In this work, we evaluate the effectiveness of a multicomponent program that includes psychoeducation in academic stress, mindfulness training, and biofeedback-assisted mindfulness, while enhancing the Resilience to Stress Index (RSI) of students through the control of autonomic recovery from psychological stress. Participants are university students enrolled in a program of excellence and are granted an academic scholarship. The dataset consists of an intentional sample of 38 undergraduate students with high academic performance, 71% (27) women, 29% (11) men, and 0% (0) non-binary, with an average age of 20 years. The group belongs to the “Leaders of Tomorrow” scholarship program from Tecnológico de Monterrey University, in Mexico. The program is structured in 16 individual sessions during an eight-week period, divided into three phases: pre-test evaluation, training program, and post-test evaluation. During the evaluation test, an assessment of the psychophysiological stress profile is performed while the participants undergo a stress test; it includes simultaneous recording of skin conductance, breathing rate, blood volume pulse, heart rate, and heart rate variability. Based on the pre-test and post-test psychophysiological variables, an RSI is computed under the assumption that changes in physiological signals due to stress can be compared against a calibration stage. The results show that approximately 66% of the participants improved their academic stress management after the multicomponent intervention program. A Welch’s t-test showed a difference in mean RSI scores (t = −2.30, p = 0.025) between the pre-test and post-test phases. Our findings show that the multicomponent program promoted positive changes in the RSI and in the management of the psychophysiological responses to academic stress.

BACKGROUND Early detection of mental disorders symptoms can lead to prompt and correct diagnosis and reduce the recurrence of these symptoms and associated disabilities. Creating a tool to detect early symptoms is crucial for taking the necessary measures to prevent major onsets of mental diseases. Early indicators of mental health disorders can be detected through changes in daily activity patterns, which activity trackers and speech data can capture. OBJECTIVE We aim to compare the accuracy of personalized machine-learning models with population-level models and evaluate the robustness of these models across various languages. Additionally, investigate the significance of speech data when the user reads a neutral text versus reflecting on their daily life experiences while predicting mental disorders. METHODS Our research is based on longitudinal data from each participant. Hence, we designed the collection process to capture several data points in time that could aid machine learning algorithms to capture patterns of mental disorder symptoms better. This research uses machine learning models to predict the levels of anxiety, stress, and depression in participants based on data collected from wearable devices and voice recordings. The data includes daily activity from smartwatches and voice data collected through text reading and free-form speech. RESULTS The study is ongoing, and data are collected from at least 50 participants attending two major universities, and the data collection complies with ethical and personal data privacy requirements. CONCLUSIONS The study aims to advance personalized machine learning for mental health, generate a dataset to predict DASS21 results, and deploy a framework to detect onsets of depression, anxiety, and stress, with the final goal of developing a non-invasive and objective method for collecting mental health data and prompt detection of mental disorder symptoms.