A Clinical Guide to the Treatment of the Human Stress Response (Springer Series on Stress and Coping

Springer Series on Stress and Coping A Clinical Guide to the Treatment of the Human Stress Response. Authors: Everly . Read this book on SpringerLink.
Table of contents

• Download A Clinical Guide to the Treatment of the Human Stress Response e-book
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• Development and Clinical Evaluation of an mHealth Application for Stress Management

Supplemental information for practitioners includes a flow chart on the nature of stress physiology, a relaxation report form, specific protocols for teaching the relaxation response, a self-report checklist designed for health education purposes, and over references. Comprehensive and up-to-the-minute, A Clinical Guide to the Treatment of the Human Stress Response will be of interest to students, practitioners, and researchers in the fields of psychology, psychiatry, medicine, nursing, social work, and public health.

The Link from Stress Arousal to Disease. Conflict Theory of Psychosomatic Disease. Hypnosis in the Management of Stress Reactions. History Mechanisms of Action Biofeedback in the Treatment of the Stress Response. Integrated Relationship between the Central Nervous System. Measurement of the Human Stress Response. Millons Personality Theory and Stress. Control and the Human Stress Response. Selecting a Relaxation Technique. Individuals in developing nations are particularly vulnerable 2 , 3.

In developed nations, an especially vulnerable population is military veterans. For example, approximately one-third of US military veterans suffer from some type of psychological distress, including post-traumatic stress disorder PTSD , major depressive disorder MDD , and suicidal ideation 4. Diagnostic and subthreshold levels of PTSD are associated with poor quality of life, including anger, stress, alcoholism, depression, poor physical health, and increased suicidality 5 , 6 , and cause impaired ability to function in social, educational, and work environments 7.

Among various interventions to treat depression and anxiety, cognitive behavioral therapy CBT has emerged as standard practice for reduction of psychiatric symptoms 8 , with previous studies indicating that CBT has similar therapeutic effects as anti-depressant medication 9. CBT is generally administered by mental health professionals and consists of a structured, collaborative process that helps individuals consider and alter their thought processes and behaviors associated with stress or anxiety, usually administered weekly over several months The limitations of CBT, including lack of objective data available for providers and high patient dropout rates, could be mitigated with emerging technologies.

To support real-time objective stress monitoring in mental health treatment, wearable physiological sensors and associated mobile health mHealth applications 14 have the potential to quantify biological metrics associated with stress 15 , support remote monitoring, and alert the wearer or provider to real-time changes in emotional state.

Existing approaches to stress detection use a wide array of features calculated from sensor data measuring various aspects of heartbeat, including pulse photoplethysmography PPG or ECG 15 — 17 , skin conductance measurement 18 — 20 , and measurement of respiration, all of which are responsive to increased sympathetic nervous system activity associated with stress Standard supervised machine learning methods have been used previously to develop stress classifiers, which require subjects to engage in tasks known to induce stress so that stress or non-stress labels can be assigned to the input features.

Previous work has emphasized the difficulties imposed on stress classification by individual subject variability in physiological responses to stress 16 , Another concern is the physical activity of subjects that triggers similar cardiovascular and electrodermal physiological signals as stress, leading to masking and confounds of stress detection 15 , The major challenge in using mobile physiological sensors to quantify stress is the lack of robust and clinically tested algorithms to classify stress in a mobile environment in real time Previous stress monitoring algorithms have been built with traditional laboratory physiological sensor suites that do not translate well to operational settings 17 , 19 , such as mental health treatment.

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New wearable devices with clinical grade sensors and associated mobile applications have the potential to take real-time stress monitoring outside of the laboratory. There is an opportunity to combine foundational mobile stress monitoring algorithm research methods with new mobile physiological sensor suites to create an accurate, quantitative classifier for continuous and objective real-time stress assessment. It is hypothesized that stress induced using standard methods can be classified with high accuracy using a machine learning algorithm and that the use of such an algorithm in an mHealth application can reduce post-deployment psychiatric symptoms, including stress, anger, and anxiety, in a clinical population undergoing CBT.

Thirty-five participants 24 males; average age Participants were recruited using recruitment flyers posted online and through recruitment fairs at local universities. Upon arrival, participants provided written-informed consent and completed a series of questionnaires including: Wireless physiological sensors were then placed on the participants, followed by a 5-min recording of baseline physiological activity, while participants remained seated. Following data acquisition, participants were debriefed and thanked for their participation. Participants in the classifier study responded to the SUDS, in which they reported their current level of stress on a scale of 0—, with 0 indicating that they were completely relaxed and indicating that they were experiencing severe stress Participants then completed the DASS, designed to assess current depression, anxiety, and stress using responses to 21 statements Respondents indicated the degree to which each statement has been true for them over the preceding week on a 4-point Likert scale.

