Skip to main content
Log in

Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment

  • Review
  • Published:
Biomedical Materials & Devices Aims and scope Submit manuscript

Abstract

Mental health disorders—including depression, anxiety, trauma-related, and psychotic conditions—are pervasive and impairing, representing considerable challenges for both individual well-being and public health. Often the first challenges to treatment include financial, geographic, and stigmatic barriers, which limit the accessibility of traditional assessment measures. Further, compounded by frequent misdiagnosis or delayed detection, there is a need for effective, accessible, and scalable approaches to identification and management. Considering advances in computing and the ubiquitous nature of personal mobile and wearable technology, this narrative review examines the utilization of passive sensor data as a screening and diagnostic tool for mental disorders. As an alternative to traditional screening measures, passive sensing offers a tool to overcome barriers that prevent many from seeking services. We critically assess the literature up to September 2023, exploring the use of passive data—such as heart rate variability, movement patterns, and geolocation—to predict mental health outcomes across a spectrum of disorders. From a translational perspective, our review explores the state of passive sensing science, with special emphasis on the capacity for the science to be implemented in real world clinical and general populations, a novelty specific to this review to the best of our knowledge. Toward this aim, we consider multiple study factors, including participant demographics, data collection methods, sensor modalities, outcome measures, and analytic modeling approaches. We find that passive sensing features, such as GPS, heart rate, and actigraphy offer promise for enhancing early detection and improving the diagnostic process for mental disorders. Despite this promise, however, our findings highlight important limitations in passive sensing research including (1) a trend toward smaller, specialized samples, (2) a predominance of data collection apps built on the Android operating system, and (3) a reliance on self-reported measures as proxies for important clinical outcomes. These limitations ultimately stymie efforts to implement and scale important research findings in larger and more heterogeneous populations. With future translational research in mind, we emphasize the importance of validating passive sensing findings with larger, more diverse samples and ensuring assessment tools can be deployed across multiple device types and operating systems. Further, where possible, we emphasize the need for robust, objectively validated outcome measures, such as by clinician assessment. We conclude that careful consideration of translational factors in the design of future research will aid in enhancing the impact of future passive sensing studies, ultimately enhancing mental health outcomes on a broad scale.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Data Availability

The present work represents a narrative review, and thus the manuscript does not include any primary data. All studies described in the review are cited for individual reference.

Notes

  1. It should be noted that suicide is included in the in the DSM-5 as suicidal behavior disorder under “Conditions for Further Study”; however, these criteria have not been intended for clinical use, but rather to guide further research and considerations of the disorder for future iterations of the DSM. Our treatment of suicide in this review will be as a serious transdiagnostic outcome.

References

  1. D. Arias, S. Saxena, and S. Verguet, “Quantifying the global burden of mental disorders and their economic value,” eClinicalMedicine, vol. 54, Dec. 2022, doi: https://doi.org/10.1016/j.eclinm.2022.101675.

  2. W.T. Carpenter, B. Kirkpatrick, The heterogeneity of the long-term course of schizophrenia. Schizophr. Bull. 14(4), 645–652 (1988). https://doi.org/10.1093/schbul/14.4.645

    Article  PubMed  Google Scholar 

  3. D. Goldberg, The heterogeneity of ‘major depression.’ World Psychiatry 10(3), 226–228 (2011). https://doi.org/10.1002/j.2051-5545.2011.tb00061.x

    Article  PubMed  PubMed Central  Google Scholar 

  4. G.Y. Toh, M.W. Vasey, Heterogeneity in autonomic arousal level in perseverative worry: the role of cognitive control and verbal thought. Front. Hum. Neurosci. (2017). https://doi.org/10.3389/fnhum.2017.00108

    Article  PubMed  PubMed Central  Google Scholar 

  5. M.V. Heinz, N.X. Thomas, N.D. Nguyen, T.Z. Griffin, N.C. Jacobson, Technological advances in clinical assessment. in Reference module in neuroscience and biobehavioral psychology, Elsevier, 2021.https://doi.org/10.1016/B978-0-12-818697-8.00171-0

  6. American Psychiatric Association, Diagnostic and statistical manual of mental disorders, (5th edn), in: Arlington, VA (eds.) Text Revision, 5th-Text Revision, American Psychiatric Association, Virginia, 2022

  7. SAMHSA, Key substance use and mental health indicators in the United States: results from the 2021 national survey on drug use and health. Center for Behavioral Health Statistics and Quality, HHS Publication No. PEP22-07-01-005, 2022. [Online]. Available: https://www.samhsa.gov/data/report/2021-nsduh-annual-national-report

  8. T. Insel et al., Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167(7), 748–751 (2010). https://doi.org/10.1176/appi.ajp.2010.09091379

    Article  PubMed  Google Scholar 

  9. R. Kotov et al., The hierarchical taxonomy of psychopathology (HiTOP): a dimensional alternative to traditional nosologies. J. Abnorm. Psychol. 126(4), 454–477 (2017). https://doi.org/10.1037/abn0000258

    Article  PubMed  Google Scholar 

  10. J. Torous, M.V. Kiang, J. Lorme, J.-P. Onnela, New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment. Health 3(2), e5165 (2016). https://doi.org/10.2196/mental.5165

    Article  Google Scholar 

  11. Pew Research Center, S. 800 Washington, D. 20036 U.-419-4300 | M.-857-8562 | F.-419-4372 | M. Inquiries Mobile fact sheet. Pew Research Center: Internet, Science & Tech. Accessed: 2022. [Online]. Available: https://www.pewresearch.org/internet/fact-sheet/mobile/

  12. E. a Vogels, About one-in-five Americans use a smart watch or fitness tracker. Pew Research Center. Accessed: 2023. [Online]. Available: https://www.pewresearch.org/short-reads/2020/01/09/about-one-in-five-americans-use-a-smart-watch-or-fitness-tracker/

  13. V.J. Reddi, H. Yoon, A. Knies, Two billion devices and counting. IEEE Micro 38(1), 6–21 (2018). https://doi.org/10.1109/MM.2018.011441560

    Article  Google Scholar 

  14. J. Shalf, The future of computing beyond Moore’s Law. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 378(2166), 20190061 (2020). https://doi.org/10.1098/rsta.2019.0061

    Article  Google Scholar 

  15. A. Trifan, M. Oliveira, J.L. Oliveira, Passive sensing of health outcomes through smartphones: systematic review of current solutions and possible limitations. JMIR MHealth UHealth 7(8), e12649 (2019). https://doi.org/10.2196/12649

    Article  PubMed  PubMed Central  Google Scholar 

  16. M. Sheikh, M. Qassem, P.A. Kyriacou, Wearable, environmental, and smartphone-based passive sensing for mental health monitoring. Front. Digit. Health (2021). https://doi.org/10.3389/fdgth.2021.662811

    Article  PubMed  PubMed Central  Google Scholar 

  17. I. Moura, A. Teles, D. Viana, J. Marques, L. Coutinho, F. Silva, Digital Phenotyping of mental health using multimodal sensing of multiple situations of interest: a systematic literature review. J. Biomed. Inform. 138, 104278 (2023). https://doi.org/10.1016/j.jbi.2022.104278

    Article  PubMed  Google Scholar 

  18. A. Abd-alrazaq et al., Wearable artificial intelligence for anxiety and depression: scoping review. J. Med. Internet Res. 25(1), e42672 (2023). https://doi.org/10.2196/42672

    Article  PubMed  PubMed Central  Google Scholar 

  19. N.C. Jacobson, B. Feng, Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life. Transl. Psychiatry 12(1), 1 (2022). https://doi.org/10.1038/s41398-022-02038-1

    Article  Google Scholar 

  20. D. Lekkas, N.C. Jacobson, Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma. Sci. Rep. 11(1), 1 (2021). https://doi.org/10.1038/s41598-021-89768-2

    Article  CAS  Google Scholar 

  21. D. Ben-Zeev et al., CrossCheck: integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse. Psychiatr. Rehabil. J. 40(3), 266–275 (2017). https://doi.org/10.1037/prj0000243

    Article  PubMed  PubMed Central  Google Scholar 

  22. E.K. Czyz, C.A. King, N. Al-Dajani, L. Zimmermann, V. Hong, I. Nahum-Shani, Ecological momentary assessments and passive sensing in the prediction of short-term suicidal ideation in young adults. JAMA Netw. Open 6(8), e2328005 (2023). https://doi.org/10.1001/jamanetworkopen.2023.28005

    Article  PubMed  PubMed Central  Google Scholar 

  23. S.D. Dlima, S. Shevade, S.R. Menezes, A. Ganju, Digital phenotyping in health using machine learning approaches: scoping review. JMIR Bioinf. Biotechnol. 3(1), e39618 (2022). https://doi.org/10.2196/39618

    Article  Google Scholar 

  24. S. Ware et al., Automatic depression screening using social interaction data on smartphones. Smart Health 26, 100356 (2022). https://doi.org/10.1016/j.smhl.2022.100356

    Article  Google Scholar 

  25. S.M. Narkhede et al., Machine learning identifies digital phenotyping measures most relevant to negative symptoms in psychotic disorders: implications for clinical trials. Schizophr. Bull. 48(2), 425–436 (2022). https://doi.org/10.1093/schbul/sbab134

