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Article

Studying Behaviour Change Mechanisms under Complexity

1
Faculty of Social Sciences, University of Helsinki, P.O. Box 54, 00014 Helsinki, Finland
2
School of Psychology, National University of Ireland, H91 TK33 Galway, Ireland
3
Behavioural Science Institute, Radboud University Nijmegen, Postbus 9104, 500 HE Nijmegen, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Scott D. Lane
Behav. Sci. 2021, 11(5), 77; https://doi.org/10.3390/bs11050077
Received: 17 March 2021 / Revised: 22 April 2021 / Accepted: 28 April 2021 / Published: 14 May 2021
(This article belongs to the Special Issue Health Behavior Change: Theories, Methods, and Interventions)
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. View Full-Text
Keywords: complex systems; wellbeing; methodology; behaviour change complex systems; wellbeing; methodology; behaviour change
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MDPI and ACS Style

Heino, M.T.J.; Knittle, K.; Noone, C.; Hasselman, F.; Hankonen, N. Studying Behaviour Change Mechanisms under Complexity. Behav. Sci. 2021, 11, 77. https://doi.org/10.3390/bs11050077

AMA Style

Heino MTJ, Knittle K, Noone C, Hasselman F, Hankonen N. Studying Behaviour Change Mechanisms under Complexity. Behavioral Sciences. 2021; 11(5):77. https://doi.org/10.3390/bs11050077

Chicago/Turabian Style

Heino, Matti T.J., Keegan Knittle, Chris Noone, Fred Hasselman, and Nelli Hankonen. 2021. "Studying Behaviour Change Mechanisms under Complexity" Behavioral Sciences 11, no. 5: 77. https://doi.org/10.3390/bs11050077

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