Behavioral Sciences Research Articles

Relevance of Behavioral Research in the Evolving State of Anti-obesity Medications: Is the Glass Half Empty or Half Full?

Obesity continues to be a significant public health challenge. While weight loss medications have been studied and available for several years, the newest generation of highly effective anti-obesity medications (AOMs) will shift how behavioral science professionals approach obesity treatment and research. With the unique skill set of behavioral science professionals, this commentary suggests ways to integrate behavioral science into the rapidly evolving landscape of AOM use to accelerate better obesity care and generate new lines of research. The goal of this commentary is to stimulate discussion and encourage responsive and relevant action to improve population health.

Where exactly do the social and behavioural sciences fit in One Health?

At its core, One Health promotes multidisciplinary cooperation amongst researchers and practitioners to improve the effectiveness and management of complex problems raised by the interplay of human, animal and environment interactions. Contemporary One Health literature has identified reducing disciplinary barriers as key to progress in the field, along with addressing the notable absence of social sciences from One Health frameworks, among other priorities. Efforts to position social scientists as experts on behaviour change and health decision-making has helped to articulate a concrete role for progressing One Health collaborations. Yet, there are other equally valuable functions the social scientist has in understanding complex systems, like One Health. We make explicit the multiple and diverse knowledge contributions the social sciences and humanities can make to progressing the One Health agenda. Articulating these more clearly invites a broader set of interdisciplinary perspectives to One Health discussions, allowing for stronger connections between sectors, actors, disciplines, and sub-systems. This perspective piece identifies a range of entry points for researchers and practitioners to better utilize the potential contributions social sciences and humanities scholars can make to One Health goals.

How much can personality predict prosocial behavior?

Explaining prosocial behavior is a central goal in classic and contemporary behavioral science. Here, for the first time, we apply modern machine learning techniques to uncover the full predictive potential that personality traits have for prosocial behavior. We utilize a large-scale dataset ( N = 2707; 81 personality traits) and state-of-the-art statistical models to predict an incentivized measure of prosocial behavior, Social Value Orientation (SVO). We conclude: (1) traits explain 13.9% of the variance in SVO; (2) linear models are sufficient to obtain good prediction; (3) trait–trait interactions do not improve prediction; (4) narrow traits improve prediction beyond basic personality (i.e., the HEXACO); (5) there is a moderate association between the univariate predictive power of a trait and its multivariate predictive power, suggesting that univariate estimates (e.g., Pearson’s correlation) can serve as a useful proxy for multivariate variable importance. We propose that the limited usefulness of nonlinear models may stem from current measurement practices in personality science, which tend to favor linearly related constructs. Overall, our study provides a benchmark for how well personality predicts SVO and charts a course toward better prediction of prosocial behavior.

Comparisons of stakeholders' influences, inter-relationships, and obstacles for circular economy implementation on existing building sectors

Buildings are energy- and resource-hungry: their construction and use account for around 39% of global carbon dioxide emissions; they consume around 40% of all the energy produced; they are responsible for over 35% of the EU's total waste generation; and account for about 50% of all extracted (fossil) materials. Therefore, they present a significant challenge to meeting national and international Net Zero targets of reducing greenhouse emissions and fossil resource use. The CircularB Project, is at the heart of this issue, which will underpin synergies of multi-scale circular perspectives (from materials, to components, to assets and built environments), digital transformation solutions, data-driven and complexity science, stakeholder behavioral science, and interdisciplinary capabilities towards achievable, affordable and marketable circular solutions for both new and existing buildings, for sustainable urban design, and for circular built environments across Europe. This paper contributes to the project by deriving new insights into the stakeholders’ influences, inter-relationships, and obstacles in the implementation of circular economy concepts on existing building stocks in Europe, which represent over 90% of whole building assets. In order to identify and derive the insights, our study is rigorously based on (i) a robust critical literature review of key documentations such as articles, standards, policy reports, strategic roadmaps and white papers; and (ii) interviews with relevant stakeholders and decision makers. Uniquely, our work spans across all scales of CE implementation from materials, to products and components, to existing building stocks, and to living built environments. The findings point out the current challenges and obstacles required to be tackled. Inadequacies of financial incentives and governmental enforcement (via policy, legislation, or directive) are commonly found to be the most critical obstacles found throughout Europe. Circular economy is the global challenge and not just a single country can resolve the climate issue without the cooperation of other countries. The insights thus highlight the essential need for harmonized actions and tactical/pragmatic policies promoted and regulated by the European Commission, national and local governments who can dominate the influence, promote inter-relationship, and overcome the barriers towards circular economy much more effectively.

