TY - JOUR
T1 - Turning data into better mental health
T2 - Past, present, and future
AU - Moukaddam, Nidal
AU - Sano, Akane
AU - Salas, Ramiro
AU - Hammal, Zakia
AU - Sabharwal, Ashutosh
N1 - Funding Information:
Rice University, Institute of Biosciences and Bioengineering, the Hamill Innovation Award. Sony Faculty Innovation Award. Funding for Sano: National Science Foundation (#2047296). Funding for RS: Veteran Health Administration (VHA I01CX001937). Funding for Hammal: National Institutes for Health (#R01NR018451).
Funding Information:
Rice University, Institute of Biosciences and Bioengineering, the Hamill Innovation Award. Sony Faculty Innovation Award. Funding for Sano: National Science Foundation (#2047296). Funding for RS: Veteran Health Administration (VHA I01CX001937). Funding for Hammal: National Institutes for Health (#R01NR018451).
Publisher Copyright:
2022 Moukaddam, Sano, Salas, Hammal and Sabharwal.
PY - 2022/8/17
Y1 - 2022/8/17
N2 - 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.
AB - 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.
KW - anhedonia
KW - apps and smartphones
KW - bio-behavioral sensing
KW - craving and relapse
KW - depression
KW - ecological momentary assessment
KW - mental health
UR - http://www.scopus.com/inward/record.url?scp=85137689367&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137689367&partnerID=8YFLogxK
U2 - 10.3389/fdgth.2022.916810
DO - 10.3389/fdgth.2022.916810
M3 - Review article
AN - SCOPUS:85137689367
VL - 4
JO - Frontiers in Digital Health
JF - Frontiers in Digital Health
SN - 2673-253X
M1 - 916810
ER -