scholarly journals Bayesian evaluation of behavior change interventions: A brief introduction and a practical example

2017 ◽  
Author(s):  
Matti Toivo Juhani Heino ◽  
Matti Vuorre ◽  
Nelli Hankonen
Keyword(s):  
Health Psychology ◽  
Behavior Change ◽  
Statistical Methods ◽  
Hierarchical Modeling ◽  
Impact Behavior ◽  
Basic Principles ◽  
Bayesian Hierarchical ◽  
Software Packages ◽  
Behavior Change Interventions ◽  
Central Interest

Introduction: Evaluating effects of behavior change interventions is a central interest in health psychology and behavioral medicine. Researchers in these fields routinely use frequentist statistical methods to evaluate the extent to which these interventions impact behavior and the hypothesized mediating processes in the population. However, calls to move beyond exclusive use of frequentist reasoning are now widespread in psychology and allied fields. We suggest adding Bayesian statistical methods to the researcher’s toolbox of statistical methods. Objectives: We first present the basic principles of Bayesian approach to statistics and why they are useful for researchers in health psychology. We then provide a practical example on how to evaluate intervention effects using Bayesian methods, with a focus on Bayesian hierarchical modeling. We provide the necessary materials for introductory level readers to follow the tutorial. Conclusion: Bayesian analytical methods are now available to researchers through easy-to-use software packages, and we recommend using them to evaluate the effectiveness of interventions for their conceptual and practical benefits.

scholarly journals An iterative, interdisciplinary, collaborative framework for developing and evaluating digital behavior change interventions

2019 ◽  
Author(s):  
Madalina Sucala ◽  
Nnamdi Peter Ezeanochie ◽  
Heather Cole-Lewis ◽  
Jennifer Turgiss
Keyword(s):  
Behavior Change ◽  
Data Science ◽  
Intervention Development ◽  
Evaluation Methods ◽  
Rapid Expansion ◽  
Impact Behavior ◽  
Science Field ◽  
Technology Industry ◽  
Behavior Change Interventions ◽  
Behavior Science

Abstract The rapid expansion of technology promises to transform the behavior science field by revolutionizing the ways in which individuals can monitor and improve their health behaviors. To fully live into this promise, the behavior science field must address distinct challenges, including: building interventions that are not only scientifically sound but also engaging; using evaluation methods to precisely assess intervention components for intervention optimization; and building personalized interventions that acknowledge and adapt to the dynamic ecosystem of individual and contextual variables that impact behavior change. The purpose of this paper is to provide a framework to address these challenges by leveraging behavior science, human-centered design, and data science expertise throughout the cycle of developing and evaluating digital behavior change interventions (DBCIs). To define this framework, we reviewed current models and practices for intervention development and evaluation, as well as technology industry models for product development. The framework promotes an iterative process, aiming to maximize outcomes by incorporating faster and more frequent testing cycles into the lifecycle of a DBCI. Within the framework provided, we describe each phase, from development to evaluation, to discuss the optimal practices, necessary stakeholders, and proposed evaluation methods. The proposed framework may inform practices in both academia and industry, as well as highlight the need to offer collaborative platforms to ensure successful partnerships that can lead to more effective DBCIs that reach broad and diverse populations.

scholarly journals We need reproducible health behavior change interventions to help adults with severe mental illness

2018 ◽  
Author(s):  
Paquito Bernard ◽  
Samuel St-Amour ◽  
Celia Kingsbury ◽  
Ahmed Jerome Romain
Keyword(s):  
Health Psychology ◽  
Mental Illness ◽  
Health Behavior ◽  
Behavior Change ◽  
Behavioral Medicine ◽  
Severe Mental Illness ◽  
Interdisciplinary Collaboration ◽  
Health Behavior Change ◽  
Behavior Change Interventions

In conclusion, future HB change interventions for adults with severe metal illness should require more interdisciplinary collaboration between behavioral medicine, health psychology and psychiatry.

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

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

Abstract 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.

Behavior change interventions in patients with type 2 diabetes: a systematic review of the effects on self-management and A1c

2021 ◽  
Author(s):  
Ana Paula Delgado Bomtempo Batalha ◽  
Isabela Coelho Ponciano ◽  
Gabriela Chaves ◽  
Diogo Carvalho Felício ◽  
Raquel Rodrigues Britto ◽  
...  
Keyword(s):  
Systematic Review ◽  
Type 2 Diabetes ◽  
Behavior Change ◽  
Self Management ◽  
Behavior Change Interventions

A Scoping Review of Behavior Change Interventions to Decrease Health Care Disparities for Patients With Disabilities in a Primary Care Setting: Can Social Marketing Play a Role?

