Good Science, Poor Visibility? Communicating Behavioural Nutrition and Physical Activity Evidence in the Algorithmic Attention Economy

Abstract

Behavioural nutrition and physical activity research has produced a strong and growing evidence base to inform prevention, treatment, and policy across a wide range of health outcomes. Yet the visibility of this evidence beyond academic audiences does not consistently reflect its scientific quality. Increasingly, dissemination occurs within digital environments shaped by algorithms that prioritise engagement, simplicity, novelty, and emotional salience over nuance and uncertainty. This creates a mismatch between the complexity that characterises behaviour change science and the attention-driven logics that shape what evidence becomes visible.

This Comment was prompted by our involvement in developing the NESI (Network of Early Career Researchers and Students of ISBNPA) Podcast and ASPA (Asia-Pacific Society for Physical Activity) Radio, initiatives designed to disseminate credible behavioural science. Despite prioritising high-quality, peer-reviewed research, we encountered persistent challenges in achieving visibility relative to simplified or commercially framed health narratives amplified in digital spaces.

We argue that visibility in digital environments is algorithmically mediated in ways that can systematically disadvantage complex, context-dependent evidence characteristic of behavioural nutrition and physical activity research. We explore the risks of equating visibility with impact, including oversimplification and erosion of trust, while contending that disengagement from contemporary communication platforms is not viable for a field committed to translation. We propose reframing dissemination as a collective responsibility of researchers, journals, and professional societies, focused on stewardship, integrity, and principled engagement. Addressing the visibility gap is essential to ensuring that credible evidence remains visible, interpretable, and influential within an attention-driven information ecosystem.