Neighborhood Environment, Self-efficacy, and ...

Neighborhood Environment, Self-efficacy, and Physical Activity in Urban Adolescents Carolyn C. Voorhees, PhD; Alice F. Yan, MD, PhD; Kelly J. Clifton, PhD; Min Qi Wang, PhD, FAAHB Objectives: To test the pathways between perceptions of built environment factors and physical activity in urban youth. Methods: Three hundred fifty high school students’ perceptions of neighborhood, and barrier self efficacy were measured by a Web survey. Physical activities were assessed using a one-week diary and accelerometers. Results: Land-use mix/ accessibility and neighborhood satisfaction had direct pathways to walking. Barrier self-efficacy

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romoting increased moderate-intensity physical activity to adolescents is a public health priority.1,2 Studies show that physical activity in adolescence significantly predicts adult physical activity, and continuous physical activity at school age considerably increases the probability of being active in adulthood.3 Furthermore, several studies have reported that there is substantial tracking of physical activity and weight status4,5 from childhood and adolescence into later adulthood because childhood and adolescence are important stages to developing Carolyn C. Voorhees, Research Associate Professor and Min Qi Wang, Professor both from the Department of Behavioral and Community Health University of Maryland School of Public Health, College Park, MD. Alice F. Yan, Assistant Professor, University of Texas at San Antonio, Department of Health and Kinesiology, San Antonio, TX. Kelly J. Clifton, Associate Professor, Portland State University, Portland, OR. Email correspondence to Dr Voorhees, [email protected]

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had a direct pathway to walking. In addition, land use, specifically neighborhood accessibility, influenced adolescents’ walking behavior via self-efficacy. Similar pathways were found in MVPA models. Conclusions: Neighborhood factors appear to work together with self-efficacy to facilitate physical activity. Key words: urban, built environment, self-efficacy, physical activity, walking behavior Am J Health Behav. 2011;35(6):674-688

lifelong habits.6 Walking is the most common moderate-intensity activity for adolescents, and recent studies have demonstrated the potential of active transport to and from school to increase overall physical activity levels for both elementary7,8 and middle school students.9 Transportation-related activity combined with other forms of moderate/vigorous physical activity (PA) throughout the day has the potential to reduce the chronic health risks associated with an inactive lifestyle. Improved understanding of the correlates of walking and other forms of moderate/ vigorous physical activity can lead to evidence-based policies and programs. Off-school time provides a challenge and an opportunity for obesity interventions. Most school-based intervention projects have generally produced disappointing effects with respect to improving body composition.10 Among the explanations postulated for why these obesityreduction programs have not achieved their full prevention potential is that they fail to alter the environment in a way

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conducive to such change.11 We need to examine the factors that influence adolescents’ physical activity behaviors outside of school. Given that students accrue 70%-80% of their daily physical activity away from school,12,13 efforts to understand the environmental and cognitive factors that may influence adolescents’ physical activity behaviors could have large cumulative impact. Over the past decade, urban planners and public health professionals have joined to advocate for community design features that promote walking, biking, and other forms of physical activity and active transportation. As a result, a burgeoning research area focused on the importance of community or built environment design in providing cues and opportunities for physical activity has emerged from these 2 distinct fields. Literature from public health and transportation research have shown positive associations between accessibility of destinations such as shops, stores, and interesting places within walking distance14 and moderate PA level. Recently, studies found a significant association between access to destinations and walking for transport.15,16 In addition, perceptions of certain neighborhood features (eg, welllit streets, biking or walking trails and more places to go, pedestrian safety, traffic volume and speed, and crime)17-22 are associated with physical activity. Evidence from transportation and urban planning has suggested that residents from communities with higher population density, greater connectivity, and more land-use mix (eg, shops are within walking distance of homes) report higher rates of walking/cycling for utilitarian purposes.23 In most studies, individual perceptions of the built environment are usually the independent factors of interest.24 These studies have not assessed whether and how individual perceptions of the built environment may relate to physical activity self-efficacy and ultimately how they may influence physical activity levels. Social cognitive theory describes the triadic reciprocal determinism involving bidirectional influences of environmental, personal, and behavioral factors upon one another.25 Accordingly, the environment can have direct or mediated influence on physical activity behaviors. It is unclear how self-efficacy as derived from social cognitive theory may mediate perception Am J Health Behav.™ ™ 2011;35(6):674-688

