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Description of why that population is at greater risk of PTSD?

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Description of why that population is at greater risk of PTSD?

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Incidents such as war or natural disasters force many people to flee their homes. PTSD can be developed because of facing direct violence, the death of a loved one, and other tragic events.

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EMDR Therapy: What It Is, Procedure & Effectiveness

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Eye movement desensitization and reprocessing (EMDR) therapy is a treatment for conditions involving traumatic memories. It’s best known for its use in treating post-traumatic stress disorder (PTSD).

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 (1-2 sentences) description of the treatment.

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 (1-2 sentences) description of the treatment.

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Eye movement desensitization and reprocessing therapy is one of the treatments for PTSD. What happens is that the patient moving their eyes in a specific way when processing a traumatic memory during the therapy session.

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Describe what short or long-term effect of the condition it would be designed to treat.

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Describe what short or long-term effect of the condition it would be designed to treat.

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It is designed to reduce symptoms long term, such as anxiety and depression levels, as well as improving quality of life and the way the brain processes trauma and emotions.

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The focus problem we have chosen is Post-Traumatic Stress Disorder (PTSD). It relates to the psychological functions of the brain. The focus problem examines how the brain reacts after a person experiences a traumatic event.

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People with PTSD often have 1-3 other mental health diagnoses as well. This study, in particular, showcases how people with PTSD are much more likely to suffer from other mental health issues, which can lead to harder lifestyles for those already struggling with PTSD.

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Early childhood trauma being exposed at a young age can affect ptsd. Women veterans and inviduals who experienced childhood trauma\ are more likely to develop PTSD, though anyone who experiences a traumatic event can develop it. Factors like a personal or family history of mental illness, and experiencing other stressors after the trauma also may increase risk.

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Women are more at risk due to higher exposure to high-impact trauma than men, as well as at a younger age. Studies also show that lifetime prevalence of PTSD is about two times more common in women, 10-12% in women, 5-6% in men. These differences can be affected by the varying brain chemistry and behaviors between genders.

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Another Major group that is majorly affected by PTSD is veterans and first due to repeated exposure to traumatic events. Not only that but it has been shown that professionals in high-trauma occupations, such as police, firefighters, and emergency medical personnel, face an increased risk. A constant high pressure and high tramua feild like these professions often have people feel at a constant feeling of life and death and have a high guard all the time.

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In the brain, the amygdala is a small, almond-shaped structure that is part of the larger limbic system. The amygdala helps us survive by learning and picking up cues throughout the day to recognize what to fear or avoid. It sends signals to the rest of the brain when it detects potential threats. The amygdala is involved not only in fear but also in aggression, learning through punishment, remembering how to do certain things without recalling when you learned them, interpreting someone’s intentions from their words or actions, emotions related to parenting and caregiving, emotions connected to memories, and learned behaviors linked to addiction.

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This is the source I got the info from https://my.clevelandclinic.org/health/body/hippocampus

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The hippocampus is a seahorse-shaped structure inside the brain and is also part of the limbic system. Its main job is to organize memories, moving them from short-term to long-term storage and helping with retrieval. It also learns about your environment to build spatial memory, so you can stay aware of what’s around you, and supports verbal memory, helping you remember words. In addition, it contributes to visual-spatial memory, which lets you recall the position of your body in relation to nearby objects. The hippocampus is essential for declarative (explicit) memory, which is the recollection of facts and personal experiences.

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The PFC relieves information from multiple cortical regions to process present moments. Connections to other cortical areas allow it to control reactions and adaptations to a particular moment. The PFC is also believed to have a role in emotions with its connections to mood-altering neurotransmitters such as dopamine, norepinephrine, and serotonin. The multiple regions of the PFC are also associated with other functions. The dorsolateral region tends to be associated with planning, strategy and decisions, while the orbitofrontal region is related to inhibiting primal responses from another system of the brain.

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The amygdala tends to overreact, creating intense fear and hyper vigilance.This also causes the amygdala to overreact to no threatening stimuli, driving the heightened state of fear, anxiety, and associated with PTSD symptoms.

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The hippocampus, when affected, can cause the brain to struggle with contextualizing trauma and distinguishing safety from danger. This causes the brain to overgeneralize fear, making someone more susceptible to being put into a state of fear.

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The medial frontal cortex is involved with complex thought in humans as well as blocking fear responses. However, with Post Traumatic Stress Disorder, with the amount of high levels of norepinephrine, these fear responses begin to become unable to become blocked, which is one of the primary effects of those with PTSD being unable to calm down after recalling traumatic events for a good deal of time.

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Animal research has shown that long-term activation of the stress system can lead to serious health risks, and has given us a better understanding of how stress damages the brain. When a person encounters a stressful situation, the adrenal gland releases norepinephrine and cortisol, hormones that circulate through the bloodstream to alert the rest of the body to react to the danger. These elevated levels enhance the function of the amygdala, a region of the brain that plays a role in understanding and remembering emotional events, especially ones that cause fear.

Patients with Post Traumatic Stress Disorder (PTSD) have heightened levels of norepinephrine, a chemical involved in arousal and stress. High levels of this chemical strengthen the emotional reactions of the amygdala, a brain region involved in the fear response, while weakening the rational functions of the prefrontal cortex, which normally allows us to suppress troubling memories and thoughts. Research shows that a drug called D-cycloserine, when used in combination with behavioral therapy, appears to help patients find some relief from these symptoms.

In normal situations, this is a good thing; it can push us to do our best on the report due next week. However, an especially traumatic event can cause post-traumatic stress disorder (PTSD), which occurs when the stress system fails to recover from the event. This results in recurring flashbacks that can disrupt everyday life. Animal research has led to a deeper understanding of PTSD, enabling scientists to develop therapeutics that lessen the number or intensity of a person's flashbacks.

Research in rats and monkeys has given us insight into one possible explanation for PTSD. The prefrontal cortex, the region of the brain involved in complex forethought, may also be involved in blocking repeated fear responses to the same stimulus. However, high levels of norepinephrine can impair the prefrontal cortex so that fear responses are not blocked. Instead, they are constantly brought up. Animal studies have shown that by reducing high norepinephrine levels, the appropriate activity in the prefrontal cortex can be restored and inappropriate overactivity of the amygdala can be dampened. Today, some norepinephrine-based drugs are used to help treat patients with PTSD.

Long-term or high levels of cortisol can also have damaging effects, causing toxicity and shrinkage of brain regions such as the hippocampus, a structure involved in memory formation. Research in rats has shown that cortisol can induce neuron damage and decrease neurogenesis, the birth of neurons. However, these effects may also be treatable, and animal research has turned scientists toward medications that protect the brain of people with PTSD from cortisol damage to the hippocampus.

AboutTheAuthor

Emily K. Dilger, PhD

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Animal Research Success: Post-Traumatic Stress Disorder

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Animal research has shown that long-term stress can damage the brain and has lead to drugs that are used to help treat patients with post-traumatic stress disorder.

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Hyperarousal is also seen in a lot of ptsd patients, making them more on edge than most people. This stems from previous traumatic events, making them more hyper vigilant, always feeling like they have to look out for anything that could hurt them or make them feel the same emotion they felt in that very moment they became traumatized. It even makes people so cautious that they have an increased heart rate and nightmares stemming from being super paranoid, which leads to insomnia.

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Intrusion Symptoms. Here are the common symptoms for people who have PTSD and therefore these are the symptoms.

Repetitive, involuntary memories of the event recurring, unwanted, and distressing memories, nightmares, or flashbacks of the traumatic event, where the person feels like they are reliving it

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Symptoms of negative changes in thinking and mood can occur. This includes ongoing negative emotions and thoughts like feeling emotionally numb or detached from friends and family. Additionally, patients can have a hard time feeling positive emotions or can end up uninterested in activities they once enjoyed.

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Acceptance and Commitment Therapy (ACT) for PTSD

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Acceptance and Commitment Therapy (ACT) for PTSD

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There is a type of therapy for PTSD, which severely helps with the symptom of emotional numbness in particular, but can also help with regulating the fear from the trauma of PTSD in patients, known as Acceptance and Commitment therapy (ACT). This is a form of behavioral therapy, allowing for the patient to be able to actually address the emotions they are prohibiting themselves from feeling and in which ways, in order to progress in their own healing by truly opening their minds to choosing to move past those unhealthy habits of numbing and choosing to begin feeling normally once more, though it can take some time.

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it is a type of cognitive behavioral therapy (CBT) that focuses on helping individuals change their negative thoughts and beliefs about traumatic events. identify and challenge negative thoughts and beliefs related to the trauma

1.Develop more balanced and realistic perspectives

2. Learn coping mechanisms to manage distressing symptoms

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Avoidance is seen in many PTSD patients who tend to always steer clear of previous traumatic experiences rather than face them head-on. This shows in the form of making the constant effort to steer clear of trauma-related thoughts and activities that can end up triggering distressing memories or emotions. They often do all they can just to make sure they don't have to ever feel that same emotion.

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One way to help identify PTSD is by having the person complete a questionnaire that asks about their feelings, behaviors, and thoughts. They can reflect on each question to see if it matches their experience and gradually narrow down possible symptoms. However, because this is self-reported, it cannot provide a final diagnosis. It can, however, offer helpful insight into what might be happening. For an official diagnosis, the person should visit a qualified mental-health professional, share their questionnaire results, and go along with a full clinical evaluation.

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Another approach is to talk with them about what they went through and how they feel after the traumatic event. In other words, an interview can help assess their experience and rule out or confirm PTSD symptoms.

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Those with Post Traumatic Stress Disorder (PTSD) often continuously suffer from traumatic memory, from triggers from said traumatic events in their lives, throughout the rest of their time, that can contribute heavily to irrational reactions whenever those traumatic memories come up in correlation to current events going on in their lives. This traumatic memory can disrupt a person's daily life severely or moderately, even ensuring, in some cases, that they have issues with their daily social interactions, work environments, etc.

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One of the long term effects with PTSD is affecting relationships with people. Since the person will always be in a fight or flight mode even if the threat is no longer there. It becomes difficult to trust others or feeling numb and distant from other people. When it comes to social activities can be affected as well and end up withdrawal and isolation from these things. Another thing happens is pushing away loved ones. The person may be angry and upset due to irritability and behaviors the person with PTSD exhibits. It can also be difficult to concentrate in school or work life.

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Fear of re-experiencing traumatic events through nightmares and flashbacks can create anxiety about sleeping. Things such as cardiovascullar and many physical conditions can be long term consequences for ptsd. Sleep disturbances also play a big role into the depths of ptsd. Most with ptsd are effected by these viviid nightmares. Re-experiencing the trauma during sleep can hinder the person negatively.

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Cognitive Behavioral Therapy in Regards To Treating PTSD

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Cognitive Behavioral Therapy in Regards To Treating PTSD

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A long-term treatment of Post Traumatic Stress Disorder (PTSD) is Cognitive Behavioral Therapy (CBT).

The Cost - These types of therapies can vary in cost, especially based on the type of practice a patient is going to and how many days a week. For instance, private practices are usually around $100-250 per hour, while community in-person sessions are usually $20-$100 per session, and online sessions are usually $40-$100 per week. So, the expected cost of treatment is from $20-250.

Insurance coverage - Since this therapy has to do with mental health, it is usual that, most of the time, insurance doesn’t cover anything = not covered at 1 from the 1-5 scale. Most insurance companies only like to deal with physical health issues.

If a person with PTSD chooses to not do Cognitive Behavioral Therapy (CBT) because of the cost, their specific conditions with PTSD can severely worsen, lead to eventual severe chronic conditions (whether physical or mental), an extreme lower quality in life, increasing depression, etc.

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One use of long term treatment for PTSD and to stop reoccurring nightmares is Imagery Rehearsal Therapy (IRT).

This therapy helps reduce the inviduialuls who are experiencing these nightmares by having the individual write out their scary dreams and making their thoughts positive it hopefully reform the thoughts of their daily life and then mentally rehearsing the new version.

Results indicate that imagery rehearsal had large effects on nightmare frequency, sleep quality, and PTSD symptoms. These effects were sustained through 6 to 12 month follow-up.

It depends on the individual if it can cover the insurance or not, for examples such as invidual plans,diagnosis and medical neccisity of the treatment.

IRT Treatment can vary to $100-$200 or more , and costs depends on factors such as therapists fees.

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A long-term treatment to help people who suffer from ptsd is to expose them to the thing they fear gradually, for them to adjust slowly.

The cost- This type of therapy can vary in price but often sits around $50-300 or more per session, depending on the treatment duration.

Insurance coverage- For most cases, most insurance plans will cover Prolonged Exposure therapy, either fully or partially, but in some way will pay some amount of the therapy. On a scale of 1-5 on how much they would cover it would be somewhere around a 3, saying no matter what they will cover, but whether it's fully or partially is all dependent on how bad it is.

