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Here is an interesting way to visualize how to design for behavior using the COM-B Model and the Behavior Change Wheel If you don't know the Behavior Change Wheel, it is a framework developed by Susan Michie, Robert West and colleagues at UCL It is comprised of 19 different behavior change frameworks. At the center sits The COM-B Model: COM-B is used to look for the barriers or enablers to a behavior Capability (both physical and psychological) Opportuntity (both physical and social) Motivation (both reflective and automatic) It is a powerful way to analyze what may be stopping your customers or employees or even yourself of making the choices you already wanted to do. Outside the COM-B model (center of the wheel) sit the Intervention Types - which can include Education, Incentivization, and Training. As for the example here used in diabetes prevention design: The wheel has been filled with interventions and ways to deliver the intervention in this example. (I may have done it a bit different, but still a good representation) It looks at the Patient level - to Increase the patient's awareness of pre-diabetes It looks at Provider's Level - Improve communication skills, and teachable moments at diagnosis It looks at System Level - Invitation by physicians as well as social marketing. This of course is a small example of how the model could help you go from challenge to outcome.

Implementing new practices requires changes in the behaviour of relevant actors, and this is facilitated by understanding of the determinants of current and desired behaviours. The Theoretical Domains Framework (TDF) was developed by a collaboration of behavioural scientists and implementation researchers who identified theories relevant to implementation and grouped constructs from these theories into domains. The collaboration aimed to provide a comprehensive, theory-informed approach to identify determinants of behaviour. The first version was published in 2005, and a subsequent version following a validation exercise was published in 2012. This guide offers practical guidance for those who wish to apply the TDF to assess implementation problems and support intervention design. It presents a brief rationale for using a theoretical approach to investigate and address implementation problems, summarises the TDF and its development, and describes how to apply the TDF to achieve implementation objectives. Examples from the implementation research literature are presented to illustrate relevant methods and practical considerations.

Robert Meza Miro boards compiled in one place

M. Bilal Akbar1 · Elizabeth Barnes

Chapter 1|7 pages Introduction Abstract Size: 0.09 MB Chapter 2|38 pages Sequential Techniques Of Social Influence Abstract Size: 0.20 MB Chapter 3|19 pages Techniques Involving Egotistic and Self-Presentation Mechanisms Abstract Size: 0.13 MB Chapter 4|34 pages The Role of Wording the Request Abstract Size: 0.19 MB Chapter 5|34 pages Interaction Dynamics and the Surprise Factor Abstract Size: 0.36 MB Chapter 6|26 pages Techniques of Social Influence Using Mood and Emotion Abstract Size: 0.17 MB Chapter 7|10 pages A Few More Issues and Final Remarks Abstract

Developed over several years, the BehaviourWorks Method is a tried and tested approach to changing behaviours. Consisting of three primary phases - Exploration, Deep Dive and Application - The Method can be used in full, or in parts, to gather evidence on the behaviour change approach that is most likely to work.

BC patterns are a collection of reoccurring solutions used in Behavioural Design to change people’s behaviour. They are patterns that designers, change makers and problem solvers can consider when solving people problems and designing behaviour change.

We have identified 30 “elements of value”—fundamental attributes in their most essential and discrete forms. These elements fall into four categories: functional, emotional, life changing, and social impact. Some elements are more inwardly focused, primarily addressing consumers’ personal needs.

Why people do or do not change their beliefs has been a long-standing puzzle. Sometimes people hold onto false beliefs despite ample contradictory evidence; sometimes they change their beliefs without sufficient reason. Here, we propose that the utility of a belief is derived from the potential outcomes associated with holding it. Outcomes can be internal (e.g., positive/negative feelings) or external (e.g., material gain/loss), and only some are dependent on belief accuracy. Belief change can then be understood as an economic transaction in which the multidimensional utility of the old belief is compared against that of the new belief. Change will occur when potential outcomes alter across attributes, for example because of changing environments or when certain outcomes are made more or less salient.

TRA has added a layer of thinking to the well-established habit loop – can we think beyond push notifications for cues and think beyond a discount as a reward? We analysed five different habit models and over 60 case studies in order to understand the breadth and depth of cues and rewards. Our framework takes these learnings and provides a thorough checklist for the cue, the behaviour and reward for strengthening habits. When you’re working on strengthening a one-time behaviour into a routine habit, consider the various options for each stage.

The COM-B model of behaviour is widely used to identify what needs to change in order for a behaviour change intervention to be effective. It identifies three factors that need to be present for any behaviour to occur: capability, opportunity and motivation. These factors interact over time so that behaviour can be seen as part of a dynamic system with positive and negative feedback loops. Motivation is a core part of the model and the PRIME Theory of motivation provides a framework for understanding how reflective thought processes (Planning and Evaluation processes) and emotional and habitual processes (Motive and Impulse/inhibition processes) interact at every moment leading to behaviour (Responses) at that moment.

