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  1. Article: A path towards progress: lessons from the hard things about digital mental health.

    Torous, John

    World psychiatry : official journal of the World Psychiatric Association (WPA)

    2022  Volume 21, Issue 3, Page(s) 419–420

    Language English
    Publishing date 2022-09-08
    Publishing country Italy
    Document type Journal Article
    ZDB-ID 2236130-3
    ISSN 2051-5545 ; 1723-8617
    ISSN (online) 2051-5545
    ISSN 1723-8617
    DOI 10.1002/wps.21003
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Generative artificial intelligence in mental health care: potential benefits and current challenges.

    Torous, John / Blease, Charlotte

    World psychiatry : official journal of the World Psychiatric Association (WPA)

    2024  Volume 23, Issue 1, Page(s) 1–2

    Language English
    Publishing date 2024-01-12
    Publishing country Italy
    Document type Editorial
    ZDB-ID 2236130-3
    ISSN 2051-5545 ; 1723-8617
    ISSN (online) 2051-5545
    ISSN 1723-8617
    DOI 10.1002/wps.21148
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Return of Results in Digital Phenotyping: Ethical Considerations for Real-World Use Cases.

    Torous, John / Blease, Charlotte

    The American journal of bioethics : AJOB

    2024  Volume 24, Issue 2, Page(s) 91–93

    MeSH term(s) Humans ; Psychiatry
    Language English
    Publishing date 2024-01-31
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 2060433-6
    ISSN 1536-0075 ; 1526-5161
    ISSN (online) 1536-0075
    ISSN 1526-5161
    DOI 10.1080/15265161.2024.2298146
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6814: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  4. Article ; Online: Digital Interventions for Adults With Symptoms of Depression and Children and Adolescents With Symptoms of Obsessive-Compulsive Disorder.

    Torous, John

    JAMA

    2021  Volume 325, Issue 18, Page(s) 1839–1840

    MeSH term(s) Adolescent ; Adult ; Brazil ; Child ; Depression/diagnosis ; Depression/therapy ; Diabetes Mellitus ; Humans ; Hypertension ; Obsessive-Compulsive Disorder/diagnosis ; Obsessive-Compulsive Disorder/therapy ; Peru ; Randomized Controlled Trials as Topic
    Language English
    Publishing date 2021-05-11
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 2958-0
    ISSN 1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484
    ISSN (online) 1538-3598
    ISSN 0254-9077 ; 0002-9955 ; 0098-7484
    DOI 10.1001/jama.2021.5047
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    In stock of ZB MED Cologne/Königswinter

    Ua VI Zs.88: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: Digital Mental Health's Unstable Dichotomy-Wellness and Health.

    Torous, John / Firth, Joseph / Goldberg, Simon B

    JAMA psychiatry

    2024  

    Language English
    Publishing date 2024-04-17
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2701203-7
    ISSN 2168-6238 ; 2168-622X
    ISSN (online) 2168-6238
    ISSN 2168-622X
    DOI 10.1001/jamapsychiatry.2024.0532
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    In stock of ZB MED Cologne/Königswinter

    Ui I Zs.235: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  6. Article ; Online: Increasing the value of digital phenotyping through reducing missingness: a retrospective review and analysis of prior studies.

    Currey, Danielle / Torous, John

    BMJ mental health

    2023  Volume 26, Issue 1

    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! ...

    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.
    MeSH term(s) Humans ; Retrospective Studies ; Mobile Applications ; Students ; Medicine
    Language English
    Publishing date 2023-05-17
    Publishing country England
    Document type Journal Article
    ISSN 2755-9734
    ISSN (online) 2755-9734
    DOI 10.1136/bmjment-2023-300718
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: ChatGPT and mental healthcare: balancing benefits with risks of harms.

    Blease, Charlotte / Torous, John

    BMJ mental health

    2023  Volume 26, Issue 1

    Abstract: Against the global need for increased access to mental services, health organisations are looking to technological advances to improve the delivery of care and lower costs. Since November 2022, with the public launch of OpenAI's ChatGPT, the field of ... ...

    Abstract Against the global need for increased access to mental services, health organisations are looking to technological advances to improve the delivery of care and lower costs. Since November 2022, with the public launch of OpenAI's ChatGPT, the field of generative artificial intelligence (AI) has received expanding attention. Although generative AI itself is not new, technical advances and the increased accessibility of large language models (LLMs) (eg, OpenAI's GPT-4 and Google's Bard) suggest use of these tools could be clinically significant. LLMs are an application of generative AI technology that can summarise and generate content based on training on vast data sets. Unlike search engines, which provide internet links in response to typed entries, chatbots that rely on generative language models can simulate dialogue that resembles human conversations. We examine the potential promise and the risks of using LLMs in mental healthcare today, focusing on their scope to impact mental healthcare, including global equity in the delivery of care. Although we caution that LLMs should not be used to disintermediate mental health clinicians, we signal how-if carefully implemented-in the long term these tools could reap benefits for patients and health professionals.
    MeSH term(s) Humans ; Artificial Intelligence ; Mental Health ; Communication ; Health Facilities ; Mental Health Services
    Language English
    Publishing date 2023-11-10
    Publishing country England
    Document type Journal Article
    ISSN 2755-9734
    ISSN (online) 2755-9734
    DOI 10.1136/bmjment-2023-300884
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Digital Phenotyping Data to Predict Symptom Improvement and Mental Health App Personalization in College Students: Prospective Validation of a Predictive Model.