If a person chooses not ot reach out and do this type of therapy, it could lead to their symptoms, like avoidance, negative thoughts, and exaggerated emotional responses, to continue and even grow worse, leading to a decreased quality of life and potential social or occupational impairment

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The therapist works with the patient and tries to talk about the in-depth of the trauma. For example, what triggers it and what terrible memories they have left from it. After knowing the why and what situation, the therapist then talks it out with the patient about new perspectives, meanings to it, and ways to help feel better, and how to handle themselves when it gets triggered.

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Short or Long term effect of the Condition designed to treat

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Short or Long term effect of the Condition designed to treat

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Long term effects include reduced depression and anxiety, decreased negative emotions like shame or guilt, and increased functioning overall for the mind and body after treatment.

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Psychosocial treatment for PTSD patients who develop anxiety disorders

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Psychosocial treatment for PTSD patients who develop anxiety disorders

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- As a result of having PTSD, one can often develop the psychological issue of anxiety disorders. One treatment to effectively treat this psychological issue of anxiety disorders is Cognitive Behavioral Therapy (CBT). CBT is used to treat a numerous amount of disorders, mainly focusing on improving the behaviors, both overall and underlying, that are effecting the patient badly and finding specific ways to ensure the patient starts having more healthy behaviors.

- To treat an anxiety disorder that developed from PTSD, one can undergo Cognitive Behavioral Therapy (CBT), in order to better face their fears through exposure therapy and build up confidence against those fears. This therapy can take a little bit or a while to fully kick in, but it does take time nonetheless. The treatment is designed to treat the short term behavioral effects, which make it difficult for the patient currently, to help the patient behave more normally in their every day lives and the long term effect would treat the severe underlying cause or disorder to generally improve the condition to a much more reduced state.

- Mental health therapists, psychiatrists, and some psychologists can deliver this treatment to patients with anxiety disorders as a result of having PTSD.

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cognitive behavioral therapy for anxiety

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What type of professional required to deliver this treatment?

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What type of professional required to deliver this treatment?

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The most common professionals that can help deliever Cognitive therapy consist of psychologists, neuropsychologists, and other specialists who focus on rehabilitating cognitive function.

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One specific rehabilitation treatment to treat the issue of cognitive impairments from PTSD affecting daily life is Accelerated Resolution Therapy (ART).

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For each treatment identified (if one was identified), provide a brief (1-2 sentences) description of the treatment.

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For each treatment identified (if one was identified), provide a brief (1-2 sentences) description of the treatment.

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Accelerated Resolution Therapy (ART) is a psychotherapy treatment where the particular therapist will guide you through your trauma and ask you to continuously place positive things in your line of thinking, with certain negative thoughts/memories, in order to basically replace them and begin to block out the negative with happiness. This treatment is intended to replace triggers, nightmares, negative thoughts, etc. with these happy things that the patient chooses.

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Accelerated Resolution Therapy (ART) is a unique approach to psychotherapy. ART is unique because the ART Therapist guides the client to replace the negative images in the mind that cause the symptoms of Post-Traumatic Stress with positive images of the client's choosing. And this is done quickly, most often within one session! Once the negative images have been replaced by positive ones, the triggers will be gone. Nightmares and repeated intrusive thoughts will stop.

Some of the growing body of research that shows ART can do this may be found by clicking the following link: https://acceleratedresolutiontherapy.com/evidence-based/ ART is recognized as evidenced-based by the Society of Clinical Psychology, Div. 12 of the American Psychological Association (APA).

ART is also unique in that it combines the enormous power of eye movements to allow voluntary changes in the client's mind with well-established therapies like Gestalt, Psychodynamic Therapy and Guided Imagery. Within the ART protocol, the eye movements, along with other ART enhancements, make these therapies work much faster and more effectively.

With these therapies, combined with ART's eye movements and other ART enhancements, many mental health problems other than Post Traumatic Stress can also be resolved quickly. Among the things ART can deal with are OCD, Eating Disorders, Generalized Anxiety and Generalized Depressive Disorders. ART can deal with traumas often associated with Dyslexia but also go beyond that to improve reading! (The issues that have been resolved by ART are listed more fully below.)

That eye movements are calming and therapeutic is evidenced-based. It has been shown that they produce theta waves in the brain. Theta waves have been connected to creativity, intuition, daydreaming. These brain waves are often present during meditative states.

One of the special aspects of ART is that when clients replace their negative images with positive ones they don't need to talk to the therapist about any details of the trauma. This is because ART is procedural, though creative; and the client is really in control of the process, while the ART Therapist is a helpful guide. This makes the therapy easier on the client and easier on the therapist.

Although some traumatic experiences such as rape, combat or loss of a loved one can be very painful to visualize, early in the ART session the client has already rapidly moved beyond the place where they are stuck in these past experiences and is making positive changes of their choice. ART sessions are calming for the client and very often joyful, especially at the end, for both the client and the therapist.

Here are some issues that have been quickly and effectively treated by ART:
For answers to any questions, email Robin@AcceleratedResolutionTherapy.com or visit our Contact page.

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What Is ART? - Accelerated Resolution Therapy

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Accelerated Resolution Therapy (ART) is a unique approach to psychotherapy. ART is unique because the ART Therapist guides the client to replace the negative images in the mind that cause the symptoms of Post-Traumatic Stress with positive images of the client's choosing. And this is done quickly, most often within one session!

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For each treatment identified (if one was identified), describe what short or long-term effect of the condition it would be designed to treat.

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For each treatment identified (if one was identified), describe what short or long-term effect of the condition it would be designed to treat.

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The short/long-term effects of the Post Traumatic Stress Disorder (PTSD) that this treatment would be designed to treat are a lack of interest in hobbies, jobs, and relationships, consistent disruption of sleep through nightmares, inability to focus because of hallucinations/flashbacks, etc. (ART) would ensure that some manner of focus, be it small or large, would be reintroduced consistently into the patient's life to get them back on track where they have been lacking in because of the effects of PTSD in their life so far.

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For each treatment identified (if one was identified), identify what type of professional would be required to deliver the treatment.

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For each treatment identified (if one was identified), identify what type of professional would be required to deliver the treatment.

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A licensed and well trained therapist would be required to deliver this treatment.

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Identify what type of professional would be required to deliver the treatment.

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Identify what type of professional would be required to deliver the treatment.

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Professional people that can deliver this treatment are the licensed mental health professionals like psychologist, licensed counselor or licensed clinical social worker.

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For each special population identified, provide a brief (1-2 sentences) description of why that population is at greater risk or is more negatively affected.  

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For each special population identified, provide a brief (1-2 sentences) description of why that population is at greater risk or is more negatively affected.  

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Veterans of war are specifically affected by having PTSD from war, as this can often make them sensitive to loud noises (bomb noise) and can make it difficult to sleep or even cope during the day from the recurring flashbacks of constant fear and death. Also, the severe exposure to this type of extreme stress, coupled with PTSD can often lead to other significant mental health issues, such as depression in less worse cases and borderline personality disorder in worse cases.

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For each special population, identify what special challenges they may face with prevention, diagnosis or treatment of their condition.

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For each special population, identify what special challenges they may face with prevention, diagnosis or treatment of their condition.

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Veterans can often struggle getting healthcare after they come home, due to inability to acquire jobs because of the physical and mental health issues they come home with or lack of a higher education with many veterans having gone into the army with only a high school diploma or less. Thus, they can often not acquire accurate and necessary care for PTSD because of a lack of stable occupation. Some veterans often choose to not go and attain health care in other cases of denial of their new mental struggles.

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June 30, 2023
With healthcare from the U.S. Department of Veterans Affairs and insurance programs like TRICARE, it's easy to assume that all veterans get the health services they need after military service.

But that's not the reality.

In truth, half of post-9/11 veterans are not enrolled in Veterans Administration health care. This is despite the fact that nationally, 36% of post-9/11 veterans have a service-related disability and 50% of returning service members report some type of mental health issue.

Many veterans aren't receiving services from the US Department of Veterans Affairs (VA) and are not getting the health care they need.

1. Access to Services has Been a Commonly Known Barrier for Veterans

Access to health services is the most well-known reason veterans aren't getting critical health services. Inconvenient locations and long wait times can be a barrier to healthcare.

But we've all had frustrating moments with getting to appointments, getting needed insurance coverage, or having to wait longer than we'd like for an appointment with our provider.

It turns out, there are other, less commonly understood reasons why veterans aren't getting help.

2. Cost of Healthcare Vary Widely Depending on Veteran Benefits

The majority of veterans, service members, and their families qualify for healthcare coverage from the VA, their employer, or Medicare. The extent of insurance coverage often depends on health issues incurred during military service, the status of a veteran's health at the time their military service ends, and their income level.

Some veterans still struggle to afford health care because:

* Their type or duration of service doesn't qualify for government insurance.

* Their discharge status does not qualify for government insurance.

* Even with insurance, not all needed services, copays, and other out-of-pocket expenses are covered or affordable.

3. Veterans Continue to Carry Heavy Stigmas, Particularly About Mental Health

One of the most frequently reported barriers to seeking help amongst military members is public stigma-negative stereotypes-related to mental health and disability.

When individuals internalize these negative public stereotypes, the result is self-stigma. Self-stigma lowers our self-esteem and motivation to seek help. Here are some examples of how negative public stigma can inhibit help-seeking behavior among service members and veterans:

* They may believe that their military values of self-sufficiency and prioritizing needs mean they are better off solving health problems on their own.

* They may falsely believe that certain labels or diagnosis (such as "PTSD" or "disability") are to be avoided or minimized.

* They may worry that seeking mental health treatment may damage their career or cause them to be treated differently by leadership.

Self-stigmas formed during military service can be difficult to ignore even after service has ended.

4. Historical Government Policies Limit Healthcare Access for LGBTQ+ Community

From 1994 to 2011 the Don't Ask Don't Tell law prohibited gay, lesbian, and bisexual Americans from serving in the armed forces and created an environment where discrimination was acceptable.

Despite the law's repeal, many veterans who were discharged under Don't Ask Don't Tell (or who witnessed peers discharged by the policy) still struggle with the loss of service time, access to veteran benefits, recognition, and social acceptance.

LGBTQ+ veterans who served under Don't Ask Don't Tell may not fully use government health services because they don't believe they're eligible or they fear continued mistreatment.

Some LGBTQ+ veterans who do use government health services continue to hide their identity out of fear that being open would disqualify them from vital benefits and disability compensation.

5. Veterans are Selflessly Trained to Put the Needs of Others Before Their Own

Some veterans worry that if they use a support service, then that service won't be available to a fellow veteran who is also in need. That's not necessarily the reality, but it's a testament to the training veterans receive to protect their comrades no matter the personal cost to themselves.

In addition, some veterans rationalize that they don't deserve treatment for their conditions or disabilities because:

* They served in the military for a shorter period of time than others.

* The location of their post was less intense than others.

* They carry invisible wounds (such as PTSD or other traumas) that some believe are less important than physical wounds, even though that's not always the case.

6. Veterans Struggle to Find Trusted Providers Who Understand Military Culture

Military veterans share a culture and subcultures with their own values, standards, customs, and traditions. Yet health care professionals are often not equipped to deliver culturally competent care to veterans.

When it comes to finding a provider they can trust, many service members and veterans don't want to explain military culture and the unique challenges of military life to their health providers. They feel more at ease when working with a provider who just "gets it."

It takes a lot of persistence to find a health provider you can trust. Many veterans aren't getting critical health care because they simply can't find a provider they trust, or there are too many hurdles to jump.

What Veterans Are Saying

At RVP, our clients have experienced all kinds of barriers when looking for the health services they need.

In their own words, here are some reasons veterans have turned to RVP for support:

* "I would never have been able to afford the kind of health care I got through RVP. Having the financial roadblock removed by RVP allowed me to get support for as long as I needed."

* "I wanted a provider who understood military service and trauma."

* "I wanted a care plan that didn't require medication."

* "My psychologist in the military recommended a therapist in Portland for when I got home, but she wasn't taking any more clients. She referred me to RVP."

* "I couldn't get to appointments."

* "I didn't want to have to explain military culture to my health provider."

* "It was difficult to navigate our complex healthcare system."

* "I didn't feel like I belonged [at the VA], because at the time, we didn't have a mass exodus of younger veterans [like me] getting out."

* "There's a mentality in the Marine Corps that women marines...you're not veterans when you discharge. So don't try to access services. And we heard that. We were told that."
Share this online:

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6 Reasons Veterans are Left Without the Critical Health Care They Need

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June 30, 2023 With healthcare from the U.S. Department of Veterans Affairs and insurance programs like TRICARE, it's easy to assume that all veterans get the health services they need after military service. But that's not the reality. In truth, half of post-9/11 veterans are not enrolled in Veterans Administration health care.

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For each special population, identify what special challenges they may face with prevention, diagnosis or treatment of their condition.

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For each special population, identify what special challenges they may face with prevention, diagnosis or treatment of their condition.

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Some of the challenges that refugees face when it comes to dealing with PTSD are that they may find it harder to resettle and migrate to other places, seeing as they leave the only home they have ever known. Not only that, but they can also struggle with making a living because they are constantly facing discrimination or poor living conditions, even after fleeing conflict. With this comes the the difference in cultures and maken diagnosising treatment a little hard but not only that but the treatment offered to them can most times go against their cultural belifes and lead to them not accepting the help.

