Using EP50 to forecast treatment adherence in individuals with multiple sclerosis

The Neural Response Process of Cognitive Decision Making: An ERP Study

Cognitive decision has the basic characteristics of risk avoidance and benefit seeking. To explore the neural response process of cognitive decision making, we asked 32 undergraduates to make a decision on whether to accept a specific treatment option with a certain cure rate and a certain risk rate while recording their electrical brain responses. The results showed that more participants chose the treatment option with a high cure rate and moderate or low risk. Compared with low and high risk, medium risk produced greater N1 and smaller P300. Low risk produced larger LPP than the moderate risk in the left hemisphere. The right prefrontal region appeared to have a smaller LPP for low risk than for high risk. The results suggest that individuals prioritize risk when making cognitive decisions. In addition, in medium-risk conditions, solution integration is more difficult. The effect of benefit size appears at the late stage of cognitive decision making and adjusts the effect of risk. These results support the satisfaction principle of decision making.

Reinforcer pathology, probabilistic choice, and medication adherence in patients with multiple sclerosis

The reinforcer pathology model posits that core behavioral economic mechanisms, including delay discounting and behavioral economic demand, underlie adverse health decisions and related clinical disorders. Extensions beyond substance use disorder and obesity, however, are limited. Using a reinforcer pathology framework, this study evaluates medical adherence decisions in patients with multiple sclerosis. Participants completed behavioral economic measures, including delay discounting, probability discounting, and a medication purchase task. A medical decision-making task was also used to evaluate how sensitivity to mild side effect risk and efficacy contributed to the likelihood of taking a hypothetical disease-modifying therapy. Less steep delay discounting and more intense (greater) medication demand were independently associated with greater adherence to the medication decision-making procedure. More generally, the pattern of interrelations between the medication-specific and general behavioral economic metrics was consistent with and contributes to the reinforcer pathology model. Additional research is warranted to expand these models to different populations and health behaviors, including those of a positive health orientation (i.e., medication adherence).

Patient-based benefit-risk assessment of medicines: development, refinement, and validation of a content search strategy to retrieve relevant studies

Introduction:

Poor indexing and inconsistent use of terms and keywords may prevent efficient retrieval of studies on the patient-based benefit-risk assessment (BRA) of medicines. We aimed to develop and validate an objectively derived content search strategy containing generic search terms that can be adapted for any search for evidence on patient-based BRA of medicines for any therapeutic area.

Methods:

We used a robust multistep process to develop and validate the content search strategy: (1) we developed a bank of search terms derived from screening studies on patient-based BRA of medicines in various therapeutic areas, (2) we refined the proposed content search strategy through an iterative process of testing sensitivity and precision of search terms, and (3) we validated the final search strategy in PubMed by firstly using multiple sclerosis as a case condition and secondly computing its relative performance versus a published systematic review on patient-based BRA of medicines in rheumatoid arthritis.

Results:

We conceptualized a final search strategy to retrieve studies on patient-based BRA containing generic search terms grouped into two domains, namely the patient and the BRA of medicines (sensitivity 84%, specificity 99.4%, precision 20.7%). The relative performance of the content search strategy was 85.7% compared with a search from a published systematic review of patient preferences in the treatment of rheumatoid arthritis. We also developed a more extended filter, with a relative performance of 93.3% when compared with a search from a published systematic review of patient preferences in lung cancer.

Behavioral economic methods to inform infectious disease response: Prevention, testing, and vaccination in the COVID-19 pandemic

The role of human behavior to thwart transmission of infectious diseases like COVID-19 is evident. Psychological and behavioral science are key areas to understand decision-making processes underlying engagement in preventive health behaviors. Here we adapt well validated methods from behavioral economic discounting and demand frameworks to evaluate variables (e.g., delay, cost, probability) known to impact health behavior engagement. We examine the contribution of these mechanisms within a broader response class of behaviors reflecting adherence to public health recommendations made during the COVID-19 pandemic. Four crowdsourced samples (total N = 1,366) completed individual experiments probing a response class including social (physical) distancing, facemask wearing, COVID-19 testing, and COVID-19 vaccination. We also measure the extent to which choice architecture manipulations (e.g., framing, opt-in/opt-out) may promote (or discourage) behavior engagement. We find that people are more likely to socially distance when specified activities are framed as high risk, that facemask use during social interaction decreases systematically with greater social relationship, that describing delay until testing (rather than delay until results) increases testing likelihood, and that framing vaccine safety in a positive valence improves vaccine acceptance. These findings collectively emphasize the flexibility of methods from diverse areas of behavioral science for informing public health crisis management.

