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Tlachac ML, Heinz M. Mental Health and Mobile Communication Profiles of Crowdsourced Participants. IEEE J Biomed Health Inform 2024; 28:7683-7692. [PMID: 39093670 PMCID: PMC11787405 DOI: 10.1109/jbhi.2024.3436654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are highly prevalent and burdensome. To increase mental health screening rates, the digital health research community has been exploring the ability to augment self reporting instruments with digital logs. Crowdsourced workers are being increasingly recruited for behavioral health research studies as demographically representative samples are desired for later translational applications. Overshadowed by predictive modeling, descriptive modeling has the ability to expand knowledge and understanding of the clinical generalizability of models trained on data from crowdsourced participants. In this study, we identify mobile communication profiles of a crowdsourced sample. To achieve this, we cluster features derived from time series of call and text logs. The psychiatric, behavioral, and demographic characteristics were notably different across the four identified mobile communication profiles. For example, the profile that had the lowest average depression and anxiety screening scores only shared incoming text logs. This cluster had statistically significantly different depression and anxiety screening scores in comparison to the cluster that shared the most outgoing text logs. These profiles expose important insights regarding the generalizability of crowdsourced samples to more general clinical populations and increase understanding regarding the limitations of crowdsourced samples for translational mental health research.
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Adler DA, Yang Y, Viranda T, Xu X, Mohr DC, VAN Meter AR, Tartaglia JC, Jacobson NC, Wang F, Estrin D, Choudhury T. Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare. PROCEEDINGS OF THE ACM ON INTERACTIVE, MOBILE, WEARABLE AND UBIQUITOUS TECHNOLOGIES 2024; 8:160. [PMID: 39639863 PMCID: PMC11620792 DOI: 10.1145/3699755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/07/2024]
Abstract
Researchers in ubiquitous computing have long promised that passive sensing will revolutionize mental health measurement by detecting individuals in a population experiencing a mental health disorder or specific symptoms. Recent work suggests that detection tools do not generalize well when trained and tested in more heterogeneous samples. In this work, we contribute a narrative review and findings from two studies with 41 mental health clinicians to understand these generalization challenges. Our findings motivate research on actionable sensing, as an alternative to detection research, studying how passive sensing can augment traditional mental health measures to support actions in clinical care. Specifically, we identify how passive sensing can support clinical actions by revealing patients' presenting problems for treatment and identifying targets for behavior change and symptom reduction, but passive data requires additional contextual information to be appropriately interpreted and used in care. We conclude by suggesting research at the intersection of actionable sensing and mental healthcare, to align technical research in ubiquitous computing with clinical actions and needs.
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Affiliation(s)
| | | | | | | | - David C Mohr
- Northwestern University Feinberg School of Medicine, USA
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Zhuang M, Cheng D, Lu X, Tan X. Postgraduate psychological stress detection from social media using BERT-Fused model. PLoS One 2024; 19:e0312264. [PMID: 39480765 PMCID: PMC11527284 DOI: 10.1371/journal.pone.0312264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Accepted: 10/03/2024] [Indexed: 11/02/2024] Open
Abstract
Postgraduate students face various academic, personal, and social stressors that increase their risk of anxiety, depression, and suicide. Identifying cost-effective methods of detecting and intervening before stress turns into severe problems is crucial. However, existing stress detection methods typically rely on psychological scales or devices, which can be complex and expensive. Therefore, we propose a BERT-fused model for rapidly and automatically detecting postgraduate students' psychological stress via social media. First, we construct an improved BERT-LDA feature extraction algorithm to extract group stress features from large-scale and complex social media data. Then, we integrate the BiLSTM-CRF named entity recognition model to construct a multi-dimensional psychological stress profile and analyze the fine-grained feature representation under the fusion of multi-dimensional features. Experimental results demonstrate that the proposed model outperforms traditional models such as BiLSTM, achieving an accuracy of 92.55%, a recall of 93.47%, and an F1-score of 92.18%, with F1-scores exceeding 89% for all three types of entities. This research provides both theoretical and practical foundations for universities or institutions to conduct fine-grained perception and intervention for postgraduate students' psychological stress.
