.578, −0.077) *
	Dining places	−0.032 (−0.062, −0.002)d	−0.024 (−0.045, −0.002)d
	Parks	−0.029 (−0.074, 0.017)	−0.060 (−0.094, −0.026) **
	Bus/train stops	−0.002 (−0.008, 0.004)	−0.001 (−0.008, 0.005)
20 MNa	Yes versus no	−0.668 (−1.362, 0.027)	−0.656 (−1.188, −0.124) *

Abbreviations: CI = confidence interval; 20 MN = 20-min neighborhood.

Boldface indicates statistical significance after adjusting for multiple comparisons using False Discovery Rate.

^*p < 0.05;

^**p < 0.01,

^***p < 0.001

^aAt least one destination/place within 20-min walk of home from 7 domains: health care, social destination, shopping, grocery, dining, parks, transit stops.

^bModel 1 controls for age (years), education (years), sex, racial group (White, Black, Other), Hispanic ethnicity, living alone, cognitive status, and area deprivation index (i.e., neighborhood disadvantage).

^cModel 2 controls for covariates from Model 1 and additionally for comorbidities (i.e., diabetes, hypertension, hypercholesterolemia, heart disease, stroke, obesity).

^dp < 0.05 before adjusting for multiple comparisons; no longer significant after adjusting for multiple comparisons using False Discovery Rate.

4. Discussion

In our cohort of older adults residing in South Florida, we found that those with greater densities of parks and grocery/supermarkets within a 20-min walk of home had better brain health indicated by lower WMH volumes. We also found a significant association between living in a 20 MN and lower WMH volumes, as well as a suggestive association (no longer significant after adjustment for multiple testing) between living in a 20 MN and greater hippocampal volumes. This may suggest that those living in 20 MN have lower risk of cerebrovascular disease and dementia compared to those not living in a 20 MN. Overall, our study suggests better brain health among older adults living in 20 MN and among those with greater densities of parks and grocery/supermarkets within a 20-min walk of home.

Four known studies have directly examined whether 20 MNs (and related X-minute concepts) are associated with health behaviors/outcomes (i.e., physical activity (Contardo Ayala et al., 2022), body mass index (BMI) (Yang et al., 2022), food consumption (Oostenbach et al., 2023a)), with one focused specifically on poorer brain health (Zhang et al., 2023). Three of these prior studies were based on the Places and Locations for Activity and Nutrition study (ProjectPLAN) in Melbourne and Adelaide, Australia. 20 MNs were defined based on the presence of destinations/resources within a ~1.5 km pedestrian network distance spanning 5 domains (healthy food, community facilities, recreational facilities, public open space, public transport) (Thornton et al., 2022). In the first study of 769 ≥ 18-years-olds in ProjectPLAN, individuals were more likely to eat unhealthy foods (i.e., snacks, soft drinks, takeout foods) if they did not live in a 20 MN (Oostenbach et al., 2023a). In the second ProjectPLAN study of 1329 adults, mean BMI was lower in 20 MNs compared to non-20MNs (Yang et al., 2022). The third ProjectPLAN study of 843 adults found that individuals living in 20 MNs in Melbourne had higher odds of walking for transport than non-20MNs, but this association was not found for residents in Adelaide (Contardo Ayala et al., 2022). Beyond ProjectPLAN, a study of 3756 > 50-year-olds in Shanghai, China, investigated associations between multiple neighborhood characteristics measured within a 15-min walk of the residence and cognitive impairment (Zhang et al., 2023). The study found that individuals with greater access to educational and cultural facilities within a 15-min walk of their residence had lower odds of being diagnosed with cognitive impairment (i.e., mild cognitive impairment or dementia). While preliminary, those initial studies suggest that 20 MNs and similar concepts may help reduce known dementia risk factors, namely physical inactivity, obesity, and diabetes (increased risk with unhealthy food consumption), and may lower risk of cognitive impairment, supporting the premise that living in neighborhoods with most necessities within walking distance may help sustain and bolster brain health via dementia risk reduction. Our study contributes significantly to the literature by suggesting associations between 20 MN and better brain health measured via MRI outcomes.

The mechanisms by which 20 MN impact brain health are currently unknown. Physical inactivity, depression, social isolation, and air pollution exposure, four dementia risk factors named by the Lancet Commission report on dementia (Livingston et al., 2024), are plausible mechanisms linking neighborhoods with more social/walking destinations including grocery/supermarkets and parks and better brain health. Numerous studies suggest that individuals living in neighborhoods with more social/walking destinations have greater physical activity and more social engagement (Barnett et al., 2017; Hasselder et al., 2022; Portegijs et al., 2020). In addition to being associated with greater physical and social activity, greenspaces have been shown to help improve mental health (e.g., reduced anxiety, stress, and depression) and reduce ambient air pollution (Ai et al., 2023; Besser and Mitsova, 2021e; Deng et al., 2025; Liu et al., 2023). Future studies could expand upon our findings to examine whether physical activity, improved mental health, reduced air pollution exposure, and social engagement help to explain (i.e., are mediators) of associations between 20 MN and better brain health.

