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Journal ArticleDOI

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.

AbstractObjectives 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.

Topics: Chronic condition (61%), Behavior change (58%), Poison control (53%), Smoking cessation (52%), Disease management (health) (52%)

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Citations
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Journal ArticleDOI
TL;DR: Little evidence that a cancer diagnosis motivates health-protective changes among UK cancer survivors is found, and strategies for effective support for behaviour change in cancer survivors need to be identified.
Abstract: A healthy lifestyle following a cancer diagnosis may improve long-term outcomes. No studies have examined health behaviour change among UK cancer survivors, or tracked behaviours over time in survivors and controls. We assessed smoking, alcohol and physical activity at three times (0–2 years before a cancer diagnosis, 0–2 years post-diagnosis and 2–4 years post-diagnosis) and at matched times in a comparison group. Data were from waves 1–5 of the English Longitudinal Study of Ageing; a cohort of older adults in England. Behavioural measures were taken at each wave. Generalised estimating equations were used to examine differences by group and time, and group-by-time interactions. Of the 5146 adults included in the analyses, 433 (8.4%) were diagnosed with cancer. Those with a cancer diagnosis were less likely to be physically active (P<0.01) and more likely to be sedentary (P<0.001). There were no group differences in alcohol or smoking. Smoking, alcohol and activity reduced over time in the whole group. Group-by-time interactions were not significant for smoking (P=0.17), alcohol (P=0.20), activity (P=0.17) or sedentary behaviour (P=0.86), although there were trends towards a transient improvement from pre-diagnosis to immediately post-diagnosis. We found little evidence that a cancer diagnosis motivates health-protective changes. Given the importance of healthy lifestyles, strategies for effective support for behaviour change in cancer survivors need to be identified.

101 citations


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

  • ...%) than those without any new serious diagnosis (22.8 to 20.8%), but there was no significant group difference in alcohol intake, and a greater reduction in physical activity in the cancer group (Newsom et al, 2012a)....

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  • ...For two of the studies, this could be because the comparison group was not only free of a cancer diagnosis, but also free from heart disease, diabetes, stroke and lung disease, and these conditions could also contribute to the motivation to change (Keenan, 2009; Newsom et al, 2012a)....

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  • ...Previous research has found evidence for higher rates of smoking cessation following a cancer diagnosis (Falba, 2005; Keenan, 2009; Karlsen et al, 2012; Newsom et al, 2012a)....

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  • ...In a Canadian sample (Newsom et al, 2012b), a cancer diagnosis was associated with a greater reduction in smoking rates (from 17.2% to 13.5...

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Journal ArticleDOI
TL;DR: Results support the hypothesis that a cancer diagnosis presents a teachable moment that can be capitalized on to promote cessation, and a diagnosis of cancer, even a cancer not strongly related to smoking and with a relatively good prognosis, may be associated with increased quitting well after diagnosis.
Abstract: Purpose Quitting smoking provides important health benefits to patients with cancer. A cancer diagnosis may motivate quitting—potentially providing a teachable moment in which oncologists can encourage and assist patients to quit—but little is known about whether a recent cancer diagnosis (including diagnosis of a cancer that is less strongly linked to smoking) is associated with increased quitting. Methods Cancer Prevention Study-II Nutrition Cohort participants reported smoking status at enrollment in 1992 to 1993 and approximately biennially through 2009. Quit rates of smokers diagnosed with cancer during 2- and 4-year intervals were compared with those of smokers not diagnosed with cancer (12,182 and 12,538 smokers in 2- and 4-year analyses, respectively). Cancers likely to cause physical limitations or symptoms that could influence smoking (cancers of the lung, head and neck, esophagus, or any metastatic cancer) were excluded. Logistic regressions calculated quit rates controlling for age, sex, surve...

86 citations


Journal ArticleDOI
TL;DR: The insufficient evidence related to pharmacotherapy as well as providing an overview of using physiologic rather than chronologic age for identifying suitable candidates for bariatric surgery are discussed.
Abstract: Obesity in older adults affects not only morbidity and mortality but, importantly, quality of life and the risk of institutionalization. Weight loss interventions can effectively lead to improved physical function. Diet-alone interventions can detrimentally affect muscle and bone physiology and, without interventions to affect these elements, can lead to adverse outcomes. Understanding social and nutritional issues facing older adults is of utmost importance to primary care providers. This article will also discuss the insufficient evidence related to pharmacotherapy as well as providing an overview of using physiologic rather than chronologic age for identifying suitable candidates for bariatric surgery.

64 citations


Journal ArticleDOI
TL;DR: Improved survivorship education for health professionals may increase the number of patients receiving lifestyle advice, and improve their long-term outcomes.
Abstract: A healthy lifestyle following a cancer diagnosis is linked with better long-term outcomes. Health professionals can play an important role in promoting healthy lifestyles after cancer, but little is known about the factors that influence whether or not they give lifestyle advice. We conducted an online survey to examine levels of, and predictors of, health professionals' provision of lifestyle advice to cancer patients in the United Kingdom. The survey included questions on awareness of lifestyle guidelines for cancer survivors, current practices with regard to giving advice on smoking, diet, exercise, weight and alcohol, and perceived barriers to giving advice. Nurses, surgeons and physicians (N=460) responded to the survey. Many (36%) were not aware of any lifestyle guidelines for cancer survivors, but 87% reported giving some lifestyle advice; although this was lower for individual behaviours and often to 1.76, all P's<0.05). Not believing lifestyle would affect outcomes was associated with lower odds of giving lifestyle advice (all OR's<0.48, all P's<0.05). Improved survivorship education for health professionals may increase the number of patients receiving lifestyle advice, and improve their long-term outcomes.

62 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.

18,186 citations



BookDOI
26 Aug 2002

5,587 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
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.

4,501 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
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.

4,319 citations