Close to one billion individuals globally grapple with mental health conditions, Major Depressive Disorders (MDD) is one of the most widespread among them (Institute of Health Metrics and Evaluation, 2022). MDDs are primarily distinguished by a reduction in energy and interest in daily activities, feelings of sadness, impaired concentration, sleep disturbances, emptiness, irritability, and the presence of suicidal ideation, planning, and attempts. With over 700,000 suicides reported annually, MDD stands as the fourth leading cause of death among individuals aged 15–29 years old (Malhi et al., 2018; World Health Organization, 2022). The onset of the disease occurs during adolescence in nearly 50% of cases, and a high risk of recurrence and chronicity across the lifespan in 5% of cases. Depression is about 50% more common among women than men (Lam et al., 2016).

After experiencing a first depressive episode, the recurrence rate within 3 years is more than 30%, and some patients have recurrent episodes; thus, the course of the disease can be chronic, placing a heavy burden on both patients and society (Kuehner, 2017). Despite its high prevalence, depression remains undiagnosed and untreated in half of all cases (Kohn et al., 2004).

The causes of MDD are complex and include multiple converging factors, such as genetic, immune system, endocrine factors, and stress-related psychosocial conditions (Ruiz et al., 2022). The specific pathways contributing to MDD onset and progression remain unclear (McIntyre et al., 2014; Lai et al., 2019; Wölfer et al., 2019), partly due to its clinical heterogeneity. MDD might have biomarkers from various sources, including inflammation, immune responses, neurotrophins, neurotransmitters, metabolic processes, and neuroendocrine systems (Belzeaux et al., 2018; Mora et al., 2018). Consequently, the use of any potential objective biomarker for accurately diagnosing depression warrants in-depth investigation.

The inflammatory immune-mediated hypothesis supported that immune dysfunction may contribute to both comorbid depression (associated with clinical inflammation) and primary depression in some individuals with low-grade inflammation indices (Averna et al., 2020). Literature data show that inflammatory biomarkers are altered in individuals with MDD (Soskin et al., 2012; Beurel et al., 2020; Osimo et al., 2020). The literature revealed significantly elevated levels of various inflammatory biomarkers, including CRP, IL-3, IL-6, IL-12, IL-18, sIL-2R, and TNFα, in individuals with MDD. Additionally, Osimo et al., observed a decreased mean-scaled variability (CVR) in patients with MDD for CRP, IL-12, and sIL-2R, indicating a more consistent inflammatory phenotype. The overall findings support the association between MDD and a pro-inflammatory state, underscoring the importance of understanding inflammation's role in shaping the clinical phenotype and affecting treatment response (Soskin et al., 2012; Beurel et al., 2020; Osimo et al., 2020).

Several studies support an important correlation between air pollution and depression. Exposure to air pollution has been found to lead to depression-like behaviors in animal studies, and exposure to environmental pollutants such as PM, PM 2.5 (Outdoor and Indoor), PM 10, CO, CO₂, CH4 (Methane), NO₂ (Nitrogen Dioxide), even for extended periods, which can lead to an increase or worsening of depressive symptoms (Szyszkowicz et al., 2016; Kioumourtzoglou et al., 2017; Lin et al., 2017). Air pollution has consistently been associated with increases in pro-inflammatory biomarkers in the blood and systemic oxidative stress, both involved in the pathogenesis of psychiatric disorders such as MDD (Jones et al., 2013; Buoli et al., 2018). Based on current scientific evidence (Ventriglio et al., 2021; Cuijpers et al., 2023), cumulative exposure to pollution can be measured with environmental devices essential for monitoring air quality and pollutant concentrations in the home environment. Also the prolonged exposure to air pollution may adversely affect mental health, underscoring its associations with MDD (Nobile et al., 2023). Comparing short-term and long-term exposure to environmental pollutants with the development of MDD can provide interesting data from a prognostic perspective regarding integrating technology into mental health prevention and treatment processes.

Although biological biomarkers remain central in determining pathology, significant progress has been made in processing social signals as a diagnostic tool (Scherer et al., 2012; Joshi et al., 2013; Williamson et al., 2013). As a supplement to these methodologies, digital indicators of depression have the potential to contribute valuable insights to clinician evaluations. This is particularly advantageous when these indicators can be effortlessly and inconspicuously gathered outside of traditional clinical settings.

