The Effect of Sleep on the Association Between Built Environment and Good Health Status

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Duszynski, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3880413/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Sleep quantity of city residents and environmental assets that support physical activity may jointly improve residents’ general health. Sufficient sleep also may mediate the effect of activity-related environmental factors on the general health. However, evidence regarding such associations is lacking. Thus, we aimed to investigate the moderating and mediating effects of sleep duration of residents on the association between environmental factors and general health status of city residents. Methods Our panel study used 2018/2019, 2021 to 2022 American Fitness Index® data for the 100 most populated US cities. Study outcome was good health status and exposures were environmental factors – percent of parks within a 10-minute walk, Walk Score®, Bike Score®, Complete Streets policy. Sleeping 7 + hours/day was used as a potential mediator or moderator. For analyses, we adopted crude and multivariable-adjusted linear mixed models. Results Our findings showed that most large cities whose residents slept longer had better baseline health and improvement in the general health status of their residents over time. Sufficient daily sleep showed a moderating effect on the association between environmental indicators and general health status. In the cities with higher percent of sufficient daily sleep, the magnitudes of the positive associations were increased, implying synergistic interactions between sufficient daily sleep and better environmental factors on good health status. However, no mediating effect of sufficient daily sleep was observed on the association between environmental indicators and good health status. Conclusion Our findings suggested a synergistic interaction effect between sufficient daily sleep and physical activity-related environmental factors on good health status. However, sleep duration was not found to be a mediator of the association between environmental indicators and good health status. Population Health Management Built Environment Sleep Duration Health status Local Government Introduction General health is complete physical, mental, and social well-being and not merely the absence of disease or infirmity ( 1 ). Availability of parks within a 10-minute walking distance, city walkability score, whether a city’s environment is good for biking, and having a Complete Streets policy are related to a person’s level of physical activity ( 5 – 8 ), affecting the overall health of residents in a city ( 9 ). Sufficient sleep is also important for optimal personal physical and mental health and well-being ( 10 – 13 ). Synergistic interaction between community physical activity assets and sleep duration are plausible, because such beneficial environmental factors positively affect personal physical activity ( 5 – 8 ), and physical activity and sleep are known to have helpful synergistic effects on health at an individual level ( 14 , 15 ). In addition, recent research reported a possible mediating role of sleep on the association between an individual’s physical activity and health ( 16 – 18 ). Thus, the sleep quantity of city residents may also mediate the association between the availability of community assets and the proportion of residents with good health status. However, no published study was found that examined the effect of sleep on the relationships between community assets and general health status at the city level in the US. Thus, a study which investigates the moderating and mediating effect of sleep on the associations between built environment and population health status will help fill a void in the research literature. The American College of Sports Medicine (ACSM) American Fitness Index® (AFI) program provides annual city-level data that includes environmental factors, sleep, and general health status of the 100 largest US cities. Further, residents included in the AFI cities represent about 20% of the total US population ( 19 ). Therefore, using the AFI data would be of great public health interest for examination of sleep’s moderating or mediating role in the associations between community assets and general health status. We hypothesized that the positive association between environmental factors that are related to the personal level of physical activity and good health status of residents would differ or be mediated by the amount city residents slept. To test these hypotheses, we used the baseline, 2021, and 2022 AFI panel data to examine the potential effect of sleep duration on the association between significant environmental indicators and good health status, and also to evaluate the potential mediating role of sleep duration on the associations between the environmental factors and good health status. Methods Study Data and Unit of Measurement Our study is a panel study in which all indicators were measured at 3 time points ( i.e. , baseline-2018 or 2019, 2021, and 2022) for the same cities, using AFI city-level data between 2018 and 2022. AFI indicators were originally selected by ACSM content experts and other nationally recognized health and fitness experts who understood the importance of community assets in improving healthy behaviors and outcomes ( 20 ). Only modifiable measures were included as environmental factors to enable city policy makers and community stakeholders to effectively improve the most important community assets included in the AFI ( 20 ) and to thereby support the health of residents. The AFI uniquely includes both community assets and personal health indicators for the 100 largest US cities, per current US Census statistics, from reputable, regularly updated, publicly available data sources ( 9 , 20 – 22 ). Because Baton Rouge, LA, was not included in the 2022 AFI data, the city’s 2021 data was substituted, based on assuming an insignificant change between 2021 and 2022. As Spokane, WA has only 2022 AFI data, this city was not included in this study. The 100 cities included in this study are listed in the online AFI report ( 9 ). Because the data used for the AFI were previously collected by other organizations, were publicly available, and were deidentified to protect the confidentiality of respondents, this study was judged to be exempt from review by the Institutional Review Board of Indiana University, and informed consent did not need to be obtained. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. Study Outcome The outcome is the percent of residents in excellent, very good, or good health (hereinafter referred to as good health status) ( 23 ). Health status was gathered from Centers for Disease Control and Prevention (CDC) Behavioral Risk Surveillance System survey (BRFSS)-County data ( 20 ). In the BRFSS Questionnaire, the question regarding health status is “Would you say that in general your health is —" and the responses consist of 1 Excellent, 2 Very Good, 3 Good, 4 Fair, and 5 Poor ( 24 ). The dichotomous variable of health status was calculated based on combining responses 1 to 3 (good or better health) and responses 4 and 5 (fair or poor) ( 23 ). Study Exposures Exposures were the percentage of parks within a 10-minute walk, Walk Score®, Bike Score®, and having a Complete Streets policy. Those variables were selected from among the environmental indicators in the AFI data report because they showed significantly positive associations with general health status of residents in preliminary analyses. They also showed positive correlations with intensive physical activity indicators ( i.e. , meeting aerobic activity guidelines, and meeting both aerobic and strengthening activity guidelines). Walk Score® measures the walkability of any US address, and Bike Score® measures whether a location is good for biking ( 25 ). The Complete Streets policy specifies how a community will plan, design, and maintain streets in order to make all users of all ages and abilities safe ( 26 ). Complete Streets policy was graded on a scale from 0 to 2 by type of policy at the city level, with those including enforcement mechanisms receiving the highest grade ( 20 ). Because Bike Score® and Complete Streets policy were not collected in 2018, our study used their 2019 values as baseline data for analyses of these exposures. For other exposure variables, 2018 data were used as baseline. Sleep as potential effect moderator or mediator The percent of city residents who reported sleeping 7 + hours/day was selected as our potential effect moderator or mediator of the associations between environmental factors and general health status based on published evidence ( 14 – 18 ). The percentage of residents with sufficient sleep also showed a strong positive correlation with both meeting aerobic activity guidelines and meeting both aerobic and strengthening activity guidelines in our preliminary analyses. The sleep variable was collected through the BRFSS Questionnaire ( 20 ). The question for this variable is “On average, how many hours of sleep do you get in a 24 hour period?” ( 27 ). Selection of Covariates The AFI data report included population characteristics for each city from the US American Community Survey and the US Census Bureau data. Percent of residents who were 65 + years old, white, high school graduates+, and their median household income were selected as study covariates, based on published evidence ( 28 – 31 ) and based on preliminary correlation tests between potential confounding factors and the variables of interest. Even though the percent of high school graduate + is correlated with both percent white and median household income, we included the education variable, because all tolerances were above 0.5 (indicating no multicollinearity ( 32 )) in all analyses that adjusted for the 4 covariates when testing the association between environment indicators and health status. We used mean values of covariates between 2018 and 2022 because of the small amount of variation across years and there was missing data in some years. Statistical Analysis Continuous variables were presented as means (standard deviations) and categorical variables were presented as frequency (column percentages). Mean differences on continuous variables were examined by t-tests in cities showing < median vs. ≥median percent in sleeping 7 + hours/day from 2018 to 2022. This study also used Fisher’s exact tests for the comparisons between the two sleep groups in categorical variables. Baseline values were defined as 2018 data, and changes over time in all continuous variables were calculated by subtracting the 2018 data from the 2022 data. Because Bike Score® and Complete Streets policy have been collected since 2019, their 2019 data were used as baseline values and their changes were calculated by subtracting the 2019 data from the 2022 data. To evaluate the hypothesis that sleep health plays a moderating role in the positive association between environmental factors and the good health status of residents, this study tested interaction terms ( i.e. , each exposure × sleep) as independent variables ( 33 ) in all analyses. Sleep was considered a moderator if the interaction term explained a statistically significant amount of variance in the percent of residents with good health status ( 33 , 34 ). Identifying a significant interaction effect between exposure and sleep on good health status would imply that the relationship between exposure and good health depends on sufficient daily sleep. All analyses