From a false sense of safety to resilience under uncertainty.

Understanding and acting upon risk is notably challenging, and navigating complexity with understandings developed for stable environments may inadvertently build a false sense of safety. Neglecting the potential for non-linear change or "black swan" events - highly impactful but uncommon occurrences - may lead to naive optimisation under assumed stability, exposing systems to extreme risks. For instance, loss aversion is seen as a cognitive bias in stable environments, but it can be an evolutionarily advantageous heuristic when complete destruction is possible. This paper advocates for better accounting of non-linear change in decision-making by leveraging insights from complex systems and psychological sciences, which help to identify blindspots in conventional decision-making and to develop risk mitigation plans that are interpreted contextually. In particular, we propose a framework using attractor landscapes to visualize and interpret complex system dynamics. In this context, attractors are states toward which systems naturally evolve, while tipping points - critical thresholds between attractors - can lead to profound, unexpected changes impacting a system's resilience and well-being. We present four generic attractor landscape types that provide a novel lens for viewing risks and opportunities, and serve as decision-making contexts. The main practical contribution is clarifying when to emphasize particular strategies - optimisation, risk mitigation, exploration, or stabilization - within this framework. Context-appropriate decision making should enhance system resilience and mitigate extreme risks.

How do behavioral public policy experts see the role of complex systems perspectives? An expert interview study.

Amidst the global momentum of behavioral insights (BI), there has been a shift from mostly nudge-based BI applications to systemic approaches. This is particularly pressing in public health, where interacting issues regularly produce unanticipated consequences. Regardless, little is known about adopting complex systems approaches in behavioral public policy. This study aims to capture current practices of international BI experts on the definition, application, drivers, and hindering factors in adopting complex systems approaches in public policy. Semi-structured individual expert interviews (n = 12) of international BI experts with extensive experience in educating, cooperating with, and/or advising public servants were analyzed with inductive content analysis. While the working definition of BI aligned with published definitions, experts varied in their descriptions of complex systems approaches and its significance for public policy, including socioecological aspects, systematic BI use across policy stages, recognizing intertwined behavior, and lack of ready-made solutions. They emphasized the importance of systems approaches, identifying drivers (e.g. need for a broader focus) and hindrances (e.g. pressure for quick results). Embracing complex systems in behavioral public policy provides a holistic perspective, extending beyond simple nudges, sometimes presumed as universally applicable. While complexity perspectives would align with policymakers' worldview, applications require more work to tailor to local situations and to evaluate. Recognizing that, given their distinct expertise content, BI expertise can be quite different from complex systems expertise. The field would benefit from clear descriptions and specialized training for effective integration and advocacy for these approaches.

In recent years, the use of behavioral insights (BI) in public policy, including health policy, has been shifting from nudges to more comprehensive approaches. This study examines how international BI experts view the use of complex systems approaches in behavioral public policy. Through interviews with 12 experts advising public servants, we found that while BI definitions align with earlier published definitions, experts interpret and apply complex systems approaches differently. Some consider socioecological factors, advocate applying the systematic use of BI throughout the policy cycle, and for some using complex systems approaches means considering intertwined nature of behavioral phenomena and that there are no one-size-fits-all solutions. Experts stress the importance of a systems approach, identifying drivers (e.g. need for a broader focus, experience-based expertise) and hindering factors (e.g. advisory settings, pressure for quick results) in its implementation. This study presents BI's evolving role in public policy, highlighting the need for complex systems approaches to address multifaceted societal challenges.

Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation.

Models and theories in behaviour change science are not in short supply, but they almost exclusively pertain to a particular facet of behaviour, such as automaticity or reasoned action, or to a single scale of observation such as individuals or communities. We present a highly generalisable conceptual model which is widely used in complex systems research from biology to physics, in an accessible form to behavioural scientists. The proposed model of attractor landscapes can be used to understand human behaviour change on different levels, from individuals to dyads, groups and societies. We use the model as a tool to present neglected ideas in contemporary behaviour change science, such as hysteresis and nonlinearity. The model of attractor landscapes can deepen understanding of well-known features of behaviour change (research), including short-livedness of intervention effects, problematicity of focusing on behavioural initiation while neglecting behavioural maintenance, continuum and stage models of behaviour change understood within a single accommodating framework, and the concept of resilience. We also demonstrate potential methods of analysis and outline avenues for future research.

