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: When clinical pharmacists are active members of the care team, they enhance proficiency by: Providing critical input on medicine use and dosing and working with patients to solve problems with their medications and improve compliance.
Abstract: Pharmacy practice has changed substantially in recent years. The professionals have the opportunity to contribute directly to patient care in order to reduce morbimortality related to medication use, promoting health and preventing diseases. Healthcare organizations worldwide are under substantial pressure from increasing patient demand. Unfortunately, a cure is not always possible particularly in this era of chronic diseases, and the role of physicians has become limited to controlling and palliating symptoms. The increasing population of patients with long-term conditions are associated with high levels of morbidity, healthcare costs and GP workloads. Clinical pharmacy took over an aspect of medical care that had been partially abandoned by physicians. Overburdened by patient loads and the explosion of new drugs, physicians turned to pharmacists more and more for drug information, especially within institutional settings. Once relegated to counting and pouring, pharmacists headed institutional reviews of drug utilization and served as consultants to all types of health-care facilities. In addition, when clinical pharmacists are active members of the care team, they enhance efficiency by: Providing critical input on medication use and dosing. Working with patients to solve problems with their medications and improve adherence.
Keywords: Chronic care, Pharmacy intervention; diabetes care, CVD prevention, Inflammatory bowel disease.
19 citations
Cites background from "Health behavior change following ch..."
...Unlike acute conditions, chronic diseases require consistent care and management outside of the healthcare setting, in the community or primary care setting, in terms of medication, lifestyle management, and health behavior modification [41-45]....
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TL;DR: In this paper, the association between educational level and incident cardiovascular disease (CVD) and all-cause mortality in Hong Kong Chinese patients with type 2 diabetes was described, where patients with the highest educational level had shorter diabetes duration and better glycemic control at enrollment than those with the lowest educational level.
Abstract: Purpose The aim of this study was to describe the association between educational level and incident cardiovascular disease (CVD) and all-cause mortality in Hong Kong Chinese patients with type 2 diabetes. Patients and methods We included 12,634 patients with type 2 diabetes who were enrolled into the Joint Asia Diabetes Evaluation Program between June 1, 2007, and June 30, 2017. We classified patients' educational level into the following three groups: ≤6 years, 6-13 years, and >13 years. Incident CVD events were identified using hospital discharge diagnoses. Death was identified from Hong Kong Death Register. We estimated HRs for incident CVD and all-cause mortality using Cox regression models. Results Patients with the highest educational level were younger and had shorter diabetes duration and better glycemic control at enrollment than those with the lowest educational level. During the median follow-up of 6.2 years for CVD and 6.4 years for all-cause mortality, 954 CVD events and 833 deaths were recorded. HRs for CVD and all-cause mortality were 0.73 (95% CI: 0.57, 0.94) and 0.71 (95% CI: 0.54, 0.94) for the highest educational level compared to the lowest educational level, after adjustment for age, sex, diabetes duration, and family history of diabetes. Conclusion Educational level is inversely associated with the risk of CVD and all-cause mortality among Hong Kong Chinese patients with type 2 diabetes. Hong Kong Chinese patients with type 2 diabetes and low educational level should be given special attention for the prevention of key complications of diabetes.
18 citations
Cites background from "Health behavior change following ch..."
...However, there is limited information available about the association between SES and incident CVD in patients with type 2 diabetes, whose healthrelated behaviors, treatment patterns, health surveillance, and the variation by SES may differ importantly from those of general populations.(12) The effect of the epidemiological transition on changes in number and distribution of patients with type 2 diabetes means that more evidence is required to clarify the relationship between SES and complications of type 2 diabetes in populations in Asia and around the world, including countries at a different stage of the epidemiological transition....
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TL;DR: Findings provide new insights into the complex relationship between socioeconomic factors and BMI, and help to inform the design of health policies and interventions related to weight control among older adults with diverse socioeconomic backgrounds.
Abstract: Objectives This research analyzed the body mass index (BMI) level and rate of change, and their association with socioeconomic status among older Japanese adults. Methods Data came from a national sample of over 4,800 Japanese adults aged 60 and older at baseline, with up to 7 repeated observations over a period of 19 years (1987-2006). Hierarchical linear modeling was used to analyze the intrapersonal and interpersonal differences in BMI. Results Average BMI among older Japanese was 22.26 at baseline and decreased with an accelerating rate over time. Relative to those with less education, BMI among older Japanese with more education was lower and it declined linearly at a faster rate over time. In contrast, higher household income at baseline was associated with a higher level of BMI but similar rates of decline over time. Furthermore, we found no evidence for age variations in the SES-BMI linkage as predicted by prior investigators. Discussion These findings provide new insights into the complex relationship between socioeconomic factors and BMI, and help to inform the design of health policies and interventions related to weight control among older adults with diverse socioeconomic backgrounds.
17 citations
TL;DR: Higher levels of health literacy and self-efficacy were significantly associated with general health behaviors and wellness maintenance and fewer substance use behaviors and fewer addictive behaviors in college students with chronic conditions.
Abstract: Background: Every year, young adults with chronic conditions matriculate into college, which is a unique transitional period in that students may be managing a chronic condition on their own for the first time. Therefore, it is important to examine which factors may contribute to positive health behaviors and risky behaviors in college students with chronic conditions. Purpose: The current study examined associations between health literacy, self-efficacy, and health behaviors in a sample of college students with chronic conditions. Methods: Data were collected from 147 undergraduate students at a Mid-Atlantic U.S. university. Students completed an online consent and questionnaires assessing chronic conditions, health literacy, self-efficacy, and health behaviors (general behavior, wellness maintenance, substance use). Results: Asthma was the most prevalent self-reported chronic condition (26.1%). Higher levels of health literacy and self-efficacy were significantly associated with general health ...
17 citations
Cites background from "Health behavior change following ch..."
...behaviors following the diagnosis of a chronic condition in middle to late adulthood.(10,11) Researchers have examined health behavior changes in emerging adults 1 year after completing high school, including substance use,...
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TL;DR: Using a kiosk within a clinic setting is a feasible method of providing health information and self-monitoring and multi-session educational content can provide beneficial short-term outcomes in overweight adults.
17 citations
Cites background from "Health behavior change following ch..."
...Lifestyles that include making appropriate food choices, maintaining a healthy weight, not using tobacco, and getting adequate physical activity can greatly reduce the risk of developing cardiovascular disease [3–5]....
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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