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Over 4 Million Venezuelan refugees and migrants struggle to meet basic needs across the Americas | UNHCR

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IOM and UNHCR call for investments in solutions that also benefit host communities.

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Treatment identified/brief description (1-2 sentences) on treatment

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Treatment identified/brief description (1-2 sentences) on treatment

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Sound therapy can be used to treat hyperacusis in Post Traumatic Stress Disorder patients. Hyperacusis is a hearing problem where it seems every noise is too loud for the auditory system.

Treatment description:  Sound therapy, in the case of treating hyperacusis with someone with PTSD, would open the door for a person struggling with hearing noises at too loud of a level, which causes them to isolate, to ease themselves back into hearing all kinds of noises at all ranges of level without feeling any discomfort after the treatment is through.

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The BAST Method of Sound Therapy

The BAST method combines carefully considered therapeutic sound techniques which have been shown to affect mind, body and emotions in different ways.

The BAST method of sound therapy also uses reflective techniques to enable a client to gain insight into their personal 'wholing' process. Our method of reflective process is based around the 'Cooper Sax 7 Rs Model of Experiential Processing'. This model is also affectionately known as 'The 7Rs' (which is less of a mouthful!). Reflective techniques combined with the therapeutic sound really enables us to gain a deep insight into a person's process and empower them to make life-changing improvements to their health and wellbeing.

The sound therapy and voice therapy techniques are delivered using tonal and rhythmic instruments and voice. The tonal instruments used are Himalayan and crystal singing bowls, gongs, monochord, tuning forks, crystal pyramids and drum. The vocal techniques are toning (the singing of one tone - usually using a vowel sound) and overtoning (a technique where more than one tone is sung simultaneously). A practitioner of Holistic Voice Therapy or Group Voice Therapy may also use 'Vocal Processing Techniques' which combine voice, movement, breath and visualisation. A drum therapy treatment/session is given using frame drum. At the end of every session that involves deep relaxation known as an Altered State of Consciousness (ASC), therapeutic percussion is used this helps to bring people back to a normal state of awareness and can also be a deeply therapeutic process. Therapeutic percussion comprises rainsticks, shakers, chimes and other percussion tools delivered in a specific order.

Information gathered over the last 25 years has informed us that certain instruments affect us in different ways. This information is used by a BAST trained therapist to create a treatment specific to their client's symptomatic state and their intention for the treatment - a relaxing treatment would be very different from an energising treatment, for example. If a client had muscle tension in their back, Himalayan bowls may be placed on the body and played in a specific way - whereas if a client was in chronic pain a gong or crystal bowl may be used.

At BAST we specialise in combining instruments in a specific way to influence brainwave frequencies, enabling a person to enter an altered state of consciousness (ASC) similar to very deep relaxation or meditation. In this state many different therapeutic processes occur, which will be explained further in our articles section.

It is now widely accepted that most illness is stress related. Therefore treatment methodologies that promote relaxation and help reduce stress can be a very effective way to prevent and treat illness. The BAST method of sound therapy is very effective at reducing stress (2). It also enables an individual to relax deeply, achieving an altered state of consciousness similar to deep meditation (4).

Research undertaken at BAST (and also independently) has shown that our methodology is 16% more relaxing than conventional relaxation music (1), 6% more relaxing than other treatments such as massage (1) and, on average, 64% more relaxing than other general relaxation methods (2). Our research has consistently shown positive benefits with regard to community sound work. (3)

1. Research undertaken at Mindlab, Sussex University

2. Research by Cook, R (2003), The Effects of a BAST Sound Therapy Session on the Autonomic Nervous System

3. Research by Cooper, L (2009), Introducing the Therapeutic Sound in Schools Programme (TheSiS)

4. Research by Cooper, L (2013), Sound Affects - the effect of therapeutic sound on consciousness.

# data[].attributes.attachment_link.title
What is Sound Therapy? - The British Academy of Sound Therapy

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For each treatment identified (if one was identified), describe what short or long-term effect of the condition it would be designed to treat.

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For each treatment identified (if one was identified), describe what short or long-term effect of the condition it would be designed to treat.

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Sound therapy: The treatment would start at low volume noises at a level the patient is already comfortable with and then they would move up from there to increase the patient’s tolerance with noise levels until they are able to listen to sounds without that intense volume in their head. The long-term effect of the condition that this therapy would be designed to treat would be the patient's inability to listen to certain noises, in order to eventually establish the ability of the patient to once again, without feeling fear or needing to isolate to a complete lack of sound, hear all types of noises.

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How people with PTSD can develop hearing/sound difficulties – Page 3 – PTSD UK

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a community for everyone in the UK affected by Post Traumatic Stress Disorder

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For each treatment identified (if one was identified), identify what type of professional would be required to deliver the treatment.

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For each treatment identified (if one was identified), identify what type of professional would be required to deliver the treatment.

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The type of professional that would be required to deliver this treatment of sound therapy to PTSD patients would be certified sound therapists or occupational therapists. There are more options of who could deliver this treatment, however, these two are the most professional for Post Traumatic Stress Disorder patients who have the issue of hyperacusis.

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For each technology identified, provide a brief (1-2 sentences) description of that technology and whether it is used to prevent, diagnose or treat the focus condition.

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For each technology identified, provide a brief (1-2 sentences) description of that technology and whether it is used to prevent, diagnose or treat the focus condition.

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There is virtual reality exposure therapy (VRET) that can be used to treat Post Traumatic Stress Disorder patients through an updated type of exposure therapy coupled with technology to unleash potential healing in a more realistic type of setting, rather than just a therapist using her words. This treatment is a, much less used, type of exposure therapy used with people with not only PTSD, but anxiety, and other types of such illnesses. However, it is far likely to be more used in the future, due to how this therapy is much more controllable for therapists to truly contain the emotional experience and control which emotions the patient feels. This technology offers a more stable way for this to happen than through general exposure therapy through in person description techniques and the like.

This is used as a treatment for Post Traumatic Stress Disorder patients.

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ABSTRACT

Contrary to specific phobias, for which Virtual Reality Exposure Therapy (VRET) constitutes an effective treatment, uncertainty still exists regarding the usefulness of VRET for posttraumatic stress disorder (PTSD). Therefore, this meta-analysis investigated the efficacy of VRET for PTSD as compared to waitlist and active comparators. A literature search yielded nine controlled studies encompassing 296 participants (124 VRET, 172 controls). The differences between conditions regarding the primary outcome of PTSD symptom severity and the secondary outcome of depressive and anxiety symptoms post-treatment were calculated using Hedges' g. Compared to waitlist controls, VRET showed a significantly better outcome for PTSD symptoms ( g = 0.62, p = .017) and depressive symptoms ( g = 0.50, p = .008). There was no significant difference between VRET and active comparators regarding PTSD symptoms ( g = 0.25, p = .356) and depressive symptoms ( g = 0.24, p = .340) post-treatment. No significant effects emerged for anxiety symptoms. These findings suggest that VRET may be as effective as active comparators for PTSD patients. However, the results must be interpreted with caution due to the limited number of trials and the substantial number of - predominantly male - military service members studied. Additional controlled trials, considering a wider range of trauma types and balanced gender, are required to strengthen the evidence.

KEYWORDS: Posttraumatic stress disorder, virtual reality, exposure therapy, meta-analysis, depression, anxiety

HIGHLIGHTS

* There was evidence of a medium sized effect for VRET over WL for PTSD and depression.* No significant difference between VRET and active controls in reducing PTSD symptoms.* Results indicate no significant changes in anxiety after VRET.

Abstract

Al contrario de las fobias específicas, para las cuales la Terapia de Exposición de Realidad Virtual (VRET en sus siglas en inglés) constituye un tratamiento efectivo, existe todavía incertidumbre con respecto a la utilidad de la VRET para el trastorno de estrés postraumático (TEPT). Por lo tanto, este meta-análisis investigó la efectividad de la VRET para el TEPT en comparación con la lista de espera y los comparadores activos. Una búsqueda de literatura arrojó nueve estudios controlados involucrando a 296 participantes (124 VRET, 172 controles). Las diferencias entre las condiciones con respecto al resultado principal de la severidad de los síntomas del TEPT y el resultado secundario de los síntomas depresivos y ansiosos luego del tratamiento, fueron calculados usando la g de Hedges. En comparación a los controles de lista de espera, la VRET mostró un resultado significativamente mejor para los síntomas del TEPT (g=0.62, p=.017) y los síntomas depresivos (g=0.50, p=.008). No hubo diferencias significativas entre la VRET y los comparadores activos con respecto a los síntomas del TEPT (g=0.25, p=.356) y los síntomas depresivos (g=0.24, p=.340) luego del tratamiento. No surgieron efectos significativos para los síntomas ansiosos. Estos hallazgos sugieren que la VRET podría ser tan efectiva como los comparadores activos para los pacientes con TEPT. Sin embargo, los resultados deben ser interpretados con cautela debido al número limitado de ensayos y el sustancial número de miembros del servicio militar -predominantemente hombres- estudiados. Ensayos controlados adicionales, que consideren un rango más amplio de tipos de trauma y balanceados en género, son requeridos para fortalecer la evidencia.

PALABRAS CLAVES: trastorno de estrés postraumático, Realidad Virtual, terapia de exposición, meta-análisis, depresión, ansiedad

Abstract

虚拟现实暴露疗法（VRET）可有效治疗特定恐惧症，与之相比，VRET对创伤后应激障碍（PTSD）的可用性仍不确定。因此，这项元分析研究了VRET对创伤后应激障碍的疗效，及其与与等候名单和有效对照疗法的比较。文献检索产生了9项对照研究，包括296名参与者（124名VRET，172名对照组）。使用Hedges'g计算关于接受不同治疗后PTSD症状严重程度的差异，以及抑郁和焦虑症状的差异。与等候名单对照组相比，VRET显示出PTSD症状（g = 0.62，p = .017）和抑郁症状（g = 0.50，p = .008）的显著更好的结果。 VRET和有效对照疗法之间在治疗后PTSD症状（g = 0.25，p = .356）和抑郁症状（g = 0.24，p = .340）上没有显著差异。对焦虑症状没有显著效果。这些发现表明，VRET可能与PTSD患者的有效对照疗法一样有效。但是，因为纳入试验数量有限，并且研究了大量以男性为主的军事服务人员，必须谨慎解释研究结果。需要进一步的对照试验以加强证据，未来的研究需要考虑更广泛的创伤类型和对参与者性别进行平衡。

关键词: 创伤后应激障碍, 虚拟现实, 暴露治疗, 元分析, 抑郁, 焦虑

1. Introduction

According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5, American Psychiatric Association [APA], 2013), posttraumatic stress disorder (PTSD) develops as a result of directly experiencing, witnessing, or being repeatedly exposed to aversive details of, a potentially traumatic event such as death, combat, sexual assault or serious injury. While a substantial number of people show considerable resilience post-exposure, up to a third of those confronted with a traumatic event subsequently develop clinically relevant PTSD symptoms such as re-experiencing, avoidance, hyperarousal and alterations in mood and cognitions (e.g. persistent, negative and distorted cognitions; Cusack et al., 2016).

A recent study (Koenen et al., 2017) reported a cross-national lifetime prevalence of PTSD of 3.9% in the general population and 5.6% among persons who had previously been exposed to a traumatic event. Furthermore, up to half of those diagnosed with PTSD tend to show persistent symptoms and an unremitting chronic course (Atwoli, Stein, Koenen, & McLaughlin, 2015; Cusack et al., 2016; Koenen et al., 2017). The disorder is associated with particularly high social and health care costs. In a sample of US veterans, the two-year social costs were estimated at $923 million (Kilmer, Eibner, Ringel, & Pacula, 2011), and in a representative sample of US women, the median annual health care costs lay at $1283 million (Walker et al., 2003). In light of these high costs, as well as the debilitating nature of the disorder, timely and effective intervention is paramount.

A broad range of psychological treatments exist for PTSD. Current reviews (e.g. Cusack et al., 2016) suggest that trauma-focused cognitive-behavioural therapy (CBT), cognitive processing therapy (CPT), cognitive therapy (CT), Eye Movement Desensitization and Reprocessing (EMDR), and narrative exposure therapy are efficacious. Moreover, exposure therapy has been found to be highly effective in reducing PTSD symptoms and is regarded as a first-line treatment for PTSD according to numerous guidelines (e.g. APA, 2017; International Society for Traumatic Stress Studies [ISTSS], 2018). The mechanisms underlying exposure therapy may be explained by the Emotional Processing Theory (Foa & Kozak, 1986), which states that a phobic fear structure is activated upon confrontation with trauma-relevant information. Accordingly, mechanisms for symptom reduction involve activation of the fear structure by means of (repeated) confrontation with a feared stimulus (imaginal or in vivo) to achieve habituation and extinction of the anxious reaction. Although emotional engagement is key to treatment outcome (Foa, Huppert, & Cahill, 2006), this is particularly difficult to achieve in the context of imaginal exposure, as many patients show problems with visualizing the traumatic event or related details (Rizzo & Shilling, 2018). In vivo exposure, in turn, poses the challenge of providing real-life stimuli that are suitable for a systematic, graded exposure (Bohil, Alicea, & Biocca, 2011).