Studying Behaviour Change Mechanisms under Complexity.

Understanding the mechanisms underlying the effects of behaviour change interventions is vital for accumulating valid scientific evidence, and useful to informing practice and policy-making across multiple domains. Traditional approaches to such evaluations have applied study designs and statistical models, which implicitly assume that change is linear, constant and caused by independent influences on behaviour (such as behaviour change techniques). This article illustrates limitations of these standard tools, and considers the benefits of adopting a complex adaptive systems approach to behaviour change research. It (1) outlines the complexity of behaviours and behaviour change interventions; (2) introduces readers to some key features of complex systems and how these relate to human behaviour change; and (3) provides suggestions for how researchers can better account for implications of complexity in analysing change mechanisms. We focus on three common features of complex systems (i.e., interconnectedness, non-ergodicity and non-linearity), and introduce Recurrence Analysis, a method for non-linear time series analysis which is able to quantify complex dynamics. The supplemental website provides exemplifying code and data for practical analysis applications. The complex adaptive systems approach can complement traditional investigations by opening up novel avenues for understanding and theorising about the dynamics of behaviour change.

Visualisation and network analysis of physical activity and its determinants: Demonstrating opportunities in analysing baseline associations in the Let's Move It trial.

Background: Visualisations and readily-accessible web-based supplementary files can improve data reporting and transparency. In this paper, we make use of recent developments in software and psychological network analysis to describe the baseline cohort of a trial testing the Let's Move It intervention, which aimed to increase physical activity (PA) and reduce sedentary behaviours (SB) among vocational school students. Methods: At baseline, 1166 adolescents, distributed across 6 school clusters and four educational tracks, completed measures of PA and SB, theoretical predictors of these behaviours, and body composition. Within a comprehensive website supplement, which includes all code and analyses, data were tabulated and visualised, and network analyses explored relations between predictor variables and outcomes. Results: Average daily moderate-to-vigorous PA was 65 min (CI95: 57min-73 min), and SB 8h44 min (CI95: 8h04min-9h24 min), with 25.8 (CI95: 23.5-28.0) interruptions to sitting. Cluster randomisation appeared to result in balanced distributions for baseline characteristics between intervention and control groups, but differences emerged across the four educational tracks. Self-reported behaviour change technique (BCT) use was low for many but not all techniques. A network analysis revealed direct relationships between PA and behavioural experiments, planning and autonomous motivation, and several BCTs were connected to PA via autonomous motivation. Visualisation uncovered a case of Simpson's paradox. Conclusions: Data-visualisation and data exploration techniques (e.g. network analysis) can help reveal the dynamics involved in complex multi-causal systems - a challenging task with traditional data presentations. The benefits of presenting complex data visually should encourage researchers to publish extensive analyses and descriptions as website supplements, which would increase the speed and quality of scientific communication, as well as help to address the crisis of reduced confidence in research findings. We hope that this example will serve as a template for other investigators to improve upon in the future.

Simple and rationale-providing SMS reminders to promote accelerometer use: a within-trial randomised trial comparing persuasive messages.

