| | Objective Measures of Neighborhood Environment and Self-Reported Physical Activity in Spinal Cord Injured MenAbstract Liang H, Tomey K, Chen D, Savar NL, Rimmer JH, Braunschweig CL. Objective measures of neighborhood environment and self-reported physical activity in spinal cord injured men. ObjectivesTo assess the relationship between objective neighborhood environment and self-reported physical activity (PA) and between PA and obesity-related risk factors in people with spinal cord injury (SCI). DesignA cross-sectional study. ParticipantsMen with SCI (N=131), 20 to 59 years old, at least 1 year postinjury and using wheelchair for mobility most of the time. InterventionsNot applicable. Main Outcome MeasuresMetabolic syndrome (abdominal obesity, elevated blood pressure, glucose, triglycerides, and low-high density lipoprotein cholesterol) and high C-reactive protein (CRP), as well as total PA metabolic equivalent score. ResultsLower PA was associated with higher prevalence rate for elevated triglycerides, metabolic syndrome, and high CRP. Compared with those in low PA tertile, those in high PA tertile had significantly lower odds for elevated triglycerides (odds ratio [OR]=.19; 95% confidence interval [CI], .04–.80), metabolic syndrome (OR=.15; 95% CI, .03–.66) and high CRP (OR=.17; 95% CI, .04–.71) while adjusting for relevant factors. In crude analysis, lower PA was associated with neighborhood environmental characteristics including shorter distance to nearest transit stops, smaller mean block area, greater number of transit stops, high vacant housing, and higher neighborhood crime rate. In multivariate analysis higher total crime was the only risk factor significantly associated with lower PA level. Those living in higher crime rate neighborhoods had 86% lower odds of having greater than median PA level (OR=.14; 95% CI, .04–.49) than their counterparts. ConclusionsIn men with SCI, lower PA is independently associated with having elevated triglycerides, metabolic syndrome, and high CRP. Additionally, lower PA is associated with higher neighborhood crime rate. List of Abbreviations: CI, confidence interval, CRP, C-reactive protein, DBP, diastolic blood pressure, HDL-C, high-density lipoprotein cholesterol, MET, metabolic equivalent, PA, physical activity, PASIPD, Physical Activity Scale for Individuals with Physical Disabilities, OR, odds ratio, SBP, systolic blood pressure, SCI, spinal cord injury RECENT STUDIES HAVE revealed that men with SCI have higher CRP and lower HDL-C than able-bodied counterparts,1 which may contribute to their increased cardiovascular disease risks.2, 3 Both elevated CRP and low HDL-C have been associated with lower PA in the able-bodied.4 Limited data suggest that sedentary lifestyle is associated with low HDL-C and high CRP and that vigorous PA tends to be avoided by people with SCI1, 5, 6; however, their associations with PA in SCI remain unknown. Neighborhood environmental features such as increased street connectivity and safety have been associated with higher PA in the able-bodied.7, 8 People with SCI face many unique barriers to PA, including wheelchair accessibility,9 transportation, and social environment.10 To our knowledge, no previous studies have explored objective measures of neighborhood environment, its association with PA and risk factors for cardiac disease in SCI populations. We hypothesize that unfavorable neighborhood environment is associated with less PA, which in turn is associated with more obesity-related risks in SCI men. Findings from this study will provide insights into environmental risk factors that potentially can be modified to facilitate increased PA. Increasing quality and years of healthy life and eliminating health disparities have been cited as major goals in Healthy People 2010.11 Understanding the impact of environmental characteristics on PA and obesity-related risks in this underserved population is a highly relevant public health area. Methods  Participants We recruited men with SCI for this cross-sectional study using flyers and word of mouth from clinics at the Rehabilitation Institute of Chicago. Eligibility included men with SCI, aged 20 to 59 years, at least 1 year postinjury and using wheelchair for mobility most of the time. This research was approved by the Institutional Review Boards of Northwestern University and the University of Illinois at Chicago. Study Variables Demographics Demographic parameters included age, race, education, employment status, major mode of transportation, wheelchair type, marital status, living arrangement, smoking, and household income at poverty level.12 Additionally, we assessed subjects for injury years and completeness of injury.13 Obesity-related outcome variables The supine waist circumference was taken at the highest point of the iliac crest to the nearest 0.1cm with minimal expiration because standing is not feasible for wheelchair users.14 Blood pressure was taken in the morning on the right arm in a sitting position.15 Fasting (≥9h) serum