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.

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.

scholarly journals Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review (Preprint)

2017 ◽  
Author(s):  
Sheik Mohammad Roushdat Ally Elaheebocus ◽  
Mark Weal ◽  
Leanne Morrison ◽  
Lucy Yardley
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Behavior Change ◽  
Behavioral Interventions ◽  
Selection Process ◽  
Future Research ◽  
Behavioral Risk ◽  
Lack Of Information ◽  
Behavior Change Interventions ◽  
High Level

BACKGROUND Incorporating social media features into digital behavior change interventions (DBCIs) has the potential to contribute positively to their success. However, the lack of clear design principles to describe and guide the use of these features in behavioral interventions limits cross-study comparisons of their uses and effects. OBJECTIVE The aim of this study was to provide a systematic review of DBCIs targeting modifiable behavioral risk factors that have included social media features as part of their intervention infrastructure. A taxonomy of social media features is presented to inform the development, description, and evaluation of behavioral interventions. METHODS Search terms were used in 8 databases to identify DBCIs that incorporated social media features and targeted tobacco smoking, diet and nutrition, physical activities, or alcohol consumption. The screening and review process was performed by 2 independent researchers. RESULTS A total of 5264 articles were screened, and 143 articles describing a total of 134 studies were retained for full review. The majority of studies (70%) reported positive outcomes, followed by 28% finding no effects with regard to their respective objectives and hypothesis, and 2% of the studies found that their interventions had negative outcomes. Few studies reported on the association between the inclusion of social media features and intervention effect. A taxonomy of social media features used in behavioral interventions has been presented with 36 social media features organized under 7 high-level categories. The taxonomy has been used to guide the analysis of this review. CONCLUSIONS Although social media features are commonly included in DBCIs, there is an acute lack of information with respect to their effect on outcomes and a lack of clear guidance to inform the selection process based on the features’ suitability for the different behaviors. The proposed taxonomy along with the set of recommendations included in this review will support future research aimed at isolating and reporting the effects of social media features on DBCIs, cross-study comparisons, and evaluations.

scholarly journals Do Combinations of Behavior Change Techniques That Occur Frequently in Interventions Reflect Underlying Theory?

2020 ◽  
Vol 54 (11) ◽  
pp. 827-842
Author(s):  
Lauren Connell Bohlen ◽  
Susan Michie ◽  
Marijn de Bruin ◽  
Alexander J Rothman ◽  
Michael P Kelly ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Behavior Change ◽  
Behavioral Interventions ◽  
Intervention Development ◽  
Evidence Base ◽  
Behavior Change Techniques ◽  
Multiple Behavior Change ◽  
Behavior Change Interventions ◽  
Theory Use ◽  
Behavior Change Theories

Abstract Background Behavioral interventions typically include multiple behavior change techniques (BCTs). The theory informing the selection of BCTs for an intervention may be stated explicitly or remain unreported, thus impeding the identification of links between theory and behavior change outcomes. Purpose This study aimed to identify groups of BCTs commonly occurring together in behavior change interventions and examine whether behavior change theories underlying these groups could be identified. Methods The study involved three phases: (a) a factor analysis to identify groups of co-occurring BCTs from 277 behavior change intervention reports; (b) examining expert consensus (n = 25) about links between BCT groups and behavioral theories; (c) a comparison of the expert-linked theories with theories explicitly mentioned by authors of the 277 intervention reports. Results Five groups of co-occurring BCTs (range: 3–13 BCTs per group) were identified through factor analysis. Experts agreed on five links (≥80% of experts), comprising three BCT groups and five behavior change theories. Four of the five BCT group–theory links agreed by experts were also stated by study authors in intervention reports using similar groups of BCTs. Conclusions It is possible to identify groups of BCTs frequently used together in interventions. Experts made shared inferences about behavior change theory underlying these BCT groups, suggesting that it may be possible to propose a theoretical basis for interventions where authors do not explicitly put forward a theory. These results advance our understanding of theory use in multicomponent interventions and build the evidence base for further understanding theory-based intervention development and evaluation.

scholarly journals Becoming FEW Conscious: A Conceptual Typology of Household Behavior Change Interventions Targeting the Food-Energy-Water (FEW) Nexus

2019 ◽  
Vol 11 (18) ◽  
pp. 5034 ◽  
Author(s):  
Holly Berman ◽  
Rachael Shwom ◽  
Cara Cuite
Keyword(s):  
Behavior Change ◽  
Behavioral Interventions ◽  
Price Change ◽  
Large Body ◽  
Household Behavior ◽  
Specific Information ◽  
Material Technology ◽  
Food Energy ◽  
Behavior Change Interventions ◽  
Tailored Information

