Neighborhood Walkability Associated with Cardiometabolic Risk Factors among Japanese Older Adults:

Differences across physical activity status and fracture history

Abstract

Cardiovascular diseases (CVDs) significantly contribute to global mortality, and few studies have explored the relationship between walkability and cardiometabolic risk factors (CMRFs) especially in Japan. Previous research often focused on gender differences without considering gender, physical activity (PA) status, and fracture history simultaneously. Given Japan’s aging population and the unique impact of the built environment on older adults, it is crucial to examine the relationship between neighborhood walkability and CMRFs in this demographic. In this study, we aimed to clarify the association between neighborhood walkability and four cardiometabolic risk factors (CMRFs) among Japanese older adults and to see if these associations differ by gender, physical activity (PA) status, and fracture history. We used data from 39,577 older adults in Kumamoto Prefecture, Japan, from 2021, to measure residential neighborhood walkability. We analyzed the associations between the walkability index and the four CMRFs (obesity, type-2 diabetes mellitus, dyslipidemia, and hypertension) using SPSS. The results revealed significant associations between walkability and the CMRFs among Japanese older adults. Furthermore, the association varied not only across gender, PA status, and fracture history but also among different CMRFs. This study found the strongest association between walkability and hypertension. The associations between walkability and the CMRFs were observed primarily in physically inactive men, physically active women, and non-fractured women. Moreover, unexpected findings revealed a significant association between higher walkability and increased risks of obesity and type-2 diabetes mellitus in physically inactive men, as well as dyslipidemia in physically inactive men, fractured men, and non-fractured men. These findings highlight the complex effects of walkable environments on the cardiometabolic health of Japanese older adults. This study emphasizes the importance of promoting PA and preventing falls and fractures to reduce the risk of CVDs among older adults.

Introduction

Non-communicable diseases (NCDs) are the leading cause of death globally, accounting for 60% of all deaths (Lopez, Mathers et al., 2006). Among NCDs, cardiovascular diseases (CVDs) are the main contributors to mortality, resulting in approximately 18 million deaths annually. While half of CVD cases are estimated to occur in Asia, Japan has experienced a unique trend, with a continuous decline in CVD mortality rates (Ohira and Iso, 2013). Notably, the incidence of stroke decreased by approximately 80% between 1965 and 1990 (Ueshima, H., Sekikawa et al., 2008). This success can be attributed to nationwide efforts to prevent hypertension and anti-smoking campaigns (Ohira and Iso, 2013). However, the decline in stroke incidence has reportedly been slowing, and the incidence of coronary heart disease among older adults has been increasing in recent years (Kubo, Kiyohara et al., 2003). CVDs remain the second leading cause of death in Japan after cancer (Hirata, Matsumoto et al., 2023). Additionally, the incidence of coronary heart disease has recently increased among urban men, posing a potential public health concern in Japan (Iso, 2011).

Physical activity (PA) is an important modifiable factor that can decrease the risk of all-cause and CVD mortality (Ueshima, K., Ishikawa-Takata et al., 2010). Numerous reviews have confirmed the impact of the built environment on human behavior and PA (Barnett, Barnett et al., 2017; Hajna, Ross et al., 2015; Kärmeniemi, Lankila et al., 2018; Liang and Guan, 2023; Van Cauwenberg, De Bourdeaudhuij et al., 2011). Built environment measures such as residential density, street connectivity, and mix of land use have been commonly analyzed for their relationships with PA (Frank, Sallis et al., 2006; Kikuchi, Nakaya et al., 2018; Owen, Cerin et al., 2007). Although the findings of various studies are inconsistent, the overall evidence suggests that a more walkable built environment is likely to be associated with more daily steps and higher levels of PA.

