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Low moderate-to-vigorous physical activity (MVPA) levels and obesity are associated with increased cardiometabolic disease risk.The aim of this study was to describe MVPA and cardiometabolic risk characteristics of insufficiently active African American women with obesity (N = 60) enrolled in a culturally tailored MVPA intervention.We assessed accelerometer-measured and self-reported MVPA, blood pressure, serum lipid profiles, cardiorespiratory fitness (VO2 peak), and aortic pulse wave velocity.Participants (mean age, 38.4; mean body mass index, 40.6 kg/m2) averaged 15 min/d of accelerometer-measured MVPA and 30 min/wk of self-reported MVPA. Systolic and diastolic blood pressure levels were elevated (135.4 and 84.0 mm Hg, respectively). With the exception of low-density lipoprotein cholesterol (121.4 mg/dL) and high-density lipoprotein cholesterol (47.6 mg/dL), lipid profiles were within reference ranges. Compared with normative reference values, average VO2 peak was low (18.7 mL/kg/min), and pulse wave velocity was high (7.4 m/s).Our sample of insufficiently active African American women with obesity was at an elevated risk for cardiometabolic disease.

Potent lifestyle interventions to increase moderate-to-vigorous physical activity are urgently needed for population-level chronic disease prevention. This trial tested the independent and joint effects of a mobile health system automating adaptive goal setting and immediate financial reinforcement for increasing daily walking among insufficiently active adults.Participants were randomized into a 2 (adaptive versus static goal setting) X 2 (immediate versus delayed financial incentive timing) condition factorial trial to increase walking.Participants (N=512 adults) were recruited between 2016 and 2018 and were 64.5% female, aged 18-60 years, 18.8% Hispanic, 6.1% African American, and 83% White.Principles of reinforcement and behavioral economics directed intervention design.Participants wore accelerometers daily (133,876 day-level observations) that remotely measured moderate-to-vigorous physical activity bout minutes of ≥3 minutes/day for 1 year. Primary outcomes were between-condition differences in (1) engaging ≥1 bout of moderate-to-vigorous physical activity on each day and (2) on days with ≥1 bout, daily total moderate-to-vigorous physical activity minutes.Mixed-effects hurdle models tested treatment group X phase (time) interactions using an intent-to-treat approach in 2021. Engaging in any ambulatory moderate-to-vigorous physical activity was greater for Adaptive than for Static Goal groups (OR=2.34, 95% CI=2.10, 2.60 vs OR=1.66, 95% CI=1.50, 1.84; p0.001) and for Immediate than for Static Reinforcement groups (OR=2.16 95% CI=1.94, 2.40 vs OR=1.77, 95% CI=1.59, 1.97; p0.01). The Immediate Reinforcement group increased by 16.54 moderate-to-vigorous physical activity minutes/day, whereas the Delayed Reinforcement group increased by 9.91 minutes/day (p0.001). The combined Adaptive Goals + Immediate Reinforcement group increased by 16.52 moderate-to-vigorous physical activity minutes/day, significantly more than that of either Delayed Reinforcement group.This study offers automated and scalable-behavior change strategies for increasing walking among adults most at-risk for chronic diseases attributed to sedentary lifestyles.This study is registered at www.clinicaltrials.gov (ClinicalTrials.gov Identifier: NCT02717663).

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.

The European Space Agency SnowSAR instrument is a side looking, dual polarized (VV/VH), X/Ku band synthetic aperture radar (SAR), operable from a small aircraft. Between 2010 and 2013, the instrument was deployed at several sites in Northern Finland, Austrian Alps, and northern Canada. The purpose of the airborne campaigns was to measure the backscattering properties of snow-covered terrain to support the development of snow water equivalent retrieval techniques using SAR. SnowSAR was deployed in Sodankylä, Northern Finland for a single flight mission in March 2011 and twelve missions at two sites (tundra and boreal forest) in the winter of 2011–2012. Over the Austrian Alps, three flight missions were performed between November 2012 and February 2013 over three sites located in different elevation zones, representing a montane valley, Alpine tundra, and a glacier environment. In Canada, a total of two missions were flown in March and April 2013, over sites in the Trail Valley Creek watershed, Northwest Territories, representative of the tundra snow regime. This paper introduces the airborne SAR data, as well as coincident in situ information on land cover, vegetation and snow properties. To facilitate easy access to the data record the datasets described here are deposited in a permanent data repository (https://doi.pangaea.de/10.1594/PANGAEA.933255; Lemmetyinen et al., 2021). A temporary link to access the data without login information is provided for reviewers of this manuscript: https://www.pangaea.de/tok/e8c562c3c8a15ac34daa83d00c76fcb347330884.