Self-Efficacy Effects on Symptom Experiences in Daily Life and Early Treatment Success in Anxiety Patients

Self-efficacy is a key construct in behavioral science affecting mental health and psychopathology. Here, we expand on previously demonstrated between-persons self-efficacy effects. We prompted 66 patients five times daily for 14 days before starting cognitive behavioral therapy (CBT) to provide avoidance, hope, and perceived psychophysiological-arousal ratings. Multilevel logistic regression analyses confirmed self-efficacy’s significant effects on avoidance in daily life (odds ratio [ OR] = 0.53, 95% confidence interval [CI] = [0.34, 0.84], p = .008) and interaction effects with anxiety in predicting perceived psychophysiological arousal ( OR = 0.79, 95% CI = [0.62, 1.00], p = .046) and hope ( OR = 1.21, 95% CI = [1.03, 1.42], p = .02). More self-efficacious patients also reported greater anxiety-symptom reduction early in treatment. Our findings assign a key role to self-efficacy for daily anxiety-symptom experiences and for early CBT success. Self-efficacy interventions delivered in patients’ daily lives could help improve treatment outcome.

Computational modeling of animal behavior in T-mazes: Insights from machine learning

This study investigates the intricacies of animal decision-making in T-maze environments through a synergistic approach combining computational modeling and machine learning techniques. Focusing on the binary decision-making process in T-mazes, we examine how animals navigate choices between two paths. Our research employs a mathematical model tailored to the decision-making behavior of fish, offering analytical insights into their complex behavioral patterns. To complement this, we apply advanced machine learning algorithms, specifically Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and a hybrid approach involving Principal Component Analysis (PCA) for dimensionality reduction followed by SVM for classification to analyze behavioral data from zebrafish and rats. The above techniques result in high predictive accuracies, approximately 98.07% for zebrafish and 98.15% for rats, underscoring the efficacy of computational methods in decoding animal behavior in controlled experiments. This study not only deepens our understanding of animal cognitive processes but also showcases the pivotal role of computational modeling and machine learning in elucidating the dynamics of behavioral science.

Causal inference in ethnographic research: Refining explanations with abductive logic, strength of evidence assessments, and graphical models.

In their classic accounts, anthropological ethnographers developed causal arguments for how specific sociocultural structures and processes shaped human thought, behavior, and experience in particular settings. Despite this history, many contemporary ethnographers avoid establishing in their work direct causal relationships between key variables in the way that, for example, quantitative research relying on experimental or longitudinal data might. As a result, ethnographers in anthropology and other fields have not advanced understandings of how to derive causal explanations from their data, which contrasts with a vibrant "causal revolution" unfolding in the broader social and behavioral sciences. Given this gap in understanding, we aim in the current article to clarify the potential ethnography has for illuminating causal processes related to the cultural influence on human knowledge and practice. We do so by drawing on our ongoing mixed methods ethnographic study of games, play, and avatar identities. In our ethnographic illustrations, we clarify points often left unsaid in both classic anthropological ethnographies and in more contemporary interdisciplinary theorizing on qualitative research methodologies. More specifically, we argue that for ethnographic studies to illuminate causal processes, it is helpful, first, to state the implicit strengths and logic of ethnography and, second, to connect ethnographic practice more fully to now well-developed interdisciplinary approaches to causal inference. In relation to the first point, we highlight the abductive inferential logic of ethnography. Regarding the second point, we connect the ethnographic logic of abduction to what Judea Pearl has called the ladder of causality, where moving from association to intervention to what he calls counterfactual reasoning produces stronger evidence for causal processes. Further, we show how graphical modeling approaches to causal explanation can help ethnographers clarify their thinking. Overall, we offer an alternative vision of ethnography, which contrasts, but nevertheless remains consistent with, currently more dominant interpretive approaches.

