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Abstract

Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.

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2023-05-09
2024-04-28
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Literature Cited

  1. Abbas A, Hansen BJ, Koesmahargyo V, Yadav V, Rosenfield PJ et al. 2022. Facial and vocal markers of schizophrenia measured using remote smartphone assessments: observational study. JMIR Form. Res. 6:1e26276 https://doi.org/10.2196/26276
    [Google Scholar]
  2. Abbas A, Sauder C, Yadav V, Koesmahargyo V, Aghjayan A et al. 2021a. Remote digital measurement of facial and vocal markers of major depressive disorder severity and treatment response: a pilot study. Front. Digit. Health 3:610006 https://doi.org/10.3389/fdgth.2021.610006
    [Google Scholar]
  3. Abbas A, Yadav V, Smith E, Ramjas E, Rutter SB et al. 2021b. Computer vision–based assessment of motor functioning in schizophrenia: use of smartphones for remote measurement of schizophrenia symptomatology. Digit. Biomark. 5:129–36. https://doi.org/10.1159/000512383
    [Google Scholar]
  4. Aldao A, Tull MT. 2015. Putting emotion regulation in context. Curr. Opin. Psychol. 3:100–7. https://doi.org/10.1016/j.copsyc.2015.03.022
    [Google Scholar]
  5. Annett J 2002. Subjective rating scales: science or art?. Ergonomics 45:14966–87. https://doi.org/10.1080/00140130210166951
    [Google Scholar]
  6. Ayer AJ. 1959. Logical Positivism New York: Simon & Schuster
  7. Barkus E, Badcock JC. 2019. A transdiagnostic perspective on social anhedonia. Front. Psychiatry 10:216 https://doi.org/10.3389/fpsyt.2019.00216
    [Google Scholar]
  8. Barnett I, Onnela J-P. 2020. Inferring mobility measures from GPS traces with missing data. Biostatistics 21:2e98–112. https://doi.org/10.1093/biostatistics/kxy059
    [Google Scholar]
  9. Bartholomew DJ, Knott M, Moustaki I. 2011. Latent Variable Models and Factor Analysis: A Unified Approach New York: Wiley
  10. Bernardo JM, Smith AF. 2001. Bayesian Theory New York: Wiley
  11. Beukenhorst AL, Collins E, Burke KM, Rahman SM, Clapp M et al. 2021. Smartphone data during the COVID-19 pandemic can quantify behavioral changes in people with ALS. Muscle Nerve 63:2258–62. https://doi.org/10.1002/mus.27110
    [Google Scholar]
  12. Boesen K, Gøtzsche PC, Ioannidis JPA. 2021. EMA and FDA psychiatric drug trial guidelines: assessment of guideline development and trial design recommendations. Epidemiol. Psychiatr. Sci. 30:e35 https://doi.org/10.1017/S2045796021000147
    [Google Scholar]
  13. Bollen KA. 2002. Latent variables in psychology and the social sciences. Annu. Rev. Psychol. 53:605–34. https://doi.org/10.1146/annurev.psych.53.100901.135239
    [Google Scholar]
  14. Bos FM, Schoevers RA, aan het Rot M. 2015. Experience sampling and ecological momentary assessment studies in psychopharmacology: a systematic review. Eur. Neuropsychopharmacol. 25:111853–64. https://doi.org/10.1016/j.euroneuro.2015.08.008
    [Google Scholar]
  15. Boureau YL, Ponce J, LeCun Y. 2010. A theoretical analysis of feature pooling in visual recognition. Proceedings of the 27th International Conference on Machine Learning (ICML ’10)111–18. New York: ACM https://www.di.ens.fr/willow/pdfs/icml2010b.pdf
    [Google Scholar]
  16. Breslau N, Lucia VC, Davis GC. 2004. Partial PTSD versus full PTSD: an empirical examination of associated impairment. Psychol. Med. 34:71205–14. https://doi.org/10.1017/s0033291704002594
    [Google Scholar]
  17. Bronfenbrenner U. 1977. Toward an experimental ecology of human development. Am. Psychol. 32:7513–31. https://doi.org/10.1037/0003-066X.32.7.513
    [Google Scholar]
  18. Brunswik E. 1943. Organismic achievement and environmental probability. Psychol. Rev. 50:3255–72. https://doi.org/10.1037/h0060889
    [Google Scholar]
  19. Buzsáki G. 2019. The Brain from Inside Out Oxford, UK: Oxford Univ. Press
  20. Campbell DT, Stanley JC. 2015. Experimental and Quasi-Experimental Designs for Research Cambridge, UK: Ravenio
  21. Carter GC, Cantrell RA, Zarotsky V, Haynes VS, Phillips G et al. 2012. Comprehensive review of factors implicated in the heterogeneity of response in depression. Depress. Anxiety 29:4340–54. https://doi.org/10.1002/da.21918
