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Bilal AM, Pagoni K, Iliadis SI, Papadopoulos FC, Skalkidou A, Öster C. Exploring User Experiences of the Mom2B mHealth Research App During the Perinatal Period: Qualitative Study. JMIR Form Res 2024; 8:e53508. [PMID: 39115893 DOI: 10.2196/53508] [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: 10/11/2023] [Revised: 02/27/2024] [Accepted: 05/26/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND Perinatal depression affects a significant number of women during pregnancy and after birth, and early identification is imperative for timely interventions and improved prognosis. Mobile apps offer the potential to overcome barriers to health care provision and facilitate clinical research. However, little is known about users' perceptions and acceptability of these apps, particularly digital phenotyping and ecological momentary assessment apps, a relatively novel category of apps and approach to data collection. Understanding user's concerns and the challenges they experience using the app will facilitate adoption and continued engagement. OBJECTIVE This qualitative study explores the experiences and attitudes of users of the Mom2B mobile health (mHealth) research app (Uppsala University) during the perinatal period. In particular, we aimed to determine the acceptability of the app and any concerns about providing data through a mobile app. METHODS Semistructured focus group interviews were conducted digitally in Swedish with 13 groups and a total of 41 participants. Participants had been active users of the Mom2B app for at least 6 weeks and included pregnant and postpartum women, both with and without depression symptomatology apparent in their last screening test. Interviews were recorded, transcribed verbatim, translated to English, and evaluated using inductive thematic analysis. RESULTS Four themes were elicited: acceptability of sharing data, motivators and incentives, barriers to task completion, and user experience. Participants also gave suggestions for the improvement of features and user experience. CONCLUSIONS The study findings suggest that app-based digital phenotyping is a feasible and acceptable method of conducting research and health care delivery among perinatal women. The Mom2B app was perceived as an efficient and practical tool that facilitates engagement in research as well as allows users to monitor their well-being and receive general and personalized information related to the perinatal period. However, this study also highlights the importance of trustworthiness, accessibility, and prompt technical issue resolution in the development of future research apps in cooperation with end users. The study contributes to the growing body of literature on the usability and acceptability of mobile apps for research and ecological momentary assessment and underscores the need for continued research in this area.
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Affiliation(s)
- Ayesha-Mae Bilal
- Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
- Centre for Women's Mental Health During the Reproductive Lifespan (WOMHER), Uppsala University, Uppsala, Sweden
| | - Konstantina Pagoni
- Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Stavros I Iliadis
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | | | - Alkistis Skalkidou
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Caisa Öster
- Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
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Varidel M, Hickie IB, Prodan A, Skinner A, Marchant R, Cripps S, Oliveria R, Chong MK, Scott E, Scott J, Iorfino F. Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care. NPJ MENTAL HEALTH RESEARCH 2024; 3:26. [PMID: 38849429 PMCID: PMC11161660 DOI: 10.1038/s44184-024-00071-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 04/25/2024] [Indexed: 06/09/2024]
Abstract
There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population (N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual's level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care.
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Affiliation(s)
- Mathew Varidel
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia.
| | - Ian B Hickie
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Ante Prodan
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Translational Health Research Institute, Western Sydney University, Sydney, NSW, Australia
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia
| | - Adam Skinner
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Roman Marchant
- Human Technology Institute, University of Technology, Sydney, NSW, Australia
- School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, NSW, Australia
| | - Sally Cripps
- Human Technology Institute, University of Technology, Sydney, NSW, Australia
- School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, NSW, Australia
| | | | - Min K Chong
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Elizabeth Scott
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Jan Scott
- Academic Psychiatry, Institute of Neuroscience, Newcastle University, Newcastle, UK
| | - Frank Iorfino
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
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Yang Y(S, Law M, Vaghri Z. New Brunswick's mental health action plan: A quantitative exploration of program efficacy in children and youth using the Canadian Community Health Survey. PLoS One 2024; 19:e0301008. [PMID: 38848408 PMCID: PMC11161078 DOI: 10.1371/journal.pone.0301008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/09/2024] [Indexed: 06/09/2024] Open
Abstract
In 2011, the New Brunswick government released the New Brunswick Mental Health Action Plan 2011-2018 (Action Plan). Following the release of the Action Plan in 2011, two progress reports were released in 2013 and 2015, highlighting the implementation status of the Action Plan. While vague in their language, these reports indicated considerable progress in implementing the Action Plan, as various initiatives were undertaken to raise awareness and provide additional resources to facilitate early prevention and intervention in children and youth. However, whether these initiatives have yielded measurable improvements in population-level mental health outcomes in children and youth remains unclear. The current study explored the impact of the Action Plan by visualizing the trend in psychosocial outcomes and service utilization of vulnerable populations in New Brunswick before and after the implementation of the Action Plan using multiple datasets from the Canadian Community Health Survey. Survey-weighted ordinary least square regression analyses were performed to investigate measurable improvements in available mental health outcomes. The result revealed a declining trend in the mental wellness of vulnerable youth despite them consistently reporting higher frequencies of mental health service use. This study highlights the need for a concerted effort in providing effective mental health services to New Brunswick youth and, more broadly, Canadian youth, as well as ensuring rigorous routine outcome monitoring and evaluation plans are consistently implemented for future mental health strategies at the time of their initiation.
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Affiliation(s)
- Yuzhi (Stanford) Yang
- Department of Psychology, Faculty of Science, Applied Science, and Engineering, University of New Brunswick, Saint John, New Brunswick, Canada
| | - Moira Law
- Department of Psychology, Faculty of Science, St. Mary’s University, Halifax, Nova Scotia, Canada
| | - Ziba Vaghri
- Global Child Program, Integrated Health Initiative, Faculty of Business, University of New Brunswick, Saint John, New Brunswick, Canada
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Noori S, Khasnavis S, DeCroce-Movson E, Blay-Tofey M, Vitiello E. A Curriculum on Digital Psychiatry for a US-Based Psychiatry Residency Training Program: Pilot Implementation Study. JMIR Form Res 2024; 8:e41573. [PMID: 38739423 PMCID: PMC11130773 DOI: 10.2196/41573] [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: 07/31/2022] [Revised: 07/01/2023] [Accepted: 10/12/2023] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Digital psychiatry, defined as the application of health technologies to the prevention, assessment, and treatment of mental health illnesses, is a growing field. Interest in the clinical use of these technologies continues to grow. However, psychiatric trainees receive limited or no formal education on the topic. OBJECTIVE This study aims to pilot a curriculum on digital psychiatry for a US-based psychiatry residency training program and examine the change in learner confidence regarding appraisal and clinical recommendation of digital mental health apps. METHODS Two 60-minute sessions were presented through a web-based platform to postgraduate year 2-4 residents training in psychiatry at a US-based adult psychiatry residency program. Learner confidence was assessed using pre- and postsession surveys. RESULTS Matched pre- and postsession quizzes showed improved confidence in multiple domains aligning with the course objectives. This included the structured appraisal of digital mental health apps (P=.03), assessment of a patient's digital health literacy (P=.01), formal recommendation of digital health tools (P=.03), and prescription of digital therapeutics to patients (P=.03). Though an improvement from baseline, mean ratings for confidence did not exceed "somewhat comfortable" on any of the above measures. CONCLUSIONS Our study shows the feasibility of implementing a digital psychiatry curriculum for residents in multiple levels of training. We also identified an opportunity to increase learner confidence in the appraisal and clinical use of digital mental health apps through the use of a formal curriculum.
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Affiliation(s)
- Sofia Noori
- Yale Department of Psychiatry, New Haven, CT, United States
| | | | - Eliza DeCroce-Movson
- Residency Training Program, Yale Department of Psychiatry, New York-Presbyterian Child and Adolescent Psychiatry, New York, NY, United States
| | - Morkeh Blay-Tofey
- Department of Psychiatry, Baylor Scott & White Health, Temple, TX, United States
| | - Evan Vitiello
- Department of Psychiatry, University of North Carolina School of Medicine, Chapel Hill, NC, United States
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Zhang X, Lewis S, Chen X, Zhou J, Wang X, Bucci S. Acceptability and experience of a smartphone symptom monitoring app for people with psychosis in China (YouXin): a qualitative study. BMC Psychiatry 2024; 24:268. [PMID: 38594713 PMCID: PMC11003104 DOI: 10.1186/s12888-024-05687-2] [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: 09/20/2023] [Accepted: 03/14/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Access to high-quality mental healthcare remains challenging for people with psychosis globally, including China. Smartphone-based symptom monitoring has the potential to support scalable mental healthcare. However, no such tool, until now, has been developed and evaluated for people with psychosis in China. This study investigated the acceptability and the experience of using a symptom self-monitoring smartphone app (YouXin) specifically developed for people with psychosis in China. METHODS Semi-structured interviews were conducted with 10 participants with psychosis to explore the acceptability of YouXin. Participants were recruited from the non-randomised feasibility study that tested the validity, feasibility, acceptability and safety of the YouXin app. Data analysis was guided by the theoretical framework of acceptability. RESULTS Most participants felt the app was acceptable and easy to use, and no unbearable burdens or opportunity costs were reported. Participants found completing the self-monitoring app rewarding and experienced a sense of achievement. Privacy and data security were not major concerns for participants, largely due to trust in their treating hospital around data protection. Participants found the app easy to use and attributed this to the training provided at the beginning of the study. A few participants said they had built some form of relationship with the app and would miss the app when the study finished. CONCLUSIONS The YouXin app is acceptable for symptom self-monitoring in people with experience of psychosis in China. Participants gained greater insights about their symptoms by using the YouXin app. As we only collected retrospective acceptability in this study, future studies are warranted to assess hypothetical acceptability before the commencement of study to provide a more comprehensive understanding of implementation.
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Affiliation(s)
- Xiaolong Zhang
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Shôn Lewis
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Xu Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jiaojiao Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xingyu Wang
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK.
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Wright AC, Palmer-Cooper E, Cella M, McGuire N, Montagnese M, Dlugunovych V, Liu CWJ, Wykes T, Cather C. Experiencing hallucinations in daily life: The role of metacognition. Schizophr Res 2024; 265:74-82. [PMID: 36623979 DOI: 10.1016/j.schres.2022.12.023] [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: 08/28/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND Hallucinations have been linked to failures in metacognitive reflection suggesting an association between hallucinations and overestimation of performance, although the cross-sectional findings are inconsistent. This inconsistency may relate to the fluctuating hallucinatory experiences that are not captured in cross-sectional studies. Ecological Momentary Assessment (EMA) captures in-the-moment experiences over time so can identify causal relationships between variables such as the associations between metacognition and hallucinatory experience in daily life and overcome problems in cross-sectional designs. METHODS Participants (N = 41) experiencing daily hallucinations completed baseline questionnaires and smartphone surveys 7 times per day for 14 days. They were prompted to identify a task they would complete in the next 4 h and to make metacognitive predictions around the likelihood of completing the task, the difficulty of the task, and how well they would complete it (standard of completion). RESULTS 76 % finished the 14-days of assessment with an average of 42.2 % survey completion. Less accurate metacognition was associated with more hallucinations, but less accurate likelihood and standard of completion was associated with fewer hallucinations. Using a cross-lagged analysis, metacognitive predictions around the likelihood of completion (p < .001) and standard of completion (p = .01) predicted hallucination intensity at the following timepoint, and metacognitive predictions regarding likelihood of completion (p = .02) predicted hallucination control at the following timepoint. DISCUSSION Interventions that aim to improve metacognitive ability in-the-moment may serve to reduce the intensity and increase the control of hallucinations.
