The Experts below are selected from a list of 353820 Experts worldwide ranked by ideXlab platform
Wayne F Velicer - One of the best experts on this subject based on the ideXlab platform.
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Automated Indexing of Internet Stories for Health Behavior Change: Weight Loss Attitude Pilot Study
Journal of Medical Internet Research, 2014Co-Authors: Ramesh Manuvinakurike, Wayne F Velicer, Timothy W. BickmoreAbstract:Background: Automated Health Behavior change interventions show promise, but suffer from high attrition and disuse. The Internet abounds with thousands of personal narrative accounts of Health Behavior change that could not only provide useful information and motivation for others who are also trying to change, but an endless source of novel, entertaining stories that may keep participants more engaged than messages authored by interventionists. Objective: Given a collection of relevant personal Health Behavior change stories gathered from the Internet, the aim of this study was to develop and evaluate an automated indexing algorithm that could select the best possible story to provide to a user to have the greatest possible impact on their attitudes toward changing a targeted Health Behavior, in this case weight loss. Methods: An indexing algorithm was developed using features informed by theories from Behavioral medicine together with text classification and machine learning techniques. The algorithm was trained using a crowdsourced dataset, then evaluated in a 2×2 between-subjects randomized pilot study. One factor compared the effects of participants reading 2 indexed stories vs 2 randomly selected stories, whereas the second factor compared the medium used to tell the stories: text or animated conversational agent. Outcome measures included changes in self-efficacy and decisional balance for weight loss before and after the stories were read. Results: Participants were recruited from a crowdsourcing website (N=103; 53.4%, 55/103 female; mean age 35, SD 10.8 years; 65.0%, 67/103 precontemplation; 19.4%, 20/103 contemplation for weight loss). Participants who read indexed stories exhibited a significantly greater increase in self-efficacy for weight loss compared to the control group ( F 1,107 =5.5, P =.02). There were no significant effects of indexing on change in decisional balance ( F 1,97 =0.05, P =.83) and no significant effects of medium on change in self-efficacy ( F 1,107 =0.04, P =.84) or decisional balance ( F 1,97 =0.78, P =.38). Conclusions: Personal stories of Health Behavior change can be harvested from the Internet and used directly and automatically in interventions to affect participant attitudes, such as self-efficacy for changing Behavior. Such approaches have the potential to provide highly tailored interventions that maximize engagement and retention with minimal intervention development effort. [J Med Internet Res 2014;16(12):e285]
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the benefits and challenges of multiple Health Behavior change in research and in practice
Preventive Medicine, 2010Co-Authors: Judith J Prochaska, Wayne F Velicer, Bonnie Spring, Claudio R Nigg, James O. ProchaskaAbstract:Objective The major chronic diseases are caused by multiple risks, yet the science of multiple Health Behavior change (MHBC) is at an early stage, and factors that facilitate or impede scientists' involvement in MHBC research are unknown. Benefits and challenges of MHBC interventions were investigated to strengthen researchers' commitment and prepare them for challenges.
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evaluating theories of Health Behavior change a hierarchy of criteria applied to the transtheoretical model
Applied Psychology, 2008Co-Authors: James O. Prochaska, Julie A Wright, Wayne F VelicerAbstract:The most common criteria recommended by philosophers of science for evaluating theories were organised within a hierarchy ranging from the least to the most risky tests for theories of Health Behavior change. The hierarchy progressed across: (1) Clarity; (2) Consistency; (3) Parsimony; (4) Testable; (5) Predictive Power; (6) Explanatory Power; (7) Productivity; (8) Generalisable; (9) Integration; (10) Utility; (11) Efficacy; and (12) Impact. The hierarchy was applied to the Transtheoretical Model (TTM) as an example of a Health Behavior change theory. The application was from the perspective of critics and advocates of TTM. Examples of basic and applied research challenging and supporting TTM across the hierarchy of criteria are presented. The goal is to provide a model for comparing alternative theories and to evaluate progress across the hierarchy within a particular theory. As theories meet criteria at each step in the hierarchy, the research and applications they generate can have increasing impacts on the science and practice of Health Behavior change.