were conducted by applying crude and multivariable-adjusted linear mixed models (LMMs) to account for the statistical dependency (and yield correct SEs) arising from repeated annual observations from the same cities; we used maximum likelihood estimation and reported unstandardized coefficients, standard errors (SEs), and P -values. In the crude LMM, only the exposure of interest and the time factor were included. In the multivariable-adjusted LMM, this study further adjusted for the four selected covariates ( i.e. , percent of older age, percent of high school+, percent white, and median household income). Stratified analyses by the median value of sleep were conducted using the same LMM approach (separately for the two sleep groups) while excluding the interaction terms. To examine the hypothesis that sleep mediates the positive association between significant environment indicators and general health status, we adopted 3 different types of models required to determine mediation ( 33 , 34 ). Model 1 tested whether the environmental exposures were significantly associated with good health status; Model 2 tested whether the environmental exposures were significantly associated with sufficient sleep; In Model 3, both the exposures and the sleep indicator were entered simultaneously and used to test the association with good health status. Full mediation was established if all results met the following criteria ( 33 , 34 ): 1) statistically significant associations were seen in both Models 1 and 2; 2) in Model 3, sleep was significantly associated with good health status; and 3) the direct relationship between the exposure and good health status was reduced to 0 by the effect of sleep. If the exposure was reduced in absolute size but was different from 0 in Model 3, partial mediation could be concluded ( 33 , 34 ). In all analyses, this study adopted the crude and multivariable-adjusted LMMs. Multivariable-adjusted LMMs included the same covariates that were used in the above models. This study presented the coefficients (SEs) and P -values of the exposure from the first to third model and also of sleep from the third model in all LMMs. PROC MIXED procedures were used for the examination of all hypotheses, in SAS software (Unix 9.4; SAS Institute, Inc., Cary, North Carolina). All tests were two-sided and statistical significance was determined by P < 0.05. Results Demographic and environmental factors and good health status are presented by the median of the baseline sleep indicator among the 100 most populous US cities between 2018 and 2022 ( Table 1 ). Baseline percent (53.7% vs. 50.2%) and change values (4.6% vs. 3.1%) of good health status were higher in cities whose sufficient sleep percent was greater than or equal to the median. The range of baseline good health status was 23.5–63.9% (mean = 53.7%) in cities with better sleep health and 34.2–62.8% (mean = 50.2%) in cities with poorer sleep health. The range of change values in good health status between 2018 and 2022 was from − 6.3–13.1% (mean = 4.6%) in cities with better sleep health and from − 11.6–12.6% (mean = 3.1%) in cities with poorer sleep health. Percent of white residents (61.2% vs. 49.8%) and median household incomes (62,458.8 vs. 52,861.8) were significantly higher in the cities with better sleep health. Among environmental indicators, the baseline values of parks within walking distance, Bike Score®, and Walk Score® were in the 40s to 60s on a 100-point scale in all cities. Baseline values and change values of parks within a walking distance were slightly higher in cities with better sleep health but did not reach statistical significance. Baseline values of Bike Score® were significantly higher in cities with more sufficient sleep, but its change value did not differ between cities with < median vs. ≥median percent in sufficient sleep. Having a Complete Streets policy was more frequent, but not statistically significant, in cities with better sleep health. Walk Score® showed almost no difference between the city sleep groups. Table 1 Demographic and environmental factors and good health status by the median of baseline percent of sufficient daily sleep Baseline percent of residents who get 7 + hours of sleep/day Characteristics <Median (N = 50) ≥Median (N = 50) Baseline percent of good health status c 50.2 (5.8) 53.7 (5.7) Change in percent of good health status 3.1 (4.8) 4.6 (4.0) Demographic factors a Percent 65 years old and older 13.1 (2.3) 12.7 (3.0) Percent white c 49.8 (15.5) 61.2 (13.5) Percent high school graduate+ 86.1 (4.8) 87.0 (7.6) Median household income d 52,861.8 (15,446.6) 62,458.8 (18,601.6) Environmental indicators Baseline percent of parks within a 10-minute walk 64.2 (18.9) 67.2 (19.2) Change in percent of parks within a 10-minute walk 5.0 (4.8) 6.1 (3.9) Baseline Walk Score® 48.7 (16.9) 47.4 (15.8) Change in Walk Score® -0.2 (1.4) 0.05 (1.0) Baseline Bike Score® b 47.8 (9.7) 52.4 (13.5) Change in Bike Score b, e 4.6 (2.6) 3.5 (1.8) Baseline Complete Streets policy b No policy type 15 (30.0) 10 (20.0) PDPOR 20 (40.0) 29 (58.0) Ordinance/law, or tax levy 15 (30.0) 11 (22.0) Recent Complete Streets policy b No policy type 14 (28.0) 9 (18.0) PDPOR 21 (42.0) 29 (58.0) Ordinance/law, or tax levy 15 (30.0) 12 (24.0) Abbreviation: PDPOR, policy, design manual/guide, plan, internal policy/executive order, or resolution. Note: Values are means (standard deviations) for continuous variables and frequencies (column percentages) for categorical variables. Mean differences of all continuous variables between low and high sleep groups were examined by t-tests, and Fisher's exact tests were used for categorical variables for comparisons between groups. All baseline values were collected in 2018 and changes in all continuous variables were calculated by subtracting the 2018 data from the 2022 data. The range of baseline good health status was from 23.5% to 63.9% in cities with better sleep health and from 34.2% to 62.8% in cities with poorer sleep health. The range of change values in good health status between 2018 and 2022 was from -6.3% to 13.1% in cities with better sleep health and from -11.6% to 12.6% in cities with poorer sleep health. a Demographic factors are averages of values between 2018 and 2022; b Bike Score ® , and Complete Streets policy have been collected since 2019, which led to including their 2019 data as baseline values, and change in Bike Score ® was calculated by subtracting the 2019 data from the 2022 data; c P < 0.005; d P < 0.01; e P < 0.05. In Table 2 , the moderating effect of sufficient daily sleep on the associations between environmental indicators and good health status was examined. P -values for the interactions, as well as coefficients (SEs) of associations between exposure and good health status in the stratified analyses by the median of percent of sufficient daily sleeping, are presented. A significant effect of interaction between sleep and parks within a 10-minute walk was found on good health status in both crude and multivariable-adjusted LMMs ( P for interaction = 0.0003 and 0.005, respectively). In the stratified analysis by sleep, the positive association between parks within walking distance and good health status was stronger in cities with better sleep health, compared either with cities with poorer sleep health [Coefficients (SEs): 0.09 (0.03) ( P = 0.005) in ≥ Median vs. 0.03 (0.03) ( P = 0.33) in < Median]. The effect of Walk Score® on good health status was also significantly moderated by sufficient daily sleep in crude and multivariable-adjusted LMMs ( P for interaction = 0.004 and 0.02, respectively). The association between Walk Score® and good health status was stronger in cities with better sleep health compared with cities with poorer sleep health [Coefficients (SEs): 0.14 (0.04) ( P = 0.0008) in ≥ Median vs. 0.05 (0.04) ( P = 0.18) in < Median]. The moderating effect of sufficient sleep on the association between Bike Score® and good health status was observed in crude LMM [ P for interaction = 0.02; Coefficients (SEs): 0.14 (0.06) ( P = 0.01) in ≥ Median vs. 0.07 (0.06) ( P = 0.28) in < Median], but not in the multivariable-adjusted LMM. Meanwhile, the association between having a Complete Streets policy and good health status did not differ by sufficient daily sleep ( i.e. , non-significant interaction). Table 2 Associations between environmental factors and good health status, stratified by sufficient sleep Percent of residents who get 7 + hours of sleep/day a <Median ≥Median Total (All Cities) Coefficient SE P Coefficient SE P P for interaction Coefficient SE P Percent of parks within a 10-minute walk Crude LMM 0.04 0.03 0.23 0.12 0.04 0.002 0.0003 0.05 0.03 0.06 Multivariable-adjusted LMM 0.03 0.03 0.33 0.09 0.03 0.005 0.005 0.05 0.02 0.04 Walk Score® Crude LMM 0.02 0.04 0.61 0.12 0.05 0.01 0.004 0.02 0.03 0.56 Multivariable-adjusted LMM 0.05 0.04 0.18 0.14 0.04 0.0008 0.02 0.06 0.03 0.04 Bike Score® b Crude LMM 0.07 0.06 0.28 0.14 0.06 0.01 0.02 0.10 0.05 0.03 Multivariable-adjusted LMM 0.07 0.06 0.24 0.08 0.05 0.13 0.12 0.06 0.04 0.09 Complete Streets policy (Ref: No policy type) b Crude LMM PDPOR 3.84 1.48 0.007 4.74 1.56 0.003 0.10 3.83 1.15 0.001 Ordinance/law, or tax levy 3.64 1.64 0.02 3.47 1.86 0.07 0.21 2.12 1.34 0.12 Multivariable-adjusted LMM PDPOR 3.88 1.22 0.002 3.57 1.39 0.01 0.16 3.12 0.99 0.002 Ordinance/law, or tax levy 3.30 1.41 0.02 2.16 1.64 0.19 0.35 1.69 1.16 0.15 Abbreviations: LMM, linear mixed model; SE, standard error; PDPOR, policy, design manual/guide, plan, internal policy/executive order, or resolution. Note: Panel data with 3 time points were used in all analyses because percent of residents who get 7+ hours of sleep per day was collected only in 2018, 2021, and 2022. In multivariable-adjusted LMMs, demographic factors, including percent age 65+, percent white, percent high school graduate+, and median household income in addition to time, were adjusted for. a Baseline data is 2018 data; b This study used 2019 data instead of 2018 data for these variables because Bike Score® and Complete Streets policy have been collected since 2019. The mediating effect of sufficient daily sleep on the associations between environmental indicators and good health status is presented in Table 3 . For analyses focused on parks within a 10-minute walk, Models 1, 2, and 3 showed significant associations but sleep did not reduce the association strength between exposure and outcome; furthermore, sleep was not significantly associated with general health status in Model 3. Sleep duration did not show any mediating effect on other analyses using other exposure variables, nor was sleep associated significantly in model 3 for other exposure variables. Table 3 Mediating effect of sufficient daily sleep on associations between environmental factors and good health status Crude LMM Multivariable-adjusted LMM Coefficient SE P Coefficient SE P Percent of parks within a 10-minute walk Model 1: exposure↔outcome 0.05 0.03 0.06 0.05 0.02 0.04 Model 2: exposure↔mediator 0.01 0.02 0.49 0.04 0.02 0.01 Model 3: exposure↔outcome 0.05 0.03 0.05 0.05 0.02 0.04 Model 3: mediator↔outcome 0.03 0.06 0.62 0.02 0.06 0.77 Walk Score® Model 1: exposure↔outcome 0.02 0.03 0.56 0.06 0.03 0.04 Model 2: exposure↔mediator -0.01 0.02 0.66 0.03 0.02 0.10 Model 3: exposure↔outcome 0.02 0.03 0.50 0.06 0.03 0.04 Model 3: mediator↔outcome 0.03 0.06 0.67 0.02 0.06 0.73 Bike Score® a Model 1: exposure↔outcome 0.10 0.05 0.03 0.06 0.04 0.09 Model 2: exposure↔mediator 0.07 0.03 0.02 0.06 0.02 0.02 Model 3: exposure↔outcome 0.10 0.04 0.03 0.06 0.04 0.09 Model 3: mediator↔outcome 0.02 0.06 