Little published research explores the perceptions of school nutrition professionals and youth regarding existing school nutrition marketing materials. A two-phased approach was taken to address this gap. In Phase 1, a national convenience sample of US school nutrition professionals (n = 1546; 89% female; 83.3% white; mean age 50 ± 10.2 years) evaluated a sample of marketing materials via a web-based survey. Phase 2 involved a sample of youth (n = 61; 51% female, 52.5% Hispanic; 98% participation in free/reduced price lunch) living in the Phoenix, Arizona metro area who completed interviews on the top-rated materials from the school nutrition professionals (Phase 1). Main outcome measures included perceived attractiveness/appeal, comprehension, relevance, motivation/persuasion, and uniqueness of marketing materials. In Phase 1, means/standard deviations of school nutrition professionals’ perceptions across materials are provided. For Phase 2, a thematic/subtheme analysis of the youth interviews is provided. School nutrition professionals found the materials easy to understand (82%); however, they rated the materials low in motivational appeal (48%) and low in their ability to influence the selection or consumption of fruits/vegetables (95%). Youth participants discussed their likes, dislikes, comprehension, social aspects, and health aspects of the materials. Results indicate that existing marketing materials were not highly rated by professionals or youth. Greater investment and research are needed to test and develop nutrition marketing materials for schools, with a focus on enhancing their appeal and impact on fruit/vegetable consumption.

Displacement rates of mountain slope deformations that can affect entire valley mountain flanks are often measured spatially distributed in‐situ without spatial significance. The spatially explicit measurement and recording of time series of slope deformations is a challenge, as the unstable slopes are often disintegrated into several subdomains, which move with different deformation rates. The current state‐of‐the‐art monitoring systems detect slow to very slow deformation rates between mm/a and several m/a. Using the examples of slope deformations in Saalbach‐Hinterglemm and the deep rock slide Marzellkamm in Austria this paper presents the results of terrestrial laser scans, extensometer measurements, Spaceborne InSAR data, unmanned Aerial System Photogrammetry (UAS‐P), and fixed‐point measurements. The different measurements complement each other and are optimally aligned for different application areas. InSAR data can help to identify hot spots on regional and local scale, while UAS‐P enables for spatially high level accuracy in the detection of subdomains moving at different speeds. For local warning systems TLS, extensometers and GBInSAR deliver higher accuracy.

The study purpose was to train and validate a deep learning approach to detect microscale streetscape features related to pedestrian physical activity. This work innovates by combining computer vision techniques with Google Street View (GSV) images to overcome impediments to conducting audits (e.g., time, safety, and expert labor cost). The EfficientNETB5 architecture was used to build deep learning models for eight microscale features guided by the Microscale Audit of Pedestrian Streetscapes Mini tool: sidewalks, sidewalk buffers, curb cuts, zebra and line crosswalks, walk signals, bike symbols, and streetlights. We used a train–correct loop, whereby images were trained on a training dataset, evaluated using a separate validation dataset, and trained further until acceptable performance metrics were achieved. Further, we used trained models to audit participant (N = 512) neighborhoods in the WalkIT Arizona trial. Correlations were explored between microscale features and GIS-measured and participant-reported neighborhood macroscale walkability. Classifier precision, recall, and overall accuracy were all over >84%. Total microscale was associated with overall macroscale walkability (r = 0.30, p < 0.001). Positive associations were found between model-detected and self-reported sidewalks (r = 0.41, p < 0.001) and sidewalk buffers (r = 0.26, p < 0.001). The computer vision model results suggest an alternative to trained human raters, allowing for audits of hundreds or thousands of neighborhoods for population surveillance or hypothesis testing.

Background Healthy aging requires support from local built and social environments. Using latent profile analysis, this study captured the multidimensionality of the built environment and examined relations between objective and perceived built environment profiles, neighborhood social cohesion and quality of life among seniors. Methods In total, 693 participants aged 66–97 were sampled from two US locales in 2005–2008 as part of the Senior Neighborhood Quality of Life Study (SNQLS). Perceived social cohesion and quality of life were assessed using validated surveys. Six objective (geographic information system (GIS)-based) and seven perceived built environment latent profiles generated in previous SNQLS publications were used for analyses. Mixed-effects models estimated social cohesion and quality of life separately as a function of the built environment profiles. Results More walkable and destination-rich perceived built environment profiles were associated with higher social cohesion and quality of life. Objective built environment profiles were not associated with social cohesion and only positively associated with quality of life in only one locale (Baltimore/DC). Conclusions Latent profile analysis offered a comprehensive approach to assessing the built environment. Seniors who perceived their neighborhoods to be highly walkable and recreationally dense experienced higher neighborhood social cohesion and quality of life, which may set the stage for healthier aging.

