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  1. Postprandial hyperglycemia (PPHG) is detrimental to health and increases risk of cardiovascular diseases, reduced eyesight, and life-threatening conditions like cancer. Detecting PPHG events before they occur can potentially help with providing early interventions. Prior research suggests that PPHG events can be predicted based on information about diet. However, such computational approaches (1) are data hungry requiring significant amounts of data for algorithm training; and (2) work as a black-box and lack interpretability, thus limiting the adoption of these technologies for use in clinical interventions. Motivated by these shortcomings, we propose, DietNudge 1 , a machine learning based framework that integrates multi-modal data about diet, insulin, and blood glucose to predict PPHG events before they occur. Using data from patients with diabetes, we demonstrate that our model can predict PPHG events with up to 90% classification accuracy and an average F1 score of 0.93. The proposed decision-tree-based approach also identifies modifiable factors that contribute to an impending PPHG event while providing personalized thresholds to prevent such events. Our results suggest that we can develop simple, yet effective, computational algorithms that can be used as preventative mechanisms for diabetes and obesity management. 
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  2. Abstract Objective

    The objective of this research note is to use both sequence analysis (SA) and repeated‐measures latent class analysis (LCA) to identify children's family structure trajectories from birth through age 15 and compare how the two sets of trajectories predict alcohol use across the transition from adolescence into young adulthood.


    Contemporary family scholars have studied the influence of changes in family structure, often referred to as family structure instability, on child and adolescent development. Typically, this research has focused on either the number or type of transitions children have experienced, but statistical advances are increasing the viability of more complex person‐centered approaches to this issue, such as SA and LCA. The choice to use one approach or the other, however, is often discipline specific and relies on different assumptions and estimation techniques that may produce different results.


    The authors used data from the National Longitudinal Study of Youth–Child and Youth Cohort (N= 11,515) to identify clusters (using SA) and classes (using repeated‐measures LCA) that represented children's family structure trajectories from birth through age 15. Using two multiple‐group random slope models, the authors predicted alcohol use across adolescence and young adulthood (ages 16–24) among the clusters (Model 1) and classes (Model 2).


    The SA identified five clusters, but the LCA further differentiated the sample with more detail on timing and identified eight classes. The sensitivity to timing in the LCA solution was substantively relevant to alcohol use across the transition to young adulthood.


    Overall, the SA is perhaps more suited to research questions requiring exclusive group membership in large, comparative analyses, and the LCA more appropriate when the research questions include timing or focus on transitioning into or out of single‐parent and stepfamily homes.

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