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: Health behaviors decline during emerging adulthood, and social network members may attempt to influence or regulate emerging adults' health behaviors using various health-related social control str... as discussed by the authors, 2013.
Abstract: Health behaviors decline during emerging adulthood, and social network members may attempt to influence or regulate emerging adults’ health behaviors using various health-related social control str...
7 citations
Cites background from "Health behavior change following ch..."
...…addressing health behavior change in emerging adults is important to prevent the development of chronic illness later in life, especially because many middleaged and older adults fail to adopt healthy behavior change once they have been diagnosed with a chronic illness (Newsom et al., 2012)....
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02 Nov 2017
TL;DR: Evidence is found for increased diabetes medication and weight loss around the high risk threshold for diabetes, where information is combined with prompting for a secondary examination and subsequent medical treatment, but there are no differences around other thresholds.
Abstract: Health screening provides information on disease risk and diagnosis, but whether this promotes health is unclear. We estimate the impacts of the National Health Screening Program in Korea for diabetes, obesity, and hyperlipidemia. In this setting, information on disease risk and prompting for a secondary examination vary at different biomarker thresholds. We find evidence for increased diabetes medication and weight loss around the high risk threshold for diabetes, where information is combined with prompting for a secondary examination and subsequent medical treatment. However, we find no differences around other thresholds, where information is not combined with further intervention.
6 citations
TL;DR: Intelligent Keyword PAMSIK featuring the use of intelligent keywords, which has been design to navigate users to the target in-time knowledge and also leverage the collective power-peer learning to encourage patients.
Abstract: Health behavior change is the key challenge for healthcare intervention adherence management. For each treatment planning, patient adherence can be managed, audited, and improved by the Patient Adherence Management System applying Intelligent Keyword PAMSIK featuring the use of intelligent keywords, which has been design to navigate users to the target in-time knowledge and also leverage the collective power-peer learning to encourage patients. Three major components of PAMSIK are: an autonomous, intelligent, friendly User Behavior Collector to identify patient's personal adherence problems, a Patient Similarity Analyzer to dynamically cluster peers, and a Cure Service Dispatcher to recommend suitable cures and thus deliver prompt services and in-time contents to users.
5 citations
Cites background from "Health behavior change following ch..."
...International Journal of E-Health and Medical Communications, 4(4), 102-119, October-December 2013 103 not adopt healthier behaviors (Newsom et al., 2012)....
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TL;DR: In this article, the authors assessed a pathway from early-life disadvantage to suboptimal later-life health via health behavior using UK Household Longitudinal Study data and found that growing up under disadvantageous socioeconomic circumstances may initiate a chain of risk by predisposing people to health behavior profiles associated with poorer later life health.
Abstract: Objectives: Drawing on UK Household Longitudinal Study data, this study assessed a pathway from early-life disadvantage to suboptimal later-life health via health behavior. Methods: Latent class analysis was used to identify distinct smoking, nutrition, alcohol, and physical activity health behavior profiles. Mediation analyses were performed to assess indirect effects of early-life disadvantage via health behavior on allostatic load, an objective measure of physiological wear and tear. Results: Four health behavior profiles were identified: (1) broadly healthy and high alcohol consumption, (2) low smoking and alcohol consumption, healthy nutrition, and physically inactive, (3) broadly unhealthy and low alcohol consumption, and (4) broadly moderately unhealthy and high alcohol consumption. Having grown up in a higher socioeconomic position family was associated with lower later-life allostatic load. This was partly attributable to health behavioral differences. Discussion: Growing up under disadvantageous socioeconomic circumstances may initiate a chain of risk by predisposing people to health behavior profiles associated with poorer later-life health.
5 citations
TL;DR: Walking in their real-world provided a meaningful, desirable, but challenging goal for participants that required significant emotional effort and can contribute to psychological wellbeing by providing opportunities for successful mastery and social connectedness.
Abstract: Purpose: Understanding personal experiences of real-world walking for stroke survivors could assist clinicians to tailor interventions to their clients' specific needs. We explored the research questions: "What does real-world walking mean to people after stroke and how do they think it can be better?"Method: Using an Interpretive Descriptive methodology, we purposively sampled eight stroke survivors who reported difficulty walking in the real-world. We sought diversity on key participant characteristics. Participants were interviewed using a semi-structured guide. Data were analysed with thematic analysis.Results: Many found real-world walking, particularly in the outdoors, created opportunities for freedom from dependence and a visible step by step progress, which generated hope for future recovery. Conversely, when participants did not experience sufficient progress, they expressed negative emotions. Participants strove to overcome challenges to their walking goals using everyday routines, planning skills, and confidence building experiences to motivate themselves. They also drew on, and extended, social resources highlighting the relational aspects of real-world walking.Conclusions: Walking in their real-world provided a meaningful, desirable, but challenging goal for participants that required significant emotional effort. Successful progress in real-world walking builds confidence and hope and can contribute to psychological wellbeing by providing opportunities for successful mastery and social connectedness.IMPLICATIONS FOR REHABILITATIONReal-world settings can be unpredictable which makes walking in the real-world after stroke demanding.Positive experiences of walking in the real-world can provide significant psychological benefits to stroke survivors.Many survivors need to carefully concentrate on the act of walking in outdoor settings.Pre-planning routes, confidence-building experiences and developing daily routines may help patients overcome these challenges.
5 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