Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

Carnosine in health and disease

Abstract

Carnosine was originally discovered in skeletal muscle, where it exists in larger amounts than in other tissues. The majority of research into the physiological roles of carnosine have been conducted on skeletal muscle. Given this and the potential for muscle carnosine content to be increased with supplementation, there is now a large body of research examining the ergogenic effects (or otherwise) of carnosine. More recent research, however, points towards a potential for carnosine to exert a wider range of physiological effects in other tissues, including the brain, heart, pancreas, kidney and cancer cells. Taken together, this is suggestive of a potential for carnosine to have therapeutic benefits in health and disease, although this is by no means without complication. Herein, we will provide a review of the current literature relating to the potential therapeutic effects of carnosine in health and disease.     

Effects of Mass and Interpersonal Communication on Breast Cancer Screening: Advancing Agenda-Setting Theory in Health Contexts

Drawing on components of agenda-setting theory and the two-step flow of information from mass media to news audiences, this study examines the effects of mass and interpersonal communication on breast cancer screening practices among college- and middle-aged women (n = 284). We theorized that screening behaviors among younger women would be influenced more by interpersonal sources of information while screening among middle-aged women would be more influenced by exposure to mass-mediated information. Findings supported anticipated patterns, revealing important and varying roles for both mass and interpersonal communication in the health behaviors of women. Implications for health practitioners and campaign planners, as well as recommendations for future research, are discussed.     

Setting the Students’ Bearings and Balance in Appraisal

The author, a professor at the University of Toronto, touches briefly on the extensive and rich archival literature that supports the teaching of macroappraisal, but notes that this is not the only educational material she offers her students when teaching appraisal theory. She discusses the usefulness to archivists of literature from the fields of ethnography, organizational knowing, records in history, personal documentary behaviour, memory, and communications, noting that the use of texts from these fields can encourage students to reflect on their own presumptions and to develop a taste for the wide reading and research that must support appraisal.     

Using word embeddings in Twitter election classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).     

Modern dietary guidelines for healthy pregnancy; maximising maternal and foetal outcomes and limiting excessive gestational weight gain

Abstract

Maternal dietary habits influence maternal and foetal health, representing a pathway for intervention to maximise pregnancy outcomes. Advice on energy intake is provided on a trimester basis, with no additional calories required in the first trimester and an additional 340?kcal?d^?1 and 452?kcal?d^?1 needed for the second and third trimesters. Energy intake depends on pre-gravid body mass index (BMI); underweight women are recommended an increase of 150, 200 and 300?kcal?d^?1 during the first, second and third trimester, normal weight women an increase of 0, 350 and 500?kcal?d^?1 and obese women an increase of 0, 450 and 350?kcal?day^?1. The recommendations for carbohydrate and protein intake are 175?g?d^?1 and 0.88–1.1?g?kgBM?d^?1, with no change to fat intake. The number of pre-gravid obese women is rising; therefore, we need to regulate weight in women of childbearing age and limit gestational weight gain to within the recommended ranges [overweight women 6.8–11.3?kg and obese women 5.0–9.1?kg]. This can be achieved using nutritional interventions, as dietary changes have been shown to help with gestational weight management. As pregnancy has been identified as a risk factor for the development of obesity, normal weight women should gain 11.5–16.0?kg during pregnancy. While some research has shown that dietary interventions help to regulate gestational weight gain and promote postpartum weight loss to some extent, future research is needed to provide safe and effective guidelines to maximise these effects, while benefitting maternal and foetal health.     

Meeting players where they are: Digital games and learning ecologies

This paper explores the relationship between digital games and learning along the continuum from formal to informal learning. Games are an increasingly important component of our learning ecologies but their potential can prove difficult to fully harness due to the downsides of gaming in informal contexts and to the limitations games can present when adopted in formal learning contexts. The aim of this paper is to shed light on the strengths and weaknesses of games for learning, and to gather stakeholder recommendations for the main actors in the field. The adopted research approach is qualitative and participatory, involving players, teachers, parents, researchers and developers. Data from 25 interviews and 11 focus groups yielded insights into the educational, psychological and ethical aspects of play. Results suggest that gameplay influences individuals in a very personal manner, and that its effects cover a wide range of skills, extending to identity development. Hence, to harness this potential, all actors in the ecology need strong awareness of both the affordances and the risks of games, and deep respect for individuals’ LEs. The study has implications for a wide range of actors, including educators, players, researchers, parents, game developers and policymakers.