We find that, acrossdomains, interventions designed to change individual determinantscan be ordered by increasing impact as those targeting knowledge,general skills, general attitudes, beliefs, emotions, behavioural skills,behavioural attitudes and habits. Interventions designed to changesocial-structural determinants can be ordered by increasing impactas legal and administrative sanctions; programmes that increaseinstitutional trustworthiness; interventions to change injunctivenorms; monitors and reminders; descriptive norm interventions;material incentives; social support provision; and policies that increaseaccess to a particular behaviour. We find similar patterns for health andenvironmental behavioural change specifically. Thus, policymakersshould focus on interventions that enable individuals to circumventobstacles to enacting desirable behaviours rather than targeting salientbut ineffective determinants of behaviour such as knowledge andbeliefs. (PDF) Determinants of behaviour and their efficacy as targets of behavioural change interventions. Available from: https://www.researchgate.net/publication/380329032_Determinants_of_behaviour_and_their_efficacy_as_targets_of_behavioural_change_interventions [accessed Jan 23 2025].

Free Behavior Design, Innovation and Change Tools These frameworks started out as internal tools we would use on client projects at Aim For Behavior, that would help us save time and create better outcomes for the customers and the companies we were working with. We are always adding more frameworks or iterating the current ones based on the feedback.

Causal layered analysis, a theory and practice of organisational, social and civilisational change, seeks to transform the present and the future, through deconstructing and reconstructing reality at four levels. The levels are: the litany or day to day unquestioned views of reality, the systemic, the worldview/stakeholder perspective and the deepest, often unconscious, myths and metaphors. Problems are considered at all four levels and multiple worldviews and stakeholders are brought into to consider alternatives. By moving up and down layers and considering alternative perspectives, transformative policy and strategic solutions are created.

I hope this post gives some ideas to product leads on how to use the Decision Stack as a mental model in all sorts of conversations. The stack is a really powerful coaching tool. It is a framework that helps you to discuss things like: How to achieve alignment and directional clarity across the board. Use the Stack to connect the dots. Ask why things are the way they are and how the organization is planning to reach their goals. Use it to discuss goals and where shared goals would be possible. Use it to discuss team topologies, team empowerment, and mandate.

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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.

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.

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

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.

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.

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?

templates showing different graphic ways to present frameworks

From my own experience, there appears to be a scientific trend (that I have not systematically evaluated) that successful behavior change programs tend to run for approximately 2-months, and that after this point, there is a large drop in adherence and impact. The big statistical meta-analysis that I carried out a few years back (http://www.jmir.org/2011/1/e17/), showed that online programs lasting more than 4 months, all failed. So as a rule of thumb, for most general purposes, 8-weeks is not a bad approximate time duration for many programs.