of environment and adolescents’ physical activity levels. Few studies tested selfefficacy’s mediating role. Motl et al26 concluded that the effect of perceived equipment accessibility on physical activity is mediated by self-efficacy for overcoming barriers among adolescent girls. This is consistent with the reciprocal relationships among the environment, person, and behavior described by social cognitive theory. The current study filled in the gaps of previous studies. First of all, our investigation looked specifically at physical activity during the weekday before school and after school periods. The findings of this study will provide unique contributions to the literature on after-school obesity interventions. Second, contrary to testing the independent contributions of either perceived environment or selfefficacy on physical activity, this study attempted to test the mediating role of self-efficacy27 and to expand on this relationship among a predominately minority adolescent population. The aim of this study was to test a model that specified both direct and indirect pathways between perceptions of built environmental factors (eg, land use/accessibility, neighborhood satisfaction, and pedestrian/traffic safety) and physical activity in a sample of urban, predominately minority youth. It was hypothesized that the perceptions of built environment have both direct and indirect pathways to physical activity and that self-efficacy for overcoming barriers would serve as a mediator of the effects of perceived neighborhood environment factors. The current study combined both subjective and objective measures of physical activity outcomes. Specifically, self-reported walking behavior and objective, accelerometer-measured physical activity were investigated in 2 separate structural models with neighborhood perception and self-efficacy as predictors. METHODS Participants and Procedure The Baltimore Active Living Teens Study (BALTS) is a cross-sectional study investigating the effects of multifactor risk and protective factors on walking behavior and MVPA in a sample of 350 African American urban high school students (grades 9 through 12). Participants were recruited in 2 magnet high schools located in Baltimore City, Maryland.28 A

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magnet school is a public school that draws students from surrounding regions who are interested in specific subjects. One of the study schools focuses on math and engineering. Its student body is 70% African American. The other study school has a liberal arts focus. The student body is 84% African American. At both schools, students in 29 noncore classes (biology, health, physical education, engineering practicum, science, chemistry, psychology, and sociology) were recruited for participation in the study. These classes were selected by the school administration. In deciding which classes would participate, they chose to include noncore classes in order to avoid using class time during required courses. They were also chosen to address the principal investigator’s request to recruit students from all 4 grades. Participation was then solicited through in-class presentations about the project and the purpose of the study by the trained research assistants.28 The recruitment rate was 54%, which was based on 649 students who were recruited and 350 who agreed to participate.29 A comparison of demographic variables of those who declined and those who participated suggested no difference between the 2 groups.29 Students’ self-reported perceptions of neighborhood built environment and their self-efficacy were assessed through a Web-based survey that included adapted subscales from the Neighborhood Environment Walkability Scale (NEWS)30 and socio-demographic items. Walking behavior and physical activity levels were measured using standardized protocols described below. The participants replied to the Web survey from school computers and were able to retake the survey if any technical problems prevented them from continuing (technical problems were extremely rare). The survey took approximately one hour to administer. Each participant’s parent or guardian provided written informed consent, and all subjects assented to participation. Each participant received incentives valued at $15 for participation in each measurement visit. The institutional review board approved the study. The data were collected from January to June 2006. Measures The student survey contained items that were related to individual self-effi-

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cacy and perception of built environment (eg, land use) factors. Six constructs were included in 2 structural equation models: pedestrian/traffic safety, land-use mix/ accessibility, neighborhood satisfaction, barrier self-efficacy, walking trips, and percentage of moderate-to-vigorous physical activity (MVPA). Table 1 presents descriptive information on demographics, levels of physical activity (ie, sedentary, light, and moderate to vigorous), and walking variables. Table 2 presents response categories, one-week test-retest reliability results, and descriptive statistics for individual self-efficacy and the built-environment variable scales. Internal reliability measures for each scale and subscale are described below. The testretest reliability coefficients are reported in Table 2. Individual-level Variables Barrier self-efficacy was measured using items from a scale originally developed by Saunders et al.31 Based on social cognitive theory, Self-efficacy subscale was defined as confidence in ability to be physically active regardless of barriers.32,33 Examples of items include (1) I think I can be active no matter how tired I feel; (2) I think I can be active even if I have a lots of homework; (3) I think I can be active after school even if I could watch TV. The 6-item subscale (α=.83) examines participants’ confidence in overcoming various situational, mood, and environmental barriers to physical activity. The response format included a 5-point Likert scale ranging from definitely disagree to definitely agree. Saunders’s self-efficacy scale was validated for an after-school physical activity study among rural, predominately African American (69%) fifth graders.31 Perception of Built Environmental Variables All subscales measuring built environment perceptions were adopted from the Neighborhood Environment Walkability Survey (NEWS).30 The 4-item land-use mix/ accessibility subscale (α=0.69) queried respondents about (1) whether there were stores within easy walking distance of home, (2) whether there were good places to go within easy walking distance of home, (3) whether respondents could do most of their shopping at local stores, and (4) whether it was easy to walk to a bus or train stop from home. The response options were

