Methods: Study participants (n=1469) were elementary and middle school students who ate school lunch on the day of data collection. Photographs and weights (to nearest 2 g) were taken of fruits and vegetables on students’ trays before and after lunch. Trained research assistants viewed photographs and sorted trays into variable categories: color of main tray, presence/absence of secondary fruit/vegetable container, and color of secondary fruit/vegetable container. Fruit and vegetable selection, consumption, and waste were calculated using tray weights. Negative binomial regression models adjusted for gender, grade level, race/ethnicity, free/reduced price lunch status, and within-school similarities were used to examine relationships between tray color and fruit and vegetable selection, consumption, and waste.
Results: Findings indicated that students with a light tray selected (IRR= 0.44), consumed (IRR=0.73) and wasted (IRR=0.81) less fruit and vegetables. Students without a secondary fruit/vegetable container selected (IRR=0.66) and consumed (IRR=0.49) less fruit and vegetables compared to those with a secondary container. Light or clear secondary fruit and vegetable containers were related to increased selection (IRR=2.06 light, 2.30 clear) and consumption (IRR=1.95 light, 2.78 clear) compared to dark secondary containers, while light secondary containers were related to decreased waste (IRR= 0.57).
Conclusion: Tray color may influence fruit and vegetable selection, consumption, and waste among students eating school lunch. Further research is needed to determine if there is a cause and effect relationship. If so, adjusting container colors may be a practical intervention for schools hoping to increase fruit and vegetable intake among students.
Purpose: The purpose of this study was to understand how implementing EIM influenced provider behaviors in a university-based healthcare system, using a process evaluation.
Methods: A multiple baseline, time series design was used. Providers were allocated to three groups. Group 1 (n=11) was exposed to an electronic medical record (EMR) systems change, EIM-related resources, and EIM training session. Group 2 (n=5) received the EMR change and resources but no training. Group 3 (n=6) was only exposed to the systems change. The study was conducted across three phases. Outcomes included asking about patient physical activity (PA) as a vital sign (PAVS), prescribing PA (ExRx), and providing PA resources or referrals. Patient surveys and EMR data were examined. Time series analysis, chi-square, and logistic regression were used.
Results: Patient survey data revealed the systems change increased patient reports of being asked about PA, χ2(4) = 95.47, p < .001 for all groups. There was a significant effect of training and resource dissemination on patients receiving PA advice, χ2(4) = 36.25, p < .001. Patients receiving PA advice was greater during phase 2 (OR = 4.7, 95% CI = 2.0-11.0) and phase 3 (OR = 2.9, 95% CI = 1.2-7.4). Increases were also observed in EMR data for PAVS, χ2(2) = 29.27, p <. 001 during implementation for all groups. Increases in PA advice χ2(2) = 140.90, p < .001 occurred among trained providers only. No statistically significant change was observed for ExRx, PA resources or PA referrals. However, visual analysis showed an upwards trend among trained providers.
Conclusions: An EMR systems change is effective for increasing the collection of the PAVS. Training and resources may influence provider behavior but training alone increased provider documentation. The low levels of documented outcomes for PA advice, ExRx, resources, or referrals may be due to the limitations of the EMR system. This approach was effective for examining the EIM Solution and scaled-up, longer trials may yield more robust results.
Vision and Change in Undergraduate Biology Education outlined five core concepts intended to guide undergraduate biology education: 1) evolution; 2) structure and function; 3) information flow, exchange, and storage; 4) pathways and transformations of energy and matter; and 5) systems. We have taken these general recommendations and created a Vision and Change BioCore Guide—a set of general principles and specific statements that expand upon the core concepts, creating a framework that biology departments can use to align with the goals of Vision and Change. We used a grassroots approach to generate the BioCore Guide, beginning with faculty ideas as the basis for an iterative process that incorporated feedback from more than 240 biologists and biology educators at a diverse range of academic institutions throughout the United States. The final validation step in this process demonstrated strong national consensus, with more than 90% of respondents agreeing with the importance and scientific accuracy of the statements. It is our hope that the BioCore Guide will serve as an agent of change for biology departments as we move toward transforming undergraduate biology education.
The U.S. scientific research community does not reflect America's diversity. Hispanics, African Americans, and Native Americans made up 31% of the general population in 2010, but they represented only 18 and 7% of science, technology, engineering, and mathematics (STEM) bachelor's and doctoral degrees, respectively, and 6% of STEM faculty members (National Science Foundation [NSF], 2013). Equity in the scientific research community is important for a variety of reasons; a diverse community of researchers can minimize the negative influence of bias in scientific reasoning, because people from different backgrounds approach a problem from different perspectives and can raise awareness regarding biases (Intemann, 2009). Additionally, by failing to be attentive to equity, we may exclude some of the best and brightest scientific minds and limit the pool of possible scientists (Intemann, 2009). Given this need for equity, how can our scientific research community become more inclusive?