0.76 0.02 0.06 0.77 Complete Streets policy (Ref: No policy type) a Model 1: exposure↔outcome PDPOR 3.83 1.15 0.001 3.12 0.99 0.002 Ordinance/law, or tax levy 2.12 1.34 0.12 1.69 1.16 0.15 Model 2: exposure↔mediator PDPOR 0.45 0.84 0.59 0.94 0.70 0.18 Ordinance/law, or tax levy -1.09 0.96 0.26 0.56 0.82 0.50 Model 3: exposure↔outcome PDPOR 3.74 1.12 0.00 3.05 0.96 0.002 Ordinance/law, or tax levy 1.96 1.29 0.13 1.58 1.13 0.16 Model 3: mediator↔outcome 0.04 0.06 0.49 0.04 0.06 0.57 Abbreviations: LMM, linear mixed model; SE, standard error; PDPOR, policy, design manual/guide, plan, internal policy/executive order, or resolution. Note: Panel data with 3 time points were used in all analyses because percent of residents who get 7+ hours of sleep per day was collected only in 2018, 2021, and 2022. To test for mediation, Models 1, 2, and 3 were used, in which exposure, outcome, and mediator indicate built environment/policy factor, percent of good health status, and percent of residents who get 7+ hours of sleep/day, respectively. In multivariable-adjusted LMMs, demographic factors including percent age 65+, percent white, percent high school graduate+, and median household income in addition to time, were adjusted for. a This study used 2019 data instead of 2018 data for these variables because Bike Score® and Complete Streets policy have been collected since 2019. Discussion Our findings showed that most large cities whose residents slept longer had better baseline health and improvement in general health status of their residents over time. However, the maximum baseline value of good health status was 63.9% and maximum change was 13.1% in cities with better sleep health, implying room for improvement in all cities. Except for Walk Score®, baseline values in exposure variables were slightly higher in cities with better sleep health than in cities with poorer sleep health. Change in exposures were not significantly different between the city groups. However, overall values of the environmental indicators reflected the need for improvement. As we hypothesized, sufficient daily sleep showed a moderating effect on the association between environmental indicators and general health status. In the cities with residents who had higher percentages of sufficient daily sleep, the magnitudes of the positive associations were increased, implying synergistic interactions between sufficient daily sleep and better environmental factors on good health status. However, no mediating effect of sufficient daily sleep was observed on the association between environmental indicators and good health status. We demonstrated the synergistic moderating effect of sufficient daily sleep on the association of each of the following with good health status: parks within a walking distance, Walk Score®, and Bike Score®. Such an interaction effect on general health was also found in recent studies in which unhealthy behaviors were jointly associated with poor-quality built environment, and too little or an excessive amount of sleep were associated with poor health status ( 36 , 37 ) and the risk of morbidity and mortality ( 38 , 39 ). Our findings also show that selected environment factors are positively correlated with intensive physical activity, which implies that sufficient sleep amplifies the beneficial effects of physical activity on general health. In fact, many recent studies have demonstrated the synergistic effect of sleep and physical activity on general health status at the individual level ( 14 , 15 ). In a recent mid-aged UK Biobank study, the association between low physical activity and risk of all-cause and cause-specific mortality was exacerbated by poor sleep, as classified with a composite sleep score that included duration ( 14 ). Another secondary analysis that used the 2017–2018 National Survey of Children’s Health demonstrated the synergistic interaction effect between insufficient sleep duration and low physical activity on mental health disorders ( 15 ). This synergistic interaction between sleep quantity and physical activity level can be explained by their interrelationship ( i.e. , 2 bi-directional mechanisms). To be specific, physical activity positively affects sleep duration ( 40 ) and lack of sleep also may be a key impediment to promoting and maintaining a physically active lifestyle ( 41 ). The first mechanism is related to the effects of exercise on central nervous system, body temperature, cardiac/autonomic, endocrine and metabolic functions during sleep and overall mood ( 42 ). The beneficial effects of exercise on sleep have been proven in many studies ( 43 – 45 ) in which physical activity is an effective intervention for those having insufficient sleep duration. The second mechanism is more complex and rarely discussed. Insufficient sleep impairs cognitive performance, mood, glucose metabolism, appetite regulation, and immune function ( 46 ), which may play a negative role in physical activity. Moreover, the process of sleep may affect the brain at an endocrine level independent of the hormonal regulation of metabolism and waste removal at the cellular level ( 47 ), which may lead to changes in body temperature, cytokine concentrations, energy metabolic rate, growth hormone secretion, nervous/cardiovascular system, symptoms and disorders of mood and anxiety, neurotrophic factor secretion, and fitness level ( 48 ). Thus, these 2 mechanisms may help to explain the synergistic interaction effect on general health observed in the current study. Though no mediating effect of sleep quantity on the association between environmental factors and good health status was found here, previous studies have shown sleep’s mediating role in the association between greater physical activity and good health status ( 16 – 18 ). The mediating mechanism can be explained in two steps. The first step is that the activity-related environmental factors affect sleep quantity; in the second step, the change in sleep quantity influences general health status. There is evidence supporting the first mechanism because sleep has been shown to be affected by neighborhood green space, walkability, safety, and built environment ( 49 ). In another study, green space exposures were associated with better sleep quality and quantity ( 50 ), which may be due to increased physical activity and spending time in a place with better air quality. Additionally, a study reported that step counts and daily active time (which are related to neighborhood walkability) can lead to better sleep quality and longer sleep duration ( 51 ). A high-quality environment that enables bicycling safely may result in better sleep as well as stress relief ( 52 ). Having a Complete Streets policy can translate into high-quality walkability and a good location for cycling, which in turn can promote and support a resident’s physical activity, because it ensures safe access for all pedestrians, bicyclists, motorists, and transit riders ( 53 ). The second mechanism can be partially explained by our finding of a positive correlation between sufficient daily sleep and good health status. In general, sufficient sleep is acknowledged as being essential to optimal general health status ( 10 ), and to influence brain performance ( 11 ), mental health ( 12 ), and physical health ( 13 ). Thus, insufficient sleep is more likely to cause significant morbidity and mortality ( 54 ). Taken together, the literature suggests a mediating effect of sleep on the association between activity-related environmental factors and good health status. A potential reason that the current study could not replicate the effect is insufficient statistical power, which was likely exacerbated in the stratified analyses. In addition, sleep was collected only for the last 3 years, which could reduce information and reduce power. We also found that all cities studied still had much room for improvement in both quality of environmental and personal sleep health. City and public health leaders’ collaboration, including setting and sharing best practices of the fittest city, would help to gradually satisfy unmet needs. There have been some federal strategies for integrating active living and health ( 55 ). Arlington, VA, the current fittest city in the AFI, shared strategies for advocating cycling paths ( 56 ). Smart growth approaches also contributed to development of mixed land use, walkable and active communities, and increased availability of diverse public transportation choices ( 57 ). Such approaches could serve as a basis for developing better strategies to improve public health. In addition, updated regulations and continuous public investment are needed for development of walkable districts, greening of abandoned areas, and development of cycling paths and lanes ( 55 ). Our study has several strengths. First, this is the first investigation of the moderating or mediating effect of sleep on the associations between activity-related environmental factors and general health status using US city data. Second, AFI data integrated environmental indicators and personal health indicators. Third, we included only modifiable measures in order to enable city leaders to implement targeted programs and policies to advance population fitness ( 20 ). Fourth, the indicators were selected, reviewed, and updated each year by Fitness Index content experts, ACSM staff, and technical consultants to ensure that the data are up-to-date and include the best measures available ( 20 ). Fifth, we also considered demographic and socioeconomic factors that allowed us to control for potential confounders in our analyses. Our study also has several limitations. First, 2022 data on Baton Rouge, Louisiana, were not collected, which necessitated the replacement of the missing values with the city’s 2021 values, based on assuming little change between 2021 and 2022. This might have introduced some bias. However, Baton Rouge indicators changed little between 2018 and 2021. In addition, data imputation of only one city is unlikely to greatly affect overall results, which may help justify our analysis and results. Second, because Bike Score® and Complete Streets policy were not collected in 2018, we used 2 different baseline years ( i.e. , 2018 or 2019) according to the available baseline year of each exposure. This may disrupt the uniformity of our examination of the interaction effects on general health, but this is a common issue when using secondary data ( 58 ), and the current study had no further missing values in the tailored panel data. Moreover, once the AFI data was gathered, the missing data was addressed ( 20 ). Third, much of our study used self-reported data gathered via questionnaires and surveys, which might bring about some biases ( 59 ). However, the AFI included only indicators from reputable and regularly updated public sources to ensure validity and reliability ( 20 ). Fourth, we have only 100 city samples, which might introduce lower statistical power, depending on the unknown population effect size magnitudes. Lower power might explain statistically nonsignificant findings for some associations. In conclusion, we identified a synergistic interaction effect between sufficient daily sleep among city residents and physical activity-related environmental factors related on good health status of residents. Sleep duration was not found to be a mediator of the association between environmental indicators and good health status. Our findings also suggested that the largest US cities still have much room for improvement in both environment and sleep adequacy, which might be addressed by continuous proactive