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. 2018 Nov;2(11):797-799.
doi: 10.1038/s41562-018-0471-8. Epub 2018 Nov 5.

Using Rigorous Methods to Advance Behaviour Change Science

Jennifer A Sumner  1 , Rachel N Carey  2 , Susan Michie  2 , Marie Johnston  3 , Donald Edmondson  4 , Karina W Davidson  4
Affiliations

Using Rigorous Methods to Advance Behaviour Change Science

Jennifer A Sumner et al. Nat Hum Behav. 2018 Nov.

Erratum in

No abstract available

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Conflict of interest statement

Competing Interests Susan Michie (Principal Investigator), Marie Johnston (Co-Investigator), and Rachel N. Carey are members of the Human Behaviour-Change Project. Karina W. Davidson (Co-Principal Investigator), Donald Edmondson (Co-Principal Investigator), and Jennifer A. Sumner (Co-Investigator) are members of the Resource and Coordinating Center for the Science of Behavior Change Research Network.

Figures

Figure 1.
Figure 1.. Examples of the Human Behaviour-Change Project (HBCP) and Science of Behavior Change (SOBC) approaches to behavior change science.
Figure 1a presents components of the HBCP Knowledge System for generating new evidence about behaviour change interventions (BCIs). Schematic relationships between the BCI literature, the Behaviour Change Intervention Ontology (BCIO), and a machine and human user interface are depicted [Michie, S., et al. The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation. Implementation Science 12, 121 (2017)]. Figure 1b presents the steps of the SOBC experimental medicine approach. This method is used to identify key mechanisms underlying behavior change.
Figure 2.
Figure 2.. How the approaches of the Human Behaviour-Change Project (HBCP) and Science of Behavior Change (SOBC) can advance behavior change research.
Using artificial intelligence, natural language processing and machine learning, the HBCP aims to organize the fragmented and nonsystematic existing literature in ways that can generate new, accessible evidence. These outputs can show the field where to “go deep” and further examine mechanisms underlying behavior change in new research using rigorous and systematic methods, like the SOBC experimental medicine approach. These additions to the empirical literature, with further processing with artificial intelligence, natural language processing, and machine learning methods, will further refine the evidence base.
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