Karen A. Kirtland

Associations of Perceived Social and Physical Environmental Supports With Physical Activity and Walking Behavior

We evaluated perceived social and environmental supports for physical activity and walking using multivariable modeling. Perceptions were obtained on a sample of households in a southeastern county. Respondents were classified according to physical activity levels and walking behaviors. Respondents who had good street lighting; trusted their neighbors; and used private recreational facilities, parks, playgrounds, and sports fields were more likely to be regularly active. Perceiving neighbors as being active, having access to sidewalks, and using malls were associated with regular walking.

Measuring the Environment for Friendliness Toward Physical Activity: A Comparison of the Reliability of 3 Questionnaires

OBJECTIVES We tested the reliability of 3 instruments that assessed social and physical environments. METHODS We conducted a test-retest study among US adults (n = 289). We used telephone survey methods to measure suitableness of the perceived (vs objective) environment for recreational physical activity and nonmotorized transportation. RESULTS Most questions in our surveys that attempted to measure specific characteristics of the built environment showed moderate to high reliability. Questions about the social environment showed lower reliability than those that assessed the physical environment. Certain blocks of questions appeared to be selectively more reliable for urban or rural respondents. CONCLUSIONS Despite differences in content and in response formats, all 3 surveys showed evidence of reliability, and most items are now ready for use in research and in public health surveillance.

Descriptive epidemiology of pedometer-determined physical activity

PURPOSE The dual purposes of this study were: 1) to provide preliminary descriptive epidemiology data representing pedometer-determined physical activity (PA) and 2) to explore sources of intra-individual variability in steps per day. METHODS All participants (76 males, age = 48.4 +/- 16.3 yr, body mass index (BMI) = 27.1 +/- 5.1 kg x m(-2); 133 females, age = 47.4 +/- 17.5 yr, BMI = 26.9 +/- 5.7 kg x m(-2)) resided in Sumter County, SC, and were recruited by telephone to receive a mailed kit to self-monitor PA for 1 wk. Statistical analyses compared mean steps per day between sexes, races, age groups, education and income levels, and BMI categories. Mean steps per day were also compared between: 1) weekdays versus weekend days, 2) workdays versus nonworkdays, and 3) days of sport/exercise versus no participation. RESULTS The entire sample took 5931 +/- 3664 steps x d(-1) (males = 7192 +/- 3596 vs females = 5210 +/- 3518 steps x d(-1), t = 7.88, P < 0.0001). Significant differences were also indicated by race, age, education, income, and BMI. In addition, weekdays were significantly higher than weekend days, workdays were higher than nonworkdays, and sport/exercise days were higher than nonsport/exercise days. CONCLUSIONS The large standard deviations reflect a wide distribution of ambulatory behavior. Regardless, important differences are still evident by demographic characteristics, BMI categories, day of the week, and reported engagement in work or sport/exercise.

Physical Activity Levels Among Overweight and Obese Adults in South Carolina

Background Obesity in the United States has reached epidemic proportions and is a major cause of morbidity and mortality. Methods We describe the activity levels of South Carolina adults on the basis of data derived from the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance System. Results Overweight and obese men and women reported less leisure time physical activity than did people of normal weight, with women found to be less active than men. Conclusion Physical inactivity is more prevalent among obese and overweight men and women than among people of normal weight. Visiting the physicians office offers a unique opportunity to educate patients about the health benefits and appropriate amount of physical activity.

Development and Use of a Tool for Assessing Sidewalk Maintenance as an Environmental Support of Physical Activity

The importance of regular physical activity is well documented, yet according to epidemiological surveillance data, physical inactivity among all age groups persists. Past attempts to promote physical activity focused on individual-level changes; current approaches focus on environmental changes that will provide opportunities for whole communities to be active. The current ecological focus has led to an increase in funding and research regarding environmental supports of physical activity. As this is a new area of research, much work needs to be done to improve the ability to assess environmental features that support physical activity. This article describes a partnership between researchers and community members to develop and test an objective tool to measure sidewalk maintenance. Community members used data collected with the tool to increase awareness about sidewalk maintenance issues among local policy makers. Collaboration between researchers and community partners was critical for the success of this study.

Physical activity trends in South Carolina, 1994-2000.

Objectives: During the 1990s, physical activity recommendations and surveillance methods were developed in an attempt to increase and monitor, respectively, regular physical activity prevalence rates. For this article, Behavioral Risk Factor Surveillance System data were analyzed to determine whether regular physical activity proportions in South Carolina adults changed from 1994 to 2000. The physical activity prevalence rates for South Carolina were compared with national rates and Healthy People 2000 goals to measure progress. The rate of physical activity counseling by physicians and other health professionals was also analyzed from 1998 to 1999. Methods: Total subjects included 10,495 adults ages 18 years and older from South Carolina and 545,445 from the remainder of the United States. Using random-digit dialing procedures in 1994, 1996, 1998 and 2000, the two most frequent types of leisure-time physical activity performed in the past month were identified. For activities listed, the frequency (days/wk) and duration (minutes/d) were obtained. Linear regressions were performed on regular physical activity and inactivity for the total population and by gender, race, age, and body mass index (BMI). Data pertaining to whether or not a physician or other health professional had provided physical activity counseling were also obtained for 1998 and 1999. Results: From 1994 to 2000, the proportion of South Carolina adults participating in regular leisure time physical activity significantly increased (10.8%). Interestingly, the prevalence of regular physical activity in the rest of the nation remained unchanged during this time. Although significant increases were observed in nearly all subgroups, physical activity prevalence rates for South Carolina adults lagged behind national levels and did not meet Healthy People 2000 goals. Physical activity counseling by physicians and other health professionals increased from 1998 (24.1%) to 1999 (30.4%). Conclusions: While it is not known what factors influenced regular physical activity from 1994 to 2000, they seem to have been equally effective in South Carolina adults of both genders, both races, regular and overweight status, and nearly all age groups. Despite these positive trends, additional efforts are needed to develop and implement effective community and primary care physical activity interventions that facilitate improvements among the nearly two-thirds of South Carolina adults who do not participate in sufficient physical activity to reap significant health benefits.

