Unlocking Mental Health: Exploring College Students' Well-being through Smartphone Behaviors

• URL: http://arxiv.org/abs/2502.08766v1
• Date: Wed, 12 Feb 2025 20:12:45 GMT
• Title: Unlocking Mental Health: Exploring College Students' Well-being through Smartphone Behaviors
• Authors: Wei Xuan, Meghna Roy Chowdhury, Yi Ding, Yixue Zhao,
• Abstract summary: This study is the first to examine the relationship between students' smartphone unlocking behaviors and their mental health at scale in real-world settings. Our findings reveal important variations in smartphone usage across genders and locations, offering a deeper understanding of the interplay between digital behaviors and mental health.
• Score: 4.362487258697971
• License:
• Abstract: The global mental health crisis is a pressing concern, with college students particularly vulnerable to rising mental health disorders. The widespread use of smartphones among young adults, while offering numerous benefits, has also been linked to negative outcomes such as addiction and regret, significantly impacting well-being. Leveraging the longest longitudinal dataset collected over four college years through passive mobile sensing, this study is the first to examine the relationship between students' smartphone unlocking behaviors and their mental health at scale in real-world settings. We provide the first evidence demonstrating the predictability of phone unlocking behaviors for mental health outcomes based on a large dataset, highlighting the potential of these novel features for future predictive models. Our findings reveal important variations in smartphone usage across genders and locations, offering a deeper understanding of the interplay between digital behaviors and mental health. We highlight future research directions aimed at mitigating adverse effects and promoting digital well-being in this population.

Related papers

• 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. 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)
• Leveraging LLMs to Predict Affective States via Smartphone Sensor Features [6.1930355276269875]
  Digital phenotyping involves collecting and analysing data from personal digital devices to infer behaviours and mental health. The emergence of large language models (LLMs) offers a new approach to make sense of smartphone sensing data. Our study aims to bridge this gap by employing LLMs to predict affect outcomes based on smartphone sensing data from university students.
  arXiv  Detail & Related papers  (2024-07-11T07:37:52Z)
• 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)
• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
  According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2022-07-03T11:33:52Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Objective Prediction of Tomorrow's Affect Using Multi-Modal Physiological Data and Personal Chronicles: A Study of Monitoring College Student Well-being in 2020 [0.0]
  The goal of our study was to investigate the capacity to more accurately predict affect through a fully automatic and objective approach using multiple commercial devices. Longitudinal physiological data and daily assessments of emotions were collected from a sample of college students using smart wearables and phones for over a year. Results showed that our model was able to predict next-day affect with accuracy comparable to state of the art methods.
  arXiv  Detail & Related papers  (2022-01-26T23:06:20Z)
• Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data [74.60507696087966]
  Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. One promising data source to help monitor human behavior is daily smartphone usage. We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
  arXiv  Detail & Related papers  (2021-06-24T17:46:03Z)
• 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)
• Understanding College Students' Phone Call Behaviors Towards a Sustainable Mobile Health and Wellbeing Solution [7.441071965808005]
  We try to assess college students' communication patterns that vary across various geographical contexts. Findings from this work will help foster the design and delivery of smartphone-based health interventions.
  arXiv  Detail & Related papers  (2020-11-11T19:00:13Z)
• 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)

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.