    Currey, Danielle / Torous, John

    Journal of medical Internet research

    2023  Volume 25, Page(s) e39258

    Abstract: Background: Mental health apps offer a transformative means to increase access to scalable evidence-based care for college students. Yet low rates of engagement currently preclude the effectiveness of these apps. One promising solution is to make these ... ...

    Abstract Background: Mental health apps offer a transformative means to increase access to scalable evidence-based care for college students. Yet low rates of engagement currently preclude the effectiveness of these apps. One promising solution is to make these apps more responsive and personalized through digital phenotyping methods able to predict symptoms and offer tailored interventions.
    Objective: Following our protocol and using the exact model shared in that paper, our primary aim in this study is to assess the prospective validity of mental health symptom prediction using the mindLAMP app through a replication study. We also explored secondary aims around app intervention personalization and correlations of engagement with the Technology Acceptance Model (TAM) and Digital Working Alliance Inventory scale in the context of automating the study.
    Methods: The study was 28 days in duration and followed the published protocol, with participants collecting digital phenotyping data and being offered optional scheduled and algorithm-recommended app interventions. Study compensation was tied to the completion of weekly surveys and was not otherwise tied to engagement or use of the app.
    Results: The data from 67 participants were used in this analysis. The area under the curve values for the symptom prediction model ranged from 0.58 for the UCLA Loneliness Scale to 0.71 for the Patient Health Questionnaire-9. Engagement with the scheduled app interventions was high, with a study mean of 73%, but few participants engaged with the optional recommended interventions. The perceived utility of the app in the TAM was higher (P=.01) among those completing at least one recommended intervention.
    Conclusions: Our results suggest how digital phenotyping methods can be used to create generalizable models that may help create more personalized and engaging mental health apps. Automating studies is feasible, and our results suggest targets to increase engagement in future studies.
    International registered report identifier (irrid): RR2-10.2196/37954.
    MeSH term(s) Humans ; Mobile Applications ; Mental Health ; Surveys and Questionnaires ; Students
    Language English
    Publishing date 2023-02-09
    Publishing country Canada
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2028830-X
    ISSN 1438-8871 ; 1438-8871
    ISSN (online) 1438-8871
    ISSN 1438-8871
    DOI 10.2196/39258
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  9. Article: Integrated Digital Mental Health Care: A Vision for Addressing Population Mental Health Needs.

    Lim, Christopher T / Fuchs, Cara / Torous, John

    International journal of general medicine

    2024  Volume 17, Page(s) 359–365

    Abstract: The unmet need for mental health care continues to rise across the world. This article synthesizes the evidence supporting the components of a hypothetical model of integrated digital mental health care to meet population-wide mental health needs. This ... ...

    Abstract The unmet need for mental health care continues to rise across the world. This article synthesizes the evidence supporting the components of a hypothetical model of integrated digital mental health care to meet population-wide mental health needs. This proposed model integrates two approaches to broadening timely access to effective care: integrated, primary care-based mental health services and digital mental health tools. The model solves for several of the key challenges historically faced by digital health, through promoting digital literacy and access, the curation of evidence-based digital tools, integration into clinical practice, and electronic medical record integration. This model builds upon momentum toward the integration of mental health services within primary care and aligns with the principles of the Collaborative Care Model. Finally, the authors present the major next steps toward implementation of integrated digital mental health care at scale.
    Language English
    Publishing date 2024-02-01
    Publishing country New Zealand
    Document type Journal Article
    ZDB-ID 2452220-X
    ISSN 1178-7074
    ISSN 1178-7074
    DOI 10.2147/IJGM.S449474
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  10. Article ; Online: Beyond the Impact Factor: Reflecting on Twenty Years of Leading Efforts in Research, Innovation in Publishing, and Investment in People.

    Torous, John

    Journal of medical Internet research

    2019  Volume 21, Issue 10, Page(s) e16390

    Abstract: This viewpoint celebrates the accomplishments of the Journal of Medical Internet Research on its twentieth anniversary and reviews accomplishments around research publications, journal innovation, and supporting people. ...

    Abstract This viewpoint celebrates the accomplishments of the Journal of Medical Internet Research on its twentieth anniversary and reviews accomplishments around research publications, journal innovation, and supporting people.
    MeSH term(s) Biomedical Research/methods ; Humans ; Internet ; Publishing/standards ; Telemedicine/methods
    Language English
    Publishing date 2019-10-31
    Publishing country Canada
    Document type Journal Article
    ZDB-ID 2028830-X
    ISSN 1438-8871 ; 1439-4456
    ISSN (online) 1438-8871
    ISSN 1439-4456
    DOI 10.2196/16390
    Database MEDical Literature Analysis and Retrieval System OnLINE

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