Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

Summary

This study introduces a novel polar histogram visualization method for analyzing Acute Stress Disorder Scale (ASDS) scores, focusing on elderly ICU caregivers. The technique displayed all 19 ASDS variables simultaneously, revealing distinct stress patterns between healthy controls and caregivers while demonstrating significant improvements post-intervention in hyperarousal and avoidance symptoms.

Abstract

The Acute Stress Disorder Scale (ASDS) is crucial for assessing acute stress disorder (ASD), especially in high-stress environments like Intensive Care Units (ICUs). Traditional methods struggle to interpret all 19 ASDS variables simultaneously. This study introduces a novel polar histogram visualization approach to enhance ASDS score analysis, focusing on elderly ICU caregivers. A polar histogram visualization for ASDS scores was developed using MATLAB. Data from healthy elderly controls (n=106) and elderly ICU caregivers (EC-ICU; n=309) were used to compare ASDS profiles. A subgroup of EC-ICU participants (n=109) received interventions on social support and positive coping strategies. Intervention effectiveness and stress dysregulation patterns were analyzed using this new technique and traditional statistical methods. The polar histogram effectively displayed all 19 ASDS variables simultaneously, revealing distinct patterns between healthy controls (mean ASDS score: 29.36) and EC-ICU participants (mean ASDS score: 62.61). This technique highlighted significant differences in stress profiles not apparent in conventional bar charts. Post-intervention, the EC-ICU subgroup showed a 5%-8% reduction across ten ASDS indicators related to avoidance, hyperarousal, and emotional distress. The most significant improvements were physical reactions to trauma reminders, hypervigilance, and sleep disturbances. This polar histogram approach offers comprehensive, intuitive visualization of ASDS scores, enhancing clinical interpretation and ASD assessment. Integrating multi-dimensional psychological indicators into a single visual framework enables a more precise analysis of stress states and intervention efficacy. The technique shows particular utility in identifying stress patterns in elderly ICU caregivers and evaluating targeted interventions. This innovative method has significant implications for developing personalized support strategies, improving ASD assessment, and advancing stress disorder research across clinical settings.

Introduction

Acute Stress Disorder (ASD)¹ is a significant mental health concern that can occur in the immediate aftermath of a traumatic event. It is characterized by a cluster of symptoms, including intrusion, negative mood, dissociation, avoidance, and arousal^1,2,3. The accurate assessment and timely intervention of ASD are crucial, particularly in high-stress environments such as Intensive Care Units (ICUs), where both patients and caregivers are at elevated risk for developing stress-related disorders^4,5,6

The Acute Stress Disorder Scale (ASDS) is a widely used tool for assessing ASD symptoms, consisting of 19 items that correspond to the diagnostic criteria outlined in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, 5th Edition). While the ASDS has proven to be a reliable and valid measure, the interpretation and analysis of its multidimensional data present significant challenges for clinicians and researchers alike. Traditional methods of data presentation, such as simple statistical summaries or bar charts, often fail to capture the complex interplay between the various symptom clusters and may obscure important patterns within the data^7,8.

In recent years, there has been a growing recognition of the importance of data visualization in enhancing the understanding of complex psychological phenomena⁹. Effective visualization techniques can reveal patterns, trends, and relationships that might otherwise remain hidden in raw data or basic statistical outputs. This is particularly relevant in the context of ASD assessment, where a comprehensive understanding of the symptom profile is essential for accurate diagnosis and targeted intervention^10,11.

The limitations of current visualization methods for ASDS data have become increasingly apparent. Linear representations of the 19 ASDS variables often struggle to effectively convey the relationships between different symptom clusters. At the same time, three-dimensional visualizations can be difficult to interpret and may introduce perceptual biases. There is a clear need for a novel approach that can simultaneously represent all ASDS variables in an intuitive and easily interpretable format^8,9.

In response to these challenges, this study introduces a polar histogram visualization technique for analyzing ASDS scores. This innovative method leverages the circular nature of polar coordinates to represent all 19 ASDS variables concurrently, allowing for a more holistic view of the ASD symptom profile. By employing this technique, the study aims to enhance the clinical interpretation and assessment of ASD, particularly in high-stress environments such as ICUs^12,13.