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Affiliation(s)
- Muni Zhuang
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, China
- School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Dongsheng Cheng
- Shenzhen Institute of Information Technology, School of Software Engineering, Shenzhen, Guangdong, China
| | - Xin Lu
- College of Systems Engineering, National University of Defense Technology, Changsha, Hunan, China
| | - Xu Tan
- Shenzhen Institute of Information Technology, Career-Oriented Multidisciplinary Education Center, Shenzhen, Guangdong, China
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Bloom PA, Lan R, Galfalvy H, Liu Y, Bitran A, Joyce K, Durham K, Porta G, Kirshenbaum JS, Kamath R, Tse TC, Chernick L, Kahn LE, Crowley R, Trivedi E, Brent D, Allen NB, Pagliaccio D, Auerbach RP. Identifying factors impacting missingness within smartphone-based research: Implications for intensive longitudinal studies of adolescent suicidal thoughts and behaviors. JOURNAL OF PSYCHOPATHOLOGY AND CLINICAL SCIENCE 2024; 133:577-597. [PMID: 39023923 PMCID: PMC12083753 DOI: 10.1037/abn0000930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Intensive longitudinal research-including experience sampling and smartphone sensor monitoring-has potential for identifying proximal risk factors for psychopathology, including suicidal thoughts and behaviors (STB). Yet, missing data can complicate analysis and interpretation. This study aimed to address whether clinical and study design factors are associated with missing data and whether missingness predicts changes in symptom severity or STB. Adolescents ages 13- to 18 years old (N = 179) reporting depressive, anxiety, and/or substance use disorders were enrolled; 65% reported current suicidal ideation and 29% indicated a past-year attempt. Passively acquired smartphone sensor data (e.g., global positioning system, accelerometer, and keyboard inputs), daily mood surveys, and weekly suicidal ideation surveys were collected during the 6-month study period using the effortless assessment research system smartphone app. First, acquisition of passive smartphone sensor data (with data on ∼80% of days across the whole sample) was strongly associated with survey data acquisition on the same day (∼44% of days). Second, STB and psychiatric symptoms were largely not associated with missing data. Rather, temporal features (e.g., length of time in study, weekends, and summer) explained more missingness of survey and passive smartphone sensor data. Last, within-participant changes in missing data over time neither followed nor predicted subsequent change in suicidal ideation and psychiatric symptoms. Findings indicate that considering technical and study design factors impacting missingness is critical and highlight several factors that should be addressed to maximize the validity of clinical interpretations in intensive longitudinal research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
| | | | | | - Ying Liu
- Columbia University Irving Medical Center
| | | | - Karla Joyce
- Department of Psychiatry, University of Pittsburgh Medical Center
| | | | - Giovanna Porta
- Western Psychiatric Hospital, University of Pittsburgh Medical Center
| | | | | | | | - Lauren Chernick
- Department of Emergency Medicine, Columbia University Medical Center
| | | | | | | | - David Brent
- Department of Psychiatry, University of Pittsburgh Medical Center
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Hur JK, Heffner J, Feng GW, Joormann J, Rutledge RB. Language sentiment predicts changes in depressive symptoms. Proc Natl Acad Sci U S A 2024; 121:e2321321121. [PMID: 39284070 PMCID: PMC11441484 DOI: 10.1073/pnas.2321321121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 07/26/2024] [Indexed: 10/02/2024] Open
Abstract
The prevalence of depression is a major societal health concern, and there is an ongoing need to develop tools that predict who will become depressed. Past research suggests that depression changes the language we use, but it is unclear whether language is predictive of worsening symptoms. Here, we test whether the sentiment of brief written linguistic responses predicts changes in depression. Across two studies (N = 467), participants provided responses to neutral open-ended questions, narrating aspects of their lives relevant to depression (e.g., mood, motivation, sleep). Participants also completed the Patient Health Questionnaire (PHQ-9) to assess depressive symptoms and a risky decision-making task with periodic measurements of momentary happiness to quantify mood dynamics. The sentiment of written responses was evaluated by human raters (N = 470), Large Language Models (LLMs; ChatGPT 3.5 and 4.0), and the Linguistic Inquiry and Word Count (LIWC) tool. We found that language sentiment evaluated by human raters and LLMs, but not LIWC, predicted changes in depressive symptoms at a three-week follow-up. Using computational modeling, we found that language sentiment was associated with current mood, but language sentiment predicted symptom changes even after controlling for current mood. In summary, we demonstrate a scalable tool that combines brief written responses with sentiment analysis by AI tools that matches human performance in the prediction of future psychiatric symptoms.