2021 ◽  
Vol 27 (1) ◽  
pp. 48-63
Author(s):  
Angela Makris ◽  
Mahmooda Khaliq ◽  
Elizabeth Perkins
Keyword(s):  
Primary Care ◽  
Health Care ◽  
Behavior Change ◽  
Social Marketing ◽  
Health Care Disparities ◽  
Social Model ◽  
Care Providers ◽  
Social Model Of Disability ◽  
Behavior Change Interventions ◽  
The Social

Background: One in four Americans have a disability but remain an overlooked minority population at risk for health care disparities. Adults with disabilities can be high users of primary care but often face unmet needs and poor-quality care. Providers lack training, knowledge and have biased practices and behaviors toward people with disabilities (PWD); which ultimately undermines their quality of care. Focus of the Article: The aim is to identify behavior change interventions for decreasing health care disparities for people with disabilities in a healthcare setting, determine whether those interventions used key features of social marketing and identify gaps in research and practice. Research Question: To what extent has the social marketing framework been used to improve health care for PWD by influencing the behavior of health care providers in a primary health care setting? Program Design/Approach: Scoping Review. Importance to the Social Marketing Field: Social marketing has a long and robust history in health education and public health promotion, yet limited work has been done in the disabilities sector. The social marketing framework encompasses the appropriate features to aligned with the core principles of the social model of disability, which espouses that the barriers for PWD lie within society and not within the individual. Incorporating elements of the social model of disability into the social marketing framework could foster a better understanding of the separation of impairment and disability in the healthcare sector and open a new area of research for the field. Results: Four articles were found that target primary care providers. Overall, the studies aimed to increase knowledge, mostly for clinically practices and processes, not clinical behavior change. None were designed to capture if initial knowledge gains led to changes in behavior toward PWD. Recommendations: The lack of published research provides an opportunity to investigate both the applicability and efficacy of social marketing in reducing health care disparities for PWD in a primary care setting. Integrating the social model of disability into the social marketing framework may be an avenue to inform future interventions aimed to increase health equity and inclusiveness through behavior change interventions at a systems level.

scholarly journals Variance prior specification for a basket trial design using Bayesian hierarchical modeling

2018 ◽  
Vol 16 (2) ◽  
pp. 142-153 ◽  
Author(s):  
Kristen M Cunanan ◽  
Alexia Iasonos ◽  
Ronglai Shen ◽  
Mithat Gönen
Keyword(s):  
Standard Deviation ◽  
Prior Distribution ◽  
Hierarchical Modeling ◽  
Bayesian Hierarchical Modeling ◽  
Bayesian Hierarchical ◽  
Inverse Gamma ◽  
Wide Range ◽  
Basket Trial ◽  
Basket Trials ◽  
Variance Parameter

Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. Methods: A common approach used to capture the correlated binary endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a non-randomized basket trial and investigate three popular prior specifications: an inverse-gamma prior on the basket-level variance, a uniform prior and half-t prior on the basket-level standard deviation. Results: From our simulation study, we can see that the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero [Formula: see text], this can lead to unacceptably high false-positive rates [Formula: see text] in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that places sufficient mass in the tail, such as the uniform or half-t prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior and the scale parameter of the half-t prior must be larger than 1. Conclusion: Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior or half-t prior on the standard deviation.

scholarly journals Catalyzing sustainable fisheries management through behavior change interventions

2020 ◽  
Vol 34 (5) ◽  
pp. 1176-1189 ◽  
Author(s):  
Gavin McDonald ◽  
Molly Wilson ◽  
Diogo Veríssimo ◽  
Rebecca Twohey ◽  
Michaela Clemence ◽  
...  
Keyword(s):  
Behavior Change ◽  
Fisheries Management ◽  
Sustainable Fisheries ◽  
Behavior Change Interventions

Training commercial energy users in behavior change: A case study

2020 ◽  
Author(s):  
Sea Rotmann ◽  
Beth Karlin
Keyword(s):  
Behavior Change ◽  
Behavioral Interventions ◽  
Professional Training ◽  
Energy Use ◽  
Behavioral Science ◽  
Research Process ◽  
Commercial Sector ◽  
Great Opportunity ◽  
Behavior Change Interventions ◽  
Commercial Energy

Within the commercial sector, energy managers and building operators have a large impact over their organizations’ energy use. However, they mostly focus on technology solutions and retrofits, rather than human or corporate behaviors, and how to change them. This gap in targeted commercial sector research and behavioral interventions provides a great opportunity which is currently not being addressed. This paper presents a field research pilot where an empirical behavior change research process was applied and taught to commercial energy users in Ontario, Canada. This course served to fill an identified market gap and to improve commercial energy managers’ literacy in behavioral science theory and techniques. A needs assessment identified a clear gap in behavioral training for energy managers, and high interest in the course further proved out the market opportunity for professional training on how to design, implement and evaluate behavior change interventions. Evaluation results identified positive feedback in terms of course reaction, self-reported learning and behavioral outcomes, and tangible results when course participants returned to work to apply their learnings. Evaluation results suggest that such training fills a vital gap in the current Strategic Energy Management (SEM) landscape, and could unlock significant savings in the commercial energy sector.

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