Factors influencing walking practice among urban residents before and after the COVID-19 pandemic

doi:10.21203/rs.3.rs-4878476/v1

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Research Article

Factors influencing walking practice among urban residents before and after the COVID-19 pandemic

https://doi.org/10.21203/rs.3.rs-4878476/v1

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Background

Walking is vital for urban residents' health, but the COVID-19 pandemic has significantly altered daily life and walking practices. This study examinedthe factors influencing walking practices before and after COVID-19 at individual and community levels to understand the pandemic's impact on health behaviors and inform future public health strategies.

Methods

Secondary data from Korea's Community Health Survey (2018–2021) were analyzed, focusing on adults aged 19 and older in Busan Metropolitan City. Multilevel regression analysis was conducted on a total of 54,092 participants, and IBM SPSS Statistics 27.0 was used for statistical analysis. For the multilevel regression analysis, individual-level factors included demographic and social characteristics, health behavior, and subjective perceptions of the community. Community-level factors included urban parks, pedestrian paths, vehicular roads, public and private sports facilities, traditional markets, and social network difficulty.

Results

At the individual level, walking decreased among women and overall from 0.98 (95% CI: 0.97–0.99) before COVID-19 to 0.92 (95% CI: 0.91–0.93) after COVID-19 but increased among those aged 65 and older from 0.96 (95% CI: 0.94–0.99) to 1.05 (95% CI: 1.02–1.08). Subjective health status declined from 1.10 (95% CI: 1.08–1.12) to 1.05 (95% CI: 1.03–1.07). Obesity (OR=0.98, 95% CI: 0.96–1.00) and depression (OR=0.93, 95% CI: 0.90–0.96) were associated with lower walking, while individuals with one chronic disease (OR=1.04, 95% CI: 1.02–1.06) and smokers (OR=1.07, 95% CI: 1.04–1.09) walked more. Neighbors’ trust (OR=1.04, 95% CI: 1.02–1.06) and neighborhood safety (OR=1.03, 95% CI: 1.00–1.06) positively influenced walking. At the community level, walking increased with more pedestrian paths before and after COVID-19. Traditional markets positively influenced walking before the pandemic (OR=1.01, 95% CI: 1.00–1.01. Social network difficulties were influenced bylower walking (OR=0.84, 95% CI: 0.77–0.93).

Conclusions

The study reveals significant changes in walking practices before and after COVID-19, influenced by individual factors such as sex, age, health status, and community factors like pedestrian paths, traditional markets, and social network difficulties. These insights highlight the need for targeted public health strategies to promote physical activity during global health crises.

Walking Practice

COVID-19 Pandemic

Urban Residents

Multilevel Regression

Walking is a fundamental activity that many engage in at the beginning and end of their day. It is important for urban residents in various aspects. First, walking is crucial for promoting health and preventing disease [1]. Regular walking helps to control and manage chronic diseases, such as cardiovascular disease, diabetes, obesity, and cancer [2, 3], maintain bone density, improve joint health, and enhance muscle strength and flexibility [4, 5]. Second, walking benefits mental health by reducing stress, lowering the risk of depression and anxiety disorders, and improving overall emotional well-being [6, 7]. Contact with nature positively affects mental health, especially when walking in city parks or green spaces [8, 9]. Additionally, walking promotes community interaction and helps to build social support networks in public spaces [10]. Third, walking is an eco-friendly means of transportation that promotes urban sustainability by reducing car usage, traffic congestion, and air pollution [11]. Fourth, walking helps to maintain functional independence, especially in the elderly, by enhancing physical stability and decreasing the likelihood of falls [12]. Finally, from an economic perspective, walking can reduce transportation and medical expenses [13, 14]. These benefits highlight the importance of incorporating walking into the daily lives of city residents.

Meanwhile, the coronavirus disease 2019 (COVID-19) pandemic has profoundly altered the everyday lives of urban dwellers. Social distancing measures, the shift to working from home, and restrictions on the use of public spaces have altered physical activity patterns, primarily walking, which is a fundamental physical activity [15, 16]. During the COVID-19 pandemic, obesity and mental health problems, which are directly related to reduced physical activity, have increased globally [17]. Walking is a crucial factor in these health issues [18]. A decrease in physical activity levels, especially walking practice rates, occurred during the COVID-19 pandemic [19, 20]. In Korea, the rate of physical activity through walking decreased by 3.0 percentage points from 40.4% in 2019 to 37.4% in 2020 [21]. The World Health Organization has reported that >80% of adolescents and 27% of adults do not meet the recommended physical activity levels and that inequalities in opportunity and access to physical activity have worsened in some countries due to the COVID-19 pandemic [1]. This highlights the effects of the COVID-19 pandemic on walking practices and underscores the importance of comparing factors affecting walking before and after the pandemic. Therefore, it is essential to investigate the factors affecting walking practices among urban residents during the pandemic.

Research has mainly focused on sociodemographic factors, such as age, sex, presence of a spouse, presence of family members living together, education, and income level. Additionally, research has been conducted on individual health behaviors and health levels, such as drinking, smoking, subjective body shape awareness, obesity, activity limitations, stress, and depression [22-24]. Salvo et al. [25] systematically reviewed how environmental factors influence physical activity. However, further studies are needed to analyze the factors influencing walking practices by dividing them into personal and community factors. This presents an even more critical research gap, especially when considering the diverse effects of the COVID-19 pandemic on the walking practices of urban residents.

This study aimed to analyze the factors influencing the walking practices of urban residents at the individual and community levels before and after the COVID-19 pandemic. Further, it aimed to comprehensively understand the effects of the COVID-19 pandemic on the health behaviors of urban residents and to identify the underlying factors contributing to these changes. Additionally, this study sought to generate evidence for developing health behavior promotion strategies that can be used in future global public health crises.

Data source and study population

This study analyzed the relationship between physical and social environmental factors affecting adults' walking practices using data from the Community Health Survey, which employs a representative sample design in Korea. The raw data from the Community Health Survey were obtained by requesting the data from the Korea Disease Control and Prevention Agency (KDCA)'s Community Health Survey website and receiving approval. The dataset includes geographic data from the 2018-2021 Community Health Survey, the Korean Statistical Information Service (KOSIS), and the Busan Open Data Portal. Additionally, some physical environment data were merged to create secondary data for comprehensive analysis. The study subjects were adults aged 19 and above residing in Busan Metropolitan City, selected from the 58,042 participants in the Community Health Survey data. After excluding 3,950 cases due to missing values and non-responses, 54,092 individuals were included in the final analysis. The sample comprised 29,018 people in 2018-2019 and 25,074 people in 2020-2021.

