Health behavior change following chronic illness in middle and later life
01 May 2012-Journals of Gerontology Series B-psychological Sciences and Social Sciences (Oxford University Press)-Vol. 67, Iss: 3, pp 279-288
TL;DR: Results provide important new information on health behavior changes among those with chronic disease and suggest that intensive efforts are required to help initiate and maintain lifestyle improvements among this population.
Abstract: Objectives Understanding lifestyle improvements among individuals with chronic illness is vital for targeting interventions that can increase longevity and improve quality of life. Methods Data from the U.S. Health and Retirement Study were used to examine changes in smoking, alcohol use, and exercise 2-14 years after a diagnosis of heart disease, diabetes, cancer, stroke, or lung disease. Results Patterns of behavior change following diagnosis indicated that the vast majority of individuals diagnosed with a new chronic condition did not adopt healthier behaviors. Smoking cessation among those with heart disease was the largest observed change, but only 40% of smokers quit. There were no significant increases in exercise for any health condition. Changes in alcohol consumption were small, with significant declines in excessive drinking and increases in abstention for a few health conditions. Over the long term, individuals who made changes appeared to maintain those changes. Latent growth curve analyses up to 14 years after diagnosis showed no average long-term improvement in health behaviors. Discussion Results provide important new information on health behavior changes among those with chronic disease and suggest that intensive efforts are required to help initiate and maintain lifestyle improvements among this population.
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TL;DR: It is demonstrated that a large proportion of Korean adults with chronic hepatitis B have poor health behaviors, and high-risk alcohol consumption and physical inactivity were significantly associated with self-perceived health status.
Abstract: Health behaviors of Korean adults with hepatitis B: Findings of the 2016 Korean National Health and Nutrition Examination Survey
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TL;DR: The inclusion of online-delivery and co-receipt of tangible benefits when designing an SMES program for seniors results in favorable reception and could facilitate sustained adherence to health behavior recommendations.
Abstract: Introduction Self-management education and support (SMES) programs can prevent adverse chronic disease outcomes, but factors modifying their reception remain relatively unexplored. We examined how perceptions of an SMES program were influenced by the mode of delivery, and co-receipt of a paired financial benefit. Methods and Patients Using a cross-sectional survey, we evaluated the perceived helpfulness of a SMES program among 446 low-income seniors at high risk for cardiovascular events in Alberta, Canada. Secondary outcomes included frequency of use, changes in perspectives on health, satisfaction with the program, and comprehensibility of the material. Participants received surveys after engaging with the program for at least 6 months. We used modified Poisson regression to calculate relative risks. Open-ended questions were analyzed inductively. Results The majority of participants reported that the SMES program was helpful (>80%). Those who also received the financial benefit (elimination of medication copayments) were more likely to report that the SMES program was helpful (RR 1.24, 95% CI 1.11–1.39). Those who received the program electronically were more likely to use the program weekly (RR 1.51, 1.25–1.84). Both those who received the intervention electronically (RR 1.18, 1.06–1.33), and those who also received copayment elimination (RR 1.17, 1.05–1.31) were more likely to state that the program helped change their perspectives on health. Conclusion When designing SMES programs, providing the option for electronic delivery appears to promote greater use for seniors. The inclusion of online-delivery and co-receipt of tangible benefits when designing an SMES program for seniors results in favorable reception and could facilitate sustained adherence to health behavior recommendations. Participants also specifically expressed that what they enjoyed most was that the SMES program was informative, helpful, engaging, and supportive.
1 citations
01 Jan 2019
TL;DR: A dynamic perturbation of miRNA and protein expression in villus-crypt axis contributing to potential biological consequences of altering CD98 expression is demonstrated and adds another layer of complexity in the interplay between the host, genetics, immune system, microbiota, and food and environment in the pathogenesis of IBD.