Bonus talks Why You Forget Everything And What to Do About It w/ Bec Weeks – https://youtu.be/VoDlOmHbaWE The Sneaky Things That Keep Good Habits From Sticking w/ Jessica Malone – https://youtu.be/oCwMXY7u73A Nicolas Fieulaine from NFÉtudes – https://youtu.be/E-XNZUGvVT0 ––– Timestamps 0:00 Event Intro 6:53 The Science of Habit Change with David Neal 38:10 The Science of Mindfulness with Dr. Clare Purvis 53:03 Creatures of Context with David Perrott 1:21:05 Time Smart: How to Reclaim Your Time and Live a Happier Life with Ashley Whillans 2:05:36 The Invisibility of Habit with Wendy Wood 2:34:19 Digital Behavior Change in Health with Jennifer La Guardia & Aline Holzwarth 2:59:11 Better Decision Making at Work: 5 Core Heuristics (& How to Manage Them) with Scott Young, BVA Nudge Unit 3:22:32 All the small things - How behavioral science can help you unlock success in love and at work with Logan Ury & Liz Fosslien 4:09:53 How to apply behavioral insights to cyber security training with Harriet Rowthron from BestAtDigital 4:22:44 Making Meaning When Life Stinks with Yael Schonbrun 4:54:47 The Power of Identity with Dominic Packer 5:30:40 The Untapped Science of Less with Leidy Klotz 5:55:10 Day Wrap-Up with Samuel Salzer & Peter Judodihardjo

Having a standardized unit of measure for risk would be helpful for our personal calculations, but it could also become a core part of the way the media or public health authorities talk about threats like epidemic disease, or even seasonal flu. Post-COVID—if we ever get there—I suspect I will still be interested to know if the flu risk starts to climb in New York, even by a few micromorts—I wouldn’t radically change my plans, but I might put on a mask in the subway for a few weeks. For the past seventy years, every single local news broadcast has been telling you what the temperature is going to be tomorrow, and the chance of precipitation. Why shouldn’t they also include genuinely life-or-death odds?

A difficulty for investigating the accuracy of everyday risks perception has been the lack of an obvious objective framework on which to compare subjective responses. This difficulty stands in contrast to other fields of risk research. For example, risk perception in health contexts, uses the probability of death or ill health (e.g. as compiled by disease data registries) as the objective comparator [2, 3, 27]; and in financial fields, losses and gains in gambling tasks can be used as an objective comparator [28, 29]. In the current study the concept of MicroMorts is introduced as an objective risk framework to investigate the accuracy of everyday risk perception. We have around a one in a million chance of dying from an accident or incident every day, and this acute risk is quantified as one MicroMort [30, 31]. That is, MicroMorts are units that index acute risk (i.e. sudden death): one MicroMort is a one-in-a-million chance of death. We increase our risk through our choices of activities, for example, skydiving has a MicroMort value of 10, walking 27 miles has a MicroMort value of one, and giving birth has a MicroMort value of 120 (i.e. 10, 1 or 120 chance in a million chance of dying respectively) [31]. MicroMorts enable us to compare the acute risk of death from various activities, for example, a general anaesthetic and a sky-dive both carry the same acute risk of death, 10 MicroMorts (10 in one million people will die as a result of doing either). This MicroMort framework is being increasingly being used to index health risks and provide a framework for risk communication, including patient consent [31–33]

The SMART acronym (e.g., Specific, Measurable, Achievable, Realistic, Timebound) is a highly prominent strategy for setting physical activity goals. While it is intuitive, and its practical value has been recognised, the scientific underpinnings of the SMART acronym are less clear. Therefore, we aimed to narratively review and critically examine the scientific underpinnings of the SMART acronym and its application in physical activity promotion. Specifically, our review suggests that the SMART acronym: is not based on scientific theory; is not consistent with empirical evidence; does not consider what type of goal is set; is not applied consistently; is lacking detailed guidance; has redundancy in its criteria; is not being used as originally intended; and has a risk of potentially harmful effects. These issues are likely leading to sub-optimal outcomes, confusion, and inconsistency. Recommendations are provided to guide the field towards better practice and, ultimately, more effective goal setting interventions to help individuals become physically active.

The presentarticle reviews the debate and research on nudges byfocusing on three main dimensions: (1) the exact defi-nition of nudges; (2) the justification of nudge policies,with a focus on “libertarian paternalism”; and (3) theeffectiveness of nudges, both over time and in compari-son with standard policies.

Excellent contrast with Embrace Life of gain vs loss framing!

Which theory of behavior change can help you plan a health communication intervention for a large audience? There is no single right answer, but some theories will fit your needs better than others. The purpose of this tool is to rank-order some commonly used theories by their degree of fit with your behavior change challenge.

