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  1. Article ; Online: Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch Data.

    McGinnis, Ellen W / Loftness, Bryn / Lunna, Shania / Berman, Isabel / Bagdon, Skylar / Lewis, Genevieve / Arnold, Michael / Danforth, Christopher M / Dodds, Peter S / Price, Matthew / Copeland, William E / McGinnis, Ryan S

    IEEE open journal of engineering in medicine and biology

    2024  Volume 5, Page(s) 14–20

    Abstract: Objective: Panic attacks are an impairing mental health problem that affects 11% of adults every year. Current criteria describe them as occurring without warning, despite evidence suggesting individuals can often identify attack triggers. We aimed to ... ...

    Abstract Objective: Panic attacks are an impairing mental health problem that affects 11% of adults every year. Current criteria describe them as occurring without warning, despite evidence suggesting individuals can often identify attack triggers. We aimed to prospectively explore qualitative and quantitative factors associated with the onset of panic attacks.
    Results: Of 87 participants, 95% retrospectively identified a trigger for their panic attacks. Worse individually reported mood and state-level mood, as indicated by Twitter ratings, were related to greater likelihood of
    Conclusions: These promising results suggest that individuals who experience panic attacks may be able to anticipate their next attack which could be used to inform future prevention and intervention efforts.
    Language English
    Publishing date 2024-01-15
    Publishing country United States
    Document type Journal Article
    ISSN 2644-1276
    ISSN (online) 2644-1276
    DOI 10.1109/OJEMB.2024.3354208
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Pilot trial comparing COVID-19 publication database to conventional online search methods.

    Torfs-Leibman, Camille / Ashikaga, Takamaru / Krag, David / Lunna, Shania / Robtoy, Sarah / Bombardier, Rachel

    BMJ health & care informatics

    2022  Volume 29, Issue 1

    Abstract: Background and objectives: Literature review using search engines results in a list of manuscripts but does not provide the content contained in the manuscripts. Our goal was to evaluate user performance-based criteria of concept retrieval accuracy and ... ...

    Abstract Background and objectives: Literature review using search engines results in a list of manuscripts but does not provide the content contained in the manuscripts. Our goal was to evaluate user performance-based criteria of concept retrieval accuracy and efficiency using a new database system that contained information extracted from 1000 COVID-19 articles.
    Methods: A sample of 17 students from the University of Vermont were randomly assigned to use the COVID-19 publication database or their usual preferred search methods to research eight prompts about COVID-19. The relevance and accuracy of the evidence found for each prompt were graded. A Cox proportional hazards' model with a sandwich estimator and Kaplan-Meier plots were used to analyse these data in a time-to-correct answer context.
    Results: Our findings indicate that students using the new information management system answered significantly more prompts correctly and, in less time, than students using conventional research methods. Bivariate models for demographic factors indicated that previous research experience conferred an advantage in study performance, though it was found to be independent from the assigned research method.
    Conclusions: The results from this pilot randomised trial present a potential tool for more quickly and thoroughly navigating the literature on expansive topics such as COVID-19.
    MeSH term(s) Humans ; COVID-19 ; Pilot Projects ; Online Systems
    Language English
    Publishing date 2022-11-09
    Publishing country England
    Document type Journal Article
    ISSN 2632-1009
    ISSN (online) 2632-1009
    DOI 10.1136/bmjhci-2022-100616
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Pilot trial comparing COVID-19 publication database to conventional online search methods

    Takamaru Ashikaga / Shania Lunna / Camille Torfs-Leibman / Sarah Robtoy / David Krag / Rachel Bombardier

    BMJ Health & Care Informatics, Vol 29, Iss

    2022  Volume 1

    Abstract: Background and objectives Literature review using search engines results in a list of manuscripts but does not provide the content contained in the manuscripts. Our goal was to evaluate user performance-based criteria of concept retrieval accuracy and ... ...