Abstract: IEC-specific overexpression of CD98 mediates intestinal inflammation and intestinal epithelial barrier dysfunction in experimental colitis and increase the susceptibility to colitisasociated cancer. Here we demonstrated homeostatic gene profile dysregulation in the villus-crypt axis via CD98 overexpression. Using miRNA-target gene prediction module, we observed differentially expressed miRNAs to target proteins of villus and crypt profoundly affected by CD98 overexpression. We have utilized online bioinformatics as methods to further scrutinize the biological meanings of miRNA-target data. We identified significant interactions among the differentially regulated proteins targeted by altered miRNAs in Tg mice. The biological processes affected by the predicted targets of miRNAs deviate from the homeostatic functions of the miRNA-gene-protein axis of the wildtype mice. Our results emphasize a dynamic perturbation of miRNA and protein expression in villus-crypt axis contributing to potential biological consequences of altering CD98 expression. Such mechanism of endogenous miRNA gene-protein network dysregulation via host gene modification as modeled in our animal study has great implication in the translational understanding of the etiology of inflammatory bowel disease (IBD) in human population. It is hypothesized that diet-derived miRNAs, or exogenous miRNAs, can also potentially modify host gene expression profile. Various miRNAs have been detected in both plant and animial-derived foods. Furthermore, diet has been implicated as a potential facilitator of microbiota activities in gut health. We found evidence of consumption of foods typically known as junk food in US adult population with IBD. The idea that miRNAs from the exogenous source such as food can be another co-factor as a potential underlying mechanism behind the differential effect of food and diet on IBD risk and pathogenesis is quite feasible. The concept adds another layer of complexity in the interplay between the host, genetics, immune system, microbiota, and food and environment in the pathogenesis of IBD. INDEX WORDS: CD98, MiRNA, Proteomics, Bioinformatics, Intestinal Homeostasis, Inflammatory Bowel Disease, Diet, microbiota, Food THE ROLE OF CD98 IN DYSREGULATION OF MIRNA AND PROTEIN EXPRESSION ALONG THE VILLUS-CRYPT AXIS IN INTESTINAL EPITHELIUM
1 citations
TL;DR: In this paper , the authors analyzed changes in drinking following diagnosis of chronic health conditions and how the short-term post-disease change in drinking was associated with a change in long-term heavy drinking from the decade before diagnosis to the decade following diagnosis.
Abstract: Literature on changes in drinking following diagnosis of chronic health conditions is limited, especially differential response to specific conditions or across demographic subgroups. Methods Data were analyzed from the 2020 National Alcohol Survey of the U.S. adult population (n = 9968). Predictors of change in drinking following first diagnosis of hypertension, heart disease, diabetes, and cancer, and how the short-term post-disease change in drinking was associated with a change in long-term heavy (5 +) drinking from the decade before diagnosis to the decade following diagnosis were analyzed. Results The majority of respondents reported no change in drinking after diagnosis. Men were more likely than women to reduce drinking after hypertension (OR=1.47) but less likely to quit after heart disease (OR=0.46). Black and Hispanic/Latinx drinkers were more likely than white or other drinkers to reduce (OR=2.68, 2.35, respectively) or quit (OR=2.69, 2.34) after hypertension, and more likely to quit after diabetes (OR=3.44, 2.74) and cancer (OR=5.00, 5.27). Black drinkers were more likely to quit after heart disease (OR=3.26). Heavier drinkers were more likely to reduce or quit drinking than lighter drinkers. For all disease types, those who quit drinking after disease onset were less likely to report heavy drinking in the following decade. Conclusions Just cutting down had little effect on subsequent long-term heavy drinking compared to quitting. These data are important for informing efforts aimed at harm reduction in patients diagnosed with a chronic health condition and suggest specific demographic subgroups.
