Assessing vulnerability to panic: a systematic review of psychological and physiological responses to biological challenges as prospective predictors of panic attacks and panic disorder

Robinaugh, Donald J.; Ward, Meredith J.; Toner, Emma R.; Brown, Mackenzie; Losiewicz, Olivia M; Bui, Éric; Orr, Scott P.

doi:10.1136/gpsych-2019-100140

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…Each question has 4 choices: 0, not at all; 1 mildly, but it didn't bother me much; 2, moderately-it wasn't pleasant at times; and 3, severely-it bothered me a lot. The cut-off points of the sum are minimal (0-7), mild (8)(9)(10)(11)(12)(13)(14)(15), moderate (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26), and severe (26-63) depressive symptoms.…”

Section: Bdi and Baimentioning

confidence: 99%

Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study (Preprint)

Tsai¹,

Chen²,

Wu³

et al. 2021

Preprint

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BACKGROUND A panic attack (PA) is an intense form of anxiety accompanied by multiple somatic presentations, leading to frequent emergency department visits and repetitive medical examinations before a formal diagnosis. Designing a PA prediction model could help prevent iatrogenic harm to patients and facilitate more personalized treatment. OBJECTIVE This study aimed to provide a seven-day PA prediction model and determine the relationship between physiological factors, anxiety and depressive factors, and air quality index. METHODS We enrolled 59 participants with PD (DMS-5 and MINI interview). Participants used smartwatches (Garmin vivosmart 4) and mobile applications to collect their sleep, heart rate, activity level, anxiety, and depression scores (BDI, BAI, STAI-S, STAI-T, and PDSS-SR) in their real life for a duration of one year. We also included air quality indexes from open data. To analyze these data, our team used six machine learning methods: random forests, decision trees, LDA, AdaBoost, XgBoost, and regularized greedy forests. RESULTS For seven-day PA prediction, the random forest produced the best prediction rate. The model achieved an accuracy of 97.5% on the training set. Overall, the accuracy of the testing set was 67.4–81.3% for different machine learning algorithms. The most critical variables in the model were questionnaire and physiological features such as BAI, BDI, STAI, MINI, average heart rate, resting heart rate, and deep sleep duration. CONCLUSIONS It is possible to predict panic attacks using a combination of data from questionnaires, and physiological and environmental data. Prediction accuracy was 97.5% on the training data and 81.3% on the testing data.

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Section: Bdi and Baimentioning

confidence: 99%

Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study (Preprint)

Tsai¹,

Chen²,

Wu³

et al. 2021

Preprint

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show abstract

“…Furthermore, palpitations and accelerated heart rate (HR) are frequent symptoms both during and beyond PA events [ 1 , 2 , 3 , 4 ]. Empirical evidence has pointed to respiratory dysregulation as a biomarker of panic vulnerability [ 5 , 6 ] and several abnormalities in resting-state respiratory patterns have repeatedly been documented in patients diagnosed with PD when compared with healthy controls (HCs) or patients with other anxiety disorders [ 4 , 7 , 8 , 9 ]. Subclinical abnormalities in autonomic and cardiac function have also been found in patients with PD [ 4 , 7 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Cardiorespiratory Assessments in Panic Disorder Facilitated by Wearable Devices: A Systematic Review and Brief Comparison of the Wearable Zephyr BioPatch with the Quark-b2 Stationary Testing System

et al. 2023

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Abnormalities in cardiorespiratory measurements have repeatedly been found in patients with panic disorder (PD) during laboratory-based assessments. However, recordings performed outside laboratory settings are required to test the ecological validity of these findings. Wearable devices, such as sensor-imbedded garments, biopatches, and smartwatches, are promising tools for this purpose. We systematically reviewed the evidence for wearables-based cardiorespiratory assessments in PD by searching for publications on the PubMed, PsycINFO, and Embase databases, from inception to 30 July 2022. After the screening of two-hundred and twenty records, eight studies were included. The limited number of available studies and critical aspects related to the uncertain reliability of wearables-based assessments, especially concerning respiration, prevented us from drawing conclusions about the cardiorespiratory function of patients with PD in daily life. We also present preliminary data on a pilot study conducted on volunteers at the Villa San Benedetto Menni Hospital for evaluating the accuracy of heart rate (HR) and breathing rate (BR) measurements by the wearable Zephyr BioPatch compared with the Quark-b2 stationary testing system. Our exploratory results suggested possible BR and HR misestimation by the wearable Zephyr BioPatch compared with the Quark-b2 system. Challenges of wearables-based cardiorespiratory assessment and possible solutions to improve their reliability and optimize their significant potential for the study of PD pathophysiology are presented.

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“…Researchers have used a variety of data sources to predict PD severity and prognosis, including demographic features, clinical scales, diagnostic information, medical history, functional magnetic resonance imaging (fMRI), electrocardiogram (ECG), electroencephalogram (EEG), and genetic data, such as DNA methylation signatures [ 8 ]. In recent years, a few studies have begun to use watch-type computers, wearable devices, or physical challenge by CO 2 [ 9 ] to predict PAs. However, there is no clear evidence showing which features are superior for prediction.…”

Section: Introductionmentioning

confidence: 99%

Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study

Tsai¹,

Chen²,

Liu³

et al. 2022

JMIR Med Inform

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Background A panic attack (PA) is an intense form of anxiety accompanied by multiple somatic presentations, leading to frequent emergency department visits and impairing the quality of life. A prediction model for PAs could help clinicians and patients monitor, control, and carry out early intervention for recurrent PAs, enabling more personalized treatment for panic disorder (PD). Objective This study aims to provide a 7-day PA prediction model and determine the relationship between a future PA and various features, including physiological factors, anxiety and depressive factors, and the air quality index (AQI). Methods We enrolled 59 participants with PD ( Diagnostic and Statistical Manual of Mental Disorders, 5th edition , and the Mini International Neuropsychiatric Interview). Participants used smartwatches (Garmin Vívosmart 4) and mobile apps to collect their sleep, heart rate (HR), activity level, anxiety, and depression scores (Beck Depression Inventory [BDI], Beck Anxiety Inventory [BAI], State-Trait Anxiety Inventory state anxiety [STAI-S], State-Trait Anxiety Inventory trait anxiety [STAI-T], and Panic Disorder Severity Scale Self-Report) in their real life for a duration of 1 year. We also included AQIs from open data. To analyze these data, our team used 6 machine learning methods: random forests, decision trees, linear discriminant analysis, adaptive boosting, extreme gradient boosting, and regularized greedy forests. Results For 7-day PA predictions, the random forest produced the best prediction rate. Overall, the accuracy of the test set was 67.4%-81.3% for different machine learning algorithms. The most critical variables in the model were questionnaire and physiological features, such as the BAI, BDI, STAI, MINI, average HR, resting HR, and deep sleep duration. Conclusions It is possible to predict PAs using a combination of data from questionnaires and physiological and environmental data.

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Assessing vulnerability to panic: a systematic review of psychological and physiological responses to biological challenges as prospective predictors of panic attacks and panic disorder

Cited by 8 publications

References 18 publications

Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study (Preprint)

Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study (Preprint)

Cardiorespiratory Assessments in Panic Disorder Facilitated by Wearable Devices: A Systematic Review and Brief Comparison of the Wearable Zephyr BioPatch with the Quark-b2 Stationary Testing System

Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study

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