Few Americans engage in sufficient physical activity to realize health benefits (1). Health benefits extend beyond weight control and improving the cardiometabolic profile to prevention and management of diseases such as dementia and some cancers (2,3). There is also evidence that habitual physical activity improves functional ability and reduces the risk of falls and fractures in older adults (4). Physical inactivity is said to be a pandemic (5) and the result of inadequate policy development and implementation (6). This may be due, in part, to the failure to scale many physical activity interventions, that is, to create viable, long-term programs with population-wide effects (6). Modifications to the built environment, such as building and improving sidewalks, are considered to be scalable because they can be far reaching and sustainable (7). In addition, enhanced sidewalk infrastructure may lead to physical activity by increasing safety and access to key destinations and improving esthetics. The Centers for Disease Control and Prevention's Guide to Community Preventive Services highlights the need for pedestrian friendly built environments that promote active transportation, and/or leisure-time physical activity (7).
However, the association between sidewalk infrastructure and physical activity is not sufficiently clear, and a causal relation has yet to be established. Early cross-sectional analyses found positive associations between sidewalks and physical activity (8–11). Recently, prospective study design has been undertaken (12,13), yet techniques to measure physical activity remain limited. In their review, McCormack and Shiell (14) examined the relation between the built environment and physical activity and found that self-reports alone (without device-based assessments) were the most common method to assess physical activity. Rarely were the measurement properties of the self-report instruments provided. These methodological flaws have made it difficult to draw an uncontested conclusion on the relation between sidewalk infrastructure and physical activity and make clear the need for further research on the topic.
The purpose of this study is to examine the association of living near recently installed or improved sidewalks with changes in self-reported and accelerometer-derived physical activity among a diverse, community-based sample from the Houston Travel Related Activity in Neighborhoods (TRAIN) Study. We addressed major gaps in the literature by using robust estimates of physical activity in a natural experiment of the association between improving sidewalk infrastructure and changes in physical activity.
Study Design, Setting, and Participants
We examined baseline (2014–2016) and first follow-up (2015–2017) assessment data of the Houston TRAIN Study (median interval, 14.3 months). Briefly, the TRAIN Study is a prospective cohort study designed to evaluate the effect of a public light rail transit system on transit use and various domains of physical activity among adults living in Houston, Texas (15). Eligible participants were at least 18 yr of age, resided in the study area (3-mile Euclidean buffer around the light rail system), and did not reside with another TRAIN participant at enrollment. Study materials were available in English and Spanish, and participants were provided incentives. The TRAIN Study methodology has been described by Durand et al (15). For this study, the analytic sample included participants with complete, self-reported physical activity at baseline and follow-up (n = 430). At enrollment, all participants were offered to participate in the accelerometer protocol (wear an accelerometer on the hip during waking hours for seven consecutive days) in addition to completing the questionnaire. Approximately 53% (228/430) of participants opted in and had returned an accelerometer at baseline and follow-up. These participants were included as an analytic subsample (16).
Transportation-related and leisure-time physical activity
The Self-Administered Modifiable Activity Questionnaire (S-MAQ) was used to estimate transportation-related and leisure-time physical activity over the previous 7 d (i.e., week). The S-MAQ is derived from the Modifiable Activity Questionnaire, which is a reliable and valid measure of physical activity (17,18). For both transportation and leisure-time domains, the MET of task minutes per week was calculated.
For the transportation-related domain, participants reported the total duration, including each one-way trip of walking, bicycling, and other activities (e.g., skateboarding) each day. To obtain the MET-minutes per week summary estimate for each activity type, the reported duration was multiplied by a standardized MET value (19) (i.e., 3.5, 6.8, and 5.0 METs for walking, bicycling, or other activity, respectively) and summed across activities.
For the leisure-time domain, 38 common activity types were included in the S-MAQ. For each type, the participant reported if he/she did that activity in the previous 7 d, and if so, additionally recorded the total duration (number of minutes) on each day. For each activity type, the reported duration was multiplied by a standardized MET value (19) to obtain the MET-minutes per week estimate.
Transportation and leisure walking and their totals were reported in minutes per week, independent of the total transportation-related and leisure-time estimates.
Accelerometer-derived physical activity
Participants that opted in to the accelerometer protocol were asked to wear accelerometers on the right hip, secured by an adjustable elastic belt, during their waking hours for seven consecutive days. Accelerometer data were sampled at 40 Hz, and data were reintegrated to 60-s epochs for further processing. Choi algorithms were used to screen for wear time, and valid wear was considered as ≥4 of the 7 d, ≥10 h·d−1 (16). The primary summary estimates, from the vertical axis, were the average accelerometer counts per minute per day. Summed daily counts detected over wear periods and time spent per day in minutes per day in different intensity levels, were estimated using count threshold values proposed by Freedson et al (20). In addition, we used vector magnitude (m·d−1), an estimate reflecting the square root of the sum of squares from each of the three axes over detected wear periods.
