The Moderating Effect of Socioeconomic Status and Walkability on the Efficacy of Physical Activity Interventions

To enable physical activity (PA) interventions to better tailor procedures to participant characteristics, we investigated the role of neighborhood socioeconomic status (SES) and walkability on the differential effectiveness of adaptive versus static activity goals (AG vs. SG) and immediate versus delayed (IR vs. DR) reinforcement in a PA trial. Data was collected as a part of the WalkIT Arizona study, where healthy, inactive adults (n = 512) were instructed to wear an accelerometer daily for one year and were provided with daily goals for moderate-to-vigorous PA (MVPA). The intersection of goal types (adaptive and static) as well as reinforcement types (immediate and delayed) created four groups. Participants were block-randomized into one of four groups according to high/low neighborhood walkability and high/low neighborhood income. A linear regression model was fit to the data to predict mean daily MVPA based on the interaction of intervention condition and neighborhood walkability/income quadrant. Each neighborhood walkability/SES quadrant level and intervention group interaction was statistically significant. In high walkability/high SES and low walkability/high SES groups, daily MVPA was highest for the AG/IR intervention and lowest for the SG/DR intervention (β = 12.18, p \u3c .001; β = 9.11, p \u3c .001, respectively). In the low walkability/low SES group, MVPA was also lowest for the SG/DR intervention but was highest for the SG/IR intervention. (β= 9.12, p \u3c .001). Results were qualitatively different in the high walkability/low SES group, where the most MVPA was seen for the SG/DR intervention, while the least was observed for AG/DR (β = 5.66 , p \u3c .001). The results show that in a low-income/high-walkability environment, static goals and delayed reinforcement were most effective, which is the opposite of what was seen in other neighborhoods. These findings can be used to customize future physical activity interventions so that intervention strategies are most appropriate for participants’ demographic/environmental settings

The Rise of Community-Based Natural Resource Management Strategies as Explained by Transaction Costs

Community-based natural resource management (CBNRM) is favored over its predecessor, the fortress approach, for common pool resource (CPR) management. We strive to identify variables associated with successful CBNRM programs, and analyze whether their presence transaction costs can explain the shift in favored conservation strategy. By examining nine case studies of large mammals in Africa, we found that out of eight variables, moderate monitoring and adequate program incentives were the most critical various factors in determining CBNRM program outcomes. Furthermore, these variables, as well as others, contributed to a decrease in transaction costs

Associations of neighborhood characteristics with active park use : an observational study in two cities in the USA and Belgium

Background: Public parks can be an important setting for physical activity promotion, but to increase park use and the activity levels of park users, the crucial attributes related to active park use need to be defined. Not only user characteristics and structural park attributes, but also characteristics of the surrounding neighborhood are important to examine. Furthermore, internationally comparable studies are needed, to find out if similar intervention strategies might be effective worldwide. The main aim of this study was to examine whether the overall number of park visitors and their activity levels depend on study site, neighborhood walkability and neighborhood income. Methods: Data were collected in 20 parks in Ghent, Belgium and San Diego, USA. Two trained observers systematically coded park characteristics using the Environmental Assessment of Public Recreation Spaces (EAPRS) tool, and park user characteristics using the System for Observing Play and recreation in Communities (SOPARC) tool. Multilevel multiple regression models were conducted in MLwiN 2.25. Results: In San Diego parks, activity levels of park visitors and number of vigorously active visitors were higher than in Ghent, while the number of visitors walking and the overall number of park visitors were lower. Neighborhood walkability was positively associated with the overall number of visitors, the number of visitors walking, number of sedentary visitors and mean activity levels of visitors. Neighborhood income was positively associated with the overall number of visitors, but negatively with the number of visitors being vigorously active. Conclusions: Neighborhood characteristics are important to explain park use. Neighborhood walkability-related attributes should be taken into account when promoting the use of existing parks or creating new parks. Because no strong differences were found between parks in high-and low-income neighborhoods, it seems that promoting park use might be a promising strategy to increase physical activity in low-income populations, known to be at higher risk for overweight and obesity