Variables of the study

Outcome variable

The dependent variable in this study is walking practice, defined as walking for more than 10 minutes at a time and for more than 30 minutes per day on more than five days a week during the past week. The specific questions about the variables used to measure this variable are: “During the past week, how many days did you walk at least 10 minutes at a time?” and "On one of these days, how long did you usually walk?" The responses to these questions were used to determine the walking practice of the participants.

Independent variables

Factors affecting adults' walking practice were divided into individual- and community-level factors. Demographic and social characteristics factors, health status and behavior factors, and subjective perception of the community factors were selected as individual-level factors. Demographic and social characteristics factors included sex, age, education, household income, and marital status. Specifically, sex was classified as ‘men’ and ‘women,’ and age was classified as ‘19 to 34 years,’ ‘35 to 49 years’, ‘50 to 64 years’, and ‘65 years or older. Education was categorized as ‘junior high school,’ ‘high school,’ and ‘university.’ Household income is household monthly income and is divided into ‘first quintile (bottom 20%)’, ‘second quintile (bottom 40%)’, ‘third quintile (bottom 60%)’, ‘fourth quintile (top 40%)’, and ‘fifth quintile (top 20%)’. Marital status was classified as ‘partnered’ or ‘single.’ Health status and health behavior factors include subjective health status, obesity, depression, number of chronic diseases, smoking, and alcohol drinking [26, 27]. Subjective health status was classified as 'yes' or 'no,' while obesity was classified as 'yes' if the individual was obese and 'no' if not, based on a body mass index (BMI) threshold of 25. Depression was assessed using a 'yes' or 'no' response to the question, “Have you ever felt so sad or hopeless that it interfered with your daily life for more than two consecutive weeks in the past year?”. The number of chronic diseases diagnosed by a doctor was categorized as '0,' '1,' or '2 or more.' The specific variables used in this study were derived from 'yes' or 'no' responses to the questions, 'Have you ever been diagnosed with high blood pressure by a doctor?' and 'Have you ever been diagnosed with diabetes by a doctor?'. Smoking status was categorized as 'yes' or 'no' based on current smoking behavior, as determined by responses to the question, 'Do you currently smoke?' Alcohol drinking status was categorized as 'yes' or 'no' based on current drinking behavior, using responses to the question, 'How often do you drink alcohol?'. The factors influencing the subjective perception of the community, as identified in the study [23], include neighbors' trust, neighborhood safety level, natural environment, and living environment. Subjective perception of the community refers to an individual's subjective thoughts about the physical and social environment. The specific questions used for the variables in this study were: 'People in our neighborhood trust and believe in each other,' 'The overall level of safety in my neighborhood (natural disasters),' 'I am satisfied with the natural environment in my neighborhood, including aspects such as air quality and water quality.’ and 'I am content with the living conditions in my neighborhood, including services such as electricity, water and sewage, garbage collection, and sports facilities.' Responses to these questions were recorded as 'yes' or 'no.'

Community-level factors that influence adults' walking practices include urban parks [22], pedestrian paths [28], vehicle roadways, public sports facilities [23, 24], private sports facilities [29], traditional markets [14], and social network difficulty [24]. Urban parks were defined as the urban park area (㎢). An urban park is a space within an urban area utilized daily to preserve the city's natural landscape and citizens’ health, recreation, and emotional well-being. Urban parks consisted of residential and neighborhood parks (historical, cultural, waterside, cemetery, sports, and urban agricultural parks). Data from the Busan Metropolitan City Basic Statistics (Parks) [30] were used. Pedestrian paths included pedestrian, promenades, and road extensions (km). A pedestrian path was a designated area for pedestrian passage. At the same time, promenades were intended for walking for exercise or enjoyment (such as trails, neighborhood alleys, historical site trails, and forest paths). Data from Busan Galmaet-gil and Busan Downtown Galmaet-gil [31] of Busan’s walking paths [32] were used. The promenade data were sourced from the National Road Tourism Information Standard Data [33, 34], and the pedestrian-only roads data were obtained from the Urban Planning Status [35, 36]. Vehicle roadways were defined as roadway length (km). The roadway consisted of general and automobile-only. This included several overpasses and underground roads. Data from the Urban Planning Status [35, 36] were used. Public sports facilities are referred to as public physical activity facilities. Data on the status of village sports facilities among public sports facilities nationwide [37, 38] were used. This included several simple sports facilities such as soccer fields, basketball courts, volleyball courts, badminton courts, tennis courts, gateball fields, and community gyms. Private sports facilities are referred to as private physical activity facilities. Data on the status of these facilities were obtained from the Busan Metropolitan City reported/registered sports facilities [39]. This included comprehensive sports facilities, swimming pools, gyms, golf driving ranges, physical training centers, and ice rinks. Private sports facilities are quantified by their number, including comprehensive sports facilities, swimming pools, sports gyms, golf driving ranges, physical training centers, and ice rinks. Traditional markets [40, 41] were defined by the number of markets Busan Metropolitan City recognizes. Data on social network difficulties were obtained from the Busan Metropolitan City Community Security Survey [42, 43]. Social network difficulties refer to the rate of experiencing issues in social relationships, such as conflicts with relatives or neighbors, estrangement and disconnection, and difficulties within affiliated groups. Responses were measured on a scale from '1 (not at all experienced)' to '5 (very frequently experienced).' In this study, higher scores indicated more significant difficulties in social networks. The reference points for community-level factors were 2019 for the period before COVID-19 and 2021 for the period after COVID-19.

Data management processes and analysis

This study investigated the impact of individual-level and community-level factors on walking practices based on the social-ecological theory. The research data were categorized into Level 1 (individual level) and Level 2 (community level), where individual characteristics at Level 1 are embedded within the physical or social environment at Level 2. The study comprised data from 54,092 individuals at the individual level and data from 16 districts at the community level.

The general characteristics of the study subjects and the distribution of individual-level factors were analyzed using the Chi-square test. In contrast, the distribution of community-level factors was analyzed using descriptive statistics. Multilevel regression analysis examined individual- and community-level factors [44]. The intraclass correlation coefficient (ICC) was calculated using the variance of individual-level factors (Level 1 σ²) and community-level factors (Level 2 σ²) with the formula ICC = Level 2 σ² / (Level 1 σ² + Level 2 σ²).

To identify the ecological factors influencing walking practice, a series of models were analyzed sequentially: the Null model (Model 1), the individual-level factors model (Model 2), the community-level factors model (Model 3), and the combined individual and community-level factors model (Model 4). The basic form of the multilevel regression model is as follows, all analyses were performed using IBM SPSS Statistics 27.0.

