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Cohen AS, Rodriguez Z, Opler M, Kirkpatrick B, Milanovic S, Piacentino D, Szabo ST, Tomioka S, Ogirala A, Koblan KS, Siegel JS, Hopkins S. Evaluating speech latencies during structured psychiatric interviews as an automated objective measure of psychomotor slowing. Psychiatry Res 2024; 340:116104. [PMID: 39137558 DOI: 10.1016/j.psychres.2024.116104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 07/23/2024] [Accepted: 07/24/2024] [Indexed: 08/15/2024]
Abstract
We sought to derive an objective measure of psychomotor slowing from speech analytics during a psychiatric interview to avoid potential burden of dedicated neurophysiological testing. Speech latency, which reflects response time between speakers, shows promise from the literature. Speech data was obtained from 274 subjects with a diagnosis of bipolar I depression enrolled in a randomized, doubleblind, 6-week phase 2 clinical trial. Audio recordings of structured Montgomery-Åsberg Depression Rating Scale (MADRS) interviews at 6 time points were examined (k = 1,352). We evaluated speech latencies, and other aspects of speech, for temporal stability, convergent validity, sensitivity/responsivity to clinical change, and generalization across seven socio-linguistically diverse countries. Speech latency was minimally associated with demographic features, and explained nearly a third of the variance in depression (categorically defined). Speech latency significantly decreased as depression symptoms improved over time, explaining nearly 20 % of variance in depression remission. Classification for differentiating people with versus without concurrent depression was high (AUCs > 0.85) both cross-sectionally and longitudinally. Results replicated across countries. Other speech features offered modest incremental contribution. Neurophysiological speech parameters with face validity can be derived from psychiatric interviews without the added patient burden of additional testing.
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Affiliation(s)
- Alex S Cohen
- Louisiana State University, Department of Psychology, USA; Louisiana State University, Center for Computation and Technology, USA; Quantic Innovation, Inc, USA.
| | - Zachary Rodriguez
- Louisiana State University, Department of Psychology, USA; Louisiana State University, Center for Computation and Technology, USA
| | - Mark Opler
- Quantic Innovation, Inc, USA; WCG, Inc, USA
| | - Brian Kirkpatrick
- Quantic Innovation, Inc, USA; Psychiatric Research Institute, University of Arkansas for Medical Sciences, USA
| | | | | | | | | | | | | | - Joshua S Siegel
- Sumitomo Pharmaceuticals Inc, USA; Washington University in St. Louis, Department of Psychiatry, USA
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Soleimani L, Ouyang Y, Cho S, Kia A, Beeri MS, Lin H, Ravona‐Springer R, Ramsingh N, Liberman MY, Grossman M, Nevler N. Speech markers of depression dimensions across cognitive status. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12604. [PMID: 39092182 PMCID: PMC11292393 DOI: 10.1002/dad2.12604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 04/20/2024] [Accepted: 04/24/2024] [Indexed: 08/04/2024]
Abstract
Introduction Depression and its components significantly impact dementia prediction and severity, necessitating reliable objective measures for quantification. Methods We investigated associations between emotion-based speech measures (valence, arousal, and dominance) during picture descriptions and depression dimensions derived from the geriatric depression scale (GDS, dysphoria, withdrawal-apathy-vigor (WAV), anxiety, hopelessness, and subjective memory complaint). Results Higher WAV was associated with more negative valence (estimate = -0.133, p = 0.030). While interactions of apolipoprotein E (APOE) 4 status with depression dimensions on emotional valence did not reach significance, there was a trend for more negative valence with higher dysphoria in those with at least one APOE4 allele (estimate = -0.404, p = 0.0846). Associations were similar irrespective of dementia severity. Discussion Our study underscores the potential utility of speech biomarkers in characterizing depression dimensions. In future research, using emotionally charged stimuli may enhance emotional measure elicitation. The role of APOE on the interaction of speech markers and depression dimensions warrants further exploration with greater sample sizes. Highlights Participants reporting higher apathy used more negative words to describe a neutral picture.Those with higher dysphoria and at least one APOE4 allele also tended to use more negative words.Our results suggest the potential use of speech biomarkers in characterizing depression dimensions.
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Affiliation(s)
| | - Yuxia Ouyang
- Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Sunghye Cho
- Linguistic Data ConsortiumUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arash Kia
- Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | | | - Hung‐Mo Lin
- Department of AnesthesiologyYale School of MedicineNew HavenConnecticutUSA
| | - Ramit Ravona‐Springer
- The Joseph Sagol Neuroscience CenterSheba Medical CenterTel‐HashomerIsrael
- Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
| | - Nadia Ramsingh
- Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Mark Y Liberman
- Linguistic Data ConsortiumUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Murray Grossman
- Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Naomi Nevler
- Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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de Azevedo Cardoso T, Kochhar S, Torous J, Morton E. Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions. JMIR Ment Health 2024; 11:e58631. [PMID: 38557724 PMCID: PMC11019420 DOI: 10.2196/58631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 03/25/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024] Open
Abstract
Bipolar disorder (BD) impacts over 40 million people around the world, often manifesting in early adulthood and substantially impacting the quality of life and functioning of individuals. Although early interventions are associated with a better prognosis, the early detection of BD is challenging given the high degree of similarity with other psychiatric conditions, including major depressive disorder, which corroborates the high rates of misdiagnosis. Further, BD has a chronic, relapsing course, and the majority of patients will go on to experience mood relapses despite pharmacological treatment. Digital technologies present promising results to augment early detection of symptoms and enhance BD treatment. In this editorial, we will discuss current findings on the use of digital technologies in the field of BD, while debating the challenges associated with their implementation in clinical practice and the future directions.
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Affiliation(s)
- Taiane de Azevedo Cardoso
- The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Australia
- JMIR Publications, Toronto, ON, Canada
| | | | - John Torous
- Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Emma Morton
- School of Psychological Sciences, Monash University, Clayton, Australia
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Lyu H, Huang H, He J, Zhu S, Hong W, Lai J, Gao T, Shao J, Zhu J, Li Y, Hu S. Task-state skin potential abnormalities can distinguish major depressive disorder and bipolar depression from healthy controls. Transl Psychiatry 2024; 14:110. [PMID: 38395985 PMCID: PMC10891315 DOI: 10.1038/s41398-024-02828-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Early detection of bipolar depression (BPD) and major depressive disorder (MDD) has been challenging due to the lack of reliable and easily measurable biological markers. This study aimed to investigate the accuracy of discriminating patients with mood disorders from healthy controls based on task state skin potential characteristics and their correlation with individual indicators of oxidative stress. A total of 77 patients with BPD, 53 patients with MDD, and 79 healthy controls were recruited. A custom-made device, previously shown to be sufficiently accurate, was used to collect skin potential data during six emotion-inducing tasks involving video, pictorial, or textual stimuli. Blood indicators reflecting individual levels of oxidative stress were collected. A discriminant model based on the support vector machine (SVM) algorithm was constructed for discriminant analysis. MDD and BPD patients were found to have abnormal skin potential characteristics on most tasks. The accuracy of the SVM model built with SP features to discriminate MDD patients from healthy controls was 78% (sensitivity 78%, specificity 82%). The SVM model gave an accuracy of 59% (sensitivity 59%, specificity 79%) in classifying BPD patients, MDD patients, and healthy controls into three groups. Significant correlations were also found between oxidative stress indicators in the blood of patients and certain SP features. Patients with depression and bipolar depression have abnormalities in task-state skin potential that partially reflect the pathological mechanism of the illness, and the abnormalities are potential biological markers of affective disorders.
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Affiliation(s)
- Hailong Lyu
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine; Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Huimin Huang
- The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325200, China
- Ruian People's Hospital, Wenzhou, 325200, China
| | - Jiadong He
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Sheng Zhu
- Department of Psychiatry, The Ruian Fifth People's Hospital, Wenzhou, 325200, China
| | - Wanchu Hong
- Department of Psychiatry, The Ruian Fifth People's Hospital, Wenzhou, 325200, China
| | - Jianbo Lai
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine; Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | | | - Jiamin Shao
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine; Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Jianfeng Zhu
- Department of Psychiatry, The Ruian Fifth People's Hospital, Wenzhou, 325200, China
| | - Yubo Li
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.
| | - Shaohua Hu
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine; Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, 310003, China.
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China.
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China.
- Ruian People's Hospital, Wenzhou, 325200, China.
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Langholm C, Breitinger S, Gray L, Goes F, Walker A, Xiong A, Stopel C, Zandi P, Frye MA, Torous J. Classifying and clustering mood disorder patients using smartphone data from a feasibility study. NPJ Digit Med 2023; 6:238. [PMID: 38129571 PMCID: PMC10739731 DOI: 10.1038/s41746-023-00977-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023] Open
Abstract
Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.
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Affiliation(s)
- Carsten Langholm
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
| | - Scott Breitinger
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, 55902, USA
| | - Lucy Gray
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
| | - Fernando Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21218, USA
| | - Alex Walker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21218, USA
| | - Ashley Xiong
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, 55902, USA
| | - Cindy Stopel
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, 55902, USA
| | - Peter Zandi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21218, USA
| | - Mark A Frye
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, 55902, USA
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA.
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Lane E, D'Arcey J, Kidd S, Onyeaka H, Alon N, Joshi D, Torous J. Digital Phenotyping in Adults with Schizophrenia: A Narrative Review. Curr Psychiatry Rep 2023; 25:699-706. [PMID: 37861979 DOI: 10.1007/s11920-023-01467-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/04/2023] [Indexed: 10/21/2023]
Abstract
PURPOSE OF REVIEW As care for older adult patients with schizophrenia lacks innovation, technology can help advance the field. Specifically, digital phenotyping, the real-time monitoring of patients' behaviors through smartphone sensors and symptoms through surveys, holds promise as the method can capture the dynamicity and environmental correlates of disease. RECENT FINDINGS Few studies have used digital phenotyping to elucidate adult patients' experiences with schizophrenia. In this narrative review, we summarized the literature using digital phenotyping on adults with schizophrenia. No study focused solely on older adult patients. Studies including all adult patients were heterogeneous in measures used, duration, and outcomes. Despite limited research, digital phenotyping shows potential for monitoring outcomes such as negative, positive, and functional symptoms, as well as predicting relapse. Future research should work to target the symptomology persistent in chronic schizophrenia and ensure all patients have the digital literacy required to benefit from digital interventions and homogenize datasets to allow for more robust conclusions.
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Affiliation(s)
- Erlend Lane
- Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA, 02115, USA
| | - Jessica D'Arcey
- Slaight Centre for Youth in Transition, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Sean Kidd
- Slaight Centre for Youth in Transition, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Henry Onyeaka
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, Massachusetts General/McLean Hospital, Boston, MA, USA
| | - Noy Alon
- Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA, 02115, USA
| | - Devayani Joshi
- Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA, 02115, USA
| | - John Torous
- Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA, 02115, USA.
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Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. J Med Internet Res 2023; 25:e44502. [PMID: 37792430 PMCID: PMC10585447 DOI: 10.2196/44502] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 07/10/2023] [Accepted: 08/21/2023] [Indexed: 10/05/2023] Open
Abstract
The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.
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Affiliation(s)
- Antoine Oudin
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Redwan Maatoug
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Alexis Bourla
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
- Medical Strategy and Innovation Department, Clariane, Paris, France
- NeuroStim Psychiatry Practice, Paris, France
| | - Florian Ferreri
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Olivier Bonnot
- Department of Child and Adolescent Psychiatry, Nantes University Hospital, Nantes, France
- Pays de la Loire Psychology Laboratory, Nantes University, Nantes, France
| | - Bruno Millet
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Félix Schoeller
- Institute for Advanced Consciousness Studies, Santa Monica, CA, United States
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Stéphane Mouchabac
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Vladimir Adrien
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
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Lagera PGD, Chan SR, Yellowlees PM. Asynchronous Technologies in Mental Health Care and Education. CURRENT TREATMENT OPTIONS IN PSYCHIATRY 2023; 10:1-13. [PMID: 37360962 PMCID: PMC10157570 DOI: 10.1007/s40501-023-00286-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 06/28/2023]
Abstract
Purpose of review Patients, providers, and trainees should understand the current types of asynchronous technologies that can be used to enhance the delivery and accessibility of mental health care. Asynchronous telepsychiatry (ATP) removes the need for real time communication between the clinician and patient, which improves efficiency and enables quality specialty care. ATP can be applied as distinct consultative and supervisory models in clinician-to-clinician, clinician-to-patient, and patient-to-mobile health settings. Recent findings This review is based on research literature and the authors' clinical and medical training, using experiences with asynchronous telepsychiatry from before, during, and after the COVID-19 pandemic. Our studies demonstrate that ATP provides positive outcomes in the clinician-to-patient model with demonstrated feasibility, outcomes and patient satisfaction. One author's medical education experience in the Philippines during COVID-19 highlights the potential to utilize asynchronous technology in areas with limitations to online learning. We emphasize the need to teach media skills literacy around mental health to students, coaches, therapists, and clinicians when advocating for mental well-being. Several studies have demonstrated the feasibility of incorporating asynchronous e-tools such as self-guided multimedia and artificial intelligence for data collection at the clinician-to-clinician and patient-to-mobile health level. In addition, we offer fresh perspectives on recent trends in asynchronous telehealth in wellness, applying concepts such as "tele-exercise" and "tele-yoga." Summary Asynchronous technologies continue to be integrated into mental health care services and research. Future research must ensure that the design and the usability of this technology puts the patient and provider first.
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Affiliation(s)
- Pamela Gail D. Lagera
- Division of Hospital Medicine, Clinical Informatics, University of California, San Francisco, CA USA
| | - Steven R. Chan
- Division of Hospital Medicine, Clinical Informatics, University of California, San Francisco, CA USA
- Department of Psychiatry, University of California, San Francisco, CA USA
- Department of Psychiatry, University of California, Davis, CA USA
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