An essential characteristic of a healthy and sustainable city is a physically active population. Effective policies for healthy and sustainable cities require evidence-informed quantitative targets. We aimed to identify the minimum thresholds for urban design and transport features associated with two physical activity criteria: at least 80% probability of engaging in any walking for transport and WHO's target of at least 15% relative reduction in insufficient physical activity through walking. The International Physical Activity and the Environment Network Adult (known as IPEN) study (N=11 615; 14 cities across ten countries) provided data on local urban design and transport features linked to walking. Associations of these features with the probability of engaging in any walking for transport and sufficient physical activity (≥150 min/week) by walking were estimated, and thresholds associated with the physical activity criteria were determined. Curvilinear associations of population, street intersection, and public transport densities with walking were found. Neighbourhoods exceeding around 5700 people per km 2, 100 intersections per km 2, and 25 public transport stops per km 2 were associated with meeting one or both physical activity criteria. Shorter distances to the nearest park were associated with more physical activity. We use the results to suggest specific target values for each feature as benchmarks for progression towards creating healthy and sustainable cities.

This article reports the results of Smart Walk: a randomized pilot trial of an 8-month culturally tailored, smartphone-delivered physical activity (PA) intervention for African American women with obesity. Sixty participants (age range = 24–49 years; BMI range = 30–58 kg/m2) were randomized to the Smart Walk intervention (n = 30) or a wellness comparison intervention (n = 30). Results supported the acceptability and feasibility of the intervention, as demonstrated by participant retention (85% at 4 months and 78% at 8 months), Smart Walk app use, and intervention satisfaction (i.e., 100% of PA participants completing the intervention [n = 24] reported they would recommend it to friend). Smart Walk participants also reported greater increases in moderate-to-vigorous PA (4-month between-arm difference in change [b] = 43.3 min/week; p = 0.018; Cohen’s d = 0.69; 8-month b = 56.6 min/week; p = 0.046; d = 0.63) and demonstrated clinically relevant, although not statistically significant (p-values > 0.05), baseline to 4 months improvements in cardiorespiratory fitness (b = 1.67 mL/kg/min; d = 0.40), systolic blood pressure (b = −3.33 mmHg; d = 0.22), diastolic blood pressure (b = −4.28 mmHg; d = 0.37), and pulse wave velocity (b = −0.46 m/s; d = 0.33). Eight-month cardiometabolic outcomes followed similar trends, but had high rates of missing data (45–53%) due to COVID-19 restrictions. Collectively, findings demonstrated favorable outcomes for acceptability and feasibility, while also highlighting key areas for refinement in future research.

Benchmarking and monitoring of urban design and transport features is crucial to achieving local and international health and sustainability goals. However, most urban indicator frameworks use coarse spatial scales that either only allow between-city comparisons, or require expensive, technical, local spatial analyses for within-city comparisons. This study developed a reusable, open-source urban indicator computational framework using open data to enable consistent local and global comparative analyses. We show this framework by calculating spatial indicators-for 25 diverse cities in 19 countries-of urban design and transport features that support health and sustainability. We link these indicators to cities' policy contexts, and identify populations living above and below critical thresholds for physical activity through walking. Efforts to broaden participation in crowdsourcing data and to calculate globally consistent indicators are essential for planning evidence-informed urban interventions, monitoring policy effects, and learning lessons from peer cities to achieve health, equity, and sustainability goals.

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.

The original article has been corrected to update figures 1, 4, and 5 captions and the corrected captions are listed below

Little change over the decades has been seen in adults meeting moderate-to-vigorous physical activity (MVPA) guidelines. Numerous individual-level interventions to increase MVPA have been designed, mostly static interventions without consideration for neighborhood context. Recent technologies make adaptive interventions for MVPA feasible. Unlike static interventions, adaptive intervention components (e.g., goal setting) adjust frequently to an individual's performance. Such technologies also allow for more precise delivery of “smaller, sooner incentives” that may result in greater MVPA than “larger, later incentives”. Combined, these factors could enhance MVPA adoption. Additionally, a central tenet of ecological models is that MVPA is sensitive to neighborhood environment design; lower-walkable neighborhoods constrain MVPA adoption and maintenance, limiting the effects of individual-level interventions. Higher-walkable neighborhoods are hypothesized to enhance MVPA interventions. Few prospective studies have addressed this premise. This report describes the rationale, design, intervention components, and baseline sample of a study testing individual-level adaptive goal-setting and incentive interventions for MVPA adoption and maintenance over 2 years among adults from neighborhoods known to vary in neighborhood walkability. We scaled these evidenced-based interventions and tested them against static-goal-setting and delayed-incentive comparisons in a 2 × 2 factorial randomized trial to increase MVPA among 512 healthy insufficiently-active adults. Participants (64.3% female, M age = 45.5 ± 9.1 years, M BMI = 33.9 ± 7.3 kg/m2, 18.8% Hispanic, 84.0% White) were recruited from May 2016 to May 2018 from block groups ranked on GIS-measured neighborhood walkability and socioeconomic status (SES) and classified into four neighborhood types: “high walkable/high SES,” “high walkable/low SES,” “low walkable/high SES,” and “low walkable/low SES.” Results from this ongoing study will provide evidence for some of the central research questions of ecological models.

