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Journal ArticleDOI

Health behavior change following chronic illness in middle and later life

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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Citations
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Journal ArticleDOI
TL;DR: A large majority of middle-aged and older smokers continued to smoke after diagnosis with a major chronic disease, and black participants demonstrated the largest reductions in smoking behavior.
Abstract: Middle-aged and older Americans from underrepresented racial and ethnic backgrounds are at risk for greater chronic disease morbidity than their white counterparts. Cigarette smoking increases the severity of chronic illness, worsens physical functioning, and impairs the successful management of symptoms. As a result, it is important to understand whether smoking behaviors change after the onset of a chronic condition. We assessed the racial/ethnic differences in smoking behavior change after onset of chronic diseases among middle-aged and older adults in the US. We use longitudinal data from the Health and Retirement Study (HRS 1992–2010) to examine changes in smoking status and quantity of cigarettes smoked after a new heart disease, diabetes, cancer, stroke, or lung disease diagnosis among smokers. The percentage of middle-aged and older smokers who quit after a new diagnosis varied by racial/ethnic group and disease: for white smokers, the percentage ranged from 14% after diabetes diagnosis to 32% after cancer diagnosis; for black smokers, the percentage ranged from 15% after lung disease diagnosis to 40% after heart disease diagnosis; the percentage of Latino smokers who quit was only statistically significant after stoke, where 38% quit. In logistic models, black (OR = 0.43, 95% CI: 0.19–0.99) and Latino (OR = 0.26, 95% CI: 0.11–0.65) older adults were less likely to continue smoking relative to white older adults after a stroke, and Latinos were more likely to continue smoking relative to black older adults after heart disease onset (OR = 2.69, 95% CI [1.05–6.95]). In models evaluating changes in the number of cigarettes smoked after a new diagnosis, black older adults smoked significantly fewer cigarettes than whites after a new diagnosis of diabetes, heart disease, stroke or cancer, and Latino older adults smoked significantly fewer cigarettes compared to white older adults after newly diagnosed diabetes and heart disease. Relative to black older adults, Latinos smoked significantly fewer cigarettes after newly diagnosed diabetes. A large majority of middle-aged and older smokers continued to smoke after diagnosis with a major chronic disease. Black participants demonstrated the largest reductions in smoking behavior. These findings have important implications for tailoring secondary prevention efforts for older adults.

23 citations


Cites background or result from "Health behavior change following ch..."

  • ...There is a higher likelihood of cessation that has been shown relative to controls in a previous study [18]....

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  • ...Further, the adoption and maintenance of healthful behaviors—such as smoking reductions and cessation—may be of particular importance in the prevention of disease and compromised function associated with aging [18, 19]....

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Journal ArticleDOI
TL;DR: Among women with CRC, higher anxiety and depression symptoms were associated with subsequent unhealthier lifestyle in the 10 years following diagnosis, which may suggest that treating psychological symptoms early in the cancer trajectory may not solely reduce psychological distress but also promote healthier lifestyle.
Abstract: Objective Although medical professionals recommend lifestyle changes following a colorectal cancer (CRC) diagnosis to improve outcomes, such changes are not consistently implemented. This study examines whether higher distress is associated with lower likelihood of engaging in favorable behaviors after CRC diagnosis. Method Women from the Nurses' Health Study prospective cohort who completed anxiety (n = 145) and depression (n = 227) symptom scales within 4 years after receiving a CRC diagnosis were included. Measures of lifestyle (diet, physical activity, alcohol, smoking, body mass index [BMI]) were queried prediagnosis, when psychological symptoms were assessed (1988 and 1992, respectively), and then every 4 years thereafter until 2010. Women were categorized according to initial psychological symptoms levels and followed through 2010 or until last follow-up completed. Results Higher versus lower anxiety symptoms were significantly related to unhealthier lifestyle scores throughout follow-up (β = -0.25, CI [-0.44, -0.05]); however, the rate of change over time was similar across groups (pinteraction effect = 0.41). Stratified analyses hinted that higher anxiety and depression symptoms were related to increased odds of reporting a future unhealthy lifestyle within 10-years postdiagnosis. Beyond 10 years, anxiety became statistically unrelated with future lifestyle, and higher depressive symptoms were associated with lower odds of subsequently having an unhealthy lifestyle, albeit nonstatistically significant (OR = 0.35, 95% CI [0.10, 1.24], p = 0.10). Conclusions Among women with CRC, higher anxiety and depression symptoms were associated with subsequent unhealthier lifestyle in the 10 years following diagnosis. With replication, such findings may suggest that treating psychological symptoms early in the cancer trajectory may not solely reduce psychological distress but also promote healthier lifestyle. (PsycINFO Database Record

23 citations


Cites background from "Health behavior change following ch..."

