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StudentSADD: Rapid Mobile Depression and Suicidal Ideation Screening of College Students during the Coronavirus Pandemic

Authors:

Ricardo Flores,

Miranda Reisch,

Rimsha Kayastha,

Veronica Melican,

Connor Bruneau,

Hunter Caouette,

Joshua Lovering,

Elke A. RundensteinerAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 6, Issue 2

Article No.: 76, Pages 1 - 32

https://doi.org/10.1145/3534604

Published: 07 July 2022 Publication History

Abstract

The growing prevalence of depression and suicidal ideation among college students further exacerbated by the Coronavirus pandemic is alarming, highlighting the need for universal mental illness screening technology. With traditional screening questionnaires too burdensome to achieve universal screening in this population, data collected through mobile applications has the potential to rapidly identify at-risk students. While prior research has mostly focused on collecting passive smartphone modalities from students, smartphone sensors are also capable of capturing active modalities. The general public has demonstrated more willingness to share active than passive modalities through an app, yet no such dataset of active mobile modalities for mental illness screening exists for students. Knowing which active modalities hold strong screening capabilities for student populations is critical for developing targeted mental illness screening technology. Thus, we deployed a mobile application to over 300 students during the COVID-19 pandemic to collect the Student Suicidal Ideation and Depression Detection (StudentSADD) dataset. We report on a rich variety of machine learning models including cutting-edge multimodal pretrained deep learning classifiers on active text and voice replies to screen for depression and suicidal ideation. This unique StudentSADD dataset is a valuable resource for the community for developing mobile mental illness screening tools.

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Tlachac MHeinz MReisch MOgden S(2024)Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety ScreeningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435548:1(1-28)Online publication date: 6-Mar-2024
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StudentSADD: Rapid Mobile Depression and Suicidal Ideation Screening of College Students during the Coronavirus Pandemic

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 6, Issue 2

June 2022

1551 pages

EISSN:2474-9567

DOI:10.1145/3547347

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Tlachac MHeinz MReisch MOgden S(2024)Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety ScreeningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435548:1(1-28)Online publication date: 6-Mar-2024
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Kumar PPunia ADagar AGaur S(2024)MindWell: A Dataset Related to Mental HealthProceedings of the NIELIT's International Conference on Communication, Electronics and Digital Technology10.1007/978-981-97-3601-0_34(477-487)Online publication date: 31-Jul-2024
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