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Affiliation(s)
- Abigail C Wright
- Center of Excellence for Psychosocial and Systemic Research, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Emma Palmer-Cooper
- Centre for Innovation in Mental Health, School of Psychology, University of Southampton, UK
| | - Matteo Cella
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London & Maudsley NHS Foundation Trust, Maudsley Hospital, London, UK
| | - Nicola McGuire
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Marcella Montagnese
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Chih-Wei Joshua Liu
- Physics of Living Systems Group, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London & Maudsley NHS Foundation Trust, Maudsley Hospital, London, UK
| | - Corinne Cather
- Center of Excellence for Psychosocial and Systemic Research, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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Hurley ME, Sonig A, Herrington J, Storch EA, Lázaro-Muñoz G, Blumenthal-Barby J, Kostick-Quenet K. Ethical considerations for integrating multimodal computer perception and neurotechnology. Front Hum Neurosci 2024; 18:1332451. [PMID: 38435745 PMCID: PMC10904467 DOI: 10.3389/fnhum.2024.1332451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Background Artificial intelligence (AI)-based computer perception technologies (e.g., digital phenotyping and affective computing) promise to transform clinical approaches to personalized care in psychiatry and beyond by offering more objective measures of emotional states and behavior, enabling precision treatment, diagnosis, and symptom monitoring. At the same time, passive and continuous nature by which they often collect data from patients in non-clinical settings raises ethical issues related to privacy and self-determination. Little is known about how such concerns may be exacerbated by the integration of neural data, as parallel advances in computer perception, AI, and neurotechnology enable new insights into subjective states. Here, we present findings from a multi-site NCATS-funded study of ethical considerations for translating computer perception into clinical care and contextualize them within the neuroethics and neurorights literatures. Methods We conducted qualitative interviews with patients (n = 20), caregivers (n = 20), clinicians (n = 12), developers (n = 12), and clinician developers (n = 2) regarding their perspective toward using PC in clinical care. Transcripts were analyzed in MAXQDA using Thematic Content Analysis. Results Stakeholder groups voiced concerns related to (1) perceived invasiveness of passive and continuous data collection in private settings; (2) data protection and security and the potential for negative downstream/future impacts on patients of unintended disclosure; and (3) ethical issues related to patients' limited versus hyper awareness of passive and continuous data collection and monitoring. Clinicians and developers highlighted that these concerns may be exacerbated by the integration of neural data with other computer perception data. Discussion Our findings suggest that the integration of neurotechnologies with existing computer perception technologies raises novel concerns around dignity-related and other harms (e.g., stigma, discrimination) that stem from data security threats and the growing potential for reidentification of sensitive data. Further, our findings suggest that patients' awareness and preoccupation with feeling monitored via computer sensors ranges from hypo- to hyper-awareness, with either extreme accompanied by ethical concerns (consent vs. anxiety and preoccupation). These results highlight the need for systematic research into how best to implement these technologies into clinical care in ways that reduce disruption, maximize patient benefits, and mitigate long-term risks associated with the passive collection of sensitive emotional, behavioral and neural data.
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Affiliation(s)
- Meghan E. Hurley
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Anika Sonig
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - John Herrington
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Eric A. Storch
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Gabriel Lázaro-Muñoz
- Center for Bioethics, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry and Behavioral Sciences, Massachusetts General Hospital, Boston, MA, United States
| | | | - Kristin Kostick-Quenet
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
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Shen FX, Baum ML, Martinez-Martin N, Miner AS, Abraham M, Brownstein CA, Cortez N, Evans BJ, Germine LT, Glahn DC, Grady C, Holm IA, Hurley EA, Kimble S, Lázaro-Muñoz G, Leary K, Marks M, Monette PJ, Jukka-Pekka O, O’Rourke PP, Rauch SL, Shachar C, Sen S, Vahia I, Vassy JL, Baker JT, Bierer BE, Silverman BC. Returning Individual Research Results from Digital Phenotyping in Psychiatry. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2024; 24:69-90. [PMID: 37155651 PMCID: PMC10630534 DOI: 10.1080/15265161.2023.2180109] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants' locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant's real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas.
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Affiliation(s)
- Francis X. Shen
- Harvard Medical School
- Massachusetts General Hospital
- Harvard Law School
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Mason Marks
- Harvard Law School
- Florida State University College of Law
- Yale Law School
| | | | | | | | - Scott L. Rauch
- Harvard Medical School
- McLean Hospital
- Mass General Brigham
| | | | | | | | - Jason L. Vassy
- Harvard Medical School
- Brigham and Women’s Hospital
- VA Boston Healthcare System
| | | | - Barbara E. Bierer
- Harvard Medical School
- Brigham and Women’s Hospital
- Multi-Regional Clinical Trials Center of Brigham and Women’s Hospital and Harvard
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Zhang X, Lewis S, Carter LA, Chen X, Zhou J, Wang X, Bucci S. Evaluating a smartphone-based symptom self-monitoring app for psychosis in China (YouXin): A non-randomised validity and feasibility study with a mixed-methods design. Digit Health 2024; 10:20552076231222097. [PMID: 38188856 PMCID: PMC10768587 DOI: 10.1177/20552076231222097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/05/2023] [Indexed: 01/09/2024] Open
Abstract
Background Psychosis causes a significant burden globally, including in China, where limited mental health resources hinder access to care. Smartphone-based remote monitoring offers a promising solution. This study aimed to assess the validity, feasibility, acceptability, and safety of a symptom self-monitoring smartphone app, YouXin, for people with psychosis in China. Methods A pre-registered non-randomised validity and feasibility study with a mixed-methods design. Participants with psychosis were recruited from a major tertiary psychiatric hospital in Beijing, China. Participants utilised the YouXin app to self-monitor psychosis and mood symptoms for four weeks. Feasibility outcomes were recruitment, retention and outcome measures completeness. Active symptom monitoring (ASM) validity was tested against corresponding clinical assessments (PANSS and CDS) using Spearman correlation. Ten participants completed qualitative interviews at study end to explore acceptability of the app and trial procedures. Results Feasibility parameters were met. The target recruitment sample of 40 participants was met, with 82.5% completing outcome measures, 60% achieving acceptable ASM engagement (completing >33% of all prompts), and 33% recording sufficient passive monitoring data to extract mobility indicators. Five ASM domains (hallucinations, suspiciousness, guilt feelings, delusions, grandiosity) achieved moderate correlation with clinical assessment. Both quantitative and qualitative evaluation showed high acceptability of YouXin. Clinical measurements indicated no symptom and functional deterioration. No adverse events were reported, suggesting YouXin is safe to use in this clinical population. Conclusions The trial feasibility, acceptability and safety parameters were met and a powered efficacy study is indicated. However, refinements are needed to improve ASM validity and increase passive monitoring data completeness.
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Affiliation(s)
- Xiaolong Zhang
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Shôn Lewis
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Lesley-Anne Carter
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Xu Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jiaojiao Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xingyu Wang
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
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10
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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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Loftness BC, Halvorson-Phelan J, OLeary A, Bradshaw C, Prytherch S, Berman I, Torous J, Copeland WL, Cheney N, McGinnis RS, McGinnis EW. The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health. IEEE J Biomed Health Inform 2023; PP:10.1109/JBHI.2023.3337649. [PMID: 38019617 PMCID: PMC11133764 DOI: 10.1109/jbhi.2023.3337649] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.
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Loftness BC, Halvorson-Phelan J, O'Leary A, Bradshaw C, Prytherch S, Berman I, Torous J, Copeland WL, Cheney N, McGinnis RS, McGinnis EW. The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.19.23284753. [PMID: 38076802 PMCID: PMC10705626 DOI: 10.1101/2023.01.19.23284753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.
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Affiliation(s)
- Bryn C Loftness
- University of Vermont's Complex Systems Center and M-Sense Research Group
| | | | | | - Carter Bradshaw
- University of Vermont Medical Center Department of Psychiatry
| | | | - Isabel Berman
- University of Vermont Medical Center Department of Psychiatry
| | - John Torous
- Digital Psychiatry Division for Beth Israel Deaconess Medical Center at Harvard Medical School
| | | | - Nick Cheney
- University of Vermont Complex Systems Center
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Eisner E, Berry N, Bucci S. Digital tools to support mental health: a survey study in psychosis. BMC Psychiatry 2023; 23:726. [PMID: 37803367 PMCID: PMC10559432 DOI: 10.1186/s12888-023-05114-y] [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: 03/07/2023] [Accepted: 08/16/2023] [Indexed: 10/08/2023] Open
Abstract
BACKGROUND There is a notable a gap between promising research findings and implementation of digital health tools. Understanding and addressing barriers to use is key to widespread implementation. METHODS A survey was administered to a self-selecting sample in-person (n = 157) or online (n = 58), with questions examining: i) ownership and usage rates of digital devices among people with psychosis; ii) interest in using technology to engage with mental health services; and iii) facilitators of and barriers to using digital tools in a mental healthcare context. RESULTS Device ownership: Virtually all participants owned a mobile phone (95%) or smartphone (90%), with Android phones slightly more prevalent than iPhones. Only a minority owned a fitness tracker (15%) or smartwatch (13%). Device ownership was significantly lower in unemployed people and those without secondary education. Device cost and paranoid ideation were barriers to ownership. Technology and mental health services: Most participants (88%) said they would willingly try a mental health app. Symptom monitoring apps were most popular, then appointment reminders and medication reminders. Half the sample would prefer an app alongside face-to-face support; the other half preferred remote support or no other mental health support. Facilitators: Participants thought using a mental health app could increase their understanding of psychosis generally, and of their own symptoms. They valued the flexibility of digital tools in enabling access to support anywhere, anytime. Barriers: Prominent barriers to using mental health apps were forgetting, lack of motivation, security concerns, and concerns it would replace face-to-face care. Overall participants reported no substantial effects of technology on their mental health, although a quarter said using a phone worsened paranoid ideation. A third used technology more when psychotic symptoms were higher, whereas a third used it less. Around half used technology more when experiencing low mood. CONCLUSIONS Our findings suggest rapidly increasing device ownership among people with psychosis, mirroring patterns in the general population. Smartphones appear appropriate for delivering internet-enabled support for psychosis. However, for a sub-group of people with psychosis, the sometimes complex interaction between technology and mental health may act as a barrier to engagement, alongside more prosaic factors such as forgetting.
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Affiliation(s)
- Emily Eisner
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, 2nd Floor Zochonis Building, Brunswick Street, Manchester, M13 9PL, UK
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Natalie Berry
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, 2nd Floor Zochonis Building, Brunswick Street, Manchester, M13 9PL, UK
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, 2nd Floor Zochonis Building, Brunswick Street, Manchester, M13 9PL, UK.
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK.
