ASU Scholarship Showcase

Objective: This cross sectional study aims to determine the effects of gender and parental perception of safety at school on children’s physical activity (PA) levels.

Materials and Methods: Parents of school aged Mexican children residing in Guadalajara, Mexico City, and Puerto Vallarta, completed surveys about their children’s PA measures. The physical activity indicators were evaluated using linear and logistical regression models.

Results: Analysis did not indicate that gender moderated the relationship between parental perception of safety and PA measures, but significant gender issues exist with girls participating less than boys in the three measures of PA in this study (p<0.001).

Conclusion: Results suggest the need for additional interventions promoting physical activity in girls in Mexico.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

Resource-poor social environments predict poor health, but the mechanisms and processes linking the social environment to psychological health and well-being remain unclear. This study explored psychosocial mediators of the association between the social environment and mental health in African American adults. African American men and women (n = 1467) completed questionnaires on the social environment, psychosocial factors (stress, depressive symptoms, and racial discrimination), and mental health. Multiple-mediator models were used to assess direct and indirect effects of the social environment on mental health. Low social status in the community (p < .001) and U.S. (p < .001) and low social support (p < .001) were associated with poor mental health. Psychosocial factors significantly jointly mediated the relationship between the social environment and mental health in multiple-mediator models. Low social status and social support were associated with greater perceived stress, depressive symptoms, and perceived racial discrimination, which were associated with poor mental health. Results suggest the relationship between the social environment and mental health is mediated by psychosocial factors and revealed potential mechanisms through which social status and social support influence the mental health of African American men and women. Findings from this study provide insight into the differential effects of stress, depression and discrimination on mental health. Ecological approaches that aim to improve the social environment and psychosocial mediators may enhance health-related quality of life and reduce health disparities in African Americans.

Background: Interaction in the form of cooperation, communication, and friendly competition theoretically precede the development of group cohesion, which often precedes adherence to health promotion programs. The purpose of this manuscript was to explore longitudinal relationships among dimensions of group cohesion and group-interaction variables to inform and improve group-based strategies within programs aimed at promoting physical activity.

Methods: Ethnic minority women completed a group dynamics-based physical activity promotion intervention (N = 103; 73% African American; 27% Hispanic/Latina; mage = 47.89 + 8.17 years; mBMI = 34.43+ 8.07 kg/m[superscript 2]) and assessments of group cohesion and group-interaction variables at baseline, 6 months (post-program), and 12 months (follow-up).

Results: All four dimensions of group cohesion had significant (ps < 0.01) relationships with the group-interaction variables. Competition was a consistently strong predictor of cohesion, while cooperation did not demonstrate consistent patterns of prediction.

Conclusions: Facilitating a sense of friendly competition may increase engagement in physical activity programs by bolstering group cohesion.

The purpose of this review was to determine the degree to which physical activity interventions for Latin American populations reported on internal and external validity factors using the RE-AIM framework (reach & representativeness, effectiveness, adoption, implementation, maintenance). We systematically identified English (PubMed; EbscoHost) and Spanish (SCIELO; Biblioteca Virtual en Salud) language studies published between 2001 and 2012 that tested physical activity, exercise, or fitness promotion interventions in Latin American populations. Cross-sectional/descriptive studies, conducted in Brazil or Spain, published in Portuguese, not including a physical activity/fitness/exercise outcome, and with one time point assessment were excluded. We reviewed 192 abstracts and identified 46 studies that met the eligibility criteria (34 in English, 12 in Spanish). A validated 21-item RE-AIM abstraction tool was used to determine the quality of reporting across studies (0-7 = low, 8-14 = moderate, and 15-21 = high). The number of indicators reported ranged from 3–14 (mean = 8.1 ± 2.6), with the majority of studies falling in the moderate quality reporting category. English and Spanish language articles did not differ on the number of indicators reported (8.1 vs. 8.3, respectively). However, Spanish articles reported more across reach indicators (62% vs. 43% of indicators), while English articles reported more across effectiveness indicators (69% vs 62%). Across RE-AIM dimensions, indicators for reach (48%), efficacy/effectiveness (67%), and implementation (41%) were reported more often than indicators of adoption (25%) and maintenance (10%). Few studies reported on the representativeness of participants, staff that delivered interventions, or the settings where interventions were adopted. Only 13% of the studies reported on quality of life and/or potential negative outcomes, 20% reported on intervention fidelity, and 11% on cost of implementation. Outcomes measured after six months of intervention, information on continued delivery and institutionalization of interventions, were also seldom reported. Regardless of language of publication, physical activity intervention research for Latin Americans should increase attention to and measurement of external validity and cost factors that are critical in the decision making process in practice settings and can increase the likelihood of translation into community or clinical practice.