In addition to PA, the built environment may influence other cardiometabolic risk factors (CMRFs) such as obesity, type-2 diabetes mellitus, dyslipidemia, and hypertension. The characteristics of the built environment can affect these factors, either directly or through their influence on PA (Müller-Riemenschneider, Pereira et al., 2013). While the relationship between walkability and obesity has been extensively studied (Berke, Koepsell et al., 2007; Ding and Gebel, 2012; Koohsari, Kaczynski et al., 2019; Leslie, Saelens et al., 2005; Rundle, Neckerman et al., 2009), less attention has been paid to exploring its associations with type-2 diabetes mellitus, dyslipidemia, and hypertension. Few studies have examined the relationship between walkability and all four CMRFs (Malambo, Kengne et al., 2016) or cumulative cardiometabolic risk (Coffee, Howard et al., 2013). Moreover, these studies were concentrated in Western countries, and a scarcity of research on this topic exists in Japan. Meanwhile, they focused only on gender differences, without emphasizing the gender and PA status group differences in the association between walkability and CMRFs simultaneously. Furthermore, fractures may significantly influence the association between walkability and CMRFs (Veronese, Stubbs et al., 2017). Investigating sex-, PA status-, and fracture history-specific associations between walkability and CMRFs is essential.

Studies examining the relationship between the built environment and health outcomes have focused on adults without distinguishing between the different life stages (Cao, Yang et al., 2024; Papas, Alberg et al., 2007). Nevertheless, built environment reportedly has a distinct impact on older adults (Bowling, Barber et al., 2006). Given the rapidly increasing number of older adults in Japan and their functional decline, evaluating the specific characteristics of the relationship between the built environment and health outcomes among older adults is crucial (Berke, Koepsell et al., 2007; Zhou, Homma et al., 2024). By understanding these characteristics, targeted interventions and policies that cater to the needs of older adults and promote healthy aging can be developed.

Therefore, in this study, we aimed to (1) clarify the association between neighborhood walkability and four CMRFs among Japanese older adults and (2) examine whether the association differs by gender, PA status, and fracture history.

Methods

Study region and sample

The study region is the Kumamoto Prefecture, located in the central part of Kyushu Island, Japan. The prefecture encompasses an area of 7,409.46 km² and has a population of 1.709 million as of April 2023, making it the second largest prefecture in Kyushu. According to the 2020 National Census, the aging rate in Kumamoto Prefecture reached 31.4%, and people aged ≥75 years accounted for 16.4% of the total population (Statistics Bureau, 2021). In the context of a rapidly aging population, promoting healthcare and preventing frailty are critical goals for public health in Kumamoto Prefecture. To achieve these goals, the Kumamoto Prefecture Senior Citizens’ Medical and Wide Area Alliance annually collects health data on some early-stage older adults (65 to 74 years) and all older adults (≥75 years) who have undergone a health check-up. In this study, we obtained health data pertinent to health checks, medical history, medication use, bone fracture history for a five-year period from 2017 to 2021, and health promotion questionnaire results. The questionnaire assessed self-reported PA, smoking status, and meal frequency. After excluding individuals aged <75 years and those from other prefectures, a total of 39,577 older adults aged ≥75 were included in the study. The 2020 National Census revealed that 285,081 older adults resided in the prefecture, implying that the study sample accounted for nearly 14% of the entire older adult population in the prefecture (Statistics Bureau, 2021).

Cardiometabolic risk factors

Four cardiometabolic risk factors in older adults in the Kumamoto Prefecture were investigated, including obesity, type-2 diabetes mellitus, dyslipidemia, and hypertension. Obesity was assessed based on body measurement and defined as a BMI ≥25 kg/m². Older adults were considered to have type-2 diabetes mellitus, dyslipidemia, or hypertension if they had received a diagnosis from a physician or were currently undergoing treatment with prescribed medications for any of these conditions. Among 39,577 older adults, 23.2% were obese, 30.0% had type 2 diabetes mellitus, 51.0% had dyslipidemia, and 67.9% had hypertension.