The food-energy-water (FEW) nexus presents an opportunity to rethink predominant approaches to household behavior change science. We linked emerging FEW nexus research with existing literature examining household consumption and pro-environmental behaviors. While a large body of work examines the environmental impacts of household life and explores pathways to behavior change for sustainability, the literature lacks studies that test interventions in multiple FEW resource categories, leaving researchers unable to identify tensions and tradeoffs in the household system. To guide this developing field and accumulate findings on household behavior across disciplines, we proposed a conceptual typology that synthesizes interdisciplinary analytic traditions to classify behavioral interventions targeting the household FEW nexus. The typology synthesizes behavioral interventions as active, passive, or structural, and household-specific or non-specific, illustrating six distinct categories: information, tailored information, action, gamification, policy/price change, and material/technology provision. A review of 40 studies that guided the typology identifies four significant lessons for future intervention research: household non-specific information and tailored information work better together, feedback is more effective when it is persistent, price-based interventions (information or incentives) are often ineffective, and material/technology provision is very effective but utilized in few household studies. To push forward household resource consumption science, we advocated for a holistic nexus focus that is rooted in interdisciplinarity, coalition building with stakeholders, and data reporting that facilitates knowledge accumulation.

scholarly journals Core processes: how to use evidence, theories and research in planning behavior change interventions

2018 ◽  
Author(s):  
Robert A.C. Ruiter ◽  
Rik Crutzen
Keyword(s):  
Behavior Change ◽  
Behavioral Science ◽  
Research Data ◽  
Problem Definition ◽  
Problem Solution ◽  
Behavior Change Interventions ◽  
Systematic Development ◽  
Intervention Planning ◽  
Correct Application ◽  
Societal Problems

Psychology is not only a basic behavioral science but also an applied discipline that is used to solve societal problems. In a problem-driven context, the search for existing literature, the correct application of appropriate theories, and the collection of additional research data are basic tools essential for the systematic development of any intervention. These ‘Core Processes’ can be used in different phases/steps of intervention planning and within different planning frameworks. However, in practice intervention planners often do not systematically follow all necessary steps. In this paper, ‘Core Processes’ are presented in order to provide empirical and theoretical guidance to planners from problem definition to problem solution. Specific emphasis is put on finding theories that are potentially useful within the parameters that the theory describes using a combination of approaches (i.e., the topic, concept and general theories approaches).

scholarly journals Translation of findings from laboratory studies of food and alcohol intake into behavior change interventions: The experimental medicine approach

2019 ◽  
Author(s):  
Matt Field ◽  
Paul Christiansen ◽  
Charlotte Hardman ◽  
Ashleigh Haynes ◽  
Andrew Jones ◽  
...  
Keyword(s):  
Behavior Change ◽  
Behavioral Interventions ◽  
Alcohol Intake ◽  
Relevant Literature ◽  
Experimental Medicine ◽  
Laboratory Studies ◽  
Methodological Issues ◽  
Promising Candidate ◽  
Randomized Controlled ◽  
Behavior Change Interventions

Objectives: Laboratory studies have contributed important information about the determinants of food and alcohol intake, and they have prompted the development of behavior change interventions that have been evaluated in randomized controlled trials conducted in the field. In this paper we apply a recent experimental medicine (EM) framework to this body of research. Methods: A conceptual review and focused discussion of the relevant literature is presented. Results: We illustrate how it is possible to translate findings from studies of food and alcohol intake in the laboratory into interventions that are effective for changing behavior in the real world. We go on to demonstrate how systematic failures can occur at different stages within the EM framework, and how these failures ultimately result in interventions that are ineffective for changing behavior. We also consider methodological issues that may constrain the external validity of findings from laboratory studies including demand effects, participant characteristics, and the timing and dose of behavioral interventions. Throughout, we make recommendations to improve the translation of findings from laboratory studies into behavior change interventions that are effective in the field. Conclusions: Consideration of the EM framework will help to ensure that promising candidate interventions for eating and drinking that are identified in laboratory studies can fulfill their translational promise.

scholarly journals Identifying Behavior Change Techniques Used in Tobacco Cessation Interventions by Oral Health Professionals and Their Relation to Intervention Effects—A Review of the Scientific Literature

2021 ◽  
Vol 18 (14) ◽  
pp. 7481
Author(s):  
Ibtisam Moafa ◽  
Ciska Hoving ◽  
Bart van den Borne ◽  
Mohammed Jafer
Keyword(s):  
Behavior Change ◽  
Goal Setting ◽  
Tobacco Use ◽  
Behavioral Interventions ◽  
Tobacco Cessation ◽  
Action Planning ◽  
Effective Interventions ◽  
Behavior Change Interventions ◽  
Dental Setting ◽  
Readiness To Quit