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Calder S, Andreotta M, Morris T, Atee M. Improving quality in pastoral care using the Pastoral Care Activity Tracker (PCAT): A feasibility study of a digital tool within an Australian healthcare organization. J Health Care Chaplain 2023; 29:353-367. [PMID: 35820050 DOI: 10.1080/08854726.2022.2091837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
BACKGROUND Activity reporting of Pastoral Care Coordinators (PCCs) is often inadequate within care settings because of suboptimal analog data collection methods. This study aims to render pastoral care activity reporting more efficient through digitizing data collection in pastoral care settings. METHODS A one-year feasibility (pilot) study of a digital tool, the "Pastoral Care Activity Tracker" (PCAT) was conducted between June 1, 2020 and May 31, 2021 at HammondCare, an Australian nonprofit healthcare organization. Feasibility was measured using electronic activity logs collected by the tool and user feedback surveys by PCCs. RESULTS Of the 43 PCCs working in the organization, 42 (97.7%) used the PCAT tool to complete the logging of 66,298 pastoral care activities (M [SD] = 1,578.5 [827.8] activities per PCC). Most activities were logged successfully (98.3%) and took less than one minute (89.5%). Survey responses (n = 20, 46.6%) indicated many PCCs found the PCAT more convenient (n = 15, 75.0%) and easier to use (n = 10, 50.0%) than paper-based method. CONCLUSIONS PCCs found the PCAT to be feasible, favorable, and easier to use for report generation compared to paper-based methods. The feasibility of the PCAT improved pastoral care activity data capture, as perceived by PCCs.
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Affiliation(s)
| | | | - Thomas Morris
- The Dementia Centre, HammondCare, St. Leonards, NSW, Australia
| | - Mustafa Atee
- The Dementia Centre, HammondCare, Osborne Park, WA, Australia
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Zhang X, Lewis S, Carter LA, Bucci S. A Digital System (YouXin) to Facilitate Self-Management by People With Psychosis in China: Protocol for a Nonrandomized Validity and Feasibility Study With a Mixed Methods Design. JMIR Res Protoc 2023; 12:e45170. [PMID: 37698905 PMCID: PMC10523209 DOI: 10.2196/45170] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Psychosis is one of the most disabling mental health conditions and causes significant personal, social, and economic burden. Accurate and timely symptom monitoring is critical to offering prompt and time-sensitive clinical services. Digital health is a promising solution for the barriers encountered by conventional symptom monitoring approaches, including accessibility, the ecological validity of assessments, and recall bias. However, to date, there has been no digital health technology developed to support self-management for people with psychosis in China. OBJECTIVE We report the study protocol to evaluate the validity, feasibility, acceptability, usability, and safety of a symptom self-monitoring smartphone app (YouXin; Chinese name ) for people with psychosis in China. METHODS This is a nonrandomized validity and feasibility study with a mixed methods design. The study was approved by the University of Manchester and Beijing Anding Hospital Research Ethics Committee. YouXin is a smartphone app designed to facilitate symptom self-monitoring for people with psychosis. YouXin has 2 core functions: active monitoring of symptoms (ie, smartphone survey) and passive monitoring of behavioral activity (ie, passive data collection via embedded smartphone sensors). The development process of YouXin utilized a systematic coproduction approach. A series of coproduction consultation meetings was conducted by the principal researcher with service users and clinicians to maximize the usability and acceptability of the app for end users. Participants with psychosis aged 16 years to 65 years were recruited from Beijing Anding Hospital, Beijing, China. All participants were invited to use the YouXin app to self-monitor symptoms for 4 weeks. At the end of the 4-week follow-up, we invited participants to take part in a qualitative interview to explore the acceptability of the app and trial procedures postintervention. RESULTS Recruitment to the study was initiated in August 2022. Of the 47 participants who were approached for the study from August 2022 to October 2022, 41 participants agreed to take part in the study. We excluded 1 of the 41 participants for not meeting the inclusion criteria, leaving a total of 40 participants who began the study. As of December 2022, 40 participants had completed the study, and the recruitment was complete. CONCLUSIONS This study is the first to develop and test a symptom self-monitoring app specifically designed for people with psychosis in China. If the study shows the feasibility of YouXin, a potential future direction is to integrate the app into clinical workflows to facilitate digital mental health care for people with psychosis in China. This study will inform improvements to the app, trial procedures, and implementation strategies with this population. Moreover, the findings of this trial could lead to optimization of digital health technologies designed for people with psychosis in China. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/45170.
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Affiliation(s)
- Xiaolong Zhang
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Shôn Lewis
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Lesley-Anne Carter
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
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Sun J, Dong QX, Wang SW, Zheng YB, Liu XX, Lu TS, Yuan K, Shi J, Hu B, Lu L, Han Y. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatr 2023; 87:103705. [PMID: 37506575 DOI: 10.1016/j.ajp.2023.103705] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.
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Affiliation(s)
- Jie Sun
- Pain Medicine Center, Peking University Third Hospital, Beijing 100191, China; Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Qun-Xi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - San-Wang Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yong-Bo Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Xiao-Xing Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Tang-Sheng Lu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Kai Yuan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Ying Han
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China.
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Lee SH, Hwang HH, Kim S, Hwang J, Park J, Park S. Clinical Implication of Maumgyeol Basic Service-the 2 Channel Electroencephalography and a Photoplethysmogram-based Mental Health Evaluation Software. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2023; 21:583-593. [PMID: 37424425 PMCID: PMC10335898 DOI: 10.9758/cpn.23.1062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/24/2023] [Accepted: 03/29/2023] [Indexed: 07/11/2023]
Abstract
Objective Maumgyeol Basic service is a mental health evaluation and grade scoring software using the 2 channels EEG and photoplethysmogram (PPG). This service is supposed to assess potential at-risk groups with mental illness more easily, rapidly, and reliably. This study aimed to evaluate the clinical implication of the Maumgyeol Basic service. Methods One hundred one healthy controls and 103 patients with a psychiatric disorder were recruited. Psychological evaluation (Mental Health Screening for Depressive Disorders [MHS-D], Mental Health Screening for Anxiety Disorders [MHS-A], cognitive stress response scale [CSRS], 12-item General Health Questionnaire [GHQ-12], Clinical Global Impression [CGI]) and digit symbol substitution test (DSST) were applied to all participants. Maumgyeol brain health score and Maumgyeol mind health score were calculated from 2 channel frontal EEG and PPG, respectively. Results Participants were divided into three groups: Maumgyeol Risky, Maumgyeol Good, and Maumgyeol Usual. The Maumgyeol mind health scores, but not brain health scores, were significantly lower in the patients group compared to healthy controls. Maumgyeol Risky group showed significantly lower psychological and cognitive ability evaluation scores than Maumgyeol Usual and Good groups. Maumgyel brain health score showed significant correlations with CSRS and DSST. Maumgyeol mind health score showed significant correlations with CGI and DSST. About 20.6% of individuals were classified as the No Insight group, who had mental health problems but were unaware of their illnesses. Conclusion This study suggests that the Maumgyeol Basic service can provide important clinical information about mental health and be used as a meaningful digital mental healthcare monitoring solution to prevent symptom aggravation.
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Affiliation(s)
- Seung-Hwan Lee
- Bwave Inc., Goyang, Korea
- Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
- Clinical Emotion and Cognition Research Laboratory, Department of Psychiatry, Inje University, Goyang, Korea
| | - Hyeon-Ho Hwang
- Clinical Emotion and Cognition Research Laboratory, Department of Psychiatry, Inje University, Goyang, Korea
- Department of Human-Computer Interaction, Hanyang University, Ansan, Korea
| | - Sungkean Kim
- Department of Human-Computer Interaction, Hanyang University, Ansan, Korea
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Weis J, Wolf LR, Boerries M, Kassahn D, Boeker M, Dresch C. Identification of the Needs and Preferences of Patients With Cancer for the Development of a Clinic App: Qualitative Study. JMIR Cancer 2023; 9:e40891. [PMID: 37498653 PMCID: PMC10415940 DOI: 10.2196/40891] [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: 07/11/2022] [Revised: 04/29/2023] [Accepted: 05/09/2023] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND Mobile health (mHealth) tools were developed during the past decades and are increasingly used by patients in cancer care too. Scientific research in the development of mHealth services is required in order to meet the various needs of patients and test usability. OBJECTIVE The aim of this study is to assess patients' needs, preferences, and usability of an app (My University Clinic [MUC] app) developed by the Comprehensive Cancer Center Freiburg (CCCF) Germany. METHODS Based on a qualitative cross-sectional approach, we conducted semistructured interviews with patients with cancer, addressing their needs, preferences, and usability of the designed MUC app. Patients treated by the CCCF were recruited based on a purposive sampling technique focusing on age, sex, cancer diagnoses, and treatment setting (inpatient, outpatient). Data analysis followed the qualitative content analysis according to Kuckartz and was performed using computer-assisted software (MAXQDA). RESULTS For the interviews, 17 patients with cancer were selected, covering a broad range of sampling parameters. The results showed that patients expect benefits in terms of improved information about the disease and communication with the clinic staff. Demands for additional features were identified (eg, a list of contact persons and medication management). The most important concerns referred to data security and the potential restriction of personal contacts with health care professionals of the clinical departments of the CCCF. In addition, some features for improving the design of the MUC app with respect to usability or for inclusion of interacting tools were suggested by the patients. CONCLUSIONS The results of this qualitative study were discussed within the multidisciplinary team and the MUC app providers. Patients' perspectives and needs will be included in further development of the MUC app. There will be a second study phase in which patients will receive a test version of the MUC app and will be asked about their experiences with it. TRIAL REGISTRATION Deutsches Register Klinischer Studien DRKS00022162; https://drks.de/search/de/trial/DRKS00022162.
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Affiliation(s)
- Joachim Weis
- Chair for Self-Help Research, Comprehensive Cancer Center, Medical Faculty, University Clinic Freiburg, Freiburg, Germany
| | - Lucy Raphaela Wolf
- Chair for Self-Help Research, Comprehensive Cancer Center, Medical Faculty, University Clinic Freiburg, Freiburg, Germany
| | - Melanie Boerries
- Institut für Medizinische Bioinformatik und Systemmedizin, Medical Faculty University Freiburg, University Clinic Freiburg, Freiburg, Germany
| | - Daniela Kassahn
- Institut für Medizinische Bioinformatik und Systemmedizin, Medical Faculty University Freiburg, University Clinic Freiburg, Freiburg, Germany
| | - Martin Boeker
- Institute of Artificial Intelligence and Informatics in Medicine, Medical Center rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
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Melcher J, Lavoie J, Hays R, D'Mello R, Rauseo-Ricupero N, Camacho E, Rodriguez-Villa E, Wisniewski H, Lagan S, Vaidyam A, Torous J. Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students. JOURNAL OF AMERICAN COLLEGE HEALTH : J OF ACH 2023; 71:736-748. [PMID: 33769927 DOI: 10.1080/07448481.2021.1905650] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Objective: This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants: Participants were 100 students enrolled in 4-year universities. Methods: Each participant attended a virtual visit to complete a series of gold-standard mental health assessments, and then used a mobile app for 28 days to complete mood assessments and allow for passive collection of GPS, accelerometer, phone call, and screen time data. Students completed another virtual visit at the end of the study to collect a second round of mental health assessments. Results: In-app daily mood assessments were strongly correlated with their corresponding gold standard clinical assessment. Sleep variance among students was correlated to depression scores (ρ = .28) and stress scores (ρ = .27). Conclusions: Digital Phenotyping among college students is feasible on both an individual and a sample level. Studies with larger sample sizes are necessary to understand population trends, but there are practical applications of the data today.
