1932

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.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-clinpsy-080921-073212
2023-05-09
2024-10-15
Loading full text...

Full text loading...

/deliver/fulltext/clinpsy/19/1/annurev-clinpsy-080921-073212.html?itemId=/content/journals/10.1146/annurev-clinpsy-080921-073212&mimeType=html&fmt=ahah

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:(1):e26276. 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:(1):2936 https://doi.org/10.1159/000512383
    [Google Scholar]
  4. Aldao A, Tull MT. 2015.. Putting emotion regulation in context. . Curr. Opin. Psychol. 3::1007 https://doi.org/10.1016/j.copsyc.2015.03.022
    [Google Scholar]
  5. Annett J. 2002.. Subjective rating scales: science or art?. Ergonomics 45:(14):96687 https://doi.org/10.1080/00140130210166951
    [Google Scholar]
  6. Ayer AJ. 1959.. Logical Positivism. New York:: Simon & Schuster
    [Google Scholar]
  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:(2):e98112 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
    [Google Scholar]
  10. Bernardo JM, Smith AF. 2001.. Bayesian Theory. New York:: Wiley
    [Google Scholar]
  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:(2):25862 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::60534 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:(11):185364 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. . In Proceedings of the 27th International Conference on Machine Learning (ICML ’10), pp. 11118 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:(7):120514 https://doi.org/10.1017/s0033291704002594
    [Google Scholar]
  17. Bronfenbrenner U. 1977.. Toward an experimental ecology of human development. . Am. Psychol. 32:(7):51331 https://doi.org/10.1037/0003-066X.32.7.513
    [Google Scholar]
  18. Brunswik E. 1943.. Organismic achievement and environmental probability. . Psychol. Rev. 50:(3):25572 https://doi.org/10.1037/h0060889
    [Google Scholar]
  19. Buzsáki G. 2019.. The Brain from Inside Out. Oxford, UK:: Oxford Univ. Press
    [Google Scholar]
  20. Campbell DT, Stanley JC. 2015.. Experimental and Quasi-Experimental Designs for Research. Cambridge, UK:: Ravenio
    [Google Scholar]
  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:(4):34054 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:(11):81014 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::36784 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. . In Handbook of Clinical Rating Scales and Assessment in Psychiatry and Mental Health, ed. L Baer, MA Blais , pp. 735 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:(9):e0138095. 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:(1):613 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:(5):58288 https://doi.org/10.1001/jamaneurol.2014.4524
    [Google Scholar]
  28. Elble RJ. 2016.. The essential tremor rating assessment scale. . J. Neurol. Neuromed. 1:(4):3438 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:(3):285301 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
    [Google Scholar]
  32. First MB, Spitzer RL. 2003.. The DSM: not perfect, but better than the alternative. . Psychiatr. Times 20:(4):7378
    [Google Scholar]
  33. Floridi L, Chiriatti M. 2020.. GPT-3: its nature, scope, limits, and consequences. . Minds Mach. 30:(4):68194 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:(9):e95052 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:(6):e25199. 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:(6):65162 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:(2):16180 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::4155 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::6876 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.
    [Google Scholar]
  42. Goodfellow I, Bengio Y, Courville A. 2016.. Deep Learning. Cambridge, MA:: MIT Press
    [Google Scholar]
  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:(4):30515 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:(6):9931003 https://doi.org/10.1016/j.cpr.2011.06.005
    [Google Scholar]
  45. Gottman JM. 1993.. Studying emotion in social interaction. . In Handbook of Emotions, ed. M Lewis , pp. 47587 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:(11):743656 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:(3):50712 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
    [Google Scholar]
  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:(3):25158 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::24370 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::15579 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:(13):121516 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:(7):74851 https://doi.org/10.1176/appi.ajp.2010.09091379
    [Google Scholar]
  55. Jaklevic MC. 2020.. Device approved for adults with nightmare disorder. . JAMA 324:(23):2357. 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
    [Google Scholar]
  57. James W. 1890.. The Principles of Psychology. London:: Taylor & Francis
    [Google Scholar]
  58. Kessler RC. 1994.. The National Comorbidity Survey of the United States. . Int. Rev. Psychiatry 6:(4):36576 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:(6):61727 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
    [Google Scholar]
  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:(2):16570 https://doi.org/10.1037/a0020847
    [Google Scholar]
  63. LeCun Y, Bengio Y. 1998.. Convolutional networks for images, speech, and time series. . In The Handbook of Brain Theory and Neural Networks, ed. MA Arbib , pp. 25558 Cambridge, MA:: MIT Press
    [Google Scholar]
  64. LeDoux J. 2012.. Rethinking the emotional brain. . Neuron 73:(4):65376 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
    [Google Scholar]
  66. Levine RL, Hunter JE. 1971.. Statistical and psychometric inference in principal components analysis. . Multivar. Behav. Res. 6:(1):10516 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:(1):18197 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::290307 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::85776
    [Google Scholar]
  70. Lilienfeld SO. 2014.. The Research Domain Criteria (RDoC): an analysis of methodological and conceptual challenges. . Behav. Res. Ther. 62::12939 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:(8):177784 https://doi.org/10.1093/jamia/ocab069
    [Google Scholar]
  72. Luus R. 2000.. Iterative Dynamic Programming. London:: Taylor & Francis
    [Google Scholar]
  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:(1):6775 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:(11):167785 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::26775 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:(3):54143 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::577605 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:(2):11528 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:(2):e28735. 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.
    [Google Scholar]
  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::120522 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. . In Longitudinal Data Analysis, ed. G Fitzmaurice, M Davidian, G Verbeke, G Molenberghs , pp. 14366 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:(6):88291
    [Google Scholar]
  84. Nabhan C, Klink A, Prasad V. 2019.. Real-world evidence—what does it really mean?. JAMA Oncol. 5:(6):78183 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:(3):291293 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:(1):4554 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:(7):169196 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. . In 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 238997 Piscataway, NJ:: IEEE
    [Google Scholar]
  89. Reise SP, Waller NG, Comrey AL. 2000.. Factor analysis and scale revision. . Psychol. Assess. 12:(3):28797 https://doi.org/10.1037//1040–3590.12.3.287
    [Google Scholar]
  90. Schapire RE. 1990.. The strength of weak learnability. . Mach. Learn. 5:(2):197227 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:(7):108488 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:(1):1623 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:(5):95767 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:(9):e29875. 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:(23):229397 https://doi.org/10.1056/NEJMsb1609216
    [Google Scholar]
  97. Shorter E. 2009.. The history of lithium therapy. . Bipolar Disord. 11:(Suppl. 2):49 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::91105 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:(13):25045 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:(6):74656 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:(11):118792 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:(6):77382 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::10016 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:(4):74148 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::5869 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:(3):13256 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:(6):42146
    [Google Scholar]
  110. Wundt WM, Judd CH. 1902.. Outlines of Psychology. Leipzig, Ger.:: Engelmann
    [Google Scholar]
  111. Zavaleta D, Samuel K, Mills CT. 2017.. Measures of social isolation. . Soc. Indic. Res. 131:(1):36791 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. . In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 825966 Piscataway, NJ:: IEEE
    [Google Scholar]
  113. Zhou Z-H. 2021.. Ensemble learning. . In Machine Learning, pp. 181210 New York:: Springer. https://doi.org/10.1007/978-981-15-1967-3_8
    [Google Scholar]
/content/journals/10.1146/annurev-clinpsy-080921-073212
Loading
/content/journals/10.1146/annurev-clinpsy-080921-073212
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error