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: In economically constrained urban environments, gender, pain, and smoking but not age, SES, depression, and health may predict binge drinking for African American older adults with type 2 diabetes mellites.
Abstract: Purpose. This study investigated the effect of demographic, socioeconomic, and psychological factors as well as the role of health determinants on alcohol consumption and binge drinking among economically disadvantaged African American older adults with type 2 diabetes mellites (T2DM). Methods. This survey recruited 231 African Americans who were older adults (age 65+ years) and had T2DM. Participants were selected from economically disadvantaged areas of South Los Angeles. A structured face-to-face interview was conducted to collect data on demographic factors, objective and subjective socioeconomic status (SES) including education and financial difficulty, living arrangement, marital status, health, and drinking behaviors (drinking and binge drinking). Results. Age, gender, living alone, pain, comorbid conditions, and smoking were associated with drinking/binge drinking. Male gender, pain, and being a smoker were associated with higher odds of drinking/binge drinking, while individuals with more comorbid medical conditions had lower odds of binge drinking. Conclusion. In economically constrained urban environments, gender, pain, and smoking but not age, SES, depression, and health may predict binge drinking for African American older adults with T2DM. African Americans older adult men with T2DM with comorbid pain should be screened for binge drinking.
4 citations
Cites background from "Health behavior change following ch..."
...Alternatively, people may quit drinking and other risk behaviors as a response to being diagnosed with a new CMC [34,35]....
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...However, we expected loneliness/living alone [36,37], pain [39], and CMC [34,35] to predict drinking and binge drinking in our sample....
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...That means, CMCs and drinking may show positive [33] or negative [34,35] associations....
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TL;DR: The perceived threat of the disease plays an important role in the process of changing to a healthy lifestyle in Iranian with chronic illnesses and it is necessary for healthcare providers, especially nurses, to use this threat as a golden opportunity to accelerate changes in patients' behaviours.
Abstract: Aim
This study aimed to explore and describe the experience of making a healthy lifestyle change among the patients with chronic illness.
Background
Despite the existence of different evidence on the critical role of lifestyle in the prevention and management of chronic diseases, many people face challenges in terms of starting and maintaining a healthy lifestyle.
Methods
A descriptive qualitative study with in-depth semi-structured interviews was carried out in 2015 in Iran. Thirty-four patients with common chronic illnesses were invited to the study using purposive sampling. The collected data were analysed by content analysis.
Findings
The main themes were: trying to remove the perceived threat, considering and trying to do physical activities, considering and planning for a healthy diet, striving to manage stress and having gradual acceptance of new habits and coping with them.
Limitations
The participants were selected from among those with chronic illness. However, there is also a need to assess the family and healthcare providers’ perspectives.
Conclusion and implications for nursing
The perceived threat of the disease plays an important role in the process of changing to a healthy lifestyle in Iranian with chronic illnesses. It is necessary for healthcare providers, especially nurses, to use this threat as a golden opportunity to accelerate changes in patients’ behaviours.
Implications for nursing policy
Findings may help policy makers become aware of the need for nurses to create community-based nursing in Iran. Community nurses can remind patients of perceived threats to their health to motivate them for continued healthy behaviours. Therefore, nursing curricula should be revised and educational programs utilise a community-based health approach.
4 citations
TL;DR: The study indicates that, the level of current cigarette smoking among HIV/TB co-infected patients in Dar es Salaam is low, Nevertheless, the preponderance ofcigarette smoking among men, alcohol drinkers, and those who use illicit substances provides a unique opportunity for targeting such population with smoking cessation interventions.
4 citations
TL;DR: Prospect theory adequately predicts screening behavior when diagnosed or faced with a possible chronic disease diagnosis for most screening tests except for females screening for breast cancer, which is more sensitive to incentives only for HIV screening.
Abstract: Background: Prospect theory suggests that people avoid risks when faced with the benefits of a decision but take risks when faced with the costs of a decision. Screening for diseases can be defined as a ‘risk’, in the context of uncertainty. The outcome can either be a ‘benefit’ of good health or a ‘cost’ of ill health or poor-quality health.
Purpose: To assess whether prospect theory can predict screening behavior in the context of a chronic disease diagnosis as well as the exposure to incentives to screen.
Methods: A retrospective longitudinal case-control study for the period 2008-2011 was conducted using a random 1% sample of 170,471 health-insured members, assessing screening for cancers, chronic diseases of lifestyle and HIV, some of whom voluntarily join an incentivized wellness program.
Results: Individuals diagnosed with a chronic disease screened up to 9.0% less for some diseases over time. Mammogram screening however increased (p<0.001). Where a family member was diagnosed with a chronic disease, individual screening decreased up to 8.6%. Similarly females in families where a member was diagnosed with a chronic disease screened more for breast cancer (p<0.001). Males were more sensitive to incentives only for HIV screening (p<0.001), while the female responses to incentives were inconsistent.
Conclusion: A chronic disease diagnosis or the risk of developing a chronic disease resulted in reduced future screening behavior for most diseases. The role of incentives was inconsistent. Prospect theory adequately predicts screening behavior when diagnosed or faced with a possible chronic disease diagnosis for most screening tests except for females screening for breast cancer.
3 citations
Cites background or methods from "Health behavior change following ch..."
...Volume 5 • Issue 5 • 1000208 J Psychol Psychother ISSN: 2161-0487 JPPT, an open access journal motivate lifestyle changes [24]....
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...Data from the United States Health and Retirement Study also found very low levels of behavior change 2-14 years after heart disease, stroke, cancer, diabetes and lung disease diagnoses [24]....
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TL;DR: Optimal biomarker health profiles and patients with strong motivation pertaining to their T2DM care yielded better outcomes, and a longer duration of diabetes was correlated with an increased risk of retinopathy events as well as being elderly.
Abstract: Background: To determine whether long-term self-management among patients with type 2 diabetes mellitus has the risk of developing complications. Methods: We conducted a survey of self-management behavior using diabetes self-management scales (DMSES-C and TSRQ-d) from November 2019 to May 2020 linked with biomarkers (glucose, lipid profile, blood pressure, and kidney function), and the varying measure values were transformed into normal rate proportions. We performed latent profile analysis (LPA) to categorize the patient into different patient health profiles using five classes (C1–C5), and we predicted the risk of retinopathy after adjusting for covariates. Results: The patients in C1, C2, and C4 had a higher likelihood of retinopathy events than those in C5, with odds ratios (ORs) of 1.655, 2.168, and 1.788, respectively (p = 0.032). In addition, a longer duration of diabetes was correlated with an increased risk of retinopathy events as well as being elderly. Conclusions: Optimal biomarker health profiles and patients with strong motivation pertaining to their T2DM care yielded better outcomes. Health profiles portraying patient control of diabetes over the long term can categorize patients with T2DM into different behavior groups. Customizing diabetes care information into different health profiles raises awareness of control strategies for caregivers and patients.
3 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