BACKGROUND: Literature on persuasion suggests compliance increases when requests are accompanied with a reason (i.e. the "because-heuristic"). The reliability of outcomes in physical activity research is dependent on sufficient accelerometer wear-time. This study tested whether SMS reminders-especially those that provided a rationale-are associated with increased accelerometer wear-time. METHODS: We conducted a within-trial partially randomised controlled trial during baseline data collection in a school-based physical activity intervention trial. Of 375 participants (mean age = 18.1), 280 (75%) opted to receive daily SMS reminders to wear their accelerometers. These 280 participants were then randomised to receive either succinct reminders or reminders including a rationale. Data was analyzed across groups using both frequentist and Bayesian methods. RESULTS: No differences in total accelerometer wear minutes were detected between the succinct reminder group (Mdn = 4909, IQR = 3429-5857) and the rationale group (Mdn = 4808, IQR = 3571-5743); W = 8860, p = 0.65, CI95 = - 280.90-447.20. Similarly, we found no differences in wear time between participants receiving SMS reminders (Mdn = 4859, IQR = 3527-5808) and those not receiving them (Mdn = 5067, IQR = 3201-5885); W = 10,642.5, p = 0.77, CI95 = - 424.20-305.30. Bayesian ANOVA favored a model of equal weartime means, over one of unequal means, by a Bayes Factor of 12.05. Accumulated days of valid accelerometer wear data did not differ either. Equivalence testing indicated rejection of effects more extreme than a Cohen's d (standardised mean difference) of ±~0.3. CONCLUSIONS: This study casts doubt on the effectiveness of using the because-heuristic via SMS messaging, to promote accelerometer wear time among youth. The because-heuristic might be limited to face-to-face communication and situations where no intention for or commitment to the behavior has yet been made. Other explanations for null effects include non-reading of messages, and reminder messages undermining the self-reminding strategies which would occur naturally in the absence of reminders. TRIAL REGISTRATION: DRKS DRKS00007721 . Registered 14.04.2015. Retrospectively registered.

Bayesian evaluation of behavior change interventions: a brief introduction and a practical example.

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 the 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 the 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.

Randomised controlled feasibility study of a school-based multi-level intervention to increase physical activity and decrease sedentary behaviour among vocational school students.

BACKGROUND: No school-based physical activity (PA) interventions among older adolescents have demonstrated long-term effectiveness, and few of them so far have addressed sedentary behaviour (SB). Based on behavioural theories and evidence, we designed a multi-level intervention to increase PA and decrease SB among vocational school students. This study investigates feasibility and acceptability of two main intervention components and research procedures. We also examine uptake of behaviour change techniques (BCTs) by the participants. METHODS: Design was an outcome assessor blinded, cluster-randomised controlled trial. Four classes of students (matched pairs) were randomised into one intervention and one control arm. The intervention consisted of (1) a 6-h group-based intervention for students, (2) two 2-h training workshops to reduce their students' sitting in class for teachers, and (3) provision of light PA equipment in classrooms. At baseline (T1), mid-intervention (T2) at 3 weeks, post-intervention (T3) and 6 months after baseline (T4) we measured hypothesised psychosocial mediators and self-reported PA and sitting. Objective assessment of PA and SB (7-day accelerometry) was conducted at T1, T3 and T4. Body composition (bioimpedance) was measured at T1 and T4. Students and teachers in the intervention arm filled in acceptability questionnaires at T3. RESULTS: Recruitment rate was 64% (students) and 88.9% (teachers), and at T3, all post-intervention measurements were completed by 33 students (retention 76.7%) and 15 teachers (retention 93.8%). Acceptability ratings of sessions were high (students M = 6.29, scale 1-7), and data collection procedures were feasible. Intervention arm students reported increased use of BCTs, but uptake of some key BCTs was suboptimal. BCT use correlated highly with objective measures of PA. Based on both self-report and student evaluation, teachers in the intervention arm increased the use of sitting reduction strategies at post-intervention and T4 follow-up (p < .05). CONCLUSIONS: We detected willingness of the target groups to participate, good response rates to questionnaires, adequate retention, as well as acceptability of the trial protocol. Investigation of BCT use among students helped further enhance intervention procedures to promote BCT use. After making necessary modifications identified, intervention effectiveness can next be tested in a definitive trial. TRIAL REGISTRATION: ISRCTN34534846 . Registered 23 May 2014. Retrospectively registered.

What explains the socioeconomic status gap in activity? Educational differences in determinants of physical activity and screentime.

