research-article

Public Access

DepreST-CAT: Retrospective Smartphone Call and Text Logs Collected during the COVID-19 Pandemic to Screen for Mental Illnesses

Authors:

Ricardo Flores,

Miranda Reisch,

Katie Houskeeper,

Elke A. RundensteinerAuthors Info & Claims

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

Article No.: 75, Pages 1 - 32

https://doi.org/10.1145/3534596

Published: 07 July 2022 Publication History

Abstract

The rates of mental illness, especially anxiety and depression, have increased greatly since the start of the COVID-19 pandemic. Traditional mental illness screening instruments are too cumbersome and biased to screen an entire population. In contrast, smartphone call and text logs passively capture communication patterns and thus represent a promising screening alternative. To facilitate the advancement of such research, we collect and curate the DepreST Call and Text log (DepreST-CAT) dataset from over 365 crowdsourced participants during the COVID-19 pandemic. The logs are labeled with traditional anxiety and depression screening scores essential for training machine learning models. We construct time series ranging from 2 to 16 weeks in length from the retrospective smartphone logs. To demonstrate the screening capabilities of these time series, we then train a variety of unimodal and multimodal machine and deep learning models. These models provide insights into the relative screening value of the different types of logs, lengths of log time series, and classification methods. The DepreST-CAT dataset is a valuable resource for the research community to model communication patterns during the COVID-19 pandemic and further the development of machine learning algorithms for passive mental illness screening.

Supplementary Material

tlachac (tlachac.zip)

Supplemental movie, appendix, image and software files for, DepreST-CAT: Retrospective Smartphone Call and Text Logs Collected during the COVID-19 Pandemic to Screen for Mental Illnesses

Download
130.21 KB

References

[1]

Substance Abuse and Mental Health Services Administration. 2020. Key substance use and mental health indicators in the United States: Results from the 2019 National Survey on Drug Use and Health. (2020).

[2]

David Bakker, Nikolaos Kazantzis, Debra Rickwood, and Nikki Rickard. 2018. A randomized controlled trial of three smartphone apps for enhancing public mental health. Behaviour Research and Therapy 109 (2018), 75--83.

[3]

David Bakker and Nikki Rickard. 2018. Engagement in mobile phone app for self-monitoring of emotional wellbeing predicts changes in mental health: MoodPrism. Journal of affective disorders 227 (2018), 432--442.

[4]

Marília Barandas, Duarte Folgado, Letícia Fernandes, Sara Santos, Mariana Abreu, Patrícia Bota, Hui Liu, Tanja Schultz, and Hugo Gamboa. 2020. TSFEL: Time series feature extraction library. SoftwareX 11 (2020), 100456.

[5]

Nasser F BinDhim, Ahmed M Shaman, Lyndal Trevena, Mada H Basyouni, Lisa G Pont, and Tariq M Alhawassi. 2015. Depression screening via a smartphone app: cross-country user characteristics and feasibility. Journal of the American Medical Informatics Association 22, 1 (2015), 29--34.

[6]

Mehdi Boukhechba, Alexander R Daros, Karl Fua, Philip I Chow, Bethany A Teachman, and Laura E Barnes. 2018. DemonicSalmon: Monitoring mental health and social interactions of college students using smartphones. Smart Health 9 (2018), 192--203.

[7]

Michael Buhrmester, Tracy Kwang, and Samuel D Gosling. 2016. Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality data? American Psychological Association (2016).

[8]

Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. 1293--1304.

Digital Library

[9]

Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li. 2018. Brits: Bidirectional recurrent imputation for time series. arXiv preprint arXiv:1805.10572 (2018).

[10]

Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. 2018. Recurrent neural networks for multivariate time series with missing values. Scientific reports 8, 1 (2018), 1--12.

[11]

Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785--794.

Digital Library

[12]

Prerna Chikersal, Afsaneh Doryab, Michael Tumminia, Daniella K Villalba, Janine M Dutcher, Xinwen Liu, Sheldon Cohen, Kasey G Creswell, Jennifer Mankoff, J David Creswell, Mayank Goel, and Anind K Dey. 2021. Detecting depression and predicting its onset using longitudinal symptoms captured by passive sensing: a machine learning approach with robust feature selection. ACM Transactions on Computer-Human Interaction (TOCHI) 28, 1 (2021), 1--41.