Financial incentives can increase physical activity (PA), but differences in the immediacy of reward delivery and individual differences in delay discount rates (i.e., higher discount values associated with less tolerance for delayed rewards) may explain differential responding. The current study tested whether delay discount rate moderated the relative effectiveness of immediate financial rewards on increasing daily PA. Inactive, overweight adults (ages 18-60, N = 96) were randomized to receive either smaller, immediate goal-contingent rewards or larger, delayed rewards for participation. Delay discount rates were derived for those who completed the Monetary Choice Questionnaire (N = 85). Linear mixed models tested interactions between discount rate and intervention arm on changes in mean daily Fitbit-measured steps from baseline to intervention phases, and rates of change during the intervention phase. Across all groups, participants increased by 2258 steps/day on average from baseline to intervention and declined by 9 steps/day across the 4-month intervention phase. The mean increase in daily steps was greater for immediate reward-arm participants across all discount rates. Descriptive exploration of reward effects by delay discount rate suggested that the magnitude of reward effects decreased at higher discount rates. During the 4-month intervention phase, rates of decline in daily steps were similar in both reward arms, but declines became more pronounced at higher discount rates. Overall, intervention efficacy decreased with less tolerance for delays. The importance of financial reward immediacy for increasing PA appears to increase with greater delay discount rates.

Background African American (AA) women perform low levels of physical activity (PA) and are disproportionally burdened by cardiometabolic disease conditions when compared to White women and the U.S. population as a whole. These disparities emphasize the need for innovative and effective interventions to increase PA and reduce cardiometabolic disease risk among AA women. Recent evidence suggests that mobile health (mHealth) interventions have the potential to increase PA and reduce cardiometabolic disease risk factors. Few studies have examined the efficacy of mHealth PA interventions among racial/ethnic minorities, including AA women. This represents a missed opportunity given the reported success of technology-delivered PA interventions in predominately White populations and the high use of technology among AA women. Objective To describe the design, theoretical rationale, and cultural relevance of Smart Walk, a culturally sensitive smartphone-delivered PA intervention for AA women. Design and methods Smart Walk is an 8-month, randomized controlled pilot trial designed to increase PA and reduce cardiometabolic disease risk among AA women. Sixty physically inactive AA women with obesity will be assigned to receive either a culturally relevant intervention designed to increase PA (by targeting leisure-time, household chore/task-related, and occupational PA) or a culturally relevant wellness attention-matched control condition. Outcomes will be assessed at baseline, 4 months, and 8 months, and include feasibility and acceptability of the PA intervention and evaluation of effects on PA and cardiometabolic risk factors. Summary Smart Walk represents a culturally relevant, theory-based approach to promote PA and reduce cardiometabolic disease risk in AA women.

Building healthy fruit and vegetable (FV) consumption habits early in life is critical for primary prevention. However, U.S. youth do not come close to meeting national recommendations for FV intake. School-lunch salad bars are one of the most heavily promoted ways to meet FV guidelines. Contrary to popular belief, no rigorous randomized trials have examined whether salad bars increase students' FV consumption. This paper describes the design and rationale of a federally funded trial to evaluate whether introducing salad bars in elementary, middle, and high schools affects students' FV consumption and waste during lunch. A cluster factorial randomized trial will test new salad bars against waitlist controls, with and without an additional marketing intervention (N = 36 schools, N = 6804 students: n = 12 elementary, n = 12 middle and n = 12 high schools). Objective plate waste measurements of individual student's selection of FVs, consumption, and waste will be conducted using digital scales. Primary aim includes comparing FV consumption in schools without salad bars to those with new salad bars by grade level. Secondary aims include: a) whether FV marketing impacts the success of salad bars for FV consumption; b) whether salad bars differentially result in more FV waste compared to traditional serving methods; c) cost-benefit of using salad bars for consumption over traditional serving methods. When complete, this study stands to be one of the most definitive on the effectiveness of salad bars and contextual factors impacting their success. Findings will provide evidence for how to best spend limited federal dollars to improve FV intake in schools. Trial registration: ClinicalTrials.gov Identifier: NCT03283033 (date of registration: 9/14/2017).

Highlights • Newly-developed measures of crime, environmental context, and activity are valid. • Factorial validity of measures supported across four across age groups. • Minimal measurement redundancy found across measures., Valid and reliable measures are needed to better understand the relationship between physical activity and crime. This paper provides a comprehensive psychometric evaluation of measures developed in the Safe and Fit Environments (SAFE) Study to assess a crime-PA conceptual framework. In addition to assessing the basic psychometric properties of each measure (e.g., variable distributions [item/scale level], internal consistency reliability), this study formally examined the measurement validity and invariance of measures across four age groups using confirmatory factor analysis. The sample (n = 2173) included 336 Adolescents (aged 12–17), 532 Young adults (aged 18–39), 838 Middle Age Adults, and 467 Older Adults (aged 66+). The psychometric evaluation of (sub)scales showed consistent factorial validity and internal consistency reliability across the majority of the measures and across the four age groups. Specifically, 14 of the 17 measures displayed statistically and practically significant factor loadings and internal consistency values in the overall sample and across the age groups. The pattern of correlations for each (sub)scale with other (sub)scales/indexes largely did not exhibit redundancy across measures. The findings expanded upon the test–retest reliability evaluation reported in Patch et al. (2019), and clarified key aspects of the construct validity of these indicators. The latter bodes well for potential utility of these indicators in future predictive models.