Participants also responded to the PROMIS anger scale, which consists of an 8-item measure on which respondents indicate the frequency of each item from the past week on a 5-point Likert scale A second system was used to ensure the stress algorithm was compatible across multiple hardware solutions and to provide for mobile stress classification in future studies.

Event times and physiological data were stored in Biopac. All data were read into Python analysis scripts running under the Enthought Canopy environment. The numpy, scipy, pandas, and matplotlib libraries were used for feature extraction and data analysis 27 , and the scikit-learn library 28 , 29 was used for classifier development. Visual inspection of the raw data in the Biopac software and the interactive Python environment was used to discard physiologically noisy or missing participant data.

From the raw data, non-overlapping 1-min windows were analyzed to yield feature vectors for the minute blocks. For minutes with less than 40 valid IBI samples, the block of data was discarded; for remaining blocks, the mean IBI was calculated. The HR and EDA means were then normalized separately for each participant by subtracting the average of the 5-min baseline. Matplotlib boxplots and scatterplots were used to explore the distributions of the task-specific patterns e.

A 2-feature linear model classifier was trained and tested on the E3 dataset using stochastic gradient descent. Five-fold cross-validation was implemented to evaluate the average performance of the algorithm on the train set: While the cross-validation measures were used to compare performance of different learning algorithms e. Accuracy was defined as the ratio of the sum of hits and correct rejections over the sum of minute blocks classified as either stressed or non-stressed. The hit rate was defined as the number of hit minute blocks divided by the total number true stress blocks TSST-S , and the false-alarm rate was defined as the number of false-alarm blocks divided by the total number of true non-stress blocks baseline.

Following development and evaluation of the stress classifier, 16 participants [13 males; average age Exclusion criteria consisted of: All treatment was administered in an individual format by the study clinicians, who are licensed mental health professionals. The study clinicians were directed to use standard CBT treatment manuals 32 as a foundation for CBT while using clinical judgment to determine what content to cover in each session and how many sessions to schedule. This approach was chosen rather than utilizing a fixed protocol in order to represent routine clinical practice.

Weekly sessions continued until: Compliance in the experimental group was quantified by use of the mobile application. Respondents indicated the extent to which they had experienced each symptom described in the past month using a 5-point scale, from 1 not at all to 5 very often. The PCL-M was considered a secondary outcome metric in the clinical study. An mHealth application and stress classifier were used for data collection in the clinical study.

The mHealth application was implemented in Android on a Samsung Galaxy S4 phone and received data from the E3 band Empatica, Milan, Italy , classified stress using the algorithm developed in the classifier study, alerted the user when stress was detected, and presented stress mitigation techniques to the user, such as breathing exercises.

A web-based provider portal that resided on a secure cloud server was also implemented and allowed the provider to view physiological data for individual patients and enter reminders e. Non-parametric statistical analysis was used to compare within groups measures across the two timepoints initial and final assessment and consisted of Wilcoxon signed-ranks tests with significance set to 0.

Between groups differences were assessed using Mann—Whitney U tests with significance set to 0. All statistical testing was done in SPSS software version The average age of the participants was Baseline cortisol in the study subset was 0.

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Self-reported distress at baseline and following the TSST. Stress, depression, and anxiety scores were considered normal Each task phase in the experiment was regarded as having distinct ground truth values for whether the participant would be considered stressed or not stressed.

The baseline resting task was considered to be a non-psychological stress phase. Task-dependent heart rate and skin conductance measures across participants. For the E3 data, the training accuracy was For the Biopac data, the testing accuracy was Stress classification using the E3-collected data is shown at left and with the Biopac-collected data shown at right. The decision boundary is shown as a line; data points to the left of this boundary were classified as non-stress.

Nine individuals in the study dropped out prior to completion of therapy and follow-up visit. The remaining participants that completed the study included five in the experimental group and two in the control group. One participant in the experimental group that completed the study did not use the mHealth application but completed standard CBT and was reassigned to the control group.

For the initial assessment, stress and depression for the participants was in the 96 th percentile, and anxiety was in the 99 th percentile as compared to a normative sample Anxiety scores were considered extremely severe, while stress and depression scores were in the severe range The follow-up assessment was completed by four participants in the experimental group and three participants in the control group.

