Playing catch-up in building an open research commons.

As efforts advance around the globe, the US falls behind.

Prediction of BMI change in young children with the family nutrition and physical activity (FNPA) screening tool.

BACKGROUND: Youth obesity prevention practices would be enhanced if modifiable risk factors can be identified before children become overweight. PURPOSE: This study evaluated the predictive validity of the Family Nutrition and Physical Activity (FNPA) screening tool, a behaviorally based screening tool designed to assess family environments and behaviors that may predispose youth to becoming overweight. METHODS: Parents from a large urban school district completed the FNPA screening when children were in first grade. One-year change in measured body mass index (BMI) was used as the primary outcome, and this was computed using the relative change in distance from the BMI value at the 50th percentile. Descriptive, correlation, and mixed modeling analyses were used for survey validation. RESULTS: Over half of the participants exhibited an increase in BMI percentile over the 1-year follow-up with an average change of 0.51 +/- 11.5% which is indicative of trends to overweight. Although baseline BMI predicted BMI at follow-up, the FNPA total score explained unique variance in child BMI at follow-up after accounting for baseline BMI, parent BMI, and other demographic variables (p = 0.049). CONCLUSIONS: This study demonstrates the potential validity of a simple, easy-to-use screening tool for identifying children that may be at risk for becoming overweight.

Development and preliminary validation of a Family Nutrition and Physical Activity (FNPA) screening tool.

BACKGROUND: Parents directly influence children's physical activity and nutrition behaviors and also dictate the physical and social environments that are available to their children. This paper summarizes the development of an easy to use screening tool (The Family Nutrition and Physical Activity (FNPA) Screening Tool) designed to assess family environmental and behavioral factors that may predispose a child to becoming overweight. METHODS: The FNPA instrument was developed using constructs identified in a comprehensive evidence analysis conducted in collaboration with the American Dietetics Association. Two or three items were created for each of the ten constructs with evidence grades of II or higher. Parents of first grade students from a large urban school district (39 schools) were recruited to complete the FNPA screening tool and provide permission to link results to BMI data obtained from trained nurses in each school. A total of 1085 surveys were completed out of the available sample of 2189 children in the district. Factor analysis was conducted to examine the factor structure of the scale. Mixed model analyses were conducted on the composite FNPA score to determine if patterns in home environments and behaviors matched some of the expected socio-economic (SES) and ethnic patterns in BMI. Correlations among FNPA constructs and other main variables were computed to examine possible associations among the various factors. Finally, logistic regression was used to evaluate the construct validity of the FNPA scale. RESULTS: Factor analyses revealed the presence of a single factor and this unidimensional structure was supported by the correlation analyses. The correlations among constructs were consistently positive but the total score had higher correlations with child BMI than the other individual constructs. The FNPA scores followed expected demographic patterns with low income families reporting lower (less favorable) scores than moderate or high income families. Children with a total score in the lowest tertile (high risk family environment and behaviors) had an odds ratio (OR) of 1.7 (95% CI = 1.07 - 2.80) compared to children with a total score in the highest tertile (more favorable family environment and behaviors) but this effect was reduced when parent BMI was included as a covariate. CONCLUSION: The results support the contention that the FNPA tool captures important elements of the family environment and behaviors that relate to risk for child overweight.

The principles of tomorrow's university.

In the 21st Century, research is increasingly data- and computation-driven. Researchers, funders, and the larger community today emphasize the traits of openness and reproducibility. In March 2017, 13 mostly early-career research leaders who are building their careers around these traits came together with ten university leaders (presidents, vice presidents, and vice provosts), representatives from four funding agencies, and eleven organizers and other stakeholders in an NIH- and NSF-funded one-day, invitation-only workshop titled "Imagining Tomorrow's University." Workshop attendees were charged with launching a new dialog around open research - the current status, opportunities for advancement, and challenges that limit sharing. The workshop examined how the internet-enabled research world has changed, and how universities need to change to adapt commensurately, aiming to understand how universities can and should make themselves competitive and attract the best students, staff, and faculty in this new world. During the workshop, the participants re-imagined scholarship, education, and institutions for an open, networked era, to uncover new opportunities for universities to create value and serve society. They expressed the results of these deliberations as a set of 22 principles of tomorrow's university across six areas: credit and attribution, communities, outreach and engagement, education, preservation and reproducibility, and technologies. Activities that follow on from workshop results take one of three forms. First, since the workshop, a number of workshop authors have further developed and published their white papers to make their reflections and recommendations more concrete. These authors are also conducting efforts to implement these ideas, and to make changes in the university system.  Second, we plan to organise a follow-up workshop that focuses on how these principles could be implemented. Third, we believe that the outcomes of this workshop support and are connected with recent theoretical work on the position and future of open knowledge institutions.

Methodologic issues in a population-based health survey of Gulf War veterans.

This report describes the principal methods used in the development, conduct, and analysis of the research study "Health Assessment of Persian Gulf War Veterans from Iowa" (Iowa Gulf War Study). The methods presented include an outline of the organizational structure, study timeline, hypotheses, outcome definitions, and study design. Adhering to a strict timeline, the study protocol and instruments were developed, and a stratified sample of 3,695 military personnel (76% participation) was located and surveyed by structured telephone interview. The study tracked personnel from all service branches residing nationally and internationally, including those discharged from service. This study required development and implementation of methods appropriate to analysis of data collected in a complex sampling framework and methodological procedures to ensure scientific rigor in a highly public and politicized environment. Statistical analyses were conducted on a priori health outcomes and required development of methods to compute Cochran-Mantel-Haenszel adjusted rate differences. This environment facilitated rapid implementation, critique by scientific and public advisors, a high participation rate, and rapid publication.