    [Google Scholar]
  22. Casey BJ, Craddock N, Cuthbert BN, Hyman SE, Lee FS, Ressler KJ. 2013. DSM-5 and RDoC: progress in psychiatry research?. Nat. Rev. Neurosci. 14:11810–14. https://doi.org/10.1038/nrn3621
    [Google Scholar]
  23. Chaix B. 2018. Mobile sensing in environmental health and neighborhood research. Annu. Rev. Public Health 39:367–84. https://doi.org/10.1146/annurev-publhealth-040617-013731
    [Google Scholar]
  24. Cusin C, Yang H, Yeung A, Fava M 2010. Rating scales for depression. Handbook of Clinical Rating Scales and Assessment in Psychiatry and Mental Health L Baer, MA Blais 7–35. Totowa, NJ: Humana https://doi.org/10.1007/978-1-59745-387-5_2
    [Google Scholar]
  25. Dodge HH, Zhu J, Mattek NC, Austin D, Kornfeld J, Kaye JA. 2015. Use of high-frequency in-home monitoring data may reduce sample sizes needed in clinical trials. PLOS ONE 10:9e0138095 https://doi.org/10.1371/journal.pone.0138095
    [Google Scholar]
  26. Dorsey ER, Papapetropoulos S, Xiong M, Kieburtz K. 2017. The first frontier: digital biomarkers for neurodegenerative disorders. Digit. Biomark. 1:16–13. https://doi.org/10.1159/000477383
    [Google Scholar]
  27. Dorsey ER, Venuto C, Venkataraman V, Harris DA, Kieburtz K. 2015. Novel methods and technologies for 21st-century clinical trials: a review. JAMA Neurol. 72:5582–88. https://doi.org/10.1001/jamaneurol.2014.4524
    [Google Scholar]
  28. Elble RJ. 2016. The essential tremor rating assessment scale. J. Neurol. Neuromed. 1:434–38. https://doi.org/10.29245/2572.942X/2016/4.1038
    [Google Scholar]
  29. El-Hajj C, Kyriacou PA. 2020. A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure. Biomed. Signal Process. Control 58:101870 https://doi.org/10.1016/j.bspc.2020.101870
    [Google Scholar]
  30. Endicott J, Spitzer RL, Fleiss JL. 1975. Mental Status Examination Record (MSER): reliability and validity. Compr. Psychiatry 16:3285–301. https://doi.org/10.1016/0010-440x(75)90055-3
    [Google Scholar]
  31. Everett B. 2013. An Introduction to Latent Variable Models New York: Springer
  32. First MB, Spitzer RL. 2003. The DSM: not perfect, but better than the alternative. Psychiatr. Times 20:473–78
    [Google Scholar]
  33. Floridi L, Chiriatti M. 2020. GPT-3: its nature, scope, limits, and consequences. Minds Mach. 30:4681–94. https://doi.org/10.1007/s11023-020-09548-1
    [Google Scholar]
  34. Galatzer-Levy IR. 2014. Empirical characterization of heterogeneous posttraumatic stress responses is necessary to improve the science of posttraumatic stress. J. Clin. Psychiatry 75:9e950–52. https://doi.org/10.4088/JCP.14com09372
    [Google Scholar]
  35. Galatzer-Levy IR, Abbas A, Ries A, Homan S, Sels L et al. 2021. Validation of visual and auditory digital markers of suicidality in acutely suicidal psychiatric inpatients: proof-of-concept study. J. Med. Internet Res. 23:6e25199 https://doi.org/10.2196/25199
    [Google Scholar]
  36. Galatzer-Levy IR, Bryant RA 2013. 636,120 ways to have posttraumatic stress disorder. Perspect. Psychol. Sci. 8:6651–62. https://doi.org/10.1177/1745691613504115
    [Google Scholar]
  37. Galatzer-Levy IR, Galatzer-Levy RM. 2007. The revolution in psychiatric diagnosis: problems at the foundations. Perspect. Biol. Med. 50:2161–80. https://doi.org/10.1353/pbm.2007.0016
    [Google Scholar]
  38. Galatzer-Levy IR, Huang SH, Bonanno GA. 2018a. Trajectories of resilience and dysfunction following potential trauma: a review and statistical evaluation. Clin. Psychol. Rev. 63:41–55. https://doi.org/10.1016/j.cpr.2018.05.008
    [Google Scholar]
  39. Galatzer-Levy IR, Karstoft K-I, Statnikov A, Shalev AY. 2014. Quantitative forecasting of PTSD from early trauma responses: a machine learning application. J. Psychiatr. Res. 59:68–76. https://doi.org/10.1016/j.jpsychires.2014.08.017
    [Google Scholar]
  40. Galatzer-Levy IR, Ruggles K, Chen Z. 2018b. Data science in the research domain criteria era: relevance of machine learning to the study of stress pathology, recovery, and resilience. Chronic Stress 2018:2 https://doi.org/10.1177/2470547017747553
    [Google Scholar]
  41. Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. 2013. Bayesian Data Analysis Boca Raton, FL: CRC. , 3rd ed..