<Level-1 Model (Individual Level)>

Y_ij= β_0j + β_1j(Age_ij) + β₂_j(Education_ij) + β₃_j(Household income_ij) + β₄_j(Marital status_ij) + β₅_j(Subjective health status_ij) + β₆_j(Obesity_ij) + β₇_j(Depression_ij) + β₈_j(No. of chronic diseases_ij) + β₉_j(Smoking_ij) + β₁₀_j(Alcohol_ij) + β₁₁_j(Neighbors’ Trust_ij) + β₁₂_j(Neighborhood Safety Level_ij) + β₁₃_j(Natural Environment_ij) + β₁₄_j(Living Environment_ij) + ε_ij

Y_ij: Outcome variable for individual i in group j

β_0j : Intercept for group j

β_{1 j} , β_{2 j} , …, β_14 j: Slope for predictor X_ijin group j

X_ij: Predictor variable for individual i in group j

ε_ij: Random error term at level 1

<Level-2 Model (Community Level)>

β_0j = γ₀₀+ γ₀₁W_j+ u_0j

β_1j = γ₁₀+ γ₁₁W_j+ u_1j

β_0j: Random effect for the intercept at level-2 for group j

β_1j: Random effect for the slope at level-2 for group j

γ₀₀, γ₀₁: Fixed effects

W_j: Group-level predictor

u_0j, u_1j:Random error terms at level-2

General characteristics of the study participants before and after coronavirus disease 2019 (COVID-19)

Before COVID-19, 53.8% of the participants engaged in walking. Age distribution was determined by analyzing the demographic and social characteristics of individual-level variables. The 50–64 age group had the highest proportion (29.9%), followed by those aged >65 years (29.3%), 35–49 years (23.4%), and 19–34 years (17.4%). Regarding educational attainment, 40.7% of the participants had a university education or higher, 29.8% had a junior high school education or lower, and 29.5% had a high school education. Regarding household income, the highest proportion of men fell into the fourth quartile at 26.8%, whereas the highest proportion of women were in the fourth quartile at 24.5%. Among the participants, 63.2% had a partner, and similar trends were observed across the sexes for various factors. When analyzing health status and behavioral factors, 34.0% of the participants reported a good subjective health status. Obesity was observed in 28.5% of the participants, and 6.7% reported depression. Regarding the number of chronic diseases, 70.3% of the participants had none, 23.1% had one, and 6.6% had two or more chronic diseases. Smoking and alcohol consumption were reported by 16.9% and 53.4% of the participants, respectively. Analysis of health status and health behavior factors based on sex revealed similar trends between the sexes in subjective health status, obesity, depression, number of chronic diseases, and smoking status. However, different trends were observed between the sexes in terms of alcohol consumption. Analyzing the factors of subjective perception of the community, 64.2% of the participants perceived their neighbors’ trust as good, 80.0% perceived the neighborhood safety level as good, and 84.1% perceived the living environment as good. Perception of the natural environment yielded insignificant results.

After COVID-19, 36.4% of the participants engaged in walking practices. Analyzing the demographic and social characteristics of individual-level variables, the age distribution was as follows: individuals aged 50–64 years comprised the highest proportion at 29.9%, followed by those aged >65 years (29.1%), 35–49 years (22.9%), and 19–34 years (18.1%). Regarding educational attainment, 44.4% of the participants had a university education or higher, 29.6% had a high school education, and 26.0% had a junior high school education or lower. Of the participants, 59.6% had partners. Similar trends were observed for various factors across sexes. Regarding household income, the highest proportion of men fell into the fourth quartile (24.1%), whereas the highest proportion of women fell into the first quartile (25.7%). In the analysis of health status and health behavior factors, 44.5% of the participants reported good subjective health status. Obesity was observed in 31.2% of the participants, and 7.2% had depression. Regarding the number of chronic diseases, 71.1% of the participants had none, 22.9% had one, and 6.0% had two or more chronic diseases. Smoking and alcohol consumption were reported by 15.6% and 47.4% of the participants, respectively. Analysis of health status and health behavior factors based on sex revealed similar trends between the sexes in subjective health status, obesity, depression, number of chronic diseases, and smoking status. However, different trends were observed between the sexes in terms of alcohol consumption. Analyzing the factors of subjective perception of the community, 64.0% of the participants perceived their neighbors’ trust as good, 84.8% perceived the neighborhood safety level as good, and 80.8% perceived the natural environment as good. Perceptions of living environment did not yield significant results (Table 1).

