Establishing a university food pantry: growth, changes in shopper characteristics and recommendations.

Objective: Describe trends in usage and shoppers of Eastern Michigan University's (EMU) food pantry over four academic years. Participants: Shoppers of EMU's pantry between September 2015 and April 2019. Methods: Data come from shopper questionnaires and pantry records of daily visits and food distribution. Descriptive statistics, t-tests, and chi-square analyses were used to explore shopper characteristics and pantry use over time. Results: Pantry use increased over four academic years (from 1,021 to 3,080 visits/year). An increasing proportion of shoppers use the pantry ≥ once/month (6.1% in 2015/2016; 15.1% in 2018/2019). Compared to irregular shoppers (≤7 visits/year), regular shoppers (≥8 visits) reported higher rates of housing instability and were less likely to have a university meal plan. Conclusion: Data revealed substantial growth of the campus pantry, likely reflecting greater awareness and greater need. The findings highlight financial and social challenges faced by Michigan's college students. Recommendations for pantry establishment/maintenance are indicated.

Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines.

The proliferation of approaches for analyzing accelerometer data using raw acceleration or novel analytic approaches like machine learning ('novel methods') outpaces their implementation in practice. This may be due to lack of accessibility, either because authors do not provide their developed models or because these models are difficult to find when included as supplementary material. Additionally, when access to a model is provided, authors may not include example data or instructions on how to use the model. This further hinders use by other researchers, particularly those who are not experts in statistics or writing computer code.Objective: We created a repository of novel methods of analyzing accelerometer data for the estimation of energy expenditure and/or physical activity intensity and a framework and reporting guidelines to guide future work.Approach: Methods were identified from a recent scoping review. Available code, models, sample data, and instructions were compiled or created.Main Results: Sixty-three methods are hosted in the repository, in preschoolers (n = 6), children/adolescents (n = 20), and adults (n = 42), using hip (n = 45), wrist (n = 25), thigh (n = 4), chest (n = 4), ankle (n = 6), other (n = 4), or a combination of monitor wear locations (n = 9). Fifteen models are implemented in R, while 48 are provided as cut-points, equations, or decision trees.Significance: The developed tools should facilitate the use and development of novel methods for analyzing accelerometer data, thus improving data harmonization and consistency across studies. Future advances may involve including models that authors did not link to the original published article or those which identify activity type.

Accessibility and use of novel methods for predicting physical activity and energy expenditure using accelerometry: a scoping review.

Use of raw acceleration data and/or 'novel' analytic approaches like machine learning for physical activity measurement will not be widely implemented if methods are not accessible to researchers.Objective: This scoping review characterizes the validation approach, accessibility and use of novel analytic techniques for classifying energy expenditure and/or physical activity intensity using raw or count-based accelerometer data.Approach: Three databases were searched for articles published between January 2000 and February 2021. Use of each method was coded from a list of citing articles compiled from Google Scholar. Authors' provision of access to the model (e.g., by request, sample code) was recorded.Main Results: Studies (N = 168) included adults (n = 143), and/or children (n = 38). Model use ranged from 0 to 27 uses/year (average 0.83) with 101 models that have never been used. Approximately half of uses occurred in a free-living setting (52%) and/or by other authors (56%). Over half of included articles (n = 107) did not provide complete access to their model. Sixty-one articles provided access to their method by including equations, coefficients, cut-points, or decision trees in the paper (n = 48) and/or by providing access to code (n = 13).Significance: The proliferation of approaches for analyzing accelerometer data outpaces the use of these models in practice. As less than half of the developed models are made accessible, it is unsurprising that so many models are not used by other researchers. We encourage researchers to make their models available and accessible for better harmonization of methods and improved capabilities for device-based physical activity measurement.