    Abstract Background and objectives Literature review using search engines results in a list of manuscripts but does not provide the content contained in the manuscripts. Our goal was to evaluate user performance-based criteria of concept retrieval accuracy and efficiency using a new database system that contained information extracted from 1000 COVID-19 articles.Methods A sample of 17 students from the University of Vermont were randomly assigned to use the COVID-19 publication database or their usual preferred search methods to research eight prompts about COVID-19. The relevance and accuracy of the evidence found for each prompt were graded. A Cox proportional hazards’ model with a sandwich estimator and Kaplan-Meier plots were used to analyse these data in a time-to-correct answer context.Results Our findings indicate that students using the new information management system answered significantly more prompts correctly and, in less time, than students using conventional research methods. Bivariate models for demographic factors indicated that previous research experience conferred an advantage in study performance, though it was found to be independent from the assigned research method.Conclusions The results from this pilot randomised trial present a potential tool for more quickly and thoroughly navigating the literature on expansive topics such as COVID-19.
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Language English
    Publishing date 2022-02-01T00:00:00Z
    Publisher BMJ Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  4. Article ; Online: ‘Refbin’ an online platform to extract and classify large-scale information

    Shania Lunna / Isabelle Flinn / James Prytherch / Camille Torfs-Leibman / Sarah Robtoy / Matt Bansak / David Krag

    BMJ Health & Care Informatics, Vol 29, Iss

    a pilot study of COVID-19 related papers

    2022  Volume 1

    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher BMJ Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  5. Article ; Online: 'Refbin' an online platform to extract and classify large-scale information: a pilot study of COVID-19 related papers.

    Lunna, Shania / Flinn, Isabelle / Prytherch, James / Torfs-Leibman, Camille / Robtoy, Sarah / Bansak, Matt / Krag, David

    BMJ health & care informatics

    2022  Volume 29, Issue 1

    Abstract: Introduction: The number of new biomedical manuscripts published on important topics exceeds the capacity of single persons to read. Integration of literature is an even more elusive task. This article describes a pilot study of a scalable online system ...

    Abstract Introduction: The number of new biomedical manuscripts published on important topics exceeds the capacity of single persons to read. Integration of literature is an even more elusive task. This article describes a pilot study of a scalable online system to integrate data from 1000 articles on COVID-19.
    Methods: Articles were imported from PubMed using the query 'COVID-19'. The full text of articles reporting new data was obtained and the results extracted manually. An online software system was used to enter the results. Similar results were bundled using note fields in parent-child order. Each extracted result was linked to the source article. Each new data entry comprised at least four note fields: (1) result, (2) population or sample, (3) description of the result and (4) topic. Articles underwent iterative rounds of group review over remote sessions.
    Results: Screening 4126 COVID-19 articles resulted in a selection of 1000 publications presenting new data. The results were extracted and manually entered in note fields. Integration from multiple publications was achieved by sharing parent note fields by child entries. The total number of extracted primary results was 12 209. The mean number of results per article was 15.1 (SD 12.0). The average number of parent note fields for each result note field was 6.8 (SD 1.4). The total number of all note fields was 28 809. Without sharing of parent note fields, there would have been a total of 94 986 note fields.
    Conclusion: This pilot study demonstrates the feasibility of a scalable online system to extract results from 1000 manuscripts. Using four types of notes to describe each result provided standardisation of data entry and information integration. There was substantial reduction in complexity and reduction in total note fields by sharing of parent note fields. We conclude that this system provides a method to scale up extraction of information on very large topics.
    MeSH term(s) COVID-19 ; Humans ; Pilot Projects ; Research Design ; SARS-CoV-2
    Language English
    Publishing date 2022-03-09
    Publishing country England
    Document type Journal Article
    ISSN 2632-1009
    ISSN (online) 2632-1009
    DOI 10.1136/bmjhci-2021-100452
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Discovering Digital Biomarkers of Panic Attack Risk in Consumer Wearables Data.

    McGinnis, Ellen W / Lunna, Shania / Berman, Isabel / Loftness, Bryn C / Bagdon, Skylar / Danforth, Christopher M / Price, Matthew / Copeland, William E / McGinnis, Ryan S

    medRxiv : the preprint server for health sciences

    2023  

    Abstract: Panic attacks are an impairing mental health problem that impacts more than one out of every 10 adults in the United States (US). Clinical guidelines suggest panic attacks occur without warning and their unexpected nature worsens their impact on quality ... ...