The research focuses on elderly caregivers in ICU scenarios, a population that is particularly vulnerable to acute stress due to the demanding nature of caregiving and the often critical condition of their loved ones^3,14. This study delves into the mental health status of elderly caregivers facing critically ill patients, revealing complex relationships between anxiety, depression, traumatic stress, social support, and coping strategies. These findings not only contribute to the development of more targeted support policies and interventions, improving healthcare systems but also raise public awareness about this group. In the long term, this research has profound implications for refining long-term care policies, promoting interdisciplinary research, and improving the quality of life for both elderly caregivers and the patients they care for, thereby providing important scientific evidence and practical guidance for addressing the social challenges brought about by an aging population.

By applying the polar histogram visualization method to this population, the study seeks to demonstrate its utility in identifying distinct stress patterns and evaluating the effectiveness of interventions^9,15. The research aimed to explore the mental health status of the caregivers, revealing complex relationships between anxiety, depression, traumatic stress, social support, and coping strategies. Data collection methods included psychological consultations and interviews with ASD specialists. The intervention comprised Cognitive Behavioral Therapy (CBT) and mindfulness-based positive coping strategies, applied to a subset of 109 subjects. The study design incorporated a quantitative comparison between ASDS scores of elderly ICU caregivers and healthy elderly controls, as well as an evaluation of intervention effectiveness.

Protocol

This study introduces a novel polar histogram visualization technique for analyzing Acute Stress Disorder Scale (ASDS) scores, focusing on elderly caregivers aged 60-80 years in Intensive Care Units (ICUs). The research was conducted at the Intensive Care Medicine Department of Dongzhimen Hospital, Beijing University of Chinese Medicine, in Beijing, China. Participant recruitment and data collection were conducted over a three-year period from January 2021 to December 2023. The study setup involved recruiting elderly ICU caregivers and healthy elderly controls from the local community. All participants provided informed consent before undergoing an ASDS assessment conducted by professional psychologists using the standardized 19-item ASDS questionnaire¹. The software tools used in this study are listed in the Table of Materials.

1. Data collection and preparation

NOTE: The specific ASDS questionnaire version used is not critical to the research method. In this investigation, genuine ASDS data collected from clinical assessments were employed. The data were acquired using the standardized 19-item ASDS questionnaire. The dataset comprises scores for four distinct symptom clusters as defined by the DSM-IV: dissociation, re-experiencing, avoidance, and arousal.

1. Data collection for ASDS
   1. Recruitment of participants
      1. Recruit study participants from two groups: elderly ICU caregivers aged 60-80 years and healthy elderly controls from the local community. The elderly caregivers (60-80 years) in this study were family members of ICU patients, not ICU patients themselves. Ensure all participants were cognitively capable of engaging in professional consultations with psychological counselors regarding the ASDS questionnaire.
      2. The acute stressor in the ASDS assessment specifically referred to their experience of having a family member in the ICU. Carefully screen to ensure that these caregivers did not have any major physiological illnesses themselves that could confound the stress assessment.
      3. For the healthy control group, recruit elderly individuals from the local community who have no current major illnesses and no immediate family members in critical care situations. Those who fall outside these criteria are considered part of the exclusion category. Ensure that all participants provide informed consent prior to any assessment or data collection.
   2. ASDS assessment
      1. Administer the standardized 19-item ASDS questionnaire¹ (Figure 1) to all participants. Ask professional psychologists to conduct the ASDS assessment interviews to ensure consistency and reliability. Collect and record ASDS scores for all participants following the completion of the questionnaire.
   3. Intervention
      1. From the group of elderly ICU caregivers, randomly select a subset of 109 individuals to participate in the intervention. Apply Cognitive Behavioral Therapy (CBT)¹⁶ techniques to this intervention group. Additionally, implement mindfulness-based positive coping strategies as part of the comprehensive intervention approach.
      2. The intervention program integrated CBT techniques and mindfulness-based positive coping strategies delivered through a structured approach. For the CBT component, carry out two-weekly cognitive restructuring sessions (45 min each), daily progressive muscle relaxation training (15 min), and weekly problem-solving therapy (60 min), focusing on helping caregivers identify and modify negative thought patterns about ICU situations.
      3. For the mindfulness component, carry out daily morning meditation (20 min), evening body scan exercises (15 min), and brief mindful breathing exercises during ICU visits. Implement these interventions through a combination of weekly individual counseling, bi-weekly group workshops, take-home audio guides, and daily app-based check-ins for support and compliance monitoring.
      4. Ensure all sessions are conducted by certified clinical psychologists with at least 3 years of experience in CBT and mindfulness training to ensure intervention quality and consistency.
      5. For all EC-ICU participants who agreed to participate in the intervention program, ensure that the intervention was initiated within 24-48 h after their initial ASDS assessment. Use this standardized timing to minimize potential temporal confounding factors and ensure consistency in the intervention protocol.
      6. Administer the intervention over a 30-day period for all participants, starting from their initial intervention session. This standardized duration ensures the comparability of intervention outcomes across participants. Following the completion of the intervention, repeat the ASDS assessment process (Step 1.1.2) for the intervention group participants.
2. Examination of the original ASDS data
   1. Open the spreadsheet data file. Browse through different sheets (Figure 1): Healthy, Elderly Caregiver - ICU (24H), EC-ICU (Intervention), and EC-ICU (Control).
   2. Ensure that the data matrices are as follows: Healthy group 19 x 106 Matrix, Elderly Caregiver - ICU (24 h) 19 x 309 Matrix, EC-ICU(Intervention) 19 x 109 Matrix, and EC-ICU (Control) 19 x 200 Matrix.
      1. The ASDS questionnaire comprises 19 items that assess four distinct symptom clusters: Dissociative Symptoms (Items 1-5, measuring emotional numbness, daze states, unreality experiences, physical detachment, and trauma-related amnesia), Reexperiencing Symptoms (Items 6-9, evaluating intrusive thoughts, nightmares, flashbacks, and emotional reactivity to trauma reminders), Avoidance Symptoms (Items 10-13, assessing cognitive and behavioral avoidance of trauma-related thoughts, conversations, situations, and emotional responses), and Arousal Symptoms (Items 14-19, examining sleep disturbance, irritability, concentration problems, hypervigilance, startle response, and physical reactions to trauma reminders). For the intervention approaches targeting these symptom clusters see step 1.1.3, which describes the implemented CBT techniques and mindfulness-based strategies.