collaboration between city and public health leaders and by creating a model informed by lessons from the fittest cities as reported in the ACSM AFI. Further research is warranted to better understand the biological mechanisms that modulate the dynamic interplay between physical activity level and sleep quantity, and to examine the mediating role of sleep health on the association between environmental assets and general health status using a larger data set with more than 100 US cities. Declarations Conflict of interest The authors declare no conflicting interests. Acknowledgements The content is solely the responsibility of the authors and does not necessarily represent the official views of the American College of Sports Medicine. We would like to thank Gretchen S. Patch from American College of Sports Medicine for the valuable contribution to data provision. Statement of authors’ contributions to manuscript BS, JH, and TWZ designed the research; BS analyzed data and wrote the paper; TWZ provided the data; POM provided statistical expertise; All authors contributed significant review, revisions, and data interpretation; BS and JH had primary responsibility for final content. All authors read and approved the final manuscript. Clinical Trial Number Not applicable Ethical Approval Because the American Fitness Index® data used for our study was made by collecting diverse, publicly available data and modified into deidentified data to protect the confidentiality of respondents, this study was judged to be exempt from review by the Institutional Review Board of Indiana University. Funding There was no financial support. Availability of data and materials Data described in the manuscript and analytic code will be made available upon request to the American College of Sports Medicine pending application review and approval. 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SAS Global Forum 2009; 2019. Allison PD. Longitudinal Data Analysis Using SAS: Statistical Horizons; 2019 [Available from: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://statisticalhorizons.com/wp-content/uploads/2021/11/Longitudinal-Data-Analysis-Using-SAS.pdf. Geiger SD, Sabanayagam C, Shankar A. The relationship between insufficient sleep and self-rated health in a nationally representative sample. J Environ Public Health. 2012;2012:518263. Shankar A, Charumathi S, Kalidindi S. Sleep duration and self-rated health: the national health interview survey 2008. Sleep. 2011;34(9):1173-7. Martinez-Gomez D, Guallar-Castillon P, Leon-Munoz LM, Lopez-Garcia E, Rodriguez-Artalejo F. Combined impact of traditional and non-traditional health behaviors on mortality: a national prospective cohort study in Spanish older adults. BMC Med. 2013;11:47. Ding D, Rogers K, Macniven R, Kamalesh V, Kritharides L, Chalmers J, et al. Revisiting lifestyle risk index assessment in a large Australian sample: should sedentary behavior and sleep be included as additional risk factors? Prev Med. 2014;60:102-6. Dolezal BA, Neufeld EV, Boland DM, Martin JL, Cooper CB. Interrelationship between Sleep and Exercise: A Systematic Review. Adv Prev Med. 2017;2017:1364387. Kline CE. The bidirectional relationship between exercise and sleep: Implications for exercise adherence and sleep improvement. Am J Lifestyle Med. 2014;8(6):375-9. Uchida S, Shioda K, Morita Y, Kubota C, Ganeko M, Takeda N. Exercise effects on sleep physiology. Front Neurol. 2012;3:48. Durcan L, Wilson F, Cunnane G. The effect of exercise on sleep and fatigue in rheumatoid arthritis: a randomized controlled study. J Rheumatol. 2014;41(10):1966-73. Loppenthin K, Esbensen BA, Jennum P, Ostergaard M, Christensen JF, Thomsen T, et al. Effect of intermittent aerobic exercise on sleep quality and sleep disturbances in patients with rheumatoid arthritis - design of a randomized controlled trial. BMC Musculoskelet Disord. 2014;15:49. Yamamoto U, Mohri M, Shimada K, Origuchi H, Miyata K, Ito K, et al. Six-month aerobic exercise training ameliorates central sleep apnea in patients with chronic heart failure. J Card Fail. 2007;13(10):825-9. Halson SL. Sleep in elite athletes and nutritional interventions to enhance sleep. Sports Med. 2014;44 Suppl 1(Suppl 1):S13-23. Petit JM, Burlet-Godinot S, Magistretti PJ, Allaman I. Glycogen metabolism and the homeostatic regulation of sleep. Metab Brain Dis. 2015;30(1):263-79. Kredlow MA, Capozzoli MC, Hearon BA, Calkins AW, Otto MW. The effects of physical activity on sleep: a meta-analytic review. J Behav Med. 2015;38(3):427-49. Hunter JC, Hayden KM. The association of sleep with neighborhood physical and social environment. Public Health. 2018;162:126-34. Shin JC, Parab KV, An R, Grigsby-Toussaint DS. Greenspace exposure and sleep: A systematic review. Environ Res. 2020;182:109081. Sullivan Bisson AN, Robinson SA, Lachman ME. Walk to a better night of sleep: testing the relationship between physical activity and sleep. Sleep Health. 2019;5(5):487-94. Knight B. Cycling and Sleep: 7 Ways Riding Helps You Snooze Better: Brooklyn Fixed Gear; [updated 27 July 2022. Complete Streets: Smart Growth America; [Available from: https://smartgrowthamerica.org/what-are-complete-streets/. Grandner MA. Sleep, Health, and Society. Sleep Med Clin. 2017;12(1):1-22. Hutch DJ, Bouye KE, Skillen E, Lee C, Whitehead L, Rashid JR. Potential strategies to eliminate built environment disparities for disadvantaged and vulnerable communities. Am J Public Health. 2011;101(4):587-95. Hanson R, Young G. Active living and biking: tracing the evolution of a biking system in Arlington, Virginia. J Health Polit Policy Law. 2008;33(3):387-406. Dalbey M. Implementing smart growth strategies in rural America: development patterns that support public health goals. J Public Health Manag Pract. 2008;14(3):238-43. Davis-Kean PE, Jager J, Maslowsky J. Answering Developmental Questions Using Secondary Data. Child Dev Perspect. 2015;9(4):256-61. Althubaiti A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc. 2016;9:211-7. Additional Declarations No competing interests reported. Supplementary Files STROBEStatement.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3880413","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269159202,"identity":"2de1fe03-5fa5-44e2-b6be-65865f229018","order_by":0,"name":"Bojung Seo","email":"","orcid":"","institution":"Indiana University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bojung","middleName":"","lastName":"Seo","suffix":""},{"id":269159203,"identity":"9cd1e3fe-3139-4214-9e17-af12934b55e5","order_by":1,"name":"Hongmei Nan","email":"","orcid":"","institution":"Indiana University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongmei","middleName":"","lastName":"Nan","suffix":""},{"id":269159204,"identity":"2cf89650-a415-4922-a139-94b0d8a058db","order_by":2,"name":"Patrick O Monahan","email":"","orcid":"","institution":"Indiana University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"O","lastName":"Monahan","suffix":""},{"id":269159205,"identity":"40c36a37-dede-4c9e-b26f-9fb4a1044e95","order_by":3,"name":"Thomas J. Duszynski","email":"","orcid":"","institution":"Indiana University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"J.","lastName":"Duszynski","suffix":""},{"id":269159206,"identity":"008a3838-a066-465a-b1a6-6009a2257382","order_by":4,"name":"Walter R. Thompson","email":"","orcid":"","institution":"Georgia State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Walter","middleName":"R.","lastName":"Thompson","suffix":""},{"id":269159207,"identity":"e73c0318-230e-434d-97c5-552ce713a51a","order_by":5,"name":"Terrell W. Zollinger","email":"","orcid":"","institution":"Indiana University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Terrell","middleName":"W.","lastName":"Zollinger","suffix":""},{"id":269159208,"identity":"cce4af2d-462e-4a7c-a3c5-a84b7ba77449","order_by":6,"name":"Jiali Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACxmYGxsNAWo4NxOMhUgsDSIsx8VpAAKQlsYFoLcztzA8OF9TcSe+TSGB88LaNKIexGRyecexZbptEArPhXOK0MBgc5mE7DNLCJs1LnBb2D4d5/h1OZ5NIYP9NpBYeg8O8bYcTgFrYmInVUnCYt++wYRvPw2bJOeeI0GLYf3zjY55vh+Xl25MPfnhTRoyWBoSFDThVoQB54pSNglEwCkbBiAYAYYgza6CWoSoAAAAASUVORK5CYII=","orcid":"","institution":"Indiana University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jiali","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-01-20 02:44:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3880413/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3880413/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50632850,"identity":"4fe7f8e5-2b24-4b4e-833c-d1d9a5c5a419","added_by":"auto","created_at":"2024-02-05 01:53:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":363094,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3880413/v1/befc6b93-6841-4025-8706-524f5b1b78e1.pdf"},{"id":50315043,"identity":"8b9a5099-9b5c-4ac8-9c45-2e5d3af122ba","added_by":"auto","created_at":"2024-01-29 15:34:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31797,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEStatement.docx","url":"https://assets-eu.researchsquare.com/files/rs-3880413/v1/067992e929d42fb33e840d18.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effect of Sleep on the Association Between Built Environment and Good Health Status","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGeneral health is complete physical, mental, and social well-being and not merely the absence of disease or infirmity (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Availability of parks within a 10-minute walking distance, city walkability score, whether a city\u0026rsquo;s environment is good for biking, and having a Complete Streets policy are related to a person\u0026rsquo;s level of physical activity (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), affecting the overall health of residents in a city (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Sufficient sleep is also important for optimal personal physical and mental health and well-being (\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Synergistic interaction between community physical activity assets and sleep duration are plausible, because such beneficial environmental factors positively affect personal physical activity (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and physical activity and sleep are known to have helpful synergistic effects on health at an individual level (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In addition, recent research reported a possible mediating role of sleep on the association between an individual\u0026rsquo;s physical activity and health (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Thus, the sleep quantity of city residents may also mediate the association between the availability of community assets and the proportion of residents with good health status. However, no published study was found that examined the effect of sleep on the relationships between community assets and general health status at the city level in the US. Thus, a study which investigates the moderating and mediating effect of sleep on the associations between built environment and population health status will help fill a void in the research literature.\u003c/p\u003e \u003cp\u003eThe American College of Sports Medicine (ACSM) American Fitness Index\u0026reg; (AFI) program provides annual city-level data that includes environmental factors, sleep, and general health status of the 100 largest US cities. Further, residents included in the AFI cities represent about 20% of the total US population (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Therefore, using the AFI data would be of great public health interest for examination of sleep\u0026rsquo;s moderating or mediating role in the associations between community assets and general health status.