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strengths and weaknesses of Health Behavior change programs on the internet
Journal of Health Psychology, 2003Co-Authors: Kerry E Evers, Janice M Prochaska, Marymargaret Driskell, Carol O Cummins, Wayne F VelicerAbstract:Full reviews were conducted on 37 public websites on Health Behavior change for disease prevention and management. All had at least four of five of the `5A's for effective Health Behavior change tr...
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the transtheoretical model of Health Behavior change
American Journal of Health Promotion, 1997Co-Authors: James O. Prochaska, Wayne F VelicerAbstract:The transtheoretical model posits that Health Behavior change involves progress through six stages of change: precontemplation, contemplation, preparation, action, maintenance, and termination. Ten processes of change have been identified for producing progress along with decisional balance, self-efficacy, and temptations. Basic research has generated a rule of thumb for at-risk populations: 40% in precontemplation, 40% in contemplation, and 20% in preparation. Across 12 Health Behaviors, consistent patterns have been found between the pros and cons of changing and the stages of change. Applied research has demonstrated dramatic improvements in recruitment, retention, and progress using stage-matched interventions and proactive recruitment procedures. The most promising outcomes to date have been found with computer-based individualized and interactive interventions. The most promising enhancement to the computer-based programs are personalized counselors. One of the most striking results to date for stag...
Noel T Brewer - One of the best experts on this subject based on the ideXlab platform.
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anticipated regret and Health Behavior a meta analysis
Health Psychology, 2016Co-Authors: Noel T Brewer, Jessica T Defrank, Melissa B GilkeyAbstract:OBJECTIVE Risk beliefs are central to most theories of Health Behavior, yet many unanswered questions remain about an increasingly studied risk construct, anticipated regret. The authors sought to better understand anticipated regret's role in motivating Health Behaviors. METHOD The authors systematically searched electronic databases for studies of anticipated regret and Behavioral intentions or Health Behavior. They used random effects meta-analysis to synthesize effect sizes from 81 studies (n = 45,618). RESULTS Anticipated regret was associated with both intentions (r+ = .50, p < .001) and Health Behavior (r+ = .29, p < .001). Greater anticipated regret from engaging in a Behavior (i.e., action regret) predicted weaker intentions and Behavior, whereas greater anticipated regret from not engaging in a Behavior (i.e., inaction regret) predicted stronger intentions and Behavior. Anticipated action regret had smaller associations with Behavioral intentions related to less severe and more distal hazards, but these moderation findings were not present for inaction regret. Anticipated regret generally was a stronger predictor of intentions and Behavior than other anticipated negative emotions and risk appraisals. CONCLUSIONS Anticipated inaction regret has a stronger and more stable association with Health Behavior than previously thought. The field should give greater attention to understanding how anticipated regret differs from similar constructs, its role in Health Behavior theory, and its potential use in Health Behavior interventions. (PsycINFO Database Record
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anticipated regret and Health Behavior a meta analysis
The European health psychologist, 2015Co-Authors: Noel T Brewer, Jessica T Defrank, Melissa B GilkeyAbstract:Objective. Regret is a cognitive emotion that is unique to decisions and that people seek to avoid. We sought to understand anticipated regret’s role in motivating Health Behaviors. Methods. We systematically searched electronic databases for studies of anticipated regret and Behavioral intentions or Health Behavior. We used random effects meta-analysis to synthesize effect sizes from 81 studies (n=45,618). Results. Anticipated regret was associated with both intentions (r+= .50, p<.001) and Health Behavior (r+= .29, p<.001), such that greater anticipated inaction regret predicted stronger intentions and Behavior, while anticipated action regret showed the opposite association. Anticipated regret generally was a stronger predictor of intentions and Behavior than other anticipated negative emotions and risk appraisals. Conclusions. Anticipated inaction regret has a stronger and more stable association with Health Behavior than previously thought. The field should give greater attention to understanding how anticipated regret differs from similar constructs, its role in Health Behavior theory, and its potential use in Health Behavior interventions.