Figure 1: Acute Stress Disorder Scale (ASDS) data. This figure displays the organization of ASDS data in spreadsheets for different participant groups. Please click here to view a larger version of this figure.

3. Importing ASDS data into MATLAB workspace
   1. Open the spreadsheets options menu, click the Add-ins tab, and enter the Add-ins management interface (Figure 2). Browse and add MATLAB's excllink.xlam, which is usually located in MATLAB's toolbox\exlink directory.
   2. In spreadsheet, select the Data for Each Group as viewed in Step 1.1.2, then click on the MATLAB plugin's Send data to MATLAB option. Then, all data can be seen in MATLAB's workspace. The Variable data_h contains the Healthy group 19 x 106 Matrix; the Variable data_ec contains the EC-ICU(Intervention) 19 x 109 Matrix; and the Variable data_control contains the EC-ICU (control) 19 x 200 Matrix.

Figure 2: Add-ins management interface for integrating MATLAB plugin. This image shows the spreadsheet interface for adding the MATLAB plugin, enabling data transfer between spreadsheet and MATLAB. Please click here to view a larger version of this figure.

2. Ordinary Statistics for ASDS of Elderly Caregiver - ICU

NOTE: Conventional statistical analysis can provide a general overview of the data's basic characteristics. However, it can also form a distinct contrast with the subsequent Polar Histogram Visualization.

1. Using the commands means = mean (data_ec, 2); and total_scores = sum (data, 1) obtain the mean values of the 19 variables and the sum scores for the ASDS of Elderly Caregiver - ICU, respectively.
2. Use the code in Supplementary Coding File 1 to view the distribution of mean values and sum scores for the 19 variables of the ASDS of Elderly Caregiver - ICU, as shown in Figure 3.

Figure 3: Distribution of mean values and sum scores for the 19 variables of the ASDS of Elderly Caregiver - ICU. These graphs illustrate the mean scores for each ASDS item and the distribution of total ASDS scores among elderly caregivers in the ICU. Please click here to view a larger version of this figure.

3. Polar histogram perspective of ASDS for elderly caregivers

NOTE: Using a polar histogram to compare healthy elderly individuals with Elderly Caregivers-ICU provides a more intuitive visualization of the differences between the two sample groups.

1. Using the commands mean_ec = mean (data_ec, 2); and mean_h = mean (data_h, 2) calculate the ASDS sample means for the Elderly Caregivers group and the healthy group, respectively.
2. Use the polarhistogram command to create an overlaid plot of the two variables mean_ec and mean_h (Figure 4).
3. Add coordinates and annotations to the figure using the code provided in Supplementary Coding File 1.