\u003c/p\u003e \u003cp\u003eWe hypothesized that the positive association between environmental factors that are related to the personal level of physical activity and good health status of residents would differ or be mediated by the amount city residents slept. To test these hypotheses, we used the baseline, 2021, and 2022 AFI panel data to examine the potential effect of sleep duration on the association between significant environmental indicators and good health status, and also to evaluate the potential mediating role of sleep duration on the associations between the environmental factors and good health status.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Data and Unit of Measurement\u003c/h2\u003e\n \u003cp\u003eOur study is a panel study in which all indicators were measured at 3 time points (\u003cem\u003ei.e.\u003c/em\u003e, baseline-2018 or 2019, 2021, and 2022) for the same cities, using AFI city-level data between 2018 and 2022. AFI indicators were originally selected by ACSM content experts and other nationally recognized health and fitness experts who understood the importance of community assets in improving healthy behaviors and outcomes (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e). Only modifiable measures were included as environmental factors to enable city policy makers and community stakeholders to effectively improve the most important community assets included in the AFI (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e) and to thereby support the health of residents. The AFI uniquely includes both community assets and personal health indicators for the 100 largest US cities, per current US Census statistics, from reputable, regularly updated, publicly available data sources (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e). Because Baton Rouge, LA, was not included in the 2022 AFI data, the city\u0026rsquo;s 2021 data was substituted, based on assuming an insignificant change between 2021 and 2022. As Spokane, WA has only 2022 AFI data, this city was not included in this study. The 100 cities included in this study are listed in the online AFI report (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eBecause the data used for the AFI were previously collected by other organizations, were publicly available, and were deidentified to protect the confidentiality of respondents, this study was judged to be exempt from review by the Institutional Review Board of Indiana University, and informed consent did not need to be obtained. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Outcome\u003c/h2\u003e\n \u003cp\u003eThe outcome is the percent of residents in excellent, very good, or good health (hereinafter referred to as good health status) (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e). Health status was gathered from Centers for Disease Control and Prevention (CDC) Behavioral Risk Surveillance System survey (BRFSS)-County data (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e). In the BRFSS Questionnaire, the question regarding health status is \u003cem\u003e\u0026ldquo;Would you say that in general your health is \u0026mdash;\u0026quot;\u003c/em\u003e and the responses consist of 1 Excellent, 2 Very Good, 3 Good, 4 Fair, and 5 Poor (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e). The dichotomous variable of health status was calculated based on combining responses 1 to 3 (good or better health) and responses 4 and 5 (fair or poor) (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Exposures\u003c/h2\u003e\n \u003cp\u003eExposures were the percentage of parks within a 10-minute walk, Walk Score\u0026reg;, Bike Score\u0026reg;, and having a Complete Streets policy. Those variables were selected from among the environmental indicators in the AFI data report because they showed significantly positive associations with general health status of residents in preliminary analyses. They also showed positive correlations with intensive physical activity indicators (\u003cem\u003ei.e.\u003c/em\u003e, meeting aerobic activity guidelines, and meeting both aerobic and strengthening activity guidelines).\u003c/p\u003e\n \u003cp\u003eWalk Score\u0026reg; measures the walkability of any US address, and Bike Score\u0026reg; measures whether a location is good for biking (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e). The Complete Streets policy specifies how a community will plan, design, and maintain streets in order to make all users of all ages and abilities safe (\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e). Complete Streets policy was graded on a scale from 0 to 2 by type of policy at the city level, with those including enforcement mechanisms receiving the highest grade (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eBecause Bike Score\u0026reg; and Complete Streets policy were not collected in 2018, our study used their 2019 values as baseline data for analyses of these exposures. For other exposure variables, 2018 data were used as baseline.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eSleep as potential effect moderator or mediator\u003c/h2\u003e\n \u003cp\u003eThe percent of city residents who reported sleeping 7\u0026thinsp;+\u0026thinsp;hours/day was selected as our potential effect moderator or mediator of the associations between environmental factors and general health status based on published evidence (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e). The percentage of residents with sufficient sleep also showed a strong positive correlation with both meeting aerobic activity guidelines and meeting both aerobic and strengthening activity guidelines in our preliminary analyses. The sleep variable was collected through the BRFSS Questionnaire (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e). The question for this variable is \u003cem\u003e\u0026ldquo;On average, how many hours of sleep do you get in a 24 hour period?\u0026rdquo;\u003c/em\u003e (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSelection of Covariates\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe AFI data report included population characteristics for each city from the US American Community Survey and the US Census Bureau data. Percent of residents who were 65\u0026thinsp;+\u0026thinsp;years old, white, high school graduates+, and their median household income were selected as study covariates, based on published evidence (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e) and based on preliminary correlation tests between potential confounding factors and the variables of interest. Even though the percent of high school graduate\u0026thinsp;+\u0026thinsp;is correlated with both percent white and median household income, we included the education variable, because all tolerances were above 0.5 (indicating no multicollinearity (\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e)) in all analyses that adjusted for the 4 covariates when testing the association between environment indicators and health status. We used mean values of covariates between 2018 and 2022 because of the small amount of variation across years and there was missing data in some years.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eContinuous variables were presented as means (standard deviations) and categorical variables were presented as frequency (column percentages). Mean differences on continuous variables were examined by t-tests in cities showing\u0026thinsp;\u0026lt;\u0026thinsp;median vs. \u0026ge;median percent in sleeping 7\u0026thinsp;+\u0026thinsp;hours/day from 2018 to 2022. This study also used Fisher\u0026rsquo;s exact tests for the comparisons between the two sleep groups in categorical variables. Baseline values were defined as 2018 data, and changes over time in all continuous variables were calculated by subtracting the 2018 data from the 2022 data. Because Bike Score\u0026reg; and Complete Streets policy have been collected since 2019, their 2019 data were used as baseline values and their changes were calculated by subtracting the 2019 data from the 2022 data.\u003c/p\u003e\n \u003cp\u003eTo evaluate the hypothesis that sleep health plays a moderating role in the positive association between environmental factors and the good health status of residents, this study tested interaction terms (\u003cem\u003ei.e.\u003c/em\u003e, each exposure \u0026times; sleep) as independent variables (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e) in all analyses. Sleep was considered a moderator if the interaction term explained a statistically significant amount of variance in the percent of residents with good health status (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e). Identifying a significant interaction effect between exposure and sleep on good health status would imply that the relationship between exposure and good health depends on sufficient daily sleep. All analyses were conducted by applying crude and multivariable-adjusted linear mixed models (LMMs) to account for the statistical dependency (and yield correct SEs) arising from repeated annual observations from the same cities; we used maximum likelihood estimation and reported unstandardized coefficients, standard errors (SEs), and \u003cem\u003eP\u003c/em\u003e-values. In the crude LMM, only the exposure of interest and the time factor were included. In the multivariable-adjusted LMM, this study further adjusted for the four selected covariates (\u003cem\u003ei.e.\u003c/em\u003e, percent of older age, percent of high school+, percent white, and median household income). Stratified analyses by the median value of sleep were conducted using the same LMM approach (separately for the two sleep groups) while excluding the interaction terms.\u003c/p\u003e\n \u003cp\u003eTo examine the hypothesis that sleep mediates the positive association between significant environment indicators and general health status, we adopted 3 different types of models required to determine mediation (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e). Model 1 tested whether the environmental exposures were significantly associated with good health status; Model 2 tested whether the environmental exposures were significantly associated with sufficient sleep; In Model 3, both the exposures and the sleep indicator were entered simultaneously and used to test the association with good health status. Full mediation was established if all results met the following criteria (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e): 1) statistically significant associations were seen in both Models 1 and 2; 2) in Model 3, sleep was significantly associated with good health status; and 3) the direct relationship between the exposure and good health status was reduced to 0 by the effect of sleep. If the exposure was reduced in absolute size but was different from 0 in Model 3, partial mediation could be concluded (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e). In all analyses, this study adopted the crude and multivariable-adjusted LMMs. Multivariable-adjusted LMMs included the same covariates that were used in the above models. This study presented the coefficients (SEs) and \u003cem\u003eP\u003c/em\u003e-values of the exposure from the first to third model and also of sleep from the third model in all LMMs.