Neighborhood walkability

The Walk Score is a widely used and convenient tool for objectively measuring walkability (Wang and Yang, 2019; Zhou and Homma, 2022). The original Walk Score algorithm measures the accessibility of facilities based on the distance to 13 categories of facilities from a given location. In 2011, the Walk Score algorithm was modified by adding the intersection density and block length to the model to measure pedestrian friendliness as well (Simbaqueba, 2018). While the Walk Score has been validated in many studies, including in Japan (Carr, Dunsiger et al., 2010, 2011; Koohsari, Sugiyama et al., 2018), its limitations in capturing all relevant factors influencing walkability have been highlighted in systematic reviews (Hall and Ram, 2018; Wang and Yang, 2019). Glazier et al. revealed that both density and destinations can have a significant impact on transportation behaviors and health outcomes (Glazier, Creatore et al., 2014). Dovey and Pafka indicated that walkability is a complex synergy of density, mix, and access, referred to as DMA (Dovey and Pafka, 2020). Frank et al. developed a walkability index by combining residential density, street connectivity, mix of land use, and retail floor area ratio (Frank, Sallis et al., 2006). Alves et al. suggested a walkability index for older adults by including the existence of slopes and stairs (Alves, Cruz et al., 2020). The present study proposes a walkability index that measures four aspects of the built environment: density, accessibility, connectivity, and comfort. Six indicators, including population density, Walk Score, mix of function, intersection density, accessibility of bus service, and slope, were used to calculate the walkability index.

Health data included the residential addresses of all older adults. Each address was geocoded using Google API and Python, and its location was plotted using GIS. The neighborhood walkability index was calculated for each residential address. Given the walking distance for older adults is approximately 500 m (Ministry of Land, Infrastructure, Transport and Tourism, 2014), only facilities within 500 m were considered. Instead of the Euclidean distance, network distances were used to create 500-m buffer zones. Measurements of the built environment and calculations of the walkability index were conducted using ArcGIS Pro 2.7.0.

Population density

Population density was calculated by dividing the population of the census tract to which each older adult belonged by the area of that specific census tract. Population data and area measurements of each census tract were obtained from e-Stat, the official statistics portal of Japan.

Intersection density

The nodes corresponding to the intersections were identified using the road network data, obtained from the Road Network of Kumamoto Prefecture version 2017. This dataset includes detailed information on road networks across Kumamoto Prefecture. Subsequently, the spatial join tool in ArcGIS Pro was utilized to determine the number of intersections within the 500-m network distance buffer zone surrounding each residential address.

Walk Score

Based on data from the 2012 Kumamoto Person Trip survey and a survey on the Awareness of Trips among individuals aged ≥60 years, we identified 12 categories of facilities commonly used by older adults. These categories include health facility, supermarket, convenience store, bank & post office, public assembly space, park, outdoor & sports facility, drug store, grocery store, catering establishment, cultural facility, and religious facility. Each category was assigned a specific weight based on its perceived importance to older adults and categorized as high, medium, or low. Table 1 presents these categories along with their corresponding weights, providing a clear framework for understanding the significance of different facilities for older adults in Kumamoto Prefecture.

Table 1. Categories and corresponding weights of facilities

Category	Weight	Category	Weight
Health facility	3	Outdoor & sports facility	2
Supermarket	3	Drug store	2
Convenience store	3	Grocery store	2
Bank & Post office	3	Catering establishment	1
Public assembly space	2	Cultural facility	1
Park	2	Religious facility	1

The interaction between facility location and residential address is inversely proportional to the square of the distance. We utilized a Gaussian decay function to determine the interaction value between residential address and facility location. According to the Handbook for Urban Structure Evaluation (Ministry of Land, Infrastructure, Transport and Tourism, 2014), we limited our analysis to facilities within a 500-m buffer zone, where the interaction value diminishes to zero as the network walking distance approaches 500 m. We performed curve fitting using Python to derive the parameters of the Gaussian decay function and ensure that it accurately represented the observed interaction pattern. Figure 1 shows the decay curve. The decay function is formulated as follows:

（1）

Figure 1. Distance decay function

We then calculated the scores for each category of facility within 500-m walking distance of all older adult’s residential addresses. The final Walk Score was obtained by summing the scores of all categories of facilities. Because intersection density was calculated separately, no penalty was imposed on the Walk Score for inadequate pedestrian-friendliness metrics. In this context, the Walk Score primarily serves as an indicator of facility accessibility.

