Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

Figure 1 Depression symptomatology.

Figure 2 Depression diagnosis and ecological momentary assessment.

Figure 3 LifeRhythm: Integration of active and passive ecological momentary assessment to predict depression. Adapted from Ware et al[86] with permission from the Association for Computing Machinery (ACM) Citation: Ware S, Yue C, Morillo R, Lu J, Shang C, Kamath J, Bamis A, Bi J, Russell A, Wang B. Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018; 2: 1-27. Copyright © The Association for Computing Machinery (ACM).

Figure 4 DepWatch: Integrating active and passive ecological momentary assessment in clinical setting. Adapted from Kamath et al[13] an open access article distributed under the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) with permission from the J Psychiatr Brain Sci (JPBS). Citation: Kamath J, Bi J, Russell A, Wang B. Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics. J Psychiatr Brain Sci 2020; 5: e200010. Copyright © The J Psychiatr Brain Sci (JPBS).

Figure 5 Hybrid clinical care model: Integration of in-person and digital care. EMA: Ecological momentray assessment; EMR: Electronic medical record.