triglyceride was assessed using a glycerokinase assay and HDL-C examined using a cholesterol oxidase assay. Plasma glucose was measured with a glucose oxidase assay. High-sensitivity CRP was analyzed using latex-enhanced nephelometry.a Metabolic syndrome was defined as presence of at least 3 of 5 risk factors as recommended by the American Heart Association16: abdominal obesity (waist circumference >102cm), elevated triglycerides (triglycerides ≥1.69mmol/L [150mg/dL]), reduced HDL (HDL <1.04mmol/L [40mg/dL]), elevated blood pressure (SBP ≥130mmHg, DBP ≥85mmHg), and elevated glucose (fasting plasma glucose ≥5.6mmol/L [100mg/dL]). Disease history and medication use was also considered in defining the aforementioned conditions. High CRP was defined as equal to 3mg/L or higher.17 Physical activity We assessed PA with the PASIPD questionnaire consisting of 13 items: 6 leisure time, 6 household, and 1 occupational activity item.18 The PASIPD requests information on leisure activities, including walking and wheeling outside the home other than specifically for exercise; light, moderate, and strenuous sport and recreation; exercise to increase muscle strength and endurance; household activity including light and heavy housework; home repair; lawn work; outdoor gardening; caring for another person; and occupational activity other than office work.18 Respondents were asked to recall the number of days in the past 7 days that they participated in these activities as never, seldom (1–2d/wk), sometimes (3–4d/wk), or often (5–7d/wk) and on average how many hours a day they participated (<1h, 1 to <2h, 2–4h, >4h). The response categories for hours a day for the occupational item were (<1h, 1 to <4h, 5 but <8h, ≥8h).18 The MET score for the PASIPD was created by multiplying the average hours a day for each item by an MET score value associated with the intensity of the activity and summing over items 2 through 13.19 The first item, which requests information on sedentary activities, was included only to familiarize respondents with the item format and was not scored. Among the 12 items that were scored, only 1 item asked about wheelchair-pushing activities. Therefore, participants who used a power wheelchair most of the time were not excluded from this study. By using this scoring procedure, the total maximum possible score is 199.5 MET score hours a day. This questionnaire has been validated in people with physical disabilities (n=372) with 80.3% of them having SCI.18 The validation study showed that Pearson correlations between each survey item and the total PASIPD score were all statistically significant (P<.05) and greater than or equal to .20 (range, .20–.67). Factor analysis revealed 5 latent factors (eigenvalues ≥1.0, factor loadings, ≥.40) which accounted for 63% of the total variance. Cronbach α coefficients ranged from .37 to .65, indicating low-to-moderate internal consistency within factors. In addition, it can differentiate people with different self-rated health status (fair/poor, good, excellent) and PA (not active at all, moderately active, active, and extremely active).18 Neighborhood environment measures: geo-coding addresses We assigned the home addresses of participants within Chicago to a geographic latitude and longitude through geo-coding using geographic information system software ArcGIS.b The U.S. Census Bureau TIGER 2000 street centerline file for Cook County, IL, was used as reference addresses. A total of 108 Chicago addresses were geo-coded and converted to points on the map; the non-Chicago addresses were not used. Half-mile radius buffers were generated from the points, assuming that a half-mile area surrounding the home is reasonable for daily life among wheelchair users.20 These buffers were used to select the blocks that have their centroids (or center points) inside the buffer. The Federal Information Processing Standards Code for state, county, tract, and block, the block identifier, was used to join with the block demographic data from the U.S. Census Bureau. Four measures were obtained from the buffer: street connectivity, density, transit availability, and neighborhood safety. Neighborhood environment measures: street connectivity The average census block area, defined as the mean area for census blocks with a centroid falling within the buffer, was an approximation for street connectivity because the shape of the blocks also affects the street pattern.20 This has often been used in previous studies.21, 22, 23 The U.S. 2000 Census block boundary shape file was used in area calculation and Xtoolsc used to generate centroids for census blocks.20 Neighborhood environment measures: density Housing density, defined as total housing units per unit land area, was used as a proxy for population density. For all blocks with centroids falling into the neighborhood buffer, the census data on total housing units were summed to calculate housing density. The housing information was from the U.S. 2000 Census data and the area was calculated on the