Burstiness and Stochasticity in the Malleability of Physical Activity

This study examined whether patterns of self-organization in physical activity (PA) predicted long-term success in a yearlong PA intervention. Increased moderate to vigorous PA (MVPA) was targeted in insufficiently active adults (N = 512) via goal setting and financial reinforcement. The degree to which inverse power law distributions, which are reflective of self-organization, summarized (a) daily MVPA and (b) time elapsed between meeting daily goals (goal attainment interresponse times) was calculated. Goal attainment interresponse times were also used to calculate burstiness, the degree to which meeting daily goals clustered in time. Inverse power laws accurately summarized interresponse times, but not daily MVPA. For participants with higher levels of MVPA early in the study, burstiness in reaching goals was associated with long-term resistance to intervention, while stochasticity in meeting goals predicted receptiveness to intervention. These results suggest that burstiness may measure self-organizing resistance to change, while PA stochasticity could be a precondition for behavioral malleability

Residential proximity to major roadways and prevalent hypertension among postmenopausal women: results from the Women's Health Initiative San Diego Cohort.

BackgroundLiving near major roadways has been linked with increased risk of cardiovascular events and worse prognosis. Residential proximity to major roadways may also be associated with increased risk of hypertension, but few studies have evaluated this hypothesis.Methods and resultsWe examined the cross-sectional association between residential proximity to major roadways and prevalent hypertension among 5401 postmenopausal women enrolled into the San Diego cohort of the Women's Health Initiative. We used modified Poisson regression with robust error variance to estimate the association between prevalence of hypertension and residential distance to nearest major roadway, adjusting for participant demographics, medical history, indicators of individual and neighborhood socioeconomic status, and for local supermarket/grocery and fast food/convenience store density. The adjusted prevalence ratios for hypertension were 1.22 (95% CI: 1.07, 1.39), 1.13 (1.00, 1.27), and 1.05 (0.99, 1.12) for women living ≤100, >100 to 200, and >200 to 1000 versus >1000 m from a major roadway (P for trend=0.006). In a model treating the natural log of distance to major roadway as a continuous variable, a shift in distance from 1000 to 100 m from a major roadway was associated with a 9% (3%, 16%) higher prevalence of hypertension.ConclusionsIn this cohort of postmenopausal women, residential proximity to major roadways was positively associated with the prevalence of hypertension. If causal, these results suggest that living close to major roadways may be an important novel risk factor for hypertension

Cyclone Boiler Field Testing of Advanced Layered NOx Control Technology in Sioux Unit 1

A four week testing program was completed during this project to assess the ability of the combination of deep staging, Rich Reagent Injection (RRI), and Selective Non-Catalytic Reduction (SNCR) to reduce NOx emissions below 0.15 lb/MBtu in a cyclone fired boiler. The host site for the tests was AmerenUE's Sioux Unit 1, a 500 MW cyclone fired boiler located near St. Louis, MO. Reaction Engineering International (REI) led the project team including AmerenUE, FuelTech Inc., and the Electric Power Research Institute (EPRI). This layered approach to NOx reduction is termed the Advanced Layered Technology Approach (ALTA). Installed RRI and SNCR port locations were guided by computational fluid dynamics (CFD) based modeling conducted by REI. During the parametric testing, NOx emissions of 0.12 lb/MBtu were achieved consistently from overfire air (OFA)-only baseline NOx emissions of 0.25 lb/MBtu or less, when firing the typical 80/20 fuel blend of Powder River Basin (PRB) and Illinois No.6 coals. From OFA-only baseline levels of 0.20 lb/MBtu, NOx emissions of 0.12 lb/MBtu were also achieved, but at significantly reduced urea flow rates. Under the deeply staged conditions that were tested, RRI performance was observed to degrade as higher blends of Illinois No.6 were used. NOx emissions achieved with ALTA while firing a 60/40 blend were approximately 0.15 lb/MBtu. NOx emissions while firing 100% Illinois No.6 were approximately 0.165 lb/MBtu. Based on the performance results of these tests, economics analyses of the application of ALTA to a nominal 500 MW cyclone unit show that the levelized cost to achieve 0.15 lb/MBtu is well below 75% of the cost of a state of the art SCR

