James L. Norris

A naturalistic study of the impact of acute physical activity on feeling states and affect in women.

This field study used experience sampling procedures to examine the relationship of feeling states and affect to acute bouts of physical activity in women. Participants (N = 86) completed brief affect and feeling state measures (a) in response to random stratified pager tones and (b) before and after acute bouts of vigorous physical activity for 6 weeks. Analysis of averaged difference scores revealed that acute vigorous physical activity was associated with significant improvements in affect and feeling states, particularly in feelings of revitalization. Moreover, within-subject analyses indicated that the effects were moderated by preactivity scores, with the greatest improvements seen when women felt worst before activity.

Effects of baseline responses, in-task feelings, and duration of activity on exercise-induced feeling states in women.

This study investigated the hypothesis that the effects of acute aerobic exercise on feeling states may be influenced by the objective dose of activity, subjective responses during exercise, and preexisting levels of feeling states. College-age women (N = 80) completed baseline measures and were then randomly assigned to 1 of 4 conditions: attention control for 10 min, or exercise for 10 min, 25 min, or 40 min. Levels of exertion and affect were assessed during exercise, and posttesting occurred 20 min following activity. Exercise enhanced revitalization in comparison with the control condition; however, this effect occurred only for participants scoring low to moderate on the pretest. In addition, in-task feeling states predicted postexercise revitalization even after we controlled for the treatment, the pretest, and the Pretest x Treatment interaction.

The Curse of Inactivity Failure of Acute Exercise to Enhance Feeling States in a Community Sample of Sedentary Adults

This laboratory experiment used random digit dialing procedures to recruit a community sample of physically inactive participants into a study that was designed to examine the impact of different intensities of acute aerobic exercise on feeling states. Sedentary men (n 5 36) and women (n 5 36) were randomly assigned to experience a single bout of aerobic exercise at one of three intensities (30%, 50%, 70% of HRR). Each participant attended two counterbalanced laboratory sessions, one involving an attention-control manipulation and the other involving aerobic exercise. Before and after each session, they completed a feeling state questionnaire. Data suggested that there were neither widespread mood-enhancing effects of acute exercise nor evidence for a strong dose-response relationship.

Open Capture–Recapture Models with Heterogeneity: II. Jolly–Seber Model

Estimation of abundance is important in both open and closed population capture-recapture analysis, but unmodeled heterogeneity of capture probability leads to negative bias in abundance estimates. This article defines and develops a suite of open population capture-recapture models using finite mixtures to model heterogeneity of capture and survival probabilities. Model comparisons and parameter estimation use likelihood-based methods. A real example is analyzed, and simulations are used to check the main features of the heterogeneous models, especially the quality of estimation of abundance, survival, recruitment, and turnover. The two major advances in this article are the provision of realistic abundance estimates that take account of heterogenetiy of capture, and an appraisal of the amount of overestimation of survival arising from conditioning on the first capture when heterogeneity of survival is present.

Bayesian capture-recapture analysis and model selection allowing for heterogeneity and behavioral effects

In this article, we present Bayesian analysis of capture-recapture models for a closed population which allows for heterogeneity of capture probabilities between animals and bait/trap effects. We use a flexible discrete mixture model to account for the heterogeneity and behavioral effects. In addition we present a solid model selection criterion. Through illustrations with a motivating dataset, we demonstrate how Bayesian analysis can be applied in this setting and discuss some major benefits which result, including consideration of informative priors based on historical data.

Influence of Weight Loss, Body Composition, and Lifestyle Behaviors on Plasma Adipokines: A Randomized Weight Loss Trial in Older Men and Women with Symptomatic Knee Osteoarthritis

Objective. To investigate effects of weight loss on adipokines and health measures in obese older adults with symptomatic knee osteoarthritis. Methods. Participants were randomly assigned to either weight loss (WL) (men: 12, women: 14) or weight stable (WS) group (men: 12, women: 13). WL intervention included meal replacements and structured exercise training. Measurements of leptin, adiponectin, soluble leptin receptor, lifestyle behaviors, and body composition were collected at baseline and 6 months. Univariate analysis of covariance was performed on 6 month variables, and Spearman and partial correlations were made between variables. Results. Weight loss was 13.0% and 6.7% in WL for men and women, respectively. Women in WL had lower whole body and trunk fat than WS. The leptin : adiponectin ratio was lower for women in WL than WS at 6 months, with no group differences in adipokines for men. Leptin and free leptin index correlated with body fat in both genders at baseline. Interestingly, only women showed reductions in leptin (P < 0.100) and correlations between the percentage change leptin and trunk fat and the percentage changes in free leptin index with total fat and trunk fat. Partial correlations between 6 month adipokines after adjustments for covariates and group/time period show potential multivariate influences. Conclusions. In the presence of an effective weight loss intervention in older obese adults, there are significant relationships between weight and fat loss and leptin in women, but not men, suggesting gender-specific features of adipokine metabolism in this age group.