intersect shape file of the census block boundary centroids shape file and the half-mile buffer shape file. Neighborhood environment measures: transit availability Numbers of transit stops and distance to nearest transit stop in neighborhood buffer were used to describe transit availability. All streets in Chicago have curb-cuts and all public transportation is wheelchair accessible.24 More transit stops (buses and trains) and shorter distance to nearest transit stop are associated with more PA in the able-bodied.7 Distance to the nearest transit stop was defined as the network distance, the distance from residence to nearest transit stop along actual travel routes. The Chicago transit stops data were provided by a Chicago Transit Authority Freedom of Information officer (http://www.transitchicago.com/business/freedom.html). Neighborhood environment measures: neighborhood safety The percentage of vacant housing units was used as an indicator for safety, as people have been shown to exercise less outdoors in unsafe neighborhoods.25, 26, 27 For all blocks with centroids falling into the neighborhood buffer, the census data on vacant housing and total housing units were summed and used to calculate the percentage of vacant housing units.20 Crime rates were defined as number of total crimes and number of total outdoor person crimes during a 2-week period of time in the buffer. It was obtained from the Chicago Citizen Information Collection for Automated Mapping system (http://12.17.79.6/ctznicam/ctznicam.asp). To increase generalizability, crime data from different seasons were collected and means were used for analysis. Statistical Analysis We calculated statistics for subject demographics (N=131). Data on 104 subjects were used to examine the association of PA and obesity-related risks (metabolic syndrome, CRP). Those who took medications for heart disease, stroke, diabetes, hypertension, or high blood cholesterol (n=27) were excluded to eliminate the effect of medication use on continuous biochemical indices as well as the effect of disease history on PA. Participants' characteristics and the association of PA and obesity-related risks were first examined by PA tertile. Next, the Jonckheere-Terpstra test and Kendall τ-b test were used to assess trends across PA tertiles for categorical (metabolic syndrome, high CRP) and continuous (waist circumference, blood pressure, glucose, triglyceride, HDL-C, CRP) variables, respectively. Logistic regression was conducted to obtain the OR and 95% CIs of middle and high versus low PA tertile for obesity-related risks (metabolic syndrome and its components, high CRP) while adjusting for age, race (black: yes, no), complete injury (yes, no), employment (yes, no), and marital status (yes, no). To examine the association between neighborhood environment and PA, tertiles for environment measures were first created (n=108). Next, medians and interquartile ranges of PA MET score were obtained for each tertile and a Kendall τ-b trend test was conducted. Spearman correlation coefficients were calculated. Finally, logistic regression was used to quantify the effect of neighborhood environment on PA while controlling for age, race, complete injury (yes, no), employment (yes, no), abdominal obesity (yes, no), and smoking (yes, no). Neighborhood environment factors that were significantly associated with low PA were identified and their effects were reported as OR and 95% CI. Data were managed and analyzed using SAS.d Results A total of 131 subjects were included in the study (table 1); they were on average 39.1 years old and 12 years postinjury. Close to 40% were black; one third were white; and one fourth were Hispanic. Over half (58%) had a complete injury; 79% were unemployed; about three fourths (74%) were neither married nor living with a significant other; and 87% used manual wheelchairs. | | |  | Characteristics | Values | |
|---|
| Age (y) | 39.1±10.6 | | | Postinjury years | 12.0±9.3 | | | Race | | | | White | 43(32.8) | | | Black | 51(38.9) | | | Hispanic | 33(25.2) | | | Other race | 4(3.1) | | | Complete injury | 76(58.0) | | | Manual wheelchair user | 112(87.0) | | | Education > high school | 68(51.9) | | | Employed | 28(21.4) | | | Household income at poverty level | 60(45.8) | | | Cigarette smoker | 63(48.1) | | | Single marital status | 97(74.0) | | | Live alone | 46(35.1) | | | Major mode of transportation | | | | Driving | 84(64.1) | | | Transits | 22(16.8) | | | Regional paratransit buses | 12(9.2) | | | Minivan taxis | 8(6.1) | | | Wheelchair pushing | 5(3.8) | | | | |