Digital Library

[13]

Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).

[14]

Mark É Czeisler, Rashon I Lane, Emiko Petrosky, Joshua F Wiley, Aleta Christensen, Rashid Njai, Matthew D Weaver, Rebecca Robbins, Elise R Facer-Childs, Laura K Barger, et al. 2020. Mental health, substance use, and suicidal ideation during the COVID-19 pandemic---United States, June 24-30, 2020. Morbidity and Mortality Weekly Report 69, 32 (2020), 1049.

[15]

Giovanni De Girolamo, Jessica Dagani, R Purcell, Angelo Cocchi, and PD McGorry. 2012. Age of onset of mental disorders and use of mental health services: needs, opportunities and obstacles. Epidemiology and psychiatric sciences 21, 1 (2012), 47--57.

[16]

Koen Demyttenaere, Anke Bonnewyn, Ronny Bruffaerts, Terry Brugha, Ron De Graaf, and Jordi Alonso. 2006. Comorbid painful physical symptoms and depression: prevalence, work loss, and help seeking. Journal of affective disorders 92 (2006), 185--193.

[17]

Daniel Di Matteo, Kathryn Fotinos, Sachinthya Lokuge, Julia Yu, Tia Sternat, Martin A Katzman, and Jonathan Rose. 2020. The relationship between smartphone-recorded environmental audio and symptomatology of anxiety and depression: exploratory study. JMIR Formative Research 4, 8 (2020), e18751.

[18]

Ada Dogrucu, Alex Perucic, Anabella Isaro, Damon Ball, Ermal Toto, Elke A Rundensteiner, Emmanuel Agu, Rachel Davis-Martin, and Edwin Boudreaux. 2020. Moodable: On feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data. Smart Health 17 (2020), 100118.

[19]

Dominic B Dwyer, Peter Falkai, and Nikolaos Koutsouleris. 2018. Machine learning approaches for clinical psychology and psychiatry. Annual review of clinical psychology 14 (2018), 91--118.

[20]

Ronald M Epstein, Paul R Duberstein, Mitchell D Feldman, Aaron B Rochlen, Robert A Bell, Richard L Kravitz, Camille Cipri, Jennifer D Becker, Patricia M Bamonti, and Debora A Paterniti. 2010. "I didn't know what was wrong:" how people with undiagnosed depression recognize, name and explain their distress. Journal of general internal medicine 25, 9 (2010), 954--961.

[21]

Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. 2019. Deep learning for time series classification: a review. Data mining and knowledge discovery 33, 4 (2019), 917--963.

Digital Library

[22]

Jayde AM Flett, Harlene Hayne, Benjamin C Riordan, Laura M Thompson, and Tamlin S Conner. 2019. Mobile mindfulness meditation: a randomised controlled trial of the effect of two popular apps on mental health. Mindfulness 10, 5 (2019), 863--876.

[23]

Walter Gerych, Emmanuel Agu, and Elke Rundensteiner. 2019. Classifying depression in imbalanced datasets using an autoencoder-based anomaly detection approach. In 2019 IEEE 13th International Conference on Semantic Computing (ICSC). IEEE, 124--127.

[24]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT press.

Digital Library

[25]

Aron Halfin. 2007. Depression: the benefits of early and appropriate treatment. American Journal of Managed Care 13, 4 (2007), S92.

[26]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780.

Digital Library

[27]

Jacqueline Howard. 2021. All 50 states now have expanded or will expand Covid vaccine eligibility to everyone 16 and up. Cable News Network (CNN) Health (2021).

[28]

Zhaocheng Huang, Julien Epps, Dale Joachim, and Michael Chen. 2018. Depression Detection from Short Utterances via Diverse Smartphones in Natural Environmental Conditions. In INTERSPEECH. 3393--3397.

[29]

Jeremy F Huckins, Alex W DaSilva, Weichen Wang, Elin Hedlund, Courtney Rogers, Subigya K Nepal, Jialing Wu, Mikio Obuchi, Eilis I Murphy, Meghan L Meyer, et al. 2020. Mental health and behavior of college students during the early phases of the COVID-19 pandemic: Longitudinal smartphone and ecological momentary assessment study. Journal of medical Internet research 22, 6 (2020), e20185.