Spatial Clusters of County-Level Diagnosed Diabetes and Associated RiskFactors in the United States

Introduction: We examined whether spatial clusters of county-level diagnosed diabetes prevalence exist in the United States and whether socioeconomic and diabetes risk factors were associated with these clusters. Materials and Methods: We used estimated county-level age-adjusted data on diagnosed diabetes prevalence for adults in 3109 counties in the United States (2007 data). We identified four types of diabetes clusters based on spatial autocorrelations: high-prevalence counties with high-prevalence neighbors (High-High), low-prevalence counties with low-prevalence neighbors (Low-Low), low-prevalence counties with high-prevalence neighbors (Low-High), and high- prevalence counties with low-prevalence neighbors (High-Low). We then estimated relative risks for clusters being associated with several socioeconomic and diabetesrisk factors. Results: Diabetes prevalence in 1551 counties was spatially associated (p<0.05) with prevalence in neighboring counties. The rate of obesity, physical inactivity, poverty, and the proportion of non-Hispanic blacks were associated with a county being in a High-High cluster versus being a non-cluster county (7% to 36% greater risk) or in a Low-Low cluster (13% to 67% greater risk). The percentage of non-Hispanic blacks was associated with a 7% greater risk for being in a Low-High cluster. The rate of physical inactivity and the percentage of Hispanics or non-Hispanic American Indians were associated with being in a High-Low cluster (5% to 21% greater risk). Discussion: Distinct spatial clusters of diabetes prevalence exist in the United States. Strong association between diabetes clusters and socioeconomic and other diabetes risk factors suggests that interventions might be tailored according to the prevalence of modifiable factors in specific counties.

Diabetes Interactive Atlas.

The Diabetes Interactive Atlas is a recently released Web-based collection of maps that allows users to view geographic patterns and examine trends in diabetes and its risk factors over time across the United States and within states. The atlas provides maps, tables, graphs, and motion charts that depict national, state, and county data. Large amounts of data can be viewed in various ways simultaneously. In this article, we describe the design and technical issues for developing the atlas and provide an overview of the atlas’ maps and graphs. The Diabetes Interactive Atlas improves visualization of geographic patterns, highlights observation of trends, and demonstrates the concomitant geographic and temporal growth of diabetes and obesity.

The prevalence of leisure-time Physical activity among diabetics in South Carolina

Background Diabetes is the seventh leading cause of death among South Carolinians. The benefit of physical activity on the control and prevention of diabetes has been established. This study determined the prevalence of leisure-time physical activity among South Carolinians with and without diabetes and compared the physical activity of those with diabetes between 1990 and 2000. Methods Data from the South Carolina Behavioral Risk Factor Surveillance System were used to classify adults with and without diabetes into categories of physical activity. Results Physical inactivity was higher among South Carolinians with diabetes (42%) than in those without (27%). A comparison of physical activity in diabetics between 1990 and 2000 demonstrated a slight decrease (2%) in physical inactivity. Conclusion The decrease in physical inactivity among diabetics is encouraging; however, further promotion of physical activity is recommended to encourage diabetics to engage in physical activity on a regular basis.

Changes in diagnosed diabetes, obesity, and physical inactivity prevalence in US counties, 2004-2012

Recent studies suggest that prevalence of diagnosed diabetes in the United States reached a plateau or slowed around 2008, and that this change coincided with obesity plateaus and increases in physical activity. However, national estimates can obscure important variations in geographic subgroups. We examine whether a slowing or leveling off in diagnosed diabetes, obesity, and leisure time physical inactivity prevalence is also evident across the 3143 counties of the United States. We used publicly available county estimates of the age-adjusted prevalence of diagnosed diabetes, obesity, and leisure-time physical inactivity, which were generated by the Centers for Disease Control and Prevention (CDC). Using a Bayesian multilevel regression that included random effects by county and year and applied cubic splines to smooth these estimates over time, we estimated the average annual percentage point change (APPC) from 2004 to 2008 and from 2008 to 2012 for diabetes, obesity, and physical inactivity prevalence in each county. Compared to 2004–2008, the median APPCs for diabetes, obesity, and physical inactivity were lower in 2008–2012 (diabetes APPC difference = 0.16, 95%CI 0.14, 0.18; obesity APPC difference = 0.65, 95%CI 0.59, 0.70; physical inactivity APPC difference = 0.43, 95%CI 0.37, 0.48). APPCs and APPC differences between time periods varied among counties and U.S. regions. Despite improvements, levels of these risk factors remained high with most counties merely slowing rather than reversing, which suggests that all counties would likely benefit from reductions in these risk factors. The diversity of trajectories in the prevalence of these risk factors across counties underscores the continued need to identify high risk areas and populations for preventive interventions. Awareness of how these factors are changing might assist local policy makers in targeting and tracking the impact of efforts to reduce diabetes, obesity and physical inactivity.