A Markov Approach for Increasing Precision in the Assessment of Data-Intensive Behavioral Interventions

Health interventions using real-time sensing technology are characterized by intensive longitudinal data, which has the potential to enable nuanced evaluations of individuals’ responses to treatment. Existing analytic tools were not developed to capitalize on this opportunity as they typically focus on first-order findings such as changes in the level and/or slope of outcome variables over different intervention phases. This paper introduces an exploratory, Markov-based empirical transition method that offers a more comprehensive assessment of behavioral responses when intensive longitudinal data are available. The procedure projects a univariate time-series into discrete states and empirically determines the probability of transitioning from one state to another. State transition probabilities are summarized separately in phase-specific transition matrices. Comparing transition matrices illuminates intricate, quantifiable differences in behavior between intervention phases. Statistical significance is estimated via bootstrapping techniques. This paper introduces the methodology via three case studies from a secondhand smoke reduction trial utilizing real-time air particle sensors. Analysis enabled the identification of complex phenomena such as avoidance and escape behavior in response to punitive contingencies for tobacco use. Additionally, the largest changes in behavior dynamics were associated with the introduction of behavioral feedback. The Markov approach‘s ability to elucidate subtle behavioral details has not typically been feasible with standard methodologies, mainly due to historical limitations associated with infrequent repeated measures. These results suggest that the evaluation of intervention effects in data-intensive single-case designs can be enhanced, providing rich information that can ultimately be used to develop interventions uniquely tailored to specific individuals

Correction to: Variable Magnitude and Frequency Financial Reinforcement is Effective at Increasing Adults’ Free-Living Physical Activity

The original article has been corrected to update figures 1, 4, and 5 captions. Original article available on Springer\u27s website or in Chapman University Digital Commons

Variable Magnitude and Frequency Financial Reinforcement is Effective at Increasing Adults’ Free-Living Physical Activity

Financial rewards can increase health behaviors, but little research has quantified the effects of different reinforcement schedules on this process. This analysis compares the average moderate-to-vigorous physical activity (MVPA) associated with six distinct positive reinforcement schedules implemented within a physical activity promotion clinical trial. In this trial, participants (N = 512) wore an accelerometer for 1 year and were prescribed one of two types of MVPA goals: a static 30-min goal or an adaptive goal based on the MVPA produced over the previous 9 days. As participants met goals, they transitioned through a sequence of reinforcement stages, beginning with a continuous-fixed magnitude (CRF-FM), then CRF-variable magnitude (CRF-VM), followed by a series of variable ratio-VM (VR-VM) schedules. The average accumulation of MVPA bouts over the last 24 days of each stage was compared to each other. Average MVPA during stage transitions was also examined. The results indicated that immediate reinforcement resulted in more MVPA relative to a comparison group and that the relative effectiveness of adaptive versus static goals was dependent on the magnitude of daily MVPA goals. Schedule effects were qualitatively different for individuals who frequently met their daily goals (Large Intervention Effect subgroup) versus those who did not (Small Intervention Effect subgroup). For the Large Intervention Effect group, the CRF-VM schedule produced the most MVPA, in particular within the adaptive goal condition, with increases observed immediately upon encountering this schedule. In contrast, the CRF-FM schedule produced small amounts of MVPA. This pattern was reversed for the Small Intervention Effect subgroup, where the most MVPA was associated with the CRF-FM stage. Future interventions should focus on discriminating small versus large intervention effects as quickly as possible so that the optimal reinforcement schedule can be used

Lattice Glass Models

Motivated by the concept of geometrical frustration, we introduce a class of statistical mechanics lattice models for the glass transition. Monte Carlo simulations in three dimensions show that they display a dynamical glass transition which is very similar to that observed in other off-lattice systems and which does not depend on a specific dynamical rule. Whereas their analytic solution within the Bethe approximation shows that they do have a discontinuous glass transition compatible with the numerical observations.Comment: 4 pages, 2 figures; minor change