A synthesis of evidence for policy from behavioural science during COVID-19.

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Open-source, Python-based, hardware and software for controlling behavioural neuroscience experiments.

Laboratory behavioural tasks are an essential research tool. As questions asked of behaviour and brain activity become more sophisticated, the ability to specify and run richly structured tasks becomes more important. An increasing focus on reproducibility also necessitates accurate communication of task logic to other researchers. To these ends, we developed pyControl, a system of open-source hardware and software for controlling behavioural experiments comprising a simple yet flexible Python-based syntax for specifying tasks as extended state machines, hardware modules for building behavioural setups, and a graphical user interface designed for efficiently running high-throughput experiments on many setups in parallel, all with extensive online documentation. These tools make it quicker, easier, and cheaper to implement rich behavioural tasks at scale. As important, pyControl facilitates communication and reproducibility of behavioural experiments through a highly readable task definition syntax and self-documenting features. Here, we outline the system's design and rationale, present validation experiments characterising system performance, and demonstrate example applications in freely moving and head-fixed mouse behaviour.

Megastudies improve the impact of applied behavioural science.

Policy-makers are increasingly turning to behavioural science for insights about how to improve citizens' decisions and outcomes1. Typically, different scientists test different intervention ideas in different samples using different outcomes over different time intervals2. The lack of comparability of such individual investigations limits their potential to inform policy. Here, to address this limitation and accelerate the pace of discovery, we introduce the megastudy-a massive field experiment in which the effects of many different interventions are compared in the same population on the same objectively measured outcome for the same duration. In a megastudy targeting physical exercise among 61,293 members of an American fitness chain, 30 scientists from 15 different US universities worked in small independent teams to design a total of 54 different four-week digital programmes (or interventions) encouraging exercise. We show that 45% of these interventions significantly increased weekly gym visits by 9% to 27%; the top-performing intervention offered microrewards for returning to the gym after a missed workout. Only 8% of interventions induced behaviour change that was significant and measurable after the four-week intervention. Conditioning on the 45% of interventions that increased exercise during the intervention, we detected carry-over effects that were proportionally similar to those measured in previous research3-6. Forecasts by impartial judges failed to predict which interventions would be most effective, underscoring the value of testing many ideas at once and, therefore, the potential for megastudies to improve the evidentiary value of behavioural science.

B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors.

Studying naturalistic animal behavior remains a difficult objective. Recent machine learning advances have enabled limb localization; however, extracting behaviors requires ascertaining the spatiotemporal patterns of these positions. To provide a link from poses to actions and their kinematics, we developed B-SOiD - an open-source, unsupervised algorithm that identifies behavior without user bias. By training a machine classifier on pose pattern statistics clustered using new methods, our approach achieves greatly improved processing speed and the ability to generalize across subjects or labs. Using a frameshift alignment paradigm, B-SOiD overcomes previous temporal resolution barriers. Using only a single, off-the-shelf camera, B-SOiD provides categories of sub-action for trained behaviors and kinematic measures of individual limb trajectories in any animal model. These behavioral and kinematic measures are difficult but critical to obtain, particularly in the study of rodent and other models of pain, OCD, and movement disorders.

Everything counts in sending the right message: science-based messaging implications from the 2020 WHO guidelines on physical activity and sedentary behaviour.

The World Health Organization (WHO) released the 2020 global guidelines on physical activity and sedentary behaviour. The new guidelines contain a significant change from the 2010 guidelines on physical activity for adults and older adults that has important implications for next-generation physical activity messaging: The removal of the need for aerobic activity to occur in bouts of at least 10 min duration. This change in the guidelines provides an opportunity to communicate in new ways that align with behavioural science, permitting physical activity communicators and promoters to better support people's psychological needs, motivation, and ability to fit healthy levels of physical activity into their lives. The frames and messages we use to communicate about the guidelines matter because they influence whether activity is perceived as relevant, meaningful, and feasible - or not. When developing new physical activity communications there are some overarching principles, based on behavioural science, to keep in mind. Using established theory, this commentary aims to support the creation of more strategic frames and messages for increasing the value and integration of physical activity into daily living. Country-specific physical activity campaigns using these ideas will be discussed.

Scaling up behavioral science interventions in online education.

Online education is rapidly expanding in response to rising demand for higher and continuing education, but many online students struggle to achieve their educational goals. Several behavioral science interventions have shown promise in raising student persistence and completion rates in a handful of courses, but evidence of their effectiveness across diverse educational contexts is limited. In this study, we test a set of established interventions over 2.5 y, with one-quarter million students, from nearly every country, across 247 online courses offered by Harvard, the Massachusetts Institute of Technology, and Stanford. We hypothesized that the interventions would produce medium-to-large effects as in prior studies, but this is not supported by our results. Instead, using an iterative scientific process of cyclically preregistering new hypotheses in between waves of data collection, we identified individual, contextual, and temporal conditions under which the interventions benefit students. Self-regulation interventions raised student engagement in the first few weeks but not final completion rates. Value-relevance interventions raised completion rates in developing countries to close the global achievement gap, but only in courses with a global gap. We found minimal evidence that state-of-the-art machine learning methods can forecast the occurrence of a global gap or learn effective individualized intervention policies. Scaling behavioral science interventions across various online learning contexts can reduce their average effectiveness by an order-of-magnitude. However, iterative scientific investigations can uncover what works where for whom.

mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences.