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Affiliation(s)
- Jennifer Melcher
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Joel Lavoie
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Ryan Hays
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Ryan D'Mello
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Natali Rauseo-Ricupero
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Erica Camacho
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Elena Rodriguez-Villa
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Hannah Wisniewski
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Sarah Lagan
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Aditya Vaidyam
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - John Torous
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
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Langholm C, Byun AJS, Mullington J, Torous J. Monitoring sleep using smartphone data in a population of college students. NPJ MENTAL HEALTH RESEARCH 2023; 2:3. [PMID: 38609478 PMCID: PMC10955805 DOI: 10.1038/s44184-023-00023-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/20/2023] [Indexed: 04/14/2024]
Abstract
Sleep is fundamental to all health, especially mental health. Monitoring sleep is thus critical to delivering effective healthcare. However, measuring sleep in a scalable way remains a clinical challenge because wearable sleep-monitoring devices are not affordable or accessible to the majority of the population. However, as consumer devices like smartphones become increasingly powerful and accessible in the United States, monitoring sleep using smartphone patterns offers a feasible and scalable alternative to wearable devices. In this study, we analyze the sleep behavior of 67 college students with elevated levels of stress over 28 days. While using the open-source mindLAMP smartphone app to complete daily and weekly sleep and mental health surveys, these participants also passively collected phone sensor data. We used these passive sensor data streams to estimate sleep duration. These sensor-based sleep duration estimates, when averaged for each participant, were correlated with self-reported sleep duration (r = 0.83). We later constructed a simple predictive model using both sensor-based sleep duration estimates and surveys as predictor variables. This model demonstrated the ability to predict survey-reported Pittsburgh Sleep Quality Index (PSQI) scores within 1 point. Overall, our results suggest that smartphone-derived sleep duration estimates offer practical results for estimating sleep duration and can also serve useful functions in the process of digital phenotyping.
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Affiliation(s)
- Carsten Langholm
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Andrew Jin Soo Byun
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Janet Mullington
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA.
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Marciano L, Saboor S. Reinventing mental health care in youth through mobile approaches: Current status and future steps. Front Psychol 2023; 14:1126015. [PMID: 36968730 PMCID: PMC10033533 DOI: 10.3389/fpsyg.2023.1126015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/17/2023] [Indexed: 03/12/2023] Open
Abstract
In this perspective, we aim to bring together research on mobile assessments and interventions in the context of mental health care in youth. After the COVID-19 pandemic, one out of five young people is experiencing mental health problems worldwide. New ways to face this burden are now needed. Young people search for low-burden services in terms of costs and time, paired with high flexibility and easy accessibility. Mobile applications meet these principles by providing new ways to inform, monitor, educate, and enable self-help, thus reinventing mental health care in youth. In this perspective, we explore the existing literature reviews on mobile assessments and interventions in youth through data collected passively (e.g., digital phenotyping) and actively (e.g., using Ecological Momentary Assessments—EMAs). The richness of such approaches relies on assessing mental health dynamically by extending beyond the confines of traditional methods and diagnostic criteria, and the integration of sensor data from multiple channels, thus allowing the cross-validation of symptoms through multiple information. However, we also acknowledge the promises and pitfalls of such approaches, including the problem of interpreting small effects combined with different data sources and the real benefits in terms of outcome prediction when compared to gold-standard methods. We also explore a new promising and complementary approach, using chatbots and conversational agents, that encourages interaction while tracing health and providing interventions. Finally, we suggest that it is important to continue to move beyond the ill-being framework by giving more importance to intervention fostering well-being, e.g., using positive psychology.
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Affiliation(s)
- Laura Marciano
- Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Lee Kum Sheung Center for Health and Happiness and Dana Farber Cancer Institute, Boston, MA, United States
- *Correspondence: Laura Marciano,
| | - Sundas Saboor
- Harvard T.H. Chan School of Public Health, Boston, MA, United States
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22
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Espinoza J, Xu NY, Nguyen KT, Klonoff DC. The Need for Data Standards and Implementation Policies to Integrate CGM Data into the Electronic Health Record. J Diabetes Sci Technol 2023; 17:495-502. [PMID: 34802286 PMCID: PMC10012359 DOI: 10.1177/19322968211058148] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The current lack of continuous glucose monitor (CGM) data integration into the electronic health record (EHR) is holding back the use of this wearable technology for patient-generated health data (PGHD). This failure to integrate with other healthcare data inside the EHR disrupts workflows, removes the data from critical patient context, and overall makes the CGM data less useful than it might otherwise be. Many healthcare organizations (HCOs) are either struggling with or delaying designing and implementing CGM data integrations. In this article, the current status of CGM integration is reviewed, goals for integration are proposed, and a consensus plan to engage key stakeholders to facilitate integration is presented.
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Affiliation(s)
- Juan Espinoza
- Division of General Pediatrics,
Children’s Hospital Los Angeles, University of Southern California, Los Angeles, CA,
USA
- Juan Espinoza, MD, FAAP, Division of
General Pediatrics, Department of Pediatrics, Children’s Hospital Los Angeles,
University of Southern California, 4650 Sunset Boulevard, Los Angeles, CA 90027,
USA.
| | - Nicole Y. Xu
- Diabetes Technology Society,
Burlingame, CA, USA
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23
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Yang X, Knights J, Bangieva V, Kambhampati V. Association Between the Severity of Depressive Symptoms and Human-Smartphone Interactions: Longitudinal Study. JMIR Form Res 2023; 7:e42935. [PMID: 36811951 PMCID: PMC9996420 DOI: 10.2196/42935] [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: 09/24/2022] [Revised: 12/13/2022] [Accepted: 01/26/2023] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Various behavioral sensing research studies have found that depressive symptoms are associated with human-smartphone interaction behaviors, including lack of diversity in unique physical locations, entropy of time spent in each location, sleep disruption, session duration, and typing speed. These behavioral measures are often tested against the total score of depressive symptoms, and the recommended practice to disaggregate within- and between-person effects in longitudinal data is often neglected. OBJECTIVE We aimed to understand depression as a multidimensional process and explore the association between specific dimensions and behavioral measures computed from passively sensed human-smartphone interactions. We also aimed to highlight the nonergodicity in psychological processes and the importance of disaggregating within- and between-person effects in the analysis. METHODS Data used in this study were collected by Mindstrong Health, a telehealth provider that focuses on individuals with serious mental illness. Depressive symptoms were measured by the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) Self-Rated Level 1 Cross-Cutting Symptom Measure-Adult Survey every 60 days for a year. Participants' interactions with their smartphones were passively recorded, and 5 behavioral measures were developed and were expected to be associated with depressive symptoms according to either theoretical proposition or previous empirical evidence. Multilevel modeling was used to explore the longitudinal relations between the severity of depressive symptoms and these behavioral measures. Furthermore, within- and between-person effects were disaggregated to accommodate the nonergodicity commonly found in psychological processes. RESULTS This study included 982 records of DSM Level 1 depressive symptom measurements and corresponding human-smartphone interaction data from 142 participants (age range 29-77 years; mean age 55.1 years, SD 10.8 years; 96 female participants). Loss of interest in pleasurable activities was associated with app count (γ10=-0.14; P=.01; within-person effect). Depressed mood was associated with typing time interval (γ05=0.88; P=.047; within-person effect) and session duration (γ05=-0.37; P=.03; between-person effect). CONCLUSIONS This study contributes new evidence for associations between human-smartphone interaction behaviors and the severity of depressive symptoms from a dimensional perspective, and it highlights the importance of considering the nonergodicity of psychological processes and analyzing the within- and between-person effects separately.
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Affiliation(s)
- Xiao Yang
- Mindstrong Health, Menlo Park, CA, United States
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24
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Currey D, Torous J. Increasing the value of digital phenotyping through reducing missingness: a retrospective review and analysis of prior studies. BMJ MENTAL HEALTH 2023; 26:e300718. [PMID: 37197799 PMCID: PMC10231441 DOI: 10.1136/bmjment-2023-300718] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 04/26/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND Digital phenotyping methods present a scalable tool to realise the potential of personalised medicine. But underlying this potential is the need for digital phenotyping data to represent accurate and precise health measurements. OBJECTIVE To assess the impact of population, clinical, research and technological factors on the digital phenotyping data quality as measured by rates of missing digital phenotyping data. METHODS This study analyses retrospective cohorts of mindLAMP smartphone application digital phenotyping studies run at Beth Israel Deaconess Medical Center between May 2019 and March 2022 involving 1178 participants (studies of college students, people with schizophrenia and people with depression/anxiety). With this large combined data set, we report on the impact of sampling frequency, active engagement with the application, phone type (Android vs Apple), gender and study protocol features on missingness/data quality. FINDINGS Missingness from sensors in digital phenotyping is related to active user engagement with the application. After 3 days of no engagement, there was a 19% decrease in average data coverage for both Global Positioning System and accelerometer. Data sets with high degrees of missingness can generate incorrect behavioural features that may lead to faulty clinical interpretations. CONCLUSIONS Digital phenotyping data quality requires ongoing technical and protocol efforts to minimise missingness. Adding run-in periods, education with hands-on support and tools to easily monitor data coverage are all productive strategies studies can use today. CLINICAL IMPLICATIONS While it is feasible to capture digital phenotyping data from diverse populations, clinicians should consider the degree of missingness in the data before using them for clinical decision-making.
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Affiliation(s)
- Danielle Currey
- Harvard Medical School, Boston, Massachusetts, USA
- Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - John Torous
- Harvard Medical School, Boston, Massachusetts, USA
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25
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Cohen A, Naslund JA, Chang S, Nagendra S, Bhan A, Rozatkar A, Thirthalli J, Bondre A, Tugnawat D, Reddy PV, Dutt S, Choudhary S, Chand PK, Patel V, Keshavan M, Joshi D, Mehta UM, Torous J. Relapse prediction in schizophrenia with smartphone digital phenotyping during COVID-19: a prospective, three-site, two-country, longitudinal study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:6. [PMID: 36707524 PMCID: PMC9880926 DOI: 10.1038/s41537-023-00332-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/13/2023] [Indexed: 01/28/2023]
Abstract
Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior and symptoms that typically precede schizophrenia relapse. To take advantage of the aforementioned, this study examines the feasibility of predicting schizophrenia relapse by identifying statistically significant anomalies in patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited in Boston, MA in the United States, and Bangalore and Bhopal in India, were invited to use mindLAMP for up to a year. The passive data (geolocation, accelerometer, and screen state), active data (surveys), and data quality metrics collected by the app were then retroactively fed into a relapse prediction model that utilizes anomaly detection. Overall, anomalies were 2.12 times more frequent in the month preceding a relapse and 2.78 times more frequent in the month preceding and following a relapse compared to intervals without relapses. The anomaly detection model incorporating passive data proved a better predictor of relapse than a naive model utilizing only survey data. These results demonstrate that relapse prediction models utilizing patient data gathered by a smartphone app can warn the clinician and patient of a potential schizophrenia relapse.