Table 1 General Characteristics of Study Participants Before and After COVID-19

Variables		Before COVID-19 (2018~2019)				After COVID-19(2020~2021)
Variables		Total (n=29,018)	Men (n=12,559)	Women (n=16,459)	χ²	Total (n=25,074)	Men (n=11,008)	Women (n=14,066)	χ²
Dependent variable
Walking practice	Yes	15,605(53.8)	6,943(55.3)	8,662(52.6)	20.21^***	9,134(36.4)	4,529(41.1)	4,605(32.7)	188.36^***
	No	13,413(46.2)	5,616(44.7)	7,797(47.4)	20.21^***	15,940(63.6)	6,479(58.9)	9,461(67.3)	188.36^***
Demographic and social characteristics
Age	19~34	5,051(17.4)	2,368(18.9)	2,683(16.3)	46.98^***	4,528(18.1)	2,087(19.0)	2,441(17.4)	39.47^***
	35~49	6,780(23.4)	3,014(24.0)	3,766(22.9)		5,748(22.9)	2,645(24.0)	3,103(22.1)
	50~64	8,683(29.9)	3,609(28.7)	5,074(30.8)		7,503(29.9)	3,103(28.2)	4,400(31.3)
	65 and over	8,504(29.3)	3,568(28.4)	4,936(30.0)		7,295(29.1)	3,173(28.8)	4,122(29.3)
Education	Junior high school	8,640(29.8)	2,755(21.9)	5,885(35.8)	654.88^***	6,529(26.0)	2,133(19.4)	4,396(31.3)	463.67^***
	High school	8,570(29.5)	4,051(32.3)	4,519(27.5)		7,420(29.6)	3,439(31.2)	3,981(28.3)
	University	11,797(40.7)	5,748(45.8)	6,049(36.8)		11,125(44.4)	5,436(49.4)	5,689(40.4)
Household income	Q1	5,581(21.0)	2,059(18.0)	3,522(23.2)	131.69^***	5,100(23.8)	1,973(21.2)	3,127(25.7)	75.45^***
	Q2	5,509(20.7)	2,314(20.2)	3,195(21.1)		4,340(20.2)	1,849(19.8)	2,491(20.5)
	Q3	4,665(17.5)	2,147(18.7)	2,518(16.6)		3,674(17.1)	1,716(18.4)	1,958(16.1)
	Q4	6,787(25.5)	3,074(26.8)	3,713(24.5)		4,974(23.2)	2,246(24.1)	2,728(22.5)
	Q5	4,060(15.3)	1,858(16.2)	2,202(14.5)		3,375(15.7)	1,533(16.5)	1,842(15.2)
Marital status	Partnered	18,330(63.2)	8,611(68.6)	9,719(59.1)	277.79^***	14,935(59.6)	6,961(63.2)	7,974(56.7)	109.86^***
	Single	10,676(36.8)	3,942(31.4)	6,734(40.9)	277.79^***	10,139(40.4)	4,047(36.8)	6,092(43.3)	109.86^***
Health behavior
Subjective health status	Yes	9,873(34.0)	4,768(38.0)	5,105(31.0)	153.33^***	11,154(44.5)	5,462(49.6)	5,692(40.5)	209.31^***
	No	19,144(66.0)	7,790(62.0)	11,354(69.0)	153.33^***	13,919(55.5)	5,546(50.4)	8,373(59.5)	209.31^***
Obesity	Yes	4,046(28.5)	2,250(35.7)	1,796(22.7)	289.78^***	3,838(31.2)	2,115(39.3)	1,723(24.9)	291.40^***
	No	10,175(71.5)	4,059(64.3)	6,116(77.3)	289.78^***	8,451(68.8)	3,264(60.7)	5,187(75.1)	291.40^***
Depression	Yes	976(6.7)	295(4.8)	681(8.2)	65.67^***	915(7.2)	286(5.1)	629(8.8)	66.35^***
	No	13,531(93.8)	5,887(95.2)	7,644(91.8)	65.67^***	11,813(92.8)	5,332(94,9)	6,481(91.2)	66.35^***
No. of chronic diseases	0	20,398(70.3)	8,617(68.6)	11,781(71.6)	31.48^***	17,818(71.1)	7,651(69.5)	10,167(72.3)	29.65^***
	1	6,703(23.1)	3,044(24.2)	3,659(22.2)		5,739(22.9)	2,612(23.7)	3,127(22.2)
	2 or more	1,913(6.6)	897(7.1)	1,016(6.2)		1,516(6.0)	745(6.8)	771(5.5)
Smoking	Yes	4,893(16.9)	4,369(34.8)	524(3.2)	5075.41^***	3,914(15.6)	3,527(32.0)	387(2.8)	4,022.31^***
	No	24,125(83.1)	8,190(65.2)	15,935(96.8)	5075.41^***	21,158(84.4)	7,479(68.0)	13,679(97.2)	4,022.31^***
Alcohol consumption	Yes	15,482(53.4)	8,698(69.3)	6,784(41.2)	2,249.90^***	11,897(47.4)	6,890(62.6)	5,007(35.6)	1,804.66^***
	No	13,535(46.6)	3,861(30.7)	9,674(58.8)	2,249.90^***	13,177(52.6)	4,118(37.4)	9,059(64.4)	1,804.66^***
Subjective perception of the community
Neighbors’ trust	Yes	9,053(64.2)	3,711(61.9)	5,342(66.0)	24.33^***	7,959(64.0)	3,449(62.9)	4,510(64.9)	5.28^*
	No	5,040(35.8)	2,282(38.1)	2,758(34.0)	24.33^***	4,478(36.0)	2,036(37.1)	2,442(35.1)	5.28^*
Neighborhood safety level	Yes	11,542(80.0)	4,976(81.0)	6,566(79.3)	6.19^*	10,748(84.8)	4,812(86.0)	5,936(83.9)	10.48^**
	No	2,878(20.0)	1,167(19.0)	1,711(20.7)	6.19^*	1,922(15.2)	784(14.0)	1,138(16.1)	10.48^**
Natural environment	Yes	11,002(76.0)	4,715(76.4)	6,287(75.6)	1.20	10,263(80.8)	4,611(82.2)	5,652(79.7)	12.81^***
	No	3,483(24.0)	1,456(23.6)	2,027(24.4)	1.20	2,441(19.2)	999(17.8)	1,442(20.3)	12.81^***
Living environment	Yes	12,176(84.1)	5,255(85.3)	6,921(83.3)	10.06^**	11,032(86.8)	4,898(87.3)	6,134(86.4)	2.24
	No	2,296(15.9)	909(14.7)	1,387(16.7)	10.06^**	1,675(13.2)	711(12.7)	964(13.6)	2.24
^* p<.001, ^ p<.01, ^* p<.05

Community environment before and after COVID-19

Analyzing community-level factors before COVID-19, urban parks ranged from 0.07 km² to 12.83 km², with a mean of 2.93 km² (standard deviation [SD]=3.50). Pedestrian paths ranged from 8.29 km to 66.26 km, with a mean of 32.55 km (SD=17.31). Vehicle roadways ranged from 20.51 km to 575.53 km, with a mean of 172.32 km (SD=156.83). The number of public sports facilities ranged from 1 to 31, with a mean of 11.75 (SD=7.55). The number of private sports facilities ranged from 47 to 268, with a mean of 138.45 (SD=65.39). The number of traditional markets ranged from 3 to 33, with a mean of 12.51 (SD=7.52). Social network difficulties ranged from 1.08 to 1.99, with a mean of 1.44 (SD=0.22).

Analyzing community-level factors after COVID-19, urban parks ranged from 0.10 km² to 18.95 km², with a mean of 3.74 km² (SD=5.33). Pedestrian paths ranged from 9.50 km to 74.70 km, with a mean of 39.30 km (SD=18.52). Vehicle roadways ranged from 19.52 km to 564.85 km, with a mean of 162.10 km (SD=154.32). The number of public sports facilities ranged from 5 to 40, with a mean of 15.12 (SD=8.39). The number of private sports facilities ranged from 46 to 322, with a mean of 165.77 (SD=81.81). The number of traditional markets ranged from 1 to 33, with a mean of 11.92 (SD=7.96). Social network difficulties ranged from 1.38 to 1.91, with a mean of 1.64 (SD=0.13) (Table 2).