    Article  PubMed  Google Scholar 

  26. A.S. Cakmak et al., Classification and prediction of post-trauma outcomes related to PTSD using circadian rhythm changes measured via wrist-worn research watch in a large longitudinal cohort. IEEE J. Biomed. Health Inform. 25(8), 2866–2876 (2021). https://doi.org/10.1109/JBHI.2021.3053909

    Article  PubMed  PubMed Central  Google Scholar 

  27. P.S. Wang, P.A. Berglund, M. Olfson, R.C. Kessler, Delays in initial treatment contact after first onset of a mental disorder. Health Serv. Res. 39(2), 393–416 (2004). https://doi.org/10.1111/j.1475-6773.2004.00234.x

    Article  PubMed  PubMed Central  Google Scholar 

  28. M.A. Whooley, J.M. Wong, Depression and cardiovascular disorders. Annu. Rev. Clin. Psychol. 9(1), 327–354 (2013). https://doi.org/10.1146/annurev-clinpsy-050212-185526

    Article  PubMed  Google Scholar 

  29. O.M. Farr et al., Posttraumatic stress disorder, alone or additively with early life adversity, is associated with obesity and cardiometabolic risk. Nutr Metab Cardiovasc Dis 25(5), 479–488 (2015). https://doi.org/10.1016/j.numecd.2015.01.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. null The Lancet Global Health, Mental health matters. Lancet Glob. Health 8(11), e1352 (2020). https://doi.org/10.1016/S2214-109X(20)30432-0

    Article  Google Scholar 

  31. Health Resources & Services Administration, Health professional shortage areas. Health Workforce Shortage Areas. Accessed: Sep. 04, 2023. [Online]. Available: https://data.hrsa.gov/topics/health-workforce/shortage-areas

  32. D. Vigo, G. Thornicroft, R. Atun, Estimating the true global burden of mental illness. Lancet Psychiatry 3(2), 171–178 (2016). https://doi.org/10.1016/S2215-0366(15)00505-2

    Article  PubMed  Google Scholar 

  33. B. Druss, E. Walker, Mental disorders and medical comorbidity. Synth. Proj. Res. Synth. Rep., pp. 1–26, 2011

  34. E.T. Isometsä, Psychological autopsy studies–a review. Eur. Psychiatry 16(7), 379–385 (2001). https://doi.org/10.1016/S0924-9338(01)00594-6

    Article  PubMed  Google Scholar 

  35. A.D. Moreland, J.E. Dumas, Categorical and dimensional approaches to the measurement of disruptive behavior in the preschool years: a meta-analysis. Clin. Psychol. Rev. 28(6), 1059–1070 (2008). https://doi.org/10.1016/j.cpr.2008.03.001

    Article  PubMed  PubMed Central  Google Scholar 

  36. World Health Organization, ICD-11. Accessed: Sep. 02, 2023. [Online]. Available: https://icd.who.int/en

  37. M.L. Savoy, D.T. O’Gurek, Screening your adult patients for depression. Fam. Pract. Manag. 23(2), 16–20 (2016)

    PubMed  Google Scholar 

  38. K. Kroenke, R.L. Spitzer, J.B.W. Williams, The PHQ-9. J. Gen. Intern. Med. 16(9), 606–613 (2001). https://doi.org/10.1046/j.1525-1497.2001.016009606.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. D. Colombo et al., Affect recall bias: being resilient by distorting reality. Cogn. Ther. Res. 44(5), 906–918 (2020). https://doi.org/10.1007/s10608-020-10122-3

    Article  Google Scholar 

  40. S.D. Targum, C. Sauder, M. Evans, J.N. Saber, P.D. Harvey, Ecological momentary assessment as a measurement tool in depression trials. J. Psychiatr. Res. 136, 256–264 (2021). https://doi.org/10.1016/j.jpsychires.2021.02.012

    Article  PubMed  Google Scholar 

  41. S. Shiffman, A. Stone, M. Hufford, Ecolocial momentary assessment. Ann. Rev. Clin. Psychol. 4, 1–32 (2008). https://doi.org/10.1146/annurev.clinpsy.3.022806.091415

    Article  Google Scholar 

  42. M.D. Nemesure et al., Depressive symptoms as a heterogeneous and constantly evolving dynamical system: idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder. PsyArXiv, 2022. https://doi.org/10.31234/osf.io/pf4kc

  43. D.B. Dwyer, P. Falkai, N. Koutsouleris, Machine learning approaches for clinical psychology and psychiatry. Annu. Rev. Clin. Psychol. 14(1), 91–118 (2018). https://doi.org/10.1146/annurev-clinpsy-032816-045037

    Article  PubMed  Google Scholar 

  44. B. Buck et al., Capturing behavioral indicators of persecutory ideation using mobile technology. J. Psychiatr. Res. 116, 112–117 (2019). https://doi.org/10.1016/j.jpsychires.2019.06.002

    Article  PubMed  PubMed Central  Google Scholar 

  45. The Balanced Accuracy and Its Posterior Distribution | IEEE Conference Publication | IEEE Xplore. Accessed: Nov. 05, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5597285?casa_token=XytYNuJq_e8AAAAA:j0h1o2NmgQKwrWiWKJjzIB2YBTY7rlwo8qvk0xxyPDoB5Spy_U7hmKv_fJLQ_bVaYvcaTc7n

  46. D. Hand, P. Christen, A note on using the F-measure for evaluating record linkage algorithms. Stat. Comput. 28(3), 539–547 (2017). https://doi.org/10.1007/s11222-017-9746-6

    Article  Google Scholar 

  47. F. Pedregosa et al., Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(85), 2825–2830 (2011)

    Google Scholar 

  48. T. Chen, C. Guestrin, XGBoost: a scalable tree boosting system. in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco California USA: ACM, pp. 785–794. 2016. https://doi.org/10.1145/2939672.2939785

  49. M. Tlachac et al., StudentSADD: rapid mobile depression and suicidal ideation screening of college students during the coronavirus pandemic. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6(2), 1–32 (2022). https://doi.org/10.1145/3534604

    Article  Google Scholar 

  50. E. Toto, M. Tlachac, E.A. Rundensteiner, AudiBERT: a deep transfer learning multimodal classification framework for depression screening. in Proceedings of the 30th ACM international conference on information & knowledge management, Virtual Event Queensland Australia: ACM, pp. 4145–4154. 2021. https://doi.org/10.1145/3459637.3481895

  51. I. Goodfellow, Y. Bengio, A. Courville, Deep learning, in Adaptive computation and machine learning. (The MIT Press, Cambridge, 2016)

    Google Scholar 

  52. S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  CAS  PubMed  Google Scholar 

  53. K. Cho et al., Learning phrase representations using RNN encoder–decoder for statistical machine translation. in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Doha, Qatar: Association for Computational Linguistics, pp. 1724–1734. 2014. https://doi.org/10.3115/v1/D14-1179

  54. M.V. Heinz et al., Association of selective serotonin reuptake inhibitor use with abnormal physical movement patterns as detected using a piezoelectric accelerometer and deep learning in a nationally representative sample of noninstitutionalized persons in the US. JAMA Netw. Open 5(4), e225403 (2022). https://doi.org/10.1001/jamanetworkopen.2022.5403

    Article  PubMed  PubMed Central  Google Scholar 

  55. G. Price, M.V. Heinz, A.C. Collins, N.C. Jacobson, Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample. PsyArXiv, 2023. https://doi.org/10.31234/osf.io/9p4xr

  56. S.G. Luke, Evaluating significance in linear mixed-effects models in R. Behav. Res. Methods 49(4), 1494–1502 (2017). https://doi.org/10.3758/s13428-016-0809-y

    Article  PubMed  Google Scholar 

  57. M. Holko et al., Wearable fitness tracker use in federally qualified health center patients: strategies to improve the health of all of us using digital health devices. NPJ Digit. Med. 5, 53 (2022). https://doi.org/10.1038/s41746-022-00593-x

    Article  PubMed  PubMed Central  Google Scholar 

  58. A. Henriksen et al., Using fitness trackers and smartwatches to measure physical activity in research: analysis of consumer wrist-worn wearables. J. Med. Internet Res. 20(3), e9157 (2018). https://doi.org/10.2196/jmir.9157

    Article  Google Scholar 

  59. Y. Cheng, K. Wang, H. Xu, T. Li, Q. Jin, D. Cui, Recent developments in sensors for wearable device applications. Anal. Bioanal. Chem. 413(24), 6037–6057 (2021). https://doi.org/10.1007/s00216-021-03602-2

    Article  CAS  PubMed  Google Scholar 

  60. C. Acebo, M.K. LeBourgeois, Actigraphy. Respir. Care Clin. N. Am. 12(1), 23–30 (2006). https://doi.org/10.1016/j.rcc.2005.11.010

    Article  PubMed  Google Scholar 

  61. Z. Huang, J. Epps, D. Joachim, M. Chen, Depression detection from short utterances via diverse smartphones in natural environmental conditions. in Interspeech 2018, ISCA, pp. 3393–3397. 2018. https://doi.org/10.21437/Interspeech.2018-1743

  62. N. Cummins, S. Scherer, J. Krajewski, S. Schnieder, J. Epps, T.F. Quatieri, A review of depression and suicide risk assessment using speech analysis. Speech Commun. 71, 10–49 (2015). https://doi.org/10.1016/j.specom.2015.03.004

