Decision-Based Disorders

Bickel, Warren K.; Pope, Derek A.; Moody, Lara; Snider, Sarah E.; Stein, Jeffrey S.; Mellis, Alexandra M.

doi:10.1177/2372732216686085

Cited by 6 publications

(3 citation statements)

References 87 publications

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“…The field of health psychology has burgeoned since these early observations by Matarazzo (1980Matarazzo ( , 1982, but the insight that decision making is a common denominator in preventable disease remains acutely applicable today. Individual decisions about health behaviors constitute the single greatest health care cost in the United States and lead to roughly 40% of preventable deaths each year (Bickel et al, 2017;McGinnis, Williams-Russo, & Knickman, 2002;Schroeder, 2007). This article introduces readers to theories, tools, and evidence from the field of neuroeconomics (also referred to as "decision neuroscience") and describes how health psychology and neuroeconomics can mutually inform the study of preventative health behavior.…”

Section: Introductionmentioning

confidence: 99%

Neuroeconomics, health psychology, and the interdisciplinary study of preventative health behavior

DeStasio¹,

Clithero²,

Berkman³

2019

Social & Personality Psych

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The goal of this article is to introduce readers to theories, tools, and evidence from the field of neuroeconomics and to describe how health psychology and neuroeconomics can be mutually informative in the study of preventative health behaviors. Preventative health behavior here refers to both individual actions that impact one's health (e.g., exercise) and broader behavioral patterns, such as those captured in personality constructs. Although neuroeconomic researchers have begun to incorporate health‐relevant behaviors into their studies, the full potential of this research to inform preventative health models is as yet unrealized. What is needed to “translate up” is the unification of rich theoretical content from health psychology with investigations by neuroeconomic researchers of the decision‐making process during health‐relevant choices. We identify choice as a central, shared feature across models of preventative health behavior that can serve as an inroad for neuroeconomics to contribute to existing models and highlight commonalities that might not otherwise be apparent. A central premise of our argument is that, because health decisions are nearly always multiply determined, a more precise and mechanistic understanding of how choices are made is an important but understudied topic in health psychology. A partnership between health psychologists and neuroeconomic researchers can yield valuable insights into how preventative health choice is made and to identify targets and methods for intervention.

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Section: Introductionmentioning

confidence: 99%

Neuroeconomics, health psychology, and the interdisciplinary study of preventative health behavior

DeStasio¹,

Clithero²,

Berkman³

2019

Social & Personality Psych

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show abstract

“…A framework is a network of interlinked concepts, ideas, theories, and/or scientific knowledge that together can provide a comprehensive understanding of a phenomenon. 7 Behavioral change theory and science has provided decades of progress, and have identified basic behavioral mechanisms and processes 8 whereby behavior, whether of a patient, a provider, an organization, or a society, can be analyzed and altered. 9 A useful, comprehensive behavioral change science framework is provided by Kane and others.…”

Section: A Behavioral Change Framework For Deimplementationmentioning

confidence: 99%

Commentary: De-implementation Science: A Virtuous Cycle of Ceasing and Desisting Low-Value Care Before Implementing New High Value Care

Davidson

Mensah

2017

Ethn Dis

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Implementation science has traditionally focused on increasing the delivery of evidence-based care. The science of systematically stopping low-value and wasteful care is substantially under-recognized, and if successful, may decrease the workload of clinicians. De-implementation science identifies problem areas of low-value and wasteful practice, carries out rigorous scientific examination of the factors that initiate and maintain such behaviors, and then employs evidence-based interventions to cease these practices. In this commentary, we describe how this approach for de-implementation might require a different set of health systems supports, economic and non-economic levers, and behavior change techniques that can lead to a virtuous cycle, ie, a complex chain of events that positively reinforce themselves through a feedback loop of removing low-value care to make room for high quality care. Ethn Dis. 2017;27(4):463-468; doi:10.18865/ ed.27.4.463.

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“…The movement to develop evidence-based treatments (Smith, 2013) or evidence-based policy (Bickel et al, 2016;Higgins, Davis, & Kurti, 2017;Madden, Price, & Sosa, 2016;Newland & Bailey, 2016) relies on the publication of statistically significant effects under the assumption that they will be reproducible. If that foundation is weak, then the treatments or policies will be misguided.…”

mentioning

confidence: 99%

An Information Theoretic Approach to Model Selection: A Tutorial with Monte Carlo Confirmation

Newland

2019

Perspect Behav Sci

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A reliance on null hypothesis significance testing (NHST) and misinterpretations of its results are thought to contribute to the replication crisis while impeding the development of a cumulative science. One solution is a data-analytic approach called Information-Theoretic (I-T) Model Selection, which builds upon Maximum Likelihood estimates. In the IT approach, the scientist examines a set of candidate models and determines for each one the probability that it is the closer to the truth than all others in the set. Although the theoretical development is subtle, the implementation of IT analysis is straightforward. Models are sorted according to the probability that they are the best in light of the data collected. It encourages the examination of multiple models, something investigators desire and that NHST discourages. This article is structured to address two objectives. The first is to illustrate the application of IT data analysis to data from a virtual experiment. A noisy delay-discounting data set is generated and seven quantitative models are examined. In the illustration, it is demonstrated that it is not necessary to know the "truth" is to identify the one that is closest to it and that the most likely models conform to the model that generated the data. Second, we examine claims made by advocates of the IT approach using Monte Carlo simulations in which 10,000 different data sets are generated and analyzed. The simulations showed that 1) the probabilities associated with each model returned by the single virtual experiment approximated those that resulted from the simulations, 2) models that were deemed close to the truth produced the most precise parameter estimates, and 3) adding a single replicate sharpens the ability to identify the most probable model.

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Decision-Based Disorders

Cited by 6 publications

References 87 publications

Neuroeconomics, health psychology, and the interdisciplinary study of preventative health behavior

Neuroeconomics, health psychology, and the interdisciplinary study of preventative health behavior

Commentary: De-implementation Science: A Virtuous Cycle of Ceasing and Desisting Low-Value Care Before Implementing New High Value Care

An Information Theoretic Approach to Model Selection: A Tutorial with Monte Carlo Confirmation

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