Table 2 Community Environment Before and After COVID-19

Variables	Before COVID-19 (2018~2019)				After COVID-19 (2020~2021)
Variables	Mean	S.D	Min.	Max.	Mean	S.D	Min.	Max.
Urban parks(㎢)	2.93	3.50	0.07	12.83	3.74	5.33	0.10	18.95
Pedestrian paths(km)	32.55	17.31	8.29	66.26	39.30	18.52	9.50	74.70
Vehicle roadways(km)	172.32	156.83	20.51	575.53	162.10	154.32	19.52	564.85
Public sports facilities(count)	11.75	7.55	1.00	31.00	15.12	8.39	5.00	40.00
Private sports facilities(count)	138.45	65.39	47.00	268.00	165.77	81.81	46.00	322.00
Traditional markets(count)	12.51	7.52	3.00	33.00	11.92	7.96	1.00	33.00
Social network difficulty(score)	1.44	0.22	1.08	1.99	1.64	0.13	1.38	1.91

Effects of individual- and community-level factors on walking practices before and after COVID-19

Multilevel regression analysis was conducted to investigate the impact of individual- and community-level factors on walking practices among urban residents.

Before COVID-19, in the baseline model (Model 1), we analyzed the regional variance in adult walking practices without including any independent variables. The variance of community-level factors affecting walking practices (Level 2 σ²) was 0.01 (95% confidence interval [CI], 0.00–0.01), with an ICC of 0.02, indicating a variance at the community level. As walking practices among urban residents are influenced by individual- and community-level factors, performing a multilevel regression analysis including both levels was deemed appropriate to comprehensively analyze walking practices. In the individual-level factor model (Model 2), the fixed effects of individual-level (Level 1) factors on walking practices were statistically significant. When Model 2 included only individual-level factors, the variance of community-level factors (Level 2 σ²) remained at 0.01 (95% CI, 0.00–0.01), with an ICC of 0.02, indicating that there was still variance at the community-level despite including individual-level variables. In the community-level factor model (Model 3), the fixed effects of community-level (Level 2) factors on walking practices were statistically significant. When only community-level factors were included in Model 3, the variance of community-level factors (Level 2 σ²) was 0.01 (95% CI, 0.01–0.01), with an ICC of 0.01, indicating variance at the community level even without including individual-level variables. To examine the effectiveness of community-level factors on walking practices while controlling for individual-level variables, we analyzed Model 4, which included individual- and community-level factors. In Model 4, with both levels included, the variance of community-level factors (Level 2 σ²) was 0.01 (95% CI, 0.00–0.01), with an ICC of 0.01, indicating that there was still variance at the community level even when individual-level factors were controlled.

After COVID-19, in the null model (Model 1), the variance of community-level factors affecting walking practices (Level 2 σ²) was 0.01 (95% CI, 0.00–0.01), with an ICC of 0.01, indicating that there was a variance at the community level. In the individual-level factors model (Model 2), community-level factors model (Model 3), and combined individual- and community-level factors model (Model 4), the variance of community-level factors (Level 2 σ²) remained at 0.01 (95% CI, 0.00–0.01) for each model, with an ICC of 0.01, indicating consistent variance at the community level.

The results of the analysis of factors influencing walking practices among urban residents before and after the COVID-19 pandemic are presented (Table 3). Among the individual-level demographic and social characteristic factors, women engaged in walking practices less than men. The practice of walking before and after COVID-19 decreased from 0.98 (95% CI, 0.97–0.99) to 0.92 (95% CI, 0.91–0.93). Regarding age, before COVID-19, walking practices were lower among those aged 35–49 years (odds ratio [OR]=0.95; 95% CI, 0.93–0.97) and those aged >65 years (OR=0.96; 95% CI, 0.94–0.99) compared with those aged 19–34 years. However, after COVID-19, walking practices increased among those aged 50–64 years (OR=1.04; 95% CI, 1.01–1.06) and >65 years (OR=1.05; 95% CI, 1.02–1.08). The walking practice among those aged >65 years increased from 0.96 (95% CI, 0.94–0.99) to 1.05 (95% CI, 1.02–1.08). Regarding educational level, no statistical correlation was observed with walking before COVID-19. However, walking practice was lower among those with a university education or higher (OR=0.96; 95% CI, 0.94–0.99) than junior school graduates or lower after COVID-19. In terms of household income, before COVID-19, walking practice was higher in the second quartile (OR=1.06; 95% CI, 1.04–1.08) to the fourth quartile (OR=1.04; 95% CI, 1.01–1.06) than that in the first quartile. After COVID-19, the walking practice was higher in the second quartile (OR=1.04; 95% CI, 1.02–1.07) to the fifth quartile (OR=1.04; 95% CI, 1.01–1.07) than in the first quartile. In terms of household income, before COVID-19, walking practice was higher in the second quartile (OR=1.06; 95% CI, 1.04–1.08) to the fourth quartile (OR=1.04; 95% CI, 1.01–1.06) than that in the first quartile. After COVID-19, the walking practice was higher in the second quartile (OR=1.04; 95% CI, 1.02–1.07) to the fifth quartile (OR=1.04; 95% CI, 1.01–1.07) than in the first quartile. No statistically significant relationship was observed between having a partner and walking before COVID-19. However, in Model 2, after COVID-19, walking practice increased with a partner (OR=1.02; 95% CI, 1.00–1.03). In Model 4, no significant relationship was observed. Regarding health status and health behavior factors, good subjective health status was highly associated with walking practice; however, this association decreased from 1.10 (95% CI, 1.08–1.12) to 1.05 (95% CI, 1.03–1.07). Obesity (OR=0.98; 95% CI, 0.96–1.00) and depression (OR=0.93; 95% CI, 0.90–0.96) were associated with lower walking practice, but no significant relationship was found after COVID-19. Compared with those without chronic diseases, individuals with two or more chronic diseases had lower walking practice before COVID-19 (OR=0.96; 95% CI, 0.93–1.00), but no significant relationship was found after COVID-19. Additionally, significant results showed that individuals with one chronic disease (OR=1.04; 95% CI, 1.02–1.06) had higher walking practice after COVID-19. Smoking did not show a statistically significant relationship before COVID-19, but a significant result showed that walking practice was high at 1.07 (95% CI, 1.04–1.09) after COVID-19. In terms of subjective perception of the community, before COVID-19, a good perception of neighbors’ trust (OR=1.04; 95% CI, 1.02–1.06) was associated with high walking practice, whereas a good perception of the living environment (OR=0.97; 95% CI, 0.94–0.99) was associated with low walking practice. No significant relationships were observed during COVID-19. Although no statistically significant relationship was found before COVID-19, significant results showed that walking practice was high due to a good perception of the local safety level (OR=1.03; 95% CI, 1.00–1.06) after COVID-19.

