PMID- 35830230 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20220731 IS - 1929-0748 (Print) IS - 1929-0748 (Electronic) IS - 1929-0748 (Linking) VI - 11 IP - 7 DP - 2022 Jul 13 TI - Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study. PG - e36417 LID - 10.2196/36417 [doi] LID - e36417 AB - BACKGROUND: Current standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient's outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinician's interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared, and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders. OBJECTIVE: We present a research protocol that utilizes facial expression, eye gaze, voice and speech, locomotor, heart rate, and electroencephalography monitoring to assess schizophrenia symptoms and to distinguish patients with schizophrenia from those with other psychiatric disorders and control subjects. METHODS: We plan to recruit three outpatient groups: (1) 50 patients with schizophrenia, (2) 50 patients with unipolar major depressive disorder, and (3) 50 individuals with no psychiatric history. Using an internally developed semistructured interview, psychometrically validated clinical outcome measures, and a multimodal sensing system utilizing video, acoustic, actigraphic, heart rate, and electroencephalographic sensors, we aim to evaluate the system's capacity in classifying subjects (schizophrenia, depression, or control), to evaluate the system's sensitivity to within-group symptom severity, and to determine if such a system can further classify variations in disorder subtypes. RESULTS: Data collection began in July 2020 and is expected to continue through December 2022. CONCLUSIONS: If successful, this study will help advance current progress in developing state-of-the-art technology to aid clinical psychiatric assessment and treatment. If our findings suggest that these technologies are capable of resolving diagnoses and symptoms to the level of current psychometric testing and clinician judgment, we would be among the first to develop a system that can eventually be used by clinicians to more objectively diagnose and assess schizophrenia and depression with the possibility of less risk of bias. Such a tool has the potential to improve accessibility to care; to aid clinicians in objectively evaluating diagnoses, severity of symptoms, and treatment efficacy through time; and to reduce treatment-related morbidity. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36417. CI - (c)Robert O Cotes, Mina Boazak, Emily Griner, Zifan Jiang, Bona Kim, Whitney Bremer, Salman Seyedi, Ali Bahrami Rad, Gari D Clifford. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 13.07.2022. FAU - Cotes, Robert O AU - Cotes RO AUID- ORCID: 0000-0001-9903-8807 AD - Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States. FAU - Boazak, Mina AU - Boazak M AUID- ORCID: 0000-0002-3908-5887 AD - Animo Sano Psychiatry, Durham, NC, United States. FAU - Griner, Emily AU - Griner E AUID- ORCID: 0000-0001-9161-8874 AD - Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States. FAU - Jiang, Zifan AU - Jiang Z AUID- ORCID: 0000-0002-3570-9461 AD - Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States. AD - Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States. FAU - Kim, Bona AU - Kim B AUID- ORCID: 0000-0003-1230-6755 AD - Visual Medical Education, Emory School of Medicine, Atlanta, GA, United States. FAU - Bremer, Whitney AU - Bremer W AUID- ORCID: 0000-0002-2757-574X AD - Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States. FAU - Seyedi, Salman AU - Seyedi S AUID- ORCID: 0000-0002-6017-7049 AD - Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States. FAU - Bahrami Rad, Ali AU - Bahrami Rad A AUID- ORCID: 0000-0002-5654-4301 AD - Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States. FAU - Clifford, Gari D AU - Clifford GD AUID- ORCID: 0000-0002-5709-201X AD - Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States. AD - Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States. LA - eng GR - UL1 TR002378/TR/NCATS NIH HHS/United States PT - Journal Article DEP - 20220713 PL - Canada TA - JMIR Res Protoc JT - JMIR research protocols JID - 101599504 PMC - PMC9330209 OTO - NOTNLM OT - acoustic OT - biomarker OT - computer vision OT - depression OT - digital biomarker OT - heart rate OT - machine learning OT - multimodal OT - schizophrenia OT - technology COIS- Conflicts of Interest: ROC received institutional research funding from Alkermes, Roche, and Otsuka and is a consultant to Saladax Biomedical and the American Psychiatric Association. The remaining authors declare no conflicts of interest. EDAT- 2022/07/14 06:00 MHDA- 2022/07/14 06:01 PMCR- 2022/07/13 CRDT- 2022/07/13 11:53 PHST- 2022/01/13 00:00 [received] PHST- 2022/05/31 00:00 [accepted] PHST- 2022/05/30 00:00 [revised] PHST- 2022/07/13 11:53 [entrez] PHST- 2022/07/14 06:00 [pubmed] PHST- 2022/07/14 06:01 [medline] PHST- 2022/07/13 00:00 [pmc-release] AID - v11i7e36417 [pii] AID - 10.2196/36417 [doi] PST - epublish SO - JMIR Res Protoc. 2022 Jul 13;11(7):e36417. doi: 10.2196/36417.