  • ...These habits are also well-documented risk factors for other illnesses, including cardiovascular disease (Chiuve et al., 2008; Newsom et al., 2012), whereas adopting at least 4 of those behaviors would strongly protect against early death (Loef & Walach, 2012)....

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  • ...…(to capture the specific impact of cancer diagnosis on lifestyle and because these chronic conditions may affect subsequent health behaviors; Newsom et al., 2012), and had no missing data on health behaviors and measures of potential covariates when psychological symptom measures were…...

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  • ...Although individuals might reevaluate their health behaviors following a cancer diagnosis, any related changes are not always sustained (Newsom et al., 2012; Williams, Steptoe, & Wardle, 2013), with suboptimal levels of health behaviors noted in studies conducted between 2 and 7 years postdiagnosis…...

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Journal ArticleDOI
TL;DR: Persistence and change in PA level was associated with mortality, largely explained by fitness status, and Randomized controlled studies are needed to test whether maintaining or increasing PA level could lengthen the life of old people.

21 citations


Cites result from "Health behavior change following ch..."

  • ...Among those with chronic diseases, however, change in health behavior is more difficult.(26) The results of this study support this view....

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Journal ArticleDOI
TL;DR: Gender-tailored smoking cessation programs that address drinking for AA men and depression for AA women may help reduce the burden of smoking in AA older adults in economically disadvantaged urban areas.
Abstract: The current study aims to explore gender differences in the risk of cigarette smoking among African-American (AA) older adults who live in economically disadvantaged urban areas of southern Los Angeles. This cross-sectional study enrolled 576 older AA adults (age range between 65 and 96 years) who were residing in Service Planning Area 6 (SPA 6), one of the most economically challenged areas in southern Los Angeles. All participants had cardiometabolic disease (CMD). Data were collected using structured face-to-face interviews. Demographic factors (age and gender), socioeconomic status (educational attainment and financial difficulty), health (number of comorbid medical conditions and depressive symptoms), and health behaviors (current alcohol drinking and current smoking) were measured. Logistic regressions were used to analyze the data without and with interaction terms between gender and current drinking, depressive symptoms, and financial difficulty. AA men reported more smoking than AA women (25.3% versus 9.3%; p < 0.05). Drinking showed a stronger association with smoking for AA men than AA women. Depressive symptoms, however, showed stronger effects on smoking for AA women than AA men. Gender did not interact with financial difficulty with regard to current smoking. As AA older men and women differ in psychological and behavioral determinants of cigarette smoking, gender-specific smoking cessation interventions for AA older adults who live in economically deprived urban areas may be more successful than interventions and programs that do not consider gender differences in determinants of smoking. Gender-tailored smoking cessation programs that address drinking for AA men and depression for AA women may help reduce the burden of smoking in AA older adults in economically disadvantaged urban areas. Given the non-random sampling, there is a need for replication of these findings in future studies.

21 citations


Cites background from "Health behavior change following ch..."

  • ...While the former results in a positive association between CMD and smoking [13–17], the latter is reverse [18,19]....

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  • ...Second, individuals may decide to quit smoking when they are diagnosed with a new CMC or CMD [18,19]....

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Journal ArticleDOI
TL;DR: The results suggest that the stroke-induced DLS infarction evoked abnormal plasticity in nigrostriatal dopaminergic neurons and DMS D1-neurons, contributing to increased post-stroke alcohol-seeking and relapse.
Abstract: Excessive alcohol consumption is a known risk factor for stroke, but the effect of stroke on alcohol intake is unknown The dorsomedial striatum (DMS) and midbrain areas of the nigrostriatal circuit are critically associated to stroke and alcohol addiction Here we sought to explore the influence of stroke on alcohol consumption and to uncover the underlying nigrostriatal mechanism Rats were trained to consume alcohol using a two-bottle choice or operant self-administration procedure Retrograde beads were infused into the DMS or midbrain to label specific neuronal types, and ischemic stroke was induced in the dorsolateral striatum (DLS) Slice electrophysiology was employed to measure excitability and synaptic transmission in DMS and midbrain neurons We found that ischemic stroke-induced DLS infarction produced significant increases in alcohol preference, operant self-administration, and relapse These increases were accompanied by enhanced excitability of DMS and midbrain neurons In addition, glutamatergic inputs onto DMS D1-neurons was potentiated, whereas GABAergic inputs onto DMS-projecting midbrain dopaminergic neurons was suppressed Importantly, systemic inhibition of dopamine D1 receptors attenuated the stroke-induced increase in operant alcohol self-administration Our results suggest that the stroke-induced DLS infarction evoked abnormal plasticity in nigrostriatal dopaminergic neurons and DMS D1-neurons, contributing to increased post-stroke alcohol-seeking and relapse

21 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

BookDOI
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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MonographDOI
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