Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch Data
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...
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| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10399901/ |
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| author | Ellen W. McGinnis Bryn Loftness Shania Lunna Isabel Berman Skylar Bagdon Genevieve Lewis Michael Arnold Christopher M. Danforth Peter S. Dodds Matthew Price William E. Copeland Ryan S. McGinnis |
| author_facet | Ellen W. McGinnis Bryn Loftness Shania Lunna Isabel Berman Skylar Bagdon Genevieve Lewis Michael Arnold Christopher M. Danforth Peter S. Dodds Matthew Price William E. Copeland Ryan S. McGinnis |
| author_sort | Ellen W. McGinnis |
| collection | DOAJ |
| description | 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 <italic>next-day</italic> panic attack. In a subsample of participants who uploaded their wearable sensor data (n = 32), louder ambient noise and higher resting heart rate were related to greater likelihood of <italic>next-day</italic> panic attack. 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. |
| format | Article |
| id | doaj-art-448cd9b8ebcd45a986ca2b6ff0fcd474 |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-448cd9b8ebcd45a986ca2b6ff0fcd4742025-01-28T00:02:07ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-015142010.1109/OJEMB.2024.335420810399901Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch DataEllen W. McGinnis0Bryn Loftness1https://orcid.org/0000-0003-4597-0783Shania Lunna2https://orcid.org/0009-0009-5992-7912Isabel Berman3Skylar Bagdon4Genevieve Lewis5Michael Arnold6https://orcid.org/0000-0002-1218-0315Christopher M. Danforth7https://orcid.org/0000-0002-9857-2845Peter S. Dodds8https://orcid.org/0000-0003-1973-8614Matthew Price9https://orcid.org/0000-0001-5637-9230William E. Copeland10https://orcid.org/0000-0002-1348-7781Ryan S. McGinnis11https://orcid.org/0000-0001-8396-6967M-Sense Research Group, Wake Forest School of Medicine, Winston-Salem, NC, USAVermont Center for Children, Youth and Families, University of Vermont, Burlington, VT, USAVermont Center for Children, Youth and Families, University of Vermont, Burlington, VT, USAVermont Center for Children, Youth and Families, University of Vermont, Burlington, VT, USAVermont Center for Children, Youth and Families, University of Vermont, Burlington, VT, USAVermont Center for Children, Youth and Families, University of Vermont, Burlington, VT, USAVermont Complex Systems Center, University of Vermont, Burlington, VT, USAVermont Complex Systems Center, University of Vermont, Burlington, VT, USAVermont Complex Systems Center, University of Vermont, Burlington, VT, USACenter for Research on Emotion, Stress and Technology, University of Vermont, Burlington, VT, USAVermont Center for Children, Youth and Families, University of Vermont, Burlington, VT, USAM-Sense Research Group, Wake Forest School of Medicine, Winston-Salem, NC, USAObjective: 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 <italic>next-day</italic> panic attack. In a subsample of participants who uploaded their wearable sensor data (n = 32), louder ambient noise and higher resting heart rate were related to greater likelihood of <italic>next-day</italic> panic attack. 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.https://ieeexplore.ieee.org/document/10399901/Panic attackswearablesapple watchmental healthtwitter |
| spellingShingle | Ellen W. McGinnis Bryn Loftness Shania Lunna Isabel Berman Skylar Bagdon Genevieve Lewis Michael Arnold Christopher M. Danforth Peter S. Dodds Matthew Price William E. Copeland Ryan S. McGinnis Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch Data IEEE Open Journal of Engineering in Medicine and Biology Panic attacks wearables apple watch mental health |
| title | Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch Data |
| title_full | Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch Data |
| title_fullStr | Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch Data |
| title_full_unstemmed | Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch Data |
| title_short | Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch Data |
| title_sort | expecting the unexpected predicting panic attacks from mood twitter and apple watch data |
| topic | Panic attacks wearables apple watch mental health |
| url | https://ieeexplore.ieee.org/document/10399901/ |
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