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Affiliation(s)
- Asher Cohen
- grid.38142.3c000000041936754XDivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - John A. Naslund
- grid.38142.3c000000041936754XDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA USA
| | - Sarah Chang
- grid.38142.3c000000041936754XDivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Srilakshmi Nagendra
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | | | - Abhijit Rozatkar
- grid.464753.70000 0004 4660 3923Department of Psychiatry, AIIMS Bhopal, All India Institute of Medical Sciences Bhopal, Bhopal, India
| | - Jagadisha Thirthalli
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | | | | | - Preethi V. Reddy
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | - Siddharth Dutt
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | - Soumya Choudhary
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | - Prabhat Kumar Chand
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | - Vikram Patel
- grid.38142.3c000000041936754XDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA USA
| | - Matcheri Keshavan
- grid.38142.3c000000041936754XDivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Devayani Joshi
- grid.38142.3c000000041936754XDivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Urvakhsh Meherwan Mehta
- grid.416861.c0000 0001 1516 2246Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka India
| | - John Torous
- grid.38142.3c000000041936754XDivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
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26
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Chang S, Alon N, Torous J. An exploratory analysis of the effect size of the mobile mental health Application, mindLAMP. Digit Health 2023; 9:20552076231187244. [PMID: 37434734 PMCID: PMC10331229 DOI: 10.1177/20552076231187244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 06/23/2023] [Indexed: 07/13/2023] Open
Abstract
Objectives Despite the proliferation of mobile mental health apps, evidence of their efficacy around anxiety or depression is inadequate as most studies lack appropriate control groups. Given that apps are designed to be scalable and reusable tools, insights concerning their efficacy can also be assessed uniquely through comparing different implementations of the same app. This exploratory analysis investigates the potential to report a preliminary effect size of an open-source smartphone mental health app, mindLAMP, on the reduction of anxiety and depression symptoms by comparing a control implementation of the app focused on self-assessment to an intervention implementation of the same app focused on CBT skills. Methods A total of 328 participants were eligible and completed the study under the control implementation and 156 completed the study under the intervention implementation of the mindLAMP app. Both use cases offered access to the same in-app self-assessments and therapeutic interventions. Multiple imputations were utilized to impute the missing Generalized Anxiety Disorder-7 and Patient Health Questionnaire-9 survey scores of the control implementation. Results Post hoc analysis revealed small effect sizes of Hedge's g = 0.34 for Generalized Anxiety Disorder-7 and Hedge's g = 0.21 for Patient Health Questionnaire-9 between the two groups. Conclusions mindLAMP shows promising results in improving anxiety and depression outcomes in participants. Though our results mirror the current literature in assessing mental health apps' efficacy, they remain preliminary and will be used to inform a larger, well-powered study to further elucidate the efficacy of mindLAMP.
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Affiliation(s)
- Sarah Chang
- Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Noy Alon
- Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - John Torous
- Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
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27
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Chang S, Gray L, Torous J. Smartphone app engagement and clinical outcomes in a hybrid clinic. Psychiatry Res 2023; 319:115015. [PMID: 36549096 DOI: 10.1016/j.psychres.2022.115015] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/12/2022] [Accepted: 12/17/2022] [Indexed: 12/23/2022]
Abstract
Despite the growing prevalence of mental health-related smartphone apps, low real-world engagement has prevented these apps from transforming the mental health landscape. Integrating mental health apps into more traditional therapeutic models appears to support better clinical outcomes, but also raises questions about the relationship between app engagement, the app itself, and the coach or clinician. This study explores patient app engagement patterns and the associated clinical outcomes gathered from piloting a digital clinic. Patients with anxiety or depression completed eight clinical visits and coach visits over a median of 83 days with a standard deviation of 17.25 days. Between clinical visits, patients completed therapeutic activities on the mindLAMP app. Mean PHQ-9 and GAD-7 scores decreased from the intake visit to both visit 4 and visit 8. Patients had high app engagement, but engagement did not correlate with outcomes. From intake visit to visit 4, the interaction effects indicate significant differences in the change of both PHQ-9 and GAD-7 depending on participants' average app satisfaction and clinician/coach satisfaction (as measured by WAI-SR) with engagement. Overall, results support the feasibility of incorporating an app into a hybrid clinic.
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Affiliation(s)
- Sarah Chang
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Lucy Gray
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
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28
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Harvey PD, Depp CA, Rizzo AA, Strauss GP, Spelber D, Carpenter LL, Kalin NH, Krystal JH, McDonald WM, Nemeroff CB, Rodriguez CI, Widge AS, Torous J. Technology and Mental Health: State of the Art for Assessment and Treatment. Am J Psychiatry 2022; 179:897-914. [PMID: 36200275 DOI: 10.1176/appi.ajp.21121254] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Technology is ubiquitous in society and is now being extensively used in mental health applications. Both assessment and treatment strategies are being developed and deployed at a rapid pace. The authors review the current domains of technology utilization, describe standards for quality evaluation, and forecast future developments. This review examines technology-based assessments of cognition, emotion, functional capacity and everyday functioning, virtual reality approaches to assessment and treatment, ecological momentary assessment, passive measurement strategies including geolocation, movement, and physiological parameters, and technology-based cognitive and functional skills training. There are many technology-based approaches that are evidence based and are supported through the results of systematic reviews and meta-analyses. Other strategies are less well supported by high-quality evidence at present, but there are evaluation standards that are well articulated at this time. There are some clear challenges in selection of applications for specific conditions, but in several areas, including cognitive training, randomized clinical trials are available to support these interventions. Some of these technology-based interventions have been approved by the U.S. Food and Drug administration, which has clear standards for which types of applications, and which claims about them, need to be reviewed by the agency and which are exempt.
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Affiliation(s)
- Philip D Harvey
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - Colin A Depp
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - Albert A Rizzo
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - Gregory P Strauss
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - David Spelber
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - Linda L Carpenter
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - Ned H Kalin
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - John H Krystal
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - William M McDonald
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - Charles B Nemeroff
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - Carolyn I Rodriguez
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - Alik S Widge
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
| | - John Torous
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, and Miami VA Medical Center (Harvey); Department of Psychiatry, UC San Diego Medical Center, La Jolla (Depp); USC Institute for Creative Technologies, University of Southern California, Los Angeles (Rizzo); Department of Psychology, University of Georgia, Athens (Strauss); Department of Psychiatry, Dell Medical Center, University of Texas at Austin (Spelber, Nemeroff); Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, R.I. (Carpenter); Department of Psychiatry, University of Wisconsin Medical School, Madison (Kalin); Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. (Krystal); Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta (McDonald); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford; Veterans Affairs Palo Alto Health Care System, Palo Alto (Rodriguez); Department of Psychiatry and Behavioral Sciences and Medical Discovery Team-Addictions, University of Minnesota, Minneapolis (Widge); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston (Torous)
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Orsolini L, Appignanesi C, Pompili S, Volpe U. The role of digital tools in providing youth mental health: results from an international multi-center study. Int Rev Psychiatry 2022; 34:809-826. [PMID: 36786119 DOI: 10.1080/09540261.2022.2118521] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Since the traditional mental health system showed significant limitations in the early identification, diagnosis and treatment of the current new youth psychopathological trajectories, by substantially failing in targeting the needs of the current young generation, there is the demand to redesign and digitally adapt youth mental health care and systems. Indeed, the level of digital literacy and the level of digital competency and knowledge in the field of digital psychiatry is still under-investigated among mental health professionals, particularly in youth mental health. Therefore, we aimed at: (a) carrying out a post-hoc analysis of an international multi-centre study, to investigate the opinions of mental health professionals regarding the feasibility, efficacy and clinical experience in delivering digital mental health interventions (DMHIs) in youths; (b) providing a comprehensive overview on the integrated digitally-based youth mental health care models and innovations. Mental health professionals declared the lack of a formal training in digital psychiatry, particularly in youth mental health. Subjects who received a formal theoretical/practical training on DMHIs displayed a statistical trend towards a positive feasibility of digital psychiatry in youth mental health (p = 0.053) and a perceived increased efficacy of digital psychiatry in youths (p = 0.051). Respondents with higher Digital Psychiatry Opinion (DPO) scores reported a positive perceived feasibility of DMHIs in youths (p < 0.041) and are more prone to deliver DMHIs to young people (p < 0.001). Respondents with higher knowledge scores (KS) declared that DMHIs are more effective in youth mental health (p < 0.001). Overall, the digitalisation indeed allowed young people to keep in touch with a mental health professional, facilitating a more dynamic and fluid mental health care access and monitoring, generally preferred and considered more feasible by post-Millennial youngsters. Accordingly, our findings demonstrated that mental health professionals are more prone to offer DMHIs in youth mental health, particularly whether previously trained and knowledgeable on the topic.
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Affiliation(s)
- Laura Orsolini
- Unit of Clinical Psychiatry, Department of Clinical Neurosciences/DIMSC, Polytechnic University of Marche, Ancona, Italy
| | - Cristina Appignanesi
- Unit of Clinical Psychiatry, Department of Clinical Neurosciences/DIMSC, Polytechnic University of Marche, Ancona, Italy
| | - Simone Pompili
- Unit of Clinical Psychiatry, Department of Clinical Neurosciences/DIMSC, Polytechnic University of Marche, Ancona, Italy
| | - Umberto Volpe
- Unit of Clinical Psychiatry, Department of Clinical Neurosciences/DIMSC, Polytechnic University of Marche, Ancona, Italy
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Karthan M, Martin R, Holl F, Swoboda W, Kestler HA, Pryss R, Schobel J. Enhancing mHealth data collection applications with sensing capabilities. Front Public Health 2022; 10:926234. [PMID: 36187627 PMCID: PMC9521646 DOI: 10.3389/fpubh.2022.926234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/11/2022] [Indexed: 01/24/2023] Open
Abstract
Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of different sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development effort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.
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Affiliation(s)
- Maximilian Karthan
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany,Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany,*Correspondence: Maximilian Karthan
| | - Robin Martin
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Felix Holl
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany,Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Walter Swoboda
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany
| | - Hans A. Kestler
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Johannes Schobel
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany
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Kwon JY, Lee JS, Park TS. Analysis of Strategies to Increase User Retention of Fitness Mobile Apps during and after the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10814. [PMID: 36078523 PMCID: PMC9517841 DOI: 10.3390/ijerph191710814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 08/25/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
The COVID-19 pandemic has changed the fitness-related field. More people started working out at home, and the use of fitness mobile apps that can measure the amount of exercise through a scientific method has increased compared to before the COVID-19 pandemic. This phenomenon is likely to continue even after the COVID-19 pandemic, and therefore this study aimed to investigate the importance of and satisfaction with a fitness app's functions according to consumers while using the fitness mobile app. Through this study, we intended to provide data for creating an environment where users can use fitness mobile apps consistently. A total of 420 questionnaires were distributed through Google Survey for about 3 months, from 13 September to 20 November 2020, and a total of 399 complete questionnaires were analyzed in this study. Regarding the data processing methods, frequency analysis, exploratory factor analysis, reliability analysis, descriptive statistical analysis, and IPA were used. The results are as follows. First, the first quadrant of the IPA matrix indicated the high importance of and satisfaction with the fitness mobile app, and included five attributes: cost-effectiveness, easy-to-understand information, ease of use and application, privacy protection, and compatibility with other devices. Second, the second quadrant of the matrix indicated relatively low satisfaction in association to high importance and included five attributes: accurate exercise information provision, design efficiency, daily exercise amount setting, convenient icons and interface, and provision of images and videos in appropriate proportions. Third, the third quadrant of the matrix, indicating low importance and low satisfaction, included five attributes: not sharing personal information, overall design composition and color, customer service, reliable security level, and providing information on goal achievement after exercising. Fourth, in the quadrant of the matrix, indicating low importance and high satisfaction, five attributes were included: exercise notification function, continuous service provision, step count and heart rate information, individual exercise recommendation, and individual body type analysis information.