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meta analysis of the relationship between risk perception and Health Behavior the example of vaccination
Health Psychology, 2007Co-Authors: Noel T Brewer, Gretchen B Chapman, Frederick X Gibbons, Meg Gerrard, Kevin D Mccaul, Neil D. WeinsteinAbstract:Background: Risk perceptions are central to many Health Behavior theories. However, the relationship between risk perceptions and Behavior, muddied by instances of inappropriate assessment and analysis, often looks weak. Method: A meta-analysis of eligible studies assessing the bivariate association between adult vaccination and perceived likelihood, susceptibility, or severity was conducted. Results: Thirty-four studies met inclusion criteria (N 15,988). Risk likelihood (pooled r .26), susceptibility (pooled r .24), and severity (pooled r .16) significantly predicted vaccination Behavior. The risk perception Behavior relationship was larger for studies that were prospective, had higher quality risk measures, or had unskewed risk or Behavior measures. Conclusions: The consistent relationships between risk perceptions and Behavior, larger than suggested by prior meta-analyses, suggest that risk perceptions are rightly placed as core concepts in theories of Health Behavior.
James O. Prochaska - One of the best experts on this subject based on the ideXlab platform.
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the benefits and challenges of multiple Health Behavior change in research and in practice
Preventive Medicine, 2010Co-Authors: Judith J Prochaska, Wayne F Velicer, Bonnie Spring, Claudio R Nigg, James O. ProchaskaAbstract:Objective The major chronic diseases are caused by multiple risks, yet the science of multiple Health Behavior change (MHBC) is at an early stage, and factors that facilitate or impede scientists' involvement in MHBC research are unknown. Benefits and challenges of MHBC interventions were investigated to strengthen researchers' commitment and prepare them for challenges.
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evaluating theories of Health Behavior change a hierarchy of criteria applied to the transtheoretical model
Applied Psychology, 2008Co-Authors: James O. Prochaska, Julie A Wright, Wayne F VelicerAbstract:The most common criteria recommended by philosophers of science for evaluating theories were organised within a hierarchy ranging from the least to the most risky tests for theories of Health Behavior change. The hierarchy progressed across: (1) Clarity; (2) Consistency; (3) Parsimony; (4) Testable; (5) Predictive Power; (6) Explanatory Power; (7) Productivity; (8) Generalisable; (9) Integration; (10) Utility; (11) Efficacy; and (12) Impact. The hierarchy was applied to the Transtheoretical Model (TTM) as an example of a Health Behavior change theory. The application was from the perspective of critics and advocates of TTM. Examples of basic and applied research challenging and supporting TTM across the hierarchy of criteria are presented. The goal is to provide a model for comparing alternative theories and to evaluate progress across the hierarchy within a particular theory. As theories meet criteria at each step in the hierarchy, the research and applications they generate can have increasing impacts on the science and practice of Health Behavior change.
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the transtheoretical model of Health Behavior change
American Journal of Health Promotion, 1997Co-Authors: James O. Prochaska, Wayne F VelicerAbstract:The transtheoretical model posits that Health Behavior change involves progress through six stages of change: precontemplation, contemplation, preparation, action, maintenance, and termination. Ten processes of change have been identified for producing progress along with decisional balance, self-efficacy, and temptations. Basic research has generated a rule of thumb for at-risk populations: 40% in precontemplation, 40% in contemplation, and 20% in preparation. Across 12 Health Behaviors, consistent patterns have been found between the pros and cons of changing and the stages of change. Applied research has demonstrated dramatic improvements in recruitment, retention, and progress using stage-matched interventions and proactive recruitment procedures. The most promising outcomes to date have been found with computer-based individualized and interactive interventions. The most promising enhancement to the computer-based programs are personalized counselors. One of the most striking results to date for stag...
Neil D. Weinstein - One of the best experts on this subject based on the ideXlab platform.