\u003c/p\u003e\n \u003cp\u003ePROC MIXED procedures were used for the examination of all hypotheses, in SAS software (Unix 9.4; SAS Institute, Inc., Cary, North Carolina). All tests were two-sided and statistical significance was determined by \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDemographic and environmental factors and good health status are presented by the median of the baseline sleep indicator among the 100 most populous US cities between 2018 and 2022 \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e Baseline percent (53.7% vs. 50.2%) and change values (4.6% vs. 3.1%) of good health status were higher in cities whose sufficient sleep percent was greater than or equal to the median. The range of baseline good health status was 23.5\u0026ndash;63.9% (mean\u0026thinsp;=\u0026thinsp;53.7%) in cities with better sleep health and 34.2\u0026ndash;62.8% (mean\u0026thinsp;=\u0026thinsp;50.2%) in cities with poorer sleep health. The range of change values in good health status between 2018 and 2022 was from \u0026minus;\u0026thinsp;6.3\u0026ndash;13.1% (mean\u0026thinsp;=\u0026thinsp;4.6%) in cities with better sleep health and from \u0026minus;\u0026thinsp;11.6\u0026ndash;12.6% (mean\u0026thinsp;=\u0026thinsp;3.1%) in cities with poorer sleep health. Percent of white residents (61.2% vs. 49.8%) and median household incomes (62,458.8 vs. 52,861.8) were significantly higher in the cities with better sleep health. Among environmental indicators, the baseline values of parks within walking distance, Bike Score\u0026reg;, and Walk Score\u0026reg; were in the 40s to 60s on a 100-point scale in all cities. Baseline values and change values of parks within a walking distance were slightly higher in cities with better sleep health but did not reach statistical significance. Baseline values of Bike Score\u0026reg; were significantly higher in cities with more sufficient sleep, but its change value did not differ between cities with \u0026lt;\u0026thinsp;median vs. \u0026ge;median percent in sufficient sleep. Having a Complete Streets policy was more frequent, but not statistically significant, in cities with better sleep health. Walk Score\u0026reg; showed almost no difference between the city sleep groups.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographic and environmental factors and good health status by the median of baseline percent of sufficient daily sleep\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBaseline percent of residents who get 7\u0026thinsp;+\u0026thinsp;hours of sleep/day\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;Median (N\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;Median (N\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline percent of good health status \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.2 (5.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.7 (5.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChange in percent of good health status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.1 (4.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.6 (4.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemographic factors \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercent 65 years old and older\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.1 (2.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.7 (3.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercent white \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.8 (15.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.2 (13.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercent high school graduate+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86.1 (4.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.0 (7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian household income \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52,861.8 (15,446.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62,458.8 (18,601.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnvironmental indicators\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline percent of parks within a 10-minute walk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.2 (18.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.2 (19.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChange in percent of parks within a 10-minute walk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.0 (4.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.1 (3.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline Walk Score\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.7 (16.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.4 (15.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChange in Walk Score\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.2 (1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05 (1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline Bike Score\u0026reg; \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.8 (9.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.4 (13.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChange in Bike Score \u003csup\u003eb, e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.6 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5 (1.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBaseline Complete Streets policy \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo policy type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (30.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (20.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePDPOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (58.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrdinance/law, or tax levy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (30.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (22.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecent Complete Streets policy \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo policy type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (28.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (18.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePDPOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (42.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (58.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrdinance/law, or tax levy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (30.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (24.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e\n\u003cp\u003eAbbreviation: PDPOR, policy, design manual/guide, plan, internal policy/executive order, or resolution.\u003c/p\u003e\n\u003cp\u003eNote: Values are means (standard deviations) for continuous variables and frequencies (column percentages) for categorical variables. Mean differences of all continuous variables between low and high sleep groups were examined by t-tests, and Fisher's exact tests were used for categorical variables for comparisons between groups. All baseline values were collected in 2018 and changes in all continuous variables were calculated by subtracting the 2018 data from the 2022 data.\u003c/p\u003e\n\u003cp\u003eThe range of baseline good health status was from 23.5% to 63.9% in cities with better sleep health and from 34.2% to 62.8% in cities with poorer sleep health. The range of change values in good health status between 2018 and 2022 was from -6.3% to 13.1% in cities with better sleep health and from -11.6% to 12.6% in cities with poorer sleep health.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Demographic factors are averages of values between 2018 and 2022;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Bike Score\u003csup\u003e\u0026reg;\u003c/sup\u003e, and Complete Streets policy have been collected since 2019, which led to including their 2019 data as baseline values, and change in Bike Score\u003csup\u003e\u0026reg;\u003c/sup\u003e was calculated by subtracting the 2019 data from the 2022 data;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.005;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; \u003csup\u003ee\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the moderating effect of sufficient daily sleep on the associations between environmental indicators and good health status was examined. \u003cem\u003eP\u003c/em\u003e-values for the interactions, as well as coefficients (SEs) of associations between exposure and good health status in the stratified analyses by the median of percent of sufficient daily sleeping, are presented. A significant effect of interaction between sleep and parks within a 10-minute walk was found on good health status in both crude and multivariable-adjusted LMMs (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.0003 and 0.005, respectively). In the stratified analysis by sleep, the positive association between parks within walking distance and good health status was stronger in cities with better sleep health, compared either with cities with poorer sleep health [Coefficients (SEs): 0.09 (0.03) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) in \u0026ge;\u0026thinsp;Median vs. 0.03 (0.03) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33) in \u0026lt;\u0026thinsp;Median]. The effect of Walk Score\u0026reg; on good health status was also significantly moderated by sufficient daily sleep in crude and multivariable-adjusted LMMs (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.004 and 0.02, respectively). The association between Walk Score\u0026reg; and good health status was stronger in cities with better sleep health compared with cities with poorer sleep health [Coefficients (SEs): 0.14 (0.04) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0008) in \u0026ge;\u0026thinsp;Median vs. 0.05 (0.04) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.18) in \u0026lt;\u0026thinsp;Median]. The moderating effect of sufficient sleep on the association between Bike Score\u0026reg; and good health status was observed in crude LMM [\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.02; Coefficients (SEs): 0.14 (0.06) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) in \u0026ge;\u0026thinsp;Median vs. 0.07 (0.06) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28) in \u0026lt;\u0026thinsp;Median], but not in the multivariable-adjusted LMM. Meanwhile, the association between having a Complete Streets policy and good health status did not differ by sufficient daily sleep (\u003cem\u003ei.e.\u003c/em\u003e, non-significant interaction).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAssociations between environmental factors and good health status, stratified by sufficient sleep\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003ePercent of residents who get 7\u0026thinsp;+\u0026thinsp;hours of sleep/day \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;Median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026ge;Median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTotal (All Cities)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoefficient\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoefficient\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoefficient\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePercent of parks within a 10-minute walk\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCrude LMM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMultivariable-adjusted LMM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWalk Score\u0026reg;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCrude LMM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMultivariable-adjusted LMM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBike Score\u0026reg;\u003c/strong\u003e \u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCrude LMM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMultivariable-adjusted LMM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComplete Streets policy\u003c/strong\u003e (Ref: No policy type) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCrude LMM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePDPOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrdinance/law, or tax levy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMultivariable-adjusted LMM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePDPOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrdinance/law, or tax levy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\"\u003e\n\u003cp\u003eAbbreviations: LMM, linear mixed model; SE, standard error; PDPOR, policy, design manual/guide, plan, internal policy/executive order, or resolution.