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Affiliation(s)
- Jae-Yoon Kwon
- Department of Fitness MBA, Sangmyung University, Seoul 03016, Korea
| | - Ji-Suk Lee
- Department of Dance & Performance, Hanyang University, 55, Ansan-si 15588, Gyeonggi-do, Korea
| | - Tae-Seung Park
- Department of Physical Education, Sejong University, Seoul 05006, Korea
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de Angel V, Lewis S, White KM, Matcham F, Hotopf M. Clinical Targets and Attitudes Toward Implementing Digital Health Tools for Remote Measurement in Treatment for Depression: Focus Groups With Patients and Clinicians. JMIR Ment Health 2022; 9:e38934. [PMID: 35969448 PMCID: PMC9425163 DOI: 10.2196/38934] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/13/2022] [Accepted: 06/13/2022] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Remote measurement technologies, such as smartphones and wearable devices, can improve treatment outcomes for depression through enhanced illness characterization and monitoring. However, little is known about digital outcomes that are clinically meaningful to patients and clinicians. Moreover, if these technologies are to be successfully implemented within treatment, stakeholders' views on the barriers to and facilitators of their implementation in treatment must be considered. OBJECTIVE This study aims to identify clinically meaningful targets for digital health research in depression and explore attitudes toward their implementation in psychological services. METHODS A grounded theory approach was used on qualitative data from 3 focus groups of patients with a current diagnosis of depression and clinicians with >6 months of experience with delivering psychotherapy (N=22). RESULTS Emerging themes on clinical targets fell into the following two main categories: promoters and markers of change. The former are behaviors that participants engage in to promote mental health, and the latter signal a change in mood. These themes were further subdivided into external changes (changes in behavior) or internal changes (changes in thoughts or feelings) and mapped with potential digital sensors. The following six implementation acceptability themes emerged: technology-related factors, information and data management, emotional support, cognitive support, increased self-awareness, and clinical utility. CONCLUSIONS The promoters versus markers of change differentiation have implications for a causal model of digital phenotyping in depression, which this paper presents. Internal versus external subdivisions are helpful in determining which factors are more susceptible to being measured by using active versus passive methods. The implications for implementation within psychotherapy are discussed with regard to treatment effectiveness, service provision, and patient and clinician experience.
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Affiliation(s)
- Valeria de Angel
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Serena Lewis
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Department of Psychology, University of Bath, Bath, United Kingdom
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,School of Psychology, University of Sussex, Falmer, East Sussex, United Kingdom
| | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
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Dimitriadis I, Mavroudopoulos I, Kyrama S, Toliopoulos T, Gounaris A, Vakali A, Billis A, Bamidis P. Scalable real-time health data sensing and analysis enabling collaborative care delivery. SOCIAL NETWORK ANALYSIS AND MINING 2022. [DOI: 10.1007/s13278-022-00891-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Vial S, Boudhraâ S, Dumont M. Human-Centered Design Approaches in Digital Mental Health Interventions: Exploratory Mapping Review. JMIR Ment Health 2022; 9:e35591. [PMID: 35671081 PMCID: PMC9214621 DOI: 10.2196/35591] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/22/2022] [Accepted: 04/19/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Digital mental health interventions have a great potential to alleviate mental illness and increase access to care. However, these technologies face significant challenges, especially in terms of user engagement and adoption. It has been suggested that this issue stems from a lack of user perspective in the development process; accordingly, several human-centered design approaches have been developed over the years to consider this important aspect. Yet, few human-centered design approaches to digital solutions exist in the field of mental health, and rarely are end users involved in their development. OBJECTIVE The main objective of this literature review is to understand how human-centered design is considered in e-mental health intervention research. METHODS An exploratory mapping review was conducted of mental health journals with the explicit scope of covering e-mental health technology. The human-centered design approaches reported and the core elements of design activity (ie, object, context, design process, and actors involved) were examined among the eligible studies. RESULTS A total of 30 studies met the inclusion criteria, of which 22 mentioned using human-centered design approaches or specific design methods in the development of an e-mental health solution. Reported approaches were classified as participatory design (11/27, 41%), codesign (6/27, 22%), user-centered design (5/27, 19%), or a specific design method (5/27, 19%). Just over half (15/27, 56%) of the approaches mentioned were supported by references. End users were involved in each study to some extent but not necessarily in designing. About 27% (8/30) of all the included studies explicitly mentioned the presence of designers on their team. CONCLUSIONS Our results show that some attempts have indeed been made to integrate human-centered design approaches into digital mental health technology development. However, these attempts rely very little on designers and design research. Researchers from other domains and technology developers would be wise to learn the underpinnings of human-centered design methods before selecting one over another. Inviting designers for assistance when implementing a particular approach would also be beneficial. To further motivate interest in and use of human-centered design principles in the world of e-mental health, we make nine suggestions for better reporting of human-centered design approaches in future research.
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Affiliation(s)
- Stéphane Vial
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, École de Design, Université du Québec à Montréal, Montréal, QC, Canada
| | - Sana Boudhraâ
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, École de Design, Université du Québec à Montréal, Montréal, QC, Canada
| | - Mathieu Dumont
- Département D'ergothérapie, Université du Québec à Trois-Rivières, Drummondville, QC, Canada
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Abstract
BACKGROUND Smartphones can facilitate patients completing surveys and collecting sensor data to gain insight into their mental health conditions. However, the utility of sensor data is still being explored. Prior studies have reported a wide range of correlations between passive data and survey scores. AIMS To explore correlations in a large data-set collected with the mindLAMP app. Additionally, we explored whether passive data features could be used in models to predict survey results. METHOD Participants were asked to complete daily and weekly mental health surveys. After screening for data quality, our sample included 147 college student participants and 270 weeks of data. We examined correlations between six weekly surveys and 13 metrics derived from passive data features. Finally, we trained logistic regression models to predict survey scores from passive data with and without daily surveys. RESULTS Similar to other large studies, our correlations were lower than prior reports from smaller studies. We found that the most useful features came from GPS, call, and sleep duration data. Logistic regression models performed poorly with only passive data, but when daily survey scores were included, performance greatly increased. CONCLUSIONS Although passive data alone may not provide enough information to predict survey scores, augmenting this data with short daily surveys can improve performance. Therefore, it may be that passive data can be used to refine survey score predictions and clinical utility may be derived from the combination of active and passive data.
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Affiliation(s)
- Danielle Currey
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Massachusetts, USA
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Massachusetts, USA
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Jurin T, Šoštarić M, Jokić-Begić N, Lauri Korajlija A. mSexHealth: An Overview of Mobile Sexual Health Applications. JOURNAL OF SEX & MARITAL THERAPY 2022; 49:129-140. [PMID: 35652779 DOI: 10.1080/0092623x.2022.2079576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The aim of this study was to examine the existing mobile applications focused on sexual health and analyze the included content and therapeutic techniques. Three databases with mobile applications were searched and 47 applications met the criteria. More applications have been developed for men, most of them included content for erectile dysfunction, and only one for vaginismus. Most apps included sexuality tips and Kegel exercises, and only one technique for working on thoughts and emotions. In conclusion, a number of mobile applications have been developed to enhance sexual functioning, but scientific verification of their effectiveness has been completely lacking.
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Affiliation(s)
- Tanja Jurin
- Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia
| | - Matea Šoštarić
- Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia
| | - Nataša Jokić-Begić
- Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia
| | - Anita Lauri Korajlija
- Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia
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Software Architecture Patterns for Extending Sensing Capabilities and Data Formatting in Mobile Sensing. SENSORS 2022; 22:s22072813. [PMID: 35408426 PMCID: PMC9002566 DOI: 10.3390/s22072813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/27/2022] [Accepted: 03/31/2022] [Indexed: 02/01/2023]
Abstract
Mobile sensing—that is, the ability to unobtrusively collect sensor data from built-in phone and attached wearable sensors—have proven to be a powerful approach to understanding the behavior, well-being, and health of people in their everyday life. Different platforms for mobile sensing have been presented and significant knowledge on how to facilitate mobile sensing has been accumulated. However, most existing mobile sensing platforms only support a fixed set of mobile phone and wearable sensors which are `built into’ the platform’s generic `study app’. This creates some fundamental challenges for the creation and approval of application-specific mobile sensing studies, since there is little support for adapting the sensing capabilities to what is needed for a specific study. Moreover, most existing platforms use their own proprietary data formats and there is no standardization in how data are collected and in what formats. This poses some fundamental challenges to realizing the vision of using mobile sensing in health applications, since mobile sensing data collected across different phones and studies cannot be compared, thus hampering generalizability and reproducibility across studies. This paper presents two software architecture patterns enabling (i) dynamic extension of mobile sensing to incorporate new sensing capabilities, such as collecting data from a wearable sensor, and (ii) handling real-time transformation of data into standardized data formats. These software patterns are derived from our work on CARP Mobile Sensing (CAMS), which is a cross-platform (Android/iOS) software architecture providing a reactive and unified programming model that emphasizes extensibility. This paper shows how the framework uses the two software architecture patterns to add sampling support for an electrocardiography (ECG) device and support data transformation into the new Open mHealth (OMH) data format. The paper also presents data from a small study, demonstrating the robustness and feasibility of using CAMS for data collection and transformation in mobile sensing.
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Weizenbaum EL, Fulford D, Torous J, Pinsky E, Kolachalama VB, Cronin-Golomb A. Smartphone-Based Neuropsychological Assessment in Parkinson's Disease: Feasibility, Validity, and Contextually Driven Variability in Cognition. J Int Neuropsychol Soc 2022; 28:401-413. [PMID: 33998438 PMCID: PMC10474573 DOI: 10.1017/s1355617721000503] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVES The prevalence of neurodegenerative disorders demands methods of accessible assessment that reliably captures cognition in daily life contexts. We investigated the feasibility of smartphone cognitive assessment in people with Parkinson's disease (PD), who may have cognitive impairment in addition to motor-related problems that limit attending in-person clinics. We examined how daily-life factors predicted smartphone cognitive performance and examined the convergent validity of smartphone assessment with traditional neuropsychological tests. METHODS Twenty-seven nondemented individuals with mild-moderate PD attended one in-lab session and responded to smartphone notifications over 10 days. The smartphone app queried participants 5x/day about their location, mood, alertness, exercise, and medication state and administered mobile games of working memory and executive function. RESULTS Response rate to prompts was high, demonstrating feasibility of the approach. Between-subject reliability was high on both cognitive games. Within-subject variability was higher for working memory than executive function. Strong convergent validity was seen between traditional tests and smartphone working memory but not executive function, reflecting the latter's ceiling effects. Participants performed better on mobile working memory tasks when at home and after recent exercise. Less self-reported daytime sleepiness and lower PD symptom burden predicted a stronger association between later time of day and higher smartphone test performance. CONCLUSIONS These findings support feasibility and validity of repeat smartphone assessments of cognition and provide preliminary evidence of the effects of context on cognitive variability in PD. Further development of this accessible assessment method could increase sensitivity and specificity regarding daily cognitive dysfunction for PD and other clinical populations.
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Affiliation(s)
- Emma L. Weizenbaum
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Daniel Fulford
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- Department of Occupational Therapy and Rehabilitation Sciences, Boston University, Boston, MA, USA
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Emma Pinsky
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- Department of Psychology, Bryn Mawr College, Bryn Mawr, PA, USA
| | - Vijaya B. Kolachalama
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Computer Science, and Faculty of Computing and Data Sciences, Boston University Alzheimer’s Disease Center; Boston University, Boston, MA, USA
| | - Alice Cronin-Golomb
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
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Thorpe D, Fouyaxis J, Lipschitz JM, Nielson A, Li W, Murphy SA, Bidargaddi N. Cost and Effort Considerations for the Development of Intervention Studies Using Mobile Health Platforms: Pragmatic Case Study. JMIR Form Res 2022; 6:e29988. [PMID: 35357313 PMCID: PMC9015742 DOI: 10.2196/29988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 12/02/2021] [Accepted: 01/14/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND The research marketplace has seen a flood of open-source or commercial mobile health (mHealth) platforms that can collect and use user data in real time. However, there is a lack of practical literature on how these platforms are developed, integrated into study designs, and adopted, including important information around cost and effort considerations. OBJECTIVE We intend to build critical literacy in the clinician-researcher readership into the cost, effort, and processes involved in developing and operationalizing an mHealth platform, focusing on Intui, an mHealth platform that we developed. METHODS We describe the development of the Intui mHealth platform and general principles of its operationalization across sites. RESULTS We provide a worked example in the form of a case study. Intui was operationalized in the design of a behavioral activation intervention in collaboration with a mental health service provider. We describe the design specifications of the study site, the developed software, and the cost and effort required to build the final product. CONCLUSIONS Study designs, researcher needs, and technical considerations can impact effort and costs associated with the use of mHealth platforms. Greater transparency from platform developers about the impact of these factors on practical considerations relevant to end users such as clinician-researchers is crucial to increasing critical literacy around mHealth, thereby aiding in the widespread use of these potentially beneficial technologies and building clinician confidence in these tools.