Saudi millennials’ panic buying behavior during pandemic and post-pandemic: Role of social media addiction and religious values and commitment

The latest version of the pandemic ravaged the world for almost three years. The world is slowly coming back to normal. Behavioral sciences tried to assess its impact on consumer behavior, and panic buying behavior has become a most sought-after domain for current researchers. Millennials were the most affected as they were at the beginning of their career, and COVID-19 disrupted their plans massively. This study assesses the panic buying behavior of millennials during pandemic and post-pandemic scenarios. Scarcity and uncertainty are used as antecedents. Two opposite moderators, social media addiction and religious values and commitment, are taken as moderators. Five hundred-seven Saudi millennials participated in this study. Building upon the theory of reasoned action (TRA) and the theory of planned behavior (TPB) along with the latest literature, the conceptual model was proposed using the S–O–B–C (Stimulus/Situation-Organisms-Behavior-Consequences) framework. Structural equation modeling was used to assess the proposed hypotheses. There was no sense of scarcity, panic buying behavior, or social media addiction. On the other hand, there was a high sense of uncertainty, high religious values and commitment, and a positive post-pandemic buying intention. A low sense of scarcity positively impacts panic buying behavior, which was also low, but a heightened sense of uncertainty has a negative effect. Panic buying behavior had no impact on the post-pandemic intentions, which was positive. Social media addiction (low) and religious values and commitment (high) affected the existing direct relationships. The results were unique and broke many myths. The study has specific implications for all the stakeholders.

Neurodevelopmental outcome of pre-term babies less than 34 weeks of gestation at corrected 2 years of age, with respect to their antenatal steroid status

Objectives: The objective of this study was to determine the neurodevelopmental status of pre-term babies between <34 weeks of gestation and equal to or more than 25 weeks of gestation, at corrected 2 years of age with respect to their antenatal steroid status. Materials and Methods: This population-level prospective study was conducted over a period of 6 years from 2017 to 2023 in middle Kerala. Pre-term babies with gestational age at birth between <34 weeks and equal to or more than 25 completed weeks, presenting for follow-up within 3 months of delivery, were randomly enrolled for the study. A total of 150 children were followed up and neurodevelopmental assessment done using Bayley Scales of Infant and Toddler Development edition-3 at their corrected 2 years of age. Approval for this prospective observational study was obtained from the Institutional Ethics Committee, School of Behavioral Sciences, Mahatma Gandhi University, Kottayam, Kerala, dated September 22, 2017. Results: Eighty-one pre-term <34 weeks babies belonged to the antenatal steroid complete group, 40 pre-term <34 weeks babies belonged to the antenatal steroid partial group, and 29 pre-term <34 weeks babies belonged to the nil steroid group. Total neurodevelopmental status score and domain-wise, cognitive, language, motor, and social-emotional developmental scores show significantly higher scores for pre-term <34 weeks with antenatal history of complete course steroid administered before pre-term delivery compared to partial and nil antenatal steroid group. Conclusions: Antenatal steroids are having a significant impact on the overall neurodevelopmental outcome of pre-term babies equal to or more than 25 weeks of gestation and <34 weeks of gestation, at their corrected 2 years of age, the maximum benefit for complete antenatal steroid group pre-term babies, equal to or more than 25 weeks and <34 completed weeks of gestation when compared with pre-term babies, equal to or more than 25 weeks and <34 completed weeks of gestation without any antenatal steroids.

Economic psychology for laymen

Neuroeconomics can now replace the Paradigm of Bounded Rationality (BR) by a rooting in positivist economic psychology. The key finding is that the neuroeconomic Model of Decision-Making (NeM), based on neuroeconomic trials, suspends subjectivism, identifying risk-willingness as a basal parameter of economic behavior. Further, NeM and behavioral psychology (Big5 Taxonomy) integrates correlation studies into an Economic Psychology that simplifies Sensi-training in the Big5 to persons without specialism in Psychology. Finally, this Economic Psychology implicates a new interdisciplinary outlook on three domains of Economics: Evidence of the Florida Thesis on the Rise of the Creative Class as a qualitative growth effect A positivist framework for modern stress-management by classical meditative in-depth relaxation Recognition of prejudices as a special problem of behavioral science due to the interaction of subject and object Discussion focuses on the significance to economic growth theory to abandon BR. The conclusion is, that the simple model of accounting real growth in GDP, must be replaced by a tripartite model already termed 3P in management research (Planet, Personnel and Profit) including negative growth factors, too. The 3P has a common outcome in Quality-Adjusted Life Years (QALY) which at the global level has a negative aggregated value in the 21 Century. So, political measures to counterbalance, for instance the damage to the climate, are discussed concluding an urgent need for a global CO2 Tariff (ET).