A viable approach to overcoming these intricate problems is provided by virtual reality (VR) technology. VR offers multi-sensory cue representation in a highly interactive, ecologically valid and emotionally engaging virtual environment. It carries the advantages of increased control over stimuli, the possibility to repeat exposure infinitely, and the unique option to simulate environments that challenge the boundaries of everyday surroundings (Difede et al., 2007; Gamito et al., 2010; Miloff et al., 2016). Moreover, VR has been shown to be effective in inducing stress and anxiety reactions which are comparable to those observed in analogous real-life situations (e.g. Dibbets, 2019; Kothgassner et al., 2016). These characteristics have all contributed to the implementation of VR as a method for exposure therapy. In the past, virtual reality exposure therapy (VRET) has shown to be effective for a wide range of specific phobias (e.g. Morina, Ijntema, Meyerbröker, & Emmelkamp, 2015; Opriş et al., 2012; Parsons & Rizzo, 2008; Powers & Emmelkamp, 2008).

In contrast to specific phobias, data regarding the efficacy of VRET in PTSD patients are still inconclusive. Early studies on VRET in veterans with chronic, treatment-resistant PTSD (Rothbaum et al., 1999; Rothbaum, Hodges, Ready, Graap, & Alarcon, 2001) generally yielded promising outcomes for VR-based exposure, and a more recent qualitative review (Gonçalves, Pedrozo, Coutinho, Figueira, & Ventura, 2012) concluded that VRET is potentially useful in the treatment of different types of trauma. Similarly, VRET has been included in the ISTSS Guidelines ( 2018), for instance as a treatment with emerging evidence for PTSD, although the guidelines also emphasize that while the evidence is promising, it is still insufficient to fully support VRET as a standard treatment. To date, most meta-analytic research on the efficacy of VRET has considered anxiety disorders in general, and the analyses have only included small numbers of PTSD related studies (Carl et al., 2019; Fodor et al., 2018; Opriş et al., 2012; Parsons & Rizzo, 2008; Powers & Emmelkamp, 2008), rendering it difficult to draw specific conclusions for PTSD. Two recently published meta-analyses on VRET for anxiety disorders found small (Fodor et al., 2018) and medium (Carl et al., 2019) effect sizes in favour of VRET versus control comparisons in terms of reducing PTSD symptom severity. Nevertheless, due to the small sample sizes of the included studies and the lack of active treatment controls, these results have to be regarded as a first step, and further evidence is needed.

In light of the aforementioned evidence, the objective of the current meta-analysis was to evaluate the efficacy of VRET for PTSD on the basis of existing controlled trials comparing VRET to waitlist control conditions as well as to active control conditions. We hypothesized that VRET would have a significant positive effect on outcome measures (i.e. overall symptom severity, anxiety and depression-related symptoms) relative to waitlist controls, as well as a comparable effect to active controls.

2. Method

2.1. Inclusion criteria and search strategy

A search of MEDLINE, PSYNDEX, Scopus and Web of Science was conducted using the keywords 'PTSD OR trauma AND VR', 'VR OR Virtual Reality AND PTSD', 'VRET OR Virtual Reality Exposure Therapy AND PTSD' from the beginning of database records until December 2018. Studies were included in the meta-analysis if controlled trials evaluated the efficacy of VR-based treatment in samples with PTSD according to the DSM-IV, DSM-5, or the International Statistical Classification of Diseases and Related Health Problems; ICD-10. The primary outcome was PTSD symptom severity. Secondary outcomes included symptoms of depression and anxiety. All control group interventions were included in the present meta-analysis, and multi-group comparisons were coded separately. No further restrictions on age of participants, type of trauma, comorbidity, medication, and language or publication status were applied. In addition, Google Scholar alerts were enabled to ensure the inclusion of accepted articles and articles in preprint, and authors with a research focus on this specific field were contacted to ensure the inclusion of unpublished studies. The title, abstract, and main text of each study were examined independently by two authors (JXK and RLvE), and exclusions of studies occurred at each stage of the process (see Figure 1).

Figure 1.

PRISMA flowchart of screening, exclusion, and inclusion criteria.

2.2. Data extraction and analysis

Data of included studies were entered into a spreadsheet independently by two authors (AG and RLvE). For each study included in the meta-analysis, we coded sample and intervention characteristics. The primary outcome was the standardized mean difference (Hedges' g) in PTSD symptom severity. Secondary outcomes included Hedges' g for depressive and anxiety symptoms as assessed via various self-report measures in VRET and control conditions post-intervention. Means, standard deviations and sample sizes were retrieved and entered into a spreadsheet. The calculations of the effect sizes and the subsequent meta-analysis were then conducted using the package metafor for R (Viechtbauer, 2010). Following general convention (Cohen, 1988), an effect size an effect size of 0.20 or above was considered a small effect, 0.50 or above a medium effect, and 0.80 or above a large effect. Random effects models were applied to estimate aggregated effect sizes. Effect sizes were based on completer samples due to a lack of intention-to-treat data ( k = 2, McLay et al., 2017; Reger et al., 2016). Heterogeneity across study outcomes was reported with I ² values, where 0% to 40% might not be important, 30% to 60% may represent moderate heterogeneity, and 50% to 90% may represent substantial heterogeneity (Higgins & Green, 2011). All data and codes are stored on a repository of the Open Science Framework (doi:10.17605/OSF.IO/Y8U6G).

3. Results

The initial search yielded 451 articles. The titles and abstracts were screened for eligibility and full-text papers were obtained where necessary to evaluate their suitability for inclusion in further analyses. After screening, nine studies - eight peer-reviewed journal articles and one book chapter - were identified and included in the present meta-analysis. See Table 1 for study characteristics and measures of included studies.

Table 1.

VR treatment

Control group

Study

Country

Sample

Psychiatric history/Medication (N)

n

Age M (SD)

% male

n

Age M (SD)

% male

VR environment

Sessions

Control condition

Gamito et al. ( 2010)

Portugal
Portuguese colonial war veterans with PTSD according to DSM-IV n. r./stable medication
4

whole sample: age ( M = 63.5, SD = 4.43)

100

3

n. r.

100

Simulation of unspecified wartime environments (dense vegetation)

12
Waitlist

Reger et al. ( 2016) †

US
Active duty military personnel with past Iraq or Afghanistan deployment with PTSD according to DSM-IV-TR 53/stable medication
30

29.52 (6.47)

96

32

30.89 (7.90)

94

Simulation of Iraq and Afghanistan wartime environments

10
Imaginal exposure

47 30.39 (7.09)98 Waitlist

Gamito et al. ( 2010) used both waitlist and imaginal exposure therapy as control conditions; however, as only one participant provided pre- and post-scores of trauma severity in the imaginal therapy condition (personal communication, 22 October 2018), we only included the waitlist condition ( n = 3) as the control group in our meta-analysis. The study by Roy et al. ( 2010) was eligible to be included in our meta-analysis, but the authors reported the results of the clinical trial, focusing on efficacy, in a separate paper (Roy, Costanzo, Blair, & Rizzo, 2014). Therefore, we included Roy et al. ( 2014) and not Roy et al. ( 2010) in the meta-analysis.

Roy et al. ( 2010, but not 2014) reported a significant reduction of anxiety scores in the VR treatment group over time. However, it was not possible to retrieve sufficient data to compute efficacy as compared to the control group. Hence, this study could not be included in our analysis of anxiety scores.

3.1. Study characteristics

In total, nine controlled trials were included with a total of 296 participants (124 in VR treatment conditions, 172 in control groups).

Regarding the control groups, five studies used exposure therapy controls (Cárdenas-López, de la Rosa-Gómez, Durán-Baca, & Bouchard, 2015; McLay et al., 2017, 2011; Reger et al., 2016; Roy et al., 2014; N = 100), four studies were waitlist controls (Difede et al., 2007; Gamito et al., 2010; Miyahira et al., 2012; Reger et al., 2016; N = 68), and one study used a present-centred control group (Ready, Gerardi, Backscheider, Mascaro, & Rothbaum, 2010, N = 4). The dropout rate was 34% for the VR conditions (20% for control conditions; 24% for active control conditions, 13% for waitlist controls), and none of the studies reported any treatment-related adverse effects (e.g. worsening of symptoms or attempted suicide) over the course of the studies. Similarly, no data were provided regarding dropouts due to adverse effects.

Seven studies included military servicemen/women, either in active duty or with veteran status. Two studies (Cárdenas-López et al., 2015; Difede et al., 2007) reported results from civilian victims or witnesses of violent crime as well as firefighters, disaster relief workers, and civilians with PTSD related to the September 11 attacks. Seven studies were conducted in the USA, one in Mexico (Cárdenas-López et al., 2015), and one in Portugal (Gamito et al., 2010).

In all included studies, the reduction in PTSD symptom severity was operationalized by the Clinician-Administered PTSD Scale (CAPS, Blake et al., 1995). Gamito et al. ( 2010) additionally measured PTSD symptoms with the Impact of Events Scale-Revised (IES-R, Weiss & Marmar, 1996), Miyahira et al. ( 2012) with the PTSD Diagnostic Scale (Foa, Cashman, Jaycox, & Perry, 1997), Cárdenas-López et al. ( 2015) with the Posttraumatic Stress Symptom Scale - Self-Report version (PSS-SR, Foa, Riggs, Dancu, & Rothbaum, 1993), and Reger et al. ( 2016) with the PTSD Checklist - Civilian version (PCL-C; Weathers, Litz, Herman, Huska, & Keane, 1993). We chose only to include the CAPS score in our meta-analysis, as it is widely used and has been proven to be psychometrically sound (Weathers, Keane, & Davidson, 2001).

Seven studies reported measures of depressive symptoms, with six studies operationalizing depressive symptoms using the Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) and one study (Gamito et al., 2010) using the depression subscale of the Symptom Checklist Revised (SCL-90-R; Derogatis, 1994). Three studies reported measures of anxiety symptoms, with Reger et al. ( 2016) using the Beck Anxiety Inventory (BAI; Derogatis & Culpepper, 2004), Gamito et al. ( 2010) the anxiety subscale of the SCL, and Cárdenas-López et al. ( 2015) the state anxiety subscale of the State-Trait Anxiety Inventory (STAI; Spielberger, 1983; see Table 1 for study characteristics and measures).

Three studies compared VR treatments to waitlist controls (Difede et al., 2007; Gamito et al., 2010; Miyahira et al., 2012) and five studies (Cárdenas-López et al., 2015; McLay et al., 2017, 2011; Ready et al., 2010; Roy et al., 2014) compared VR treatments to active control groups. One study (Reger et al., 2016) compared VR treatment to both a waitlist and active comparator; this study was thus included in both comparisons. In another study (Miyahira et al., 2012), participants in the control group received minimal attention, defined as 'brief telephone contacts, every 2 weeks for 5 weeks to assess safety and to affirm their continued interest in study participation' (p. 130). We therefore included this study in our comparisons to waitlist controls. Ready et al. ( 2010) used present-centred therapy as a control condition, which does not comprise exposure therapy but rather involves nonspecific components of psychotherapy (e.g. psychoeducation, problem-solving techniques). McLay et al. ( 2011) compared VR treatment to a treatment-as-usual control group, in which participants were able to choose freely from all services provided by Veterans Affairs facilities, including, for instance, prolonged exposure or cognitive-processing therapy. Another study (McLay et al., 2017) used a specific form of control exposure therapy, namely a treatment that is very similar to VR exposure but in which stimuli are viewed as still images on a computer and not via an immersive VR headset.

Sample sizes were very small across studies, with only two studies (McLay et al., 2017; Reger et al., 2016) reporting more than 10 participants having completed treatment in the VR condition. The participants' mean age ranged from 28 to 64 years across studies. Included participants were predominantly male: Only one study had a sample that included 50% females (Cárdenas-López et al., 2015), while the remaining samples comprised 88% male participants or higher. Five studies (Cárdenas-López et al., 2015; Difede et al., 2007; Gamito et al., 2010; Ready et al., 2010; Reger et al., 2016) reported that medication of participants was held constant over the course of the treatments or that participants did not take any medication - the remainder of the studies did not report any details concerning medication. VRET treatments varied regarding the duration of individual sessions (min. 60 and max. 120 minutes) as well as the number of treatment sessions (see Table 1 for study characteristics).

3.2. Efficacy of VRET regarding PTSD symptom severity

One hundred twenty-two participants ( N = 54 in VR treatment, N = 68 in waitlist control groups, k = 4) were analysed to compare the efficacy of VRET and waitlist controls regarding PTSD symptom severity. The meta-analysis yielded an effect of g = 0.62 (CI 0.11 to 1.12) in favour of VR treatment over waitlist conditions. This effect was significant ( p = .017) and heterogeneity was considered as not important ( I ² = 29, Q(3) = 4.32, p = .229).

