Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening

Published: 06 March 2024 Publication History
  • Get Citation Alerts
  • Abstract

    Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are both heterogeneous in their clinical presentations, manifesting with unique symptom profiles. Despite this, prior digital phenotype research has primarily focused on disorder-level detection rather than symptom-level detection. In this research, we predict the existence of individual symptoms of MDD and GAD with SMS log metadata, and ensemble these symptom-level classifiers to screen for depression and anxiety, thus accounting for disorder heterogeneity. Further, we collect an additional dataset of retrospectively harvested SMS logs to augment an existing dataset collected after COVID-19 altered communication patterns, and propose two new types of distribution features: consecutive messages and conversation ratio. Our symptom-level detectors achieved a balanced accuracy of 0.7 in 13 of the 16 MDD and GAD symptoms, with reply latency distribution features achieving a balanced accuracy of 0.78 when detecting anxiety symptom trouble relaxing. When combined into disorder-level ensembles, these symptom-level detectors achieved a balanced accuracy of 0.76 for depression screening and 0.73 for anxiety screening, with tree boosting methods demonstrating particular efficacy. Accounting for disorder heterogeneity, our research provides insight into the value of SMS logs for the assessment of depression and anxiety diagnostic criteria.

    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]
    United States Environmental Protection Agency. 2013. Power Plants and Neighboring Communities. https://www.epa.gov/power-sector/power-plants-and-neighboring-communities
    [3]
    Sharifa Alghowinem, Roland Goecke, Michael Wagner, Julien Epps, Michael Breakspear, Gordon Parker, et al. 2012. From joyous to clinically depressed: Mood detection using spontaneous speech. In FLAIRS Conference, Vol. 19.
    [4]
    Gavin Andrews. 1996. Comorbidity and the General Neurotic Syndrome. The British Journal of Psychiatry 168, S30 (1996), 76--84.
    [5]
    Daniel Arias, Shekhar Saxena, and Stéphane Verguet. 2022. Quantifying the global burden of mental disorders and their economic value. eClinicalMedicine 54 (Dec. 2022).
    [6]
    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.
    [7]
    James C Ballenger. 2000. Anxiety and depression: Optimizing treatments. Prim. Care Companion J. Clin. Psychiatry 2, 3 (2000), 71--79.
    [8]
    Katja Beesdo-Baum, Elsa Jenjahn, Michael Höfler, Ulrike Lueken, Eni S. Becker, and Jürgen Hoyer. 2012. Avoidance, safety behavior, and reassurance seeking in generalized anxiety disorder. Depression and Anxiety 29, 11 (2012), 948--957.
    [9]
    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.
    [10]
    Louise Brådvik. 2018. Suicide risk and mental disorders. Int. J. Environ. Res. Public Health 15, 9 (Sept. 2018), 2028.
    [11]
    Anastasia Bryan, Michael Heinz, Abagail Salzhauer, George Price, M. L. Tlachac, and Nicholas Jacobson. 2024. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials and Devices (2024). In Press.
    [12]
    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.
    [13]
    Stevie Chancellor and Munmun De Choudhury. 2020. Methods in predictive techniques for mental health status on social media: a critical review. NPJ digital medicine 3, 1 (2020), 43.
    [14]
    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.
    [15]
    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.
    [16]
    Nicholas Cummins, Stefan Scherer, Jarek Krajewski, Sebastian Schnieder, Julien Epps, and Thomas F Quatieri. 2015. A review of depression and suicide risk assessment using speech analysis. Speech communication 71 (2015), 10--49.
    [17]
    Bruce N. Cuthbert. 2014. The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry 13, 1 (2014), 28--35.
    [18]
    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.
    [19]
    Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013. Predicting depression via social media. In Proceedings of the international AAAI conference on web and social media, Vol. 7. 128--137.
    [20]
    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.