CUBES (to Change behavior, Understand Barriers, Enablers, and Stages of change) is a comprehensive framework for analyzing behavior developed by Surgo Ventures. As described in the video with Peter Smittenaar below, CUBES builds on evidence-based behavioral models that are widely used across sectors and includes drivers that show evidence of changing behavior. It illustrates how adopting a new behavior is a process of stages; at each stage, people are influenced by internal and environmental factors (see Figure 1). The CUBES framework articulates three critical components of behavior change: The path toward a target behavior comprises distinct stages of change, progressing from knowledge to intention, action, repetition, and finally, habit. Perceptual and contextual drivers can act as enablers or barriers that influence each individual, shaping their progression through each stage of change. Influencers in the form of family and friends, community, and society can affect these drivers, either directly or via media channels.

A pressing goal in global development and other sectors is often to understand what drives people’s behaviors, and how to influence them. Yet designing behavior change interventions is often an unsystematic process, hobbled by insufficient understanding of contextual and perceptual behavioral drivers and a narrow focus on limited research methods to assess them. We propose a toolkit (CUBES) of two solutions to help programs arrive at more effective interventions. First, we introduce a novel framework of behavior, which is a practical tool for programs to structure potential drivers and match corresponding interventions. This evidence-based framework was developed through extensive cross-sectoral literature research and refined through application in large-scale global development programs. Second, we propose a set of descriptive, experimental, and simulation approaches that can enhance and expand the methods commonly used in global development. Since not all methods are equally suited to capture the different types of drivers of behavior, we present a decision aid for method selection. We recommend that existing commonly used methods, such as observations and surveys, use CUBES as a scaffold and incorporate validated measures of specific types of drivers in order to comprehensively test all the potential components of a target behavior. We also recommend under-used methods from sectors such as market research, experimental psychology, and decision science, which programs can use to extend their toolkit and test the importance and impact of key enablers and barriers. The CUBES toolkit enables programs across sectors to streamline the process of conceptualizing, designing, and optimizing interventions, and ultimately to change behaviors and achieve targeted outcomes.

Low self esteem is the best predictor

There are surely many ways in which our beliefs can be quite nuanced. We examined the different ‘styles’ of belief we come up against in a variety of the work we do and observed a number of ways these styles appear: Suspension of disbelief: We know not to look too closely at something – we think that overall it is a good thing (e.g. recycling) but aware of possible discrepancies (e.g. being poorly disposed of) that may or may not lead us to question our positive beliefs. We are aware of the possible conflicts but this does not make our belief in the value of recycling any less valid. There are a great many beliefs that we have that could be challenged yet they serve us sufficiently well that we do not need to interrogate them too closely (political representation, eating meat) Inconsistent beliefs: Linked to this, we may hold two conflicting beliefs at the same time. We may know that wild fires are a natural phenomenon that predates climate change; but also that the fires we see in many areas today are of a much greater intensity and frequency. Exactly which is responsible cannot really be picked out, we can only really see the patterns emerging at a more macro-level, so it is not unreasonably to either hold both as true for even consider that the fire you have experience is a normal wild fire. Off-loading beliefs to others: Much of the time our beliefs about how things work is not something that we each individually work out, but we rely on a community of knowledge to work on our behalf. How many of us can be sure that our beliefs are correct about how vaccines work or indeed even how a zipper work. If we are questioned, then we recognise that our belief about how something works is tenuous but we have a good enough sense of it that allows us to function. Unformed beliefs: Sometimes we have not quite worked out what our beliefs are about something, which means that we may well move about in those beliefs or in the strength to which we hold onto them. The vaccination example outlined earlier is a good case in point. Not sure fully believe it but ‘there is something in it’ beliefs: Recent work we have been doing on Conspiracy Theories suggests that people may consider something is believable (e.g. Princess Diana’s death in a car crash was not accidental) but at the same time, in a different question then say they ‘do not fully believe it but there is something in it’. So what might seem like a belief is actually something much more akin to a questioning stance.

We present a theoretical model to clarify the underlying mechanisms that drive individual decision making and responses to behavioral interventions, such as nudges. The model provides a theoretical framework that comprehensively structures the individual decision-making process applicable to a wide range of choice situations. We also identify the mechanisms behind the effectiveness of behavioral interventions—in particular, nudges—based on this structured decision-making process. Hence, the model can be used to predict under which circumstances, and in which choice situations, a nudge is likely to be effective.

