Reliability, validity and cut-off score of the Chinese version of the panic disorder severity scale self-report form in patients with panic disorder

Sleep, physical activity and panic attacks: A two-year prospective cohort study using smartwatches, deep learning and an explainable artificial intelligence model

BACKGROUND

Sleep and physical activity suggestions for panic disorder (PD) are critical but less surveyed. This two-year prospective cohort study aims to predict panic attacks (PA), state anxiety (SA), trait anxiety (TA) and panic disorder severity (PDS) in the upcoming week.

METHODS

We enrolled 114 PD patients from one general hospital. Data were collected using the DSM-5, the MINI, clinical app questionnaires (BDI, BAI, PDSS-SR, STAI) and wearable devices recording daily sleep, physical activity and heart rate from 16 June 2020 to 10 June 2022. Our teams applied RNN, LSTM, GRU deep learning and SHAP explainable methods to analyse the data.

RESULTS

The 7-day prediction accuracies for PA, SA, TA, and PDS were 92.8 %, 83.6 %, 87.2 %, and 75.6 % from the LSTM model. Using the SHAP explainable model, higher initial BDI or BAI score and comorbidities with depressive disorder, generalized anxiety disorder or agoraphobia predict a higher chance of PA. However, PA decreased under the following conditions: daily average heart rate, 72-87 bpm; maximum heart rate, 100-145 bpm; resting heart rate, 55-60 bpm; daily climbing of more than nine floors; total sleep duration between 6 h 23 min and 10 h 50 min; deep sleep, >50 min; and awake duration, <53 min.

LIMITATIONS

Moderate sample size and self-report questionnaires were the limitations.

CONCLUSIONS

Deep learning predicts recurrent PA and various anxiety domains with 75.6-92.8 % accuracy. Recurrent PA decreases under adequate daily sleep and physical activity.

Cognitive impairment in Chinese adult patients with type III spinal muscular atrophy without disease-modifying treatment

Objective

Spinal muscular atrophy (SMA) is a neurodegenerative disorder characterized by the degeneration of motor neurons in the spinal cord. It remains uncertain whether the cognitive performance of adult patients with SMA is impaired. The objective of this study was to assess the cognitive profile of adult Chinese patients with SMA and the association between clinical features and cognitive ability, particularly executive function.

Methods

This cross-sectional study included 22 untreated adult patients with type III SMA and 20 healthy subjects. The following variables were assessed: general intelligence, memory, attention, language, executive function, depression, anxiety, and other demographic and clinical parameters. In addition, physical function was evaluated using the Hammersmith Functional Motor Scale Expanded (HFMSE), the Revised Upper Limb Module (RULM), and the 6-Minute Walk Test (6MWT).

Results

SMA patients had lower scores than healthy subjects in the Verbal Fluency Test, Stroop effect, Total Errors, Perseverative Responses, Perseverative Errors, and Non-perseverative Errors in the Wisconsin Card Sorting Test, showing impaired abilities of SMA patients in executive function. In the Attention Network Test (ANT), the results indicated that the SMA patients also had selective deficits in their executive control networks. Ambulant patients had better executive function test performance than non-ambulant ones. Compromised executive abilities in patients with SMA were correlated with a younger age at onset, poorer motor function, and higher levels of anxiety and depression.

Conclusion

Our study presented the distribution of cognitive impairment in a Chinese cohort with SMA. Patients with type III SMA showed selective deficits in executive function, which may be associated with disease severity, physical impairment, depression and anxiety. Future cognitive studies, accounting for motor and emotional impairment, are needed to evaluate if executive impairment is driven by specific brain changes or by those confounding factors.

Enhancing panic disorder treatment with mobile-aided case management: an exploratory study based on a 3-year cohort analysis

Background

Individuals with panic disorder frequently face ongoing symptoms, suboptimal treatment adherence, and increased relapse rates. Although mobile health interventions have shown promise in improving treatment outcomes for numerous mental health conditions, their effectiveness, specifically for panic disorder, has yet to be determined.

Objective

This study investigates the effects of a mobile-aided case management program on symptom reduction and quality of care among individuals with panic disorder.

Methods

This 3-year cohort study enrolled 138 participants diagnosed with panic disorder. One hundred and eight participants joined the mobile-aided case management group and 30 in the treatment-as-usual group. Data were collected at baseline, 3-month, 6-month, and 12-month treatment checkpoints using self-report questionnaires, in-depth interviews, direct observation, and medical record analysis.

Results

During the maintenance treatment phase, the mobile-assisted case management group decreased both panic severity (p = 0.008) and state anxiety (p = 0.016) more than the control group at 6 months. Participants who underwent case management experienced enhanced control over panic symptoms, heightened self-awareness, and elevated interpersonal support.

Conclusion

The mobile-aided case management is beneficial in managing panic disorder, especially maintenance treatment.

French adaptation and validation of the Panic Disorder Severity Scale-self-report

BACKGROUND

The aims of this study were to conduct a cross-cultural validation of the Panic Disorder Severity Scale - Self-Report (PDSS-SR) and to examine psychometric properties of the French-Canadian version.

METHODS

A sample of 256 adults were included in the validation study based on data from the baseline interview of a clinical trial on transdiagnostic cognitive-behavioral therapy for mixed anxiety disorders. Participants completed the Anxiety and Related Disorders Interview Schedule (ADIS-5), and self-report instruments including the PDSS-SR, Beck Anxiety Inventory (BAI), Mobility Inventory for Agoraphobia (MIA), Sheehan Disability Scale (SDS), Patient Health Questionnaire (PHQ-9), Social Phobia Inventory (SPIN), Insomnia Severity Index (ISI) and Penn State Worry Questionnaire (PSWQ). The cross-cultural adaptation in French of the PDSS-SR included a rigorous back-translation process, with an expert committee review. Sensitivity to change was also examined with a subgroup of patients (n = 72) enrolled in the trial.

RESULTS

The French version of the PDSS-SR demonstrated good psychometric properties. The exploratory factor analysis supported a one factor structure with an eigenvalue > 1 that explained 64.9% of the total variability. The confirmatory factor analysis (CFA) corroborated a one-factor model with a good model fit. Internal consistency analysis showed a .91 Cronbach's alpha. The convergent validity was adequate with the ADIS-5 clinical severity ratings for panic disorder (r = .56) and agoraphobia (r = .39), as well as for self-report instruments [BAI (r = .63), MIA (accompanied: r = .50; alone: r = .47) and SDS (r = .37)]. With respect to discriminant validity, lower correlations were found with the SPIN (r = .17), PSWQ (r = .11), ISI (r = .19) and PHQ-9 (r = .28). The optimal threshold for probable diagnosis was 9 for the PDSS-SR and 4 for the very brief 2-item version. The French version showed good sensitivity to change.

CONCLUSIONS

The French version of the PDSS-SR has psychometric properties consistent with the original version and constitutes a valid brief scale to assess the severity of panic disorder and change in severity over time, both in research and clinical practice.

Panic attack prediction using wearable devices and machine learning: Development and cohort study (Preprint)

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.