    [21]
    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.
    [22]
    Samuel L Dickman, David U Himmelstein, and Steffie Woolhandler. 2017. Inequality and the health-care system in the USA. The Lancet 389, 10077 (April 2017), 1431--1441.
    [23]
    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).
    [24]
    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.
    [25]
    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.
    [26]
    Asma Ahmad Farhan, Chaoqun Yue, Reynaldo Morillo, Shweta Ware, Jin Lu, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang. 2016. Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. In 2016 IEEE wireless health (WH). IEEE, 1--8.
    [27]
    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.
    [28]
    Ricardo Flores, Avantika Shrestha, ML Tlachac, and Elke Rundensteiner. 2023. Multi-Task Learning Using Facial Features for Mental Health Screening. In 2023 IEEE International Conference on Big Data (Big Data).
    [29]
    Ricardo Flores, ML Tlachac, Avantika Shrestha, and Elke Rundensteiner. 2022. Temporal Facial Features for Depression Screening. In Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). 488--493.
    [30]
    Pascal Geldsetzer. 2020. Use of rapid online surveys to assess people's perceptions during infectious disease outbreaks: a cross-sectional survey on COVID-19. Journal of medical Internet research 22, 4 (2020), e18790.
    [31]
    David Goldberg. 2011. The heterogeneity of "major depression". World Psychiatry 10, 3 (Oct. 2011), 226--228.
    [32]
    Aron Halfin. 2007. Depression: the benefits of early and appropriate treatment. American Journal of Managed Care 13, 4 (2007), S92.
    [33]
    Daniel Highland and Gang Zhou. 2022. A review of detection techniques for depression and bipolar disorder. Smart Health 24 (2022).
    [34]
    Juyoung Hong, Jiwon Kim, Sunmi Kim, Jaewon Oh, Deokjong Lee, San Lee, Jinsun Uh, Juhong Yoon, and Yukyung Choi. 2022. Depressive symptoms feature-based machine learning approach to predicting depression using smartphone. In Healthcare, Vol. 10. MDPI.
    [35]
    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.
    [36]
    Jeremy F Huckins, Alex W DaSilva, Elin L Hedlund, Eilis I Murphy, Courtney Rogers, Weichen Wang, Mikio Obuchi, Paul Holtzheimer, Dylan Wagner, and Andrew Campbell. 2020. Causal factors of anxiety and depression in college students: longitudinal ecological momentary assessment and causal analysis using Peter and Clark momentary conditional independence. JMIR mental health 7, 6 (2020).
    [37]
    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).
    [38]
    Pfizer Inc. 1999. PHQ-9 Patient Depression Questionnaire. https://med.stanford.edu/fastlab/research/imapp/msrs/_jcr_content/main/accordion/accordion_content3/download_256324296/file.res/PHQ9%20id%20date%2008.03.pdf
    [39]
    Spencer L James, Degu Abate, Kalkidan Hassen Abate, Solomon M Abay, Cristiana Abbafati, Nooshin Abbasi, Hedayat Abbastabar, Foad Abd-Allah, Jemal Abdela, Ahmed Abdelalim, et al. 2018. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990--2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392, 10159 (2018), 1789--1858.
    [40]
    Salene MW Jones, Yuxian Du, Laura Panattoni, and Nora B Henrikson. 2019. Assessing worry about affording healthcare in a general population sample. Frontiers in Psychology 10 (2019), 2622.
    [41]
    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.
    [42]
    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.
    [43]
    Pranav Kulkarni, Reuben Kirkham, and Roisin McNaney. 2022. Opportunities for smartphone sensing in e-health research: a narrative review. Sensors 22, 10 (2022), 3893.
    [44]
    Aleksandra Kupferberg, Lucy Bicks, and Gregor Hasler. 2016. Social functioning in major depressive disorder. Neuroscience & Biobehavioral Reviews 69 (2016), 313--332. https://www.sciencedirect.com/science/article/pii/S0149763415302487
    [45]
    Tony Liu, Jonah Meyerhoff, Johannes C Eichstaedt, Chris J Karr, Susan M Kaiser, Konrad P Kording, David C Mohr, and Lyle H Ungar. 2022. The relationship between text message sentiment and self-reported depression. Journal of affective disorders 302 (2022), 7--14.
    [46]