Mobile health technologies are contributing to the increasing relevance of control engineering principles in understanding and improving health behaviors, such as physical activity. Social cognitive theory (SCT), one of the most influential theories of health behavior, has been used as the conceptual basis for behavioral interventions for smoking cessation, weight management, and other health-related outcomes. This paper presents a control-oriented dynamical systems model of SCT based on fluid analogies that can be used in system identification and control design problems relevant to the design and analysis of intensively adaptive interventions . Following model development, a series of simulation scenarios illustrating the basic workings of the model are presented. The model’s usefulness is demonstrated in the solution of two important practical problems: 1) semiphysical model estimation from data gathered in a physical activity intervention (the Mobile Interventions for Lifestyle Exercise and Eating at Stanford study) and 2) as a means for discerning the range of “ambitious but doable” daily step goals in a closed-loop behavioral intervention aimed at sedentary adults. The model is the basis for ongoing experimental validation efforts and should encourage additional research in applying control engineering technologies to the social and behavioral sciences.

Background WalkIT Arizona was a 2×2 factorial trial examining the effects of goal type (adaptive versus static) and reinforcement type (immediate versus delayed) to increase moderate to vigorous physical activity (MVPA) among insufficiently active adults. The 12-month intervention combined mobile health (mHealth) technology with behavioral strategies to test scalable population-health approaches to increasing MVPA. Self-reported physical activity provided domain-specific information to help contextualize the intervention effects. Objective The aim of this study was to report on the secondary outcomes of self-reported walking for transportation and leisure over the course of the 12-month WalkIT intervention. Methods A total of 512 participants aged 19 to 60 years (n=330 [64.5%] women; n=425 [83%] Caucasian/white, n=96 [18.8%] Hispanic/Latinx) were randomized into interventions based on type of goals and reinforcements. The International Physical Activity Questionnaire-long form assessed walking for transportation and leisure at baseline, and at 6 months and 12 months of the intervention. Negative binomial hurdle models were used to examine the effects of goal and reinforcement type on (1) odds of reporting any (versus no) walking/week and (2) total reported minutes of walking/week, adjusted for neighborhood walkability and socioeconomic status. Separate analyses were conducted for transportation and leisure walking, using complete cases and multiple imputation. Results All intervention groups reported increased walking at 12 months relative to baseline. Effects of the intervention differed by domain: a significant three-way goal by reinforcement by time interaction was observed for total minutes of leisure walking/week, whereas time was the only significant factor that contributed to transportation walking. A sensitivity analysis indicated minimal differences between complete case analysis and multiple imputation. Conclusions This study is the first to report differential effects of adaptive versus static goals for self-reported walking by domain. Results support the premise that individual-level PA interventions are domain- and context-specific and may be helpful in guiding further intervention refinement. Trial Registration Preregistered at clinicaltrials.gov: (NCT02717663) https://clinicaltrials.gov/ct2/show/NCT02717663 International Registered Report Identifier (IRRID) RR2-10.1016/j.cct.2019.05.001

Despite growing research supporting the impact of the built environment on active school transport (AST), distance persists as the most powerful predictor of walking and biking to school. There is a need to better understand how environmental features interact with distance to affect AST, and whether the influence of environmental factors persist across different distance thresholds. Multilevel models using cluster-robust standard errors were used to examine for interactions between objectively measured macroscale environmental features and several reported distances from home to school (up to &frac14, &frac14, up to &frac12, &frac12, up to 1, 1+ miles) on the likelihood of parent reported AST for children grades 3&ndash, 8 (n = 2751) at 35 schools who completed a Safe Routes to School Parent Survey about Walking and Biking to School (SRTS Parent Survey). An interaction between both intersection density and food-related land use with distance was observed. The likelihood of AST decreased as intersection density and distance increased (i.e., 31.0% reduced odds among those living within &frac14, mile compared to 18.2% using &frac12, &ndash, 1-mile criterion). The likelihood of using AST were reduced as food-related land use and distance increased (i.e., 43.67% reduced odds among those living under &frac14, mile compared to 19.83% reduced odds among those living &frac12, 1 mile). Programs and infrastructure improvements focused on overcoming environmental barriers to promote AST may be most effective when targeting neighborhoods within &frac14, mile of schools.