1 citations
References
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Book•
01 Jan 1987
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Abstract: Preface.PART I: OVERVIEW AND BASIC APPROACHES.Introduction.Missing Data in Experiments.Complete-Case and Available-Case Analysis, Including Weighting Methods.Single Imputation Methods.Estimation of Imputation Uncertainty.PART II: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA.Theory of Inference Based on the Likelihood Function.Methods Based on Factoring the Likelihood, Ignoring the Missing-Data Mechanism.Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse.Large-Sample Inference Based on Maximum Likelihood Estimates.Bayes and Multiple Imputation.PART III: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA: APPLICATIONS TO SOME COMMON MODELS.Multivariate Normal Examples, Ignoring the Missing-Data Mechanism.Models for Robust Estimation.Models for Partially Classified Contingency Tables, Ignoring the Missing-Data Mechanism.Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missing-Data Mechanism.Nonignorable Missing-Data Models.References.Author Index.Subject Index.
18,201 citations
7,643 citations
26 Aug 2002
6,148 citations
"Health behavior change following ch..." refers methods in this paper
...—Latent growth curve models using all available data assume that the data are at least missing at random (Little & Rubin, 2002), and the pattern of missing data from this study may not meet this criterion (i.e., nonignorable missingness)....
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Book•
27 Jul 2009
TL;DR: The reasoned action approach as mentioned in this paper is an integrative framework for the prediction and change of human social behavior, and it provides methodological and conceptual tools for predicting and explaining social behavior and for designing behavior change interventions.
Abstract: This book describes the reasoned action approach, an integrative framework for the prediction and change of human social behavior. It provides an up-to-date review of relevant research, discusses critical issues related to the reasoned action framework, and provides methodological and conceptual tools for the prediction and explanation of social behavior and for designing behavior change interventions.
5,005 citations
"Health behavior change following ch..." refers background in this paper
...Subjective norms in favor of changing behavior (Ajzen & Albarracín, 2007) are likely to be salient when a chronic illness has been diagnosed and also should lead to healthier behavior....
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TL;DR: In this article, the authors present a generalized linear model for categorical data, which is based on the Logit model, and use it to fit Logistic Regression models.
Abstract: Preface. 1. Introduction: Distributions and Inference for Categorical Data. 1.1 Categorical Response Data. 1.2 Distributions for Categorical Data. 1.3 Statistical Inference for Categorical Data. 1.4 Statistical Inference for Binomial Parameters. 1.5 Statistical Inference for Multinomial Parameters. Notes. Problems. 2. Describing Contingency Tables. 2.1 Probability Structure for Contingency Tables. 2.2 Comparing Two Proportions. 2.3 Partial Association in Stratified 2 x 2 Tables. 2.4 Extensions for I x J Tables. Notes. Problems. 3. Inference for Contingency Tables. 3.1 Confidence Intervals for Association Parameters. 3.2 Testing Independence in Two Way Contingency Tables. 3.3 Following Up Chi Squared Tests. 3.4 Two Way Tables with Ordered Classifications. 3.5 Small Sample Tests of Independence. 3.6 Small Sample Confidence Intervals for 2 x 2 Tables . 3.7 Extensions for Multiway Tables and Nontabulated Responses. Notes. Problems. 4. Introduction to Generalized Linear Models. 4.1 Generalized Linear Model. 4.2 Generalized Linear Models for Binary Data. 4.3 Generalized Linear Models for Counts. 4.4 Moments and Likelihood for Generalized Linear Models . 4.5 Inference for Generalized Linear Models. 4.6 Fitting Generalized Linear Models. 4.7 Quasi likelihood and Generalized Linear Models . 4.8 Generalized Additive Models . Notes. Problems. 5. Logistic Regression. 5.1 Interpreting Parameters in Logistic Regression. 