    Abstract Panic attacks are an impairing mental health problem that impacts more than one out of every 10 adults in the United States (US). Clinical guidelines suggest panic attacks occur without warning and their unexpected nature worsens their impact on quality of life. Individuals who experience panic attacks would benefit from advance warning of when an attack is likely to occur so that appropriate steps could be taken to manage or prevent it. Our recent work suggests that an individual's likelihood of experiencing a panic attack can be predicted by self-reported mood and community-level Twitter-derived mood the previous day. Prior work also suggests that physiological markers may indicate a pending panic attack. However, the ability of objective physiological, behavioral, and environmental measures to predict next-day panic attacks has not yet been explored. To address this question, we consider data from 38 individuals who regularly experienced panic attacks recruited from across the US. Participants responded to daily questions about their panic attacks for 28 days and provided access to data from their Apple Watches. Results indicate that objective measures of ambient noise (louder) and resting heart rate (higher) are related to the likelihood of experiencing a panic attack the next day. These preliminary results suggest, for the first time, that panic attacks may be predictable from data passively collected by consumer wearable devices, opening the door to improvements in how panic attacks are managed and to the development of new preventative interventions.
    Clinical relevance: Objective data from consumer wearables may predict when an individual is at high risk for experiencing a next-day panic attack. This information could guide treatment decisions, help individuals manage their panic, and inform the development of new preventative interventions.
    Language English
    Publishing date 2023-03-06
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.03.01.23286647
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Discovering Digital Biomarkers of Panic Attack Risk in Consumer Wearables Data.

    McGinnis, Ellen W / Lunna, Shania / Berman, Isabel / Loftness, Bryn C / Bagdon, Skylar / Danforth, Christopher M / Price, Matthew / Copeland, William E / McGinnis, Ryan S

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2023  Volume 2023, Page(s) 1–4

    Abstract: Panic attacks are an impairing mental health problem that impacts more than one out of every 10 adults in the United States (US). Clinical guidelines suggest panic attacks occur without warning and their unexpected nature worsens their impact on quality ... ...

    Abstract Panic attacks are an impairing mental health problem that impacts more than one out of every 10 adults in the United States (US). Clinical guidelines suggest panic attacks occur without warning and their unexpected nature worsens their impact on quality of life. Individuals who experience panic attacks would benefit from advance warning of when an attack is likely to occur so that appropriate steps could be taken to manage or prevent it. Our recent work suggests that an individual's likelihood of experiencing a panic attack can be predicted by self-reported mood and community-level Twitter-derived mood the previous day. Prior work also suggests that physiological markers may indicate a pending panic attack. However, the ability of objective physiological, behavioral, and environmental measures collected via consumer wearable sensors (referred to as digital biomarkers) to predict next-day panic attacks has not yet been explored. To address this question, we consider data from 38 individuals who regularly experienced panic attacks recruited from across the US. Participants responded to daily questions about their panic attacks for 28 days and provided access to data from their Apple Watches. Mixed Regressions, with an autoregressive covariance structure were used to estimate the prevalence of a next-day panic attack Results indicate that digital biomarkers of ambient noise (louder) and resting heart rate (higher) are indicative of experiencing a panic attack the next day. These preliminary results suggest, for the first time, that panic attacks may be predictable from digital biomarkers, opening the door to improvements in how panic attacks are managed and to the development of new preventative interventions.Clinical Relevance- Objective data from consumer wearables may predict when an individual is at high risk for experiencing a next-day panic attack. This information could guide treatment decisions, help individuals manage their panic, and inform the development of new preventative interventions.
    MeSH term(s) Adult ; Humans ; United States ; Panic Disorder/diagnosis ; Panic Disorder/epidemiology ; Panic Disorder/psychology ; Quality of Life ; Self Report ; Affect ; Wearable Electronic Devices
    Language English
    Publishing date 2023-12-11
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC40787.2023.10339982
    Database MEDical Literature Analysis and Retrieval System OnLINE

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