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Allen K, Rodriguez S, Hayani L, Rothenberger S, Moses-Kolko E, Simhan HN, Krishnamurti T. Digital phenotyping of depression during pregnancy using self-report data. J Affect Disord 2024; 364:231-239. [PMID: 39137834 DOI: 10.1016/j.jad.2024.08.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 06/26/2024] [Accepted: 08/09/2024] [Indexed: 08/15/2024]
Abstract
BACKGROUND Depression is a common pregnancy complication yet is often under-detected and, subsequently, undertreated. Data collected through mobile health tools may be used to support the identification of depression symptoms in pregnancy. METHODS An observational cohort study of 2062 pregnancies collected self-reports of patient history, mood, pregnancy-specific symptoms, and written language using a prenatal support app. These app inputs were used to model depression risk in subsequent 30- and 60-day periods throughout pregnancy. A selective inference lasso modeling approach examined the individual and additive value of each type of patient-reported app input. RESULTS Depression models ranged in predictive power (AUC value of 0.64-0.83), depending on the type of inputs. The most predictive model included personal history, daily mood, and acute pregnancy-related symptoms (e.g., severe vomiting, cramping). Across models, daily mood was the strongest indicator of depression symptoms in the following month. Models that retained natural language inputs typically improved predictive accuracy and offered insight into the lived context associated with experiencing depression. LIMITATIONS Our findings are not generalizable beyond a digitally literate patient population that is self-motivated to report data during pregnancy. CONCLUSIONS Simple patient reported data, including sparse language, shared directly via digital tools may support earlier depression symptom identification and a more nuanced understanding of depression context.
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Affiliation(s)
- Kristen Allen
- Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, United States of America; Allegheny County Department of Human Services, Pittsburgh, PA, United States of America
| | - Samantha Rodriguez
- Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Laila Hayani
- Naima Health LLC, Pittsburgh, PA, United States of America
| | - Scott Rothenberger
- Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Eydie Moses-Kolko
- University of Pittsburgh Medical Center Western Psychiatric Hospital, Pittsburgh, PA, United States of America
| | - Hyagriv N Simhan
- Department of OB-GYN and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Tamar Krishnamurti
- Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America.
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Zierer C, Behrendt C, Lepach-Engelhardt AC. Digital biomarkers in depression: A systematic review and call for standardization and harmonization of feature engineering. J Affect Disord 2024; 356:438-449. [PMID: 38583596 DOI: 10.1016/j.jad.2024.03.163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 03/21/2024] [Accepted: 03/28/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND General physicians misclassify depression in more than half of the cases. Researchers have explored the feasibility of leveraging passively collected data points, also called digital biomarkers, to provide more granular understanding of depression phenotypes as well as a more objective assessment of disease. METHOD This paper provides a systematic review following the PRISMA guidelines (Page et al., 2021) to understand which digital biomarkers might be relevant for passive screening of depression. Pubmed and PsycInfo were systematically searched for studies published from 2019 to early 2024, resulting in 161 records assessed for eligibility. Excluded were intervention studies, studies focusing on a different disease or those with a lack of passive data collection. 74 studies remained for a quality assessment, after which 27 studies were included. RESULTS The review shows that depressed participants' real-life behavior such as reduced communication with others can be tracked by passive data. Machine learning models for the classification of depression have shown accuracies up to 0.98, surpassing the quality of many standardized assessment methods. LIMITATIONS Inconsistency of outcome reporting of current studies does not allow for drawing statistical conclusions regarding effectiveness of individual included features. The Covid-19 pandemic might have impacted the ongoing studies between 2020 and 2022. CONCLUSION While digital biomarkers allow real-life tracking of participant's behavior and symptoms, further work is required to align the feature engineering of digital biomarkers. With shown high accuracies of assessments, connecting digital biomarkers with clinical practice can be a promising method of detecting symptoms of depression automatically.