Meal replacement: calming the hot-state brain network of appetite

There is a growing awareness in the field of neuroscience that the self-regulation of eating behavior is driven by complex networks within the brain. These networks may be vulnerable to “hot states” which people can move into and out of dynamically throughout the course of a day as a function of changes in affect or visceral cues. The goal of the current study was to identify and determine differences in the Hot-state Brain Network of Appetite (HBN-A) that exists after a brief period of food restraint followed either by the consumption of a meal replacement (MR) or water. Fourteen overweight/obese adults came to our laboratory on two different occasions. Both times they consumed a controlled breakfast meal and then were restricted from eating for 2.5 h prior to an MRI scan. On one visit, they consumed a meal replacement (MR) liquid meal after this period of food restriction; on the other visit they consumed an equal amount of water. After these manipulations, the participants underwent a resting fMRI scan. Our first study aim employed an exploratory, data-driven approach to identify hubs relevant to the HBN-A. Using data from the water condition, five regions were found to be the hubs or nodes of the HBN-A: insula, anterior cingulated cortex, the superior temporal pole, the amygdala, and the hippocampus. We then demonstrated that the consumption of a liquid MR dampened interconnectivity between the nodes of the HBN-A as compared to water. Importantly and consistent with these network data, the consumption of a MR beverage also lowered state cravings and hunger.

Drive for consumption, craving, and connectivity in the visual cortex during the imagery of desired food

There is considerable interest in understanding food cravings given the obesogenic environment of Western Society. In this paper we examine how the imagery of palatable foods affects cravings and functional connectivity in the visual cortex for people who differ on the power of food scale (PFS). Fourteen older, overweight/obese adults came to our laboratory on two different occasions. Both times they ate a controlled breakfast meal and then were restricted from eating for 2.5 h prior to scanning. On 1 day they consumed a BOOST® liquid meal after the period of food restriction, whereas on the other day they only consumed water (NO BOOST® condition). After these manipulations, they had an fMRI scan in which they were asked to image both neutral objects and their favorite snack foods; they also completed visual analog scales for craving, hunger, and the vividness of the imagery experiences. Irrespective of the BOOST® manipulation, we observed marked increases in food cravings when older, overweight/obese adults created images of favorite foods in their minds as opposed to creating an image of neutral objects; however, the increase in food craving following the imagery of desired food was more pronounced among those scoring high than low on the PFS. Furthermore, local efficiency within the visual cortex when imaging desired food was higher for those scoring high as compared to low on the PFS. The active imagery of desired foods seemed to have overpowered the BOOST® manipulation when evaluating connectivity in the visual cortex.

Metropolis-Hastings Algorithm and Continuous Regression for finding Next-State Models of Protein Modification using Information Scores

The reverse engineering of protein modification networks from protein modification time sequence data is an important and challenging problem. We utilize continuous regression-based techniques to generate next-state models. Three statistical diagnostics are applied to test the stationary Markov, the equal time interval influence, and the continuous regression assumptions. The standard Metropolis-Hastings algorithm is modified to use a focused model initialization based on Pearson correlations. Additionally, an infrequent step forcing multiple changes, as opposed to the predominate single change, is incorporated into the standard Metropolis-Hastings algorithm which improves the opportunity to escape local minima of information criterion scores. Both Bayesian and corrected Akaike information criteria are used in conjunction with the Metropolis-Hastings algorithm. Real protein modification data often involves only a small number of proteins which allows a direct computation of some models. Our techniques and diagnostics are applied to two sets of time course protein modification data to produce models which score optimally with respect to information criteria. These techniques and diagnostics lend themselves to applications with similar data sets.

Nonparametric MLE incorporation of heterogeneity and model testing into premarked cohort studies

We utilize mixture models and nonparametric maximum likelihood estimation to both develop a likelihood ratio test (lrt) for a common simplifying assumption and to allow heterogeneity within premarked cohort studies. Our methods allow estimation of the entire probability model and thus one can not only estimate many parameters of interest but one can also bootstrap from the estimated model to predict many things, including the standard deviations of estimators. Simulations suggest that our lrt has the appropriate protection for Type I error and often has good power. In practice, our lrt is important for determining the appropriateness of estimators and in examining if a simple design with only one capture period could be utilized for a future similar study.