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meta analysis of the relationship between risk perception and Health Behavior the example of vaccination
Health Psychology, 2007Co-Authors: Noel T Brewer, Gretchen B Chapman, Frederick X Gibbons, Meg Gerrard, Kevin D Mccaul, Neil D. WeinsteinAbstract:Background: Risk perceptions are central to many Health Behavior theories. However, the relationship between risk perceptions and Behavior, muddied by instances of inappropriate assessment and analysis, often looks weak. Method: A meta-analysis of eligible studies assessing the bivariate association between adult vaccination and perceived likelihood, susceptibility, or severity was conducted. Results: Thirty-four studies met inclusion criteria (N 15,988). Risk likelihood (pooled r .26), susceptibility (pooled r .24), and severity (pooled r .16) significantly predicted vaccination Behavior. The risk perception Behavior relationship was larger for studies that were prospective, had higher quality risk measures, or had unskewed risk or Behavior measures. Conclusions: The consistent relationships between risk perceptions and Behavior, larger than suggested by prior meta-analyses, suggest that risk perceptions are rightly placed as core concepts in theories of Health Behavior.
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Misleading tests of Health Behavior theories
Annals of Behavioral Medicine, 2007Co-Authors: Neil D. WeinsteinAbstract:Most tests of cognitively oriented theories of Health Behavior are based on correlational data. Unfortunately, such tests are often biased, overestimating the accuracy of the theories they seek to evaluate. These biases are especially strong when studies examine Health Behaviors that need to be performed repeatedly, such as medication adherence, diet, exercise, and condom use. Several misleading data analysis procedures further exaggerate the theories’ predictive accuracy. Because correlational designs are not adequate for deciding whether a particular construct affects Behavior or for testing one theory against another, most of the literature aiming to test these theories tells us little about their validity or completeness. Neither does the existing empirical literature support decisions to use these theories to design interventions. In addition to discussing problems with correlational data, this article offers ideas for alternative testing strategies.
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Stage theories of Health Behavior: conceptual and methodological issues.
Health Psychology, 1998Co-Authors: Neil D. Weinstein, Alexander J Rothman, Stephen SuttonAbstract:: Despite growing interest in stage theories of Health Behavior, there is considerable confusion in the literature concerning the essential characteristics of stage theories and the manner in which such theories should be tested. In this article, the 4 key characteristics of a stage theory-a category system, an ordering of categories, similar barriers to change within categories, and different barriers to change between categories--are discussed in detail. Examples of stage models of Health Behavior also are described. Four major types of research designs that might be used for testing stage theories are examined, including examples from the empirical literature. The most commonly used design, which involves cross-sectional comparisons of people believed to be in different stages, is shown to have only limited value for testing whether Behavior change follows a stage process.
Claudio R Nigg - One of the best experts on this subject based on the ideXlab platform.
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the benefits and challenges of multiple Health Behavior change in research and in practice
Preventive Medicine, 2010Co-Authors: Judith J Prochaska, Wayne F Velicer, Bonnie Spring, Claudio R Nigg, James O. ProchaskaAbstract:Objective The major chronic diseases are caused by multiple risks, yet the science of multiple Health Behavior change (MHBC) is at an early stage, and factors that facilitate or impede scientists' involvement in MHBC research are unknown. Benefits and challenges of MHBC interventions were investigated to strengthen researchers' commitment and prepare them for challenges.
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multiple Health Behavior change research an introduction and overview
Preventive Medicine, 2008Co-Authors: Judith J Prochaska, Bonnie Spring, Claudio R NiggAbstract:In 2002, the Society of Behavioral Medicine's special interest group on Multiple Health Behavior Change was formed. The group focuses on the interrelationships among Health Behaviors and interventions designed to promote change in more than one Health Behavior at a time. Growing evidence suggests the potential for multiple-Behavior interventions to have a greater impact on public Health than single-Behavior interventions. However, there exists surprisingly little understanding of some very basic principles concerning multiple Health Behavior change (MHBC) research. This paper presents the rationale and need for MHBC research and interventions, briefly reviews the research base, and identifies core conceptual and methodological issues unique to this growing area. The prospects of MHBC for the Health of individuals and populations are considerable.