The current series of studies shows the feasibility of creating an individualized, physiological classifier of stress with a high degree of accuracy compatible with different sensor suites. The use of such an algorithm in an mHealth application 35 may reduce symptoms of stress and anger in a small clinical population, increase the number of CBT sessions individuals will attend, and decrease their dropout rate.

Given the large number of individuals that experience mental health disorders and the unmet need for treatment, especially in developing nations, such mobile approaches have the potential to provide or augment treatment in the absence of standard, in-person care However, most commercially available apps targeting mental health remain untested 22 , Classification of stress was based on features gathered from a large user group undergoing the TSST, which has one of the highest effect sizes for eliciting stress and associated cortisol responses in laboratory settings Stress classification was based on cardiovascular and electrodermal inputs 3 , which showed high variance due to individual differences 19 , and was addressed by individual baseline normalization.

The psychoendocrine reaction to life stressors, or stressors outside of the laboratory setting, such as bereavement, declining health, or flashbacks in PTSD, are likely of higher duration and intensity than laboratory stressors Therefore, the algorithm and decision boundary developed using acute socio-evaluative stress in the current work may underperform for more severe stressors associated with MDD, PTSD, or other forms of mental health disorders. For instance, the DASS and PROMIS-anger scores from the classifier-development study sample indicated a relatively low burden of stress, depression, anxiety, and anger as opposed to a relatively high burden of mental health symptoms in the clinical evaluation study sample.

In addition, veteran post-traumatic stress is often comorbid with depression, which has recently been shown to be associated with intensified anger 2. Anger has been acknowledged as the most prevalent veteran readjustment problem Interestingly, the use of an mHealth application focused on stress identification and reduction in conjunction with CBT reduced metrics of anger and anxiety in addition to stress in a small clinical sample. This higher dropout rate may be due to characteristics of the veteran population or the high burden of mental health symptoms in the study sample.

For example, previous research has indicated that medication compliance among veterans is relatively low The high dropout rate likely also reflects that many of the participants were experiencing periods of acute stress and were often preoccupied with these stressors. The availability of validated mHealth applications that individuals could use within the context of their daily lives would help to address this issue.

Within the sample, those who used the mobile application and stress algorithm were more likely to complete the study and demonstrated reduced stress and anger as compared to the control group. This reduced stress may result from an increased awareness of their stressors due to the alerts provided through the mobile application to the user 41 , or the use of the guided breathing exercises within the application Future research will include further accuracy refinement through reduction in environmental noise, and a method to learn individual user stress thresholds Additional operational testing to reduce environmental noise is being conducted in order to determine the changes in classifier false alarms and misses when collecting data in different temperatures and while performing different physical activities 43 , ranging from typing on a keyboard to walking or running.

The 2-feature linear model trained with stochastic gradient descent employed in this effort has the advantage of including a bias term to tune the decision boundary threshold on the stress vs. The low sample size in the clinical evaluation and the high dropout rate represent a limitation in the current study.

Even though there have been an estimated , cases of US military veterans with PTSD over the past two decades 44 , many do not seek care Additional challenges include long wait times experienced in the VA medical system 46 , low participation rates in clinical studies 47 , and a high dropout rate during CBT. Further data and objective outcome measures are needed to validate the observed reductions in stress, anger, and anxiety symptoms in the study sample.

The capability to classify individual physiological stress in a mobile environment has additional uses outside of veteran mental health treatment, including military or medical training For example, training instructors could remotely and simultaneously monitor objective stress status for individual trainees during live training sessions and act on the information in real time In addition, instructors could identify individual trainees that tend to have more intense stress responses than others during training scenarios to provide targeted coping and resilience training interventions Beyond training, additional applications for this capability include stress research for laboratory and field settings, chronic disease monitoring for tracking outpatient health and long-term data capture to inform care, and objective, real-time user experience evaluations.

However, high quality, wearable devices and robust, validated algorithms remain a necessary component to realizing the potential of this technology. BW analyzed data and wrote the manuscript.

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• A Clinical Guide to the Treatment of the Human Stress Response by George S. Everly Jr..
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GC and BW developed the algorithm for stress. SD and DJ developed the mobile application and managed the experiments. PG designed the clinical evaluation. SK and JG conducted the clinical evaluation. US Patent Pending Schmidt-Daly, DJ Classifying stress in a mobile environment