    Article  Google Scholar 

  63. M.L. Tlachac, R. Flores, E. Toto, E. Rundensteiner, Early mental health uncovering with short scripted and unscripted voice recordings. in Deep Learning Applications, Volume 4, vol. 1434, ed. by M.A. Wani, V. Palade Advances in Intelligent Systems and Computing, (Springer: Singapore, 2023), pp. 79–110. https://doi.org/10.1007/978-981-19-6153-3_4

  64. E.W. McGinnis et al., Giving voice to vulnerable children: machine learning analysis of speech detects anxiety and depression in early childhood. IEEE J. Biomed. Health Inform. 23(6), 2294–2301 (2019). https://doi.org/10.1109/JBHI.2019.2913590

    Article  PubMed  PubMed Central  Google Scholar 

  65. R. Wang et al., 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, in UbiComp ’14. New York, NY, USA: Association for Computing Machinery, pp. 3–14. 2014. https://doi.org/10.1145/2632048.2632054

  66. D. Di Matteo et al., The relationship between smartphone-recorded environmental audio and symptomatology of anxiety and depression: exploratory study. JMIR Form. Res. 4(8), e18751 (2020). https://doi.org/10.2196/18751

    Article  PubMed  PubMed Central  Google Scholar 

  67. M.R. Mehl, J.W. Pennebaker, D.M. Crow, J. Dabbs, J.H. Price, The electronically activated recorder (EAR): a device for sampling naturalistic daily activities and conversations. Behav. Res. Methods Instrum. Comput. 33(4), 517–523 (2001). https://doi.org/10.3758/bf03195410

    Article  CAS  PubMed  Google Scholar 

  68. J. Rooksby, A. Morrison, D. Murray-Rust, 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, Glasgow Scotland Uk: ACM, pp. 1–14. 2019. https://doi.org/10.1145/3290605.3300655

  69. M. Boukhechba, A.R. Daros, K. Fua, P.I. Chow, B.A. Teachman, L.E. Barnes, DemonicSalmon: monitoring mental health and social interactions of college students using smartphones. Smart Health 9–10, 192–203 (2018). https://doi.org/10.1016/j.smhl.2018.07.005

    Article  Google Scholar 

  70. S. Ware et al., Large-scale automatic depression screening using meta-data from WiFi infrastructure. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2(4), 1951–19527 (2018). https://doi.org/10.1145/3287073

    Article  Google Scholar 

  71. T. Liu et al., The relationship between text message sentiment and self-reported depression. J. Affect. Disord. 302, 7–14 (2022). https://doi.org/10.1016/j.jad.2021.12.048

    Article  PubMed  Google Scholar 

  72. S.S. Ogden, T. Guo, Layercake: efficient inference serving with cloud and mobile resources. in 2023 IEEE/ACM 23rd international symposium on cluster, cloud and internet computing (CCGrid), Bangalore, India: IEEE, pp. 191–202. 2023. https://doi.org/10.1109/CCGrid57682.2023.00027

  73. R.M. Epstein et al., ‘I didn’t know what was wrong:’ how people with undiagnosed depression recognize, name and explain their distress. J. Gen. Intern. Med. 25(9), 954–961 (2010). https://doi.org/10.1007/s11606-010-1367-0

    Article  PubMed  PubMed Central  Google Scholar 

  74. M.L. Tlachac, M. Reisch, B. Lewis, R. Flores, L. Harrison, E. Rundensteiner, Impact assessment of stereotype threat on mobile depression screening using Bayesian estimation. Healthc. Anal. 2, 100088 (2022). https://doi.org/10.1016/j.health.2022.100088

    Article  Google Scholar 

  75. K. Demyttenaere, A. Bonnewyn, R. Bruffaerts, T. Brugha, R. De Graaf, J. Alonso, Comorbid painful physical symptoms and depression: prevalence, work loss, and help seeking. J. Affect. Disord. 92(2–3), 185–193 (2006). https://doi.org/10.1016/j.jad.2006.01.007

    Article  PubMed  Google Scholar 

  76. A. Halfin, Depression: the benefits of early and appropriate treatment. Am. J. Manag. Care 13(4 Suppl), S92-97 (2007)

    PubMed  Google Scholar 

  77. A. Madan, M. Cebrian, S. Moturu, K. Farrahi, and A. “Sandy” Pentland, “Sensing the ‘Health State’ of a Community,” IEEE Pervasive Comput., vol. 11, no. 4, pp. 36–45, Oct. 2012, doi: https://doi.org/10.1109/MPRV.2011.79.

  78. A. Dogrucu et al., Moodable: on feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data. Smart Health 17, 100118 (2020). https://doi.org/10.1016/j.smhl.2020.100118

    Article  Google Scholar 

  79. M.L. Tlachac, R. Flores, M. Reisch, K. Houskeeper, E.A. Rundensteiner, DepreST-CAT: retrospective smartphone call and text logs collected during the COVID-19 pandemic to screen for mental illnesses. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6(2), 1–32 (2022). https://doi.org/10.1145/3534596

    Article  Google Scholar 

  80. C.M. Jones, E.F. McCance-Katz, Co-occurring substance use and mental disorders among adults with opioid use disorder. Drug Alcohol Depend. 197, 78–82 (2019). https://doi.org/10.1016/j.drugalcdep.2018.12.030

    Article  PubMed  Google Scholar 

  81. D. Comer-HaGans, B.E. Weller, C. Story, J. Holton, Developmental stages and estimated prevalence of coexisting mental health and neurodevelopmental conditions and service use in youth with intellectual disabilities, 2011–2012. J. Intellect. Disabil. Res. 64(3), 185–196 (2020). https://doi.org/10.1111/jir.12708

    Article  CAS  PubMed  Google Scholar 

  82. L.A. Marsch et al., The application of digital health to the assessment and treatment of substance use disorders: the past, current, and future role of the national drug abuse treatment clinical trials network. J. Subst. Abuse Treat. 112S, 4–11 (2020). https://doi.org/10.1016/j.jsat.2020.02.005

    Article  PubMed  Google Scholar 

  83. D. Campolo, F. Taffoni, G. Schiavone, C. Laschi, F. Keller, E. Guglielmelli, A novel technological approach towards the early diagnosis of neurodevelopmental disorders. in 2008 30th annual international conference of the IEEE engineering in medicine and biology society, vol. 2008, pp. 4875–4878, 2008. https://doi.org/10.1109/IEMBS.2008.4650306

  84. A. Sano et al., Identifying objective physiological markers and modifiable behaviors for self-reported stress and mental health status using wearable sensors and mobile phones: observational study. J. Med. Internet Res. 20(6), e210 (2018). https://doi.org/10.2196/jmir.9410

    Article  PubMed  PubMed Central  Google Scholar 

  85. S.C. Guntuku, D.B. Yaden, M.L. Kern, L.H. Ungar, J.C. Eichstaedt, Detecting depression and mental illness on social media: an integrative review. Curr. Opin. Behav. Sci. 18, 43–49 (2017). https://doi.org/10.1016/j.cobeha.2017.07.005

    Article  Google Scholar 

  86. S. Chancellor, M. De Choudhury, Methods in predictive techniques for mental health status on social media: a critical review. Npj Digit. Med. 3(1), 43 (2020). https://doi.org/10.1038/s41746-020-0233-7

    Article  PubMed  PubMed Central  Google Scholar 

  87. M. Alkhathlan, M.L. Tlachac, L. Harrison, E. Rundensteiner, Honestly i never really thought about adding a description why highly engaged tweets are inaccessible, in Human-Computer Interaction – INTERACT 2021 Lecture Notes in Computer Science, vol. 12932, ed. by C. Ardito, R. Lanzilotti, A. Malizia, H. Petrie, A. Piccinno, G. Desolda, K. Inkpen (Springer International Publishing, Cham, 2021), pp.373–395. https://doi.org/10.1007/978-3-030-85623-6_23

    Chapter  Google Scholar 

  88. J. Shin, S.M. Bae, A systematic review of location data for depression prediction. Int. J. Environ. Res. Public Health 20(11), 5984 (2023). https://doi.org/10.3390/ijerph20115984

    Article  PubMed  PubMed Central  Google Scholar 

  89. W.F. Heckler, J.V. De Carvalho, J.L.V. Barbosa, Machine learning for suicidal ideation identification: a systematic literature review. Comput. Hum. Behav. 128, 107095 (2022). https://doi.org/10.1016/j.chb.2021.107095

    Article  Google Scholar 

  90. D. Highland, G. Zhou, A review of detection techniques for depression and bipolar disorder. Smart Health 24, 100282 (2022). https://doi.org/10.1016/j.smhl.2022.100282

    Article  Google Scholar 

  91. G.S. Malhi, J.J. Mann, Depression. Lancet Lond. Engl. 392(10161), 2299–2312 (2018). https://doi.org/10.1016/S0140-6736(18)31948-2

    Article  Google Scholar 

  92. E.I. Fried, R.M. Nesse, Depression is not a consistent syndrome: an investigation of unique symptom patterns in the STAR*D study. J. Affect. Disord. 172, 96–102 (2015). https://doi.org/10.1016/j.jad.2014.10.010