BACKGROUND WalkIT Arizona was a 2×2 factorial trial examining the effects of goal type (adaptive versus static) and reinforcement type (immediate versus delayed) to increase moderate to vigorous physical activity (MVPA) among insufficiently active adults. The 12-month intervention combined mobile health (mHealth) technology with behavioral strategies to test scalable population-health approaches to increasing MVPA. Self-reported physical activity provided domain-specific information to help contextualize the intervention effects. OBJECTIVE The aim of this study was to report on the secondary outcomes of self-reported walking for transportation and leisure over the course of the 12-month WalkIT intervention. METHODS A total of 512 participants aged 19 to 60 years (n=330 [64.5%] women; n=425 [83%] Caucasian/white, n=96 [18.8%] Hispanic/Latinx) were randomized into interventions based on type of goals and reinforcements. The International Physical Activity Questionnaire-long form assessed walking for transportation and leisure at baseline, and at 6 months and 12 months of the intervention. Negative binomial hurdle models were used to examine the effects of goal and reinforcement type on (1) odds of reporting any (versus no) walking/week and (2) total reported minutes of walking/week, adjusted for neighborhood walkability and socioeconomic status. Separate analyses were conducted for transportation and leisure walking, using complete cases and multiple imputation. RESULTS All intervention groups reported increased walking at 12 months relative to baseline. Effects of the intervention differed by domain: a significant three-way goal by reinforcement by time interaction was observed for total minutes of leisure walking/week, whereas time was the only significant factor that contributed to transportation walking. A sensitivity analysis indicated minimal differences between complete case analysis and multiple imputation. CONCLUSIONS This study is the first to report differential effects of adaptive versus static goals for self-reported walking by domain. Results support the premise that individual-level PA interventions are domain- and context-specific and may be helpful in guiding further intervention refinement. CLINICALTRIAL Preregistered at clinicaltrials.gov: (NCT02717663) https://clinicaltrials.gov/ct2/show/NCT02717663 INTERNATIONAL REGISTERED REPORT RR2-10.1016/j.cct.2019.05.001

Creating more physical activity–supportive built environments is recommended by the World Health Organization for controlling noncommunicable diseases. The IPEN (International Physical Activity and Environment Network) Adult Study was undertaken to provide international evidence on associations of built environments with physical activity and weight status in 12 countries on 5 continents ( n > 14,000). This article presents reanalyzed data from eight primary papers to identify patterns of findings across studies. Neighborhood environment attributes, whether measured objectively or by self-report, were strongly related to all physical activity outcomes (accelerometer-assessed total physical activity, reported walking for transport and leisure) and meaningfully related to overweight/obesity. Multivariable indexes of built environment variables were more strongly related to most outcomes than were single-environment variables. Designing activity-supportive built environments should be a higher international health priority. Results provide evidence in support of global initiatives to increase physical activity and control noncommunicable diseases while achieving sustainable development goals.

The use of unmanned aerial vehicles (UAV) for ground surface measurements in natural hazard studies has strongly increased in recent years. Multi-temporal 3D point clouds derived from light detection and ranging (LiDAR) sensors and photogrammetric techniques including structure-from-motion (SfM) and dense image matching (DIM) have become important tools for monitoring the activity of geomorphic processes. However, due to georeferencing errors and measurement inaccuracies, change detection with centimeter precision remains challenging, especially in study areas covered by vegetation. This study aims at quantifying the influence of low vegetation on the vertical uncertainties of 3D point clouds in a study area mostly covered by meadows and pastures with different grass heights. 3D point clouds derived from UAV-SfM and UAV-LiDAR are compared to terrestrial ground surface measurements of a differential global navigation satellite system (dGNSS) receiver in order to quantify the vertical uncertainties and to detect advantages/disadvantages of the different sensors. The results indicate that neither method is able to detect the ground surface under dense low vegetation with centimeter precision, and that surface displacement rates derived from multi temporal analyses can be highly influenced by changes in vegetation height between surveys.