Among community-level factors, walking practices increased as the length of pedestrian paths increased, both before and after COVID-19. Before COVID-19, walking practice increased as the number of traditional markets increased (OR=1.01; 95% CI, 1.00–1.01), but no significant relationship was found after COVID-19. Before COVID-19, there was no statistically significant relationship with private sports facilities; however, after COVID-19, walking practice increased as the number of private sports facilities increased (OR=1.00; 95% CI, 1.00–1.01). Before COVID-19, there was no statistically significant relationship with social network difficulty, but after COVID-19, as social network difficulty increased (OR=0.84; 95% CI, 0.77–0.93), walking practice was lower. No statistical correlation was observed for urban parks, vehicle roadways, and public sports facilities.

Table 3 Effects of Individual Level and Community Level Factors on Walking Practices Before and After COVID-19

Fixed effects variable			Before COVID-19 (2018~2019)				After COVID-19 (2020~2021)
			Model 1	Model 2	Model 3	Model 4	Model 1	Model 2	Model 3	Model 4
			OR(95% CI)	OR(95% CI)	OR(95% CI)	OR(95% CI)	OR(95% CI)	OR(95% CI)	OR(95% CI)	OR(95% CI)
(Intercept)			1.71^*** (1.65–1.78)	1.73^*** (1.66–1.81)	1.83^* (1.31–2.56)	2.32^*** (1.81–2.98)	1.44^*** (1.41–1.47)	1.35^*** (1.32–1.38)	1.81^** (1.34–2.43)	1.81^*** (1.52–2.16)
Individual level factors (Level 1	Demographic and social characteristics
	Sex (ref. Men)
	Women			0.98^**(0.97–0.99)		0.98^**(0.97–0.99)		0.92^***(0.91–0.93)		0.92^***(0.91–0.93)
	Age (ref.19~34)
		35~49		0.94^***(0.92–0.96)		0.95^***(0.93–0.97)		0.99(0.97–1.02)		1.00(0.98–1.02)
		50~64		0.98(0.96–1.00)		0.98(0.96–1.01)		1.04^**(1.02–1.06)		1.04^**(1.01–1.06)
		65 and over		0.97^**(0.94–0.99)		0.96^**(0.94–0.99)		1.05^***(1.02–1.08)		1.05^**(1.02–1.08)
	Education (ref. Junior high school)
		High school		1.02(1.00–1.03)		1.01(1.00–1.03)		1.01(0.99–1.03)		1.01(0.99–1.03)
		University		1.00(0.98–1.02)		1.00(0.98–1.02)		0.97^**(0.95–0.99)		0.96^**(0.94–0.99)
	Household income (ref. Q1)
		Q2		1.05^***(1.03–1.07)		1.06^***(1.04–1.08)		1.04^***(1.02–1.06)		1.04^***(1.02–1.07)
		Q3		1.05^***(1.02–1.07)		1.05^***(1.03–1.07)		1.05^***(1.03–1.08)		1.06^***(1.04–1.09)
		Q4		1.03^**(1.01–1.05)		1.04^**(1.01–1.06)		1.02^*(1.00–1.05)		1.03^*(1.00–1.05)
		Q5		0.99(1.00–1.03)		0.99(0.96–1.01)		1.04^**(1.01–1.06)		1.04^**(1.01–1.07)
	Marital status (ref. Single)
		Partnered		1.01(1.00–1.02)		1.01(1.00–1.02)		1.02^*(1.00–1.03)		1.02(1.00–1.03)
	Health behavior
	Subjective health status (ref. No)
		Yes		1.09^***(1.07–1.11)		1.10^***(1.08–1.12)		1.04^***(1.03–1.06)		1.05^***(1.03–1.07)
	Obesity (ref. No)
		Yes		0.97^**(0.96–0.99)		0.98^**(0.96–1.00)		1.01(0.99–1.02)		1.01(0.99–1.03)
	Depression (ref. No)
		Yes		0.92^***(0.90–0.95)		0.93^***(0.90–0.96)		0.98(0.95–1.02)		0.96(0.96–1.00)
	No. of chronic diseases (ref. 0)
		1		1.01(0.99–1.04)		1.01(0.99–1.04)		1.04^***(1.02–1.06)		1.04^***(1.02–1.06)
		2 or more		0.97(0.94–1.00)		0.96^**(0.93–1.00)		1.00(0.96–1.03)		0.98(0.95–1.02)
	Smoking (ref. No)
		Yes		0.98(0.96–1.01)		0.99(0.97–1.01)		1.07^***(1.04–1.09)		1.07^***(1.04–1.09)
	Alcohol consumption (ref. No)
		Yes		1.01(0.99–1.03)		1.01(0.99–1.03)		1.01(0.99–1.02)		1.00(0.99–1.02)
	Subjective perception of the community
	Neighbors’ Trust (ref. No)
		Yes		1.03^***(1.01–1.05)		1.04^***(1.02–1.06)		1.01(0.99–1.03)		1.01(0.99–1.03)
	Neighborhood Safety Level (ref. No)
		Yes		1.01(0.99–1.03)		1.01(0.99–1.04)		1.03(1.00–1.06)		1.03^*(1.00–1.06)
	Natural Environment (ref. No)
		Yes		0.99(0.97–1.01)		0.99(0.97–1.02)		1.02(1.00–1.05)		1.02(0.99–1.04)
	Living Environment (ref. No)
		Yes		0.97^*(0.95–1.00)		0.97^*(0.94–0.99)		0.99(0.96–1.02)		0.99(0.96–1.02)
Community level factors (Level 2)	Urban parks				0.99(0.97–1.01)	0.99(0.98–1.01)			1.00(1.00–1.01)	1.00(1.00–1.01)
	Pedestrian paths				1.00(0.99–1.00)	1.01^*(0.99–1.00)			1.00^*(0.99–1.00)	1.00^*(0.99–1.00)
	Vehicle roadways				1.00(0.99–1.00)	1.00(1.00–1.01)			1.00(1.00–1.01)	1.00(1.00–1.01)
	Public sports facilities				1.01(1.00–1.01)	1.00(0.99–1.00)			1.00(1.00–1.01)	1.00(0.99–1.00)
	Private sports facilities				1.00(0.99–1.00)	1.00(0.99–1.00)			1.00(1.00–1.01)	1.00^*(1.00–1.01)
	Traditional Markets				1.01^*(1.00–1.01)	1.01^*(1.00–1.01)			1.00(0.99–1.01)	1.00(0.99–1.00)
	Social network difficulty				0.96(0.80–1.15)	0.88(0.77–1.01)			0.87(0.74–1.03)	0.84^*(0.77–0.93)
			Est.(95% C.I)	Est.(95% C.I)	Est.(95% C.I)	Est.(95% C.I)	Est.(95% C.I)	Est.(95% C.I)	Est.(95% C.I)	Est.(95% C.I)
Level 1 σ²			0.24^*** (0.23–0.25)	0.24^*** (0.23–0.24)	0.24^*** (0.24–0.25)	0.24^*** (0.23–0.24)	0.23^*** (0.23–0.23)	0.22^*** (0.22–0.23)	0.23^*** (0.23–0.24)	0.22^*** (0.22–0.23)
Level 2 σ²			0.01^* (0.00–0.01)	0.01^** (0.00–0.01)	0.01 (0.01–0.01)	0.01 (0.00–0.01)	0.01^* (0.00–0.01)	0.01 (0.00–0.01)	0.01 (0.00–0.01)	0.01 (0.00–0.01)
ICC			0.02	0.02	0.01	0.01	0.01	0.01	0.01	0.01
^* p<.001, ^ p<.01, ^* p<.05