    Article  PubMed  Google Scholar 

  93. P. Cuijpers, C.F. Reynolds III., T. Donker, J. Li, G. Andersson, A. Beekman, Personalized treatment of adult depression: medication, psychotherapy, or both? A systematic review. Depress. Anxiety 29(10), 855–864 (2012). https://doi.org/10.1002/da.21985

    Article  PubMed  Google Scholar 

  94. A.M. Buch, C. Liston, Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics. Neuropsychopharmacology 46(1), 1 (2021). https://doi.org/10.1038/s41386-020-00789-3

    Article  Google Scholar 

  95. C. Otte et al., Major depressive disorder. Nat. Rev. Dis. Primer 2(1), 1 (2016). https://doi.org/10.1038/nrdp.2016.65

    Article  Google Scholar 

  96. J.D. Tubbs, J. Ding, L. Baum, P.C. Sham, Systemic neuro-dysregulation in depression: evidence from genome-wide association. Eur. Neuropsychopharmacol. 39, 1–18 (2020). https://doi.org/10.1016/j.euroneuro.2020.08.007

    Article  CAS  PubMed  Google Scholar 

  97. R.Z. Fisch, G. Nesher, Masked depression. Postgrad. Med. 80(3), 165–169 (1986). https://doi.org/10.1080/00325481.1986.11699519

    Article  CAS  PubMed  Google Scholar 

  98. C. Yue et al., Automatic depression prediction using internet traffic characteristics on smartphones. Smart Health Amst. Neth. 18, 100137 (2020). https://doi.org/10.1016/j.smhl.2020.100137

    Article  Google Scholar 

  99. J. Lu et al., Joint modeling of heterogeneous sensing data for depression assessment via multi-task learning. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2(1), 21:1-21:21 (2018). https://doi.org/10.1145/3191753

    Article  Google Scholar 

  100. R. Razavi, A. Gharipour, M. Gharipour, Depression screening using mobile phone usage metadata: a machine learning approach. J. Am. Med. Inform. Assoc. JAMIA 27(4), 522–530 (2020). https://doi.org/10.1093/jamia/ocz221

    Article  PubMed  Google Scholar 

  101. E. O’Connor et al., Screening for depression in adults: an updated systematic evidence review for the U.S. Preventive services task force. in U.S. preventive services task force evidence syntheses, formerly systematic evidence reviews. Rockville (MD): Agency for Healthcare Research and Quality (US), 2016. Accessed: Aug. 30, 2023. [Online]. Available: http://www.ncbi.nlm.nih.gov/books/NBK349027/

  102. A.J. Rush et al., The 16-Item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol. Psychiatry 54(5), 573–583 (2003). https://doi.org/10.1016/S0006-3223(02)01866-8

    Article  PubMed  Google Scholar 

  103. K. Opoku Asare et al., Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: a longitudinal data analysis. Pervasive Mob. Comput. 83, 101621 (2022). https://doi.org/10.1016/j.pmcj.2022.101621

    Article  Google Scholar 

  104. S. Saeb, E.G. Lattie, S.M. Schueller, K.P. Kording, D.C. Mohr, The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 4, e2537 (2016). https://doi.org/10.7717/peerj.2537

    Article  PubMed  PubMed Central  Google Scholar 

  105. C. Yue et al., Fusing location data for depression prediction. IEEE Trans. Big Data 7(2), 355–370 (2021). https://doi.org/10.1109/TBDATA.2018.2872569

    Article  PubMed  Google Scholar 

  106. A. Pratap et al., The accuracy of passive phone sensors in predicting daily mood. Depress. Anxiety 36(1), 72–81 (2019). https://doi.org/10.1002/da.22822

    Article  PubMed  Google Scholar 

  107. X. Xu et al., Leveraging routine behavior and contextually-filtered features for depression detection among college students. Proc. ACM Interact. Mob Wearable Ubiquitous Technol. 3(3), 116:1-116:33 (2019). https://doi.org/10.1145/3351274

    Article  Google Scholar 

  108. P. Chikersal et al., Detecting depression and predicting its onset using longitudinal symptoms captured by passive sensing: a machine learning approach with robust feature selection. ACM Trans Comput.-Hum. Interact. 28(1), 1–41 (2021). https://doi.org/10.1145/3422821

    Article  Google Scholar 

  109. R. Bai et al., Tracking and monitoring mood stability of patients with major depressive disorder by machine learning models using passive digital data: prospective naturalistic multicenter study. JMIR MHealth UHealth 9(3), e24365 (2021). https://doi.org/10.2196/24365

    Article  PubMed  PubMed Central  Google Scholar 

  110. B.W. Nelson, C.A. Low, N. Jacobson, P. Areán, J. Torous, N.B. Allen, Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research. NPJ Digit. Med. 3, 90 (2020). https://doi.org/10.1038/s41746-020-0297-4

    Article  PubMed  PubMed Central  Google Scholar 

  111. A.G. Horwitz et al., Using machine learning with intensive longitudinal data to predict depression and suicidal ideation among medical interns over time. Psychol. Med. (2023). https://doi.org/10.1017/S0033291722003014

    Article  PubMed  Google Scholar 

  112. E. Strubell, A. Ganesh, A. McCallum, Energy and policy considerations for deep learning in NLP. in Proceedings of the 57th annual meeting of the association for computational linguistics, Florence, Italy: Association for Computational Linguistics, pp. 3645–3650. 2019. https://doi.org/10.18653/v1/P19-1355

  113. X. Xu et al., GLOBEM: cross-dataset generalization of longitudinal human behavior modeling. Proc. ACM Interact., Mob. Wearable Ubiquitous Technol. 6(4), 1–34 (2023). https://doi.org/10.1145/3569485

    Article  CAS  Google Scholar 

  114. M. Jamalova, C. Milán, The comparative study of the relationship between smartphone choice and socio-economic indicators. Int. J. Mark. Stud. 11(3), 11 (2019). https://doi.org/10.5539/ijms.v11n3p11

    Article  Google Scholar 

  115. I. Nahum-Shani et al., Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann. Behav. Med. 52(6), 446–462 (2018). https://doi.org/10.1007/s12160-016-9830-8

    Article  PubMed  Google Scholar 

  116. M. Tlachac, S.S. Ogden, Left on read: reply latency for anxiety & depression screening. in Adjunct proceedings of the 2022 ACM international joint conference on pervasive and ubiquitous computing and the 2022 ACM international symposium on wearable computers, in UbiComp/ISWC ’22 Adjunct. New York, NY, USA: Association for Computing Machinery, pp. 500–502. 2023. https://doi.org/10.1145/3544793.3563429

  117. M. Tlachac, V. Melican, M. Reisch, E. Rundensteiner, Mobile depression screening with time series of text logs and call logs. in 2021 IEEE EMBS international conference on biomedical and health informatics (BHI), pp. 1–4. 2021. https://doi.org/10.1109/BHI50953.2021.9508582

  118. M.L. Tlachac, E.A. Rundensteiner, Depression screening from text message reply latency. in 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp. 5490–5493. 2020. https://doi.org/10.1109/EMBC44109.2020.9175690

  119. M. Tlachac, E. Rundensteiner, Screening for depression with retrospectively harvested private versus public text. IEEE J. Biomed. Health Inform. 24(11), 3326–3332 (2020). https://doi.org/10.1109/JBHI.2020.2983035

    Article  CAS  PubMed  Google Scholar 

  120. M. Tlachac, A. Shrestha, M. Shah, B. Litterer, E.A. Rundensteiner, Automated construction of lexicons to improve depression screening with text messages. IEEE J. Biomed. Health Inform. 27(6), 2751–2759 (2023). https://doi.org/10.1109/JBHI.2022.3203345

    Article  CAS  PubMed  Google Scholar 

  121. T. Ek, C. Kirkegaard, H. Jonsson, P. Nugues, Named entity recognition for short text messages. Procedia Soc. Behav. Sci. 27, 178–187 (2011). https://doi.org/10.1016/j.sbspro.2011.10.596

    Article  Google Scholar 

  122. M. Tlachac, E. Toto, E. Rundensteiner, You’re making me depressed: leveraging texts from contact subsets to predict depression. in 2019 IEEE EMBS international conference on biomedical & health informatics (BHI), Chicago, IL, USA: IEEE, pp. 1–4. 2019. https://doi.org/10.1109/BHI.2019.8834481

  123. M. Tlachac et al., 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), Osaka, Japan: IEEE, pp. 2804–2813. 2022. https://doi.org/10.1109/BigData55660.2022.10020224

  124. J. Meyerhoff et al., Analyzing text message linguistic features: do people with depression communicate differently with their close and non-close contacts? Behav. Res. Ther. 166, 104342 (2023). https://doi.org/10.1016/j.brat.2023.104342

    Article  PubMed  Google Scholar 

  125. Y. Zhang et al., Predicting depressive symptom severity through individuals’ nearby bluetooth device count data collected by mobile phones: preliminary longitudinal study. JMIR MHealth UHealth 9(7), e29840 (2021). https://doi.org/10.2196/29840

    Article  PubMed  PubMed Central  Google Scholar 

  126. F. Matcham et al., Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study. BMC Psychiatry 22(1), 136 (2022). https://doi.org/10.1186/s12888-022-03753-1

    Article  PubMed  PubMed Central  Google Scholar 

  127. C. Oetzmann et al., Lessons learned from recruiting into a longitudinal remote measurement study in major depressive disorder. NPJ Digit. Med. 5(1), 1 (2022). https://doi.org/10.1038/s41746-022-00680-z