Validity of 24-h physical activity recall: physical activity measurement survey.

PURPOSE: The primary purpose of this study was to evaluate the validity of an interviewer-administered, 24-h physical activity recall (PAR) compared with that of the SenseWear Armband (SWA) for estimation of energy expenditure (EE) and moderate-to-vigorous physical activity (MVPA) in a representative sample of adults. A secondary goal was to compare measurement errors for various demographic subgroups (gender, age, and weight status). METHODS: A sample of 1347 adults (20-71 yr, 786 females) wore an SWA for a single day and then completed a PAR, recalling the previous day's physical activity. The participants each performed two trials on two randomly selected days across a 2-yr time span. The EE and MVPA values for each participant were averaged across the 2 d. Group-level and individual-level agreement were evaluated using 95% equivalence testing and mean absolute percent error, respectively. Results were further examined for subgroups by gender, age, and body mass index. RESULTS: The PAR yielded equivalent estimates of EE (compared with those in the SWA) for almost all demographic subgroups, but none of the comparisons for MVPA were equivalent. Smaller mean absolute percent error values were observed for EE (ranges from 10.3% to 15.0%) than those for MVPA (ranges from 68.6% to 269.5%) across all comparisons. The PAR yielded underestimates of MVPA for younger, less obese people but overestimates for older, more obese people. CONCLUSIONS: For EE measurement, the PAR demonstrated good agreement relative to the SWA. However, the use of PAR may result in biased estimates of MVPA both at the group and individual level in adults.

Modeling errors in physical activity recall data.

BACKGROUND: Physical activity recall instruments provide an inexpensive method of collecting physical activity patterns on a sample of individuals, but they are subject to systematic and random measurement error. Statistical models can be used to estimate measurement error in activity recalls and provide more accurate estimates of usual activity parameters for a population. METHODS: We develop a measurement error model for a short-term activity recall that describes the relationship between the recall and an individual's usual activity over a long period of time. The model includes terms for systematic and random measurement errors. To estimate model parameters, the design should include replicate observations of a concurrent activity recall and an objective monitor measurement on a subsample of respondents. RESULTS: We illustrate the approach with preliminary data from the Iowa Physical Activity Measurement Study. In this dataset, recalls tend to overestimate actual activity, and measurement errors greatly increase the variance of recalls relative to the person-to-person variation in usual activity. Statistical adjustments are used to remove bias and extraneous variation in estimating the usual activity distribution. CONCLUSIONS: Modeling measurement error in recall data can be used to provide more accurate estimates of long-term activity behavior.

Estimating minutes of physical activity from the previous day physical activity recall: validation of a prediction equation.

BACKGROUND: This study was designed to develop a prediction algorithm that would allow the Previous Day Physical Activity Recall (PDPAR) to be equated with temporally matched data from an accelerometer. METHODS: Participants (n = 121) from a large, school-based intervention wore a validated accelerometer and completed the PDPAR for 3 consecutive days. Physical activity estimates were obtained from PDPAR by totaling 30-minute bouts of activity coded as ≥4 METS. A regression equation was developed in a calibration sample (n = 91) to predict accelerometer minutes of moderate to vigorous physical activity (MVPA) from PDPAR bouts. The regression equation was then applied to a separate, holdout sample (n = 30) to evaluate the utility of the prediction algorithm. RESULTS: Gender and PDPAR bouts accounted for 36.6% of the variance in accelerometer MVPA. The regression model showed that on average boys obtain 9.0 min of MVPA for each reported PDPAR bout, while girls obtain 4.8 min of MVPA per bout. When applied to the holdout sample, predicted minutes of MVPA from the models showed good agreement with accelerometer minutes (r = .81). CONCLUSIONS: The prediction equation provides a valid and useful metric to aid in the interpretation of PDPAR results.

Validation of a computerized 24-hour physical activity recall (24PAR) instrument with pattern-recognition activity monitors.

PURPOSE: The purpose of this study was to examine the validity of a computerized 24-hour physical activity recall instrument (24PAR). METHODS: Participants (n=20) wore 2 pattern-recognition activity monitors (an IDEEA and a SenseWear Pro Armband) for a 24-hour period and then completed the 24PAR the following morning. Participants completed 2 trials, 1 while maintaining a prospective diary of their activities and 1 without a diary. The trials were counterbalanced and completed within a week from each other. Estimates of energy expenditure (EE) and minutes of moderate-to-vigorous physical activity (MVPA) were compared with the criterion measures using 3-way (method by gender by trial) mixed-model ANOVA analyses. RESULTS: For EE, pairwise correlations were high (r>.88), and there were no differences in estimates across methods. Estimates of MVPA were more variable, but correlations were still in the moderate to high range (r>.57). Average activity levels were significantly higher on the logging trial, but there was no significant difference in the accuracy of self-report on days with and without logging. CONCLUSIONS: The results of this study support the overall utility of the 24PAR for group-level estimates of daily EE and MVPA.