Background: To combat the disproportionately higher risk of childhood obesity in Latino preschool-aged children, multilevel interventions targeting physical (in) activity are needed. These require the identification of environmental and psychosocial determinants of physical (in) activity for this ethnic group. The objectives were to examine differences in objectively-measured physical activity and sedentary behavior across objectively-determined types of locations in Latino preschool-aged children; and determine whether the differences in physical activity by location were greater in children of parents with higher neighborhood-safety perceptions and physical activity-supportive parenting practices.

Methods: An observational field study was conducted in Houston (Texas, USA) from August 2011 to April 2012. A purposive sample of Latino children aged 3–5 years and one of their parents (n = 84) were recruited from Census block groups in Houston (Texas) stratified by objectively-assessed high vs. low traffic and crime safety. Seventy-three children provided valid data. Time spent outdoors/indoors tagged with geographic locations was coded into location types based on objective data collected using Global Positioning Systems units that children wore >8 hr/day for a week. Physical activity parenting practices, perceived neighborhood-safety, and demographics were reported by parents. Time spent in sedentary behavior and moderate-to-vigorous physical activity was measured based on objective data collected using accelerometers (motion sensors) that children wore >8 hr/day for a week.

Results: The odds of children engaging in moderate-to-vigorous physical activity were 43 % higher when outdoors than indoors (95 % confidence interval: 1.30, 1.58), and the odds of being sedentary were 14 % lower when outdoors compared to indoors (95 % confidence intervals: 0.81, 0.91). This difference depended on parental neighborhood-safety perceptions and parenting practices. Children were most active in parks/playgrounds (30 % of the time spent in moderate-to-vigorous physical activity) and least active in childcare/school settings (8 % of the time spent in moderate-to-vigorous physical activity).

Conclusions: Objectively-assessed time spent in specific locations is correlated with physical activity and sedentary behavior in Latino preschoolers. Interventions and policies should identify ways to engage Latino preschool-aged children in more physical activity and less sedentary behavior while in childcare, and encourage parents to spend more time with their young children in parks/playgrounds and other safe outdoor places.

Background: Low physical activity (PA) and fruit and vegetable (F&V) consumption in early childhood are continued public health challenges. This manuscript describes outcomes from two pilot studies for Sustainability via Active Garden Education (SAGE), a program designed to increase PA and F&V consumption among 3 to 5 year old children.

Methods: SAGE was developed using community-based participatory research (CBPR) and delivered to children (N = 89) in early care and education centers (ECEC, N = 6) in two US cities. Children participated in 12 one-hour sessions that included songs, games, and interactive learning activities involving garden maintenance and taste tests. We evaluated reach, efficacy, adoption, implementation, and potential for maintenance of SAGE following the RE-AIM framework. Reach was evaluated by comparing demographic characteristics among SAGE participants and residents of target geographic areas. Efficacy was evaluated with accelerometer-measured PA, F&V consumption, and eating in the absence of hunger among children, parenting practices regarding PA, and home availability of F&V. Adoption was evaluated by the number of ECEC that participated relative to the number of ECEC that were recruited. Implementation was evaluated by completion rates of planned SAGE lessons and activities, and potential for maintenance was evaluated with a parent satisfaction survey.

Results: SAGE reached ECEC in neighborhoods representing a wide range of socioeconomic status, with participants’ sociodemographic characteristics representing those of the intervention areas. Children significantly increased PA during SAGE lessons compared to usual lessons, but they also consumed more calories in the absence of hunger in post- vs. pre-intervention tests (both p < .05). Parent reports did not suggest changes in F&V consumption, parenting PA practices, or home F&V availability, possibly due to low parent engagement. ECEC had moderate-to-high implementation of SAGE lessons and curriculum. Potential for maintenance was strong, with parents rating SAGE favorably and reporting increases in knowledge about PA and nutrition guidelines for young children.

Conclusions: SAGE successfully translated national PA guidelines to practice for young children but was less successful with nutrition guidelines. High adoption and implementation and favorable parent reports suggest high potential for program sustainability. Further work to engage parents and families of young children in ECEC-based PA and nutrition programming is needed.