    Y Maaravi and B Heller. 2020. Not all worries were created equal: the case of COVID-19 anxiety. Public Health 185 (2020), 243--245.
    [47]
    Anmol Madan, Manuel Cebrian, David Lazer, and Alex Pentland. 2010. Social sensing for epidemiological behavior change. In Proceedings of the 12th ACM international conference on Ubiquitous computing. 291--300.
    [48]
    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.
    [49]
    Robert FK Martin, Patrick Leppink-Shands, Matthew Tlachac, Megan DuBois, Christine Conelea, Suma Jacob, Vassilios Morellas, Theodore Morris, and Nikolaos Papanikolopoulos. 2021. The Use of Immersive Environments for the Early Detection and Treatment of Neuropsychiatric Disorders. Frontiers in Digital Health 2 (2021), 576076.
    [50]
    Katie A. McLaughlin and Susan Nolen-Hoeksema. 2011. Rumination as a transdiagnostic factor in depression and anxiety. Behaviour Research and Therapy 49, 3 (2011), 186--193.
    [51]
    Jonah Meyerhoff, Tingting Liu, Caitlin A Stamatis, Tony Liu, Harry Wang, Yixuan Meng, Brenda Curtis, Chris J Karr, Garrick Sherman, Lyle H Ungar, et al. 2023. Analyzing text message linguistic features: Do people with depression communicate differently with their close and non-close contacts? Behaviour Research and Therapy (2023), 104342.
    [52]
    Arijit Nandi, John R. Beard, and Sandro Galea. 2009. Epidemiologic heterogeneity of common mood and anxiety disorders over the lifecourse in the general population: a systematic review. BMC Psychiatry 9, 1 (01 Jun 2009), 31. https://doi.org/10.1186/1471-244X-9-31
    [53]
    Matthew David Nemesure, Michael V Heinz, Jason McFadden, Nicholas C Jacobson, et al. 2021. Predictive modeling approach to evaluate individual response to a physical activity digital intervention for subjects with major depressive disorder. (2021).
    [54]
    Subigya Nepal, Weichen Wang, Vlado Vojdanovski, Jeremy F Huckins, Alex Dasilva, Meghan Meyer, and Andrew Campbell. 2022. COVID student study: A year in the life of college students during the COVID-19 pandemic through the lens of mobile phone sensing. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1--19.
    [55]
    Stefan Palan and Christian Schitter. 2018. Prolific. ac---A subject pool for online experiments. Journal of Behavioral and Experimental Finance 17 (2018), 22--27.
    [56]
    Gabriele Paolacci and Jesse Chandler. 2014. Inside the Turk: Understanding Mechanical Turk as a participant pool. Current directions in psychological science 23, 3 (2014), 184--188.
    [57]
    Elizabeth J. Pawluk and Naomi Koerner. 2016. The relationship between negative urgency and generalized anxiety disorder symptoms: the role of intolerance of negative emotions and intolerance of uncertainty. Anxiety, Stress, & Coping 29, 6 (2016), 606--615.
    [58]
    Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, et al. 2011. Scikit-learn: Machine learning in Python. The journal of machine learning research 12 (2011), 2825--2830.
    [59]
    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/
    [60]
    Lisa Posch, Arnim Bleier, Fabian Flöck, Clemens M Lechner, Katharina Kinder-Kurlanda, Denis Helic, and Markus Strohmaier. 2022. Characterizing the Global Crowd Workforce: A Cross-Country Comparison of Crowdworker Demographics. Human Computation 9, 1 (2022), 22--57.
    [61]
    Andrew G Reece, Andrew J Reagan, Katharina LM Lix, Peter Sheridan Dodds, Christopher M Danforth, and Ellen J Langer. 2017. Forecasting the onset and course of mental illness with Twitter data. Scientific reports 7, 1 (2017), 13006.
    [62]
    Benjamin J Ricard, Lisa A Marsch, Benjamin Crosier, and Saeed Hassanpour. 2018. Exploring the utility of community-generated social media content for detecting depression: an analytical study on Instagram. Journal of medical Internet research 20, 12 (2018), e11817.
    [63]
    John Rooksby, Alistair Morrison, and Dave Murray-Rust. 2019. Student perspectives on digital phenotyping: The acceptability of using smartphone data to assess mental health. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1--14.
    [64]
    Sohrab Saeb, Mi Zhang, Christopher J Karr, Stephen M Schueller, Marya E Corden, Konrad P Kording, David C Mohr, et al. 2015. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. Journal of medical Internet research 17, 7 (2015), e4273.