Mountainous areas bring unique challenges for surveying and natural hazard monitoring – inaccessibility, dangerous terrain, snow coverage and line-of-sight problems often make it next to impossible to perform ground-based monitoring or even to provide a good vantage point for close-range sensing (e.g. terrestrial laser scanning (TLS) or terrestrial photogrammetry). Airborne or satellite-based methods are often the only way to gain information about geodynamically active sites. Here, structure-from-motion (SfM) photogrammetry from unmanned aerial vehicle (UAV) imagery in particular can provide an inexpensive and easily implemented monitoring option. The Vigilans research project attempts to evaluate the feasibility of UAV-photogrammetry against more established surveying methods (e.g. in situ data from extensometers or total stations).Our study site Marzellkamm is located in the Central Ötztal Alps of Western Austria. The active rock slope deformation we are monitoring in Vigilans lies at 2450-2850 m asl. on a SE-facing slope. Annual displacement rates of up to 1.5 m/year in the early 2010’s triggered monitoring and research interest. Due to the remote location, mitigation methods were not implemented, but a hiking trails was relocated. Orthoimage photogrammetry and ground-based monitoring instrumentation (extensometers, terrestrial laser scanning, total station measurements combined with GNSS and geodetic surveys) collected data 1971-2019.In the last years, movement along the slope has slowed down considerably. The rather slow current movements provide a valuable challenge for detection, with rates of In three separate missions between summer 2018 to fall of 2019, UAV-SfM 3D-models of the site were created for displacement rate evaluations; it is planned to continue this monitoring for a total of three years as part of the Vigilans project. Photogrammetric missions were performed in conjunction with total station measurements of more than 30 ground control points.The required level of precision is becoming achievable and affordable with new RTK/PPK-equipped (Real-Time-Kinematics/Post-Processed Kinematics) UAVs. However, evaluating the resulting 3D-- model in terms of movement rates remains non-trivial. The most common algorithm for change detection in point clouds, M3C2, is not well-suited to detect a laterally moving surface as a whole, as it detects changes along the normal orientation of a surface (such as subsidence). Therefore, the point cloud needs to be very selectively reduced, requiring complex filtering operations and expert input as well as expensive software packages.This contribution will present a workflow to simplify such evaluation, based on 2.5D (DEM-based) algorithms such as IMCORR and DoD (Difference-of-DEMs), in comparison with the more complex 3D-pointcloud based processing. The presented workflow is based on Agisoft Metashape and Open-Source software tools QGIS and Saga GIS. It aims to streamline UAV-based surveying work, 3D-model generation and simplified change detection into a repeatable and easily automatable framework. Special emphasis will be put on estimating the quality of the recorded data.

PurposeIdentify how higher Body Mass Index (BMI) and weight discrimination are associated with romantic relationship formation and termination in young adults, and if the association was consistent for males and females.MethodsFirst-year students (N = 1096) at entry to university (Time 1) provided BMI and self-reports of weight discrimination and romantic relationship status (in a relationship vs single); 550 were successfully resampled four months later (Time 2). Logistic generalized estimating equations (GEEs) examined if Time 1 relationship status was predicted by BMI and weight discrimination. Logistic GEEs were used to determine if Time 1 BMI and weight discrimination predicted Time 2 relationship status for the strata of students in, and out, of a relationship at Time 1.ResultsAt baseline, students were less likely to be in a relationship if they had a higher BMI (OR = 0.94, 95% CI = 0.92, 0.96) or reported weight discrimination (OR = 0.69, 95% CI = 0.53, 0.90). When stratified by gender, the association between higher BMI and weight discrimination with relationship status was only observed for females. Longitudinally, a BMI-based selection effect was observed for romantic relationship formation, but not termination. Of the students who were single at Time 1, each one unit higher baseline BMI decreased the odds of the student transitioning to a relationship by 9% at Time 2 (OR = 0.91, 95% CI = 0.85, 0.96). When stratified by gender the association of higher BMI decreased odds of relationship formation was only significant for females. No weight discrimination differences for selection in or out of a romantic relationship were observed.ConclusionsThese findings suggest a weight-related selection effect for romantic relationship initiation, but not termination, in young female adults with lower BMIs. Weight discrimination was not associated with romantic relationship initiation or termination in this sample.

This paper represents the result of the IAEG C35 Commission “Monitoring methods and approaches in engineering geology applications” workgroup aimed to describe a general overview of unmanned aerial vehicles (UAVs) and their potentiality in several engineering geology applications. The use of UAV has progressively increased in the last decade and nowadays started to be considered a standard research instrument for the acquisition of images and other information on demand over an area of interest. UAV represents a cheap and fast solution for the on-demand acquisition of detailed images of an area of interest and the creation of detailed 3D models and orthophoto. The use of these systems required a good background of data processing and a good drone pilot ability for the management of the flight mission in particular in a complex environment.

ObjectiveTo examine if first-year roommates made similar meal plan decisions.MethodsResidence 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.ResultsThe 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

Background Workplaces that provide opportunities for physical activity without requiring extra time for activity could help counteract the obesity epidemic. Desk ellipticals can contribute to activity-supportive workplace environments; however, the feasibility of engaging employees in pedaling ellipticals during simultaneous office work has not been well evaluated. Objective We aim to present the rationale and methods from an ongoing randomized trial with overweight and obese employees that will evaluate (1) the effects of pedaling a compact desk elliptical on work performance and (2) the influence of different incentive types and schedules on desk pedaling quantity. Methods Overweight and obese medical center employees are being recruited in dyads for a 2 (gift card type: healthier food vs Amazon) by 3 (gift card schedule: immediate incentive contingent on individual pedaling quantity; immediate incentive partially contingent on dyads’ joint pedaling quantity; and delayed noncontingent pedaling incentive) cluster randomized within-subjects factorial trial. All participants receive a Bluetooth-enabled desk elliptical for 4 weeks and access to a mobile app that provides real-time pedaling feedback. The primary aims are to assess (1) change in employee work performance from pre- to postelliptical installation via employee and supervisor ratings and (2) effects of gift card type and schedule on quantity of objectively measured desk pedaling completed. Results Data collection is ongoing. We expect to complete main outcome analyses in 2020. Conclusions This trial represents one of the earliest attempts to assess the effects of desk pedaling and pedaling-incentive types in real-world offices. It could help bridge the research-to-practice gap by providing evidence on whether desk pedaling can be sustained without compromising work performance. International Registered Report Identifier (IRRID) DERR1-10.2196/16275

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.