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Affiliation(s)
- Dan Thorpe
- Flinders Digital Health Research Lab, College of Medicine and Public Health, Flinders University, Clovelly Park, Australia
| | - John Fouyaxis
- Flinders Digital Health Research Lab, College of Medicine and Public Health, Flinders University, Clovelly Park, Australia
| | | | - Amy Nielson
- Flinders Digital Health Research Lab, College of Medicine and Public Health, Flinders University, Clovelly Park, Australia
| | - Wenhao Li
- Flinders Digital Health Research Lab, College of Medicine and Public Health, Flinders University, Clovelly Park, Australia
| | - Susan A Murphy
- Radcliffe Institute, Harvard University, Boston, MA, United States
| | - Niranjan Bidargaddi
- Flinders Digital Health Research Lab, College of Medicine and Public Health, Flinders University, Clovelly Park, Australia
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Analysis of Start-Up Digital Mental Health Platforms for Enterprise: Opportunities for Enhancing Communication between Managers and Employees. SUSTAINABILITY 2022. [DOI: 10.3390/su14073929] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The COVID-19 pandemic has resulted in employees being exposed to transformational stressors from within and outside the organization. This has created an opportunity for employee mental health solutions. Indeed, there has been a rapid growth in start-ups offering clinical mental health services via a digital health platform. These platforms servicing enterprise employee mental health needs have not been evaluated with respect to their ability to enhance management communication. Hence, the aims of the present study are to explore communication and service attributes across a sample of five operational leading commercial start-up platforms for mental service delivery to employees. We have observed that all platform models focused on providing on-demand mental health consultation services. Existing platforms fail to adequately support management communication for mental health solutions across 80% of platforms reviewed. We recommend that industry start-ups should understand the need for management engagement with digital mental health platforms. Digital mental health platform solutions in the workplace are ideally supported by valuing leadership communication. A culture around mental health will create sustainability in digital mental health solutions for an organization.
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Kalinich M, Ebrahim S, Hays R, Melcher J, Vaidyam A, Torous J. Applying machine learning to smartphone based cognitive and sleep assessments in schizophrenia. Schizophr Res Cogn 2022; 27:100216. [PMID: 34934638 PMCID: PMC8655108 DOI: 10.1016/j.scog.2021.100216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/09/2021] [Accepted: 09/19/2021] [Indexed: 11/19/2022] Open
Abstract
Background Methods Results Discussion
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Affiliation(s)
- Mark Kalinich
- Harvard Medical School, Boston, MA, USA
- Watershed Informatics, Inc., Boston, MA, USA
| | - Senan Ebrahim
- Harvard Medical School, Boston, MA, USA
- Delfina Inc., Boston, MA, USA
| | - Ryan Hays
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jennifer Melcher
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Aditya Vaidyam
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - John Torous
- Harvard Medical School, Boston, MA, USA
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Corresponding author at: Beth Israel Deaconess Medical Center, 330 Brookline Ave., Boston, MA 02215, USA.
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Abstract
Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance.
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Eis S, Solà-Morales O, Duarte-Díaz A, Vidal-Alaball J, Perestelo-Pérez L, Robles N, Carrion C. Mobile Applications in Mood Disorders and Mental Health: Systematic Search in Apple App Store and Google Play Store and Review of the Literature. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042186. [PMID: 35206373 PMCID: PMC8871536 DOI: 10.3390/ijerph19042186] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/03/2022] [Accepted: 02/10/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVES The main objective of this work was to explore and characterize the current landscape of mobile applications available to treat mood disorders such as depression, bipolar disorder, and dysthymia. METHODS We developed a tool that makes both the Apple App Store and the Google Play Store searchable using keywords and that facilitates the extraction of basic app information of the search results. All app results were filtered using various inclusion and exclusion criteria. We characterized all resultant applications according to their technical details. Furthermore, we searched for scientific publications on each app's website and PubMed, to understand whether any of the apps were supported by any type of scientific evidence on their acceptability, validation, use, effectiveness, etc. Results: Thirty apps were identified that fit the inclusion and exclusion criteria. The literature search yielded 27 publications related to the apps. However, these did not exclusively concern mood disorders. 6 were randomized studies and the rest included a protocol, pilot-, feasibility, case-, or qualitative studies, among others. The majority of studies were conducted on relatively small scales and 9 of the 27 studies did not explicitly study the effects of mobile application use on mental wellbeing. CONCLUSION While there exists a wealth of mobile applications aimed at the treatment of mental health disorders, including mood disorders, this study showed that only a handful of these are backed by robust scientific evidence. This result uncovers a need for further clinically oriented and systematic validation and testing of such apps.
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Affiliation(s)
- Sophie Eis
- Fundació HiTT (Health Innovation Technology Transfer), 08015 Barcelona, Spain;
| | - Oriol Solà-Morales
- Fundació HiTT (Health Innovation Technology Transfer), 08015 Barcelona, Spain;
- Correspondence:
| | - Andrea Duarte-Díaz
- Canary Islands Health Research Institute Foundation (FIISC), 38109 Tenerife, Spain;
| | - Josep Vidal-Alaball
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, 08272 Barcelona, Spain;
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina, 08007 Barcelona, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia (UVIC-UCC), 08500 Vic, Spain
| | | | - Noemí Robles
- eHealth Lab Research Group, School of Health Sciences and eHealth Centre, Universitat Oberta de Catalunya (UOC), 08035 Barcelona, Spain; (N.R.); (C.C.)
| | - Carme Carrion
- eHealth Lab Research Group, School of Health Sciences and eHealth Centre, Universitat Oberta de Catalunya (UOC), 08035 Barcelona, Spain; (N.R.); (C.C.)
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Colombo P, Busti Ceccarelli S, Pacchiarini S, Cribellati S, Molteni M. MedicalBIT: A web platform for Standardized Data Acquisition, Processing and Export in Child Psychopathology Clinical Routine. From design to implementation. (Preprint). JMIR Form Res 2022; 6:e36757. [PMID: 35816380 PMCID: PMC9315902 DOI: 10.2196/36757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/06/2022] [Accepted: 05/22/2022] [Indexed: 11/16/2022] Open
Abstract
Background The rapid extent of digital innovation for the collection of data has transformed the way in which health professionals collect, share, and analyze health information for better clinical decision-making and health care. In the last decade, there has been an increased interest in telemedicine by mental health agencies; the gap between the need for care and both diagnosis and treatment is wide, and digital technology could play an important role in filling this gap. However, there are limited data on the effectiveness of the clinical process and cost-effectiveness of most telemedicine applications. Objective This study examined the implementation of the first Italian online, web-based, comprehensive screening tool and described the screening and diagnostic process through the interactive web platform in a child psychopathology clinic. This is a feasibility study that aims to present the design and implementation of the best practices to improve patient experiences and clinical outcomes. Moreover, the paper evaluates the platform with qualitative and quantitative measures. Methods We planned, designed, and implemented a web-based system to collect, store, and manage clinical data. The platform was developed by a multidisciplinary team composed of researchers, clinicians, and informatics professionals through different steps. First, we defined the clinical information to be collected. A number of measures were chosen, tapping several clinical risk areas such as neurodevelopmental disorders and emotional and behavioral problems. The web application architecture and process were then designed. The three phases of process design are described in detail: design of the input interface, processing design, and design of the output interface. Finally, the system has been implemented and evaluated. Based on indicators recommended by the National Quality Forum and the Italian National Guidelines, we evaluated the quality of the system and used quantitative measures that were replicable and comparable over time. Results We present the implemented architecture and features of Medea Information and Clinical Assessment On-Line (MedicalBIT), and we provide performance measures for the data collected between October 2018 and June 2021. The measured concepts pertain to four domains: access to care, financial impact/cost, experience, and effectiveness. Conclusions In this study, we present the successful implementation of an innovative digital tool. The findings of this study show that the implemented web-based platform appears to be an efficient, cost-effective, and feasible way to improve digital care in the field of child psychiatry.
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Affiliation(s)
- Paola Colombo
- Child Psychopathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico Eugenio Medea, Bosisio Parini, Italy
| | - Silvia Busti Ceccarelli
- Child Psychopathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico Eugenio Medea, Bosisio Parini, Italy
| | | | | | - Massimo Molteni
- Child Psychopathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico Eugenio Medea, Bosisio Parini, Italy
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Shen FX, Silverman BC, Monette P, Kimble S, Rauch SL, Baker JT. An Ethics Checklist for Digital Health Research in Psychiatry: Viewpoint. J Med Internet Res 2022; 24:e31146. [PMID: 35138261 PMCID: PMC8867294 DOI: 10.2196/31146] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/23/2021] [Accepted: 10/29/2021] [Indexed: 01/23/2023] Open
Abstract
Background Psychiatry has long needed a better and more scalable way to capture the dynamics of behavior and its disturbances, quantitatively across multiple data channels, at high temporal resolution in real time. By combining 24/7 data—on location, movement, email and text communications, and social media—with brain scans, genetics, genomics, neuropsychological batteries, and clinical interviews, researchers will have an unprecedented amount of objective, individual-level data. Analyzing these data with ever-evolving artificial intelligence could one day include bringing interventions to patients where they are in the real world in a convenient, efficient, effective, and timely way. Yet, the road to this innovative future is fraught with ethical dilemmas as well as ethical, legal, and social implications (ELSI). Objective The goal of the Ethics Checklist is to promote careful design and execution of research. It is not meant to mandate particular research designs; indeed, at this early stage and without consensus guidance, there are a range of reasonable choices researchers may make. However, the checklist is meant to make those ethical choices explicit, and to require researchers to give reasons for their decisions related to ELSI issues. The Ethics Checklist is primarily focused on procedural safeguards, such as consulting with experts outside the research group and documenting standard operating procedures for clearly actionable data (eg, expressed suicidality) within written research protocols. Methods We explored the ELSI of digital health research in psychiatry, with a particular focus on what we label “deep phenotyping” psychiatric research, which combines the potential for virtually boundless data collection and increasingly sophisticated techniques to analyze those data. We convened an interdisciplinary expert stakeholder workshop in May 2020, and this checklist emerges out of that dialogue. Results Consistent with recent ELSI analyses, we find that existing ethical guidance and legal regulations are not sufficient for deep phenotyping research in psychiatry. At present, there are regulatory gaps, inconsistencies across research teams in ethics protocols, and a lack of consensus among institutional review boards on when and how deep phenotyping research should proceed. We thus developed a new instrument, an Ethics Checklist for Digital Health Research in Psychiatry (“the Ethics Checklist”). The Ethics Checklist is composed of 20 key questions, subdivided into 6 interrelated domains: (1) informed consent; (2) equity, diversity, and access; (3) privacy and partnerships; (4) regulation and law; (5) return of results; and (6) duty to warn and duty to report. Conclusions Deep phenotyping research offers a vision for vastly more effective care for people with, or at risk for, psychiatric disease. The potential perils en route to realizing this vision are significant; however, and researchers must be willing to address the questions in the Ethics Checklist before embarking on each leg of the journey.