Walking among urban residents improves physical and mental health and fosters social interactions, enhancing social well-being. Additionally, it reduces vehicular traffic, alleviates congestion, decreases urban air pollution, and positively affects climate change, establishing it as a significant public health behavior [45-47]. Specifically, walking is influenced by individual sociodemographic characteristics and physical, mental, and social health. It is also affected by the social and physical environments of the neighborhood. Therefore, this study performed a multilevel regression analysis that included both individual and physical factors of the community.

This study conducted a multidimensional analysis of the factors affecting the walking practices of urban residents before and after the COVID-19 pandemic, considering both individual and community levels. Through this analysis, we aimed to comprehensively understand the effect of the COVID-19 pandemic on the health behaviors of urban residents and provide foundational data for developing strategies to promote health behaviors in future global public health crises. We identified several factors associated with the outcome variables before and after COVID-19. At the individual level, sex, age, education, household income, subjective health status, obesity, depression, number of chronic diseases, smoking, perception of neighbors’ trust, perception of neighborhood safety, and perception of living environment were significantly related to walking practice. At the community level, the availability of pedestrian paths, private sports facilities, traditional markets, and social network difficulties were significantly related to walking. These findings underscore the significance of considering both individual and community contexts when studying the walking practices of urban residents.

At the individual level, women’s walking practices significantly decreased after COVID-19. The COVID-19 pandemic has significantly increased the burden on unpaid households and caregiving responsibilities [48], disproportionately affecting women who have taken on a higher share of these duties. These changes are likely to affect external physical activity. Additionally, because the economically active population comprised more men than women, some physical activity might have been maintained because of men’s continued labor participation, even under social distancing measures. Women initially walked less; however, this decline was more significant after COVID-19. These results suggest that the effect of the COVID-19 pandemic on physical activity varies by sex.

After the COVID-19 pandemic, the likelihood of older adults walking increased with age. Older individuals who did not maintain high levels of physical activity before the pandemic experienced less decline in physical activity than younger individuals. In contrast, younger individuals experienced a more significant decline in their physical activity levels. However, relatively healthy older adults may have increased their physical levels to remain healthy during the pandemic. Before COVID-19, walking practice was high among young individuals aged 19–34; however, after the pandemic, the number of individuals in their 50s and older walking increased. A previous study in Canada reported that adults maintained their physical activity levels during the COVID-19 pandemic and that the elderly increased their physical activity levels [49], which was consistent with the results of this study. These findings indicate that the COVID-19 pandemic has had different effects on physical activity according to age group. In particular, older adults were likely to have increased their walking practices owing to an awareness of the importance of maintaining health and physical activity during the pandemic.

After COVID-19, walking decreased among those with a university education or higher. According to a previous study, higher levels of education were associated with better knowledge about COVID-19. This suggests that participants with higher educational levels are likely to engage in more physical activity because of their understanding of COVID-19 and its risks [50]. They found that highly educated individuals had more vital decision-making abilities [51] and were more likely to choose healthier lifestyles, including regular physical activity [52]. Given that higher educational levels are correlated with better COVID-19 knowledge [53], these individuals are likely to have a stronger intention and capacity to use their decision-making skills to engage in physical activities other than walking [50].

Walking practices varied depending on the household income before COVID-19. However, following the COVID-19 pandemic, walking practices have increased household income proportionately. A previous study in the UK found that activity levels across society decreased after the COVID-19 pandemic, with the decline being most noticeable among individuals living in the most deprived areas. Individuals in these poorer areas have reduced activity levels and are among the least active groups overall [54]. These results suggest that the pandemic affects physical activity differently based on income. High-income individuals who recognized the importance of maintaining health increased their walking practices during the pandemic. In contrast, low-income individuals likely decreased their physical activity due to the economic and social difficulties caused by the pandemic.

After the COVID-19 pandemic, the effect of obesity on walking practices disappeared. This change appears to be due to the widespread effect of COVID-19, which has reduced walking activity across all groups, including obese individuals. Consequently, several individuals are more likely to be overweight, obese, or inactive [55]. This suggests that the pandemic reduces physical activity levels across all population groups, weakening the relationship between obesity and walking.

The effect of depression on walking practice disappeared after the COVID-19 pandemic. COVID-19 has significantly affected even those without depression, leading to a more pronounced decline in walking practices. Depression has been particularly influential during the COVID-19 pandemic [56]. This suggests that the existing relationship between depression and walking weakens because of the widespread effect of the pandemic on overall physical activity levels. Consequently, regardless of their depressive status, several individuals are prone to decreasing their physical activity during the pandemic.

Individuals with one chronic disease walk more often after the COVID-19 pandemic. However, this significance disappeared in patients with two or more chronic diseases. Although those with mild chronic diseases continued walking to maintain and manage their health, those with multiple chronic diseases appeared to have reduced their outdoor activities out of concern that their conditions would worsen due to COVID-19. Diabetes and high blood pressure are recognized risk factors for mortality from COVID-19. During this period, healthcare institutions recommended that patients with multiple chronic conditions refrain from visiting hospitals. Self-awareness of these risks leads to decreased movement, and medical institutions recommend that they refrain from outdoor activities. Additionally, having two or more chronic diseases impacts mobility [51]. A previous study has emphasized the importance of community-based strategies to increase physical activity during the COVID-19 pandemic owing to the combination of chronic diseases and the disproportionate burden of COVID-19 [53]. This highlights the effect of the pandemic on the walking practices of patients with chronic diseases.