Background Salad bars are placed in schools to promote fruit and vegetable consumption among students. This study assessed differences in school nutrition practices and perceptions in schools with and without salad bars. Methods Cross-sectional surveys were completed by school nutrition managers (N = 648) in Arizona schools participating in the National School Lunch Program during 2013-2014. Mixed general estimating equation binomial regressions assessed factors related to having a salad bar after mutually adjusting for clustering within districts, school level, free/reduced rate, and respondents' time in current position. Results On average, 61% of schools reported having a salad bar. After adjustment, school nutrition managers were significantly more likely to report having a salad bar if they served lunch by grade level (vs mixed grades), had a full-service kitchen, and their personal perception of salad bars was positive; schools were less likely to have a salad bar if menu and food service decisions were made at the school level. Conclusions Several school-level nutrition practices and perceptions were associated with having a salad bar in schools. Enhancement of these factors may facilitate having salad bars in schools.

Background Control systems engineering methods, particularly, system identification (system ID), offer an idiographic (i.e., person-specific) approach to develop dynamic models of physical activity (PA) that can be used to personalize interventions in a systematic, scalable way. The purpose of this work is to: (1) apply system ID to develop individual dynamical models of PA (steps/day measured using Fitbit Zip) in the context of a goal setting and positive reinforcement intervention informed by Social Cognitive Theory; and (2) compare insights on potential tailoring variables (i.e., predictors expected to influence steps and thus moderate the suggested step goal and points for goal achievement) selected using the idiographic models to those selected via a nomothetic (i.e., aggregated across individuals) approach. Method A personalized goal setting and positive reinforcement intervention was deployed for 14 weeks. Baseline PA measured in weeks 1–2 was used to inform personalized daily step goals delivered in weeks 3–14. Goals and expected reward points (granted upon goal achievement) were pseudo-randomly assigned using techniques from system ID, with goals ranging from their baseline median steps/day up to 2.5× baseline median steps/day, and points ranging from 100 to 500 (i.e., $0.20–$1.00). Participants completed a series of daily self-report measures. Auto Regressive with eXogenous Input (ARX) modeling and multilevel modeling (MLM) were used as the idiographic and nomothetic approaches, respectively. Results Participants (N = 20, mean age = 47.25 ± 6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ± 6.82 kg/m2) adults. Results from ARX modeling suggest that individuals differ in the factors (e.g., perceived stress, weekday/weekend) that influence their observed steps/day. In contrast, the nomothetic model from MLM suggested that goals and weekday/weekend were the key variables that were predictive of steps. Assuming the ARX models are more personalized, the obtained nomothetic model would have led to the identification of the same predictors for 5 of the 20 participants, suggesting a mismatch of plausible tailoring variables to use for 75% of the sample. Conclusion The idiographic approach revealed person-specific predictors beyond traditional MLM analyses and unpacked the inherent complexity of PA; namely that people are different and context matters. System ID provides a feasible approach to develop personalized dynamical models of PA and inform person-specific tailoring variable selection for use in adaptive behavioral interventions.

Rural children consume more calories per day on average than urban children, and they are less likely to consume fruit. Self-service salad bars have been proposed as an effective approach to better meet the National School Lunch Program's fruit and vegetable recommendations. No studies have examined how rural and urban schools differ in the implementation of school salad bars.To compare the prevalence of school-lunch salad bars and differences in implementation between urban and rural Arizona schools.Secondary analysis of a cross-sectional web-based survey.School nutrition managers (N=596) in the state of Arizona.National Center for Education Statistics locale codes defined rural and urban classifications. Barriers to salad bar implementation were examined among schools that have never had, once had, and currently have a school salad bar. Promotional practices were examined among schools that once had and currently have a school salad bar.Generalized estimating equation models were used to compare urban and rural differences in presence and implementation of salad bars, adjusting for school-level demographics and the clustering of schools within districts.After adjustment, the prevalence of salad bars did not differ between urban and rural schools (46.9%±4.3% vs 46.8%±8.5%, respectively). Rural schools without salad bars more often reported perceived food waste and cost of produce as barriers to implementing salad bars, and funding was a necessary resource for offering a salad bar in the future, as compared with urban schools (P0.05). No other geographic differences were observed in reported salad bar promotion, challenges, or resources among schools that currently have or once had a salad bar.After adjustment, salad bar prevalence, implementation practices, and concerns are similar across geographic settings. Future research is needed to investigate methods to address cost and food waste concerns in rural areas.