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.

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.

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.

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.

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.

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.

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.

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.

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.

How behavioural science can contribute to health partnerships: the case of The Change Exchange.

BACKGROUND: Health partnerships often use health professional training to change practice with the aim of improving quality of care. Interventions to change practice can learn from behavioural science and focus not only on improving the competence and capability of health professionals but also their opportunity and motivation to make changes in practice. We describe a project that used behavioural scientist volunteers to enable health partnerships to understand and use the theories, techniques and assessments of behavioural science. CASE STUDIES: This paper outlines how The Change Exchange, a collective of volunteer behavioural scientists, worked with health partnerships to strengthen their projects by translating behavioural science in situ. We describe three case studies in which behavioural scientists, embedded in health partnerships in Uganda, Sierra Leone and Mozambique, explored the behaviour change techniques used by educators, supported knowledge and skill development in behaviour change, monitored the impact of projects on psychological determinants of behaviour and made recommendations for future project developments. DISCUSSION: Challenges in the work included having time and space for behavioural science in already very busy health partnership schedules and the difficulties in using certain methods in other cultures. Future work could explore other modes of translation and further develop methods to make them more culturally applicable. CONCLUSION: Behavioural scientists could translate behavioural science which was understood and used by the health partnerships to strengthen their project work.

Developing nurse medication safety training in a health partnership in Mozambique using behavioural science.

BACKGROUND: Globally, safe and effective medication administration relies on nurses being able to apply strong drug calculation skills in their real-life practice, in the face of stressors and distractions. These may be especially prevalent for nurses in low-income countries such as Mozambique and Continuing Professional Development post-registration may be important. This study aimed to 1) explore the initial impact of an international health partnership's work to develop a drug calculation workshop for nurses in Beira, Mozambique and 2) reflect upon the role of health psychologists in helping educators apply behavioural science to the training content and evaluation. METHODS: In phase one, partners developed a training package, which was delivered to 87 Portuguese-speaking nurses. The partnership's health psychologists coded the training's behaviour change content and recommended enhancements to content and delivery. In phase two, the refined training, including an educational game, was delivered to 36 nurses in Mozambique and recoded by the health psychologists. Measures of participant confidence and intentions to make changes to healthcare practice were collected, as well as qualitative data through post-training questions and 12 short follow-up participant interviews. RESULTS: In phase one six BCTs were used during the didactic presentation. Most techniques targeted participants' capability to calculate drug doses accurately; recommendations aimed to increase participants' motivation and perceived opportunity, two other drivers of practice change. Phase two training included an extra seven BCTs, such as action planning and further skills practice. Participants reported high confidence before and after the training (p = 0.25); intentions to use calculators to check drug calculations significantly increased (p = 0.031). Qualitative data suggested the training was acceptable, enjoyable and led to practice changes, through improved capability, opportunity and motivation. Opportunity barriers to medication safety were highlighted. CONCLUSIONS: Reporting and measuring medication errors and related outcomes is a complex challenge affecting global efforts to improve medication safety. Through strong partnership working, a multi-disciplinary team of health professionals including health psychologists developed, refined and begin to evaluate a locally-led drug calculation CPD workshop for nurses in a low-resource setting. Applying behavioural science helped to collect feasible evaluation data and hopefully improved impact and sustainability.

A call to improve sampling methodology and reporting in young novice driver research.

Young drivers continue to be over-represented in road crash fatalities despite a multitude of research, communication and intervention. Evidence-based improvement depends to a great extent upon research methodology quality and its reporting, with known limitations in the peer-review process. The aim of the current research was to review the scope of research methodologies applied in 'young driver' and 'teen driver' research and their reporting in four peer-review journals in the field between January 2006 and December 2013. In total, 806 articles were identified and assessed. Reporting omissions included participant gender (11% of papers), response rates (49%), retention rates (39%) and information regarding incentives (44%). Greater breadth and specific improvements in study designs and reporting are thereby identified as a means to further advance the field.

Physician Assimilation in Medical Schools: Dualisms of Biomedical and Biopsychosocial Ideologies in the Discourse of Physician Educators.

Although health communication research and popular literature on physicians have heightened awareness of the dualisms physicians face, research is yet to focus on the discourse of physician educators who assimilate students into medicine for dualisms of the biomedical (BMD) and biopsychosocial (BPS) ideologies. The study drew on a dualism-centered model to analyze the discourse of 19 behavioral science course directors at 10 medical schools for the emergence of dualisms in instantiations of BPS ideologies and for the management of dualism in discourse that instantiated both BMD and BPS ideologies as part of the curriculum. Dualism emerged in the BPS ideologies of "patient-centeredness" and "cultural competence." While a dualism between "patients' data" and "patients' stories" emerged in the patient-centeredness ideology, a dualism between enhancing "interaction skill" and "understanding" emerged in the cultural competence ideology. Moreover, the study found educator discourse managing dualism between BMD and BPS ideologies through the strategies of "connection" and "separation." The study concludes with a discussion and the implications for theory and research.