Furthermore, data of 204 participants ( k = 6) were analysed to calculate the effect of VRET on PTSD symptom severity in comparison with active control groups ( N = 100 in VRET conditions, N = 104 in active control conditions). The meta-analysis yielded a non-significant effect of g = 0.25 (CI −0.28 to 0.79, p = .356). Heterogeneity was significant and substantial for the six studies, I ² = 67 ( Q(5) = 13.97, p = .016). A forest plot is provided in Figure 2.

Figure 2.

Forest plot of the standardized mean difference ( Hedges' g) in post-treatment CAPS scores of VR treatments compared to control conditions (waitlist and active control). A positive effect size indicates that the outcome was in favour of VR treatment. Average effect was calculated using a random-effects model.

3.3. Efficacy of VRET regarding depressive symptoms

For the comparison between VRET and waitlist controls ( N = 54 and N = 68, respectively, k = 4), we found a significant summary effect of g = 0.50 (CI 0.13 to 0.87, p = .008) and low, non-significant heterogeneity of I ² = 0 ( Q(3) = 3.25, p = .354), all in favour of VRET. Four effect sizes from the data of 110 participants were analysed to compare the efficacy of VR treatment against active controls with respect to the reduction of depressive symptoms ( N = 54 in VR treatment, N = 56 active controls). The meta-analysis yielded an effect of g = 0.24 (CI −0.25 to 0.39) and this comparison was not significant ( p = .340). Heterogeneity was considered as not important and non-significant ( I ² = 27, Q(3) = 4.92, p = .178) for these studies. A forest plot is provided in Figure 3.

Figure 3.

Forest plot of the standardized mean difference ( Hedges' g) in post-treatment depression scores of VR treatments compared to control conditions (waitlist and active control). A positive effect size indicates that the outcome was in favour of VR treatment. Average effect was calculated using a random-effects model.

3.4. Efficacy of VRET regarding anxiety symptoms

Based on the data of 84 participants ( N = 34 in VR treatment, N = 50 controls, k = 2) we compared VRET with waitlist controls regarding the reduction of anxiety symptoms. The meta-analysis revealed a non-significant effect of g = 0.47 (CI −0.60 to 1.54, p = .387).

For the comparison of VRET and active controls, the data of 82 participants ( N = 40 in VR treatment, N = 42 controls, k = 2) was analysed to compare anxiety symptoms after VRET and active treatment. Again, no significant effect was found in this comparison: g = −0.26 (CI −0.70 to 0.18, p = .241).

3.5. Sensitivity analysis

To evaluate a potential sampling bias, we excluded the two studies that investigated civilian samples and re-analysed our main outcome (reduction of PTSD symptom severity) with the more homogenous samples of only military service members. This did not change any of our results, irrespective of control comparison (PTSD symptom severity: VRET vs. waitlist, k = 3, N = 132, g = 0.40, CI 0.01 to 0.80, p < .05; VRET vs. active control, k = 5, N = 243, g = 0.06, CI −0.43 to 0.54, p > .05). We further re-analysed our active control groups without the participants of Ready et al. ( 2010) and McLay et al. ( 2011), since these studies did not use exposure therapy in their control group and instead employed present-centred therapy and several other standard treatments. Likewise, this new analysis (with k = 4 and N = 223) did not significantly change the direction or magnitude of our results ( g = 0.16, CI −0.54 to 0.86, p > .05).

3.6. Publication bias

Due to the small sample size of most preliminary or pilot studies evaluating the efficacy of VRET, the probability of traditional dissemination bias (i.e. non-publication of entire studies) should not be dismissed. The fact that most studies did not find significant effects in favour of VRET makes the occurrence of publication bias less likely. Furthermore, guidelines for conducting funnel plot asymmetry tests recommend the inclusion of at least 10 studies to maintain sufficient power for distinguishing chance from real asymmetry (Egger, Smith, Schneider, & Minder, 1997; Sterne et al., 2011). While it was therefore unfeasible to conduct Egger's regression test (Sterne & Egger, 2005) or trim-and-fill analysis (Duval & Tweedie, 2000) in our analysis, it should be noted that the omission of results and selective reporting are well-documented (Dwan et al., 2008; Ioannidis, Munafo, Fusar-Poli, Nosek, & David, 2014) and are expected to be the main reason for bias in our meta-analysis.

4. Discussion

Given the need for stronger evidence regarding the added value of using VR for PTSD treatment, the current meta-analysis set out to systematically review relevant controlled trials and to analyse the efficacy of VRET in relation to waitlist controls and active controls. Our meta-analysis reveal encouraging preliminary findings regarding the efficacy of VRET compared to waitlist controls in terms of improving PTSD symptom severity, as well as symptoms of depression, but not anxiety. Effect sizes for PTSD symptom reduction in favour of VRET were medium ( g = 0.62 for PTSD symptoms; g = 0.50 for depressive symptoms), and effects for treatment-related changes in anxiety symptoms were non-significant ( g = 0.47, p = .387). These results are in line with previous research (Carl et al., 2019), but depict better outcomes than another meta-analysis (Fodor et al., 2018). Furthermore, our findings suggest that there is no significant difference between VRET and active controls regarding the improvement of PTSD symptoms ( g = 0.25) and depressive symptoms ( g = 0.24). Given that exposure therapy is suggested as a first-line treatment for PTSD (APA Guidelines, 2017; ISTSS Guidelines, 2018), VRET has been proposed as a potentially efficacious tool for exposure therapy in PTSD (Gonçalves et al., 2012; Opriş et al., 2012). However, our results illustrate that VRET has only medium effects or non-significant effects when compared to waitlist and active controls. In contrast, recent meta-analytic research (Cusack et al., 2016) found large effects for specific manualized therapies (e.g. CBT exposure therapy, CPT/CT, EMDR) when compared to waitlist (CBT exposure therapy: d = 1.16; CPT/CT: d = 1.56-1.61; EMDR: d = 1.37) as well as active controls (CBT exposure therapy: d = 1.79; CPT/CT: d = 0.60). This suggests a greater efficacy of traditional exposure therapy, CPT/CT or EMDR compared to VRET with respect to PTSD symptom severity improvement. In light of these findings, it seems appropriate to infer that these well-controlled therapies should be the first choice of treatment whenever feasible, with VRET providing an alternative in scenarios in which the opportunity for in vivo treatment, for instance, is lacking.

4.1. Applicability of findings

The generalizability and applicability of the current meta-analysis is limited by a number of factors. For instance, most studies examined active-duty military members or veteran samples with combat-related trauma. Only two (Cárdenas-López et al., 2015; Difede et al., 2007) of the nine studies included in this meta-analysis considered civilians, and among these, one study (Difede et al., 2007) had a mixed sample consisting not only of civilians but also of professionals such as firefighters and disaster relief workers who are all systematically trained to handle disaster scenarios. This constitutes a source of bias and prevents wide-ranging conclusions regarding the efficacy of VRET for PTSD, as it is unclear whether these results are applicable to all adults with PTSD. Military personnel have been observed to be less responsive to (traditional) PTSD treatment than other PTSD samples, with some studies showing null effects for therapy gains or even indicating a reverse outcome with an exacerbation of trauma-related symptoms post-intervention (Hammarberg & Silver, 1994; Rothbaum, Meadows, Resick, & Foy, 2000).

Additionally, these associations seem to be impacted by another factor which potentially shapes the treatment outcome: In our meta-analysis, participants' age ( M VR = 39.3; M control = 38.9) ranged widely, from 28 years (active-duty service members, McLay et al., 2011) to 63 years (veterans, Gamito et al., 2010). It has been speculated that younger adults - as representatives of the digital generation - may be more willing to try an innovative intervention approach like VR than older groups (Rizzo & Shilling, 2018). Hence, age should be investigated more carefully in future controlled studies in relation to its possible impact on treatment adherence and attrition. Similarly, patients' preferences for certain interventions should be considered when evaluating the efficacy of PTSD treatments in general and of VRET in particular (Tarrier, Liversidge, & Gregg, 2006).

Another factor which might affect the generalizability of findings is gender. Studies included in this review predominantly included male participants. This is all the more relevant since female gender has consistently been shown to be a significant risk factor across different trauma types (Brewin, Andrews, & Valentine, 2000) and has been reported to be associated with higher PTSD-related health care costs compared to male gender (Walker et al., 2003). Moreover, the probability of being diagnosed with PTSD is twice as high for women than for men (Koenen et al., 2017). Hence, gender should be considered in the design of future trials.

Finally, regarding the effect of VRET on PTSD symptom severity, a high heterogeneity between active control trials was detected. A possible explanation for these heterogeneous findings might be that different active interventions were included in our analysis (e.g. stand-alone imaginal exposure, prolonged exposure, EMDR, CBT exposure and present-centred therapy). Furthermore, the included studies showed a high variability regarding the number of sessions and the duration of individual sessions. Due to small sample sizes, we were unable to control for the specific method applied, and had to subsume all of these interventions under the active control condition. Nevertheless, all of the studies in the active control condition - except for two (present-centred therapy, Ready et al., 2010; various treatment-as-usual; McLay et al., 2011) - used types of exposure therapy. Excluding the two studies using other treatments did not have a significant impact on the direction or magnitude of the effects. Notably, no study included in vivo exposure as an active comparator.

4.2. Limitations

One of the main limitations of the present meta-analysis pertains to the small sample sizes within the included studies and the restricted number of controlled trials eligible for meta-analysis. This limits our ability to draw any firm conclusions and is a major limitation. Furthermore, only one study (Difede et al., 2007) specified the type of concurrent medication, whereas the other studies either did not report any information about medication at all or did not provide details on the type, dosage, or duration. However, four studies (Difede et al., 2007; Gamito et al., 2010; Ready et al., 2010; Reger et al., 2016) did report that medication was stable during the intervention. Medication is considered as a next-line treatment by most guidelines on PTSD treatment, but may serve as a reasonable initial option if no first-line treatment is available. It should be noted that the pharmacological treatment of PTSD, especially in combination with a comorbid depressive disorder, is complex, and there are not yet any specific guidelines on this issue (e.g. American Psychological Association, 2017; for an overview see Cusack et al., 2016).

Similarly, none of the included studies provided any information about adverse treatment effects. This is a key issue as reporting adverse effects, and dropouts due to adverse effects, may foster the understanding not only of treatment efficacy but also of its effectiveness. In addition, some trials (e.g. McLay et al., 2011) examined participants with a long history of failed treatment approaches - both medical and psychological. On the one hand, this may limit cross-sample comparisons, while on the other hand, the reported positive treatment outcomes may suggest that treatment-resistant patients might particularly benefit from VRET (Difede et al., 2014). However, due to small sample sizes, it was not possible to consider these sample-specific factors in our analyses. Further studies are needed in order to evaluate a possible added value of VR-based approaches for treatment-resistant PTSD cohorts. Moreover, the included studies did not report long term follow-ups, which further limits the ability to draw conclusions about the treatment`s effectiveness.

Finally, due to the different control conditions and the low number of studies, we are unable to rule out or confirm the presence of a possible publication bias, as assessments of funnel plots and more advanced regression-based assessments are unfeasible in reviews with 10 or fewer studies (Dalton, Bolen, & Mascha, 2016). Therefore, the results of this meta-analysis have to be regarded as preliminary in nature. Furthermore, some papers using keywords outside of our search strategy, or grey literature published outside of peer-reviewed journals, may have been missed. A further limitation pertains to the substantial amount of heterogeneity within active controls regarding PTSD symptom severity ( I ² = 66%). We were unable to analyse potential confounders (e.g. via meta-regression) due to the small sample of eligible studies and the resulting low power (Hempel et al., 2013).

4.3. Conclusion and future directions

This meta-analysis revealed a medium sized effect for VRET over waitlist controls and at least no significant difference between VRET and active interventions in terms of reducing PTSD symptom severity and depressive symptoms in adults with PTSD. However, VRET did not differ from controls in reducing anxiety symptoms, either for waitlist or for active comparators. Furthermore, VRET showed a lower efficacy in comparison to other meta-analyses (e.g. Cusack et al., 2016) which investigated improvement of PTSD symptoms due to in vivo exposure therapy. Past studies show that individuals differ in their ability to become immersed in or engaged with the virtual environment (e.g. Sacau, Laarni, & Hartmann, 2008; Weibel, Wissmath, & Mast, 2010), which may hinder positive treatment outcome. Hence, future studies should further investigate specific user characteristics such as personality traits (i.e. openness to experience or neuroticism) as they could potentially mediate treatment outcome in VRET.

In general, VRET constitutes an ecologically valid, safe and controlled environment for the induction of emotional, cognitive and behavioural as well as physiological reactions which are equivalent to those found in comparable in vivo surroundings (e.g. Dibbets, 2019; Kothgassner et al., 2016). In the past, high costs for the hardware and software of the respective systems may have limited the access to this technology. Nevertheless, as the affordability of VR systems has been increasing over the last decade (Rizzo & Shilling, 2018), clinical researchers may be more inclined to include and evaluate this tool in their routine therapy and thus improve accessibility for affected samples. It is, however, essential to note that VRET should not be used as a stand-alone tool and a trained therapist is always necessary to guide the process. By drawing on pre-programmed environments and stimuli, VRET has the potential for treatment standardization. This could be an interesting option to increase comparability across multiple trials in future studies.