Mix of function

The mix of function provides a numerical measure of the diversity of facilities within walking distance. A higher value indicates a more varied mix of facilities, which is generally associated with higher walkability owing to the availability and accessibility of different services and destinations. This measure was calculated using the following formula, where n represents the total number of facility types and p_i denotes the percentage of type i facility in the total facilities:

（2）

Accessibility of bus service

The weight of the bus stop was determined using the same distance decay function used to calculate the Walk Score. If there were multiple bus stops within 500-m walking distance from a residential address, the bus stop with the highest accessibility was selected to represent the ease of bus service access. This indicator was measured using the following formula, where w_i represents the weight of i bus stop, f_i denotes the frequency of i bus stop, and n is the total number of bus stops reachable within a 500-m radius:

（3）

Slope

We estimated the slope of the entire prefecture using surface parameter tool based on Digital Elevation Model data from the Geospatial Information Authority of Japan. After that, we extracted the slope value corresponding to each residential address.

Walkability index

We calculated the basic walkability index as the average value of five indicators, excluding slope. After obtaining the values, we normalized them and calculated their average values. Subsequently, the residential address may have been penalized based on the slope value. For every one degree of slope, we applied a one percent penalty to the basic walkability index to obtain the final index.

Statistical analysis

The study participants were divided into four groups for two separate analyses. One analysis categorized them into four groups based on PA status and gender, whereas the other categorized them into four groups based on fracture history and gender. PA status was classified as either engaging in PA at least once a week or not, while fracture history was classified as having had fractures in the past five years or not. Separate logistic regression models were used to examine the associations between walkability and each of the four CMRFs. The analysis was conducted according to gender and PA status or gender and fracture history, separately. Adjusted odds ratios (OR) and 95% confidence intervals were calculated. Adjustments were made for age, smoking status, meal frequency, and sleeping pill use. An OR <1.00 indicates a lower risk of CMRFs in more walkable neighborhoods. Statistical significance was set at the level of P <0.05. All analyses were conducted using IBM SPSS Statistics 26.0

Results: Walkability and CMRFs Stratified by Gender, PA, and Fracture

Description of participants

Of the 39,577 participants, 17,064 were men (43.1%) and 22,513 were women (56.9%). The participant characteristics are listed in Table 2. Men had a higher rate of engagement in PA and a much lower rate of fractures than women. However, men had a higher risk of developing CMRFs than women, except for dyslipidemia. Among the four CMRFs, Hypertension was the most prevalent CMRF for both men (68.7%) and women (67.3%).

Table 2. Descriptive characteristics of the participants (n = 39577)

	Total (n = 39577)	Men (n = 17064)	Women (n = 22513)
Age (year)	80.80 ± 4.37	80.72 ± 4.25	80.87 ± 4.45
Physical activity	61.4	64.3	59.2
Fracture	20.8	14.6	25.6
Smoking (Current smokers)	4.0	7.4	1.4
(Former smokers)	18.0	38.6	2.4
(Non-smokers)	78.1	54.0	96.3
Meal frequency (3 meals/day)	95.7	95.3	96.0
Sleeping pill	25.9	19.9	30.5
Obesity (BMI ≥25 kg/m²)	23.2	25.1	21.8
Type-2 diabetes mellitus	30.0	35.3	25.9
Dyslipidemia	51.0	44.3	56.2
Hypertension	67.9	68.7	67.3

BMI: Body Mass Index

Data are shown as the mean ± SD or percentage.

Figure 2 (left) presents the walkability index for the four groups stratified by gender and PA status. Both physically active men and women exhibited a significantly higher walkability index than their physically inactive counterparts. Notably, the disparity in the walkability index between physically active and inactive men was nearly twice that between physically active and inactive women. Furthermore, the walkability index for physically active men and women were comparable, whereas physically inactive men displayed a considerably lower walkability index than physically inactive women.

Figure 2 (right) illustrates the walkability index across the four groups stratified by gender and fracture status. Both non-fractured men and women displayed higher walkability index than those with fractures. The disparity in the walkability index between non-fractured and fractured men was notably greater than that between non-fractured and fractured women. Furthermore, while the walkability index of non-fractured men and women were similar, fractured men exhibited a lower walkability index than fractured women.