Figure 4: Polar histogram comparison of ASDS scores between Elderly Caregivers - ICU and Healthy Elderly Individuals. This polar histogram visually compares ASDS scores between elderly caregivers in ICU and healthy elderly controls, highlighting differences across all 19 ASDS variables. Please click here to view a larger version of this figure.

4. Ordinary statistics of intervention

NOTE: Typically, comparisons of ASDS scores are conducted using bar graphs for visual contrast.

1. To analyze the Acute Stress Disorder Scale (ASDS) scores across different groups, calculate the sample means using the following MATLAB commands:
   For the Elderly Caregivers group (Original Data): mean_ec = mean(data_ec, 2);
   For the EC-ICU Intervention group (Control Group): mean_control = mean(data_control, 2);
   For the healthy control group (Intervention Group): mean_h = mean(data_h, 2);
2. Plot a comparison as shown in Figure 5 by using the command bar ([mean_ec, mean_control, mean_h]). Add figure annotations using the commands xticklabels and ylabel.

Figure 5: ASDS prognostic analysis of EC-ICU intervention group. This bar graph compares ASDS scores across three groups: original elderly caregivers, control group, and intervention group, showing the impact of interventions. Please click here to view a larger version of this figure.

5. Polar histogram perspective of intervention

NOTE: Using a Polar Histogram to compare the prognosis of the EC-ICU Intervention group (Figure 6) can result in more reliable and significant visualization outcomes.

1. Use the following code: data_diff=abs(mean_control-mean_intevention); [~, idxx]=sort(data_diff) to rank the effects on prognosis, obtaining the variable idxx for the ranking of the 19 ASDS items' prognosis effects.
2. Use the polarhistogram command to create an overlaid plot of the control group and intervention group, and plot and annotate according to the order of idxx to obtain Figure 6.

Figure 6: Polar histogram comparison of control and intervention groups. This polar histogram visualizes the differences in ASDS scores between the control and intervention groups, emphasizing the effects of the intervention on specific ASDS items. Please click here to view a larger version of this figure.

Results

Figure 1 displays the ASDS data of elderly caregivers in the ICU. Using MATLAB's plugin (Figure 2), the data can be imported into MATLAB's workspace. Figure 3 presents the characteristics of the ASDS data for elderly caregivers in the ICU: the mean is 3.3, and the cumulative value is 62.6. This indicates that the ASDS level of participants in the ICU scenario is not extremely high but significantly higher than that of healthy elderly individuals (Figure 4). Figure 4 uses the Polar Histogram visualization method to quantify the comparison between healthy elderly individuals and elderly caregivers in the ICU, highlighting the significant differences between the two.

ASDS is a 19-variable Stress Profile Analysis. To assess the effectiveness of Cognitive Behavioral Therapy (CBT) interventions, such as positive coping strategies, a Polar Histogram was employed to visualize the prognosis of elderly caregivers in the ICU. The results demonstrated that the intervention had a positive impact on several key aspects of acute stress disorder symptoms among the caregivers.

The most significant improvements were observed in the following areas (Figure 6): Experiencing physical reactions when reminded of the trauma (4.16 to 3.63), being jumpy or easily startled (3.95 to 3.44), being overly alert (4.13 to 3.66), having trouble concentrating (4.09 to 3.64), feeling irritable (3.69 to 3.28), having trouble sleeping (3.67 to 3.27).

These reductions in symptom severity suggest that the intervention was particularly effective in addressing hyperarousal symptoms, which are a core component of acute stress disorder. The improvement in sleep quality and concentration abilities may contribute to better overall functioning and well-being for the caregivers. Additionally, modest improvements were observed in: Trying to avoid situations or people that remind of the trauma (3.60 to 3.28), trying not to talk about the trauma (3.51 to 3.24), trying not to think about the trauma (3.73 to 3.49), feeling upset when reminded of the trauma (2.46 to 2.37).

These changes indicate that the intervention also had a positive effect on avoidance symptoms, potentially helping caregivers to better cope with trauma-related triggers and memories.

While the improvements in some areas were less pronounced, the overall pattern suggests that the intervention was successful in reducing the severity of acute stress symptoms across multiple domains. This comprehensive approach to symptom reduction may lead to improved quality of life and better adaptive functioning for elderly caregivers in intensive care unit settings.

The visualization clearly demonstrates the intervention's impact, with the blue area (intervention group) consistently showing lower scores than the pink area (control group) across most ASDS items. This graphical representation provides an intuitive and powerful way to communicate the effectiveness of the intervention to both clinical practitioners and patients.