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Affiliation(s)
- Carolin Zierer
- Department of Psychology, PFH Private University of Applied Sciences, Göttingen, Lower Saxony, Germany
| | - Corinna Behrendt
- Department of Psychology, PFH Private University of Applied Sciences, Göttingen, Lower Saxony, Germany.
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Funkhouser CJ, Trivedi E, Li LY, Helgren F, Zhang E, Sritharan A, Cherner RA, Pagliaccio D, Durham K, Kyler M, Tse TC, Buchanan SN, Allen NB, Shankman SA, Auerbach RP. Detecting adolescent depression through passive monitoring of linguistic markers in smartphone communication. J Child Psychol Psychiatry 2024; 65:932-941. [PMID: 38098445 PMCID: PMC11161327 DOI: 10.1111/jcpp.13931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 06/09/2024]
Abstract
BACKGROUND Cross sectional studies have identified linguistic correlates of major depressive disorder (MDD) in smartphone communication. However, it is unclear whether monitoring these linguistic characteristics can detect when an individual is experiencing MDD, which would facilitate timely intervention. METHODS Approximately 1.2 million messages typed into smartphone social communication apps (e.g. texting, social media) were passively collected from 90 adolescents with a range of depression severity over a 12-month period. Sentiment (i.e. positive vs. negative valence of text), proportions of first-person singular pronouns (e.g. 'I'), and proportions of absolutist words (e.g. 'all') were computed for each message and converted to weekly aggregates temporally aligned with weekly MDD statuses obtained from retrospective interviews. Idiographic, multilevel logistic regression models tested whether within-person deviations in these linguistic features were associated with the probability of concurrently meeting threshold for MDD. RESULTS Using more first-person singular pronouns in smartphone communication relative to one's own average was associated with higher odds of meeting threshold for MDD in the concurrent week (OR = 1.29; p = .007). Sentiment (OR = 1.07; p = .54) and use of absolutist words (OR = 0.99; p = .90) were not related to weekly MDD. CONCLUSIONS Passively monitoring use of first-person singular pronouns in adolescents' smartphone communication may help detect MDD, providing novel opportunities for early intervention.
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Affiliation(s)
- Carter J. Funkhouser
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Esha Trivedi
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Lilian Y. Li
- Department of Psychiatry and Behavioral Sciences, Northwestern University
| | - Fiona Helgren
- Department of Psychiatry and Behavioral Sciences, Northwestern University
| | - Emily Zhang
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Aishwarya Sritharan
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Rachel A. Cherner
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - David Pagliaccio
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Katherine Durham
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Mia Kyler
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Trinity C. Tse
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | | | | | | | - Randy P. Auerbach
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
- Division of Clinical Developmental Neuroscience, Sackler Institute
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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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Stamatis CA, Meyerhoff J, Meng Y, Lin ZCC, Cho YM, Liu T, Karr CJ, Liu T, Curtis BL, Ungar LH, Mohr DC. Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study. NPJ MENTAL HEALTH RESEARCH 2024; 3:1. [PMID: 38609548 PMCID: PMC10955925 DOI: 10.1038/s44184-023-00041-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 10/19/2023] [Indexed: 04/14/2024]
Abstract
While studies show links between smartphone data and affective symptoms, we lack clarity on the temporal scale, specificity (e.g., to depression vs. anxiety), and person-specific (vs. group-level) nature of these associations. We conducted a large-scale (n = 1013) smartphone-based passive sensing study to identify within- and between-person digital markers of depression and anxiety symptoms over time. Participants (74.6% female; M age = 40.9) downloaded the LifeSense app, which facilitated continuous passive data collection (e.g., GPS, app and device use, communication) across 16 weeks. Hierarchical linear regression models tested the within- and between-person associations of 2-week windows of passively sensed data with depression (PHQ-8) or generalized anxiety (GAD-7). We used a shifting window to understand the time scale at which sensed features relate to mental health symptoms, predicting symptoms 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Spending more time at home relative to one's average was an early signal of PHQ-8 severity (distal β = 0.219, p = 0.012) and continued to relate to PHQ-8 at medial (β = 0.198, p = 0.022) and proximal (β = 0.183, p = 0.045) windows. In contrast, circadian movement was proximally related to (β = -0.131, p = 0.035) but did not predict (distal β = 0.034, p = 0.577; medial β = -0.089, p = 0.138) PHQ-8. Distinct communication features (i.e., call/text or app-based messaging) related to PHQ-8 and GAD-7. Findings have implications for identifying novel treatment targets, personalizing digital mental health interventions, and enhancing traditional patient-provider interactions. Certain features (e.g., circadian movement) may represent correlates but not true prospective indicators of affective symptoms. Conversely, other features like home duration may be such early signals of intra-individual symptom change, indicating the potential utility of prophylactic intervention (e.g., behavioral activation) in response to person-specific increases in these signals.