Figure 2. Clustered boxplot of walkability index stratified by gender and physical activity status (left), by gender and fracture status (right)

Association between walkability and CMRFs

Table 3 shows the association between the walkability index and each CMRF after stratification by gender and PA status. The results are presented as adjusted OR (95% CI) after adjusting for age, smoking status, meal frequency, and sleeping pill use. For men, higher walkability was significantly associated with a higher risk of hypercholesterolemia, obesity, and type-2 diabetes mellitus in the physically inactive group, but not in the physically active group. For women, significant associations between higher walkability and lower risks of obesity, type-2 diabetes mellitus, and hypercholesterolemia were observed in the physically active group but not in the physically inactive group. For both men and women, greater walkability was associated with a lower risk of hypertension, and this association was stronger for women and the physically active group.

Table 3. Adjusted odds ratios of CMRFs for participants in high vs. low walkable neighborhoods: stratified by gender and PA status

	Men			Women
	Physically inactive	Physically active		Physically inactive	Physically active
Obesity	1.52	0.85		0.83	0.46
	(1.14, 2.02)**	(0.68, 1.06)		(0.65, 1.04)	(0.37, 0.57)**
Type-2 diabetes mellitus	1.33	0.85		0.86	0.64
	(1.02, 1.74)*	(0.70, 1.04)		(0.68, 1.09)	(0.53, 0.78)**
Dyslipidemia	1.85	1.10		1.12	0.70
	(1.43, 2.40)**	(0.91, 1.33)		(0.91, 1.38)	(0.59, 0.84)**
Hypertension	0.64	0.52		0.40	0.36
	(0.48, 0.85)**	(0.42, 0.63)**		(0.32, 0.51)**	(0.30, 0.43)**

*p ≤ 0.05; **p ≤ 0.01

Physically active: engaging in PA at least once per week.

Physically inactive: not engaging in PA at least once per week.

Data are presented as adjusted odds ratio (95% confidence interval) after adjusting for age, smoking status, meal frequency, and sleeping pill use.

Table 4 shows the association between the walkability index and each CMRF according to gender and fracture history. The results are presented as adjusted OR (95% CI) after adjusting for age, smoking status, meal frequency, and sleeping pill use. For men, higher walkability was significantly associated with a lower risk of hypertension in non-fractured group. After stratification according to the fracture history, walkability was not significantly associated with the risk of obesity and type-2 diabetes mellitus. However, higher walkability was associated with a higher risk of hypercholesterolemia in both fractured and non-fractured groups, and this relationship was stronger in non-fractured group. For women, higher walkability was significantly associated with lower risks of the four CMRFs in non-fractured group but was only associated with a lower risk of hypertension in fractured group.

Table 4. Adjusted odds ratios of CMRFs for participants in high vs. low walkable neighborhoods: stratified by gender and fracture history

	Men			Women
	Fracture	Non-fracture		Fracture	Non-fracture
Obesity	1.01	0.95		0.75	0.52
	(0.63, 1.63)	(0.79, 1.14)		(0.55, 1.03)	(0.43, 0.62)**
Type-2 diabetes mellitus	1.48	0.92		0.86	0.68
	(0.98, 2.25)	(0.78, 1.09)		(0.64, 1.14)	(0.57, 0.81)**
Dyslipidemia	1.74	1.25		1.10	0.78
	(1.16, 2.60)**	(1.06, 1.48)**		(0.85, 1.42)	(0.67, 0.91)**
Hypertension	0.73	0.48		0.37	0.36
	(0.48, 1.13)	(0.40, 0.57)**		(0.28, 0.49)**	(0.30, 0.42)**

*p ≤ 0.05; **p ≤ 0.01

Fracture: had fractures in the past five years.

Non-fracture: had no fractures in the past five years.

Data are presented as adjusted odds ratio (95% confidence interval) after adjusting for age, smoking status, meal frequency, and sleeping pill use.

Discussion: Walkability and CMRFs Among Older Adults

Using a large representative sample of older adults, this study investigated the relationship between walkability and four CMRFs. The results revealed that walkability was associated with the CMRFs among older Japanese older adults. Moreover, the association varied not only across gender, PA status, and fracture history, but also among different CMRFs after adjustment for age, smoking status, meal frequency, and sleeping pill use. The association between walkability and CMRFs was observed primarily in physically inactive men, physically active women, and non-fractured women.