Subject characteristics stratified by PA tertile are presented in table 2. Participants in low and medium PA tertiles were more likely to be black, unemployed, and have complete injury. No statistical differences in education, household income, or living arrangement were found across PA tertile. Participants in low PA tertile tended to have higher prevalence for abdominal obesity, elevated triglyceride, metabolic syndrome, and high CRP. The continuous HDL had a tendency to be lower and CRP higher in low versus medium and high PA tertile, respectively (low vs medium vs high: HDL=1.01mmol/L vs 1.05mmol/L vs 1.20mmol/L, Kendall τ-b=.185, P=.016; CRP=6.8mg/L vs 4.0mg/L vs 1.2mg/L, Kendall τ-b=−.280, P=.003). However, no significant trends were found for continuous waist circumference, triglyceride, blood pressure, or glucose (data not shown). Logistic regression results (table 3) showed that while controlling for age, complete injury, race, employment, and marital status, those in medium and high PA tertile had point ORs less than 1 for metabolic syndrome and high CRP. However, compared with those in low PA tertile, only those in high PA tertile had significant lower odds for elevated triglycerides (OR=.19; 95% CI, .04–.80), metabolic syndrome (OR=.15; 95% CI, .03–.66), and high CRP (OR=.17; 95% CI, .04–.71). | | | | Dependent Variables | OR(95% CI) | |
|---|
| Medium vs Low PA Tertile | High vs Low PA Tertile | |
|---|
| Metabolic syndrome | 0.32(0.09–1.14) | 0.15(0.03–0.66) | | | Central obesity | 0.48(0.16–1.40) | 0.37(0.11–1.22) | | | Elevated triglycerides | 0.41(0.13–1.36) | 0.19(0.04–0.80) | | | Low HDL | 0.63(0.23–1.74) | 0.42(0.13–1.29) | | | Elevated BP | 0.63(0.22–1.82) | 0.83(0.26–2.63) | | | Elevated glucose | 0.64(0.18–2.27) | 0.81(0.22–3.01) | | | High CRP | 0.80(0.21–3.12) | 0.17(0.04–0.71) | | | | |
The association of neighborhood environment and PA is shown in table 4. Lower PA was associated with shorter distance to nearest transit stops and higher neighborhood crime rate and tended to be associated with smaller mean block area, greater number of transit stops, and higher vacant housing. More transit stops was associated with shorter distance to nearest transit stops (Spearman ρ=−.39, P<.01), smaller mean block area (ρ=−.40, P<.01), greater housing density (ρ=.50, P<.01), higher vacant housing (ρ=.47, P<.01), and crime (ρ=.48, P<.01). Smaller mean block area was associated with higher density (ρ=−.34, P<.01) and crime rate (ρ=−.30, P<.01). After controlling for covariates in logistic regression, despite having ORs in the right direction for other environmental measures, higher total crime was the only proposed risk factor significantly associated with lower PA level. Those who lived in an area with a greater than median total crime rate (54 total crimes in 2wk) had 86% lower odds of having greater than median PA level (OR=.14; 95% CI, .04–.49) compared with their counterparts. Discussion The major findings from this study are that high PA tertile was associated with significantly lower odds for elevated triglycerides, metabolic syndrome, and high CRP and that this association was independent of age, race, complete injury, employment, and marital status. In addition, lower PA is associated with neighborhood environmental characteristics including higher crime rate, shorter distance to nearest transit stops, smaller mean block area, greater number of transit stops, and high vacant housing that impose barriers to PA in men with SCI. However, only higher crime rate was significantly associated with less PA in logistic regression. The inverse association between PA and obesity-related health risks including CRP and metabolic syndrome has been well documented in the able-bodied population4 but has not been reported in SCI. Manns et al28 reported that lower peak aerobic capacities were associated with lower HDL-C and PA levels in paraplegics with complete injuries. However, correlations between CRP and PA were not examined. The independent association of PA with CRP in SCI supports findings from the able-bodied populations,4 however, the magnitude of the association was much greater in SCI. In the able-bodied populations, CRP levels were 9% to 35% lower in physically active compared with less active subjects4; in our participants, median CRP levels in medium (4.0mg/L) and high (1.2mg/L) versus low PA tertile (6.8mg/L) were on average 40% to 80% lower suggesting that PA may have a greater health benefit in SCI populations. The association of lower PA and higher prevalence of metabolic syndrome supports findings from various populations in the able-bodied.29, 30, 31, 32, 33 Our finding suggests that increasing PA could be pivotal in prevention of obesity-related disease risks in this population. A strong association was found between low PA and high neighborhood crime rate in SCI. Most recent studies in adults have found less PA was associated with perceived lack of safety25, 26, 27 although none included objective measure of safety. In elderly men, higher rates of objective neighborhood crime were significantly associated with lower outdoor PA levels.34 Black adults have higher prevalence for leisure time inactivity and perceive their neighborhoods as less safe than do whites.35 Wheelchair users are particularly vulnerable to crime. It is reasonable that safety concerns may contribute to disparities in PA. These findings strengthen the importance of improvement of neighborhood environment to reduce