\u003c/p\u003e\n\u003cp\u003eNote: Panel data with 3 time points were used in all analyses because percent of residents who get 7+ hours of sleep per day was collected only in 2018, 2021, and 2022. In multivariable-adjusted LMMs, demographic factors, including percent age 65+, percent white, percent high school graduate+, and median household income in addition to time, were adjusted for.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Baseline data is 2018 data;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e This study used 2019 data instead of 2018 data for these variables because Bike Score\u0026reg; and Complete Streets policy have been collected since 2019.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe mediating effect of sufficient daily sleep on the associations between environmental indicators and good health status is presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. For analyses focused on parks within a 10-minute walk, Models 1, 2, and 3 showed significant associations but sleep did not reduce the association strength between exposure and outcome; furthermore, sleep was not significantly associated with general health status in Model 3. Sleep duration did not show any mediating effect on other analyses using other exposure variables, nor was sleep associated significantly in model 3 for other exposure variables.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMediating effect of sufficient daily sleep on associations between environmental factors and good health status\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCrude LMM\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eMultivariable-adjusted LMM\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoefficient\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoefficient\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercent of parks within a 10-minute walk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1: exposure\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2: exposure\u0026harr;mediator\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3: exposure\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3: mediator\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWalk Score\u0026reg;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1: exposure\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2: exposure\u0026harr;mediator\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3: exposure\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3: mediator\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBike Score\u0026reg; \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1: exposure\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2: exposure\u0026harr;mediator\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3: exposure\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3: mediator\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComplete Streets policy (Ref: No policy type) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1: exposure\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePDPOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrdinance/law, or tax levy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2: exposure\u0026harr;mediator\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePDPOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrdinance/law, or tax levy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3: exposure\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePDPOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrdinance/law, or tax levy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3: mediator\u0026harr;outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\n\u003cp\u003eAbbreviations: LMM, linear mixed model; SE, standard error; PDPOR, policy, design manual/guide, plan, internal policy/executive order, or resolution.\u003c/p\u003e\n\u003cp\u003eNote: Panel data with 3 time points were used in all analyses because percent of residents who get 7+ hours of sleep per day was collected only in 2018, 2021, and 2022. To test for mediation, Models 1, 2, and 3 were used, in which exposure, outcome, and mediator indicate built environment/policy factor, percent of good health status, and percent of residents who get 7+ hours of sleep/day, respectively. In multivariable-adjusted LMMs, demographic factors including percent age 65+, percent white, percent high school graduate+, and median household income in addition to time, were adjusted for.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e This study used 2019 data instead of 2018 data for these variables because Bike Score\u0026reg; and Complete Streets policy have been collected since 2019.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings showed that most large cities whose residents slept longer had better baseline health and improvement in general health status of their residents over time. However, the maximum baseline value of good health status was 63.9% and maximum change was 13.1% in cities with better sleep health, implying room for improvement in all cities. Except for Walk Score\u0026reg;, baseline values in exposure variables were slightly higher in cities with better sleep health than in cities with poorer sleep health. Change in exposures were not significantly different between the city groups. However, overall values of the environmental indicators reflected the need for improvement. As we hypothesized, sufficient daily sleep showed a moderating effect on the association between environmental indicators and general health status. In the cities with residents who had higher percentages of sufficient daily sleep, the magnitudes of the positive associations were increased, implying synergistic interactions between sufficient daily sleep and better environmental factors on good health status. However, no mediating effect of sufficient daily sleep was observed on the association between environmental indicators and good health status.\u003c/p\u003e \u003cp\u003eWe demonstrated the synergistic moderating effect of sufficient daily sleep on the association of each of the following with good health status: parks within a walking distance, Walk Score\u0026reg;, and Bike Score\u0026reg;. Such an interaction effect on general health was also found in recent studies in which unhealthy behaviors were jointly associated with poor-quality built environment, and too little or an excessive amount of sleep were associated with poor health status (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and the risk of morbidity and mortality (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings also show that selected environment factors are positively correlated with intensive physical activity, which implies that sufficient sleep amplifies the beneficial effects of physical activity on general health. In fact, many recent studies have demonstrated the synergistic effect of sleep and physical activity on general health status at the individual level (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In a recent mid-aged UK Biobank study, the association between low physical activity and risk of all-cause and cause-specific mortality was exacerbated by poor sleep, as classified with a composite sleep score that included duration (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Another secondary analysis that used the 2017\u0026ndash;2018 National Survey of Children\u0026rsquo;s Health demonstrated the synergistic interaction effect between insufficient sleep duration and low physical activity on mental health disorders (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis synergistic interaction between sleep quantity and physical activity level can be explained by their interrelationship (\u003cem\u003ei.e.\u003c/em\u003e, 2 bi-directional mechanisms). To be specific, physical activity positively affects sleep duration (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) and lack of sleep also may be a key impediment to promoting and maintaining a physically active lifestyle (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). The first mechanism is related to the effects of exercise on central nervous system, body temperature, cardiac/autonomic, endocrine and metabolic functions during sleep and overall mood (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The beneficial effects of exercise on sleep have been proven in many studies (\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) in which physical activity is an effective intervention for those having insufficient sleep duration. The second mechanism is more complex and rarely discussed. Insufficient sleep impairs cognitive performance, mood, glucose metabolism, appetite regulation, and immune function (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), which may play a negative role in physical activity. Moreover, the process of sleep may affect the brain at an endocrine level independent of the hormonal regulation of metabolism and waste removal at the cellular level (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), which may lead to changes in body temperature, cytokine concentrations, energy metabolic rate, growth hormone secretion, nervous/cardiovascular system, symptoms and disorders of mood and anxiety, neurotrophic factor secretion, and fitness level (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Thus, these 2 mechanisms may help to explain the synergistic interaction effect on general health observed in the current study.