Design of a User-Centered Electronic Health Tool for Glomerular Disease Management

Introduction: Patients with primary glomerular disease (GN) have unique management needs. We describe the design of a user-centered, patient-facing electronic health (eHealth) tool to support GN management. Methods: We surveyed patients and GN expert nephrologists on disease management tasks, educational needs, and barriers and facilitators of eHealth tool use. Results were summarized and presented to patients, nephrologists, engineers, and a behavioral and implementation science expert in stakeholder meetings to jointly design an eHealth tool. Key themes from the meetings are described using rapid qualitative analysis. Results: Sixty-six patients with minimal change disease, focal segmental glomerulosclerosis, IgA nephropathy, and membranous nephropathy responded to the survey, as well as 25 nephrologists from the NIH-funded Cure Glomerulonephropathy study network. Overall, patients performed fewer management tasks and acknowledged fewer informational needs than recommended by nephrologists. Patients were more knowledgeable about eHealth tools than nephrologists. Nine patient stakeholders reflected on the survey findings and noted a lack of awareness of key recommended management tasks and receiving little guidance from nephrologists on using eHealth. Key themes and concepts from the stakeholder meetings about eHealth tool development included the need for customizable design, trustworthy sources, seamless integration with other apps and clinical workflow, and reliable data tracking. The final design of our eHealth tool, the UrApp System, has 5 core features: “Profile” generates personalized data tracking, educational information, facilitation with provider discussions and inputting other preferences; “Data Tracking” displays patient health data with the ability to communicate important trends to patients and nephrologists; “Resources” provides trusted education information in a personalized manner; “Calendar” displays key events and generate reminders; and “Journal” facilitates information documentation using written or audio notes. Conclusion: Our theory- and evidenced-based, stakeholder-engaged design process created designs for an eHealth tool to support the unique needs of patients with GN, optimized for effectiveness and implementation.

The critical factors in producing high quality and policy-relevant research: insights from international behavioural science units

Background: There has been a rapid increase in the number of, and demand for, organisations offering behavioural science advice to government over the last ten years. Yet we know little of the state of science and the experiences of these evidence providers. Aims and objectives: To identify current practice in this emerging field and the factors that impact on the production of high-quality and policy-relevant research. Methods: A qualitative study using one-to-one interviews with representatives from a purposeful sample of 15 units in the vanguard of international behavioural science research in policy. The data were analysed thematically. Findings: Relationships with policymakers were important in the inception of units, research conduct, implementation and dissemination of findings. Knowledge exchange facilitated a shared understanding of policy issues/context, and of behavioural science. Sufficient funding was crucial to maintain critical capacity in the units’ workforces, build a research portfolio beneficial to policymakers and the units, and to ensure full and transparent dissemination. Discussion and conclusion: Findings highlight the positive impact of strong evidence-provider/user relationships and the importance of governments’ commitment to co-produced research programmes to address policy problems and transparency in the dissemination of methods and findings. From the findings we have created a framework, ‘STEPS’ (Sharing, Transparency, Engagement, Partnership, Strong relationships), of five recommendations for units working with policymakers. These findings will be of value to all researchers conducting research on behalf of government.

Harmonizing human-AI synergy: behavioral science in AI-integrated design

AbstractThis paper explores the role of integrating behavioral science to refine human-AI interaction, essential for ensuring safety and efficiency. Advocating for empathetic, user-centric design, the paper illustrates how behavioral insights can effectively inform AI-integrated designs, making AI applications more intuitive and ethically aligned with diverse human needs. This approach can ultimately enrich interaction across systems, fostering a more harmonious human-AI synergy.

Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning

<p>In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electricity usage within specific time segments, this research employs an unsupervised machine learning method, developing an anomaly detection model for the overall electricity consumption behavior of residential users. The model focuses on analyzing 2-hour intervals of electricity consumption, enabling more effective detection of abnormal usage patterns. It is trained using power consumption data collected from five actual residential users as part of an experimental study. The results indicate that the proposed anomaly detection model achieves performance metrics such as Precision, Recall, and F1-score of 0.90 or above, showcasing its potential for practical implementation.</p> <p> </p>

REPORT: Some Opportunities and Complications in Relating to Federal Agencies: A Report on the 2023 Council of Representatives Meeting of the Federation of Associations in Brain and Behavioral Sciences

REPORT: Some Opportunities and Complications in Relating to Federal Agencies: A Report on the 2023 Council of Representatives Meeting of the Federation of Associations in Brain and Behavioral Sciences

Imagine moving behavioral science findings languishing in scholarly journals to public consumption

Imagine moving behavioral science findings languishing in scholarly journals to public consumption

Understanding veterinary prices: facts or feelings?

