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

Detection of solvents using a distributed fibre optic sensor

A fibre optic sensor that is capable of distributed detection of liquid solvents is presented. Sensor interrogation using optical time domain reflectometry (OTDR) provides the capability of locating solvent spills to a precision of ±2 m over a total sensor length that may extend to 20 km

Exploratory Analyses of the Popularity and Efficacy of Four Behavioral Methods of Gradual Smoking Cessation

Introduction: Around half of smokers attempt to stop by cutting-down first. Evidence suggests that this results in similar quit rates to abrupt quitting. Evidence for the effectiveness and popularity of different gradual cessation methods is sparse. / Methods: Secondary, exploratory, analyses of a randomized trial of gradual versus abrupt smoking cessation. Gradual participants (N = 342) chose between four methods of cutting-down over 2 weeks: cutting-out the easiest cigarettes first (HR-E); cutting-out the most difficult cigarettes first (HR-D); smoking on an increasing time schedule (SR); and not smoking during particular periods (SFP). Nicotine replacement therapy and behavioral support were provided before and after quit day. We used logistic and linear regression modeling to test whether the method chosen was associated with smoking reduction, quit attempts, and abstinence, while adjusting for potential confounders. / Results: Participants were on average 49 years old, smoked 20 cigarettes per day, and had a Fagerstrom Test for Cigarette Dependence score of 6. 14.9% (51/342) chose HR-E, 2.1% (7/342) HR-D, 46.2% (158/342) SFP, and 36.8% (126/342) SR. We found no evidence of adjusted or unadjusted associations between method and successful 75% reduction in cigarette consumption, reduction in percentage cigarettes per day or exhaled carbon monoxide, quit attempts, or abstinence at 4-week or 6-month follow-up. / Conclusions: Future research and practice could focus more heavily on the SR and SFP methods as these appeared notably more popular than HR. There was substantial imprecision in the efficacy data, which should be treated with caution; however, none of the gradual cessation methods showed clear evidence of being more efficacious than others. Implications: There is evidence that people who would like to quit smoking gradually should be supported to do so. However, as this is relatively new thinking and there is large potential for variation in methods, guidance on the best way to offer support is sparse. This article is an exploratory analysis of the popularity and efficacy of various methods in an attempt to move the topic forward and inform the implementation of gradual smoking cessation methods in practice. The identified popularity of some methods over others signposts directions for future research

Increasing condom use in heterosexual men: development of a theory-based interactive digital intervention

Increasing condom use to prevent sexually transmitted infections is a key public health goal. Interventions are more likely to be effective if they are theory- and evidence-based. The Behaviour Change Wheel (BCW) provides a framework for intervention development. To provide an example of how the BCW was used to develop an intervention to increase condom use in heterosexual men (the MenSS website), the steps of the BCW intervention development process were followed, incorporating evidence from the research literature and views of experts and the target population. Capability (e.g. knowledge) and motivation (e.g. beliefs about pleasure) were identified as important targets of the intervention. We devised ways to address each intervention target, including selecting interactive features and behaviour change techniques. The BCW provides a useful framework for integrating sources of evidence to inform intervention content and deciding which influences on behaviour to target

Individual Investors and Portfolio Diversification in Late Victorian Britain: How Diversified Were Victorian Financial Portfolios?

This article investigates Victorian investor financial portfolio strategies in England and Wales during the second half of the nineteenth century. We find that investors held on average about half of their gross wealth in the form of four or five liquid financial securities, but were reluctant to adopt fully contemporary financial advice to invest equal amounts in securities or to spread risk across the globe. They generally held under-diversified portfolios and proximity to their investments may have been an alternative to diversification as a means of risk reduction, especially for the less wealthy

Developing theory-informed interventions to implement evidence into practice: a systematic approach using the Theoretical Domains Framework

Background: There is little systematic operational guidance about how best to develop complex interventions to reduce the gap between practice and evidence. This article is one in a series of articles documenting the development and use of the Theoretical Domains Framework (TDF) to advance the science of implementation research. Methods: The intervention was developed considering three main components: theory, evidence, and practical issues. We used a four-step approach, consisting of guiding questions, to direct the choice of the most appropriate components of an implementation intervention: Who needs to do what, differently? Using a theoretical framework, which barriers and enablers need to be addressed? Which intervention components (behaviour change techniques and mode(s) of delivery) could overcome the modifiable barriers and enhance the enablers? And how can behaviour change be measured and understood? Results: A complex implementation intervention was designed that aimed to improve acute low back pain management in primary care. We used the TDF to identify the barriers and enablers to the uptake of evidence into practice and to guide the choice of intervention components. These components were then combined into a cohesive intervention. The intervention was delivered via two facilitated interactive small group workshops. We also produced a DVD to distribute to all participants in the intervention group. We chose outcome measures in order to assess the mediating mechanisms of behaviour change. Conclusions: We have illustrated a four-step systematic method for developing an intervention designed to change clinical practice based on a theoretical framework. The method of development provides a systematic framework that could be used by others developing complex implementation interventions. While this framework should be iteratively adjusted and refined to suit other contexts and settings, we believe that the four-step process should be maintained as the primary framework to guide researchers through a comprehensive intervention development process

Evidence-based selection of theories for designing behaviour change interventions : using methods based on theoretical construct domains to understand clinicians' blood transfusion behaviour

Peer reviewedPostprin

Effective Feedback to Improve Primary Care Prescribing Safety (EFIPPS) a pragmatic three-arm cluster randomised trial:designing the intervention (ClinicalTrials.gov registration NCT01602705)

Peer reviewedPublisher PD

Short-term effects of announcing revised lower risk national drinking guidelines on related awareness and knowledge: A trend analysis of monthly survey data in England.

Objectives: To evaluate short-term effects of publishing revised lower-risk national drinking guidelines on related awareness and knowledge. To examine where drinkers heard about guidelines over the same period. Design: Trend analysis of the Alcohol Toolkit Study, a monthly repeat cross-sectional national survey. Setting: England, November 2015 to May 2016. Participants: A total of 11,845 adults (18+) living in private households in England Intervention: Publication of revised national drinking guidelines in January 2016 which reduced the male guideline by approximately one-third to 14 units per week. Measurements: Whether drinkers (i) had heard of drinking guidelines (awareness), (ii) stated the guideline was above, exactly or below 14 units (knowledge), and (iii) reported seeing the stated guideline number of units in the last month in each of 11 locations (exposure). Sociodemographics: sex, age (18-34, 35-64, 65+), social grade (AB, C1C2, DE). Alcohol consumption derived from graduated frequency questions: low risk (<14 units/week), increasing/high risk (14+ units/week). Results: Following publication of the guidelines, the proportion of drinkers aware of guidelines did not increase from its baseline level of 85.1% (CI:82.7-87.1). However, the proportion of male drinkers saying the guideline was 14 units or less increased from 22.6% (CI:18.9-26.7) in December to 43.3% (CI:38.9-47.8) in January and was at 35.6% (CI:31.6-39.9) in May. Last month exposure to the guidelines was below 25% in all locations except television/radio where exposure increased from 33% (CI:28.8-36.2) in December to 65% (CI:61.2-68.3) in January. Awareness and knowledge of guidelines was lowest in social grade DE and this gap remained after publication. Conclusions: Publication of new or revised lower risk drinking guidelines can improve drinkers’ knowledge of these guidelines within all sociodemographic groups; however, in the absence of sustained promotional activity, positive effects may not be maintained and social inequalities in awareness and knowledge of guidelines are likely to persist