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Applying Machine Learning Techniques to Implementation Science

Applying Machine Learning Techniques to Implementation Science

The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, describe a few limited real-world applications of ML related to implementation science, and discuss challenges that implementation scientists and methodologists may face along the way when using ML as a strategy to monitor EBI adoption or to inform the need for interventions. ML approaches can be applied across the continuum of EBI implementation.

Nathalie Huguet, Jinying Chen, Ravi B Parikh, Miguel Marino, Susan A Flocke, Sonja Likumahuwa-Ackman, Justin Bekelman, Jennifer E DeVoe

Online J Public Health Inform 2024;16:e50201

Building on Existing Classifications of Behavior Change Techniques to Classify Planned Coping Strategies: Physical Activity Diary Study

Building on Existing Classifications of Behavior Change Techniques to Classify Planned Coping Strategies: Physical Activity Diary Study

Similar considerations have prompted Knittle et al [35] to develop a compendium of self-enactable techniques. The compendium contains 123 techniques that individuals can carry out themselves in order to achieve behavioral change and maintenance. Second, the level of specificity varies among BCTs. While some techniques are highly specific, such as “information about antecedents” or “prompts or cues,” other techniques are less specific.

Maya Braun, Helene Schroé, Annick L De Paepe, Geert Crombez

JMIR Form Res 2023;7:e50573