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Affiliation(s)
- Jihyun K. Hur
- Department of Psychology, Yale University, New Haven, CT06510
| | - Joseph Heffner
- Department of Psychology, Yale University, New Haven, CT06510
| | - Gloria W. Feng
- Department of Psychology, Yale University, New Haven, CT06510
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT06510
| | - Robb B. Rutledge
- Department of Psychology, Yale University, New Haven, CT06510
- Department of Psychiatry, Yale University, New Haven, CT06511
- Wu Tsai Institute, Yale University, New Haven, CT06510
- Wellcome Centre for Human Neuroimaging, University College London, LondonWC1N 3AR, United Kingdom
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Adler DA, Stamatis CA, Meyerhoff J, Mohr DC, Wang F, Aranovich GJ, Sen S, Choudhury T. Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data. NPJ MENTAL HEALTH RESEARCH 2024; 3:17. [PMID: 38649446 PMCID: PMC11035598 DOI: 10.1038/s44184-024-00057-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/07/2024] [Indexed: 04/25/2024]
Abstract
AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.
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Affiliation(s)
- Daniel A Adler
- Cornell Tech, Information Science, 2 W Loop Rd, New York, NY, 10044, USA.
| | - Caitlin A Stamatis
- Northwestern University Feinberg School of Medicine, Center for Behavioral Intervention Technologies, Chicago, IL, 60611, USA
| | - Jonah Meyerhoff
- Northwestern University Feinberg School of Medicine, Center for Behavioral Intervention Technologies, Chicago, IL, 60611, USA
| | - David C Mohr
- Northwestern University Feinberg School of Medicine, Center for Behavioral Intervention Technologies, Chicago, IL, 60611, USA
| | - Fei Wang
- Weill Cornell Medicine, Population Health Sciences, New York, NY, 10065, USA
| | | | - Srijan Sen
- Michigan Medicine, Department of Psychiatry, Ann Arbor, MI, 48109, USA
| | - Tanzeem Choudhury
- Cornell Tech, Information Science, 2 W Loop Rd, New York, NY, 10044, USA
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Adler DA, Stamatis CA, Meyerhoff J, Mohr DC, Wang F, Aranovich GJ, Sen S, Choudhury T. Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data. RESEARCH SQUARE 2024:rs.3.rs-3044613. [PMID: 38746448 PMCID: PMC11092819 DOI: 10.21203/rs.3.rs-3044613/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from behavior should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.
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Trifu RN, Nemeș B, Herta DC, Bodea-Hategan C, Talaș DA, Coman H. Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure. Front Psychol 2024; 15:1355734. [PMID: 38510303 PMCID: PMC10953917 DOI: 10.3389/fpsyg.2024.1355734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/06/2024] [Indexed: 03/22/2024] Open
Abstract
Introduction The identification of language markers, referring to both form and content, for common mental health disorders such as major depressive disorder (MDD), can facilitate the development of innovative tools for early recognition and prevention. However, studies in this direction are only at the beginning and are difficult to implement due to linguistic variability and the influence of cultural contexts. Aim This study aims to identify language markers specific to MDD through an automated analysis process based on RO-2015 LIWC (Linguistic Inquiry and Word Count). Materials and methods A sample of 62 medicated patients with MDD and a sample of 43 controls were assessed. Each participant provided language samples that described something that was pleasant for them. Assessment tools (1) Screening tests for MDD (MADRS and DASS-21); (2) Ro-LIWC2015 - Linguistic Inquiry and Word Count - a computerized text analysis software, validated for Romanian Language, that analyzes morphology, syntax and semantics of word use. Results Depressive patients use different approaches in sentence structure, and communicate in short sentences. This requires multiple use of the punctuation mark period, which implicitly requires directive communication, limited in exchange of ideas. Also, participants from the sample with depression mostly use impersonal pronouns, first person pronoun in plural form - not singular, a limited number of prepositions and an increased number of conjunctions, auxiliary verbs, negations, verbs in the past tense, and much less in the present tense, increased use of words expressing negative affects, anxiety, with limited use of words indicating positive affects. The favorite topics of interest of patients with depression are leisure, time and money. Conclusion Depressive patients use a significantly different language pattern than people without mood or behavioral disorders, both in form and content. These differences are sometimes associated with years of education and sex, and might also be explained by cultural differences.