    Article  Google Scholar 

  128. W. Gerych, E. Agu, E. Rundensteiner, Classifying depression in imbalanced datasets using an autoencoder- based anomaly detection approach. in 2019 IEEE 13th international conference on semantic computing (ICSC), Newport Beach, CA, USA: IEEE, pp. 124–127. 2019. https://doi.org/10.1109/ICOSC.2019.8665535

  129. S. Saeb et al., Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J. Med. Internet Res. 17(7), e175 (2015). https://doi.org/10.2196/jmir.4273

    Article  PubMed  PubMed Central  Google Scholar 

  130. L. Canzian, M. Musolesi, 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, Osaka Japan: ACM, pp. 1293–1304. 2015. https://doi.org/10.1145/2750858.2805845

  131. F. Wahle, T. Kowatsch, E. Fleisch, M. Rufer, S. Weidt, Mobile sensing and support for people with depression: a pilot trial in the Wild. JMIR MHealth UHealth 4(3), e111 (2016). https://doi.org/10.2196/mhealth.5960

    Article  PubMed  PubMed Central  Google Scholar 

  132. J. Meyerhoff et al., Evaluation of changes in depression, anxiety, and social anxiety using smartphone sensor features: longitudinal cohort study. J. Med. Internet Res. 23(9), e22844 (2021). https://doi.org/10.2196/22844

    Article  PubMed  PubMed Central  Google Scholar 

  133. P. Laiou et al., The association between home stay and symptom severity in major depressive disorder: preliminary findings from a multicenter observational study using geolocation data from smartphones. JMIR MHealth UHealth 10(1), e28095 (2022). https://doi.org/10.2196/28095

    Article  PubMed  PubMed Central  Google Scholar 

  134. Y. Zhang et al., Longitudinal relationships between depressive symptom severity and phone-measured mobility: dynamic structural equation modeling study. JMIR Ment. Health 9(3), e34898 (2022). https://doi.org/10.2196/34898

    Article  PubMed  PubMed Central  Google Scholar 

  135. Y. Zhang et al., Relationship between major depression symptom severity and sleep collected using a wristband wearable device: multicenter longitudinal observational study. JMIR MHealth UHealth 9(4), e24604 (2021). https://doi.org/10.2196/24604

    Article  PubMed  PubMed Central  Google Scholar 

  136. I. Grande, M. Berk, B. Birmaher, E. Vieta, Bipolar disorder. The Lancet 387(10027), 1561–1572 (2016). https://doi.org/10.1016/S0140-6736(15)00241-X

    Article  Google Scholar 

  137. M. Berk et al., History of illness prior to a diagnosis of bipolar disorder or schizoaffective disorder. J. Affect. Disord. 103(1–3), 181–186 (2007). https://doi.org/10.1016/j.jad.2007.01.027

    Article  CAS  PubMed  Google Scholar 

  138. J.R. Calabrese, M.D. Shelton, D.J. Rapport, M. Kujawa, S.E. Kimmel, S. Caban, Current research on rapid cycling bipolar disorder and its treatment. J. Affect. Disord. 67(1), 241–255 (2001). https://doi.org/10.1016/S0165-0327(98)00161-X

    Article  CAS  PubMed  Google Scholar 

  139. T. Tanaka, K. Kokubo, K. Iwasa, K. Sawa, N. Yamada, M. Komori, Intraday activity levels may better reflect the differences between major depressive disorder and bipolar disorder than average daily activity levels. Front. Psychol. 9, 2314 (2018). https://doi.org/10.3389/fpsyg.2018.02314

    Article  PubMed  PubMed Central  Google Scholar 

  140. S. Melbye et al., Automatically generated smartphone data in young patients with newly diagnosed bipolar disorder and healthy controls. Front. Psychiatry (2021). https://doi.org/10.3389/fpsyt.2021.559954

    Article  PubMed  PubMed Central  Google Scholar 

  141. C.N. Kaufmann, A. Gershon, C.A. Depp, S. Miller, J.M. Zeitzer, T.A. Ketter, Daytime midpoint as a digital biomarker for chronotype in bipolar disorder. J. Affect. Disord. 241, 586–591 (2018). https://doi.org/10.1016/j.jad.2018.08.032

    Article  PubMed  PubMed Central  Google Scholar 

  142. M. Faurholt-Jepsen et al., Daily mobility patterns in patients with bipolar disorder and healthy individuals. J. Affect. Disord. 278, 413–422 (2021). https://doi.org/10.1016/j.jad.2020.09.087

    Article  PubMed  Google Scholar 

  143. C.C. Bennett, M.K. Ross, E. Baek, D. Kim, A.D. Leow, Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory. NPJ Digit. Med. 5(1), 1 (2022). https://doi.org/10.1038/s41746-022-00741-3

    Article  Google Scholar 

  144. Y. Wu et al., Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: A systematic review and individual participant data meta-analysis. Psychol. Med. 50(8), 1368–1380 (2020). https://doi.org/10.1017/S0033291719001314

    Article  PubMed  Google Scholar 

  145. American Psychiatric Association. Anxiety disorders. in Diagnostic and statistical manual of mental disorders, (5th edn). Text Revision. American Psychiatric Association, 2022

  146. Anxiety Disorders. [Online]. Available: https://dictionary.apa.org/anxiety-disorder

  147. B. Bandelow, M. Reitt, C. Röver, S. Michaelis, Y. Görlich, D. Wedekind, Efficacy of treatments for anxiety disorders: a meta-analysis. Int. Clin. Psychopharmacol. 30(4), 183–192 (2015). https://doi.org/10.1097/YIC.0000000000000078

    Article  PubMed  Google Scholar 

  148. R.B. Weisberg, Overview of generalized anxiety disorder: epidemiology, presentation, and course. J. Clin. Psychiatry 70, 4–9 (2009)

    Article  PubMed  Google Scholar 

  149. K.L. Szuhany, N.M. Simon, Anxiety disorders: a review. JAMA 328(24), 2431–2445 (2022). https://doi.org/10.1001/jama.2022.22744

    Article  CAS  PubMed  Google Scholar 

  150. K. Leonard, A. Abramovitch, Cognitive functions in young adults with generalized anxiety disorder. Eur. Psychiatry 56, 1–7 (2019). https://doi.org/10.1016/j.eurpsy.2018.10.008

    Article  CAS  PubMed  Google Scholar 

  151. Y. Kim et al., Screening tool for anxiety disorders: development and validation of the Korean anxiety screening assessment. Psychiatry Investig. 15(11), 1053–1063 (2018). https://doi.org/10.30773/pi.2018.09.27.2

    Article  PubMed  PubMed Central  Google Scholar 

  152. M.B. First, Structured clinical interview for the DSM (SCID), in The Encyclopedia of Clinical Psychology. (Wiley, Hoboken, 2015), pp.1–6. https://doi.org/10.1002/9781118625392.wbecp351

    Chapter  Google Scholar 

  153. Social Anxiety Disorder. [Online]. Available: https://www.nimh.nih.gov/health/statistics/social-anxiety-disorder

  154. N.C. Jacobson, B. Summers, S. Wilhelm, Digital biomarkers of social anxiety severity: digital phenotyping using passive smartphone sensors. J. Med. Internet Res. 22(5), e16875 (2020). https://doi.org/10.2196/16875

    Article  PubMed  PubMed Central  Google Scholar 

  155. J. Gong et al., Understanding behavioral dynamics of social anxiety among college students through smartphone sensors. Inf. Fusion 49, 57–68 (2019). https://doi.org/10.1016/j.inffus.2018.09.002

    Article  Google Scholar 

  156. N.C. Jacobson, S. Bhattacharya, Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behav. Res. Ther. 149, 104013 (2022). https://doi.org/10.1016/j.brat.2021.104013

    Article  PubMed  Google Scholar 

  157. M.G. Craske et al., Panic disorder: a review of DSM-IV panic disorder and proposals for DSM-V. Depress. Anxiety 27(2), 93–112 (2010). https://doi.org/10.1002/da.20654

    Article  PubMed  Google Scholar 

  158. S. A. and M. H. S. Administration, Table 3.10, Panic disorder and agoraphobia criteria changes from DSM-IV to DSM-5. Accessed: Aug. 24, 2023. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK519704/table/ch3.t10/

  159. E. W. McGinnis et al., Discovering digital biomarkers of panic attack risk in consumer wearables data. medRxiv, p. 2023.03.01.23286647, 2023. https://doi.org/10.1101/2023.03.01.23286647

  160. D.J. Stein, M.A. Craske, M.J. Friedman, K.A. Phillips, Anxiety disorders, obsessive-compulsive and related disorders, trauma- and stressor-related disorders, and dissociative disorders in DSM-5. Am. J. Psychiatry 171(6), 611–613 (2014). https://doi.org/10.1176/appi.ajp.2014.14010003

    Article  PubMed  Google Scholar 

  161. D.G. Kilpatrick, H.S. Resnick, M.E. Milanak, M.W. Miller, K.M. Keyes, M.J. Friedman, National estimates of exposure to traumatic events and PTSD prevalence using DSM-IV and DSM-5 criteria: DSM-5 PTSD prevalence. J. Trauma. Stress 26(5), 537–547 (2013). https://doi.org/10.1002/jts.21848