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Affiliation(s)
- Francis X Shen
- Harvard Medical School, Boston, MA, United States.,Law School, University of Minnesota, Minneapolis, MN, United States
| | - Benjamin C Silverman
- Harvard Medical School, Boston, MA, United States.,Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, United States
| | - Patrick Monette
- Harvard Medical School, Boston, MA, United States.,Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, United States
| | - Sara Kimble
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, United States
| | - Scott L Rauch
- Harvard Medical School, Boston, MA, United States.,Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, United States
| | - Justin T Baker
- Harvard Medical School, Boston, MA, United States.,Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, United States
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McGorry PD, Mei C, Chanen A, Hodges C, Alvarez-Jimenez M, Killackey E. Designing and scaling up integrated youth mental health care. World Psychiatry 2022; 21:61-76. [PMID: 35015367 PMCID: PMC8751571 DOI: 10.1002/wps.20938] [Citation(s) in RCA: 131] [Impact Index Per Article: 65.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Mental ill-health represents the main threat to the health, survival and future potential of young people around the world. There are indications that this is a rising tide of vulnerability and need for care, a trend that has been augmented by the COVID-19 pandemic. It represents a global public health crisis, which not only demands a deep and sophisticated understanding of possible targets for prevention, but also urgent reform and investment in the provision of developmentally appropriate clinical care. Despite having the greatest level of need, and potential to benefit, adolescents and emerging adults have the worst access to timely and quality mental health care. How is this global crisis to be addressed? Since the start of the century, a range of co-designed youth mental health strategies and innovations have emerged. These range from digital platforms, through to new models of primary care to new services for potentially severe mental illness, which must be locally adapted according to the availability of resources, workforce, cultural factors and health financing patterns. The fulcrum of this progress is the advent of broad-spectrum, integrated primary youth mental health care services. They represent a blueprint and beach-head for an overdue global system reform. While resources will vary across settings, the mental health needs of young people are largely universal, and underpin a set of fundamental principles and design features. These include establishing an accessible, "soft entry" youth primary care platform with digital support, where young people are valued and essential partners in the design, operation, management and evaluation of the service. Global progress achieved to date in implementing integrated youth mental health care has highlighted that these services are being accessed by young people with genuine and substantial mental health needs, that they are benefiting from them, and that both these young people and their families are highly satisfied with the services they receive. However, we are still at base camp and these primary care platforms need to be scaled up across the globe, complemented by prevention, digital platforms and, crucially, more specialized care for complex and persistent conditions, aligned to this transitional age range (from approximately 12 to 25 years). The rising tide of mental ill-health in young people globally demands that this focus be elevated to a top priority in global health.
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Affiliation(s)
- Patrick D McGorry
- Orygen, National Centre of Excellence in Youth Mental Health; Centre for Youth Mental Health, University of Melbourne, Parkville, VIC, Australia
| | - Cristina Mei
- Orygen, National Centre of Excellence in Youth Mental Health; Centre for Youth Mental Health, University of Melbourne, Parkville, VIC, Australia
| | - Andrew Chanen
- Orygen, National Centre of Excellence in Youth Mental Health; Centre for Youth Mental Health, University of Melbourne, Parkville, VIC, Australia
| | - Craig Hodges
- Orygen, National Centre of Excellence in Youth Mental Health; Centre for Youth Mental Health, University of Melbourne, Parkville, VIC, Australia
| | - Mario Alvarez-Jimenez
- Orygen, National Centre of Excellence in Youth Mental Health; Centre for Youth Mental Health, University of Melbourne, Parkville, VIC, Australia
| | - Eóin Killackey
- Orygen, National Centre of Excellence in Youth Mental Health; Centre for Youth Mental Health, University of Melbourne, Parkville, VIC, Australia
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Global Collaboration Around Digital Mental Health: The LAMP Consortium. JOURNAL OF TECHNOLOGY IN BEHAVIORAL SCIENCE 2022; 7:227-233. [PMID: 35071742 PMCID: PMC8764174 DOI: 10.1007/s41347-022-00240-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 12/02/2021] [Accepted: 01/03/2022] [Indexed: 11/29/2022]
Abstract
Despite the great potential, there has been a lack of progress in the development of sharable and scalable tools for digital mental health due to difficulty in reproducibility and clinical application. The LAMP Platform was developed to address this gap by creating a single platform that works for a variety of clinical and research use cases. The study aims to understand how a consortium of clinical and research sites can help onboard, execute, and expand digital health research, software, and use cases. The Division of Digital Psychiatry implemented a formal consortium with goal of expanding the reach of mindLAMP as a digital mental health platform, enabling diverse studies and expanded use cases, and supportint growth of mindLAMP and consortium members’ research. The LAMP Consortium has brought together 54 sites from across the world, encouraging collaboration and idea sharing. These sites’ locations range from the USA to the Czech Republic to Australia, and apply the many features of LAMP to research, clinical, research and clinical, and industry use. The most popular features were surveys, sharing/viewing data, and GPS passive data collection. A user support network is necessary to encourage research and clinical use of the LAMP Platform. Resources like documentation, an online forum, and newsletters are essential to promote cooperation between many types of sites that is essential to advancing the field of digital mental health.
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Vaidyam A, Halamka J, Torous J. Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study. JMIR Mhealth Uhealth 2022; 10:e30557. [PMID: 34994710 PMCID: PMC8783287 DOI: 10.2196/30557] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/18/2021] [Accepted: 11/11/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables. OBJECTIVE This study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.3), Advanced Encryption Standard-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a previous version of mindLAMP. METHODS The Learn, Assess, Manage, and Prevent (LAMP) platform addresses the abovementioned challenges in enhancing clinical insight by supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code. RESULTS With a simplified programming interface and novel data representation that captures additional metadata, the LAMP platform enables interoperability with existing Fast Healthcare Interoperability Resources-based health care systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offer robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques. CONCLUSIONS The LAMP platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and functions across a wide range of use cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple delivery of mindfulness exercises through personalized, just-in-time adaptive interventions.
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Affiliation(s)
- Aditya Vaidyam
- Beth Israel Deaconess Medical Center, Boston, MA, United States
| | | | - John Torous
- Beth Israel Deaconess Medical Center, Boston, MA, United States
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Nicolaidou I, Aristeidis L, Lambrinos L. A gamified app for supporting undergraduate students’ mental health: A feasibility and usability study. Digit Health 2022; 8:20552076221109059. [PMID: 35756831 PMCID: PMC9228636 DOI: 10.1177/20552076221109059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/18/2022] Open
Abstract
Resilience, a person's mental ability to deal with challenging situations adaptively, is an important life skill. Supporting students in building psychological resilience and coping during crises (with the COVID-19 pandemic being a prime example) is crucial. Very few mobile applications (apps) for mental health explicitly report behavioral change techniques. Moreover, only a handful of the apps that support resilience are gamified, or use smartphone sensors readily available in modern smartphones for health self-management, or were designed for use by a nonclinical population. This study describes the design of a prototype for a gamified, theory-based mobile app that utilizes the Internet of Things to provide personalized data and enhance undergraduate students’ resilience. A total of 74 participants evaluated the prototype and completed an online questionnaire during the COVID-19 lockdowns. The questionnaire included questions examining the design's feasibility for supporting resilience and questions on the System Usability Scale evaluating its usability. Regarding the evaluation of the prototype on improving psychological resilience, positive responses (M = 3.76 out of 5, SD = 0.82) were received for all functions (goal setting for studying, socializing and physical exercise, progress monitoring using sensors or self-reporting, reflection, motivational badges). The System Usability Scale returned an evaluation score of 72.9, indicating a satisfactory degree of usability. The resilience app is a promising proof of concept. Combining Internet of Things capabilities with active user interaction while incorporating behavior change techniques in a gamified environment was well accepted by students. Implications for the design of gamified environments for well-being are drawn. Future research will empirically validate its design using quasi-experimental methods.
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Affiliation(s)
- Iolie Nicolaidou
- Emerging Technologies for Learning, Department of Communication and Internet Studies, Cyprus University of Technology, Limassol, Cyprus
| | - Loizos Aristeidis
- Convertico Media/Department of Communication and Internet Studies, Cyprus University of Technology, Limassol, Cyprus
| | - Lambros Lambrinos
- New Communication Technologies and the Internet, Department of Communication and Internet Studies, Cyprus University of Technology, Limassol, Cyprus
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Lakhtakia T, Bondre A, Chand PK, Chaturvedi N, Choudhary S, Currey D, Dutt S, Khan A, Kumar M, Gupta S, Nagendra S, Reddy PV, Rozatkar A, Scheuer L, Sen Y, Shrivastava R, Singh R, Thirthalli J, Tugnawat DK, Bhan A, Naslund JA, Patel V, Keshavan M, Mehta UM, Torous J. Smartphone digital phenotyping, surveys, and cognitive assessments for global mental health: Initial data and clinical correlations from an international first episode psychosis study. Digit Health 2022; 8:20552076221133758. [PMID: 36386246 PMCID: PMC9647298 DOI: 10.1177/20552076221133758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 09/30/2022] [Indexed: 11/11/2022] Open
Abstract
Objective To examine feasibility and acceptability of smartphone mental health app use
for symptom, cognitive, and digital phenotyping monitoring among people with
schizophrenia in India and the United States. Methods Participants in Boston, USA and Bhopal and Bangalore, India used a smartphone
app to monitor symptoms, play cognitive games, access relaxation and
psychoeducation resources and for one month, with an initial clinical and
cognitive assessment and a one-month follow-up clinical assessment.
Engagement with the app was compared between study sites, by clinical
symptom severity and by cognitive functioning. Digital phenotyping data
collection was also compared between three sites. Results By Kruskal-Wallis rank-sum test, we found no difference between app
activities completed or digital phenotyping data collected across the three
study sites. App use also did not correlate to clinical or cognitive
assessment scores. When using the app for symptom monitoring, preliminary
findings suggest app-based assessment correlate with standard cognitive and
clinical assessments. Conclusions Smartphone app for symptom monitoring and digital phenotyping for individuals
with schizophrenia appears feasible and acceptable in a global context.
Clinical utility of this app for real-time assessments is promising, but
further research is necessary to determine the long-term efficacy and
generalizability for serious mental illness.
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Affiliation(s)
- Tanvi Lakhtakia
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | | | - Prabhat Kumar Chand
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| | | | - Soumya Choudhary
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| | - Danielle Currey
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Siddharth Dutt
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| | | | - Mohit Kumar
- Department of Psychiatry, AIIMS Bhopal, All India Institute of Medical Sciences Bhopal, Bhopal, India
| | - Snehil Gupta
- Department of Psychiatry, AIIMS Bhopal, All India Institute of Medical Sciences Bhopal, Bhopal, India
| | - Srilakshmi Nagendra
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| | - Preethi V Reddy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| | - Abhijit Rozatkar
- Department of Psychiatry, AIIMS Bhopal, All India Institute of Medical Sciences Bhopal, Bhopal, India
| | - Luke Scheuer
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | | | | | | | - Jagadisha Thirthalli
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| | | | | | - John A Naslund
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Vikram Patel
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Matcheri Keshavan
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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