I propose a four-stage model below that balances an understanding that each part is essential with the need to break it down into units of work that can be spread across internal teams and external vendors when necessary. But be warned: each handoff increases the potential for loss, particularly when there is an incomplete understanding of the adjoining stages. A tightly integrated process managed by people who understand the end-to-end process will always have the greatest likelihood of creating meaningful behavior change; that we can name the parts should not detract from the need for a whole. Behavioral Strategy: the defining of a desired behavioral outcome, with population, motivation, limitations, behavior, and measurement all clearly demarcated. Plain version: figuring out what “works” and “worth doing” mean in behavioral terms by collaborating with stakeholders. Behavioral Insights: the discovery of observations about the pressures that create current behaviors, both quantitative and qualitative. Plain version: figure out why people would want to do the behavior and why they aren’t already by talking to them individually and observing their behavior at scale. Behavioral Design: the design of proposed interventions, based on behavioral insights, that may create the pre-defined behavioral outcome. Plain version: design products, processes, etc. to make the behavior more likely. Behavioral Impact Evaluation: the piloting (often but not always using randomized controlled trials) of behavioral interventions to evaluate to what extent they modify the existing rates of the pre-defined behavioral outcomes. Plain version: figure out whether the products, processes, etc. actually make the behavior more likely. Behavioral Science: combining all four of those processes. Plain version: behavior as an outcome, science as a process.

We argue that the reason so little progress has been made against obesity and type 2 diabetes is because the field has been laboring, quite literally, in the sense intended by philosopher of science Thomas Kuhn, under the wrong paradigm. This energy-in-energy-out conception of weight regulation, we argue, is fatally, tragically flawed: Obesity is not an energy balance disorder, but a hormonal or constitutional disorder, a dysregulation of fat storage and metabolism, a disorder of fuel-partitioning. Because these hormonal responses are dominated by the insulin signaling system, which in turn responds primarily (although not entirely) to the carbohydrate content of the diet, this thinking is now known as the carbohydrate-insulin model. Its implications are simple and profound: People don’t get fat because they eat too much, consuming more calories than they expend, but because the carbohydrates in their diets — both the quantity of carbohydrates and their quality — establish a hormonal milieu that fosters the accumulation of excess fat.

The framework comprises 6 key stages. Each building on the insights of the previous and each with its own objectives, tools and resources: 1. What - are the target behaviours? 2. Who - should we focus our resource on? 3. Why - do/don’t those people manifest the target behaviours? 4. How - can we empower people to change? 5. So What? To what extent were our interventions effective? 6. What Now? How do we apply our learnings at scale?

There were some significant differences between BCTs reported in implementation and de-implementation interventions suggesting that researchers may have implicit theories about different BCTs required for de-implementation and implementation. These findings do not imply that the BCTs identified as targeting implementation or de-implementation are effective, rather simply that they were more frequently used. These findings require replication for a wider range of clinical behaviours. The continued accumulation of additional knowledge and evidence into whether implementation and de-implementation is different will serve to better inform researchers and, subsequently, improve methods for intervention design.

Psychological reactance theory (PRT; Brehm, 1966) posits that when something threatens or eliminates people’s freedom of behavior, they experience psychological reactance, a motivational state that drives freedom restoration. Complementing recent, discipline-specific reviews (e.g., Quick, Shen, & Dillard, 2013; Steindl, Jonas, Sittenthaler, Traut-Mattausch, & Greenberg, 2015), the current analysis integrates PRT research across fields in which it has flourished: social psychology and clinical psychology, as well as communication research.

The key in all this is crossing the chasm—performing the acts that allow the first shoots of that mainstream market to emerge. This is a do-or-die proposition for high-tech enterprises; hence it is logical that they be the crucible in which “chasm theory” is formed. But the principles can be generalized to other forms of marketing, so for the general reader who can bear with all the high-tech examples in this book, useful lessons may be learned.

SHIFT is an acronym for five psychological factors that make consumers more inclined to engage in pro-environmental behaviours: social influence, habit formation, individual self, feelings and cognition, and tangibility.

The behavioural change enterprise disproportionately focuses on promoting successes at the expense of examining the failures of behavioural change interventions. We review the literature across different fields through a causal explanatory approach to identify structural relations that impede (or promote) the success of interventions. Based on this analysis we present a taxonomy of failures of behavioural change that catalogues different types of failures and backfiring effects. Our analyses and classification offer guidance for practitioners and researchers alike, and provide critical insights for establishing a more robust foundation for evidence-based policy. Behavioural change techniques are currently used by many global organisations and public institutions. The amassing evidence base is used to answer practical and scientific questions regarding what cognitive, affective, and environment factors lead to successful behavioural change in the laboratory and in the field. In this piece we show that there is also value to examining interventions that inadvertently fail in achieving their desired behavioural change (e.g., backfiring effects). We identify the underlying causal pathways that characterise different types of failure, and show how a taxonomy of causal interactions that result in failure exposes new insights that can advance theory and practice.

Includes “periodic table“ of behavior change techniques