Data on sidewalk infrastructure improvement for the City of Houston were obtained from the ArcGIS website (http://cohgis.mycity.opendata.arcgis.com/). The City of Houston provides publicly available geospatial information systems data sets on areas of interest. For this analysis, we used a sidewalk inventory feature class that detailed active sidewalk projects at various stages, including programmed, design, construction, pending construction, and complete. The data set was accessed and downloaded on September 14, 2016. To establish the exposure variable, we first created a 250-m pedestrian network buffer around the participant's residence. The network buffer was based on the road centerline file, excluding highways and ramps (21). Others have suggested that 250-m buffer lengths may be more useful than the usual 800-m–1-km distance (13,22). Second, an ordinal count of the number of sidewalk segments within the buffer labeled as a completed project was dichotomized as 0, 1, 2, and 3 or more improvements. For this analysis, a sidewalk segment was defined as an uninterrupted stretch of sidewalk between street crossings.
We selected covariates on the basis of previous work on the correlates of physical activity related to the built environment (14,23). Sociodemographic data were self-reported at baseline. These included sex, age, race/ethnicity, household income, and highest level of education achieved. Self-reported height and weight were used to calculate body mass index (kg·m−2). Other variables included as possible covariates were transit user status, seasonality (time of year) to account for possible seasonal differences in physical activity levels (24), automobile ownership, and distance in meters between a participant's home and the nearest transit stop.
Data Management and Analysis
Participants' baseline characteristics were reported as the count and proportion of each categorical variable. The extent of missing values was assessed for each variable. Bivariate analysis of the relation between physical activity and sidewalk infrastructure improvements was stratified by transportation-related, leisure-time, walking behaviors, and accelerometer-derived activity estimates to analyze activity-level differences. Hosmer–Lemeshow tests and chi-square tests for heterogeneity were used to determine differences in the median and proportion estimates, respectively. Physical activity change was calculated as the difference in estimates from baseline to follow-up and reported as the mean (±SD) value across all participants.
Multivariable regression models were built to test the hypothesis that exposure to sidewalk improvements is related to change in an individual's physical activity, when accounting for possible confounders. The follow-up assessment estimate of physical activity was modeled as the outcome with the baseline assessment locked in the model as a covariate. This method was chosen, rather than modeling the change score, on the basis of a correlation, below 0.70 threshold, between the baseline and follow-up assessments (25).
For the self-reported physical activity models, a two-step method was used to account for the overdispersion of zero values in the outcome estimates (26). The two-step method first modeled a probit mean response function of observations where the outcome was recoded as zero for those with no reported physical activity at follow-up and one for those with any nonzero reported physical activity (participation portion). Then, a separate ordinary least-squares regression model with only those nonzero value observations was constructed (duration portion). The two separately derived models used different model-building techniques and therefore contained different covariates. For accelerometer-derived estimate models, there was not an overdispersion of zeros in the outcome. Therefore, these models were constructed as multivariable ordinary least-squares linear regressions. Data management and statistical analysis were performed using StataSE 14.1 (StataCorp, College Station, TX).
The Houston TRAIN Study protocol and this secondary analysis were reviewed and approved by The University of Texas Health Science Center at Houston Committee for the Protection of Human Subjects (The University of Texas Health Science Center Institutional Review Board). All participants provided consent to participate.
Table 1 details the participants' characteristics and shows that the majority did not live near improved sidewalks, were female, and had low incomes and education levels. Most owned vehicles and were also transit users.
Physical activity and sidewalk improvements
For a participant exposed to improved sidewalks, Table 2 shows by intensity category the median (interquartile range) duration of time that he/she was physically active, measured by self-report and accelerometer at baseline and follow-up. Among exposed participants, there were no significant differences in any estimate of physical activity from baseline to follow-up.
The adjusted linear associations between sidewalk improvements near participants' homes and changes in physical activity are presented in Table 3. For the self-reports of physical activity (transportation-related, leisure-time, and walking), the participation portion of the two-step model (modeling the association between living near a sidewalk and any nonzero reported physical activity) was not statistically significant (P > 0.05). For transportation-related physical activity, among participants reporting any physical activity (duration portion), living near sidewalk improvements was not statistically significant (P > 0.05).
Considering self-reported, leisure-time activity in the duration portion of the model, living near two sidewalk improvements was associated with 1.58 times (58%) more self-reported MET-minutes per week of leisure-time physical activity compared with those not living near a sidewalk improvement (β = 0.46, P = 0.005). This statistically significant linear relation did not persist for those living near three or more sidewalks.
We found a similar result for the reported minutes per week spent walking for transportation and leisure. Among participants who reported any walking (duration portion), living near two improved sidewalks was associated with 1.64 times more self-reported walking minutes per week compared with those not living near an improved sidewalk (β = 0.50, P = 0.02). On the basis of accelerometry, living near an improved sidewalk was not associated with physical activity (see Table 4).