Behavioral scientists are developing new methods and frameworks that leverage mobile health technologies to optimize individual level behavior change. Pervasive sensors and mobile apps allow researchers to passively observe human behaviors "in the wild" 24/7 which supports delivery of personalized interventions in the real-world environment. This is all possible because these technologies contain an incredible array of sensors that allow applications to constantly record user location and can contextualize current environmental conditions through barometers, thermometers, and ambient light sensors and can also capture audio and video of the user and their surroundings through multiple integrated high-definition cameras and microphones. These tools are a game changer in behavioral health research and, not surprisingly, introduce new ethical, regulatory/legal and social implications described in this article.

Scaling up psychology via Scientific Regret Minimization.

Do large datasets provide value to psychologists? Without a systematic methodology for working with such datasets, there is a valid concern that analyses will produce noise artifacts rather than true effects. In this paper, we offer a way to enable researchers to systematically build models and identify novel phenomena in large datasets. One traditional approach is to analyze the residuals of models-the biggest errors they make in predicting the data-to discover what might be missing from those models. However, once a dataset is sufficiently large, machine learning algorithms approximate the true underlying function better than the data, suggesting, instead, that the predictions of these data-driven models should be used to guide model building. We call this approach "Scientific Regret Minimization" (SRM), as it focuses on minimizing errors for cases that we know should have been predictable. We apply this exploratory method on a subset of the Moral Machine dataset, a public collection of roughly 40 million moral decisions. Using SRM, we find that incorporating a set of deontological principles that capture dimensions along which groups of agents can vary (e.g., sex and age) improves a computational model of human moral judgment. Furthermore, we are able to identify and independently validate three interesting moral phenomena: criminal dehumanization, age of responsibility, and asymmetric notions of responsibility.

Psychosocial aspects of diabetes technology.

AIM: To identify key psychosocial research in the domain of diabetes technology. RESULTS: Four trajectories of psychosocial diabetes technology research are identified that characterize research over the past 25 years. Key evidence is reviewed on psychosocial outcomes of technology use as well as psychosocial barriers and facilitating conditions of diabetes technology uptake. Psychosocial interventions that address modifiable barriers and psychosocial factors have proven to be effective in improving glycaemic and self-reported outcomes in diabetes technology users. CONCLUSIONS: Psychosocial diabetes technology research is essential for designing interventions and education programmes targeting the person with diabetes to facilitate optimized outcomes associated with technology uptake. Psychosocial aspects of diabetes technology use and related research will be even more important in the future given the advent of systems for automated insulin delivery and the increasingly widespread digitalization of diabetes care.

Induction of reversible bidirectional social approach bias by olfactory conditioning in male mice.

Social avoidance is a common component of neuropsychiatric disorders that confers substantial functional impairment. An unbiased approach to identify brain regions and neuronal circuits that regulate social avoidance might enable development of novel therapeutics. However, most paradigms that alter social avoidance are irreversible and accompanied by multiple behavioral confounds. Here we report a straightforward behavioral paradigm in male mice enabling the reversible induction of social avoidance or approach with temporal control. C57BL/6J mice repeatedly participated in both negative and positive social experiences. Negative social experience was induced by brief social defeat by an aggressive male CD-1 mouse, while positive social experience was induced by exposure to a female mouse, each conducted daily for five days. Each social experience valence was conducted in a specific odorant context (i.e. negative experience in odorant A, positive experience in odorant B). Odorants were equally preferred pre-conditioning. However, after conditioning, mice sniffed positive experience-paired odorants more than negative experience-paired odorants. Furthermore, positive- or negative-conditioned odorant contexts increased or decreased, respectively, the approach behavior of conditioned mice toward conspecifics. Because individual mice undergo both positive and negative conditioning, this paradigm may be useful to examine neural representations of social approach or avoidance within the same subject.

Sweet dreams or bitter nightmare: a narrative review of 25 years of research on the role of sleep in diabetes and the contributions of behavioural science.

The aim of this review was to provide an overview of developments, clinical implications and gaps in knowledge regarding the relationship between diabetes and sleep over the past 25 years, with special focus on contributions from the behavioural sciences. Multiple prospective observational and experimental studies have shown a link between suboptimal sleep and impaired glucose tolerance, decreased insulin sensitivity and the development of type 2 diabetes. While prevalence rates of suboptimal sleep vary widely according to definition, assessment and sample, suboptimal subjective sleep quality appears to be a common reality for one-third of people with type 1 diabetes and over half of people with type 2 diabetes. Both physiological and psychosocial factors may impair sleep in these groups. In turn, suboptimal sleep can negatively affect glycaemic outcomes directly or indirectly via suboptimal daytime functioning (energy, mood, cognition) and self-care behaviours. Technological devices supporting diabetes self-care may have both negative and positive effects. Diabetes and its treatment also affect the sleep of significant others. Research on the merits of interventions aimed at improving sleep for people with diabetes is in its infancy. Diabetes and sleep appear to be reciprocally related. Discussion of sleep deserves a central place in regular diabetes care. Multi-day, multi-method studies may shed more light on the complex relationship between sleep and diabetes at an individual level. Intervention studies are warranted to examine the potential of sleep interventions in improving outcomes for people with diabetes.

Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application.

BACKGROUND: Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. PURPOSES: By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). METHODS: The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. RESULTS: Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. CONCLUSIONS: AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

Psychosocial aspects and contributions of behavioural science to medication-taking for adults with type 2 diabetes.

The aim of this narrative review was to determine the contribution of behavioural and psychosocial research to the field of medication-taking for adults with type 2 diabetes over the past 25 years. We review the behavioural and psychosocial literature relevant to adults with type 2 diabetes who are treated with oral antidiabetes agents, glucagon-like peptide-1 receptor agonists and insulin. Delayed uptake of, omission of and non-persistence with medications are significant problems among adults with type 2 diabetes. At each stage of the course of diabetes, during which medication to lower blood glucose is initiated or intensified, ~50% of people take less medication than prescribed. Research aimed at increasing optimal medication-taking behaviour has targeted 'forgetfulness', developing interventions which aid medication-taking, such as reminder devices, with limited success. In parallel, investigation of beliefs about medication has provided insights into the perceived necessity of and concerns about medication and how these inform medication-taking decisions. Guidance is available for health professionals to facilitate shared decision-making, particularly with insulin therapy; however, interventions addressing medication beliefs are limited. Optimal medication-taking behaviour is essential to prevent hyperglycaemia in adults with type 2 diabetes. Evidence from the past 25 years has demonstrated the association between medication beliefs and medication-taking behaviour. Health professionals need to address medication concerns, and establish and demonstrate the utility of diabetes medication with the individual within the clinical consultation. There are interventions that may assist diabetes health professionals in the shared decision-making process, but further development and more robust evaluation of these tools and techniques is required.

Behaviour change in diabetes: behavioural science advancements to support the use of theory.

Behaviour is central to the management of diabetes, both for people living with diabetes and for healthcare professionals delivering evidence-based care. This review outlines the evolution of behavioural science and the application of theoretical models in diabetes care over the past 25 years. There has been a particular advancement in the development of tools and techniques to support researchers, healthcare professionals and policymakers in taking a theory-based approach, and to enhance the development, reporting and replication of successful interventions. Systematic guidance, theoretical frameworks and lists of behavioural techniques provide the tools to specify target behaviours, identify why ideal behaviours are not implemented, systematically develop theory-based interventions, describe intervention content using shared terminology, and evaluate their effects. Several examples from a range of diabetes-related behaviours (clinic attendance, self-monitoring of blood glucose, retinal screening, setting collaborative goals in diabetes) and populations (people with type 1 and type 2 diabetes, healthcare professionals) illustrate the potential for these approaches to be widely translated into diabetes care. The behavioural science approaches outlined in this review give healthcare professionals, researchers and policymakers the tools to deliver care and design interventions with an evidence-based understanding of behaviour. The challenge for the next 25 years is to refine the tools to increase their use and advocate for the role of theoretical models and behavioural science in the commissioning, funding and delivery of diabetes care.

Two open source designs for a low-cost operant chamber using Raspberry Pi™.

After almost a century of use and development, operant chambers remain a significant financial investment for scientists. Small powerful single-board computers such as the Raspberry Pi™ offer researchers a low-cost alternative to expensive operant chambers. In this paper, we describe two new operant chambers, one using nose-poke ports as operanda and another using a touchscreen. To validate the chamber designs, rats learned to perform both visual discrimination and delayed alternation tasks in each chamber. Designs and codes are open source and serve as a starting point for researchers to develop behavioral experiments or educational demonstrations.

How Replicable Are Links Between Personality Traits and Consequential Life Outcomes? The Life Outcomes of Personality Replication Project.

The Big Five personality traits have been linked to dozens of life outcomes. However, metascientific research has raised questions about the replicability of behavioral science. The Life Outcomes of Personality Replication (LOOPR) Project was therefore conducted to estimate the replicability of the personality-outcome literature. Specifically, I conducted preregistered, high-powered (median N = 1,504) replications of 78 previously published trait-outcome associations. Overall, 87% of the replication attempts were statistically significant in the expected direction. The replication effects were typically 77% as strong as the corresponding original effects, which represents a significant decline in effect size. The replicability of individual effects was predicted by the effect size and design of the original study, as well as the sample size and statistical power of the replication. These results indicate that the personality-outcome literature provides a reasonably accurate map of trait-outcome associations but also that it stands to benefit from efforts to improve replicability.