[30]

Ronald C Kessler, Steven Heeringa, Matthew D Lakoma, Maria Petukhova, Agnes E Rupp, Michael Schoenbaum, Philip S Wang, and Alan M Zaslavsky. 2008. Individual and societal effects of mental disorders on earnings in the United States: results from the national comorbidity survey replication. American Journal of Psychiatry 165, 6 (2008), 703--711.

[31]

Kurt Kroenke, Robert L Spitzer, and Janet BW Williams. 2001. The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine 16, 9 (2001), 606--613.

[32]

Boning Li and Akane Sano. 2020. Extraction and interpretation of deep autoencoder-based temporal features from wearables for forecasting personalized mood, health, and stress. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 2 (2020), 1--26.

Digital Library

[33]

Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130 (2017).

[34]

Anmol Madan, Manuel Cebrian, Sai Moturu, Katayoun Farrahi, et al. 2011. Sensing the "health state" of a community. IEEE Pervasive Computing 11, 4 (2011), 36--45.

Digital Library

[35]

Mehrab Bin Morshed, Koustuv Saha, Richard Li, Sidney K D'Mello, Munmun De Choudhury, Gregory D Abowd, and Thomas Plötz. 2019. Prediction of mood instability with passive sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 3 (2019), 1--21.

Digital Library

[36]

Stefan Palan and Christian Schitter. 2018. Prolific. ac---A subject pool for online experiments. Journal of Behavioral and Experimental Finance 17 (2018), 22--27.

[37]

Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013. On the difficulty of training recurrent neural networks. In International conference on machine learning. PMLR, 1310--1318.

[38]

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825--2830.

[39]

Pew Research Center. 2019. Smartphone ownership is growing rapidly around the world but not always equally. https://www.pewresearch.org/global/2019/02/05/smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/

[40]

Mashfiqui Rabbi, Shahid Ali, Tanzeem Choudhury, and Ethan Berke. 2011. Passive and in-situ assessment of mental and physical well-being using mobile sensors. In Proceedings of the 13th international conference on Ubiquitous computing. 385--394.

Digital Library

[41]

Margot L Savoy and David T O'Gurek. 2016. Screening your adult patients for depression. Family practice management 23, 2 (2016), 16--20.

[42]

Albert L Siu, Kirsten Bibbins-Domingo, David C Grossman, Linda Ciofu Baumann, Karina W Davidson, Mark Ebell, Francisco AR García, Matthew Gillman, Jessica Herzstein, Alex R Kemper, et al. 2016. Screening for depression in adults: US Preventive Services Task Force recommendation statement. Jama 315, 4 (2016), 380--387.

[43]

Steven J. Spencer, Christine Logel, and Paul Davies. 2016. Stereotype Threat. The Annual Review of Psychology 67 (2016).

[44]

Robert L Spitzer, Kurt Kroenke, Janet BW Williams, and Bernd Löwe. 2006. A brief measure for assessing generalized anxiety disorder: the GAD-7. Archives of Internal Medicine 166, 10 (2006), 1092--1097.

[45]

Sara Taylor, Natasha Jaques, Ehimwenma Nosakhare, Akane Sano, and Rosalind Picard. 2017. Personalized multitask learning for predicting tomorrow's mood, stress, and health. IEEE Transactions on Affective Computing 11, 2 (2017), 200--213.

[46]

ML Tlachac, Katherine Dixon-Gordon, and Elke Rundensteiner. 2021. Screening for Suicidal Ideation with Text Messages. In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 1--4.

[47]

ML Tlachac, Veronica Melican, Miranda Reisch, and Elke Rundensteiner. 2021. Mobile Depression Screening with Time Series of Text Logs and Call Logs. In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 1--4.

[48]

ML Tlachac and Elke Rundensteiner. 2020. Screening for depression with retrospectively harvested private versus public text. IEEE journal of biomedical and health informatics 24, 11 (2020), 3326--3332.

[49]

ML Tlachac and Elke A Rundensteiner. 2020. Depression screening from text message reply latency. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 5490--5493.