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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, USA.
| | - Jonah Meyerhoff
- Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yixuan Meng
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhi Chong Chris Lin
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Young Min Cho
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Tony Liu
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
- Roblox Corporation, San Mateo, CA, USA
| | | | - Tingting Liu
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Brenda L Curtis
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Lyle H Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA
| | - David C Mohr
- Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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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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Jamali AA, Berger C, Spiteri RJ. Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach. JMIR AI 2023; 2:e49531. [PMID: 38875532 PMCID: PMC11041470 DOI: 10.2196/49531] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/06/2023] [Accepted: 10/27/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Depression and momentary depressive feelings are major public health concerns imposing a substantial burden on both individuals and society. Early detection of momentary depressive feelings is highly beneficial in reducing this burden and improving the quality of life for affected individuals. To this end, the abundance of data exemplified by X (formerly Twitter) presents an invaluable resource for discerning insights into individuals' mental states and enabling timely detection of these transitory depressive feelings. OBJECTIVE The objective of this study was to automate the detection of momentary depressive feelings in posts using contextual language approaches. METHODS First, we identified terms expressing momentary depressive feelings and depression, scaled their relevance to depression, and constructed a lexicon. Then, we scraped posts using this lexicon and labeled them manually. Finally, we assessed the performance of the Bidirectional Encoder Representations From Transformers (BERT), A Lite BERT (ALBERT), Robustly Optimized BERT Approach (RoBERTa), Distilled BERT (DistilBERT), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and machine learning (ML) algorithms in detecting momentary depressive feelings in posts. RESULTS This study demonstrates a notable distinction in performance between binary classification, aimed at identifying posts conveying depressive sentiments and multilabel classification, designed to categorize such posts across multiple emotional nuances. Specifically, binary classification emerges as the more adept approach in this context, outperforming multilabel classification. This outcome stems from several critical factors that underscore the nuanced nature of depressive expressions within social media. Our results show that when using binary classification, BERT and DistilBERT (pretrained transfer learning algorithms) may outperform traditional ML algorithms. Particularly, DistilBERT achieved the best performance in terms of area under the curve (96.71%), accuracy (97.4%), sensitivity (97.57%), specificity (97.22%), precision (97.30%), and F1-score (97.44%). DistilBERT obtained an area under the curve nearly 12% points higher than that of the best-performing traditional ML algorithm, convolutional neural network. This study showed that transfer learning algorithms are highly effective in extracting knowledge from posts, detecting momentary depressive feelings, and highlighting their superiority in contextual analysis. CONCLUSIONS Our findings suggest that contextual language approaches-particularly those rooted in transfer learning-are reliable approaches to automate the early detection of momentary depressive feelings and can be used to develop social media monitoring tools for identifying individuals who may be at risk of depression. The implications are far-reaching because these approaches stand poised to inform the creation of social media monitoring tools and are pivotal for identifying individuals susceptible to depression. By intervening proactively, these tools possess the potential to slow the progression of depressive feelings, effectively mitigating the societal load of depression and fostering improved mental health. In addition to highlighting the capabilities of automated sentiment analysis, this study illuminates its pivotal role in advancing global public health.
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Affiliation(s)
- Ali Akbar Jamali
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Corinne Berger
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Raymond J Spiteri
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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Li LY, Trivedi E, Helgren F, Allison GO, Zhang E, Buchanan SN, Pagliaccio D, Durham K, Allen NB, Auerbach RP, Shankman SA. Capturing mood dynamics through adolescent smartphone social communication. JOURNAL OF PSYCHOPATHOLOGY AND CLINICAL SCIENCE 2023; 132:1072-1084. [PMID: 37498714 PMCID: PMC10818010 DOI: 10.1037/abn0000855] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Most adolescents with depression remain undiagnosed and untreated-missed opportunities that are costly from both personal and public health perspectives. A promising approach to detecting adolescent depression in real-time and at a large scale is through their social communication on the smartphone (e.g., text messages, social media posts). Past research has shown that language from online social communication reliably indicates interindividual differences in depression. To move toward detecting the emergence of depression symptoms intraindividually, the present study tested whether sentiment (i.e., words connoting positive and negative affect) from smartphone social communication prospectively predicted daily mood fluctuations in 83 adolescents (Mage = 16.49, 73.5% female) with a wide range of depression severity. Participants completed daily mood ratings across a 90-day period, during which 354,278 messages were passively collected from social communication apps. Greater positive sentiment (i.e., more positive weighted composite valence score and a greater proportion of words expressing positive sentiment) predicted more positive next-day mood, controlling for previous-day mood. Moreover, greater proportions of positive and negative sentiment were, respectively, associated with lower anhedonia and greater dysphoria symptoms measured at baseline. Exploratory analyses of nonaffective linguistic features showed that greater use of social engagement words (e.g., friends and affiliation) and emojis (primarily consisting of hearts) predicted more positive changes in mood. Collectively, findings suggest that language from smartphone social communication can detect mood fluctuations in adolescents, laying the foundation for language-based tools to identify periods of heightened depression risk. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Lilian Y. Li
- Department of Psychiatry and Behavioral Sciences, Northwestern University
| | - Esha Trivedi
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Fiona Helgren
- Department of Psychiatry and Behavioral Sciences, Northwestern University
| | | | - Emily Zhang
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | | | - David Pagliaccio
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | - Katherine Durham
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
| | | | - Randy P. Auerbach
- Department of Psychiatry, Columbia University
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute
- Division of Clinical Developmental Neuroscience, Sackler Institute
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10
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Nghiem J, Adler DA, Estrin D, Livesey C, Choudhury T. Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study. JMIR Form Res 2023; 7:e47380. [PMID: 37561561 PMCID: PMC10450536 DOI: 10.2196/47380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/20/2023] [Accepted: 07/06/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used. OBJECTIVE We conducted a qualitative study to understand mental health clinicians' perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants' current experiences with and visions for using passive PGHD. METHODS Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes. RESULTS Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven-we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data-participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients' mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action-participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy-participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use. CONCLUSIONS Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data-sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians.
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Affiliation(s)
- Jodie Nghiem
- Medical College, Weill Cornell Medicine, New York, NY, United States
| | - Daniel A Adler
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
| | - Deborah Estrin
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
| | - Cecilia Livesey
- Optum Labs, UnitedHealth Group, Minnetonka, MN, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Tanzeem Choudhury
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
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11
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Kruzan KP, Ng A, Stiles-Shields C, Lattie EG, Mohr DC, Reddy M. The Perceived Utility of Smartphone and Wearable Sensor Data in Digital Self-tracking Technologies for Mental Health. PROCEEDINGS OF THE SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS. CHI CONFERENCE 2023; 2023:88. [PMID: 38873656 PMCID: PMC11174977 DOI: 10.1145/3544548.3581209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Mental health symptoms are commonly discovered in primary care. Yet, these settings are not set up to provide psychological treatment. Digital interventions can play a crucial role in stepped care management of patients' symptoms where patients are offered a low intensity intervention, and treatment evolves to incorporate providers if needed. Though digital interventions often use smartphone and wearable sensor data, little is known about patients' desires to use these data to manage mental health symptoms. In 10 interviews with patients with symptoms of depression and anxiety, we explored their: symptom self-management, current and desired use of sensor data, and comfort sharing such data with providers. Findings support the use digital interventions to manage mental health, yet they also highlight a misalignment in patient needs and current efforts to use sensors. We outline considerations for future research, including extending design thinking to wraparound services that may be necessary to truly reduce healthcare burden.
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Affiliation(s)
| | - Ada Ng
- Northwestern University, Illinois, USA
| | | | | | | | - Madhu Reddy
- University of California Irvine, Irvine, USA
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12
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Owen D, Antypas D, Hassoulas A, Pardiñas AF, Espinosa-Anke L, Collados JC. Enabling Early Health Care Intervention by Detecting Depression in Users of Web-Based Forums using Language Models: Longitudinal Analysis and Evaluation. JMIR AI 2023; 2:e41205. [PMID: 37525646 PMCID: PMC7614849 DOI: 10.2196/41205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Background Major depressive disorder is a common mental disorder affecting 5% of adults worldwide. Early contact with health care services is critical for achieving accurate diagnosis and improving patient outcomes. Key symptoms of major depressive disorder (depression hereafter) such as cognitive distortions are observed in verbal communication, which can also manifest in the structure of written language. Thus, the automatic analysis of text outputs may provide opportunities for early intervention in settings where written communication is rich and regular, such as social media and web-based forums. Objective The objective of this study was 2-fold. We sought to gauge the effectiveness of different machine learning approaches to identify users of the mass web-based forum Reddit, who eventually disclose a diagnosis of depression. We then aimed to determine whether the time between a forum post and a depression diagnosis date was a relevant factor in performing this detection. Methods A total of 2 Reddit data sets containing posts belonging to users with and without a history of depression diagnosis were obtained. The intersection of these data sets provided users with an estimated date of depression diagnosis. This derived data set was used as an input for several machine learning classifiers, including transformer-based language models (LMs). Results Bidirectional Encoder Representations from Transformers (BERT) and MentalBERT transformer-based LMs proved the most effective in distinguishing forum users with a known depression diagnosis from those without. They each obtained a mean F1-score of 0.64 across the experimental setups used for binary classification. The results also suggested that the final 12 to 16 weeks (about 3-4 months) of posts before a depressed user's estimated diagnosis date are the most indicative of their illness, with data before that period not helping the models detect more accurately. Furthermore, in the 4- to 8-week period before the user's estimated diagnosis date, their posts exhibited more negative sentiment than any other 4-week period in their post history. Conclusions Transformer-based LMs may be used on data from web-based social media forums to identify users at risk for psychiatric conditions such as depression. Language features picked up by these classifiers might predate depression onset by weeks to months, enabling proactive mental health care interventions to support those at risk for this condition.
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Affiliation(s)
- David Owen
- School of Computer Science and Informatics, Cardiff University,
Cardiff, United Kingdom
| | - Dimosthenis Antypas
- School of Computer Science and Informatics, Cardiff University,
Cardiff, United Kingdom
| | - Athanasios Hassoulas
- Centre for Medical Education, School of Medicine, Cardiff
University, Cardiff, United Kingdom
| | - Antonio F Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, School of
Medicine, Cardiff University, Cardiff, United Kingdom
| | - Luis Espinosa-Anke
- School of Computer Science and Informatics, Cardiff University,
Cardiff, United Kingdom
| | - Jose Camacho Collados
- School of Computer Science and Informatics, Cardiff University,
Cardiff, United Kingdom
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13
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Krishnamurti T, Allen K, Hayani L, Rodriguez S, Rothenberger S, Moses-Kolko E, Simhan H. Using natural language from a smartphone pregnancy app to identify maternal depression. RESEARCH SQUARE 2023:rs.3.rs-2583296. [PMID: 36865248 PMCID: PMC9980211 DOI: 10.21203/rs.3.rs-2583296/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Depression is highly prevalent in pregnancy, yet it often goes undiagnosed and untreated. Language can be an indicator of psychological well-being. This longitudinal, observational cohort study of 1,274 pregnancies examined written language shared in a prenatal smartphone app. Natural language feature of text entered in the app (e.g. in a journaling feature) throughout the course of participants' pregnancies were used to model subsequent depression symptoms. Language features were predictive of incident depression symptoms in a 30-day window (AUROC = 0.72) and offer insights into topics most salient in the writing of individuals experiencing those symptoms. When natural language inputs were combined with self-reported current mood, a stronger predictive model was produced (AUROC = 0.84). Pregnancy apps are a promising way to illuminate experiences contributing to depression symptoms. Even sparse language and simple patient-reports collected directly from these tools may support earlier, more nuanced depression symptom identification.
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Stamatis CA, Meyerhoff J, Liu T, Sherman G, Wang H, Liu T, Curtis B, Ungar LH, Mohr DC. Prospective associations of text-message-based sentiment with symptoms of depression, generalized anxiety, and social anxiety. Depress Anxiety 2022; 39:794-804. [PMID: 36281621 PMCID: PMC9729432 DOI: 10.1002/da.23286] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 09/16/2022] [Accepted: 10/02/2022] [Indexed: 01/27/2023] Open
Abstract
OBJECTIVE Language patterns may elucidate mechanisms of mental health conditions. To inform underlying theory and risk models, we evaluated prospective associations between in vivo text messaging language and differential symptoms of depression, generalized anxiety, and social anxiety. METHODS Over 16 weeks, we collected outgoing text messages from 335 adults. Using Linguistic Inquiry and Word Count (LIWC), NRC Emotion Lexicon, and previously established depression and stress dictionaries, we evaluated the degree to which language features predict symptoms of depression, generalized anxiety, or social anxiety the following week using hierarchical linear models. To isolate the specificity of language effects, we also controlled for the effects of the two other symptom types. RESULTS We found significant relationships of language features, including personal pronouns, negative emotion, cognitive and biological processes, and informal language, with common mental health conditions, including depression, generalized anxiety, and social anxiety (ps < .05). There was substantial overlap between language features and the three mental health outcomes. However, after controlling for other symptoms in the models, depressive symptoms were uniquely negatively associated with language about anticipation, trust, social processes, and affiliation (βs: -.10 to -.09, ps < .05), whereas generalized anxiety symptoms were positively linked with these same language features (βs: .12-.13, ps < .001). Social anxiety symptoms were uniquely associated with anger, sexual language, and swearing (βs: .12-.13, ps < .05). CONCLUSION Language that confers both common (e.g., personal pronouns and negative emotion) and specific (e.g., affiliation, anticipation, trust, and anger) risk for affective disorders is perceptible in prior week text messages, holding promise for understanding cognitive-behavioral mechanisms and tailoring digital interventions.
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Affiliation(s)
- Caitlin A. Stamatis
- Center for Behavioral Intervention TechnologiesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Jonah Meyerhoff
- Center for Behavioral Intervention TechnologiesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Tingting Liu
- Positive Psychology CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP)National Institutes of Health (NIH)BaltimoreMarylandUSA
| | - Garrick Sherman
- Positive Psychology CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Harry Wang
- Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tony Liu
- Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- RobloxSan MateoCaliforniaUSA
| | - Brenda Curtis
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP)National Institutes of Health (NIH)BaltimoreMarylandUSA
| | - Lyle H. Ungar
- Positive Psychology CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David C. Mohr
- Center for Behavioral Intervention TechnologiesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
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15
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Potier R. Revue critique sur le potentiel du numérique dans la recherche en psychopathologie : un point de vue psychanalytique. L'ÉVOLUTION PSYCHIATRIQUE 2022. [DOI: 10.1016/j.evopsy.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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