Prognostic factors and outcome in anorexia nervosa: a follow-up study.

BACKGROUND AND AIMS: Anorexia nervosa is an eating disorder characterized by food restriction, irrational fear of gaining weight and consequent weight loss. High mortality rates have been reported, mostly due to suicide and malnutrition. Good outcomes largely vary between 18 and 42%. We aimed to assess outcome and prognostic factors of a large group of patients with anorexia nervosa. Moreover we aimed to identify clusters of prognostic factors related to specific outcomes. METHODS: We retrospectively reviewed data of 100 patients diagnosed with anorexia nervosa previously hospitalized in a tertiary level structure. Then we performed follow-up structured telephone interviews. RESULTS: We identified four dead patients, while 34% were clinically recovered. In univariate analysis, short duration of inpatient treatment (p = 0.003), short duration of disorder (p = 0.001), early age at first inpatient treatment (p = 0.025) and preserved insight (p = 0.029) were significantly associated with clinical recovery at follow-up. In multiple logistic regression analysis, duration of first inpatient treatment, duration of disorder and preserved insight maintained their association with outcome. Moreover multiple correspondence analysis and cluster analysis allowed to identify different typologies of patients with specific features. Notably, group 1 was characterized by two or more inpatient treatments, BMI ≤ 14, absence of insight, history of long-term inpatient treatments, first inpatient treatment ≥30 days. While group 4 was characterized by preserved insight, BMI ≥ 16, first inpatient treatment ≤14 days, no more than one inpatient treatment, no psychotropic drugs intake, duration of illness ≤4 years. CONCLUSIONS: We confirmed the association between short duration of inpatient treatment, short duration of disorder, early age at first inpatient treatment, preserved insight and clinical recovery. We also differentiated patients with anorexia nervosa in well-defined outcome groups according to specific clusters of prognostic factors. Our study might help clinicians to evaluate prognosis of patients with anorexia nervosa.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder.

Background: Major Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies. Methods: This prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source "Pothos" air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels. Results: Following initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness. Conclusion: This study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.