[50]

ML Tlachac, Ermal Toto, Joshua Lovering, Rimsha Kayastha, Nina Taurich, and Elke Rundensteiner. 2021. Emu: Early mental health uncovering framework and dataset. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 1311--1318.

[51]

ML Tlachac, Ermal Toto, and Elke Rundensteiner. 2019. You're making me depressed: Leveraging texts from contact subsets to predict depression. In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 1--4.

[52]

M L Tlachac, Ricardo Flores, Miranda Reisch, Rimsha Kayastha, Nina Taurich, Veronica Melican, Connor Bruneau, Hunter Caouette, Joshua Lovering, Ermal Toto, and Elke Rundensteiner. Accepted. StudentSADD: Depression and Suicidal Ideation Screening of College Students during COVID-19. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Accepted).

[53]

Ermal Toto, ML Tlachac, and Elke A Rundensteiner. 2021. AudiBERT: A Deep Transfer Learning Multimodal Classification Framework for Depression Screening. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 4145--4154.

Digital Library

[54]

Ermal Toto, ML Tlachac, Francis Lee Stevens, and Elke A Rundensteiner. 2020. Audio-based Depression Screening using Sliding Window Sub-clip Pooling. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 791--796.

[55]

Fabian Wahle, Tobias Kowatsch, Elgar Fleisch, Michael Rufer, Steffi Weidt, et al. 2016. Mobile sensing and support for people with depression: a pilot trial in the wild. JMIR mHealth and uHealth 4, 3 (2016), e5960.

[56]

Philip S Wang, Gregory Simon, and Ronald C Kessler. 2003. The economic burden of depression and the cost-effectiveness of treatment. International journal of methods in psychiatric research 12, 1 (2003), 22--33.

[57]

Rui Wang, Min SH Aung, Saeed Abdullah, Rachel Brian, Andrew T Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Michael Merrill, Emily A Scherer, Vincent WS Tsent, and Dror Ben-Zeev. 2016. CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia. In Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing. 886--897.

Digital Library

[58]

Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T Campbell. 2014. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. 3--14.

Digital Library

[59]

Rui Wang, Weichen Wang, Min SH Aung, Dror Ben-Zeev, Rachel Brian, Andrew T Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Emily A Scherer, and Megan Walsh. 2017. Predicting symptom trajectories of schizophrenia using mobile sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 1--24.

Digital Library

[60]

Rui Wang, Weichen Wang, Alex DaSilva, Jeremy F Huckins, William M Kelley, Todd F Heatherton, and Andrew T Campbell. 2018. Tracking depression dynamics in college students using mobile phone and wearable sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 1--26.

Digital Library

[61]

Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang. 2020. Predicting depressive symptoms using smartphone data. Smart Health 15 (2020), 100093.

[62]

Mark D Weist, Marcia Rubin, Elizabeth Moore, Steven Adelsheim, and Gordon Wrobel. 2007. Mental health screening in schools. Journal of School Health 77, 2 (2007), 53--58.

[63]

Britta Wetzel, Rüdiger Pryss, Harald Baumeister, Johanna-Sophie Edler, Ana Sofia Oliveira Gonçalves, and Caroline Cohrdes. 2021. "How Come You Don't Call Me?" Smartphone Communication App Usage as an Indicator of Loneliness and Social Well-Being across the Adult Lifespan during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health 18, 12 (2021), 6212.

[64]

Xuhai Xu, Prerna Chikersal, Afsaneh Doryab, Daniella K Villalba, Janine M Dutcher, Michael J Tumminia, Tim Althoff, Sheldon Cohen, Kasey G Creswell, J David Creswell, Jennifer Mankoff, and Anind K Dey. 2019. Leveraging routine behavior and contextually-filtered features for depression detection among college students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 3 (2019), 1--33.

Digital Library

[65]

BF Yan, Ayaho Miyamoto, and Eugen Brühwiler. 2006. Wavelet transform-based modal parameter identification considering uncertainty. Journal of Sound and Vibration 291, 1-2 (2006), 285--301.

[66]

Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018. Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine 13, 3 (2018), 55--75.