barriers to PA in a highly vulnerable population. In the able-bodied populations higher residential density and greater street connectivity are associated with more time walking.7, 8, 36 As expected, greater number of transit stops was associated with shorter distance to nearest transit stops and smaller block size. Different from findings from the able-bodied population, we found several environment measures including higher residential density, better transit availability (eg, greater number of transit stops and shorter distance to nearest transit stops), and greater street connectivity (smaller block size) were associated with less PA in our SCI men. It may be that higher residential density, more transit stops, shorter distance to nearest transit stops, and smaller block size are associated with greater population density and higher crime rate, which may serve as social and environmental barriers to PA in the SCI population despite higher street connectivity. The reported barriers to PA in people with SCI are different from those of the able-bodied populations: they include lack of time, energy, social supports, motivation, child care, resources for equipment, and/or convenient and safe facilities availability.37, 38, 39 They are attributable to attitudinal barriers or societal attitudes in addition to individual influences (ie, inability to perform activities and cost) and material environment (physical accessibility and access to information and resources).9, 40, 41 The different association of environment measures and PA in men with SCI from those in the general population may indicate different PA barriers in this population and that their PA is influenced by both physical and social environmental factors. Study Limitations There are several limitations to our findings. First, cross-sectional study design excludes a causal link between PA and obesity-related risks and between neighborhood environment and PA. Second, generalizability of this study is limited because of following factors: first, a majority of participants were unemployed, and they were largely minority group members compared with national SCI population.42 Second, environment data on wheelchair users with SCI who lived in urban environments were included in the analysis. Findings from this study may be skewed due to sample characteristics and cannot be generalized to SCI populations in rural areas. Third, there were potential inaccuracies of the self-reported PA questionnaire. Although the PASIPD questionnaire was validated in a sample with 80% participants having SCI,18 it did not differentiate the MET score values by injury type. Because 42% of our participants had incomplete injury, there was inevitably a potential inaccuracy in estimating MET score between injury types due to different muscle usage. Fourth, PA measurement was not limited to that which occurred only in participants' neighborhood, but, because 79% were unemployed, it is likely that the majority of their waking hours were spent near their homes. Fifth, it did not control for motivation and mental conditions that could have an impact on PA levels in this population. However, motivation is probably not a big player because over half the PA items (6 items on household activities, 1 item on occupation) of 12 PA items contributing to total MET scores were mandatory. Finally, the sample size may be too small to detect significant associations between residential environment measures and PA. Conclusions In men with SCI, lower PA is associated with higher risk for elevated triglycerides, metabolic syndrome, and high CRP as well as with higher neighborhood crime rate. 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42. 42DeVivo MJ, Chen YY, Jackson AJ. Spinal cord injury: facts and figures at a glance. Birmingham: National Spinal Cord Injury Statistical Center; 2006;. a Departments of Kinesiology and Nutrition, University of Illinois, Chicago, IL b Great Cities Urban Data Visualization Program and Lab, University of Illinois, Chicago, IL c Disability and Human Development, University of Illinois, Chicago, IL d Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI e Spinal Cord Injury Acute Care and Rehabilitation, Northwestern Memorial Hospital and Rehabilitation Institute of Chicago, Chicago, IL f Takeda Global Research and Development Center Inc, Deerfield, IL.  Reprint requests to Carol L. Braunschweig, PhD, RD, Dept of Kinesiology and Nutrition, University of Illinois, 1919 W Taylor St, Room 650 (M/C 517), Chicago, IL 60612
Supported by the Agency for Healthcare Research and Quality, National Institutes of Health (grant no. R03HS011277-01). No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the authors or upon any organization with which the authors are associated. At the time of the study, Liang was not an employee of Takeda. PII: S0003-9993(08)00352-3 doi:10.1016/j.apmr.2008.01.017 © 2008 American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved. | |
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