Student Mental Health Screening via Fitbit Data Collected During the COVID-19 Pandemic

• URL: http://arxiv.org/abs/2601.16324v1
• Date: Thu, 22 Jan 2026 21:18:01 GMT
• Title: Student Mental Health Screening via Fitbit Data Collected During the COVID-19 Pandemic
• Authors: Rebecca Lopez, Avantika Shrestha, ML Tlachac, Kevin Hickey, Xingtong Guo, Shichao Liu, Elke Rundensteiner,
• Abstract summary: We collect the Student Mental and Environmental Health (StudentMEH) Fitbit dataset from students at our institution during the pandemic.<n>We provide a comprehensive assessment of the ability of predictive machine learning models to screen for depression, anxiety, and stress using different Fitbit modalities.
• Score: 4.943603895773321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: College students experience many stressors, resulting in high levels of anxiety and depression. Wearable technology provides unobtrusive sensor data that can be used for the early detection of mental illness. However, current research is limited concerning the variety of psychological instruments administered, physiological modalities, and time series parameters. In this research, we collect the Student Mental and Environmental Health (StudentMEH) Fitbit dataset from students at our institution during the pandemic. We provide a comprehensive assessment of the ability of predictive machine learning models to screen for depression, anxiety, and stress using different Fitbit modalities. Our findings indicate potential in physiological modalities such as heart rate and sleep to screen for mental illness with the F1 scores as high as 0.79 for anxiety, the former modality reaching 0.77 for stress screening, and the latter modality achieving 0.78 for depression. This research highlights the potential of wearable devices to support continuous mental health monitoring, the importance of identifying best data aggregation levels and appropriate modalities for screening for different mental ailments.

Related papers

• ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media [48.56586765769052]
  We present ReDepress, the first clinically validated social media dataset focused on relapse.<n>Our framework draws on cognitive theories of depression, incorporating constructs such as attention bias, interpretation bias, memory bias and rumination.<n>Our findings validate psychological theories in real-world textual data and underscore the potential of cognitive-informed computational methods for early relapse detection.
  arXiv  Detail & Related papers  (2025-09-22T16:33:59Z)
• Real-Time Stress Monitoring, Detection, and Management in College Students: A Wearable Technology and Machine-Learning Approach [1.0389278083386493]
  College students are increasingly affected by stress, anxiety, and depression.<n>This study evaluated the efficacy of a mobile health (mHealth) intervention, Mental Health Evaluation and Lookout Program (mHELP)<n>mHELP integrates a smartwatch sensor and machine learning (ML) algorithms for real-time stress detection and self-management.
  arXiv  Detail & Related papers  (2025-05-21T19:45:42Z)
• Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data [2.2310516973117194]
  This study evaluated the feasibility of integrating active and passive smartphone data to predict mental disorders in non-clinical adolescents.<n>We investigated the Mindcraft app in predicting risks for internalising and externalising disorders, eating disorders, insomnia and suicidal ideation.
  arXiv  Detail & Related papers  (2025-01-15T15:05:49Z)
• Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants [2.2909783327197393]
  We conducted a cross-sectional analysis of data from 10,129 participants recruited from a UK-based general population. Participants shared wearable (Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety (GAD-7), and mood via a study app. We observed significant associations between the severity of depression and anxiety with several factors, including mood, age, gender, BMI, sleep patterns, physical activity, and heart rate.
  arXiv  Detail & Related papers  (2024-09-24T16:05:17Z)
• Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings? [0.210674772139335]
  This study aims to bridge the gap between laboratory-based studies and real-life settings by leveraging consumer-grade wearables and self-report measures. We present the initial analysis of the collected data, focusing primarily on the results of valence classification. This research opens up avenues for future research in the field of mobile mental health interventions.
  arXiv  Detail & Related papers  (2023-12-21T13:57:34Z)
• A Hybrid Approach for Depression Classification: Random Forest-ANN Ensemble on Motor Activity Signals [4.798808180453298]
  Wearable sensors provide a potential way to track and comprehend mental health issues. Recent research has used these sensors in conjunction with machine learning methods to identify patterns relating to different mental health conditions. We present a novel algorithm called the Hybrid Random forest - Neural network that has been tailored to evaluate sensor data from depressed patients.
  arXiv  Detail & Related papers  (2023-10-13T17:39:35Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• What's on your mind? A Mental and Perceptual Load Estimation Framework towards Adaptive In-vehicle Interaction while Driving [55.41644538483948]
  We analyze the effects of mental workload and perceptual load on psychophysiological dimensions. We classify the mental and perceptual load levels through the fusion of these measurements. We report up to 89% mental workload classification accuracy and provide a real-time minimally-intrusive solution.
  arXiv  Detail & Related papers  (2022-08-10T21:19:49Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)
• Anxiety Detection Leveraging Mobile Passive Sensing [53.11661460916551]
  Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods. eWellness is an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner.
  arXiv  Detail & Related papers  (2020-08-09T20:22:52Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.