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Affiliation(s)
- Raluca Nicoleta Trifu
- Department of Neurosciences, Discipline of Medical Psychology and Psychiatry, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Bogdan Nemeș
- Department of Neurosciences, Discipline of Medical Psychology and Psychiatry, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Dana Cristina Herta
- Department of Neurosciences, Discipline of Medical Psychology and Psychiatry, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Carolina Bodea-Hategan
- Special Education Department, Faculty of Psychology and Education Sciences, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Dorina Anca Talaș
- Special Education Department, Faculty of Psychology and Education Sciences, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Horia Coman
- Department of Neurosciences, Discipline of Medical Psychology and Psychiatry, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
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Stamatis CA, Liu T, Meyerhoff J, Meng Y, Cho YM, Karr CJ, Curtis BL, Ungar LH, Mohr DC. Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety. Internet Interv 2023; 34:100683. [PMID: 37867614 PMCID: PMC10589746 DOI: 10.1016/j.invent.2023.100683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/21/2023] [Accepted: 10/11/2023] [Indexed: 10/24/2023] Open
Abstract
Background Prior literature links passively sensed information about a person's location, movement, and communication with social anxiety. These findings hold promise for identifying novel treatment targets, informing clinical care, and personalizing digital mental health interventions. However, social anxiety symptoms are heterogeneous; to identify more precise targets and tailor treatments, there is a need for personal sensing studies aimed at understanding differential predictors of the distinct subdomains of social anxiety. Our objective was to conduct a large-scale smartphone-based sensing study of fear, avoidance, and physiological symptoms in the context of trait social anxiety over time. Methods Participants (n = 1013; 74.6 % female; M age = 40.9) downloaded the LifeSense app, which collected continuous passive data (e.g., GPS, communication, app and device use) over 16 weeks. We tested a series of multilevel linear regression models to understand within- and between-person associations of 2-week windows of passively sensed smartphone data with fear, avoidance, and physiological distress on the self-reported Social Phobia Inventory (SPIN). A shifting sensor lag was applied to examine how smartphone features related to SPIN subdomains 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Results A decrease in time visiting novel places was a strong between-person predictor of social avoidance over time (distal β = -0.886, p = .002; medial β = -0.647, p = .029; proximal β = -0.818, p = .007). Reductions in call- and text-based communications were associated with social avoidance at both the between- (distal β = -0.882, p = .002; medial β = -0.932, p = .001; proximal β = -0.918, p = .001) and within- (distal β = -0.191, p = .046; medial β = -0.213, p = .028) person levels, as well as between-person fear of social situations (distal β = -0.860, p < .001; medial β = -0.892, p < .001; proximal β = -0.886, p < .001) over time. There were fewer significant associations of sensed data with physiological distress. Across the three subscales, smartphone data explained 9-12 % of the variance in social anxiety. Conclusion Findings have implications for understanding how social anxiety manifests in daily life, and for personalizing treatments. For example, a signal that someone is likely to begin avoiding social situations may suggest a need for alternative types of exposure-based interventions compared to a signal that someone is likely to begin experiencing increased physiological distress. Our results suggest that as a prophylactic means of targeting social avoidance, it may be helpful to deploy interventions involving social exposures in response to decreases in time spent visiting novel places.
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Affiliation(s)
- Caitlin A. Stamatis
- Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
| | - Tingting Liu
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, United States of America
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Bethesda, MD, United States of America
| | - Jonah Meyerhoff
- Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
| | - Yixuan Meng
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Young Min Cho
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Chris J. Karr
- Audacious Software, Chicago, IL, United States of America
| | - Brenda L. Curtis
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Bethesda, MD, United States of America
| | - Lyle H. Ungar
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States of America
| | - David C. Mohr
- Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
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