From Traditional Medical Patterns to Artificial Intelligence: The Applicability of Digital Biomarkers in Reinventing Personalised Nutrition

Abstract

The purpose of this study is to assess the association between digital biomarkers on diet compliance and other health-related outcomes in adults willing to change their eating behaviours. This study employed a mixed-method, cross-sectional design with 100 participants over three months. Basic demographic and clinical data were obtained, and self-monitoring was done using digital biomarkers of diet and physical activity. The dietary compliance level was measured pre- and post-intervention using a dietary questionnaire. Quantitative analyses included paired t-tests, correlation, regression, and mediation analyses to assess the impact of digital biomarkers on nutritional and health outcomes. There was a statistically significant improvement in dietary adherence scores post-intervention (t = 9.61, p < 0.001). A positive relationship was found between digital biomarker use and diet consumption (r = 0.45, p < 0.001) and weight loss (r = 0.32, p < 0.05). Regression analysis indicated that baseline dietary adherence and biomarker use were significant predictors of weight loss (F (1, 22) = 7.92; p < 0.001 and F (1,22) = 7.69; p < 0.01), though the model explained only 20% of variance, suggesting other influencing factors. This study identified usefulness, motivation, and responsibility as key themes related to digital biomarker use. Digital biomarkers hold potential for enhancing personalised nutrition interventions and adherence to dietary recommendations. Future research should focus on data privacy, user involvement, and improving AI algorithms for clinical implementation.