Women who start college in one of the natural or physical sciences leave in greater proportions than their male peers. The reasons for this difference are complex, and one possible contributing factor is the social environment women experience in the classroom. Using social network analysis, we explore how gender influences the confidence that college-level biology students have in each other’s mastery of biology. Results reveal that males are more likely than females to be named by peers as being knowledgeable about the course content. This effect increases as the term progresses, and persists even after controlling for class performance and outspokenness. The bias in nominations is specifically due to males over-nominating their male peers relative to their performance. The over-nomination of male peers is commensurate with an overestimation of male grades by 0.57 points on a 4 point grade scale, indicating a strong male bias among males when assessing their classmates. Females, in contrast, nominated equitably based on student performance rather than gender, suggesting they lacked gender biases in filling out these surveys. These trends persist across eleven surveys taken in three different iterations of the same Biology course. In every class, the most renowned students are always male. This favoring of males by peers could influence student self-confidence, and thus persistence in this STEM discipline.
Background: Physical activity (PA) interventions typically include components or doses that are static across participants. Adaptive interventions are dynamic; components or doses change in response to short-term variations in participant's performance. Emerging theory and technologies make adaptive goal setting and feedback interventions feasible.
Objective: To test an adaptive intervention for PA based on Operant and Behavior Economic principles and a percentile-based algorithm. The adaptive intervention was hypothesized to result in greater increases in steps per day than the static intervention.
Methods: Participants (N = 20) were randomized to one of two 6-month treatments: 1) static intervention (SI) or 2) adaptive intervention (AI). Inactive overweight adults (85% women, M = 36.9±9.2 years, 35% non-white) in both groups received a pedometer, email and text message communication, brief health information, and biweekly motivational prompts. The AI group received daily step goals that adjusted up and down based on the percentile-rank algorithm and micro-incentives for goal attainment. This algorithm adjusted goals based on a moving window; an approach that responded to each individual's performance and ensured goals were always challenging but within participants' abilities. The SI group received a static 10,000 steps/day goal with incentives linked to uploading the pedometer's data.
Results: A random-effects repeated-measures model accounted for 180 repeated measures and autocorrelation. After adjusting for covariates, the treatment phase showed greater steps/day relative to the baseline phase (p<.001) and a group by study phase interaction was observed (p = .017). The SI group increased by 1,598 steps/day on average between baseline and treatment while the AI group increased by 2,728 steps/day on average between baseline and treatment; a significant between-group difference of 1,130 steps/day (Cohen's d = .74).
Conclusions: The adaptive intervention outperformed the static intervention for increasing PA. The adaptive goal and feedback algorithm is a “behavior change technology” that could be incorporated into mHealth technologies and scaled to reach large populations.
Background: An evidence-based steps/day translation of U.S. federal guidelines for youth to engage in ≥60 minutes/day of moderate-to-vigorous physical activity (MVPA) would help health researchers, practitioners, and lay professionals charged with increasing youth’s physical activity (PA). The purpose of this study was to determine the number of free-living steps/day (both raw and adjusted to a pedometer scale) that correctly classified children (6–11 years) and adolescents (12–17 years) as meeting the 60-minute MVPA guideline using the 2005–2006 National Health and Nutrition Examination Survey (NHANES) accelerometer data, and to evaluate the 12,000 steps/day recommendation recently adopted by the President’s Challenge Physical Activity and Fitness Awards Program.
Methods: Analyses were conducted among children (n = 915) and adolescents (n = 1,302) in 2011 and 2012. Receiver Operating Characteristic (ROC) curve plots and classification statistics revealed candidate steps/day cut points that discriminated meeting/not meeting the MVPA threshold by age group, gender and different accelerometer activity cut points. The Evenson and two Freedson age-specific (3 and 4 METs) cut points were used to define minimum MVPA, and optimal steps/day were examined for raw steps and adjusted to a pedometer-scale to facilitate translation to lay populations.
Results: For boys and girls (6–11 years) with ≥ 60 minutes/day of MVPA, a range of 11,500–13,500 uncensored steps/day for children was the optimal range that balanced classification errors. For adolescent boys and girls (12–17) with ≥60 minutes/day of MVPA, 11,500–14,000 uncensored steps/day was optimal. Translation to a pedometer-scaling reduced these minimum values by 2,500 step/day to 9,000 steps/day. Area under the curve was ≥84% in all analyses.
Conclusions: No single study has definitively identified a precise and unyielding steps/day value for youth. Considering the other evidence to date, we propose a reasonable ‘rule of thumb’ value of ≥ 11,500 accelerometer-determined steps/day for both children and adolescents (and both genders), accepting that more is better. For practical applications, 9,000 steps/day appears to be a more pedometer-friendly value.
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.