    Article  PubMed  PubMed Central  Google Scholar 

  162. R.H. Pietrzak, R.B. Goldstein, S.M. Southwick, B.F. Grant, Prevalence and axis I comorbidity of full and partial posttraumatic stress disorder in the United States: results from wave 2 of the national epidemiologic survey on alcohol and related conditions. J. Anxiety Disord. 25(3), 456–465 (2011). https://doi.org/10.1016/j.janxdis.2010.11.010

    Article  PubMed  Google Scholar 

  163. K.H. Seal, T.J. Metzler, K.S. Gima, D. Bertenthal, S. Maguen, C.R. Marmar, Trends and risk factors for mental health diagnoses among Iraq and Afghanistan veterans using Department of Veterans Affairs Health Care, 2002–2008. Am. J. Public Health 99(9), 1651–1658 (2009). https://doi.org/10.2105/AJPH.2008.150284

    Article  PubMed  PubMed Central  Google Scholar 

  164. J. Sareen, Posttraumatic stress disorder in adults: Impact, comorbidity, risk factors, and treatment. Can. J. Psychiatry 59(9), 460–467 (2014). https://doi.org/10.1177/070674371405900902

    Article  PubMed  PubMed Central  Google Scholar 

  165. I.R. Galatzer-Levy, R.A. Bryant, 636,120 ways to have posttraumatic stress disorder. Perspect. Psychol. Sci. 8(6), 651–662 (2013). https://doi.org/10.1177/1745691613504115

    Article  PubMed  Google Scholar 

  166. L.D. Kubzansky et al., The weight of traumatic stress: a prospective study of posttraumatic stress disorder symptoms and weight status in women. JAMA Psychiat. 71(1), 44 (2014). https://doi.org/10.1001/jamapsychiatry.2013.2798

    Article  Google Scholar 

  167. E.J. Paulus, T.R. Argo, J.A. Egge, The impact of posttraumatic stress disorder on blood pressure and heart rate in a veteran population: effect of PTSD on blood pressure and heart rate. J. Trauma. Stress 26(1), 169–172 (2013). https://doi.org/10.1002/jts.21785

    Article  PubMed  Google Scholar 

  168. M.-H. Chen et al., Risk of stroke among patients with post-traumatic stress disorder: nationwide longitudinal study. Br. J. Psychiatry 206(4), 302–307 (2015). https://doi.org/10.1192/bjp.bp.113.143610

    Article  PubMed  Google Scholar 

  169. Y. Neria et al., Long-term course of probable PTSD after the 9/11 attacks: a study in urban primary care. J. Trauma. Stress 23(4), 474–482 (2010). https://doi.org/10.1002/jts.20544

    Article  PubMed  PubMed Central  Google Scholar 

  170. K.M. Magruder et al., Prevalence of posttraumatic stress disorder in Veterans Affairs primary care clinics. Gen. Hosp. Psychiatry 27(3), 169–179 (2005). https://doi.org/10.1016/j.genhosppsych.2004.11.001

    Article  PubMed  Google Scholar 

  171. R. Kimerling et al., Brief report: Utility of a short screening scale for DSM-IV PTSD in primary care. J. Gen. Intern. Med. 21(1), 65–67 (2006). https://doi.org/10.1111/j.1525-1497.2005.00292.x

    Article  PubMed  PubMed Central  Google Scholar 

  172. A. Elklit, M. Shevlin, The structure of PTSD symptoms: A test of alternative models using confirmatory factor analysis. Br. J. Clin. Psychol. 46(3), 299–313 (2007). https://doi.org/10.1348/014466506X171540

    Article  PubMed  Google Scholar 

  173. C.P. Sullivan, A.J. Smith, M. Lewis, R.T. Jones, Network analysis of PTSD symptoms following mass violence. Psychol. Trauma Theory Res. Pract. Policy 10(1), 58–66 (2018). https://doi.org/10.1037/tra0000237

    Article  Google Scholar 

  174. R.A. Parslow, A.F. Jorm, B.I. O’Toole, R.P. Marshall, D.A. Grayson, Distress experienced by participants during an epidemiological survey of posttraumatic stress disorder. J. Trauma. Stress 13(3), 465–471 (2000). https://doi.org/10.1023/A:1007785308422

    Article  CAS  PubMed  Google Scholar 

  175. S. Akselrod, D. Gordon, F.A. Ubel, D.C. Shannon, A.C. Berger, R.J. Cohen, Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 213(4504), 220–222 (1981). https://doi.org/10.1126/science.6166045

    Article  CAS  PubMed  Google Scholar 

  176. M.B. Rissling et al., Circadian contrasts in heart rate variability associated with posttraumatic stress disorder symptoms in a young adult cohort. J. Trauma. Stress 29(5), 415–421 (2016). https://doi.org/10.1002/jts.22125

    Article  PubMed  PubMed Central  Google Scholar 

  177. A.D. McDonald, F. Sasangohar, A. Jatav, A.H. Rao, Continuous monitoring and detection of post-traumatic stress disorder (PTSD) triggers among veterans: a supervised machine learning approach. IISE Trans. Healthc. Syst. Eng. 9(3), 201–211 (2019). https://doi.org/10.1080/24725579.2019.1583703

    Article  Google Scholar 

  178. A. Minassian et al., Association of predeployment heart rate variability with risk of postdeployment posttraumatic stress disorder in active-duty marines. JAMA Psychiat. 72(10), 979–986 (2015). https://doi.org/10.1001/jamapsychiatry.2015.0922

    Article  Google Scholar 

  179. D.J. Biddle, R. Robillard, D.F. Hermens, I.B. Hickie, N. Glozier, Accuracy of self-reported sleep parameters compared with actigraphy in young people with mental ill-health. Sleep Health 1(3), 214–220 (2015). https://doi.org/10.1016/j.sleh.2015.07.006

    Article  PubMed  Google Scholar 

  180. S.M. Patterson, D.S. Krantz, L.C. Montgomery, P.A. Deuster, S.M. Hedges, L.E. Nebel, Automated physical activity monitoring: validation and comparison with physiological and self-report measures. Psychophysiology 30(3), 296–305 (1993). https://doi.org/10.1111/j.1469-8986.1993.tb03356.x

    Article  CAS  PubMed  Google Scholar 

  181. M. Sadeghi, A.D. McDonald, F. Sasangohar, Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data. PLoS ONE 17(5), e0267749 (2022). https://doi.org/10.1371/journal.pone.0267749

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. S.C. Cheng, K.G. Schepp, Early intervention in schizophrenia: a literature review. Arch. Psychiatr. Nurs. 30(6), 774–781 (2016). https://doi.org/10.1016/j.apnu.2016.02.009

    Article  PubMed  Google Scholar 

  183. M. George, S. Maheshwari, S. Chandran, J.S. Manohar, T.S. Sathyanarayana Rao, Understanding the schizophrenia prodrome. Indian J. Psychiatry 59(4), 505–509 (2017). https://doi.org/10.4103/psychiatry.IndianJPsychiatry_464_17

    Article  PubMed  PubMed Central  Google Scholar 

  184. A. Marconi, M. Di Forti, C.M. Lewis, R.M. Murray, E. Vassos, Meta-analysis of the association between the level of cannabis use and risk of psychosis. Schizophr. Bull. 42(5), 1262–1269 (2016). https://doi.org/10.1093/schbul/sbw003

    Article  PubMed  PubMed Central  Google Scholar 

  185. R. Wang et al., CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia. in Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing, in UbiComp ’16. New York, NY, USA: Association for Computing Machinery, pp. 886–897. 2016. https://doi.org/10.1145/2971648.2971740

  186. R. Wang et al., Predicting symptom trajectories of schizophrenia using mobile sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1(3), 1–24 (2017). https://doi.org/10.1145/3130976

    Article  Google Scholar 

  187. R. Emsley, B. Chiliza, L. Asmal, B.H. Harvey, The nature of relapse in schizophrenia. BMC Psychiatry 13(1), 50 (2013). https://doi.org/10.1186/1471-244X-13-50

    Article  PubMed  PubMed Central  Google Scholar 

  188. D.A. Adler et al., Predicting early warning signs of psychotic relapse from passive sensing data: an approach using encoder-decoder neural networks. JMIR MHealth UHealth 8(8), e19962 (2020). https://doi.org/10.2196/19962

    Article  PubMed  PubMed Central  Google Scholar 

  189. B. Buck et al., Relationships between smartphone social behavior and relapse in schizophrenia: a preliminary report. Schizophr. Res. 208, 167–172 (2019). https://doi.org/10.1016/j.schres.2019.03.014

    Article  PubMed  PubMed Central  Google Scholar 

  190. C.A. Depp et al., GPS mobility as a digital biomarker of negative symptoms in schizophrenia: a case control study. NPJ Digit. Med. 2(1), 1 (2019). https://doi.org/10.1038/s41746-019-0182-1

    Article  Google Scholar 

  191. G. P. Strauss et al., “Validation of accelerometry as a digital phenotyping measure of negative symptoms in schizophrenia,” Schizophrenia, vol. 8, no. 1, Art. no. 1, Apr. 2022, doi: https://doi.org/10.1038/s41537-022-00241-z.