Improving periodontal outcomes: merging clinical and behavioral science.

New data indicate that periodontal diseases are much more prevalent than previously thought, which means that there are large numbers of patients who will need to be diagnosed and treated for periodontal disease in a general dental practice. Oral hygiene procedures performed by patients between office visits are important for gingival health. No particular type of toothbrush has consistently been shown to have superior plaque-removal ability over another. Although studies on powered brushes have shown evidence for efficacy of biofilm removal and increased patient compliance, they are of short duration, making evaluation of long-term effects difficult to achieve. Interdental cleaning with dental floss can be effective but it is technique-sensitive. Interdental brushes have been shown to be superior to floss in plaque index scores, but not in gingival inflammation reduction. A systematic review of oral irrigation reported a beneficial adjunctive effect on bleeding and gingival indices and pocket depth. Antimicrobials in mouthrinses and toothpastes have shown significant reductions in plaque and gingivitis when used correctly. Even though it is considered essential for patients to utilize biofilm-removal techniques on a frequent basis, studies on adherence show that approximately 30-60% of health information is forgotten within 1 h, and 50% of health recommendations are not followed. Incorporating psychosocial aspects of behavioral change, including well-established counseling strategies, such as motivational interviewing, may elicit improved patient outcomes.

Some Behaviorial Science Measurement Concerns and Proposals.

Primarily from a measurement standpoint, we question some basic beliefs and procedures characterizing the scientific study of human behavior. The relations between observed and unobserved variables are key to an empirical approach to building explanatory theories and we are especially concerned about how the former are used as proxies for the latter. We believe that behavioral science can profitably reconsider the prevailing version of this arrangement because of its vulnerability to limiting idiosyncratic aspects of observed/unobserved variable relations. We describe a general measurement approach that takes into account idiosyncrasies that should be irrelevant to the measurement process but can intrude and may invalidate it in ways that distort and weaken relations among theoretically important variables. To clarify further our major concerns, we briefly describe one version of the measurement approach that fundamentally supports the individual as the primary unit of analysis orientation that we believe should be preeminent in the scientific study of human behavior.

Outcome and Efficacy of Interventions by a Public Figure Threat Assessment and Management Unit: A Mirrored Study of Concerning Behaviors and Police Contacts Before and After Intervention.

Specialized units for the assessment and management of concerning behaviors towards public figures have been set up in various jurisdictions. Their efficacy has been demonstrated descriptively and in terms of reduction in concern rates. This study of 100 consecutive cases from the Fixated Threat Assessment Centre (FTAC) in the UK uses a novel measure of outcome in the form of reduction in behaviors of concern and in police call-outs/stops, using data culled from police and health service records. It adopts a mirrored design, comparing individuals over 12-month and 2-year periods before and after FTAC intervention. It demonstrates significant reductions in both numbers of individuals involved in, and number of actual incidents of, concerning communication and problematic approach, as well as police call-outs/stops. Most results are consistent across subgroups with regard to gender, previous convictions, concern level, compulsory hospitalization and grievance-driven behavior. Such threat assessment units reduce risky behavior and save police time and, possibly, costs. Copyright © 2016 John Wiley & Sons, Ltd.

The Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) 2016 Statement.

Reporting guidelines, such as the Consolidated Standards of Reporting Trials (CONSORT) Statement, improve the reporting of research in the medical literature (Turner et al., 2012). Many such guidelines exist, and the CONSORT Extension to Nonpharmacological Trials (Boutron et al., 2008) provides suitable guidance for reporting between-groups intervention studies in the behavioral sciences. The CONSORT Extension for N-of-1 Trials (CENT 2015) was developed for multiple crossover trials with single individuals in the medical sciences (Shamseer et al., 2015; Vohra et al., 2015), but there is no reporting guideline in the CONSORT tradition for single-case research used in the behavioral sciences. We developed the Single-Case Reporting guideline In Behavioral interventions (SCRIBE) 2016 to meet this need. This Statement article describes the methodology of the development of the SCRIBE 2016, along with the outcome of 2 Delphi surveys and a consensus meeting of experts. We present the resulting 26-item SCRIBE 2016 checklist. The article complements the more detailed SCRIBE 2016 Explanation and Elaboration article (Tate et al., 2016) that provides a rationale for each of the items and examples of adequate reporting from the literature. Both these resources will assist authors to prepare reports of single-case research with clarity, completeness, accuracy, and transparency. They will also provide journal reviewers and editors with a practical checklist against which such reports may be critically evaluated.