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.

Mental, neurological, and substance use disorders represent a major global problem. In this context, nutrition is crucial as it affects both the severity and incidence of neuropsychiatric symptoms. In addition to drug treatments, lifestyle modifications, including dietary interventions and targeted supplementation with specific nutrients, could have a beneficial effect on various neuropsychiatric disorders. This study introduces a preliminary framework for assessing the link between nutrition and neuropsychiatric symptoms. Thus, the Evaluating the Importance of Nutrition in Neuropsychiatric Manifestations (SENM) Scale provides a structured approach to examine how diet affects mental health. The study sample of 264 participants, including patients diagnosed with depression, anxiety, and bipolar disorder, as well as mental health and nutrition specialists. Preliminary results indicate a significant correlation between nutritional deficiencies (e.g., B vitamins, omega-3) and worsening of symptoms. Also, most participants emphasised integrating nutritional education into neuropsychiatric treatments. The conclusions suggest that nutrition can be an effective adjunct in mental health treatments, with practical applications and promising future directions.

Artificial intelligence can be used to personalise the counselling plan for individuals with drug addiction, identified through our screening process. Thus, anyone who scores above the average reported in research studies is considered to have issues related to opioid abuse. Additionally, artificial intelligence can provide verified and highly relevant information essential for clarifying the situation of those who respond to the questionnaire. When certain key indicators or markers appear in their answers, it can help young people more easily accept the idea that they are dealing with a form of substance dependence. This represents a significant advantage for individuals with substance use disorders, who often do not accept criticism or being labeled by a doctor or psychologist. However, they may be more open to accepting a perspective offered by a computer-based system. Such a system is perceived as neutral, non-judgmental, and highly objective. In this context, the individual feels that their responses are being evaluated fairly, which encourages greater honesty and increases the likelihood of accepting the AI-generated conclusions as well-founded and difficult to dispute.