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“definitely disagree,” “mostly disagree,” “neither agree nor disagree,” “mostly agree,” and “definitely agree.” The 7-item neighborhood satisfaction subscale (α=0.73) measured the participants’ degrees of satisfaction with different features of their neighborhoods. The responses categories range from definitely disagree to definitely agree. Pedestrian/traffic safety was measured by 2 items (α=0.66) relating to adolescents’ perceptions about their neighborhoods’ pedestrian/traffic safety (ie, whether crosswalks and pedestrian traffic signals help walkers cross busy streets). The 5-point response categories range from definitely disagree to definitely agree. To assess test-retest stability, participants completed the NEWS questionnaire twice, with an interval of one week. This is a standard time frame in test-retest studies as it is long enough to minimize potential changes in physical activity behavior.34 Dependent Variables: Physical Activity Two dimensions of physical activity were measured in this study. Transportation-related walking. Was defined as the frequency of selfreported walking trips for school, work, recreation, or to get to places. It was measured by household travel diaries. During the measurement week, students were oriented to the process of completing a one-week travel diary. A paper travel diary was filled out to record destinations visited (including home, school, neighborhood, work, and other places), time, and mode of travel (auto/drive, walk, bus, bicycle, etc) over the course of one week. A household travel diary35 is a standard tool used within the transportation industry to capture and assess the movement between and time spent at habitual (home and work) and other locations. When assessing walking behavior, trips with the travel mode of “walking” were selected, and then student-reported 7day walking data were reduced to individual levels of walking trips per day and per week. Physical activity measurement and data reduction. Physical activity was measured by Actigraph accelerometers (Computer Science Applications). Following a standardized protocol, each monitor was initialized prior to placing it on a belt to be worn around each participant’s waist above the right hip. Participants were asked to wear the Am J Health Behav.™ ™ 2011;35(6):674-688

device at all times during the 7 consecutive monitoring days, except at night while sleeping and while bathing or swimming. Activity counts were stored in 30-second time intervals. The 6 students (1.7%) who failed to comply with minimal wear time, had a monitor malfunction, or had fewer than 7 days of data (or non-useable data) were asked to wear the monitor again until useable data were collected. The average number of wears for those 6 students was 2. Actigraph counts were summarized by quantifying the time (minutes) spent at different intensity levels. The Baltimore Active Living Teens Study (BALTS) 28,36 thresholds for activity intensities were fewer than 50.99 counts for sedentary activity, 51 to 578.99 counts for light activity, and 579 or more counts for MVPA. The threshold of 579 or more counts for MVPA corresponds approximately to the lower bound for a 2.5-mph walk, representing an activity intensity level of 3 metabolic equivalents (METs). A 3-MET cut point to define MVPA was used because it has been used as the threshold for MVPA in previous studies of youth.37-40 Accelerometer data reduction methods incorporated the following data processing issues suggested by literature: (1) Individual records that either had a value greater than 16,000 counts (the maximum accelerometer value) or constant and consecutive nonzero records for 10 minutes were excluded from the analysis.41 (2) Valid wearing time was determined by subtracting the invalid minutes (ie, interruptions) from the total wearing minutes. The total weekday out-of-school minutes were the accumulated valid wearing minutes before (5 AM – 8:30 AM) and after (3 PM – 10 PM) school combined. Interruption was estimated using 20 minutes9,42,43 of continuous zero counts. (3) The minimal wear requirement for a valid out-of- school day was 6 hours.44 Accordingly, the percentage of time spent in MVPA was defined as the combined valid moderate and vigorous physical activity minutes divided by the total valid physical activity time in each intensity category (ie, sedentary, light, moderate, and vigorous physical activity). Compared to other approaches, the accelerometer is able to adequately assess physical activity and its association with health. Pedometers provide an inexpensive overall measure of physical activity but are un-

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Figure 1 Students’ Distribution in Baltimore Neighborhoods

able to assess intensity, frequency, and duration of activity or to estimate energy expenditure. 45 Socio-demographic Variables Age, gender, race/ethnicity, grade, and parents’ educational attainment were included on the Web-based survey. Information on participants’ self-reported race/ ethnicity, age, gender, and grade and parents’ educational attainment were obtained and were controlled for as covariates in the analyses. Statistical Analysis and Missing Data Structural equation modeling was used to test the hypothesized pathways be-