Extensive research has been conducted on the association between walkability and obesity, and hypertension has received considerable attention (Malambo, Kengne et al., 2016). However, studies examining the relationship between walkability and type-2 diabetes mellitus and dyslipidemia are relatively scarce (Leal and Chaix, 2011). Overall, a significant protective association is reported between neighborhood walkability and CMRFs in the expected direction (Coffee, Howard et al., 2013; Malambo, Kengne et al., 2016). Strong evidence for longitudinal associations between neighborhood walkability and obesity and type-2 diabetes mellitus outcomes has been observed, and a very strong association between neighborhood walkability and hypertension has been identified (Chandrabose, Rachele et al., 2019).

This study further emphasizes the association between walkability and hypertension. These significant associations remained consistent across different PA statuses, indicating protective outcomes regardless of PA status. Meanwhile, significantly beneficial outcomes were observed among both fractured and non-fractured woman, as well as non-fractured men, but not among fractured man. A cross-sectional study conducted in France concluded that living in a highly walkable neighborhood was associated with lower systolic and diastolic blood pressures (Méline, Chaix et al., 2017). Similarly, a large population-based cohort study in the UK provided evidence of a protective association between walkability and hypertension (Sarkar, Webster et al., 2018). Another longitudinal study in Portland suggested that neighborhood walkability may reduce the risk of hypertension and play a vital role in promoting population health (Li, Harmer et al., 2009). In contrast to previous studies, the present study not only confirmed the protective association but also revealed that it was stronger among women than among men, and among physically active individuals than among those who were inactive. However, a comparable association was observed between fractured women and non-fractured women, suggesting that fracture history may not significantly modify the association between walkability and hypertension in women. The reason for the lack of significant association among fractured men requires further investigation.

Numerous studies have examined the relationship between obesity and the built environment. A comprehensive review reported that 84% of articles reported a significant beneficial association between obesity and certain aspects of the built environment (Papas, Alberg et al., 2007). However, another review noted that many studies did not report a statistically significant association and great heterogeneity existed across studies (Feng, Glass et al., 2010). A study focusing on older adults in the United States revealed no significant association between walkability and obesity in men or women, and the impact of walking on obesity reduction remains unclear (Berke, Koepsell et al., 2007). In contrast, a study conducted in Japan concluded that higher population density and Walk Score were associated with lower BMI. Moreover, PA was identified as a significant mediator of this association (Koohsari, Kaczynski et al., 2019). In the present study, a significant protective association was observed between walkability and obesity among physically active women and non-fractured women. These results highlight substantial gender differences and the influence of PA and fracture history on this association.

Our findings regarding type-2 diabetes mellitus were consistent with those regarding obesity. Walkability was negatively associated with type-2 diabetes mellitus in physically active women and non-fractured women. The distinction between obesity and type-2 diabetes mellitus lay in the strength of the significant association, which was stronger for obesity compared to type-2 diabetes mellitus. A comprehensive review of 108 studies indicated that higher neighborhood walkability was consistently associated with a lower risk of type-2 diabetes mellitus (Den Braver, Lakerveld et al., 2018). In contrast to previous studies, the present study emphasized the importance of promoting PA and creating environments that facilitate active lifestyles by highlighting the protective association between walkability and type-2 diabetes mellitus among physically active women and non-fractured women.

As for dyslipidemia, which was paid least attention, we found similar association with obesity and type-2 diabetes mellitus. The difference was that higher walkability was significantly associated with a higher risk of dyslipidemia among both fractured and non-fractured men, with a stronger association observed among fractured men. These findings suggest that living in neighborhoods with higher walkability may contribute to an increased risk of dyslipidemia in men, regardless of their fracture history. Further research is required to better understand the underlying reasons for this association. In contrast, an observational study of Australian adults suggested that walkability was not associated with hypercholesterolemia or hypertension (Müller-Riemenschneider, Pereira et al., 2013). It is important to acknowledge that regional differences, cultural factors, and variations in study design and population characteristics may contribute to the discrepancies in results across different studies.