BACKGROUND: Designing evidence-based interventions to address socioeconomic disparities in health and health behaviours requires a better understanding of the specific explanatory mechanisms. We aimed to investigate a comprehensive range of potential theoretical mediators of physical activity (PA) and screen time in different socioeconomic status (SES) groups: a high SES group of high school students, and a low SES group of vocational school students. The COM-B system, including the Theoretical Domains Framework (TDF), was used as a heuristic framework to synthesise different theoretical determinants in this exploratory study. METHODS: Finnish vocational and high school students (N = 659) aged 16-19, responded to a survey assessing psychological, social and environmental determinants of activity (PA and screen time). These determinants are mappable into the COM-B domains: capability, opportunity and motivation. The outcome measures were validated self-report measures for PA and screen time. The statistical analyses included a bootstrapping-based mediation procedure. RESULTS: Regarding PA, there were SES differences in all of the COM-B domains. For example, vocational school students reported using less self-monitoring of PA, weaker injunctive norms to engage in regular PA, and fewer intentions than high school students. Mediation analyses identified potential mediators of the SES-PA relationship in all of three domains: The most important candidates included self-monitoring (CI95 for b: 0.19-0.47), identity (0.04-0.25) and material resources available (0.01-0.16). However, SES was not related to most determinants of screentime, where there were mainly gender differences. Most determinants were similarly related with both behaviours in both SES groups, indicating no major moderation effect of SES on these relationships. CONCLUSIONS: This study revealed that already in the first years of educational differentiation, levels of key PA determinants differ, contributing to socioeconomic differences in PA. The analyses identified the strongest mediators of the SES-PA association, but additional investigation utilising longitudinal and experimental designs are needed. This study demonstrates the usefulness of combining constructs from various theoretical approaches to better understand the role of distinct mechanisms that underpin socioeconomic health behaviour disparities.

'Let's Move It' - a school-based multilevel intervention to increase physical activity and reduce sedentary behaviour among older adolescents in vocational secondary schools: a study protocol for a cluster-randomised trial.

BACKGROUND: Physical activity (PA) has been shown to decline during adolescence, and those with lower education have lower levels of activity already at this age, calling for targeted efforts for them. No previous study has demonstrated lasting effects of school-based PA interventions among older adolescents. Furthermore, these interventions have rarely targeted sedentary behaviour (SB) despite its relevance to health. The Let's Move It trial aims to evaluate the effectiveness and the cost-effectiveness of a school-based, multi-level intervention, on PA and SB, among vocational school students. We hypothesise that the intervention is effective in increasing moderate-to-vigorous-intensity physical activity (MVPA), particularly among those with low or moderate baseline levels, and decreasing SB among all students. METHODS: The design is a cluster-randomised parallel group trial with an internal pilot study. The trial is conducted in six vocational schools in the Helsinki Metropolitan area, Finland. The intervention is carried out in 30 intervention classes, and 27 control classes retain the standard curriculum. The randomisation occurs at school-level to avoid contamination and to aid delivery. Three of the six schools, randomly allocated, receive the 'Let's Move It' intervention which consists of 1) group sessions and poster campaign targeting students' autonomous PA motivation and self-regulation skills, 2) sitting reduction in classrooms via alterations in choice architecture and teacher behaviour, and 3) enhancement of PA opportunities in school, home and community environments. At baseline, student participants are blind to group allocation. The trial is carried out in six batches in 2015-2017, with main measurements at pre-intervention baseline, and 2-month and 14-month follow-ups. Primary outcomes are for PA, MVPA measured by accelerometry and self-report, and for SB, sedentary time and breaks in sedentary time (accelerometry). Key secondary outcomes include measured body composition, self-reported well-being, and psychological variables. Process variables include measures of psychosocial determinants of PA (e.g. autonomous motivation) and use of behaviour change techniques. Process evaluation also includes qualitative interviews. Intervention fidelity is monitored. DISCUSSION: The study will establish whether the Let's Move It intervention is effective in increasing PA and reducing SB in vocational school students, and identify key processes explaining the results. TRIAL REGISTRATION: ISRCTN10979479 . Registered: 31.12.2015.