  42. Goodfellow I, Bengio Y, Courville A. 2016. Deep Learning Cambridge, MA: MIT Press
  43. Goodkind M, Eickhoff SB, Oathes DJ, Jiang Y, Chang A et al. 2015. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72:4305–15. https://doi.org/10.1001/jamapsychiatry.2014.2206
    [Google Scholar]
  44. Gootzeit J, Markon K. 2011. Factors of PTSD: differential specificity and external correlates. Clin. Psychol. Rev. 31:6993–1003. https://doi.org/10.1016/j.cpr.2011.06.005
    [Google Scholar]
  45. Gottman JM. 1993. Studying emotion in social interaction. Handbook of Emotions M Lewis 475–87. New York: Guilford
    [Google Scholar]
  46. Han Y, Huang G, Song S, Yang L, Wang H, Wang Y. 2021. Dynamic neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44:117436–56. https://doi.org/10.1109/TPAMI.2021.3117837
    [Google Scholar]
  47. Harvey AG, Bryant RA. 1998. The relationship between acute stress disorder and posttraumatic stress disorder: a prospective evaluation of motor vehicle accident survivors. J. Consult. Clin. Psychol. 66:3507–12. https://doi.org/10.1037//0022-006x.66.3.507
    [Google Scholar]
  48. Hastie T, Friedman J, Tibshirani R. 2001. The Elements of Statistical Learning New York: Springer
  49. Hinrichs R, van Rooij SJ, Michopoulos V, Schultebraucks K, Winters S et al. 2019. Increased skin conductance response in the immediate aftermath of trauma predicts PTSD risk. Chronic Stress 2019:3 https://doi.org/10.1177/2470547019844441
    [Google Scholar]
  50. Hirschtritt ME, Insel TR. 2018. Digital technologies in psychiatry: present and future. Focus 16:3251–58. https://doi.org/10.1176/appi.focus.20180001
    [Google Scholar]
  51. Hitchcock PF, Fried EI, Frank MJ. 2022. Computational psychiatry needs time and context. Annu. Rev. Psychol. 73:243–70. https://doi.org/10.1146/annurev-psych-021621-124910
    [Google Scholar]
  52. Hyman SE. 2010. The diagnosis of mental disorders: the problem of reification. Annu. Rev. Clin. Psychol. 6:155–79. https://doi.org/10.1146/annurev.clinpsy.3.022806.091532
    [Google Scholar]
  53. Insel TR. 2017. Digital phenotyping: technology for a new science of behavior. JAMA 318:131215–16. https://doi.org/10.1001/jama.2017.11295
    [Google Scholar]
  54. Insel TR, Cuthbert B, Garvey M, Heinssen R, Pine DS et al. 2010. Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167:7748–51. https://doi.org/10.1176/appi.ajp.2010.09091379
    [Google Scholar]
  55. Jaklevic MC. 2020. Device approved for adults with nightmare disorder. JAMA 324:232357 https://doi.org/10.1001/jama.2020.24228
    [Google Scholar]
  56. James G, Witten D, Hastie T, Tibshirani R. 2013. An Introduction to Statistical Learning New York: Springer
  57. James W. 1890. The Principles of Psychology London: Taylor & Francis
  58. Kessler RC. 1994. The National Comorbidity Survey of the United States. Int. Rev. Psychiatry 6:4365–76. https://doi.org/10.3109/09540269409023274
    [Google Scholar]
  59. Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. 2005. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 62:6617–27. https://doi.org/10.1001/archpsyc.62.6.617
    [Google Scholar]
  60. Kraepelin E. 1915. Clinical Psychiatry: A Text-Book for Students and Physicians New York: Macmillan
  61. Ladouce S, Donaldson DI, Dudchenko PA, Ietswaart M. 2016. Understanding minds in real-world environments: toward a mobile cognition approach. Front. Hum. Neurosci. 10:694 https://doi.org/10.3389/fnhum.2016.00694
    [Google Scholar]