  192. P. Jakobsen et al., PSYKOSE: a motor activity database of patients with schizophrenia. in 2020 IEEE 33rd international symposium on computer-based medical systems (CBMS), Rochester, MN, USA: IEEE, pp. 303–308. 2020. https://doi.org/10.1109/CBMS49503.2020.00064

  193. World Health Organization, Mental health: suicide prevention. [Online]. Available: http://www.who.int/mental_health/suicide-prevention/en/

  194. H. Hedegaard, S.C. Curtin, M. Warner, Increase in suicide mortality in the United States, 1999–2018. NCHS Data Brief 362, 1–8 (2020)

    Google Scholar 

  195. C. Katz, J. Bolton, J. Sareen, The prevalence rates of suicide are likely underestimated worldwide: why it matters. Soc. Psychiatry Psychiatr. Epidemiol. 51(1), 125–127 (2016). https://doi.org/10.1007/s00127-015-1158-3

    Article  PubMed  Google Scholar 

  196. World Health Organization, World suicide prevention day media release: suicide prevention. [Online]. Available: http://www.who.int/mental_health/prevention/suicide/suicideprevent/en

  197. H.R. Lawrence et al., Prevalence and correlates of suicidal ideation and suicide attempts in preadolescent children: a US population-based study. Transl. Psychiatry 11(1), 489 (2021). https://doi.org/10.1038/s41398-021-01593-3

    Article  PubMed  PubMed Central  Google Scholar 

  198. G. Milos, A. Spindler, U. Hepp, U. Schnyder, Suicide attempts and suicidal ideation: links with psychiatric comorbidity in eating disorder subjects. Gen. Hosp. Psychiatry 26(2), 129–135 (2004). https://doi.org/10.1016/j.genhosppsych.2003.10.005

    Article  PubMed  Google Scholar 

  199. A. Reynders, A.J.F.M. Kerkhof, G. Molenberghs, C. Van Audenhove, Help-seeking, stigma and attitudes of people with and without a suicidal past. A comparison between a low and a high suicide rate country. J. Affect. Disord. 178, 5–11 (2015). https://doi.org/10.1016/j.jad.2015.02.013

    Article  PubMed  Google Scholar 

  200. J.T. Walkup, L. Townsend, S. Crystal, M. Olfson, A systematic review of validated methods for identifying suicide or suicidal ideation using administrative or claims data: methods for identifying suicide using claims data. Pharmacoepidemiol. Drug Saf. 21, 174–182 (2012). https://doi.org/10.1002/pds.2335

    Article  PubMed  Google Scholar 

  201. J.-I. Lee et al., Prevalence of suicidal ideation and associated risk factors in the general population. J. Formos. Med. Assoc. 109(2), 138–147 (2010). https://doi.org/10.1016/S0929-6646(10)60034-4

    Article  PubMed  Google Scholar 

  202. K. Szanto, A. Gildengers, B.H. Mulsant, G. Brown, G.S. Alexopoulos, C.F. Reynolds, Identification of suicidal ideation and prevention of suicidal behaviour in the elderly. Drugs Aging 19(1), 11–24 (2002). https://doi.org/10.2165/00002512-200219010-00002

    Article  PubMed  Google Scholar 

  203. A. Haines-Delmont et al., Testing suicide risk prediction algorithms using phone measurements with patients in acute mental health settings: feasibility study. JMIR MHealth UHealth 8(6), e15901 (2020). https://doi.org/10.2196/15901

    Article  PubMed  PubMed Central  Google Scholar 

  204. P. Moreno-Muñoz, L. Romero-Medrano, Á. Moreno, J. Herrera-López, E. Baca-García, A. Artés-Rodríguez, Passive detection of behavioral shifts for suicide attempt prevention. arXiv, Nov. 14, 2020. https://doi.org/10.48550/arXiv.2011.09848

  205. M.L. Barrigon et al., One-week suicide risk prediction using real-time smartphone monitoring: prospective cohort study. J. Med. Internet Res. 25(1), e43719 (2023). https://doi.org/10.2196/43719

    Article  PubMed  PubMed Central  Google Scholar 

  206. J. Rottenberg, F.H. Wilhelm, J.J. Gross, I.H. Gotlib, Respiratory sinus arrhythmia as a predictor of outcome in major depressive disorder. J. Affect. Disord. 71(1–3), 265–272 (2002). https://doi.org/10.1016/s0165-0327(01)00406-2

    Article  PubMed  Google Scholar 

  207. D. Adolph, T. Teismann, T. Forkmann, A. Wannemüller, J. Margraf, High frequency heart rate variability: evidence for a transdiagnostic association with suicide ideation. Biol. Psychol. 138, 165–171 (2018). https://doi.org/10.1016/j.biopsycho.2018.09.006

    Article  PubMed  Google Scholar 

  208. S.T. Wilson et al., Heart rate variability and suicidal behavior. Psychiatry Res. 240, 241–247 (2016). https://doi.org/10.1016/j.psychres.2016.04.033

    Article  PubMed  Google Scholar 

  209. A.L. Calear, P.J. Batterham, H. Christensen, Predictors of help-seeking for suicidal ideation in the community: risks and opportunities for public suicide prevention campaigns. Psychiatry Res. 219(3), 525–530 (2014). https://doi.org/10.1016/j.psychres.2014.06.027

    Article  PubMed  Google Scholar 

  210. L. Gutiérrez-Rojas, A. Porras-Segovia, H. Dunne, N. Andrade-González, J.A. Cervilla, Prevalence and correlates of major depressive disorder: a systematic review. Braz. J. Psychiatry 42, 657–672 (2020). https://doi.org/10.1590/1516-4446-2020-0650

    Article  PubMed  PubMed Central  Google Scholar 

  211. J. Hong et al., Depressive symptoms feature-based machine learning approach to predicting depression using smartphone. Healthcare 10(7), 7 (2022). https://doi.org/10.3390/healthcare10071189

    Article  Google Scholar 

  212. J. Busk, M. Faurholt-Jepsen, M. Frost, J.E. Bardram, L.V. Kessing, O. Winther, Forecasting mood in bipolar disorder from smartphone self-assessments: hierarchical bayesian approach. JMIR MHealth UHealth 8(4), e15028 (2020). https://doi.org/10.2196/15028

    Article  PubMed  PubMed Central  Google Scholar 

  213. C.-H. Cho, T. Lee, M.-G. Kim, H.P. In, L. Kim, H.-J. Lee, Mood prediction of patients with mood disorders by machine learning using passive digital phenotypes based on the circadian rhythm: prospective observational cohort study. J. Med. Internet Res. 21(4), e11029 (2019). https://doi.org/10.2196/11029

    Article  PubMed  PubMed Central  Google Scholar 

  214. J. Gideon, E.M. Provost, M. McInnis, Mood state prediction from speech of varying acoustic quality for individuals with bipolar disorder. in 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), vol. 2016, pp. 2359–2363, 2016. https://doi.org/10.1109/ICASSP.2016.7472099

  215. N. Vanello et al., Speech analysis for mood state characterization in bipolar patients. in 2012 Annual international conference of the IEEE engineering in medicine and biology society, pp. 2104–2107 2012. https://doi.org/10.1109/EMBC.2012.6346375

  216. M.S. Scheeringa, PTSD in children younger than the age of 13: toward developmentally sensitive assessment and management. J. Child Adolesc. Trauma 41(3), 181–197 (2011). https://doi.org/10.1080/19361521.2011.597079

    Article  PubMed  Google Scholar 

  217. I.M. Raugh et al., Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia. J. Psychiatr. Res. 138, 436–443 (2021). https://doi.org/10.1016/j.jpsychires.2021.04.022

    Article  PubMed  PubMed Central  Google Scholar 

  218. J. Meyerhoff, K.P. Kruzan, K.-Y.A. Kim, K. Van Orden, D.C. Mohr, Exploring the safety of a general digital mental health intervention to effect symptom reduction among individuals with and without suicidal ideation: a secondary analysis. Arch. Suicide Res. 27(3), 966–983 (2023). https://doi.org/10.1080/13811118.2022.2096520

    Article  PubMed  Google Scholar 

  219. C.G. Walsh et al., Prospective validation of an electronic health record-based, real-time suicide risk model. JAMA Netw. Open 4(3), e211428 (2021). https://doi.org/10.1001/jamanetworkopen.2021.1428

    Article  PubMed  PubMed Central  Google Scholar 

  220. E.M. Kleiman et al., Can passive measurement of physiological distress help better predict suicidal thinking? Transl. Psychiatry 11(1), 611 (2021). https://doi.org/10.1038/s41398-021-01730-y

    Article  PubMed  PubMed Central  Google Scholar 

  221. L.K. Berger, A.L. Begun, L.L. Otto-Salaj, Participant recruitment in intervention research: scientific integrity and cost-effective strategies. Int. J. Soc. Res. Methodol. 12(1), 79–92 (2009). https://doi.org/10.1080/13645570701606077

    Article  Google Scholar 

  222. M. Tlachac, E. Toto, J. Lovering, R. Kayastha, N. Taurich, E. Rundensteiner, EMU: early mental health uncovering framework and dataset. in 2021 20th IEEE international conference on machine learning and applications (ICMLA), pp. 1311–1318 2021. https://doi.org/10.1109/ICMLA52953.2021.00213