\u003c/p\u003e \u003cp\u003eThough no mediating effect of sleep quantity on the association between environmental factors and good health status was found here, previous studies have shown sleep\u0026rsquo;s mediating role in the association between greater physical activity and good health status (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The mediating mechanism can be explained in two steps. The first step is that the activity-related environmental factors affect sleep quantity; in the second step, the change in sleep quantity influences general health status. There is evidence supporting the first mechanism because sleep has been shown to be affected by neighborhood green space, walkability, safety, and built environment (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). In another study, green space exposures were associated with better sleep quality and quantity (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), which may be due to increased physical activity and spending time in a place with better air quality. Additionally, a study reported that step counts and daily active time (which are related to neighborhood walkability) can lead to better sleep quality and longer sleep duration (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). A high-quality environment that enables bicycling safely may result in better sleep as well as stress relief (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Having a Complete Streets policy can translate into high-quality walkability and a good location for cycling, which in turn can promote and support a resident\u0026rsquo;s physical activity, because it ensures safe access for all pedestrians, bicyclists, motorists, and transit riders (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The second mechanism can be partially explained by our finding of a positive correlation between sufficient daily sleep and good health status. In general, sufficient sleep is acknowledged as being essential to optimal general health status (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and to influence brain performance (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), mental health (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and physical health (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Thus, insufficient sleep is more likely to cause significant morbidity and mortality (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Taken together, the literature suggests a mediating effect of sleep on the association between activity-related environmental factors and good health status. A potential reason that the current study could not replicate the effect is insufficient statistical power, which was likely exacerbated in the stratified analyses. In addition, sleep was collected only for the last 3 years, which could reduce information and reduce power.\u003c/p\u003e \u003cp\u003eWe also found that all cities studied still had much room for improvement in both quality of environmental and personal sleep health. City and public health leaders\u0026rsquo; collaboration, including setting and sharing best practices of the fittest city, would help to gradually satisfy unmet needs. There have been some federal strategies for integrating active living and health (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Arlington, VA, the current fittest city in the AFI, shared strategies for advocating cycling paths (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Smart growth approaches also contributed to development of mixed land use, walkable and active communities, and increased availability of diverse public transportation choices (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Such approaches could serve as a basis for developing better strategies to improve public health. In addition, updated regulations and continuous public investment are needed for development of walkable districts, greening of abandoned areas, and development of cycling paths and lanes (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study has several strengths. First, this is the first investigation of the moderating or mediating effect of sleep on the associations between activity-related environmental factors and general health status using US city data. Second, AFI data integrated environmental indicators and personal health indicators. Third, we included only modifiable measures in order to enable city leaders to implement targeted programs and policies to advance population fitness (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Fourth, the indicators were selected, reviewed, and updated each year by Fitness Index content experts, ACSM staff, and technical consultants to ensure that the data are up-to-date and include the best measures available (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Fifth, we also considered demographic and socioeconomic factors that allowed us to control for potential confounders in our analyses.\u003c/p\u003e \u003cp\u003eOur study also has several limitations. First, 2022 data on Baton Rouge, Louisiana, were not collected, which necessitated the replacement of the missing values with the city\u0026rsquo;s 2021 values, based on assuming little change between 2021 and 2022. This might have introduced some bias. However, Baton Rouge indicators changed little between 2018 and 2021. In addition, data imputation of only one city is unlikely to greatly affect overall results, which may help justify our analysis and results. Second, because Bike Score\u0026reg; and Complete Streets policy were not collected in 2018, we used 2 different baseline years (\u003cem\u003ei.e.\u003c/em\u003e, 2018 or 2019) according to the available baseline year of each exposure. This may disrupt the uniformity of our examination of the interaction effects on general health, but this is a common issue when using secondary data (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), and the current study had no further missing values in the tailored panel data. Moreover, once the AFI data was gathered, the missing data was addressed (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Third, much of our study used self-reported data gathered via questionnaires and surveys, which might bring about some biases (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). However, the AFI included only indicators from reputable and regularly updated public sources to ensure validity and reliability (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Fourth, we have only 100 city samples, which might introduce lower statistical power, depending on the unknown population effect size magnitudes. Lower power might explain statistically nonsignificant findings for some associations.\u003c/p\u003e \u003cp\u003eIn conclusion, we identified a synergistic interaction effect between sufficient daily sleep among city residents and physical activity-related environmental factors related on good health status of residents. Sleep duration was not found to be a mediator of the association between environmental indicators and good health status. Our findings also suggested that the largest US cities still have much room for improvement in both environment and sleep adequacy, which might be addressed by continuous proactive collaboration between city and public health leaders and by creating a model informed by lessons from the fittest cities as reported in the ACSM AFI. Further research is warranted to better understand the biological mechanisms that modulate the dynamic interplay between physical activity level and sleep quantity, and to examine the mediating role of sleep health on the association between environmental assets and general health status using a larger data set with more than 100 US cities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicting interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe content is solely the responsibility of the authors and does not necessarily represent the official views of the American College of Sports Medicine. We would like to thank Gretchen S. Patch from American College of Sports Medicine for the valuable contribution to data provision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of authors\u0026rsquo; contributions to manuscript\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBS, JH, and TWZ designed the research; BS analyzed data and wrote the paper; TWZ provided the data; POM provided statistical expertise; All authors contributed significant review, revisions, and data interpretation; BS and JH had primary responsibility for final content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause the American Fitness Index\u0026reg; data used for our study was made by collecting diverse, publicly available data and modified into deidentified data to protect the confidentiality of respondents, this study was judged to be exempt from review by the Institutional Review Board of Indiana University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData described in the manuscript and analytic code will be made available upon request to the American College of Sports Medicine pending application review and approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO remains firmly committed to the principles set out in the preamble to the Constitution: World Health Organization; [Available from: https://www.who.int/about/governance/constitution.\u003c/li\u003e\n\u003cli\u003eLandais LL, Damman OC, Schoonmade LJ, Timmermans DRM, Verhagen E, Jelsma JGM. 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J Public Health Manag Pract. 2008;14(3):238-43.\u003c/li\u003e\n\u003cli\u003eDavis-Kean PE, Jager J, Maslowsky J. Answering Developmental Questions Using Secondary Data. Child Dev Perspect. 2015;9(4):256-61.\u003c/li\u003e\n\u003cli\u003eAlthubaiti A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc. 2016;9:211-7.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Population Health Management, Built Environment, Sleep Duration, Health status, Local Government","lastPublishedDoi":"10.21203/rs.3.rs-3880413/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3880413/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eSleep quantity of city residents and environmental assets that support physical activity may jointly improve residents\u0026rsquo; general health. Sufficient sleep also may mediate the effect of activity-related environmental factors on the general health. However, evidence regarding such associations is lacking. Thus, we aimed to investigate the moderating and mediating effects of sleep duration of residents on the association between environmental factors and general health status of city residents.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOur panel study used 2018/2019, 2021 to 2022 American Fitness Index\u0026reg; data for the 100 most populated US cities. Study outcome was good health status and exposures were environmental factors \u0026ndash; percent of parks within a 10-minute walk, Walk Score\u0026reg;, Bike Score\u0026reg;, Complete Streets policy. Sleeping 7\u0026thinsp;+\u0026thinsp;hours/day was used as a potential mediator or moderator. For analyses, we adopted crude and multivariable-adjusted linear mixed models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur findings showed that most large cities whose residents slept longer had better baseline health and improvement in the general health status of their residents over time. Sufficient daily sleep showed a moderating effect on the association between environmental indicators and general health status. In the cities with higher percent of sufficient daily sleep, the magnitudes of the positive associations were increased, implying synergistic interactions between sufficient daily sleep and better environmental factors on good health status. However, no mediating effect of sufficient daily sleep was observed on the association between environmental indicators and good health status.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings suggested a synergistic interaction effect between sufficient daily sleep and physical activity-related environmental factors on good health status. However, sleep duration was not found to be a mediator of the association between environmental indicators and good health status.\u003c/p\u003e","manuscriptTitle":"The Effect of Sleep on the Association Between Built Environment and Good Health Status","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-29 15:34:42","doi":"10.21203/rs.3.rs-3880413/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a48ccbf9-229b-4d6b-9bae-72159d370751","owner":[],"postedDate":"January 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-05T01:45:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-29 15:34:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3880413","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3880413","identity":"rs-3880413","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}