Veterinary fees can be a source of great pain, not just for clients, but also for the clinic teams that are communicating them. It's a common misconception that fees need to be lowered if we wish to make them feel more comfortable to clients, or to make their communication easier for teams; however, because the human mind perceives price in a very subjective way, there is a lot that can be done to help teams communicate them more confidently and to make them feel more comfortable to clients. An understanding of a few behavioural science principles can give veterinary teams the tools that they need to achieve this.

Dual-factor Models of Mental Health: A Systematic Review of Empirical Evidence.

Objective: Dual-factor models of mental health propose that mental health includes two interrelated yet distinct dimensions - psychopathology and well-being. However, there is no systematization of the evidence following these models. This review aims to address the following research question: what evidence exists using dual-factor models? Method: The current systematic review was conducted using PRISMA guidelines on the following databases: Web-of-science, Scopus, Academic Search Complete, APA PsycArticles, APA PsycInfo, Psychology and Behavioral Sciences Collection, ERIC, and MEDLINE. The screening process resulted in 85 manuscripts that tested the assumptions of dual-factor models. Results: Evidence revealed psychometric substantiation on the two-dimensionality of the dual-factor model, and 85% of the manuscripts provided evidence related to classifying participants into different mental health groups. Most studies showed that the Complete Mental Health or Positive Mental Health group is the most prevalent status group, and longitudinal evidence suggests that most participants (around 50%-64%) remain in the same group across time. Regarding the factors associated with mental health status groups, studies reviewed in this manuscript focus mainly on school-related outcomes, followed by supportive relationships, sociodemographic characteristics, psychological assets, individual attributes, physical health, and stressful events. Conclusions: This review highlights the importance of considering the two dimensions of mental health when conceptualizing, operationalizing, and measuring mental health. Fostering mental health must go beyond reducing symptoms, and practitioners would be able to include well-being-related interventions in their regular practice to improve individuals' mental health outcomes.

Proposing an SDGs education model: integrating design thinking and behavioral science “nudges” for high school students

AbstractThis study focuses on the development of a creative model of SDGs education that promotes behavior change in upper secondary schools using an approach that combines design thinking and behavioral science nudge methods. The SDGs education for high school students, which utilized design thinking, was used as a case study. Questionnaire surveys of high school students and teachers who participated in the project were conducted. Based on the survey results, a new education model was proposed that can effectively improve SDGs education at the high school level and support educators in this field.

Identifying barriers to outpatient appointment attendance in patient groups at risk of inequity: a mixed methods study in a London NHS trust.

There is significant health inequity in the United Kingdom (U.K.), with different populations facing challenges accessing health services, which can impact health outcomes. At one London National Health Service (NHS) Trust, data showed that patients from deprived areas and minority ethnic groups had a higher likelihood of missing their first outpatient appointment. This study's objectives were to understand barriers to specific patient populations attending first outpatient appointments, explore systemic factors and assess appointment awareness. Five high-volume specialties identified as having inequitable access based on ethnicity and deprivation were selected as the study setting. Mixed methods were employed to understand barriers to outpatient attendance, including qualitative semi-structured interviews with patients and staff, observations of staff workflows and interrogation of quantitative data on appointment communication. To identify barriers, semi-structured interviews were conducted with patients who missed their appointment and were from a minority ethnic group or deprived area. Staff interviews and observations were carried out to further understand attendance barriers. Patient interview data were analysed using inductive thematic analysis to create a thematic framework and triangulated with staff data. Subthemes were mapped onto a behavioural science framework highlighting behaviours that could be targeted. Quantitative data from patient interviews were analysed to assess appointment awareness and communication. Twenty-six patients and 11 staff were interviewed, with four staff observed. Seven themes were identified as barriers - communication factors, communication methods, healthcare system, system errors, transport, appointment, and personal factors. Knowledge about appointments was an important identified behaviour, supported by eight out of 26 patients answering that they were unaware of their missed appointment. Environmental context and resources were other strongly represented behavioural factors, highlighting systemic barriers that prevent attendance. This study showed the barriers preventing patients from minority ethnic groups or living in deprived areas from attending their outpatient appointment. These barriers included communication factors, communication methods, healthcare the system, system errors, transport, appointment, and personal factors. Healthcare services should acknowledge this and work with public members from these communities to co-design solutions supporting attendance. Our work provides a basis for future intervention design, informed by behavioural science and community involvement.