Supplementary Coding File 1: MATLAB coding file. Please click here to download this file.

Discussion

This study introduces a novel polar histogram visualization technique for analyzing Acute Stress Disorder Scale (ASDS) scores, with particular emphasis on its application in assessing elderly caregivers in Intensive Care Units (ICUs). The method provides a comprehensive and intuitive visual representation of all 19 ASDS variables simultaneously, offering significant advantages over traditional statistical approaches^1,6,17.

Critical steps
The protocol's key components consist of two essential steps, both vital for converting complex, multidimensional data into clear and easily interpretable visual patterns. The first key step is the Polar Histogram Comparison of ASDS Scores (Figure 4), which involves creating an overlaid polar histogram plot of mean ASDS scores for elderly caregivers in ICU and healthy elderly controls. This visualization allows for immediate, intuitive comparison of stress profiles between the two groups across all 19 ASDS variables^10,15,18. The second critical step is the Polar Histogram Perspective of Intervention (Figure 6), which focuses on visualizing the effectiveness of interventions by comparing control and intervention groups using the polar histogram method. The ranking of intervention effects on different ASDS items provides valuable insights into the specific areas of intervention effectiveness, particularly in hyperarousal and avoidance symptoms, showing notable improvements in these critical domains^6,13. Through these steps, rapid identification of stress profile differences and intervention effects is enabled, significantly enhancing the ability to analyze and interpret ASDS data^12,13,19.

Troubleshooting and potential limitations
Despite the advantages of the Polar Histogram method for visualizing and comparing multiple variables, several limitations and potential improvements should be noted. This study utilized the MATLAB Excel Link add-in for data transfer, which may encounter obstacles in certain environments. As an alternative, the xlsread command can be used to import data directly into the MATLAB workspace, providing a more robust data input method. Visualization clarity presents another challenge, as the polar histogram can become cluttered with more variables^6,8,9. While the Polar Histogram method excels in multi-variable visualization, an excessive number of variables can lead to complexity. To optimize visualization, techniques such as principal component analysis, correlation analysis, and significance testing can be employed to reduce the number of variables to a manageable level. Using different colors to identify different groups can achieve the best visual effect, balancing comprehensive representation with clarity and interpretability^8,12,19.

Importance compared to existing methods
Traditional methods of analyzing ASDS data often rely on basic statistical measures or simple bar charts, which can fail to capture the full complexity of the 19-variable ASDS profile^1,7,8. The polar histogram method offers several advantages: Comprehensive Visualization: It allows for simultaneous visualization of all 19 ASDS variables, providing a more holistic view of the stress profile^10,11,19. Intuitive Comparison: The overlaid polar histograms enable quick and intuitive comparisons between different groups or pre- and post-intervention states^11,15,18. Pattern Recognition: The circular arrangement facilitates the identification of patterns or clusters in ASDS profiles that might not be apparent in linear representations^6,8,10.

Potential applications and future directions
The polar histogram visualization method for ASDS has significant potential for broader applications in clinical psychology and stress research. Clinical Assessment: This method could be integrated into clinical software for rapid assessment and monitoring of ASD in various patient populations^4,5,15. Intervention Evaluation: The technique provides a powerful tool for visualizing the effectiveness of different interventions, potentially guiding the development of more targeted treatment strategies^11,16,18. Research Applications: This visualization method could be adapted for other multi-variable psychological assessment tools, expanding its utility beyond ASD research^9,10,19. Patient Education: The visualization's intuitive nature makes it a potentially valuable tool for explaining stress profiles and treatment progress to patients^3,17. Machine Learning Integration: Future research could explore combining this visualization technique with machine learning algorithms for predictive modeling of ASD development or treatment outcomes^12,13,19. Also, the research could explore cross-cultural validation of this visualization method across different populations, and with sufficient validation, the technique could potentially be integrated into clinical electronic health record systems for longitudinal stress monitoring and assessment.

While there are some technical challenges to consider, the polar histogram visualization method for ASDS offers a significant advancement in the analysis and interpretation of acute stress disorder data. Its ability to provide comprehensive, intuitive visualizations of complex stress profiles has the potential to enhance both clinical practice and research in the field of stress disorders.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
EXCEL	Microsoft	Office2021	ASDS Data Collection
MATLAB	MathWorks	2023B	Computing and visualization