Smokers walked more frequently after the COVID-19 pandemic than before. Moreover, physical activity during commuting increases the risk of smoking behavior, and efforts should be made to avoid creating environments where smoking is possible [57]. This suggests that smokers engage in more physical activities, such as walking, during the pandemic. This indicates that smokers have increased opportunities to smoke through outdoor activities.

Before the COVID-19 pandemic, higher perceptions of neighbors’ trust correlated with walking practices, but this effect disappeared after the pandemic period. According to a previous study, before the COVID-19 pandemic, positive perceptions of neighborhood safety levels were positively related to physical activity practices [58]. However, with these dynamics changing due to the widespread effect of COVID-19, the effect of perceptions of neighbors’ trust in walking practices was not observed. This suggests that the pandemic weakens the relationship between mutual trust and community physical activity. It is possible that social distancing and restrictions during the pandemic reduced interactions between neighbors, thereby diminishing the effect of neighbors’ trust in their walking practices.

It was found that the higher the awareness of community safety levels, the greater the practice of walking, a trend not observed before COVID-19. This suggests that safety awareness significantly affects walking practices during the pandemic. After COVID-19, the effect of perceived safety on walking has changed noticeably [59], and perceived neighborhood safety has been correlated with walking [60]. This highlights the importance of community safety in promoting walking practices in the post-COVID-19 period. Therefore, the safer a community is perceived to be, the more likely residents are to actively engage in physical activities, such as walking.

Although the perception of the natural environment was not statistically significant, walking practices increased as the perception of the natural environment increased after the COVID-19 pandemic. According to a study conducted before the COVID-19 pandemic, walking increased as awareness of open space accessibility increased [61]. The importance and accessibility of urban natural areas have increased or been highlighted during the COVID-19 pandemic [62].

At the community level, increases in pedestrian path length are associated with increases in walking practice [28], and this relationship remains consistent before and after the COVID-19 pandemic. According to previous studies, the availability of walking paths and green spaces, where individuals turn walking into safe exercise, played an important role in maintaining physical activity levels during the COVID-19 lockdown [63]. Additionally, a UK study found that individuals living within 800 m of green spaces experienced less mental distress during social distancing restrictions and that improving walking routes for green space access could help improve resilience to future pandemics [63]. These results highlight the importance of walking paths and green spaces and suggest that this infrastructure must be strengthened to maintain physical activity and mental health, even during crises like the pandemic.

Before the COVID-19 pandemic, an increase in traditional markets was associated with increased walking practice [14], but such an association was not observed after the pandemic. The relationship between traditional markets and walking practices has significantly changed due to the COVID-19 pandemic. Before the pandemic, traditional markets were closely associated with increased walking as individuals frequently visited these markets for daily shopping and social interactions. However, during the pandemic, this association diminished because of social distancing measures, fear of contagion, and temporary closures of markets, leading to a disruption in walking practices associated with these areas.

As social network difficulties increased, walking practices decreased, and this statistically significant relationship was evident after the COVID-19 pandemic. Difficulties in maintaining social networks during the COVID-19 pandemic were predicted to be associated with lower levels of physical activity, and individuals experiencing high social network difficulties were less likely to engage in walking or other forms of exercise [64]. These results demonstrated the effect of the pandemic on the relationship between social networks and physical activity. Individuals who lack social support have difficulty maintaining physical activity during the pandemic, leading to a decline in their walking practice.

This study compared the factors influencing urban residents’ walking practices before and after COVID-19, including individual sex, age, household income, education, marital status, subjective health status, obesity, depression, number of chronic diseases, smoking, and alcohol consumption. It also addresses changes in perceptions of neighbors’ trust, neighborhood safety levels, the natural environment, and the living environment. Additionally, this study has methodological significance, as it employed multilevel regression analysis to verify the effect of urban parks, pedestrian paths, vehicle roadways, public sports facilities, private sports facilities, traditional markets, and social network difficulties at the community level on walking practices.

This study has some limitations. First, in multilevel analysis, there is a limitation that the intragroup correlation coefficient, which shows the similarity between lower levels belonging to the same level, needs to be more significant. Second, because this study was conducted in Busan Metropolitan City, the second largest city in Korea, it ischallenging to generalize the findings to all cities. Third, because health behavior data were collected via self-administered survey reports, the possibility of overreporting walking practices and underreporting health risk behaviors cannot be ruled out. Finally, although we tried to collect as much official data as possible by focusing on autonomous districts, there were many cases in which the data were not produced or collected in some regions. This implies that some factors affecting the walking practices were omitted.

In future studies, long-term follow-up studies and short-term changes caused by COVID-19 are required to overcome some of the abovementioned limitations. This approach allows us to derive more comprehensive and generalizable results.

Acknowledgments

Not applicable.

Author contributions

The research project was conceptualized and designed by C.S. and J.P., who were responsible for the overall development and planning of the study. The methodology was designed under the supervision and responsibility of C.S. Data collection, processing, and consolidation were carried out by J.P. The initial draft of the manuscript was written by J.P., while C.S. provided critical revisions and substantial editing of the manuscript. All authors read and approved the final version of the manuscript for publication.

Funding

This study did not receive any financial support.

Data availability

Data related to health behaviors and health outcomes in this study are provided by the Korea Disease Control and Prevention Agency (KDCA). Data will be made public after a research plan is written and reviewed. Additionally, the community environmental data included in the study is also publicly available as open access. : 82-41-719-7391, https://chs.kdca.go.kr/chs/index.do

Ethics declarations

Ethics approval and consent to participate

This study was conducted by the Declaration of Helsinki. This study used raw data from the Community Health Survey. The raw data were requested and approved by the Korea Disease Control and Prevention Agency's Community Health Survey website (https://chs.kdca.go.kr/chs/rawDta/rawDtaProvdMain.do). We did not manipulate or apply microdata outside of this study. Therefore, this study did not require patient or public participation and thus did not require ethical approval.

Consent for publication

The dataset analyzed for this study does not disclose any personally identifiable information. Therefore, consent to publication was not required.

Competing interests

The authors declare no competing interests.

Authors’ Information

Author-1

¹Graduate School, Inje University, Busan, South Korea; [email protected]

Corresponding author

^*Graduate School of Public Health, Inje University, Busanjin-gu, Busan, 47392, South Korea; [email protected]

Correspondence to: Author-1

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Factors influencing walking practice among urban residents before and after the COVID-19 pandemic

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