In this study, we prospectively evaluated the association of living near sidewalk improvements with changes in physical activity levels. Few studies have combined self-reported and device-assessed physical activity data within natural experiments of the built environment. While our findings in unadjusted models were not statistically significant, in a multivariable analysis, we found that self-reported leisure-time physical activity and time spent walking were significantly greater (~60%) among those who reported some activity and lived near two sidewalk improvements. Self-reported, transportation-related physical activity and accelerometer estimates, on the other hand, indicated that there were no associations with sidewalk improvements. These mixed results could reflect the differing constructs measured with self-reports and device-based assessment of physical activity. The S-MAQ primarily operationalizes the time spent in structured, higher-intensity physical activities, like leisure walking or jogging on a sidewalk. Alternatively, accelerometry captures all ambulation, including structured and unstructured activity, the most of which does not take place on a sidewalk and is accumulated in small bursts (walking around the home/office). Therefore, the accelerometer estimates, although less prone to self-reporting bias, may be diluted by activities unrelated to sidewalk infrastructure improvements. Nonetheless, future work should continue to combine self-reports with device-based assessments of physical activity and should consider incorporating geospatial data (i.e., Global Positioning System) to further elucidate sidewalk versus non-sidewalk (e.g., indoor) physical activity.
The results of our study have implications for health promotion professionals. Most notably, the data suggest that improving sidewalks was not sufficient to increase physical activity among those who are inactive, whereas for those who are already active, living near two improved sidewalks was associated with increases in reported leisure-time and walking activity. This distinction could be important. The 2008 Physical Activity Guidelines for Americans states that any physical activity is beneficial, and at least 150 min of moderate-to-vigorous aerobic physical activity provides a variety of health benefits (4). This could indicate that sidewalks provide opportunities for already-active people to be even more physically active, especially if they seek opportunities around their home. It is interesting that the significance of this finding did not hold for three or more sidewalk improvements. We hypothesize a few possible reasons for this finding. This may be the result of a smaller sample (n = 46) of participants living near two sidewalk improvements, or a possible threshold effect that occurs as the result of three or more sidewalk improvements being inhibitive of physical activity (e.g., increased construction). There is also the possibility that the significant findings for two sidewalks is a type 1 error.
Although we did not evaluate sidewalk use, our results can be compared with other prospective evaluations of the pedestrian environment and physical activity changes. First, Evenson et al. (27) found that respondents who live within 2 miles of a newly constructed walking/biking trail were not more likely to increase their physical activity. Specifically, the authors found that those who increased their walking from baseline to follow-up were 57% less likely to have reported using the walking/biking trail compared with those who showed no change in physical activity. In addition, those who decreased their bicycling were more likely (~4.0 times) to have used the trail. Second, Burbidge and Goulias (12) conducted a similar analysis of how a newly constructed trail affected physical activity among adults who lived within 1 mile of it. After controlling for relevant factors, physical activity bouts were found to significantly decrease from baseline to year 3 follow-up among those living near the new trail. These two studies, along with our results, indicate that improving or building new sidewalks/trails may not be enough of an incentive to the people who live nearby. The Centers for Disease Control and Prevention's Guide to Community Preventive Services recommends changing the built environment to support and provide opportunities for the community to be physically active, including connected sidewalks and streets, but recognizes the need for complementary behavioral and social approaches (7). Our results underscore this recommendation. Pairing infrastructure changes with behavioral economic theories, such as asymmetric paternalism (28), could be an area for future investigation.
The current study has its limitations. Only those sidewalk projects that had a completed construction date before participant follow-up (2016) were included in the current analysis. As such, the results should be interpreted as the change in physical activity being associated with sidewalk infrastructure improvement near the home. In addition, there is no clear definition from the City of Houston of a sidewalk improvement. This could include construction of a new sidewalk, a crack repair, or a curb cut to facilitate disability access. Finally, although quasi-experimental design helped elucidate changes in physical activity over a 1-yr period, a longer follow-up period could provide a better understanding of the relation and whether the increases in physical activity diminish in subsequent years or perhaps require a critical mass to become a social norm (29).
Nonetheless, this study used a natural experiment and a prospective design to examine the changes in physical activity associated with an improved built environment. In addition, a robust measurement of physical activity that combined domain-specific assessments of self-reported physical activity with accelerometry was used along with a novel statistical approach to highlight the public health implications of the findings. The results indicate that participants who engage in some physical activity and live near two sidewalk improvements report significantly more walking and leisure-time physical activity than active participants who do not live near an improvement. This association was not observed among accelerometry participants. To examine short- and long-term effects on physical activity, future studies should use temporally structured and detailed assessments of sidewalk infrastructure and attempt to examine the effects of theory-driven behavioral intervention alongside changes to the built environment.
This research was supported primarily by the National Institutes of Health (NIH) National Institute of Diabetes and Digestive and Kidney Diseases via Grant NIH R01 DK101593 (to H. W. K. III). Additional support for manuscript preparation came to Dr. Knell through a postdoctoral fellowship at the University of Texas School of Public Health Cancer Education and Career Development Program–National Cancer Institute/NIH Grant R25 CA57712.
The authors declare that there is no conflict of interest. The results of the present study do not constitute an endorsement by the American College of Sports Medicine.
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