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tween land use, pedestrian/traffic safety, neighborhood satisfaction, self-efficacy, and 2 physical activity outcomes (walking behavior and MVPA). The structural equation model (SEM) analyses were conducted using Mplus (Version 5.1).46 Data analyses were conducted in 2008. Covariance matrices were analyzed using maximum likelihood procedure in the 2-step approach.47 The first step was to perform confirmatory factor analyses (CFAs) to test the measurement model. A measurement model describes the nature of the relationship between a number of latent variables and the observed variables (indicators) corresponding to their respective underlying latent factors. Walk-

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Table 1 Characteristics of Study Participants and Health Parameter Measures Characteristics

% or mean (s.d)b

Gender (%) Girls Boys Race/ethnicity (%) Black White, non-Hispanic Other Grade (%) ( 9-12th) 9th 10th 11th 12th Parent’s education level (%) (Father’s/Mother’s) High school College Advanced degree Average trip per day for weekday b Accelerometer measured physical activity intensity (weekday %) Sedentary Light Moderate to Vigorous

58.4 41.6 69.1 16.6 14.3 32.6 23.4 13.1 30.9 47.8/31.5 42.3/56.8 9.9/11.7 0.7 (0.86) a,b

76.3 (7.02) 16.1 (4.84) 7.65 (3.47)

Note. a Intensities of physical activity were measured by accelerometer. Percentage valid accelerometer wearing time (weekday) was calculated as the valid minutes of sedentary/light/moderate-tovigorous divided by the whole day’s valid wearing time; thus, the number represents the percent of time students were involved in the different intensities of physical activity during their valid wearing time during the day. b Average trip per day was used as the measure of the self-report walking behavior. Mean and standard deviation was provided for continuous variables.

ing trips and the MVPA measure were assessed by single items. The second step was full SEM testing. The structural model shows the theoretically based model in which the relationship among dependent and independent variables can be seen. The Satorra and Bentler robust statistic was used to assess the goodness of fit as it is the most reliable test statistic for evaluating covariance structure models with various distributions and sample sizes.48,49 Goodness of fit of models was assessed with multiple indices: (1) chi-square/degree of freedom ratio, where the recommended criterion for a good fit is below 3.00.50 (Chisquare provides a test of exact fit and can be inflated with nonnormal data; it was Am J Health Behav.™ ™ 2011;35(6):674-688

expected to be too conservative.51 The chisquare likelihood with its degrees of freedom ratio provides a better index than chisquare alone); (2) comparative fit index (CFI); (3) nonnormed fit index (NNFI), which is identical to the Tucker-Lewis index;52 (4) root mean square error of approximation (RMSEA). Models that met the criteria of a chi-square/degree of freedom ratio of less than 3.00 or a CFI and NNFI above 0.90 and RMSEA below 0.07 were judged to be acceptable fits.47 SEM requires complete data with no missing values on any variables to conduct model testing. Travel diary data were collected separately, and for the walkingtrip model, 293 students (response rate=84%) completed the diary. We ex-

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Table 2 Self-efficacy and Perception of Built Environment Independent Variables Latent Variables

Response

Self-efficacy-Barriers subscale (8 items)a 5-Point I think…be active no matter how tired I feel I think…I have skills I need to be active. I think…I can be active even if I have a lot of homework. I think…I can be active even when I’d rather do something else. I think…be active no matter how busy my day. I think…can be active most days after school. I think…can be active even if it is hot or cold outside I think…can be active after school even if I could watch TV Land-use mix: Accessibility (4 items)a 5-Point Stores within easy walking distance Good places to go within easy walking distance of home Can do most of my shopping at local stores Easy to walk to bus or train stop from my home Pedestrian/traffic Safety (2 items)a 5-Point Crosswalks to help walkers cross busy streets Pedestrian traffic signals help walkers cross busy streets Neighborhood Satisfaction (7 items)a 5-Point Satisfaction with access to entertainment Satisfaction with safety from crime Satisfaction with amount and speed of traffic Satisfaction with noise from traffic Satisfaction with neighborhood as good place to live Satisfaction with number and quality of food stores Satisfaction with number and quality of restaurants

Mean (s.d) Test-retest (P)

scale 3.2 (1.11) 4.3 (0.84) 3.4 (1.24) 3.7 (1.04) 3.2 (1.14) 3.8 (1.07) 3.7 (1.13) 4.0 (1.00)

0.69 (