Moreover, due to the rise of personalized medicine, future research should be encouraged to achieve a better understanding of the effect of moderating factors (e.g. age, gender) on the efficacy of VRET and to enable analyses of specific samples. In particular, children and adolescents with considerably high lifetime prevalence rates of PTSD should be included in trials evaluating VRET (Meiser-Stedman, Smith, Yule, Glucksman, & Dalgleish, 2017; Merikangas et al., 2010).

In sum, the evidence from the present meta-analysis was insufficient to assume efficacy of VRET for particular trauma types. While a considerable body of research supports the efficacy of VRET for the treatment of specific phobias (for an overview, see Carl et al., 2019; Fodor et al., 2018; Opriş et al., 2012), only a small number of controlled studies have investigated the efficacy of VRET in patients with PTSD. As such, we have to state that despite the moderately positive results of the present meta-analysis, more research is required to determine whether VRET constitutes a valuable tool for PTSD treatment.

Acknowledgments

The authors thank Sarah Mannion de Hernandez for correcting the English manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

References

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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

1. American Psychological Association (2017). Clinical practice guideline for the treatment of posttraumatic stress disorder (PTSD). Retrieved from https://www.apa.org/ptsd-guideline/ptsd.pdf

2. Higgins J. P. T., & Green S. (2011). Cochrane handbook for systematic reviews of interventions version 5.1.0 [updated March 2011]. The Cochrane Collaboration. Retrieved from http://handbook.cochrane.org

Articles from European Journal of Psychotraumatology are provided here courtesy of Taylor & Francis

# data[].attributes.attachment_link.title
Virtual reality exposure therapy for posttraumatic stress disorder (PTSD): a meta-analysis

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Contrary to specific phobias, for which Virtual Reality Exposure Therapy (VRET) constitutes an effective treatment, uncertainty still exists regarding the usefulness of VRET for posttraumatic stress disorder (PTSD). Therefore, this meta-analysis ...

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For each technology, identify any possible negative aspects of utilization of that technology.

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For each technology, identify any possible negative aspects of utilization of that technology.

# data[].attributes.body
The possible negative outcomes of this aspect of utilization of that technology in Post Traumatic Stress Disorder treatments for patients include causing panic attacks from the extremely realistic nature of the virtual reality presented, causing temporary distress, and the possibility of VR-induced motion sickness. The motion sickness, in particular, can be very unhelpful in the use of treatment, due to how this would put an utter blockade in the road for the treatment's efficiency on the patient.

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Abstract

Background

Virtual reality (VR) and augmented reality (AR) are emerging treatment modalities in psychiatry, which are capable of producing clinical outcomes broadly comparable to those achieved with standard psychotherapies.

Objective

Because the side effect profile associated with the clinical use of VR and AR remains largely unknown, we systematically reviewed available evidence of their adverse effects.

Methods

A systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework across 3 mental health databases (PubMed, PsycINFO, and Embase) to identify VR and AR interventions targeting mental health diagnoses.

Results

Of 73 studies meeting the inclusion criteria, 7 reported worsening clinical symptoms or an increased fall risk. Another 21 studies reported "no adverse effects" but failed to identify obvious adverse effects, mainly cybersickness, documented in their results. More concerningly, 45 of the 73 studies made no mention of adverse effects whatsoever.

Conclusions

An appropriate screening tool would help ensure that VR adverse effects are correctly identified and reported.

Keywords: virtual reality, augmented reality, mental health, side effects, adverse events, hardware, VR, software, AR, cybersickness, reporting standards

Introduction

Overview

Substantial unmet need for treatment for mental disorders arises from problems of access, efficacy, and tolerability of conventional treatments [ 1, 2]. These limitations have prompted the development and evaluation of novel interventions, including those based on virtual reality (VR) and augmented reality (AR) [ 3, 4]. Recent systematic reviews and meta-analyses have shown that VR and AR can be usefully applied to the treatment of various psychological disorders [ 5- 7], mainly using versions of cognitive behavioral therapy (CBT) and the specific CBT technique of exposure therapy (ET) [ 8]. Despite the enhanced accessibility and scalability of digital interventions, individual VR studies are often criticized for their limited quality and sample size [ 9].

Despite its therapeutic promise, the stance of VR-based psychotherapy cannot be established without a realistic appraisal of its benefits and harms. Unfortunately, little evidence is available regarding VR side effects experienced by mental health cohorts, a deficit shared with psychotherapies in general [ 10- 12]; both lack comprehensive identification of adverse effects, including symptom deterioration [ 13]. There is an urgent need to improve detection, reporting, and evaluation of adverse effects of psychotherapies, including VR [ 14].

Adverse Effects in VR and AR Clinical Trials

We define adverse effects as those perceived as unpleasant or harmful by the patient, clinician, or family, including symptomatic deterioration [ 11], addiction, or delusions manifesting in relation to VR use [ 10]. Previous studies refer to VR-induced symptoms and effects involving nausea, dizziness, disorientation, postural instability, and fatigue. These unpleasant physical symptoms experienced are often referred to as "cybersickness," which is found to be negatively correlated with the subjective experience of "presence" in the virtual environment [ 15]. VR interventions can also induce dissociative symptoms [ 16], including perceived disconnection from the self (depersonalization) and the environment (derealization).

The full mechanism for the above experiences is not known, but they are thought to be triggered by the discontinuity between digital and objective reality. While the frequency and severity of these are being reduced by technological improvements in hardware and software, as well as a reduction in the time spent in VR, the effects are often subjective experiences that cannot be mitigated in some individuals and vary greatly based on the hardware and software used [ 17]. Relevant hypotheses primarily involve the mismatch between the 2 sensory systems involved in motion detection. If a user is standing still, no motion is detected by the vestibular system; however, when using a VR device, there is a direct contradiction owing to the motion being observed by the visual system [ 18]. Measuring and addressing such adverse effects is important because they predict poorer treatment outcomes and increased nausea [ 19- 21]. ET, whether in vivo or delivered via VR, can transiently intensify distress [ 22] while improving outcomes overall [ 23].

This has led to the development of rating scales for cybersickness, including the Simulator Sickness Questionnaire (SSQ), Visual Analogue Scale, the Fast Motion Sickness Scale, and Virtual Reality Sickness Questionnaire [ 24- 26]. It has also led to the repurposing of previously reported scales such as Subjective Units of Distress Scale [ 27] and, more recently, the development of the Virtual Reality Neuroscience Questionnaire [ 28], which additionally measures user experience with the software, allowing direct comparisons between different interventions. There are also tools specifically scoring the degree of "presence" such as the Igroup Presence Questionnaire [ 22]. This is important because the intervention's ability to influence the emotional state, fundamental to therapeutic effects, is related to the sense of presence and can be disrupted by adverse effects interrupting the simulation [ 25].

Because VR studies often lack a standardized research design, adverse effect reporting is inconsistent and needs to be improved to reckon VR's appropriate place in mental health [ 6, 29]. Recommendations for the design of VR clinical trials, developed by an international consortium, address the safety and tolerability of equipment, headset, and intervention. Specific recommendations have highlighted the importance of assessing the psychological and emotional experiences of each participant [ 30]. Considering the prevalence of physical symptoms reported in the wider VR literature, it is concerning that mental health applications rarely report physical or psychological adverse effects [ 10, 31, 32], such as reduced cognitive performance [ 33, 34], physical or eye fatigue [ 35], and cybersickness [ 36]. This review examines adverse effects reported in trials of VR and AR in mental health.

Methods

Identification and Selection of Studies

A systematic search of 3 databases (PsycINFO, PubMed, and Embase) concluded on September 17, 2022. These databases were selected as the 3 largest repositories for mental health studies. The search included the terms "virtual reality" or "augmented reality" or "computer-assisted" in combination with a range of mental health disorders to capture a breadth of relevant conditions including "mental health" or "psychiatry" and "treatment" or "therapy" or "intervention" or "psychotherapy" or "attention deficit hyperactivity disorder" or "dementia" or "cognitive impairment" or "depression" or "mood disorder" or "schizophrenia" or "psychosis" or "psychotic" or "phobia" or "anxiety" or "bipolar" or "PTSD" or "post-traumatic stress disorder" or "alcohol" or "substance" or "anorexia" or "bulimia" or "eating disorder" or "psychiatric" or "mental illness" or "mental health." The reference lists of studies included for full-text review were used to identify additional articles not captured by the initial search process. The literature search was conducted in accordance with PRISMA (Preferred Reporting Items of Systematic reviews and Meta-Analyses) guidelines as depicted in Figure 1. Duplicate studies were identified and removed; the remaining studies were screened by 2 authors (YY and RL).

Figure 1.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection flowchart. HMD: head-mounted display.

Eligibility Criteria

Screened papers were included for analysis if they (1) used a VR- or AR-based intervention administered via a head-mounted display (HMD) and (2) addressed a specific Diagnostic and Statistical Manual of Mental Disorders or International Classification of Diseases mental health disorder. The authors decided to focus on the use of HMDs because the term "virtual reality" is often used to reference nonimmersive alternatives such as computer screens or Cave Automatic Virtual Environments. If both conditions were not met, the paper was excluded. Final decisions on inclusion were reached by consensus, with papers grouped on the basis of disorder and data type. Selected papers were examined for information on the type of mixed reality used, psychological intervention implemented and target disorder, the number of patients in the data set, reported side effects, and any measure of presence or immersion.

Results

Study Selection

The systematic search of databases yielded 424 hits ( Figure 1), with 84 duplicates. The examination of abstracts led to the exclusion of 85 articles, leaving 111 for full-text examination. In total, 42 studies were excluded for not using an HMD, 4 had no available translation, and 3 were based on nonclinical populations. Screening of reference lists yielded another 2 admissible reports. In total, 73 studies used VR or AR to treat a psychiatric diagnosis and were selected for analysis.

Study Characteristics

Of the 73 included studies, 67 used VR, 3 used AR, 2 used both VR and AR, and 1 used mixed reality. The main psychological intervention used was ET (n=52, 71%), followed by CBT without ET techniques (n=12, 16%). Non-CBT-based interventions (n=9, 12%) included embodiment illusion (n=2) and provision of psychoeducation (n=1), relaxation (n=1), cognitive rehabilitation (n=1), physical and cognitive training (n=1), body swapping (n=1), interreality (n=1), and aggression prevention therapy (n=1). A variety of specific problems were targeted by these interventions, which are outlined in Table 1.

Table 1.

Mental disorder

Studies, n

Phobias

30

Posttraumatic stress disorder

17

Anxiety

10

Psychosis

7

Eating disorders

6

Stress

3

Cognitive impairment

1

Suicidal ideation

1

Aggression

1

Addiction

1

Depression

1

Of 73 studies included, only 22 measured levels of immersion or presence. The most popular rating tool was the Igroup Presence Questionnaire (n=6). Other tools included the SSQ (n=3), Presence and Reality Judgment questions (n=3), and an embodiment questionnaire (n=3) where 3 other measures were used once each (n=1). The remaining studies used unknown or subjective descriptions of presence or immersion (n=7).

Reporting of Adverse Effects

Most studies (45/73, 62%) made no reference to adverse or side effects. Of the remaining 28, a total of 21 studies reported no adverse effects, but in 5 of them, adverse effects were apparent but not identified as such ( Table 2).

Table 2.

Study (year)

Participants in the exposure group, n

Adverse effect described

Seven studies reported adverse effects but provided limited information ( Table 3). Overall, the use of a tool that measured presence, immersion, or cybersickness was more common in the studies that commented on adverse effects (12/29, 27%) than in those that did not comment on adverse effects (12/44, 31%).

Table 3.

Study (year)

Participants in the exposure group, n

Adverse effect described

Data on dropouts from treatment groups were reported by 49 of 73 studies, but only 6 reported relevant reasons, including failure to arouse anxiety (n=2), high anxiety (n=2), anxiety along with a lack of engagement (n=1), finding the VR equipment "too distracting," distress from previous physical or sexual abuse, and avatars being "too unrealistic" (n=1).

Discussion

This review highlights significant gaps in reporting the adverse effects of VR interventions in mental health. In a majority of included articles, authors made no mention whatsoever of possible adverse effects associated with technology. The identified studies specifically mention cybersickness, worsening of symptoms (posttraumatic stress disorder, anxiety, and cravings), tiredness, dizziness, back pain, and carelessness. Also concerning was the fact that even studies that mentioned possible adverse effects still did not recognize and report them as such. Available evidence indicates that adverse effects associated with VR are likely to be common, but the identified lack of good data makes it difficult to estimate rates with any confidence. Some studies also specifically focus on measures of immersion or presence or only report serious adverse events that would involve significant harm or death. While this is a valid approach for traditional clinical trials with large cohorts focused on medications with serious side effects, it is unlikely to be a sufficient approach for VR interventions in mental health. What is clear from these limited results is that use of VR and AR in mental health studies has adverse effects that include traditional vestibular-related side effects, physical experiences, and psychological impacts, which will all need to be considered.