Cited By

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
https://dl.acm.org/doi/10.1145/3643554
Matias PAraújo RGraça RHenriques ABelo DValada MLotfi NMateus ERadner HRodrigues AStudenic PNunes F(2024)COTIDIANA Dataset – Smartphone-Collected Data on the Mobility, Finger Dexterity, and Mental Health of People With Rheumatic and Musculoskeletal DiseasesIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.345606928:11(6538-6547)Online publication date: Nov-2024
https://doi.org/10.1109/JBHI.2024.3456069
Tlachac MHeinz M(2024)Mental Health and Mobile Communication Profiles of Crowdsourced ParticipantsIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.343665428:12(7683-7692)Online publication date: Dec-2024
https://doi.org/10.1109/JBHI.2024.3436654
Show More Cited By

Index Terms

DepreST-CAT: Retrospective Smartphone Call and Text Logs Collected during the COVID-19 Pandemic to Screen for Mental Illnesses

Recommendations

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

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 ...
Left on Read: Reply Latency for Anxiety & Depression Screening
UbiComp/ISWC '22 Adjunct: Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers

Mental health is a critical societal issue and early screening is vital to enabling timely treatment. The rise of text-based communications provides new modalities that can be used to passively screen for mental illnesses. In this paper we present an ...
Experiences in mHealth for chronic disease management in 4 countries
ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies

This paper describes mHealth applications to deal with Non Communicable Diseases in North and Latin America: In Chile, a project focused on Diabetes Mellitus type 2; In the United States, Honduras, and Mexico, projects focused in diabetes, heart failure,...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

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

July 2022

1551 pages

EISSN:2474-9567

DOI:10.1145/3547347

Issue’s Table of Contents

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2022

Published in IMWUT Volume 6, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

NSF (National Science Foundation)
Fulbright Foreign Student Program
Chilean National Agency for Research and Development
DraftKings Fellowship
US Department of Education
AFRI

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
553
Total Downloads

Downloads (Last 12 months)244
Downloads (Last 6 weeks)31

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

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
https://dl.acm.org/doi/10.1145/3643554
Matias PAraújo RGraça RHenriques ABelo DValada MLotfi NMateus ERadner HRodrigues AStudenic PNunes F(2024)COTIDIANA Dataset – Smartphone-Collected Data on the Mobility, Finger Dexterity, and Mental Health of People With Rheumatic and Musculoskeletal DiseasesIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.345606928:11(6538-6547)Online publication date: Nov-2024
https://doi.org/10.1109/JBHI.2024.3456069
Tlachac MHeinz M(2024)Mental Health and Mobile Communication Profiles of Crowdsourced ParticipantsIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.343665428:12(7683-7692)Online publication date: Dec-2024
https://doi.org/10.1109/JBHI.2024.3436654
Bryan AHeinz MSalzhauer APrice GTlachac MJacobson N(2024)Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health AssessmentBiomedical Materials & Devices10.1007/s44174-023-00150-42:2(778-810)Online publication date: 22-Feb-2024
https://doi.org/10.1007/s44174-023-00150-4
Tlachac MShrestha AShah MLitterer BRundensteiner E(2023)Automated Construction of Lexicons to Improve Depression Screening With Text MessagesIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2022.320334527:6(2751-2759)Online publication date: Jun-2023
https://doi.org/10.1109/JBHI.2022.3203345
Tlachac MReisch MHeinz M(2023)Mobile Communication Log Time Series to Detect Depressive Symptoms2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC40787.2023.10341154(1-4)Online publication date: 24-Jul-2023
https://doi.org/10.1109/EMBC40787.2023.10341154
Flores RShrestha ATlachac MRundensteiner E(2023)Multi-Task Learning Using Facial Features for Mental Health Screening2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386191(4881-4890)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386191
Tlachac MGerych WAgrawal KLitterer BJurovich NThatigotla SThadajarassiri JRundensteiner E(2022)Text Generation to Aid Depression Detection: A Comparative Study of Conditional Sequence Generative Adversarial Networks2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020224(2804-2813)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020224
Tlachac MFlores RToto ERundensteiner E(2022)Early Mental Health Uncovering with Short Scripted and Unscripted Voice RecordingsDeep Learning Applications, Volume 410.1007/978-981-19-6153-3_4(79-110)Online publication date: 26-Nov-2022
https://doi.org/10.1007/978-981-19-6153-3_4

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents