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Dong T, Yu C, Mao Q, Han F, Yang Z, Yang Z, Pires N, Wei X, Jing W, Lin Q, Hu F, Hu X, Zhao L, Jiang Z. Advances in biosensors for major depressive disorder diagnostic biomarkers. Biosens Bioelectron 2024; 258:116291. [PMID: 38735080 DOI: 10.1016/j.bios.2024.116291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/25/2024] [Accepted: 04/09/2024] [Indexed: 05/14/2024]
Abstract
Depression is one of the most common mental disorders and is mainly characterized by low mood or lack of interest and pleasure. It can be accompanied by varying degrees of cognitive and behavioral changes and may lead to suicide risk in severe cases. Due to the subjectivity of diagnostic methods and the complexity of patients' conditions, the diagnosis of major depressive disorder (MDD) has always been a difficult problem in psychiatry. With the discovery of more diagnostic biomarkers associated with MDD in recent years, especially emerging non-coding RNAs (ncRNAs), it is possible to quantify the condition of patients with mental illness based on biomarker levels. Point-of-care biosensors have emerged due to their advantages of convenient sampling, rapid detection, miniaturization, and portability. After summarizing the pathogenesis of MDD, representative biomarkers, including proteins, hormones, and RNAs, are discussed. Furthermore, we analyzed recent advances in biosensors for detecting various types of biomarkers of MDD, highlighting representative electrochemical sensors. Future trends in terms of new biomarkers, new sample processing methods, and new detection modalities are expected to provide a complete reference for psychiatrists and biomedical engineers.
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Affiliation(s)
- Tao Dong
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; Chongqing Key Laboratory of Micro-Nano Transduction and Intelligent Systems, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China.
| | - Chenghui Yu
- Chongqing Key Laboratory of Micro-Nano Transduction and Intelligent Systems, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China.
| | - Qi Mao
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Feng Han
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Zhenwei Yang
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Zhaochu Yang
- Chongqing Key Laboratory of Micro-Nano Transduction and Intelligent Systems, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China
| | - Nuno Pires
- Chongqing Key Laboratory of Micro-Nano Transduction and Intelligent Systems, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China
| | - Xueyong Wei
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Weixuan Jing
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Qijing Lin
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Fei Hu
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xiao Hu
- Engineering Research Center of Ministry of Education for Smart Justice, School of Criminal Investigation, Southwest University of Political Science and Law, Chongqing, 401120, China.
| | - Libo Zhao
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Zhuangde Jiang
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
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2
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Rettew DC, Biel MG. Widening Our Lane: How Child and Adolescent Psychiatrists Can Embrace the Full Spectrum of Mental Health. Child Adolesc Psychiatr Clin N Am 2024; 33:293-306. [PMID: 38823804 DOI: 10.1016/j.chc.2024.02.001] [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] [Indexed: 06/03/2024]
Abstract
The majority of a psychiatrist's training and clinical attention is devoted to mental illness rather than mental health. This article suggests a broader understanding and application of mental well-being that can benefit both those already struggling with mental health challenges and those trying to stay well. Recommendations for being a well-being-oriented psychiatrist include increasing one's knowledge about well-being and health promotion and adjusting one's practice to incorporate these principles. Recommendations at the level of the field of psychiatry include revising the definition of a psychiatrist, increasing research on well-being and health promotion, improving financial incentives, expanding efforts in schools and community settings, and providing additional training.
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Affiliation(s)
- David C Rettew
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA; Lane County Behavioral Health, Eugene, OR, USA.
| | - Matthew G Biel
- MedStar Georgetown University Hospital, Georgetown University School of Medicine, Washington, DC, USA
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3
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Young HA, Cousins AL, Byrd-Bredbenner C, Benton D, Gershon RC, Ghirardelli A, Latulippe ME, Scholey A, Wagstaff L. Alignment of Consumers' Expected Brain Benefits from Food and Supplements with Measurable Cognitive Performance Tests. Nutrients 2024; 16:1950. [PMID: 38931303 PMCID: PMC11206270 DOI: 10.3390/nu16121950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/31/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024] Open
Abstract
Consumers often cite cognitive improvements as reasons for making dietary changes or using dietary supplements, a motivation that if leveraged could greatly enhance public health. However, rarely is it considered whether standardized cognitive tests that are used in nutrition research are aligned to outcomes of interest to the consumer. This knowledge gap presents a challenge to the scientific substantiation of nutrition-based cognitive health benefits. Here we combined focus group transcript review using reflexive thematic analysis and a multidisciplinary expert panel exercise to evaluate the applicability of cognitive performance tools/tasks for substantiating the specific cognitive benefits articulated by consumers with the objectives to (1) understand how consumers comprehend the potential benefits of nutrition for brain health, and (2) determine the alignment between consumers desired brain benefits and validated tests and tools. We derived a 'Consumer Taxonomy of Cognitive and Affective Health in Nutrition Research' which describes the cognitive and affective structure from the consumers perspective. Experts agreed that validated tests exist for some consumer benefits including focused attention, sustained attention, episodic memory, energy levels, and anxiety. Prospective memory, flow, and presence represented novel benefits that require the development and validation of new tests and tools. Closing the gap between science and consumers and fostering co-creative approaches to nutrition research are critical to the development of products and dietary recommendations that support realizable cognitive benefits that benefit public health.
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Affiliation(s)
- Hayley A. Young
- Department of Psychology, Swansea University, Wales SA2 8PP, UK; (A.L.C.); (D.B.)
| | - Alecia L. Cousins
- Department of Psychology, Swansea University, Wales SA2 8PP, UK; (A.L.C.); (D.B.)
| | - Carol Byrd-Bredbenner
- Department of Nutritional Sciences, Rutgers University, New Brunswick, NJ 08854, USA;
| | - David Benton
- Department of Psychology, Swansea University, Wales SA2 8PP, UK; (A.L.C.); (D.B.)
| | - Richard C. Gershon
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60208, USA;
| | | | - Marie E. Latulippe
- Institute for the Advancement of Food and Nutrition Sciences, Washington, DC 20005, USA;
| | - Andrew Scholey
- Nutrition Dietetics and Food, School of Clinical Sciences, Monash University, Notting Hill, VIC 3168, Australia;
- Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne, VIC 3122, Australia
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Ballard C, Tariot P, Soto-Martin M, Pathak S, Liu IY. Challenges and proposed solutions to conducting Alzheimer's disease psychosis trials. Front Psychiatry 2024; 15:1384176. [PMID: 38812491 PMCID: PMC11135469 DOI: 10.3389/fpsyt.2024.1384176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 04/05/2024] [Indexed: 05/31/2024] Open
Abstract
Alzheimer's disease psychosis (ADP) produces a significant burden for patients and their care partners, but at present there are no approved treatments for ADP. The lack of approved treatments may be due to the challenges of conducting clinical trials for this disease. This perspective article discusses distinct challenges and proposed solutions of conducting ADP trials involving seven key areas: (1) methods to reduce the variable and sometimes high rates of placebo response that occur for treatments of neuropsychiatric symptoms; (2) the use of combined or updated criteria that provide a precise, consensus definition of ADP; (3) the use of eligibility criteria to help recruit individuals representative of the larger ADP population and overcome the difficulty of recruiting patients with moderate-to-severe ADP; (4) consideration of multiple perspectives and implementation of technology to reduce the variability in the administration and scoring of neuropsychiatric symptom assessments; (5) the use of clinically appropriate, a priori-defined severity thresholds and responder cutoffs; (6) the use of statistical approaches that address absolute effect sizes and a three-tier approach to address the fluctuation of neuropsychiatric symptoms; and (7) the implementation of feasible diagnostic and target-engagement biomarkers as they become available. The goal of these proposed solutions is to improve the evaluation of potential ADP therapies, within the context of randomized, placebo-controlled trials with clinically meaningful endpoints and sustained treatment responses.
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Affiliation(s)
- Clive Ballard
- Institute of Health Research, University of Exeter Medical School, Exeter, United Kingdom
| | - Pierre Tariot
- Banner Alzheimer’s Institute and University of Arizona College of Medicine, Phoenix, AZ, United States
| | - Maria Soto-Martin
- Alzheimer Clinical and Research Centre, Gérontopôle, Toulouse University Hospital, University Hospital Institutes (IHU) HealthAge, Toulouse, France
| | - Sanjeev Pathak
- Department of Medical Affairs, Acadia Pharmaceuticals Inc., San Diego, CA, United States
| | - I-Yuan Liu
- Department of Medical Affairs, Acadia Pharmaceuticals Inc., San Diego, CA, United States
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5
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Dowell-Esquivel C, Czaja SJ, Kallestrup P, Depp CA, Saber JN, Harvey PD. Computerized Cognitive and Skills Training in Older People With Mild Cognitive Impairment: Using Ecological Momentary Assessment to Index Treatment-Related Changes in Real-World Performance of Technology-Dependent Functional Tasks. Am J Geriatr Psychiatry 2024; 32:446-459. [PMID: 37953132 PMCID: PMC10950539 DOI: 10.1016/j.jagp.2023.10.014] [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: 07/30/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES Cognitive and functional skills training improves skills and cognitive test performance, but the true test of efficacy is real-world transfer. We trained participants with mild cognitive impairment (MCI) or normal cognition (NC) for up to 12 weeks on six technology-related skills using remote computerized functional skills assessment and training (FUNSAT) software. Using ecological momentary assessment (EMA), we measured real-world performance of the technology-related skills over 6 months and related EMA-identified changes in performance to training gains. DESIGN Randomized clinical trial with post-training follow-up. SETTING A total of 14 Community centers in New York City and Miami. PARTICIPANTS Older adults with normal cognition (n = 72) or well-defined MCI (n = 92), ranging in age from 60 to 90, primarily female, and racially and ethnically diverse. INTERVENTION Computerized cognitive and skills training. MEASUREMENTS EMA surveys measuring trained and untrained functional skills 3 or more days per week for 6 months and training gains from baseline to end of training. RESULTS Training gains in completion times across all 6 tasks were significant (p <0.001) for both samples, with effect sizes more than 1.0 SD for all tasks. EMA surveys detected increases in performance for both trained (p <0.03) and untrained (p <0.001) technology-related skills for both samples. Training gains in completion times predicted increases in performance of both trained and untrained technology-related skills (all p <0.001). CONCLUSIONS Computerized training produces increases in real-world performance of important technology-related skills. These gains continued after the end of training, with greater gains in MCI participants.
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Affiliation(s)
| | - Sara J Czaja
- Weil Cornell School of Medicine (SJC), New York, NY; i-Function, Inc. (SJC, PK, PDH) Miami, FL
| | | | | | | | - Philip D Harvey
- University of Miami Miller School of Medicine (CDE, PDH), Miami, FL; i-Function, Inc. (SJC, PK, PDH) Miami, FL.
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Kalam KT, Rahman JM, Islam MR, Dewan SMR. ChatGPT and mental health: Friends or foes? Health Sci Rep 2024; 7:e1912. [PMID: 38361805 PMCID: PMC10867692 DOI: 10.1002/hsr2.1912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/30/2023] [Accepted: 01/31/2024] [Indexed: 02/17/2024] Open
Abstract
Background ChatGPT is an artificial intelligence (AI) language model that has gained popularity as a virtual assistant because of its exceptional capacity to solve problems and make decisions. However, there are some ways in which technological misuse and incorrect interpretations can have potentially hazardous consequences for a user's mental health. Discussion Because it lacks real-time fact-checking capabilities, ChatGPT may create misleading or erroneous information. Considering AI technology has the potential to influence a person's thinking, we anticipate ChatGPT's future repercussions on mental health by considering instances in which inappropriate usage may lead to mental disorders. While several studies have demonstrated how the AI model may transform mental health care and therapy, certain drawbacks, including bias and privacy violations, have also been identified. Conclusion Educating people and organizing workshops on AI technology usage, strengthening privacy measures, and updating ethical standards are crucial initiatives to prevent misuse and resultant dire impacts on mental health. Longitudinal research on the potential of these platforms to impact a variety of mental health problems is recommended in the future.
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Affiliation(s)
| | - Jannatul Mabia Rahman
- Department of Electrical and Electronic EngineeringUniversity of Asia PacificDhakaBangladesh
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Widge AS. Closing the loop in psychiatric deep brain stimulation: physiology, psychometrics, and plasticity. Neuropsychopharmacology 2024; 49:138-149. [PMID: 37415081 PMCID: PMC10700701 DOI: 10.1038/s41386-023-01643-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/28/2023] [Accepted: 06/20/2023] [Indexed: 07/08/2023]
Abstract
Deep brain stimulation (DBS) is an invasive approach to precise modulation of psychiatrically relevant circuits. Although it has impressive results in open-label psychiatric trials, DBS has also struggled to scale to and pass through multi-center randomized trials. This contrasts with Parkinson disease, where DBS is an established therapy treating thousands of patients annually. The core difference between these clinical applications is the difficulty of proving target engagement, and of leveraging the wide range of possible settings (parameters) that can be programmed in a given patient's DBS. In Parkinson's, patients' symptoms change rapidly and visibly when the stimulator is tuned to the correct parameters. In psychiatry, those same changes take days to weeks, limiting a clinician's ability to explore parameter space and identify patient-specific optimal settings. I review new approaches to psychiatric target engagement, with an emphasis on major depressive disorder (MDD). Specifically, I argue that better engagement may come by focusing on the root causes of psychiatric illness: dysfunction in specific, measurable cognitive functions and in the connectivity and synchrony of distributed brain circuits. I overview recent progress in both those domains, and how it may relate to other technologies discussed in companion articles in this issue.
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Affiliation(s)
- Alik S Widge
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
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Breitinger S, Gardea-Resendez M, Langholm C, Xiong A, Laivell J, Stoppel C, Harper L, Volety R, Walker A, D'Mello R, Byun AJS, Zandi P, Goes FS, Frye M, Torous J. Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study. J Med Internet Res 2023; 25:e47006. [PMID: 38157233 PMCID: PMC10787337 DOI: 10.2196/47006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 09/04/2023] [Accepted: 11/20/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams. OBJECTIVE This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study. METHODS We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders. RESULTS We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions. CONCLUSIONS Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.
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Affiliation(s)
- Scott Breitinger
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | | | | | - Ashley Xiong
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Joseph Laivell
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Cynthia Stoppel
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Laura Harper
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Rama Volety
- Research Application Solutions Unit, Mayo Clinic, Rochester, MN, United States
| | - Alex Walker
- Johns Hopkins University, Baltimore, MD, United States
| | - Ryan D'Mello
- Beth Israel Deaconess Medical Center, Boston, MA, United States
| | | | - Peter Zandi
- Johns Hopkins University, Baltimore, MD, United States
| | | | - Mark Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - John Torous
- Beth Israel Deaconess Medical Center, Boston, MA, United States
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Grasser LR, Erjo T, Goodwin MS, Naim R, German RE, White J, Cullins L, Tseng WL, Stoddard J, Brotman MA. Can peripheral psychophysiological markers predict response to exposure-based cognitive behavioral therapy in youth with severely impairing irritability? A study protocol. BMC Psychiatry 2023; 23:926. [PMID: 38082431 PMCID: PMC10712194 DOI: 10.1186/s12888-023-05421-4] [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: 10/10/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Irritability, an increased proneness to anger, is a primary reason youth present for psychiatric care. While initial evidence supports the efficacy of exposure-based cognitive behavioral therapy (CBT) for youth with clinically impairing irritability, treatment mechanisms remain unclear. Here, we propose to measure peripheral psychophysiological indicators of arousal-heart rate (HR)/electrodermal activity (EDA)-and regulation-heart rate variability (HRV)-during exposures to anger-inducing stimuli as potential predictors of treatment efficacy. The objective of this study is to evaluate whether in-situ biosensing data provides peripheral physiological indicators of in-session response to exposures. METHODS Blood volume pulse (BVP; from which HR and HRV canl be derived) and EDA will be collected ambulatorily using the Empatica EmbracePlus from 40 youth (all genders; ages 8-17) undergoing six in-person exposure treatment sessions, as part of a multiple-baseline trial of exposure-based CBT for clinically impairing irritability. Clinical ratings of irritability will be conducted at baseline, weekly throughout treatment, and at 3-month and 6-month follow-ups via the Clinical Global Impressions Scale (CGI) and the Affective Reactivity Index (ARI; clinician-, parent-, and child-report). Multilevel modeling will be used to assess within- and between-person changes in physiological arousal and regulation throughout exposure-based CBT and to determine whether individual differences are predictive of treatment response. DISCUSSION This study protocol leverages a wearable biosensor (Empatica) to continuously record HR/HRV (derived from BVP) and EDA during in-person exposure sessions for youth with clinically impairing irritability. Here, the goal is to identify changes in physiological arousal (EDA, HR) and regulation (HRV) over the course of treatment in tandem with changes in clinical symptoms. TRIAL REGISTRATION The participants in this study come from an overarching clinical trial (trial registration numbers: NCT02531893 first registered on 8/25/2015; last updated on 8/25/2023). The research project and all related materials were submitted and approved by the appropriate Institutional Review Board of the National Institute of Mental Health (NIMH).
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Affiliation(s)
- Lana Ruvolo Grasser
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Trinity Erjo
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Matthew S Goodwin
- Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA
| | - Reut Naim
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Ramaris E German
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Jamell White
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Lisa Cullins
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Wan-Ling Tseng
- Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Joel Stoddard
- Department of Psychiatry and Biomedical Informatics, University of Colorado, School of Medicine, Aurora, CO, USA
| | - Melissa A Brotman
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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Romanowicz M, Croarkin KS, Elmaghraby R, Skime M, Croarkin PE, Vande Voort JL, Shekunov J, Athreya AP. Machine Learning Identifies Smartwatch-Based Physiological Biomarker for Predicting Disruptive Behavior in Children: A Feasibility Study. J Child Adolesc Psychopharmacol 2023; 33:387-392. [PMID: 37966360 PMCID: PMC10698791 DOI: 10.1089/cap.2023.0038] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Objective: Parents frequently purchase and inquire about smartwatch devices to monitor child behaviors and functioning. This pilot study examined the feasibility and accuracy of using smartwatch monitoring for the prediction of disruptive behaviors. Methods: The study enrolled children (N = 10) aged 7-10 years hospitalized for the treatment of disruptive behaviors. The study team completed continuous behavioral phenotyping during study participation. The machine learning protocol examined severe behavioral outbursts (operationalized as episodes that preceded physical restraint) for preparing the training data. Supervised machine learning methods were trained with cross-validation to predict three behavior states-calm, playful, and disruptive. Results: The participants had a 90% adherence rate for per protocol smartwatch use. Decision trees derived conditional dependencies of heart rate, sleep, and motor activity to predict behavior. A cross-validation demonstrated 80.89% accuracy of predicting the child's behavior state using these conditional dependencies. Conclusion: This study demonstrated the feasibility of 7-day continuous smartwatch monitoring for children with severe disruptive behaviors. A machine learning approach characterized predictive biomarkers of impending disruptive behaviors. Future validation studies will examine smartwatch physiological biomarkers to enhance behavioral interventions, increase parental engagement in treatment, and demonstrate target engagement in clinical trials of pharmacological agents for young children.
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Affiliation(s)
- Magdalena Romanowicz
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
- Mayo Clinic Children's Research Center, Rochester, Minnesota, USA
| | - Kyle S. Croarkin
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Rana Elmaghraby
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA
| | - Michelle Skime
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul E. Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
- Mayo Clinic Children's Research Center, Rochester, Minnesota, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Jennifer L. Vande Voort
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
- Mayo Clinic Children's Research Center, Rochester, Minnesota, USA
| | - Julia Shekunov
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
- Mayo Clinic Children's Research Center, Rochester, Minnesota, USA
| | - Arjun P. Athreya
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
- Mayo Clinic Children's Research Center, Rochester, Minnesota, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
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Medich M, Cannedy SL, Hoffmann LC, Chinchilla MY, Pila JM, Chassman SA, Calderon RA, Young AS. Clinician and Patient Perspectives on the Use of Passive Mobile Monitoring and Self-Tracking for Patients With Serious Mental Illness: User-Centered Approach. JMIR Hum Factors 2023; 10:e46909. [PMID: 37874639 PMCID: PMC10630855 DOI: 10.2196/46909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 08/11/2023] [Accepted: 08/12/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Early intervention in mental health crises can prevent negative outcomes. A promising new direction is remote mental health monitoring using smartphone technology to passively collect data from individuals to rapidly detect the worsening of serious mental illness (SMI). This technology may benefit patients with SMI, but little is known about health IT acceptability among this population or their mental health clinicians. OBJECTIVE We used the Health Information Technology Acceptability Model to analyze the acceptability and usability of passive mobile monitoring and self-tracking among patients with serious mental illness and their mental health clinicians. METHODS Data collection took place between December 2020 and June 2021 in 1 Veterans Administration health care system. Interviews with mental health clinicians (n=16) assessed the acceptability of mobile sensing, its usefulness as a tool to improve clinical assessment and care, and recommendations for program refinements. Focus groups with patients with SMI (n=3 groups) and individual usability tests (n=8) elucidated patient attitudes about engaging in health IT and perceptions of its usefulness as a tool for self-tracking and improving mental health assessments. RESULTS Clinicians discussed the utility of web-based data dashboards to monitor patients with SMI health behaviors and receiving alerts about their worsening health. Potential benefits included improving clinical care, capturing behaviors patients do not self-report, watching trends, and receiving alerts. Clinicians' concerns included increased workloads tied to dashboard data review, lack of experience using health IT in clinical care, and how SMI patients' associated paranoia and financial instability would impact patient uptake. Despite concerns, all mental health clinicians stated that they would recommend it. Almost all patients with SMI were receptive to using smartphone dashboards for self-monitoring and having behavioral change alerts sent to their mental health clinicians. They found the mobile app easy to navigate and dashboards easy to find and understand. Patient concerns centered on privacy and "government tracking," and their phone's battery life and data plans. Despite concerns, most reported that they would use it. CONCLUSIONS Many people with SMI would like to have mobile informatics tools that can support their illness and recovery. Similar to other populations (eg, older adults, people experiencing homelessness) this population presents challenges to adoption and implementation. Health care organizations will need to provide resources to address these and support successful illness management. Clinicians are supportive of technological approaches, with adapting informatics data into their workflow as the primary challenge. Despite clear challenges, technological developments are increasingly designed to be acceptable to patients. The research development-clinical deployment gap must be addressed by health care systems, similar to computerized cognitive training. It will ensure clinicians operate at the top of their skill set and are not overwhelmed by administrative tasks, data summarization, or reviewing data that do not indicate a need for intervention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/39010.
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Affiliation(s)
- Melissa Medich
- Center for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, North Hills, CA, United States
- The Lundquist Institute for Biomedical Research, Torrance, CA, United States
| | - Shay L Cannedy
- Center for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, North Hills, CA, United States
| | - Lauren C Hoffmann
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
| | - Melissa Y Chinchilla
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
| | - Jose M Pila
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
| | - Stephanie A Chassman
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
| | - Ronald A Calderon
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
| | - Alexander S Young
- Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, U.S. Department of Veteran Affairs, Los Angeles, CA, United States
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University California Los Angeles Geffen School of Medicine, Los Angeles, CA, United States
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12
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Schumann G, Andreassen OA, Banaschewski T, Calhoun VD, Clinton N, Desrivieres S, Brandlistuen RE, Feng J, Hese S, Hitchen E, Hoffmann P, Jia T, Jirsa V, Marquand AF, Nees F, Nöthen MM, Novarino G, Polemiti E, Ralser M, Rapp M, Schepanski K, Schikowski T, Slater M, Sommer P, Stahl BC, Thompson PM, Twardziok S, van der Meer D, Walter H, Westlye L. Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience: A Review. JAMA Psychiatry 2023; 80:1066-1074. [PMID: 37610741 DOI: 10.1001/jamapsychiatry.2023.2996] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Importance Climate change, pollution, urbanization, socioeconomic inequality, and psychosocial effects of the COVID-19 pandemic have caused massive changes in environmental conditions that affect brain health during the life span, both on a population level as well as on the level of the individual. How these environmental factors influence the brain, behavior, and mental illness is not well known. Observations A research strategy enabling population neuroscience to contribute to identify brain mechanisms underlying environment-related mental illness by leveraging innovative enrichment tools for data federation, geospatial observation, climate and pollution measures, digital health, and novel data integration techniques is described. This strategy can inform innovative treatments that target causal cognitive and molecular mechanisms of mental illness related to the environment. An example is presented of the environMENTAL Project that is leveraging federated cohort data of over 1.5 million European citizens and patients enriched with deep phenotyping data from large-scale behavioral neuroimaging cohorts to identify brain mechanisms related to environmental adversity underlying symptoms of depression, anxiety, stress, and substance misuse. Conclusions and Relevance This research will lead to the development of objective biomarkers and evidence-based interventions that will significantly improve outcomes of environment-related mental illness.
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Affiliation(s)
- Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, Georgia
| | | | - Sylvane Desrivieres
- Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, United Kingdom
| | | | - Jianfeng Feng
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Soeren Hese
- Institute of Geography, Friedrich Schiller University Jena, Jena, Germany
| | - Esther Hitchen
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Per Hoffmann
- Institute of Human Genetics, University Hospital of Bonn, Bonn, Germany
| | - Tianye Jia
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Viktor Jirsa
- Institut National de la Santé et de la Recherche Médicale (Inserm), Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | | | - Frauke Nees
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University Hospital of Bonn, Bonn, Germany
| | - Gaia Novarino
- Institute of Science and Technology, Klosterneuburg, Austria
| | - Elli Polemiti
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Rapp
- Department for Social and Preventive Medicine, University of Potsdam, Potsdam, Germany
| | | | - Tamara Schikowski
- NAKO, Leibniz Institute for Environmental Medicine, Duesseldorf, Germany
| | - Mel Slater
- Campus de Mundet, ICREA-University of Barcelona, Barcelona, Spain
- Department of Computer Science, University College London, London, United Kingdom
| | | | - Bernd Carsten Stahl
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Los Angeles, California
| | - Sven Twardziok
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Henrik Walter
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lars Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
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13
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Nghiem J, Adler DA, Estrin D, Livesey C, Choudhury T. Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study. JMIR Form Res 2023; 7:e47380. [PMID: 37561561 PMCID: PMC10450536 DOI: 10.2196/47380] [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: 03/28/2023] [Revised: 06/20/2023] [Accepted: 07/06/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used. OBJECTIVE We conducted a qualitative study to understand mental health clinicians' perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants' current experiences with and visions for using passive PGHD. METHODS Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes. RESULTS Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven-we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data-participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients' mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action-participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy-participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use. CONCLUSIONS Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data-sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians.
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Affiliation(s)
- Jodie Nghiem
- Medical College, Weill Cornell Medicine, New York, NY, United States
| | - Daniel A Adler
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
| | - Deborah Estrin
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
| | - Cecilia Livesey
- Optum Labs, UnitedHealth Group, Minnetonka, MN, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Tanzeem Choudhury
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
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14
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Smith KA, Blease C, Faurholt-Jepsen M, Firth J, Van Daele T, Moreno C, Carlbring P, Ebner-Priemer UW, Koutsouleris N, Riper H, Mouchabac S, Torous J, Cipriani A. Digital mental health: challenges and next steps. BMJ MENTAL HEALTH 2023; 26:e300670. [PMID: 37197797 PMCID: PMC10231442 DOI: 10.1136/bmjment-2023-300670] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/28/2023] [Indexed: 05/19/2023]
Abstract
Digital innovations in mental health offer great potential, but present unique challenges. Using a consensus development panel approach, an expert, international, cross-disciplinary panel met to provide a framework to conceptualise digital mental health innovations, research into mechanisms and effectiveness and approaches for clinical implementation. Key questions and outputs from the group were agreed by consensus, and are presented and discussed in the text and supported by case examples in an accompanying appendix. A number of key themes emerged. (1) Digital approaches may work best across traditional diagnostic systems: we do not have effective ontologies of mental illness and transdiagnostic/symptom-based approaches may be more fruitful. (2) Approaches in clinical implementation of digital tools/interventions need to be creative and require organisational change: not only do clinicians and patients need training and education to be more confident and skilled in using digital technologies to support shared care decision-making, but traditional roles need to be extended, with clinicians working alongside digital navigators and non-clinicians who are delivering protocolised treatments. (3) Designing appropriate studies to measure the effectiveness of implementation is also key: including digital data raises unique ethical issues, and measurement of potential harms is only just beginning. (4) Accessibility and codesign are needed to ensure innovations are long lasting. (5) Standardised guidelines for reporting would ensure effective synthesis of the evidence to inform clinical implementation. COVID-19 and the transition to virtual consultations have shown us the potential for digital innovations to improve access and quality of care in mental health: now is the ideal time to act.
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Affiliation(s)
- Katharine A Smith
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
| | - Charlotte Blease
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Participatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Joseph Firth
- Division of Psychology and Mental Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Manchester Academic Health Science Centre, Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Tom Van Daele
- Expertise Unit Psychology, Technology and Society, Thomas More University of Applied Sciences, Mechelen, Belgium
| | - Carmen Moreno
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, Universidad Complutense de Madrid Facultad de Medicina, Madrid, Spain
| | - Per Carlbring
- Department of Psychology, Stockholm University, Stockholm, Sweden
| | - Ulrich W Ebner-Priemer
- Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
- mHealth Methods in Psychiatry, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, München, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Max-Planck Institute of Psychiatry, Munich, Germany
| | - Heleen Riper
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Duivendrecht, Netherlands
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Stephane Mouchabac
- Department of Psychiatry, Hôpital Saint-Antoine, Sorbonne Université, Paris, France
- Infrastructure for Clinical Research in Neurosciences (iCRIN), Brain Institute (ICM), INSERM, CNRS, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Paris, France
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
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15
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Zhang DW. Perspectives on heterogeneity-informed cognitive training for attention-deficit/hyperactivity disorder. Front Psychiatry 2023; 13:1100008. [PMID: 36713921 PMCID: PMC9878183 DOI: 10.3389/fpsyt.2022.1100008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/30/2022] [Indexed: 01/13/2023] Open
Abstract
Attention-deficit/hyperactivity disorder (AD/HD) is a heterogeneous neurodevelopmental condition, posing a severe threat to quality of life. Pharmacological therapies are the front-line treatment; however, their shortages encourage the development of alternative treatments for AD/HD. One promising method of developing alternative treatments is cognitive training (CT). A CT-based therapy was recently approved by the US Food and Drug Administration. However, due to heterogeneity in AD/HD, a CT protocol is unlikely to provide a one-size-fits-all solution for all patients with AD/HD. Therefore, this article highlights key aspects that need to be considered to further develop CT protocols for AD/HD, regarding training content, timing, suitability, and delivery mode. The perspectives presented here contribute to optimizing CT as an alternative option for treating AD/HD.
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Affiliation(s)
- Da-Wei Zhang
- Department of Psychology and Center for Place-Based Education, Yangzhou University, Yangzhou, China
- Department of Psychology, Monash University Malaysia, Bandar Sunway, Malaysia
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