    [65]
    Akane Sano, Sara Taylor, Andrew W McHill, Andrew JK Phillips, Laura K Barger, Elizabeth Klerman, and Rosalind Picard. 2018. Identifying objective physiological markers and modifiable behaviors for self-reported stress and mental health status using wearable sensors and mobile phones: observational study. Journal of medical Internet research 20, 6 (2018), e210.
    [66]
    Margot Savoy and David O'Gurek. 2016. Screening your adult patients for depression. Family practice management 23, 2 (2016), 16--20.
    [67]
    Jaeeun Shin and Sung Man Bae. 2023. A Systematic Review of Location Data for Depression Prediction. International Journal of Environmental Research and Public Health 20, 11 (2023), 5984.
    [68]
    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.
    [69]
    Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019. Energy and Policy Considerations for Deep Learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 3645--3650.
    [70]
    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.
    [71]
    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.
    [72]
    ML Tlachac, Ricardo Flores, Miranda Reisch, Katie Houskeeper, and Elke A Rundensteiner. 2022. DepreST-CAT: Retrospective smartphone call and text logs collected during the covid-19 pandemic to screen for mental illnesses. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2 (2022), 1--32.
    [73]
    ML Tlachac, Ricardo Flores, Miranda Reisch, Rimsha Kayastha, Nina Taurich, Veronica Melican, Connor Bruneau, Hunter Caouette, Joshua Lovering, Ermal Toto, et al. 2022. StudentSADD: Rapid mobile depression and suicidal ideation screening of college students during the coronavirus pandemic. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2 (2022), 1--32.
    [74]
    ML Tlachac, Ricardo Flores, Ermal Toto, and Elke Rundensteiner. 2022. Early mental health uncovering with short scripted and unscripted voice recordings. In Deep Learning Applications, Volume 4. Springer, 79--110.
    [75]
    ML Tlachac, Walter Gerych, Kratika Agrawal, Benjamin Litterer, Nicholas Jurovich, Saitheeraj Thatigotla, Jidapa Thadajarassiri, and Elke A Rundensteiner. 2022. Text Generation to Aid Depression Detection: A Comparative Study of Conditional Sequence Generative Adversarial Networks. In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2804--2813.
    [76]
    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.
    [77]
    ML Tlachac and Samuel S Ogden. 2022. Left on Read: Reply Latency for Anxiety & Depression Screening. In Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). 500--502.
    [78]
    ML Tlachac, Mirand Reisch, and Michael Heinz. 2023. Mobile Communication Log Time Series to Detect Depressive Symptoms. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE.
    [79]
    ML Tlachac, Miranda Reisch, Brittany Lewis, Ricardo Flores, Lane Harrison, and Elke Rundensteiner. 2022. Impact assessment of stereotype threat on mobile depression screening using Bayesian estimation. Healthcare Analytics 2 (2022), 100088.
    [80]
    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.
    [81]
    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.
    [82]
    ML Tlachac, Adam Sargent, Ermal Toto, Randy Paffenroth, and Elke Rundensteiner. 2020. Topological data analysis to engineer features from audio signals for depression detection. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 302--307.
    [83]
    ML Tlachac, Avantika Shrestha, Mahum Shah, Benjamin Litterer, and Elke A Rundensteiner. 2023. Automated construction of lexicons to improve depression screening with text messages. IEEE Journal of Biomedical and Health Informatics 26, 6 (2023), 2751--2759.
    [84]
    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.
    [85]
    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.
    [86]
    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.
    [87]
    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.
    [88]
    Anne M Turner, Thomas Engelsma, Jean O Taylor, Rashmi K Sharma, and George Demiris. 2020. Recruiting older adult participants through crowdsourcing platforms: Mechanical Turk versus Prolific Academic. In AMIA Annual Symposium Proceedings.
    [89]
    Monica Vermani, Madalyn Marcus, and Martin A Katzman. 2011. Rates of detection of mood and anxiety disorders in primary care: a descriptive, cross-sectional study. Prim. Care Companion CNS Disord. 13, 2 (2011).
    [90]
    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.
    [91]
    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 Proc. of the 2016 ACM int. joint conf. on pervasive and ubiquitous computing. 886--897.
    [92]
    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.
    [93]
    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.
    [94]
    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.
    [95]
    Weichen Wang, Subigya Nepal, Jeremy F Huckins, Lessley Hernandez, Vlado Vojdanovski, Dante Mack, Jane Plomp, Arvind Pillai, Mikio Obuchi, Alex Dasilva, et al. 2022. First-gen lens: Assessing mental health of first-generation students across their first year at college using mobile sensing. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 6, 2 (2022), 1--32.
    [96]
    Shweta Ware, Laura E Knouse, Israa Draz, and Alina Enikeeva. 2022. Predicting ADHD Symptoms Using Smartphone Sensing Data. In Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). 503--505.
    [97]
    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).
    [98]
    Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Jin Lu, Chao Shang, Jayesh Kamath, Athanasios Bamis, Jinbo Bi, Alexander Russell, and Bing Wang. 2018. Large-scale automatic depression screening using meta-data from wifi infrastructure. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 4 (2018), 1--27.
    [99]
    Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Dongjin Song, Athanasios Bamis, and Bing Wang. 2022. Automatic depression screening using social interaction data on smartphones. Smart Health 26 (2022).
    [100]
    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).
    [101]
    James H Wirth and Galen V Bodenhausen. 2009. The role of gender in mental-illness stigma: A national experiment. Psychological Science 20, 2 (2009), 169--173.
    [102]
    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.
    [103]
    Xuhai Xu, Prerna Chikersal, Janine M Dutcher, Yasaman S Sefidgar, Woosuk Seo, Michael J Tumminia, Daniella K Villalba, Sheldon Cohen, Kasey G Creswell, et al. 2021. Leveraging collaborative-filtering for personalized behavior modeling: a case study of depression detection among college students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 1 (2021), 1--27.
    [104]
    Xuhai Xu, Xin Liu, Han Zhang, Weichen Wang, Subigya Nepal, Yasaman Sefidgar, Woosuk Seo, Kevin S Kuehn, Jeremy F Huckins, Margaret E Morris, et al. 2023. GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 4 (2023), 1--34.
    [105]
    Xuhai Xu, Han Zhang, Yasaman Sefidgar, Yiyi Ren, Xin Liu, Woosuk Seo, Jennifer Brown, Kevin Kuehn, Mike Merrill, Paula Nurius, et al. 2022. GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization. Advances in Neural Information Processing Systems 35 (2022), 24655--24692.
    [106]
    Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang. 2020. Automatic depression prediction using internet traffic characteristics on smartphones. Smart Health 18 (2020).

    Recommendations

    Comments

    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 8, Issue 1
    March 2024
    1182 pages
    EISSN:2474-9567
    DOI:10.1145/3651875
    Issue’s Table of Contents
    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 the author(s) 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: 06 March 2024
    Published in IMWUT Volume 8, Issue 1

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. AdaBoost
    2. XGBoost
    3. digital phenotype
    4. mental health assessment
    5. mobile health

    Qualifiers

    • Research-article
    • Research
    • Refereed

    Funding Sources

    • National Institiute of Health

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 152
      Total Downloads
    • Downloads (Last 12 months)152
    • Downloads (Last 6 weeks)12
    Reflects downloads up to 10 Aug 2024

    Other Metrics

    Citations

    View Options

    Get Access

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media