  223. J. Henrich, S.J. Heine, A. Norenzayan, The weirdest people in the world? Behav. Brain Sci. 33(2–3), 61–83 (2010). https://doi.org/10.1017/S0140525X0999152X

    Article  PubMed  Google Scholar 

  224. R.A. Peterson, D.R. Merunka, Convenience samples of college students and research reproducibility. J. Bus. Res. 67(5), 1035–1041 (2014). https://doi.org/10.1016/j.jbusres.2013.08.010

    Article  Google Scholar 

  225. J.D. Runyan, T.A. Steenbergh, C. Bainbridge, D.A. Daugherty, L. Oke, B.N. Fry, A smartphone ecological momentary assessment/intervention ‘app’ for collecting real-time data and promoting self-awareness. PLoS ONE 8(8), e71325 (2013). https://doi.org/10.1371/journal.pone.0071325

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  226. C.J. Reback, D. Rünger, J.B. Fletcher, D. Swendeman, Ecological momentary assessments for self-monitoring and counseling to optimize methamphetamine treatment and sexual risk reduction outcomes among gay and bisexual Men. J. Subst. Abuse Treat. 92, 17–26 (2018). https://doi.org/10.1016/j.jsat.2018.06.005

    Article  PubMed  PubMed Central  Google Scholar 

  227. T.W. Boonstra, J. Nicholas, Q.J. Wong, F. Shaw, S. Townsend, H. Christensen, Using mobile phone sensor technology for mental health research: integrated analysis to identify hidden challenges and potential solutions. J. Med. Internet Res. 20(7), e10131 (2018). https://doi.org/10.2196/10131

    Article  PubMed  PubMed Central  Google Scholar 

  228. A. P. A. American Psychiatric Association, Diagnostic and statistical manual of mental disorders (DSM-5) (American Psychiatric Association, Arlington, 2013)

    Book  Google Scholar 

  229. A.G. Horwitz, Z. Zhao, S. Sen, Peak-end bias in retrospective recall of depressive symptoms on the PHQ-9. Psychol. Assess. 35(4), 378–381 (2023). https://doi.org/10.1037/pas0001219

    Article  PubMed  PubMed Central  Google Scholar 

  230. D.J. Hallford, D. Rusanov, B. Winestone, R. Kaplan, M. Fuller-Tyszkiewicz, G. Melvin, Disclosure of suicidal ideation and behaviours: a systematic review and meta-analysis of prevalence. Clin. Psychol. Rev. 101, 102272 (2023). https://doi.org/10.1016/j.cpr.2023.102272

    Article  CAS  PubMed  Google Scholar 

  231. M.M. Misgar, M. Bhatia, Utilizing deep convolutional neural architecture with attention mechanism for objective diagnosis of schizophrenia using wearable IoMT devices. Multimed. Tools Appl. (2023). https://doi.org/10.1007/s11042-023-17119-6

    Article  Google Scholar 

  232. H. Li et al., Modern deep learning in bioinformatics. J. Mol. Cell Biol. 12(11), 823–827 (2020). https://doi.org/10.1093/jmcb/mjaa030

    Article  PubMed  PubMed Central  Google Scholar 

  233. S. Nepal et al., COVID student study: a year in the life of college students during the COVID-19 pandemic through the lens of mobile phone sensing. in CHI Conference on human factors in computing systems, New Orleans LA USA: ACM, pp. 1–19 2022. https://doi.org/10.1145/3491102.3502043

  234. J.F. Huckins, A.W. DaSilva, E.L. Hedlund, E.I. Murphy, C. Rogers, W. Wang, M. Obuchi, P.E. Holtzheimer, D.D. Wagner, A.T. Campbell, Causal factors of anxiety and depression in college students: longitudinal ecological momentary assessment and causal analysis using Peter and Clark momentary conditional independence. JMIR Ment. Health 7(6), e16684 (2020). https://doi.org/10.2196/16684

    Article  PubMed  PubMed Central  Google Scholar 

  235. M.K. Larson, E.F. Walker, M.T. Compton, Early signs, diagnosis and therapeutics of the prodromal phase of schizophrenia and related psychotic disorders. Expert Rev. Neurother. 10(8), 1347–1359 (2010). https://doi.org/10.1586/ern.10.93

    Article  PubMed  PubMed Central  Google Scholar 

  236. W.E. Copeland et al., Impact of COVID-19 pandemic on college student mental health and wellness. J. Am. Acad. Child Adolesc. Psychiatry 60(1), 134-141.e2 (2021). https://doi.org/10.1016/j.jaac.2020.08.466

    Article  PubMed  Google Scholar 

  237. L.T. Hoyt, A.K. Cohen, B. Dull, E.M. Castro, N. Yazdani, “Constant stress has become the new normal”: stress and anxiety inequalities among US college students in the time of COVID-19. J. Adolesc. Health. 68(2), 270–276 (2021). https://doi.org/10.1016/j.jadohealth.2020.10.030

    Article  PubMed  Google Scholar 

  238. J.A. Elharake, F. Akbar, A.A. Malik, W. Gilliam, S.B. Omer, Mental health impact of COVID-19 among children and college students: a systematic review. Child Psychiatry Hum. Dev. 54(3), 913–925 (2023). https://doi.org/10.1007/s10578-021-01297-1

    Article  PubMed  Google Scholar 

  239. M. Carrasco, Colleges seek virtual mental health services. Inside Higher Ed. Accessed: Oct. 14, 2023. [Online]. Available: https://www.insidehighered.com/news/2021/09/20/colleges-expand-mental-health-services-students

  240. H. Kobayashi, R. Saenz-Escarcega, A. Fulk, F.B. Agusto, Understanding mental health trends during COVID-19 pandemic in the United States using network analysis. PLoS ONE 18(6), e0286857 (2023). https://doi.org/10.1371/journal.pone.0286857

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  241. S. Yu, Uncovering the hidden impacts of inequality on mental health: a global study. Transl. Psychiatry 8(1), 1 (2018). https://doi.org/10.1038/s41398-018-0148-0

    Article  Google Scholar 

  242. 1615 L. St NW, S. 800 Washington, D. 20036 U.-419-4300 | M.-857-8562 | F.-41-4372 | M. Inquiries, Smartphone ownership in advanced economies higher than in emerging. Pew Research Center’s global attitudes Project. Accessed: Oct. 19, 2023. [Online]. Available: https://www.pewresearch.org/global/2019/02/05/smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/pg_global-technology-use-2018_2019-02-05_0-01/

  243. R.S. McIntyre et al., Ecological momentary assessment of depressive symptoms using the mind.me application: convergence with the patient health questionnaire-9 (PHQ-9). J. Psychiatr. Res. 135, 311–317 (2021). https://doi.org/10.1016/j.jpsychires.2021.01.012

    Article  PubMed  Google Scholar 

  244. X. Xu et al., Leveraging collaborative-filtering for personalized behavior modeling: a case study of depression detection among college students. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5(1), 1–27 (2021). https://doi.org/10.1145/3448107

    Article  Google Scholar 

  245. A.A. Farhan et al., Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. in 2016 IEEE wireless health (WH), pp. 1–8 2016. https://doi.org/10.1109/WH.2016.7764553

  246. N.C. Jacobson, D. Lekkas, R. Huang, N. Thomas, Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17–18 years. J. Affect. Disord. 282, 104–111 (2021). https://doi.org/10.1016/j.jad.2020.12.086

    Article  PubMed  Google Scholar 

  247. M. Tahmasian et al., Differentiation chronic post traumatic stress disorder patients from healthy subjects using objective and subjective sleep-related parameters. Neurosci. Lett. 650, 174–179 (2017). https://doi.org/10.1016/j.neulet.2017.04.042

    Article  CAS  PubMed  Google Scholar 

  248. R. Wang et al., On predicting relapse in schizophrenia using mobile sensing in a randomized control trial. in 2020 IEEE international conference on pervasive computing and communications (PerCom), Austin, TX, USA: IEEE, pp. 1–8 2020. https://doi.org/10.1109/PerCom45495.2020.9127365

  249. M. Tlachac, M. Reisch, M. Heinz, Mobile communication log time series to detect depressive symptoms

  250. M. Tlachac, K. Dixon-Gordon, E. Rundensteiner, Screening for suicidal ideation with text messages. in 2021 IEEE EMBS international conference on biomedical and health informatics (BHI), Athens, Greece, IEEE, pp. 1–4 2021. https://doi.org/10.1109/BHI50953.2021.9508486

Download references

Funding

The funding was provided by the Dartmouth College Kaminsky Family Fund Award (Anastasia C. Bryan), Mark C. Hansen Undergraduate Research Scholarship Creativity Fund (Anastasia C. Bryan), Dartmouth Undergraduate Research Assistantship Internal Funding (Abigail J. Salzhauer), NIMM R01MH123482 (Nicholas C. Jacobson), NIH T32 CA134286 (Michael V. Heinz).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael V. Heinz.

Ethics declarations

Conflict of interest

NCJ has received a grant from Boehringer-Ingelheim. NCJ has edited a book through Academic Press and receives book royalties, and NCJ also receives speaking fees related to his research.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 18 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bryan, A.C., Heinz, M.V., Salzhauer, A.J. et al. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2, 778–810 (2024). https://doi.org/10.1007/s44174-023-00150-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s44174-023-00150-4

Keywords

Navigation