The majority of U.S. adults perform insufficient amounts of physical activity to prevent disease and maintain fitness. National recommendations prescribe fixed physical activity goals (e.g. 10,000 steps per day) that may fall outside of an individual's current physical activity repertoire. Prescribing smaller, more adaptive goals based on participant past behavior may be more efficacious at increasing physical activity to the target level. This study tested a pedometer-based intervention that prescribed adaptive goals and rewarded behavior using a percentile schedule of reinforcement. Five individuals enrolled into the intervention and were evaluated with a single-case withdrawal (ABA) design over 10 weeks. The six -week intervention consisted of one-time educational materials, daily adaptive goals, and contingent financial rewards administered on a percentile schedule. Daily goals were determined by ranking a participant's prior 9 days of physical activity (i.e. step counts) and selecting the 40th percentile of the distribution on a moving basis. A Lifecorder Plus, combined accelerometer and pedometer, measured moderate-to-vigorous physical activity (MVPA) minutes per day and steps per day simultaneously. Visual analyses and multilevel statistical models for longitudinal data tested for change across phases. Based on visual analysis, four of the five women increased their median number of steps/day, and all five increased their median MVPA minutes/day. Participants increased their activity by 851 steps/day (range -829 to 2,450 steps) or approximately 5,957 steps per week, and 3.34 MVPA minutes/ day (range 1.93 to 17.27 minutes) or approximately 23.38 MVPA minutes per week from baseline to the intervention phase. After adjusting for wear time and day of the week, the multilevel model detected a significant increase of 551.21 steps/day (SE = 258.26, p = .03) and 2.65 MVPA minutes/day (SE =1.09, p = .02) during the intervention phase compared to the baseline phase. This study provides a formal test of percentile schedules for physical activity research and provided intervention efficacy (i.e., 'proof of concept'). The findings may be used as a preliminary study to inform future work in this line of research

Objective

To examine if first-year roommates made similar meal plan decisions.

Methods

Residence information for 1186 first-year students (N = 593 roommate pairs) and 559 floormates was obtained for the 2015-2016 academic year. Linear generalized estimating equations (GEEs) were used to examine if the number of meals students used over the semester was higher if their roommate used their meal plan more frequently. A logistic GEE examined joint meal plan usage between students and roommates for each month of the semester. To determine if residence, rather than roommate, explained the results, a simulation was conducted by randomly assigning the floormates to a same-sex roommate.

Results

The number of meals students used in spring was higher if the students' roommate had used more meals in spring, even after controlling for the number of meals students used in fall (Female: β = 0.07, 99% CI = 0.00, 0.13; Male: β = 0.10, 99% CI = 0.02, 0.18). Students were more likely to use a meal with their roommate if they were on the same meal plan (Female: OR = 1.61, 99% CI = 1.27, 2.04; Male: OR = 1.57, 99% CI = 1.09, 2.25), and less likely after the first month of being roommates (Female: OR = 0.57-0.25; Male: OR = 0.50-0.22; p < 0.001). The simulation analysis indicated these findings were not due to shared residence.

Discussion

Students' meal plan choices were associated with their roommates' meal plan choices. Roommates' joint meal plan usage was highest at the start of the year. Strategic roommate pairings may result in students using their meal plan more. Further research should determine the extent of roommate influence on students' diet.

Few studies have examined the relative effectiveness of reinforcing versus aversive consequences at changing behavior in real-world environments. Real-time sensing devices makes it easier to investigate such questions, offering the potential to improve both intervention outcomes and theory. This research aims to describe the development of a real-time, operant theory-based secondhand smoke (SHS) intervention and compare the efficacy of aversive versus aversive plus reinforcement contingency systems. Indoor air particle monitors were placed in the households of 253 smokers for approximately three months. Participants were assigned to a measurement-only control group (N = 129) or one of the following groups: 1.) aversive only (AO, N = 71), with aversive audio/visual consequences triggered by the detection of elevated air particle measurements, or 2.) aversive plus reinforcement (AP, N = 53), with reinforcing consequences contingent on the absence of SHS added to the AO intervention. Residualized change ANCOVA analysis compared particle concentrations over time and across groups. Post-hoc pairwise comparisons were also performed. After controlling for Baseline, Post-Baseline daily particle counts (F = 6.42, p = 0.002), % of time >15,000 counts (F = 7.72, p < 0.001), and daily particle events (F = 4.04, p = 0.02) significantly differed by study group. Nearly all control versus AO/AP pair-wise comparisons were statistically significant. No significant differences were found for AO versus AP groups. The aversive feedback system reduced SHS, but adding reinforcing consequences did not further improve outcomes. The complexity of real-world environments requires the nuances of these two contingency systems continue to be explored, with this study demonstrating that real-time sensing technology can serve as a platform for such research.

Some researchers have proposed the prevalence of food insecurity among college students is high due to students' meal plans providing insufficient meals. The association between college students' food security status and their meal plans have not yet been examined. In this study, United States (US) first year college students (N = 534) self-reported their food security status in the Fall 2015 and/or Spring 2016 semester(s). Objective measures of students' meal plans were obtained from the university. Logistic generalized estimating equations (GEEs) were used to determine if students' meal plan, and meal plan use, predicted food insecurity. Linear GEEs were used to examine several potential reasons for lower meal plan use. We found that students did not use all of their available meals. Compared to students on the most expensive (unlimited) meal plan, students on the cheapest (8 meals/week) meal plan were the most likely to report food insecurity (OR = 2.2, 95% CI = 1.2, 4.1). However, in the Fall semester, 26% of students on unlimited meal plans also reported food insecurity. For students on the 180 meals/semester meal plan, food insecurity was associated with using fewer meals (OR = 0.9, 95% CI = 0.8, 1.0). Students who worked tended to use their meal plan less (β = -1.3, 95% CI = -2.3, -0.3). Students are reporting food insecurity while having meals left in their meal plan.

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.

Abstract Background Many studies used the older ActiGraph (7164) for physical activity measurement, but this model has been replaced with newer ones (e.g., GT3X+). The assumption that new generation models are more accurate has been questioned, especially for measuring lower intensity levels. The low-frequency extension (LFE) increases the low-intensity sensitivity of newer models, but its comparability with older models is unknown. This study compared step counts and physical activity collected with the 7164 and GT3X + using the Normal Filter and the LFE (GT3X+N and GT3X+LFE, respectively). Findings Twenty-five adults wore 2 accelerometer models simultaneously for 3Âdays and were instructed to engage in typical behaviors. Average daily step counts and minutes per day in nonwear, sedentary, light, moderate, and vigorous activity were calculated. Repeated measures ANOVAs with post-hoc pairwise comparisons were used to compare mean values. Means for the GT3X+N and 7164 were significantly different in 4 of the 6 categories (p < .05). The GT3X+N showed 2041 fewer steps per day and more sedentary, less light, and less moderate than the 7164 (+25.6, -31.2, -2.9 mins/day, respectively). The GT3X+LFE showed non-significant differences in 5 of 6 categories but recorded significantly more steps (+3597 steps/day; p < .001) than the 7164. Conclusion Studies using the newer ActiGraphs should employ the LFE for greater sensitivity to lower intensity activity and more comparable activity results with studies using the older models. Newer generation ActiGraphs do not produce comparable step counts to the older generation devices with the Normal filter or the LFE.

Abstract 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.

Objectives

To investigate whether walking mediates neighborhood built environment associations with weight status in middle- and older-aged women.

Methods

Participants (N=5085; mean age=64 ± 7.7; 75.4% White non-Hispanic) were from the Women's Health Initiative San Diego cohort baseline visits. Body mass index (BMI) and waist circumference were measured objectively. Walking was assessed via survey. The geographic information system (GIS)-based home neighborhood activity supportiveness index included residential density, street connectivity, land use mix, and number of parks.

Results

BMI was 0.22 units higher and the odds ratio for being obese (vs. normal or overweight) was 8% higher for every standard deviation decrease in neighborhood activity supportiveness. Walking partially mediated these associations (22-23% attenuation). Findings were less robust for waist circumference.

Conclusions

Findings suggest women who lived in activity-supportive neighborhoods had a lower BMI than their counterparts, in part because they walked more. Improving neighborhood activity supportiveness has population-level implications for improving weight status and health.

Since 1980, many studies have evaluated whether stair-use prompts increased physical activity by quantifying changes in stair use. To more completely evaluate changes in physical activity, this study addressed the often-overlooked assessment of climbing up escalators by evaluating the degree to which stair-use sign prompts increased active ascent-defined as stair use or escalator climbing. Over 5 months, at an airport stairs/escalator point of choice, we video-recorded passersby (N = 13,544) who ascended either stairs or escalators, on 10 days with signs and 10 days without signs. Ascenders using the stairs, standing on the escalator, and climbing the escalator were compared on days with versus without signs using multivariable logistic regression. The percentage of ascenders on days with versus without signs were as follows: stair use, 6.9 versus 3.6 percent; escalator standing, 75.2 versus 76.0 percent; and escalator climbing, 18.5 versus 20.4 percent. Signs more than doubled the odds of stair use (vs. escalator use; OR = 2.25; 95% CI = 1.90-2.68; p < .001). Signs decreased the odds of escalator climbing (vs. escalator standing or stair use); OR = 0.90; 95% CI = 0.82-0.99; p = .028). Signs increased the odds of active ascent versus escalator standing by 15 percent (OR = 1.15; 95% CI = 1.05-1.25; p = .002). Although stair-use prompts increased stair use more than twofold (125%), they increased active ascent by only 15 percent, partly because escalator climbing-a behavior not targeted by the intervention-decreased. Although our results corroborated the established consensus that point-of-choice prompts increase stair use, future studies should test interventions designed to increase active ascent.

Background

An online version of the Microscale Audit of Pedestrian Streetscapes (Abbreviated) tool was adapted to virtually audit built environment features supportive of physical activity. The current study assessed inter-rater reliability of MAPS Online between in-person raters and online raters unfamiliar with the regions.

Methods

In-person and online audits were conducted for a total of 120 quarter-mile routes (60 per site) in Phoenix, AZ and San Diego, CA. Routes in each city included 40 residential origins stratified by walkability and SES, and 20 commercial centers. In-person audits were conducted by raters residing in their region. Online audits were conducted by raters in the alternate location using Google Maps (Aerial and Street View) images. The MAPS Abbreviated Online tool consisted of four sections: overall route, street segments, crossings and cul-de-sacs. Items within each section were grouped into subscales, and inter-rater reliability (ICCs) was assessed for subscales at multiple levels of aggregation.

Results

Online and in-person audits showed excellent agreement for overall positive microscale (ICC = 0.86, 95% CI [0.80, 0.90]) and grand scores (ICC = 0.93, 95% CI [0.89, 0.95]). Substantial to near-perfect agreement was found for 21 of 30 (70%) subscales, valence, and subsection scores, with ICCs ranging from 0.62, 95% CI [0.50, 0.72] to 0.95, 95% CI [0.93, 0.97]. Lowest agreement was found for the aesthetics and social characteristics scores, with ICCs ranging from 0.07, 95% CI [-0.12, 0.24] to 0.27, 95% CI [0.10, 0.43].

Conclusions

Results support use of the MAPS Abbreviated Online tool to reliably assess microscale neighborhood features that support physical activity and may be used by raters residing in different geographic regions and unfamiliar with the audit areas.