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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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Journal ArticleDOI
TL;DR: Improvements in diet were observed, but only 36 % of those with T2D and 35% of those without diabetes consumed ≥5 servings of fruits and vegetables/d in 2009.
Abstract: Given the importance of prevention of complications in type 2 diabetes (T2D), we aimed to examine changes over time in consumption of fruits, vegetables and juice among men who were diagnosed with T2D in comparison with men without diabetes. The prospective Cohort of Swedish Men, aged 45-79 years in 1997, was used to examine changes in diet after diagnosis of T2D. Dietary intake was assessed using FFQ in 1997 and 2009. In all, 23 953 men who were diabetes free at baseline (1997) and completed both FFQ were eligible to participate in the study. Diagnosis of T2D was reported by subjects and ascertained through registers. Multivariable linear mixed models were used to examine changes in mean servings/week over time. In total, 1741 men developed T2D during the study period. Increased consumption of vegetables and fruits was observed among those who developed T2D (equivalent to 1·6 servings/week, 95 % CI 1·08, 2·03) and men who remained diabetes free (0·7 servings/week, 95 % CI 0·54, 0·84). Consumption of juice decreased by 0·6 servings/week (95 % CI -0·71, -0·39) among those who developed T2D and increased by 0·1 servings/week (95 % CI 0·05, 0·15) in those who were diabetes free. Changes over time and between groups were statistically significant. Although improvements in diet were observed, only 36 % of those with T2D and 35 % of those without diabetes consumed ≥5 servings of fruits and vegetables/d in 2009.

16 citations

Journal ArticleDOI
TL;DR: Although Active Plus65 did not outperform the original intervention, in itself active Plus65 effectuated a significant increase in the weekly minutes of MVPA and in the days per week with sufficient MVPA after three months, it potentially makes an interesting intervention.
Abstract: This study explores the effectiveness of the Active Plus65 intervention designed to stimulate physical activity among single older adults with a chronic physical impairment. A quasi-experimental pre-test post-test study was performed. The intervention group (n = 411; mean age = 76.75; SD = 7.75) was assessed at baseline, three months, and six months. Data of comparable older adults who completed the original Active Plus intervention served as reference group (n = 87; mean age = 74.36; SD = 6.26). Multilevel regression analyses were applied: outcome measures were weekly minutes of moderate to vigorous physical activity (MVPA) and days per week with at least 30 minutes of MVPA. Although Active Plus65 did not outperform the original intervention, in itself Active Plus65 effectuated a significant increase in the weekly minutes of MVPA (B = 208.26; p < 0.001; Effect Size (ES) = 0.45) and in the days per week with sufficient MVPA (B = 1.20; p < 0.001; ES = 0.61) after three months. After six months, it effectuated a significant increase in the days per week with sufficient MVPA (B = 0.67; p = 0.001; ES = 0.34) but not for the weekly minutes of MVPA (p = 0.745). As Active Plus65 increased MVPA at three months with a higher ES than average interventions for this vulnerable target group, it potentially makes an interesting intervention. Further development should focus on long-term maintenance of effects.

16 citations

Journal ArticleDOI
TL;DR: The findings of this study add a dynamic dimension to understanding the relationship among financial strain, social support, and smoking in old age when designing health policies and interventions regarding health behaviors in late life.
Abstract: Objectives. Although there is extensive research on the stress-buffering effects of social support on health, there is little understanding of this effect on health behaviors such as smoking, particularly during old age. This study aimed to estimate the effect of financial strain and the stress-buffering effect of social support, on the trajectory of smoking over an extended period of time among older Japanese. Method. Data came from a national sample of more than 4,800 adults, aged 60 and older in Japan, with up to 7 repeated observations between 1987 and 2006 (16,669 observations). Hierarchical linear modeling was used to analyze the intrapersonal and interpersonal differences in smoking. Results. Higher financial strain at baseline was associated with greater amount of smoking, and a slower rate of decline, after adjusting for sociodemographic attributes. Greater instrumental support partially offset the deleterious effect of financial strain on the rate of decline in smoking. Discussion. Our findings add a dynamic dimension to understanding the relationship among financial strain, social support, and smoking in old age. This knowledge is significant when designing health policies and interventions regarding health behaviors in late life.

16 citations


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

  • ...Moreover, it was reported that middle-aged and older people with a new diagnosis of a chronic illness tended to quit smoking (Newson et al., 2012)....

    [...]

Journal ArticleDOI
TL;DR: In this paper , a clear link between excessive alcohol consumption and cardiovascular disease (CVD) has been established, but no consensus exists on the effects of moderate alcohol consumption on CVD.
Abstract: A clear link between excessive alcohol consumption and cardiovascular disease (CVD) has been established, but no consensus exists on the effects of moderate alcohol consumption on CVD.A lower risk of coronary heart disease and myocardial infarction among moderate drinkers compared to abstainers has been consistently observed in epidemiological studies and meta-analyses of these studies. However, ambiguity remains on the effect of alcohol on other CVDs and all-cause mortality. Short-term randomized controlled trials (RCT) have identified potentially beneficial effects of alcohol consumption on cardiovascular risk factors, but studies investigating genetic polymorphisms that influence alcohol consumption (i.e., Mendelian randomization) have yielded inconclusive results. To date, a long-term RCT providing causal evidence is lacking but urgently needed. Triangulation of evidence from different study designs, including long-term RCTs, pragmatic trials and the evaluation of policy measures, combined will lead to the best available evidence.

15 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)....

    [...]

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....

    [...]

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