In addition, the observed associations between greater access to grocery/supermarkets and better brain health could relate to the availability of fresher and healthier foods that then translates into healthier diets. Studies have shown that a regular diet of healthier foods (fresh, minimal processed and fast foods), such as those seen in Mediterranean diets, have been associated with lower risk of dementia and lower WMH volumes (Fekete et al., 2025; Gardener et al., 2012). Conversely, individuals who experience food insecurity (e.g., difficulty obtaining food for all household members over the past 12 months) have demonstrated increased risk of dementia (Leung et al., 2024; Qian et al., 2023). Next steps for our research would be to examine whether individuals with a greater density of grocery/supermarkets within a 20-min walk of home demonstrate healthier diets and better brain health measures over time, to support a causal association between healthier food environments and brain health promotion.

This study has limitations that should be noted. The cross-sectional design of the current study revealed associations between the neighborhood measures and brain health but does not allow for causal inferences, which will need to be investigated in future longitudinal studies. Approximately 15 % of our sample (n = 54) was exposed to 20 MN, which may have limited statistical power to detect associations between 20 MN and the outcomes. We lacked data on how long participants lived at their addresses, which may have confounded our findings if participants lived elsewhere more recently. Time living in the current address and long-term exposure to 20 MN will need to be incorporated into future studies to reduce potential bias of findings. In addition, future studies that incorporate 20 MN measures that are assessed prior to the brain health outcome would strengthen evidence for causality. Reverse causality is a possibility, in which individuals with greater brain health choose to reside in more livable neighborhoods or are more likely to remain in livable neighborhoods into older age, thus explaining the observed positive associations. Mediation analyses require sufficient sample size, and that the exposure temporally precedes the mediator, which temporally precedes the outcome. The data for this study do not meet these criteria, and thus, future studies are needed to investigate factors such as comorbidities that mediate associations between 20 MN exposure and brain health/resilience. Our findings may not be generalizable to other geographic regions or subpopulations. For example, on average, the participants living in 20 MN (versus not) were living in more deprived neighborhoods based on the ADI, were more often identifying as Black, and were more often obese (Table 1). Depending on the city, 20 MN instead may be associated with socioeconomically advantaged, White, and/or healthier residents. Future research could benefit from studying other diverse geographic regions and from stratifying 20 MN-brain health associations by ADI (or similar measures) and ethnoracial group to determine if our findings can be replicated and have similar effect sizes across subpopulations. Our limited sample size of Black and Hispanic older adults did not allow for stratified analyses to determine if associations differed by ethnoracial group. This will be essential for future studies to inform interventions and policies to improve livability for minoritized groups, which often have been relegated to neighborhoods lacking resources and opportunities to promote health (e.g., less greenspace, fewer supermarkets) and are at a 1.5 to 2 times higher risk of developing Alzheimer’s disease and related dementias (Alzheimer’s Association, 2024). Overall, our analyses will need to be repeated in larger cohorts to determine if 20 MN-brain health associations are observed in diverse locations and populations. Lastly, the concept of 20 MNs is relatively new without a standardized definition to help guide research operationalization. Additional work will be needed to refine the domains and measures incorporated into the 20 MN definition to aptly represent livable neighborhoods for older adults, ideally done in collaboration with urban planners and policy makers.

In summary, we found that older adults with more food shopping destinations and parks within a 20-min walk of home, as well as those living in a 20 MN (with ≥1 destination from each of 7 domains within 20-min walk), had better brain health measured via WMH volume. Our study adds significantly to the extant literature by investigating whether livable neighborhoods, defined as 20 MN, are associated with multiple measures of brain health and resilience. This contrasts with a large portion of the prior studies on neighborhood environments and brain health, which focus on a single neighborhood domain and not characteristics that, when combined together, capture livability, or that examine characteristics (e.g., overall greenness measured using satellite imagery, indices of walkability) that may not be practicably translated to be implemented by urban planners and policy makers. In addition, most prior studies have investigated detrimental effects of neighborhood built environments on the brain (e.g., e.g., low greenspace access associated with cognitive decline) instead of determining neighborhood factors that promote brain health. Yet, it seems plausible that the predictors of negative (e.g., dementia) versus positive (e.g., resilience) brain health outcomes differ. Thus, our study aimed to determine whether livable neighborhoods may be able to maintain/improve brain health in older adults, and our findings, although preliminary and cross-sectional, support this hypothesis. Our findings suggest an opportunity for policy and planning interventions at the 20 MN scale that, by improving accessibility to resources including parks, healthy foods, and social destinations, may improve health behaviors and thus promote brain health among older adults. Future longitudinal studies with larger and more diverse cohorts will be needed to corroborate our findings and build upon our study to investigate differences in associations by ethnoracial group and whether other measures of brain health (e.g., decelerated brain age from brain imaging) may be more strongly associated with living in a 20 MN.

Supplementary Material

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.healthplace.2025.103502.

Data availability

Data will be made available on request.