  62. Lalande KM, Bonanno GA. 2011. Retrospective memory bias for the frequency of potentially traumatic events: a prospective study. Psychol. Trauma 3:2165–70. https://doi.org/10.1037/a0020847
    [Google Scholar]
  63. LeCun Y, Bengio Y. 1998. Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks MA Arbib 255–58. Cambridge, MA: MIT Press
    [Google Scholar]
  64. LeDoux J. 2012. Rethinking the emotional brain. Neuron 73:4653–76. https://doi.org/10.1016/j.neuron.2012.02.004
    [Google Scholar]
  65. Le Lézard 2020. NightWare receives FDA marketing permission for first and only medical device to reduce sleep disturbances related to PTSD-associated nightmares in adults Press Release Novemb. 20. https://www.lelezard.com/en/news-19509722.html
  66. Levine RL, Hunter JE. 1971. Statistical and psychometric inference in principal components analysis. Multivar. Behav. Res. 6:1105–16. https://doi.org/10.1207/s15327906mbr0601_7
    [Google Scholar]
  67. Li H, Glecia A, Kent-Wilkinson A, Leidl D, Kleib M, Risling T. 2022. Transition of mental health service delivery to telepsychiatry in response to COVID-19: a literature review. Psychiatr. Q. 93:1181–97. https://doi.org/10.1007/s11126-021-09926-7
    [Google Scholar]
  68. Liang Y, Zheng X, Zeng DD. 2019. A survey on big data–driven digital phenotyping of mental health. Inf. Fusion 52:290–307. https://doi.org/10.1016/j.inffus.2019.04.001
    [Google Scholar]
  69. Liu X, Zhang F, Hou Z, Mian L, Wang Z et al. 2023. Self-supervised learning: generative or contrastive. IEEE Trans. Knowl. Data Eng. 35:857–76
    [Google Scholar]
  70. Lilienfeld SO. 2014. The Research Domain Criteria (RDoC): an analysis of methodological and conceptual challenges. Behav. Res. Ther. 62:129–39. https://doi.org/10.1016/j.brat.2014.07.019
    [Google Scholar]
  71. Liu G, Onnela J-P. 2021. Bidirectional imputation of spatial GPS trajectories with missingness using sparse online Gaussian process. J. Am. Med. Inform. Assoc. 28:81777–84. https://doi.org/10.1093/jamia/ocab069
    [Google Scholar]
  72. Luus R. 2000. Iterative Dynamic Programming London: Taylor & Francis
  73. Manea L, Gilbody S, McMillan D. 2015. A diagnostic meta-analysis of the Patient Health Questionnaire–9 (PHQ-9) algorithm scoring method as a screen for depression. Gen. Hosp. Psychiatry 37:167–75. https://doi.org/10.1016/j.genhosppsych.2014.09.009
    [Google Scholar]
  74. Marshall RD, Spitzer R, Liebowitz MR. 1999. Review and critique of the new DSM-IV diagnosis of acute stress disorder. Am. J. Psychiatry 156:111677–85. https://doi.org/10.1176/ajp.156.11.1677
    [Google Scholar]
  75. Matsuo Y, LeCun Y, Sahani M, Precup D, Silver D et al. 2022. Deep learning, reinforcement learning, and world models. Neural Netw. 152:267–75. https://doi.org/10.1016/j.neunet.2022.03.037
    [Google Scholar]
  76. Mayzner MS, Neisser U. 1977. Cognition and reality. Am. J. Psychol. 90:3541–43. https://doi.org/10.2307/1421888
    [Google Scholar]
  77. McArdle JJ. 2009. Latent variable modeling of differences and changes with longitudinal data. Annu. Rev. Psychol. 60:577–605. https://doi.org/10.1146/annurev.psych.60.110707.163612
    [Google Scholar]
  78. McCabe R, Priebe S. 2004. The therapeutic relationship in the treatment of severe mental illness: a review of methods and findings. Int. J. Soc. Psychiatry 50:2115–28. https://doi.org/10.1177/0020764004040959
    [Google Scholar]
  79. Mendes JPM, Moura IR, Van de Ven P, Viana D, Silva FJS et al. 2022. Sensing apps and public data sets for digital phenotyping of mental health: systematic review. J. Med. Internet Res. 24:2e28735 https://doi.org/10.2196/28735
    [Google Scholar]
