Moving beyond a limited follow-up in cost-effectiveness analyses of behavioral interventions

Background Cost-effectiveness analyses of behavioral interventions typically use a dichotomous outcome criterion. However, achieving behavioral change is a complex process involving several steps towards a change in behavior. Delayed effects may occur after an intervention period ends, which can lead to underestimation of these interventions. To account for such delayed effects, intermediate outcomes of behavioral change may be used in cost-effectiveness analyses. The aim of this study is to model cognitive parameters of behavioral change into a cost-effectiveness model of a behavioral intervention. Methods The cost-effectiveness analysis (CEA) of an existing dataset from an RCT in which an high-intensity smoking cessation intervention was compared with a medium-intensity intervention, was re-analyzed by modeling the stages of change of the Transtheoretical Model of behavioral change. Probabilities were obtained from the dataset and literature and a sensitivity analysis was performed. Results In the original CEA over the first 12 months, the high-intensity intervention dominated in approximately 58% of the cases. After modeling the cognitive parameters to a future 2nd year of follow-up, this was the case in approximately 79%. Conclusion This study showed that modeling of future behavioral change in CEA of a behavioral intervention further strengthened the results of the standard CEA. Ultimately, modeling future behavioral change could have important consequences for health policy development in general and the adoption of behavioral interventions in particular

The overconfidence problem in insurance markets

Adverse selection has long been recognized as a rationale for government intervention in in- surance markets and for the adoption of public compulsory insurance. A different rationale for compulsory insurance is that overconfident individuals may underinsure because they underes- timate the relevant risks. We show that government intervention is not a Pareto improvement in an adverse selection model with a significant fraction of overcon�dent agents. We underline that behavioral biases need not be the basis for government intervention. In fact, behavioral biases may overturn existing compelling reasons for intervention in the economy. Our model also delivers novel positive implications on aggregate variables that have been at the center of recent empirical investigation

Psychological Intervention for Button Phobic

Behavior therapy approach is used in psychological intervention of button phobic, including systematic desensitization, relaxation, cognitive-behavioral therapy, modelling and skill training therapy. Partisipant (N=1) was button phobic since adolescence. Sampling technique in this research was accidental sampling. Psychological intervention design was classified as quasi experiment with single-case subject design. Data collection was performed with a multi-assessment, including observation and interviews, anxiety questionnaires and interview of cognitive change processes, before and after therapy is given. Results of psychological intervention indicated that behavioral therapy can be relied upon their role in overcoming anxiety of button phobic

Sleep Hygiene In Danbury CT: Providing a Resource to Prompt and Initiate Behavior Change

Sleeplessness is a problem encountered frequently in the primary care setting, and often is inappropriately addressed by prescribing pharmacological intervention for indefinite periods of time. This project sought to promote behavioral intervention strategies modeled after CBT-I to address sleep hygiene in patients struggling with their sleep.https://scholarworks.uvm.edu/fmclerk/1363/thumbnail.jp

Communication interventions in adult and pediatric oncology: A scoping review and analysis of behavioral targets

BackgroundImproving communication requires that clinicians and patients change their behaviors. Interventions might be more successful if they incorporate principles from behavioral change theories. We aimed to determine which behavioral domains are targeted by communication interventions in oncology.MethodsSystematic search of literature indexed in Ovid Medline, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Clinicaltrials.gov (2000-October 2018) for intervention studies targeting communication behaviors of clinicians and/or patients in oncology. Two authors extracted the following information: population, number of participants, country, number of sites, intervention target, type and context, study design. All included studies were coded based on which behavioral domains were targeted, as defined by Theoretical Domains Framework.FindingsEighty-eight studies met inclusion criteria. Interventions varied widely in which behavioral domains were engaged. Knowledge and skills were engaged most frequently (85%, 75/88 and 73%, 64/88, respectively). Fewer than 5% of studies engaged social influences (3%, 3/88) or environmental context/resources (5%, 4/88). No studies engaged reinforcement. Overall, 7/12 behavioral domains were engaged by fewer than 30% of included studies. We identified methodological concerns in many studies. These 88 studies reported 188 different outcome measures, of which 156 measures were reported by individual studies.ConclusionsMost communication interventions target few behavioral domains. Increased engagement of behavioral domains in future studies could support communication needs in feasible, specific, and sustainable ways. This study is limited by only including interventions that directly facilitated communication interactions, which excluded stand-alone educational interventions and decision-aids. Also, we applied stringent coding criteria to allow for reproducible, consistent coding, potentially leading to underrepresentation of behavioral domains

Integrating basic research with prevention/intervention to reduce risky substance use among college students

Too often basic research on etiological processes that contribute to substance use outcomes is disconnected from efforts to develop prevention and intervention programming. Substance use on college campuses is an area of concern where translational efforts that bring together basic scientists and prevention/intervention practitioners have potential for high impact. We describe an effort at a large, public, urban university in the United States to bring together researchers across the campus with expertise in college behavioral health with university administration and health/wellness practitioners to address college student substance use and mental health. The project “Spit for Science” examines how genetic and environmental influences contribute to behavioral health outcomes across the college years. We argue that findings coming out of basic research can be used to develop more tailored prevention and intervention programming that incorporates both biologically and psychosocially influenced risk factors. Examples of personalized programming suggest this may be a fruitful way to advance the field and reduce risky substance use