Although recent attempts have been made to formulate a research framework for these interventions, these have not addressed the detection and reporting of adverse reactions. It is, therefore, important that robust protocols are developed to rectify this shortcoming [ 30, 49]. This review highlights that fewer than half of all VR mental health studies report adverse effects. This points to a significant problem with reporting standards in these studies, which need to be addressed as the initial concern. This is particularly important because adverse effects can significantly influence the subject's emotional state and, therefore, the therapeutic effect of the intervention [ 16].

It is puzzling that many studies would use simple subjective measures of presence despite the many validated tools available. It has been established that the use of personal digital avatars, and especially seeing your own body in the virtual space, has a strong effect on presence [ 50]. Such factors are rarely discussed in mental health studies, which is an important omission as it would be particularly key in studies on eating disorders where participants are occasionally asked to assess body sizes.

Future studies will need to consider the range of physical and psychological adverse effects associated with AR and VR interventions and use screening tools that include physical symptoms such as cybersickness and VR-induced symptoms and effects, symptoms of dissociation, negative emotional responses such as increased rumination or thoughts of self-harm, and wider impacts on functioning. Although this would require the use of several tools, an ideal focus for future work would be to use a questionnaire covering all sections to standardize reporting of VR and AR interventions in mental health. This would include a move from older measures such as the SSQ tool to more dimensional assessments using the Virtual Reality Neuroscience Questionnaire tool, which provides grading and a broader approach to assess interventions. This could include the incorporation of established tools such as the Clinician-Administered Dissociative States Scale for dissociation and the Experience of Therapy Questionnaire [ 51].

As with the evaluation of any health care intervention, a benefit-risk analysis should be standard, where the dimension of risk includes the safety profile identified from the range and frequency of adverse effects. It is essential that all adverse effects, especially those that lead to dropouts or clinical worsening, need to be identified and reported. It is of concern that some studies specifically excluded participants experiencing cybersickness as the adverse events reported will not be representative of the general population. Thorough reporting would allow future systematic reviews to include meta-analyses of outcomes, including adverse effects, and improve our understanding of benefits, harm, and the appropriate place of VR and AR in psychiatric treatment.

This systematic review has multiple limitations. The study design focused specifically on HMDs and does not incorporate the wider (and generally older) literature regarding computer-generated environments projected onto screens. It also focused on diagnosed psychiatric disorders, and the results cannot be generalized to wider health care and nonclinical populations. There was also significant heterogeneity in the definitions of VR and AR in the literature, to the extent that our otherwise detailed search may not have captured some studies. Only papers published in English were included in the review.

Despite promising developments in VR and AR across a range of mental disorders, there is a clear need for standardized detection and reporting of adverse effects associated with these interventions.

Abbreviations

AR

augmented reality

CBT

cognitive behavioral therapy

ET

exposure therapy

HMD

head-mounted display

SSQ

Simulator Sickness Questionnaire

VR

virtual reality

Footnotes

Authors' Contributions: RML and DBM conceptualized the project. YY led the literature search and RML wrote the first draft of the manuscript. All authors edited and approved the submitted manuscript.

Conflicts of Interest: None declared.

References

31. Bouchard S, St-Jacques J, Renaud P, Wiederhold B. Side effects of immersions in virtual reality for people suffering from anxiety disorders. J Cyber Ther Rehabil. 2009;2(2):127-137. [Google Scholar]

Articles from JMIR Mental Health are provided here courtesy of JMIR Publications Inc.

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Adverse Effects of Virtual and Augmented Reality Interventions in Psychiatry: Systematic Review

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Virtual reality (VR) and augmented reality (AR) are emerging treatment modalities in psychiatry, which are capable of producing clinical outcomes broadly comparable to those achieved with standard psychotherapies. Because the side effect profile ...

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For each technology identified, provide a brief (1-2 sentences) description of that technology and whether it is used to prevent, diagnose or treat the focus condition.

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For each technology identified, provide a brief (1-2 sentences) description of that technology and whether it is used to prevent, diagnose or treat the focus condition.

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There are Wearable Stress-Monitoring Devices (e.g., HRV Sensors) that help track heart rate variability (HRV) and can detect sudden changes in a person's stress levels, potentially indicating a flashback to something traumatic. This also allows tracking the mental state of someone and helps to assess the effectiveness of therapies. This is accomplished because this device also shows what someone feels as a psychological response to their trauma and how to access it from there.

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by , and *

Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN 38152, USA

Author to whom correspondence should be addressed.

1. Introduction

According to the World Health Organization, stress is any form of change that causes physical, emotional, or psychological pressure [ 1]. Stress can lead to emotional and mental symptoms such as fear, anxiety, sadness, panic attacks, and depression. Additionally, stress can lead to physical symptoms, including elevated heart rate, difficulty breathing, disruption in sleeping patterns, change in eating habits, difficulty concentrating, and worsening of pre-existing health conditions. People under stress may increase the use of alcohol, tobacco, and other drugs, showing a strong dependence between stress and addiction to drugs and alcohol [ 2]. To some extent, everyone endures stress in their daily life. In particular, Americans are one of the most stressed-out populations in the world, experiencing a stress level 20 percentage points higher than the global average [ 3]. Whereas small amounts of stress for short periods can lead to positive changes in our lives, stress becomes harmful when it occurs for too long or is continuous (i.e., chronic). Chronic stress increases the risk of developing major health conditions and diseases such as diabetes, depression, heart disease, and cancer [ 4]. Monitoring an individual's stress level regularly can help us identify high-stress situations, enabling us to implement early stress interventions for better management. Effective stress management would reduce the probability of developing major health conditions, improving the well-being of individuals in our society. A direct consequence of improved stress management is the associated reduction in the individual and national economic cost of healthcare, increasing the economic competitiveness of any country.

Accurate stress monitoring can be provided through measuring cortisol levels using blood, urine, or saliva fluids. These methods require a specific laboratory or hospital to analyze the sample and determine the cortisol level. These methods, which can be considered minimally invasive, provide a brief glimpse of cortisol levels at a particular moment, restricting their use to continuous long-term stress studies due to their cost and the inconvenience of requiring an external facility to provide a quantitative measurement. Traditional stress detection methods are based on questionnaires in which subjects answer a validated set of stress-related questions to assess their stress level. Nonetheless, these questionnaires cannot be used for instantaneous stress detection. Real-time stress detection methods have been accomplished through analyzing imaging-based approaches that monitor any changes in individual's facial expressions, including changes in the blinking rate, pupils, and eyebrows [ 5, 6]. However, these methods could be unreliable due to participants' manipulated answers and facial expressions regarding mental stress.

Over the last decade, our research community has investigated physiological signals measured using various sensors or wearable devices to identify and measure stress levels. Blood volume pressure, electrocardiography (ECG), electromyography (EMG), electrodermal activity (EDA), and heart rate variability (HRV) are the most used physiological signals to identify stress. Reported research has used these signals independently or in combination with each other to improve the accuracy of the stress detection method. Current research in stress detection methods involves the combination of multiple physiological signals with machine learning (ML) or deep learning algorithms [ 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30].

Although multiple physiological signals have been investigated and reported in the literature, HRV signal is currently one of the most used signals for assessing mental stress [ 8, 9, 10, 11, 12, 13, 14, 18, 19, 20, 21, 22, 23, 26, 27, 30] because of its accessibility in current wearable devices. For example, most wristband devices have a photoplethysmography (PPG) sensor to measure HRV features. This paper focuses on stress detection through combining HRV features with ML algorithms to further investigate a low-cost, simple, and accurate stress detection model. Several research groups have worked on this goal. For example, in 2015, Munla et al. [ 9] investigated a stress detection method in people driving using HRV-based features and ML models. Twenty-two HRV features were obtained from an ECG sensor: maximumRRI, minimumRRI, medianRRI, meanRRI, meanHR, stHR, SDNN, RMSSD, NN50, pNN50, SDNNi, SDANN, TINN, VLF, HF, LF, LF/HF, SD1, and SD2. Among the different ML models, the authors investigated standard classifiers such as vector machine (SVM), K nearest neighbor (KNN), and radial basis function (RBF). Using the 22 HRV features, the authors reported the highest accuracy of 83.33% for an SVM classifier model to detect two physiological states of drivers (i.e., highly stressed, and normal). In 2020, Nkurikiyeyezu et al. proposed person-specific and generic models using a random forest (RF) model, and hybrid-calibrated the model using extremely randomized trees (ExtraTrees) [ 22]. In that work, the authors extracted HRV- and EDA-based features from the WESAD and SWELL datasets. In case of HRV features, the authors used 75 and 40 HRV features from SWELL and WESAD, respectively, including RMSSD, SSDD, SDRR_RMSSD, pNN25, pNN50, SD1, SD2, RELATIVE_RR, VLF, LF, HF, and LF/HF. Using these HRV-based features, the accuracy of their best RF model was 95.2 ± 0.5% for a person-specific model and 42.5 ± 19.9% for a generic model using the SWELL dataset. The accuracy of the proposed RF model improved for the WESAD dataset, being 98.9 ± 2.4% for a person-specific model and 83.9 ± 13.2% for a generic model. The performance of the hybrid model was trained on the SWELL datasets and achieved 93.09% accuracy with 100 calibration samples per subject. More recently, in 2021, Dalmeida et al. developed a predictive model to accurately classify stress levels using five different ML methods such as KNN, SVM, multilayer perceptron (MLP), RF, and gradient boosting (GB) from ECG-derived HRV features collected from wearable devices [ 26]. The authors used HRV metrics from a public dataset, DriverDB, as well as created a custom dataset through collecting HRV measurements from four Apple watch users. Using Pearson's correlation, recursive feature elimination (RFE), and ExtraTrees classifier, they selected the best 11 HRV features that were relevant in the context of the smartwatch: HR, RESP, TP, interval in seconds, AVNN, SDNN, RMSSD, pNN50, HF, LF, LF_HF. The authors reported the MLP model as the best stress-predictive model on the DriverDB dataset, providing a 75% area under receiver operator characteristic curve (AUROC), 80% recall, and 72% F1 score. The best MLP model was then tested on the four Apple Watch users, being able to predict relaxing and stress conditions with 79% and 71% accuracy, respectively. In that same year, Szakonyi et al. proposed an efficient method for an acute stress detection model using HRV features from ECG signal [ 27]. The authors created a custom dataset from seven participants using portable ECG sensors to record inter-beat (RR) interval data. Using Kubios HRV software [ 31, 32], the authors extracted 52 HRV features from RR interval data. The 52 HRV-based features were classified as 13 time-domain features, 32 frequency-domain features, and 7 non-linear features. Among the different ML algorithms tested, the authors reported the best stress-predictive model using only time-domain and non-linear HRV features (20 features = 13 + 7) and an XGBoost tree algorithm, yielding 96.31% accuracy, 95.23% sensitivity, 97.38% specificity, and 96.26% F1 score. Whereas the performance of the model was not significantly different if all HRV features were used (i.e., 96% accuracy, 95.69% sensitivity, 96.31% specificity, and 95.99% F1 score), the authors identified that the frequency-domain HRV-based features were the ones leading to the lowest predictive model with 92.10% accuracy, 90.81% sensitivity, 93.39% specificity, and 91.96% F1 score. This year, Benchekroun et al. proposed stress detection models based on HRV features using ML algorithms such as logistic regression (LR) and RF [ 30]. They extracted 61 HRV features using the pyHRV and TSFEL toolboxes from the Multi-Modal Stress Dataset (MMSD) and the University of Waterloo stress dataset (UWSD). Whereas the trained LR model using leave-one-subject-out cross-validation provided a specificity of 66% for the MMSD, the specificity of an RF model was 70% for the UWSD dataset. The authors also investigated a cross-dataset analysis through training on the MMSD dataset and testing on the UWSD one, achieving an RF model with a precision of 63%.

Despite all efforts reported in the literature, our community has not agreed yet on the number of features to identify stress conditions using wearable devices. The number of used features ranges from 7 to 75 among the reported works in the literature. In addition, most works reported in the literature are focused on individual training and testing. Stress varies from person to person; therefore, a stress detection model based on one subject might not provide accurate stress detection with another subject. An effective stress detection model should provide accurate stress classification for any individual in our society. Thanks to our community's broad acceptance of wearable devices (i.e., people wear wearable devices during their daily activities), this work investigates a hybrid global stress monitoring framework with generic training and person-specific testing. In other words, the model is trained using data from multiple subjects, but its performance is tested individually on each subject. A similar approach was used by Nkurikiyeyezu et al. in their hybrid-calibrated model using extremely randomized trees (ExtraTrees) [ 22]. Nonetheless, in that work, the authors used HRV- and EDA-based features. Our hybrid framework enables the model to learn general patterns across multiple subjects via combining HRV-based features with an ML algorithm. Among the different HRV features, our global stress model only uses eight HRV features selected using the minimum redundancy maximum relevance (mRMR) method [ 33]. The mRMR method has been previously reported in the literature to select features using HRV features [ 18, 19], selecting a subset of features with the highest correlation with the target variable but the lowest correlation among themselves [ 33]. We have validated the proposed global stress model using two open-access databases (the WESAD and SWELL databases). A key feature of the proposed framework is that the penalization on the person-specific stress prediction is negligible, identifying individual stress events with an accuracy higher than 99% after a global training framework. Although this pilot model should be further investigated, we believe this study provides a stepping-stone to developing a global stress monitoring and management model for real-world applications.