  80. Mohri M, Rostamizadeh A, Talwalkar A. 2018. Foundations of Machine Learning Cambridge, MA: MIT Press. , 2nd ed..
  81. Mumtaz F, Khan MI, Zubair M, Dehpour AR. 2018. Neurobiology and consequences of social isolation stress in animal model—a comprehensive review. Biomed. Pharmacother. 105:1205–22. https://doi.org/10.1016/j.biopha.2018.05.086
    [Google Scholar]
  82. Muthén B, Asparouhov T 2008. Growth mixture modeling: analysis with non-Gaussian random effects. Longitudinal Data Analysis G Fitzmaurice, M Davidian, G Verbeke, G Molenberghs 143–66. London: Taylor & Francis
    [Google Scholar]
  83. Muthén B, Muthén LK. 2000. Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. Alcohol. Clin. Exp. Res. 24:6882–91
    [Google Scholar]
  84. Nabhan C, Klink A, Prasad V. 2019. Real-world evidence—what does it really mean?. JAMA Oncol. 5:6781–83. https://doi.org/10.1001/jamaoncol.2019.0450
    [Google Scholar]
  85. O'Donovan MC. 2015. What have we learned from the Psychiatric Genomics Consortium?. World Psychiatry 14:3291–293. https://doi.org/10.1002/wps.20270
    [Google Scholar]
  86. Onnela J-P. 2021. Opportunities and challenges in the collection and analysis of digital phenotyping data. Neuropsychopharmacology 46:145–54. https://doi.org/10.1038/s41386-020-0771-3
    [Google Scholar]
  87. Onnela J-P, Rauch SL. 2016. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology 41:71691–96. https://doi.org/10.1038/npp.2016.7
    [Google Scholar]
  88. Reddy RR, Mamatha C, Reddy RG. 2018. A review on machine learning trends, application and challenges in internet of things. 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)2389–97. Piscataway, NJ: IEEE
    [Google Scholar]
  89. Reise SP, Waller NG, Comrey AL. 2000. Factor analysis and scale revision. Psychol. Assess. 12:3287–97. https://doi.org/10.1037//1040–3590.12.3.287
    [Google Scholar]
  90. Schapire RE. 1990. The strength of weak learnability. Mach. Learn. 5:2197–227. https://doi.org/10.1007/BF00116037
    [Google Scholar]
  91. Schultebraucks K, Shalev AY, Michopoulos V, Grudzen CR, Shin S-M et al. 2020. A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nat. Med. 26:71084–88. https://doi.org/10.1038/s41591-020-0951-z
    [Google Scholar]
  92. Schultebraucks K, Sijbrandij M, Galatzer-Levy IR, Mouthaan J, Olff M, van Zuiden M. 2021a. Forecasting individual risk for long-term Posttraumatic Stress Disorder in emergency medical settings using biomedical data: a machine learning multicenter cohort study. Neurobiol. Stress 14:100297 https://doi.org/10.1016/j.ynstr.2021.100297
    [Google Scholar]
  93. Schultebraucks K, Yadav V, Galatzer-Levy IR. 2021b. Utilization of machine learning–based computer vision and voice analysis to derive digital biomarkers of cognitive functioning in trauma survivors. Digit. Biomark. 5:116–23. https://doi.org/10.1159/000512394
    [Google Scholar]
  94. Schultebraucks K, Yadav V, Shalev AY, Bonanno GA, Galatzer-Levy IR. 2022. Deep learning–based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood. Psychol. Med. 52:5957–67. https://doi.org/10.1017/S0033291720002718
    [Google Scholar]
  95. Shandhi MMH, Goldsack JC, Ryan K, Bennion A, Kotla AV et al. 2021. Recent academic research on clinically relevant digital measures: systematic review. J. Med. Internet Res. 23:9e29875 https://doi.org/10.2196/29875
    [Google Scholar]
  96. Sherman RE, Anderson SA, Dal Pan GJ, Gray GW, Gross T et al. 2016. Real-world evidence—what is it and what can it tell us?. N. Engl. J. Med. 375:232293–97. https://doi.org/10.1056/NEJMsb1609216
    [Google Scholar]
  97. Shorter E. 2009. The history of lithium therapy. Bipolar Disord. 11:Suppl. 24–9. https://doi.org/10.1111/j.1399-5618.2009.00706.x
    [Google Scholar]
  98. Shrive FM, Stuart H, Quan H, Ghali WA. 2006. Dealing with missing data in a multi-question depression scale: a comparison of imputation methods. BMC Med. Res. Methodol. 6:57 https://doi.org/10.1186/1471-2288-6-57
    [Google Scholar]
  99. Sikdar A, Behera SK, Dogra DP. 2016. Computer-vision-guided human pulse rate estimation: a review. IEEE Rev. Biomed. Eng. 9:91–105. https://doi.org/10.1109/RBME.2016.2551778
    [Google Scholar]
  100. Smith DG. 2018. Digital phenotyping approaches and mobile devices enhance CNS biopharmaceutical research and development. Neuropsychopharmacology 43:132504–5. https://doi.org/10.1038/s41386-018-0222-6
    [Google Scholar]
  101. Spitzer RL, Endicott J. 1968. DIAGNO. A computer program for psychiatric diagnosis utilizing the differential diagnostic procedure. Arch. Gen. Psychiatry 18:6746–56. https://doi.org/10.1001/archpsyc.1968.01740060106013
    [Google Scholar]
  102. Spitzer RL, Endicott J, Robins E. 1975. Clinical criteria for psychiatric diagnosis and DSM-III. Am. J. Psychiatry 132:111187–92. https://doi.org/10.1176/ajp.132.11.1187
    [Google Scholar]
  103. Spitzer RL, Endicott J, Robins E. 1978. Research diagnostic criteria: rationale and reliability. Arch. Gen. Psychiatry 35:6773–82. https://doi.org/10.1001/archpsyc.1978.01770300115013
    [Google Scholar]
  104. Stroud C, Onnela J-P, Manji H. 2019. Harnessing digital technology to predict, diagnose, monitor, and develop treatments for brain disorders. npj Digit. Med. 2:44 https://doi.org/10.1038/s41746-019-0123-z
    [Google Scholar]
  105. Van Assche E, Ramos-Quiroga JA, Pariante CM, Sforzini L, Young AH et al. 2022. Digital tools for the assessment of pharmacological treatment for depressive disorder: state of the art. Eur. Neuropsychopharmacol. 60:100–16. https://doi.org/10.1016/j.euroneuro.2022.05.007
    [Google Scholar]
  106. Walther S, Mittal VA. 2022. Motor behavior is relevant for understanding mechanism, bolstering prediction, and improving treatment: a transdiagnostic perspective. Schizophr. Bull. 48:4741–48. https://doi.org/10.1093/schbul/sbac003
    [Google Scholar]
  107. Watson D, Stanton K, Clark LA. 2017. Self-report indicators of negative valence constructs within the Research Domain Criteria (RDoC): a critical review. J. Affect. Disord. 216:58–69. https://doi.org/10.1016/j.jad.2016.09.065
    [Google Scholar]
  108. Weathers FW, Keane TM, Davidson JR. 2001. Clinician-administered PTSD scale: a review of the first ten years of research. Depress. Anxiety 13:3132–56. https://doi.org/10.1002/da.1029
    [Google Scholar]
  109. Wilson TD, Meyers J, Gilbert DT. 2003.. “ How happy was I, anyway?” A retrospective impact bias. Soc. Cogn. N. Y. 21:6421–46
    [Google Scholar]
  110. Wundt WM, Judd CH. 1902. Outlines of Psychology Leipzig, Ger.: Engelmann
  111. Zavaleta D, Samuel K, Mills CT. 2017. Measures of social isolation. Soc. Indic. Res. 131:1367–91. https://doi.org/10.1007/s11205-016-1252-2
    [Google Scholar]
  112. Zhang L, Koesmahargyo V, Galatzer-Levy I. 2021. Estimation of clinical tremor using spatio-temporal adversarial autoencoder. 2020 25th International Conference on Pattern Recognition (ICPR)8259–66. Piscataway, NJ: IEEE
    [Google Scholar]
  113. Zhou Z-H. 2021. Ensemble learning. Machine Learning181–210. New York: Springer https://doi.org/10.1007/978-981-15-1967-3_8
    [Google Scholar]
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