Applied Quantitative Analysis of Behavior: What It Is, and Why We Care-Introduction to the Special Section

Science evolves from prior approximations of its current form. Interest in changes in species over time was not a new concept when Darwin made his famous voyage to the Galapagos Islands; concern with speciation stretches back throughout the history of modern thought. Behavioral science also does and must evolve. Such change can be difficult, but it can also yield great dividends. The focus of the current special section is on a common mutation that appears to have emerged across these areas and the critical features that define an emerging research area-applied quantitative analysis of behavior (AQAB). In this introduction to the "Special Issue on Applications of Quantitative Methods," we will outline some of the common characteristics of research in this area, an exercise that will surely be outdated as the research area continues to progress. In doing so, we also describe how AQAB is relevant to theory, behavioral pharmacology, applied behavior analysis, and health behaviors. Finally, we provide a summary for the articles that appear in this special issue. The authors of these papers are all thinking outside the Skinner box, creating new tools and approaches, and testing them against relevant data. If we can keep up this evolution of methods and ideas, behavior analysis will regain its place at the head of the table!

Integrating Operant and Cognitive Behavioral Economics to Inform Infectious Disease Response: Prevention, Testing, and Vaccination in the COVID-19 Pandemic

The role of human behavior to thwart transmission of infectious diseases like COVID-19 is evident. Yet, many areas of psychological and behavioral science are limited in the ability to mobilize to address exponential spread or provide easily translatable findings for policymakers. Here we describe how integrating methods from operant and cognitive approaches to behavioral economics can provide robust policy relevant data. Adapting well validated methods from behavioral economic discounting and demand frameworks, we evaluate in four crowdsourced samples (total N = 1,366) behavioral mechanisms underlying engagement in preventive health behaviors. We find that people are more likely to social distance when specified activities are framed as high risk, that describing delay until testing (rather than delay until results) increases testing likelihood, and that framing vaccine safety in a positive valence improves vaccine acceptance. These findings collectively emphasize the flexibility of methods from diverse areas of behavioral science for informing public health crisis management.

Further comparison of 5-trial adjusting delay and probability loss tasks over a wide range of amounts

Researchers have recently developed brief methods to measure discounting. One brief method uses 5-trial adjusting-delay or -probability tasks. These tasks have provided similar rates of discounting to traditional tasks with monetary gains, but the accuracy with losses have been mixed. Differences in loss discounting across tasks may have been due to the amounts used in previous experiments. Therefore, we had undergraduate students (N = 93) complete two types of discounting tasks across losses ranging from $10 to $10 million. Consistent with previous research using traditional measures, discounting did not differ between tasks or across amounts used. 5-trial discounting tasks of losses provide similar rates of discounting compared to traditional adjusting amount tasks for both probability and delay.

Neural mechanisms associated with treatment decision making: An fMRI study

Great progress has been made in understanding how people make financial decisions. However, there is little research on how people make health and treatment choices. Our study aimed to examine how participants weigh benefits (reduction in disease progression) and probability of risk (medications' side effects) when making hypothetical treatment decisions, and to identify the neural networks implicated in this process. Fourteen healthy participants were recruited to perform a treatment decision probability discounting task using MRI. Behavioral responses and skin conductance responses (SCRs) were measured. A whole brain analysis were performed to compare activity changes between "mild" and "severe" medications' side effects conditions. Then, orbitofrontal cortex (OFC), ventral striatum (VS), amygdala and insula were chosen for effective connectivity analysis. Behavioral data showed that participants are more likely to refuse medication when side effects are high and efficacy is low. SCRs values were significantly higher when people made medication decisions in the severe compared to mild condition. Functionally, OFC and VS were activated in the mild condition and were associated with increased likehood of choosing to take medication (higher area under the curve "AUC" side effects/efficacy). These regions also demonstrated an increased effective connectivity when participants valued treatment benefits. By contrast, the OFC, insula and amygdala were activated in the severe condition and were associated with and increased likelihood to refuse treatment. These regions showed enhanced effective connectivity when participants were confronted with increased side effects severity. This is the first study to examine the behavioral and neural bases of medical decision making.