Digital biomarkers, collected through digital devices, offer objective and quantifiable physiological and behavioral data. They enable remote data collection, potentially reducing the burden of in-clinic visits and providing insights into clinically relevant changes (McIntyre et al., 2023). Wearable technologies and smartphone apps contribute to digital phenotyping, allowing for moment-by-moment assessment of individual characteristics in real-world environments. These tools have been investigated for their utility in characterizing and diagnosing MDD, including analyzing patterns of physical activity, voice samples, light exposure, and smartphone usage data (Lee S. et al., 2021; Ahmed A. et al., 2023).

The integration of digital biomarkers for depression holds the promise of improving clinical interventions (Mohr et al., 2017). This improvement can manifest through prompt identification for early intervention, continuous assessment during treatment, and the mitigation of disparities in assessment accessibility (Kumar and Phookun, 2016; Naslund et al., 2017).

Voice analytics has shown promise for detecting symptoms of depression (Soskin et al., 2012; Beurel et al., 2020).

The idea of using vocal acoustic features as potential biomarkers for identifying or diagnosing depression is an emerging area of research within the field of computational psychiatry and affective computing. The premise behind this approach is that changes in vocal characteristics, such as pitch, tone, rhythm, and intensity, may reflect underlying emotional states, including depression (Schwoebel et al., 2021; Sverdlov et al., 2021).

Several studies have explored the relationship between vocal features and depression (Mundt et al., 2012; Huang et al., 2021). These studies typically involve collecting speech samples from individuals diagnosed with depression and comparing their vocal characteristics with those of non-depressed individuals. Some common findings include:

Researchers have developed computational algorithms to analyze these vocal features automatically, allowing for objective and quantitative assessment of depressive symptoms. Machine learning (ML) techniques, such as support vector machines, neural networks, and Gaussian mixture models, have been employed to classify individuals as depressed or non-depressed based on their vocal characteristics (Zhao et al., 2022).

While the potential of vocal acoustic features as biomarkers for depression is promising, several challenges remain. These include the need for large-scale validation studies, addressing confounding factors such as age, gender, and cultural differences, and ensuring the privacy and ethical use of speech data in clinical settings.

Integrating computer technologies and AI is a cutting-edge approach in mental health research (Graham et al., 2019), addressing the growing challenges faced by healthcare systems. AI, especially ML, aids in the precise and rapid diagnosis of mental health disorders by analyzing biomarkers related to patients' biological, physiological, and behavioral aspects (Iyortsuun et al., 2023). AI holds promise in understanding the complex interplay of biological, psychological, and social factors in mental health. The use of AI and wearable devices allows for continuous, objective patient monitoring, offering insights into mood changes and treatment effects, thereby enhancing mental health care and research (Nahavandi et al., 2022). Integrating AI algorithms with real-time data from wearables and sensors marks a substantial stride forward, enabling the creation of personalized and responsive mental health systems (Boucher et al., 2021; Omarov et al., 2023). Personal wearable devices like smartwatches and fitness trackers offer real-time data collection for healthcare. They are valuable in monitoring and assessing patients with depression, as they capture data related to physical activity and smartphone usage, among others. Advancements in smartphone-based speech analysis and wearable technologies like smartwatches offer new opportunities for real-life depression screening and the generation of digital biomarkers (Sequeira and Perrotta, 2020; Lee Y. et al., 2021). Research on digital biomarkers addresses four fundamental aspects: forecasting diagnostic status, evaluating symptom severity and progression, discerning treatment response, and monitoring real-world and ecological (Ahmed A. A. M. et al., 2023; Vignapiano et al., 2023). Various wearable technologies have been employed to gather physiological, activity/sleep, or subjective data in order to investigate their correlations with depression.

The next step to apply these findings clinically is to integrate relevant measures into non-invasive and cost-effective tools for use in clinical settings. This integration, including physical, psychopathological, and environmental indicators, could enhance clinical assessments by reducing inter-rater subjectivity relative to diagnosis and symptom evaluation and treatment response (McGuire et al., 2015).

Digital psychiatry integrates various healthcare aspects such as delivery, illness surveillance, disease management, and treatment, with advancements in AI and ML facilitating the translation of new data into clinically relevant digital biomarkers (Orsolini et al., 2024). Wearable devices are expected to play a crucial role in personalized telemedicine, with ongoing research aiming to enhance digital phenotyping precision and efficacy in mental healthcare, ultimately improving the quality of life for individuals with MDD (Courtet et al., 2023).

This study focuses on evaluating specific digital and environmental biomarkers in adolescents and young adults diagnosed with MDD. The study, conducted through a prospective investigation on the MDD population (i.e., MDD), aims to: