Turning data into better mental health: Past, present, and future
| dc.citation.articleNumber | 916810 | |
| dc.citation.journalTitle | Frontiers in Digital Health | |
| dc.citation.volumeNumber | 4 | |
| dc.contributor.author | Moukaddam, Nidal | |
| dc.contributor.author | Sano, Akane | |
| dc.contributor.author | Salas, Ramiro | |
| dc.contributor.author | Hammal, Zakia | |
| dc.contributor.author | Sabharwal, Ashutosh | |
| dc.date.accessioned | 2022-09-29T15:06:33Z | |
| dc.date.available | 2022-09-29T15:06:33Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | In this mini-review, we discuss the fundamentals of using technology in mental health diagnosis and tracking. We highlight those principles using two clinical concepts: (1) cravings and relapse in the context of addictive disorders and (2) anhedonia in the context of depression. This manuscript is useful for both clinicians wanting to understand the scope of technology use in psychiatry and for computer scientists and engineers wishing to assess psychiatric frameworks useful for diagnosis and treatment. The increase in smartphone ownership and internet connectivity, as well as the accelerated development of wearable devices, have made the observation and analysis of human behavior patterns possible. This has, in turn, paved the way to understand mental health conditions better. These technologies have immense potential in facilitating the diagnosis and tracking of mental health conditions; they also allow the implementation of existing behavioral treatments in new contexts (e.g., remotely, online, and in rural/underserved areas), and the possibility to develop new treatments based on new understanding of behavior patterns. The path to understand how to best use technology in mental health includes the need to match interdisciplinary frameworks from engineering/computer sciences and psychiatry. Thus, we start our review by introducing bio-behavioral sensing, the types of information available, and what behavioral patterns they may reflect and be related to in psychiatric diagnostic frameworks. This information is linked to the use of functional imaging, highlighting how imaging modalities can be considered “ground truth” for mental health/psychiatric dimensions, given the heterogeneity of clinical presentations, and the difficulty of determining what symptom corresponds to what disease. We then discuss how mental health/psychiatric dimensions overlap, yet differ from, psychiatric diagnoses. Using two clinical examples, we highlight the potential agreement areas in assessment/management of anhedonia and cravings. These two dimensions were chosen because of their link to two very prevalent diseases worldwide: depression and addiction. Anhedonia is a core symptom of depression, which is one of the leading causes of disability worldwide. Cravings, the urge to use a substance or perform an action (e.g., shopping, internet), is the leading step before relapse. Lastly, through the manuscript, we discuss potential mental health dimensions. | |
| dc.identifier.citation | Moukaddam, Nidal, Sano, Akane, Salas, Ramiro, et al.. "Turning data into better mental health: Past, present, and future." <i>Frontiers in Digital Health,</i> 4, (2022) Frontiers Media S.A.: https://doi.org/10.3389/fdgth.2022.916810. | |
| dc.identifier.digital | fdgth-04-916810 | |
| dc.identifier.doi | https://doi.org/10.3389/fdgth.2022.916810 | |
| dc.identifier.uri | https://hdl.handle.net/1911/113442 | |
| dc.language.iso | eng | |
| dc.publisher | Frontiers Media S.A. | |
| dc.rights | This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Turning data into better mental health: Past, present, and future | |
| dc.type | Journal article | |
| dc.type.dcmi | Text | |
| dc.type.publication | publisher version |
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