This review aimed to identify the behavioral change techniques (BCTs) used in behavioral interventions for tobacco cessation at dental practices in relation to their effect on tobacco use. Six scientific databases were searched for behavior change interventions for tobacco cessation and were coded using the BCT taxonomy of behavioral support for smoking cessation (BCTTsm). Fifteen interventions were identified, and data related to intervention characteristics were abstracted. Sixteen BCTs were identified, mainly related to increased motivation and teaching regulatory skills. Goal setting was the most commonly used BCT. Ten out of fifteen interventions effectively impacted tobacco cessation outcomes (OR = 2 to 5.25). Effective interventions more frequently included goal setting, written materials, readiness to quit and ability assessment, tobacco-use assessment, self-efficacy boost, listing reasons for quitting, action planning and environment restructuring. Other BCTs were not clearly associated with an increased effect. Among the behavioral interventions, certain techniques were associated with successful tobacco quitting. Tobacco cessation interventions in a dental setting appear to benefit from using BCTs that increase motivation and teach regulatory skills. The identified BCTs in this review could provide a source to better inform researchers and dentists about the active ingredients in behavior change interventions for tobacco cessation in a dental setting.

scholarly journals Integrating Behavioral Science With Mobile (mHealth) Technology to Optimize Health Behavior Change Interventions

2019 ◽  
Vol 24 (1) ◽  
pp. 38-48 ◽  
Author(s):  
Jane C. Walsh ◽  
Jenny M. Groarke
Keyword(s):  
Chronic Disease ◽  
Health Behavior ◽  
Behavior Change ◽  
European Commission ◽  
Behavioral Science ◽  
Digital Technologies ◽  
Health Behavior Change ◽  
World Health ◽  
Effect Change ◽  
Behavior Change Interventions

Abstract. Recent rapid advances in technology have provided us with a golden opportunity to effect change in health-related outcomes for chronic disease by employing digital technologies to encourage and support behavior change to promote and maintain health. Behavior change theories are the bedrock to developing evidence-based mHealth interventions. Digital technologies enable researchers to empirically test behavioral theories in “real-world” contexts using behavior change techniques ( Hekler, Michie, et al., 2016 ). According to the European Commission (2014) among the world’s population of 7 billion, there are over 5 billion mobile devices and over 90% of the users have their mobile device near them 24 hr a day. This provides a huge opportunity for behavior change and one that health psychologists have already begun to address. However, while a novel and exciting area of research, many early studies have been criticized for lacking a strong evidence base in both design and implementation. The European Commission conducted a public consultation in 2016 on the issues surrounding the use of mHealth tools (e.g., apps) and found a lack of global standards was a significant barrier. Recently, the World Health Organization (WHO) mHealth Technical Evidence Review Group developed the mHealth evidence reporting and assessment (mERA) checklist for specifying the content of mHealth interventions. Health psychologists play a key role in developing mHealth interventions, particularly in the management of chronic disease. This article discusses current challenges facing widespread integration of mobile technology into self-management of chronic disease including issues around security and regulation, as well as investigating mechanisms to overcoming these barriers.

scholarly journals Understanding Outcomes in Behavior Change Interventions to Prevent Pediatric Obesity: The Role of Dose and Behavior Change Techniques

2018 ◽  
Vol 46 (2) ◽  
pp. 312-321 ◽  
Author(s):  
Meghan M. JaKa ◽  
Simone A. French ◽  
Julian Wolfson ◽  
Robert W. Jeffery ◽  
Fabianna Lorencatto ◽  
...  
Keyword(s):  
Behavior Change ◽  
Pediatric Obesity ◽  
Behavioral Interventions ◽  
Specific Weight ◽  
Primary Study ◽  
Session Length ◽  
Behavior Change Techniques ◽  
Effective Interventions ◽  
Behavior Change Interventions ◽  
Child Bmi

Background. Behavioral interventions to prevent pediatric obesity have shown inconsistent results across the field. Studying what happens within the “black box” of these interventions and how differences in implementation lead to different outcomes will help researchers develop more effective interventions. Aim. To compare the implementation of three features of a phone-based intervention for parents (time spent discussing weight-related behaviors, behavior change techniques used in sessions, and intervention activities implemented by parents between sessions) with study outcomes. Methods. A random selection of 100 parent–child dyads in the intervention arm of a phone-based obesity prevention trial was included in this analysis. Sessions were coded for overall session length, length of time spent discussing specific weight-related behaviors, number of behavior change techniques used during the sessions, and number of intervention-recommended activities implemented by the parents between sessions (e.g., parent-reported implementation of behavioral practice/rehearsal between sessions). The primary study outcome, prevention of unhealthy increase in child body mass index (BMI) percentile, was measured at baseline and 12 months. Results. Overall session length was associated with decreases in child BMI percentile ( b = −0.02, p = .01). There was no association between the number of behavior change techniques used in the sessions and decreases in child BMI percentile ( b = −0.29, p = .27). The number of activities the parents reported implementing between sessions was associated with decreases in child BMI percentile ( b = −1.25, p = .02). Discussion. To improve future interventions, greater attention should be paid to the intended and delivered session length, and efforts should be made to facilitate parents’ implementation of intervention-recommended activities between sessions (ClinicalTrials.gov, No. NCT01084590).

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