5.2 Inference for Logistic Regression. 5.3 Logit Models with Categorical Predictors. 5.4 Multiple Logistic Regression. 5.5 Fitting Logistic Regression Models. Notes. Problems. 6. Building and Applying Logistic Regression Models. 6.1 Strategies in Model Selection. 6.2 Logistic Regression Diagnostics. 6.3 Inference About Conditional Associations in 2 x 2 x K Tables. 6.4 Using Models to Improve Inferential Power. 6.5 Sample Size and Power Considerations . 6.6 Probit and Complementary Log Log Models . 6.7 Conditional Logistic Regression and Exact Distributions . Notes. Problems. 7. Logit Models for Multinomial Responses. 7.1 Nominal Responses: Baseline Category Logit Models. 7.2 Ordinal Responses: Cumulative Logit Models. 7.3 Ordinal Responses: Cumulative Link Models. 7.4 Alternative Models for Ordinal Responses . 7.5 Testing Conditional Independence in I x J x K Tables . 7.6 Discrete Choice Multinomial Logit Models . Notes. Problems. 8. Loglinear Models for Contingency Tables. 8.1 Loglinear Models for Two Way Tables. 8.2 Loglinear Models for Independence and Interaction in Three Way Tables. 8.3 Inference for Loglinear Models. 8.4 Loglinear Models for Higher Dimensions. 8.5 The Loglinear Logit Model Connection. 8.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions . 8.7 Loglinear Model Fitting: Iterative Methods and their Application . Notes. Problems. 9. Building and Extending Loglinear/Logit Models. 9.1 Association Graphs and Collapsibility. 9.2 Model Selection and Comparison. 9.3 Diagnostics for Checking Models. 9.4 Modeling Ordinal Associations. 9.5 Association Models . 9.6 Association Models, Correlation Models, and Correspondence Analysis . 9.7 Poisson Regression for Rates. 9.8 Empty Cells and Sparseness in Modeling Contingency Tables. Notes. Problems. 10. Models for Matched Pairs. 10.1 Comparing Dependent Proportions. 10.2 Conditional Logistic Regression for Binary Matched Pairs. 10.3 Marginal Models for Square Contingency Tables. 10.4 Symmetry, Quasi symmetry, and Quasiindependence. 10.5 Measuring Agreement Between Observers. 10.6 Bradley Terry Model for Paired Preferences. 10.7 Marginal Models and Quasi symmetry Models for Matched Sets . Notes. Problems. 11. Analyzing Repeated Categorical Response Data. 11.1 Comparing Marginal Distributions: Multiple Responses. 11.2 Marginal Modeling: Maximum Likelihood Approach. 11.3 Marginal Modeling: Generalized Estimating Equations Approach. 11.4 Quasi likelihood and Its GEE Multivariate Extension: Details . 11.5 Markov Chains: Transitional Modeling. Notes. Problems. 12. Random Effects: Generalized Linear Mixed Models for Categorical Responses. 12.1 Random Effects Modeling of Clustered Categorical Data. 12.2 Binary Responses: Logistic Normal Model. 12.3 Examples of Random Effects Models for Binary Data. 12.4 Random Effects Models for Multinomial Data. 12.5 Multivariate Random Effects Models for Binary Data. 12.6 GLMM Fitting, Inference, and Prediction. Notes. Problems. 13. Other Mixture Models for Categorical Data . 13.1 Latent Class Models. 13.2 Nonparametric Random Effects Models. 13.3 Beta Binomial Models. 13.4 Negative Binomial Regression. 13.5 Poisson Regression with Random Effects. Notes. Problems. 14. Asymptotic Theory for Parametric Models. 14.1 Delta Method. 14.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities. 14.3 Asymptotic Distributions of Residuals and Goodnessof Fit Statistics. 14.4 Asymptotic Distributions for Logit/Loglinear Models. Notes. Problems. 15. Alternative Estimation Theory for Parametric Models. 15.1 Weighted Least Squares for Categorical Data. 15.2 Bayesian Inference for Categorical Data. 15.3 Other Methods of Estimation. Notes. Problems. 16. Historical Tour of Categorical Data Analysis . 16.1 Pearson Yule Association Controversy. 16.2 R. A. Fisher s Contributions. 16.3 Logistic Regression. 16.4 Multiway Contingency Tables and Loglinear Models. 16.5 Recent and Future? Developments. Appendix A. Using Computer Software to Analyze Categorical Data. A.1 Software for Categorical Data Analysis. A.2 Examples of SAS Code by Chapter. Appendix B. Chi Squared Distribution Values. References. Examples Index. Author Index. Subject Index. Sections marked with an asterisk are less important for an overview.
4,650 citations