Abstract
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.
Highlights
Of particular importance to human behaviour change, is that the accuracy of mediation analysis depends on four key assumptions [13]: (1) The number of variables involved is small, and dynamics can be meaningfully assessed with only a few time points; (2) The process of change is the same for all individuals, e.g., follows the same sequence; (3) The dynamic between variables is linear, additive, and does not change in time; and (4) The included variables are not entangled with the context, omitted variables, or each other in bi-directional recursive relationships
This paper will (1) outline the complexity of behaviours and behaviour change interventions; (2) introduce readers to some key features of complex systems and how these can be applied to human behaviour; and (3) provide concrete suggestions for how researchers can better account for the implications of complexity in analysing behaviour change mechanisms
We introduce interconnectedness via interaction-dominant dynamics, which flow from point 1 above; second, we present how idiosyncratic, non-stationary change trajectories lead to nonergodicity, a technical term for point 2; third, we highlight that the flexibility of complex systems leads to ubiquitous non-linear dynamics as alluded to in point 3
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The evaluation of behaviour change interventions often involves randomly assigning participants to receive an intervention of interest or a comparator, and measuring subjective and objective indicators of behaviour [4] These measurements occur immediately before and after the delivery of the intervention, though sometimes additional follow-up measurements may take place weeks or months later. Of particular importance to human behaviour change, is that the accuracy of mediation analysis depends on four key assumptions [13]: (1) The number of variables involved is small, and dynamics can be meaningfully assessed with only a few time points; (2) The process of change is the same for all individuals, e.g., follows the same sequence; (3) The dynamic between variables is linear, additive, and does not change in time; and (4) The included variables are not entangled with the context, omitted variables, or each other in bi-directional recursive relationships. This paper will (1) outline the complexity of behaviours and behaviour change interventions; (2) introduce readers to some key features of complex systems and how these can be applied to human behaviour; and (3) provide concrete suggestions for how researchers can better account for the implications of complexity in analysing behaviour change mechanisms
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