An unexpected finding of this study was that higher walkability was significantly associated with a higher risk of obesity and type-2 diabetes mellitus in physically inactive men, and dyslipidemia in physically inactive men as well as both fractured men and non-fractured men group. These findings elicit intriguing questions that warrant further investigation. Walkability is not necessarily associated with beneficial health outcomes (Frank, Sallis et al., 2006). One possible explanation for this unexpected association is the presence of environmental factors that may contribute to unhealthy behaviors. Walkable neighborhoods often have a higher density of fast-food outlets and other establishments that offer less healthy food options. Access to unhealthy food stores may sufficiently support a higher risk of obesity, type-2 diabetes mellitus, and dyslipidemia among men, especially in those who are physically inactive or fractured (Rundle, Neckerman et al., 2009). Further research is necessary to explore the underlying mechanisms and potential confounding factors to gain a comprehensive understanding of these unexpected findings.

This study had several limitations. First, walkability was measured only using objective built environment data. Previous study pointed out that a mismatch existed between objectively assessed walkability and perceived built environments (Gebel, Bauman et al., 2011). Future studies should incorporate both objective and perceived built environments to provide a comprehensive understanding of the influence of walkability on CMRFs. Second, except for fracture data, which covered a five-year period, all other health data were collected for 2021 only. Therefore, this study had a cross-sectional design and did not reveal the causal inferences. Future research should use longitudinal data to establish causal relationships between the built environment characteristics and health outcomes. A longitudinal study design can track changes in health outcomes relative to changes in the built environment, thus providing stronger evidence of causal relationships. Third, questions regarding PA status did not distinguish between domain and duration of activity. Participants were separated into physically inactive and active groups based on their self-reported activity levels. Future studies should consider differences in the relationship between walkability and CMRFs across specific domains and durations of PA. Additionally, using objective measures of PA rather than questionnaire surveys can provide more accurate data.

Despite these limitations, this study had several strengths. It was conducted with a large sample in a prefecture of Japan and clarified the differences in the association between walkability and the CMRFs across gender, PA, and fracture history for the first time in Japan. Moreover, the study focused exclusively on older adults at the highest risk of CVDs, thus addressing an important gap in the literature. To the best of our knowledge, this is the first study to explore the relationship between walkability and the four CMRFs in the same group of older adults in Japan.

Our study contributes significantly to the current knowledge of the complex relationship between walkable environments and cardiometabolic health among Japanese older adults. The association between walkability and cardiometabolic health outcomes can be influenced by factors such as PA level and fracture history. These findings emphasize the importance of proactive measures to improve neighborhood walkability, promote regular PA, and implement strategies to prevent falls and fractures. Collaborative efforts among policymakers, healthcare professionals, and urban planners are vital for establishing healthier communities that support the well-being of older adults and alleviate the burden of cardiometabolic diseases.

Conclusion

This study revealed that the associations between neighborhood walkability and CMRFs among Japanese older adults varied not only among different CMRFs but also across gender, PA status, and fracture history. Physically active women and non-fractured women exhibited the strongest beneficial associations between walkability and CMRFs. Walkability exhibited the strongest protective association with hypertension, while unexpectedly it was positively associated with obesity, type-2 diabetes mellitus, and dyslipidemia in physically inactive men. Furthermore, walkability was positively associated with dyslipidemia in men with and without a history of fractures. These findings highlight the complex effects of walkable environments on cardiometabolic health in Japanese older adults. Additionally, this study emphasizes the importance of promoting PA and preventing falls and fractures to reduce the risk of CVDs among older adults.

Author Contributions

QZ conceived the study, designed the methodology, conducted formal analysis, and drafted the manuscript. RH contributed to the supervision of the study, interpretation of results and revision of the manuscript. All the authors have read and agreed to the published version of the manuscript.

Ethics Declaration

The authors declare that they have no conflicts of interest regarding the publication of the paper.

Acknowledgments

We would like to express our deepest gratitude to the Kumamoto Prefecture Association of Medical Care Services for Older Senior Citizens and the NPO Kumamoto Machizukuri for generously providing us with a wealth of data.

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