The rest of the paper is organized as follows: Section 2 describes the methodology used in this study, describing the proposed global stress detection framework. Section 3 discusses the results obtained using the SWELL and WESAD datasets as well as their combination. Finally, Section 4 summarizes the main achievements of this work and discusses future work to validate the proposed global stress framework further.

2. Methods

Figure 1 shows our proposed global stress detection framework that takes HRV-based features generated from individual wearable devices. All users' data are saved in the cloud for global training. The cloud stores all users' data, combines them, and trains a single integrated model using an RF algorithm. This remotely trained model is then used for individual stress detection locally. The only requirement of the proposed framework is that the training should occur on a cloud, requiring data transfer from the device to a cloud. However, most wearable devices such as the Empatica E4 and Fitbit devices already have this transfer implemented, making its deployment feasible for real-world applications. The proposed framework is further described in the flowchart, including each step involved in the methodology of the proposed system, shown in Figure 2.

2.1. SWELL and WESAD Datasets

We have used two benchmark datasets: the SWELL and WESAD datasets. We used only chest sensors' data from these datasets. The WESAD dataset is a multimodal dataset containing physiological and motion data recorded using a wrist- and chest-worn device from 15 subjects during a laboratory study [ 34]. The datasets include physiological signals such as three-axis acceleration (ACC), ECG, body temperature (TEMP), respiration (RESP), EDA, and EMG. During the laboratory study, each subject was exposed to three different physiological conditions: amusement, baseline, and stress. The labeling of these classes was based on the following three events. For example, data were labeled as baseline (i.e., class 0) when the participants were reading magazines for 20 min in a standing/sitting position. During the amusement condition (i.e., class 1), the subject watched eleven funny video clips. Finally, a stress condition (i.e., class 2) was induced when the participants took the Trier Social Stress Test (TSST) [ 35], which consists of public speaking and mental arithmetic operations.

The second dataset is the SWELL dataset, which contains data from 25 subjects during regular office hours [ 36]. This multimodal dataset contains information regarding computer logging, facial expression, bodily postures, ECG signals, and skin conductance (i.e., EDA). The SWELL dataset is classified into three classes: no stress, interruption, and time pressure. The labeling of the 'no stress' class (i.e., class 0) is related to the participant's performance in assigned tasks for a maximum of 45 min without time pressure or interruption. The SWELL dataset includes two stress-related events due to interruption (i.e., class 1) and time pressure (i.e., class 2). For example, the workers received interrupting emails during their assigned tasks to interrupt their activity in class 1. Whereas some emails did not require immediate action from the participants (e.g., informative emails), other emails required immediate action by the participants. Finally, in the time pressure condition, the execution time to complete assigned tasks was reduced by two-thirds of the no-stress execution time.

2.2. Proposed Global Stress Model

As previously mentioned, we have only used HRV features in this work. The main reasons for only using HRV features are the reduction of the computational tasks and the required sensors, leading to a low-cost stress monitoring device. The HRV features can be extracted from ECG signals. The process of extracting HRV features from ECG signals is found in Ref. [ 22] using a methodology proposed by the European Society of Cardiology Task Force [ 37]. The preprocessed SWELL and WESAD datasets containing only HRV features can be downloaded from Kaggle [ 38]. The preprocessing procedure is described in Ref. [ 22]. The overview of these datasets is explained in Table 1. For binary classification (stress versus no-stress conditions), the baseline and amusement classes in the WESAD dataset were combined and formed a new class named no-stress. On the other hand, the time-pressure and interruption stress classes in the SWELL dataset were combined into a new class named stress. Finally, we have combined both binary datasets for the global stress platform.

As Figure 2 shows, the dataset was divided into training and testing datasets using a ratio of 70/30 (training/testing). In other words, the training dataset contains 70% of data instances of each subject dataset, and the testing dataset has 30% of data instances. Data standardization is related to the scaling of the features so that their range is the same, ensuring that no features have a higher impact on the model. Whereas data standardization is commonly implemented since some ML algorithms are sensitive to the scale of the input features, we have found that data standardization did not improve the model's performance in our global stress model ( Figure 2). Therefore, we have decided to not implement it, reducing the data processing steps.

2.3. Feature Selection

Training all features is impractical due to computational and storage constraints in the proposed global framework. Therefore, selecting the best features (i.e., the feature with the highest classifying power) would reduce the computational complexity of the model, decreasing the training time. In this study, we have selected features based on the mRMR method [ 33]. Several works reported in the literature have used the mRMR method to select features using HRV features [ 18, 19]. This mRMR approach chooses a subset of features with the highest correlation with the target variable but the lowest correlation among themselves [ 33]. The features are chosen one at a time using a greedy search method based on optimizing an objective function that balances relevance and redundancy. For our global stress monitory framework, we chose the top eight features selected using the mRMR approach to train and test our model.

2.4. Random Forest Algorithm

Among the different ML models, the RF algorithm is one of the most effective for detecting stress due to its better results as compared to others [ 23]. Some articles that successfully used RF for detecting stress are Refs. [ 11, 15, 17, 22, 26, 27]. The RF model is an ensemble of several decision tree algorithms. The objective of this RF model is finding a subset of features randomly among all features to be the base of constructing each decision tree [ 39]. As a result, each decision tree classifies any new dataset or unknown instances, which are categorized into a specific class based on the majority of the decision trees. Figure 3 shows the structure of an RF classifier, highlighting that the output of the RF classifier is the popular vote among the outputs of the individual decision trees. The hyperparameters of the proposed RF model were determined using grid search, identifying the values that yield the highest performance of the RF model. The best hyperparameter values were max_depth = 20, min_samples_split = 2, and n_estimators = 100. The hyperparameter of the maximum depth was found through analyzing the model's accuracy for the three datasets (see Figure S1 in the supplemental document). Because the accuracy of the model remains almost invariant from a maximum depth of 20 to 60, we have chosen 20 to reduce the complexity of the model.

Regarding the validation method of the RF model, we have implemented the stratified K-fold cross-validation method on the 70% training dataset. The stratified K-Fold is preferred over K-Fold for classification problems with unbalanced class distributions [ 40] since it maintains the proportion of samples for each class in each fold, ensuring each fold has a similar class distribution as the entire dataset. In other words, the stratified K-fold cross-validation method enables the model to be trained on a representative sample of the minority class. Although K = 10 is the preferred value, we used K = 15 since the higher the K value, the lower the prediction error as the model sees more available data [ 41].

We used Google Collab with Python 3 to investigate the proposed global stress framework. The code was implemented using Python libraries such as TensorFlow, Matplotlib, NumPy, Pandas, and Karas. The computational specs for the algorithm are 12 GB RAM using Google Collaboratory. The code is available in our GitHub [ 42].

2.5. Evaluation Metrics

The evaluation of the trained RF model is done through estimating the confusion matrix ( Table 2), which classifies the data instances into positive and negative categories. For example, true positive (TP) is the number of instances when the classifier correctly identifies stress events. False positive (FP) is the number of instances that the classifier identifies true stress events that are not. False negative (FN) is the number of instances in which the classifier labels true stress events as no-stress events, failing to detect stress. Finally, true negative (TN) is the number of instances in which the classifier correctly detects no-stress events. Based on these values, the model's performance is assessed through measuring the accuracy, precision, recall, and F1 score. The accuracy provides a score of the proportion of correct predictions out of all predictions made. The precision value provides a measurement of true positive predictions with respect to all positive predictions. The recall value provides the ratio of true positive predictions out of all actual positive cases. Finally, the F1-score is the harmonic mean between the precision and recall scores. The value of all these performance metrics ranges between 0 and 1, with 1 indicating the highest performance of the classifier model. These performance metrics are calculated using the following equations:

Precision P = TP TP+FP,

and

3. Results

This section explains the features selected and the results obtained from each dataset's global training (e.g., combining the data from all subjects) and individual testing. The performance of the best-trained model is evaluated on each subject through estimating the accuracy, precision, recall, and F1-score metric parameters.

Firstly, we investigate the SWELL and WESAD datasets separately. The SWELL dataset originally contains 67 HRV features. Applying the mRMR method, we have reduced the 67 HRV features to 8 features: HR_SQRT, pNN25, samples, MEAN_RR, HR, MEAN_RR_SQRT, MEAN_RR_LOG, and MEDIAN_RR. Information on these features is shown in Table 3. These features have enough discriminant power to differentiate between non-stressed and stress events, providing a p-value < 0.05 in Tukey's Honest Significant Differences (HSD) test. These eight HRV features were used as training features of our RF model for detecting stress conditions. We have selected the top eight features because the accuracy of the prediction model is high for all subjects. Whereas the reduction of the number of features penalizes the model's performance, the increase in them raises the training time. Empirically, we found that eight features are the minimum number of features to discriminate stress events with a high prediction performance and reduced training time.

Figure 4 shows the performance of the trained RF model for each subject of the SWELL dataset. These results are also reported in Table S1 in the supplemental document. The classifying power of our RF model is equal to or higher than 99% for all subjects of the SWELL dataset. In other words, the RF model can identify stress events with accuracy, precision, recall, and an F1 score ranging from 98 to 100%.

Next, we train and evaluate the performance of an RF model for the WESAD dataset. From the original 65 HRV features, we selected the HR, HR_SQRT, LF_PCT, MEAN_RR_LOG, MEAN_RR_SQRT, MEDIAN_RR, MEAN_RR, and HF features using the mRMR method. The description of these features is also reported in Table 3. Comparing these features with the features selected from SWELL, shown in the third column of Table 3, one realizes that the selected eight features are data dependent, being different between the SWELL and WESAD datasets. Nonetheless, five selected features are the same: HR, HR_SQRT, MEAN_RR_SQRT, MEAN_RR_LOG, and MEDIAN_RR. After selecting the eight features for the WESAD dataset, we train and test our RF model. Figure 5 and Table S2 report the performance of the trained RF model for each subject of the WESAD dataset. Again, the trained RF model has a classifying power equal to or higher than 99% for each individual subject of the dataset. In particular, except for subjects 4, 5, and 15, the RF model identifies stress events with accuracy, precision, recall, and an F1 score of 100%, validating the global framework to detect stress for the WESAD dataset.

Although the results reported in Figure 4 and Figure 5 validate the global framework for stress detection, these results do not validate the generalization of the stress model. In other words, a generalized global stress model should be able to detect stress conditions regardless of the origin/cause of it. The next validation of the global stress framework examines our capability to classify stress under two causes of stress. Therefore, for this validation, we combined the SWELL and WESAD datasets, having a total of 37 subjects. For this validation, we chose the common features between both datasets to combine them, reducing the number of features from 67 to 63 in the SWELL dataset and 65 to 63 in the WESAD dataset. As the third column of Table 3 shows, the selected mRMR features are dataset dependent; for the combined dataset, the selected eight features were KURT, VLF, MEAN_REL_RR, HR_HF, pNN25, KURT_REL_RR, TP, and MEDIAN_REL_RR_LOG. Again, the description of these features is reported in Table 3. Note that none of these features except for one were previously selected during the individual testing of each dataset. Per previous validations, we use 70% and 30% of each subject's instances from the combined dataset for training and testing, respectively. Figure 6 and Table S3 report the performance metrics (e.g., accuracy, precision, recall, and F1 score) for each subject of the combined dataset. From these results, we can see the high classifying power of the trained RF model. We can detect stress conditions with a mean accuracy of 99.5% and a standard deviation of 0.8% using KURT, VLF, MEAN_REL_RR, HR_HF, pNN25, KURT_REL_RR, TP, and MEDIAN_REL_RR_LOG as training features. Regarding the other performance metrics, the average and standard deviation of the precision, recall, and F1-score metrics are 99.4% and 0.9% for all the subjects in the combined dataset, enabling the RF model to identify stress events.

4. Discussion and Conclusions

Both young and adult people have experienced stress over the last year, becoming one of the main health issues of this decade. For this reason, it is critical to investigate and develop a global stress framework that detects stress events accurately and in a non-invasive way. This work focused on detecting stress levels using HRV features because of the broad acceptance of HRV-based wearable devices used almost daily to track daily events. In this research study, we have investigated the combination of eight HRV features recorded from a single sensor with an RF model to detect stress on an individual subject using a global stress detection framework. The eight HRV features were selected using the mRMR feature selection method [ 33]. The proposed global stress framework ( Figure 1 and Figure 2) has been tested using WESAD and SWELL datasets. The global stress framework is based on the principle that each individual dataset is split into training and testing data in a ratio of 70:30 for each subject. All subjects' training data were combined and used to select the best eight features using the mRMR feature selection method. An RF model was trained using the best eight features and a stratified 15-fold cross-validation method. Then, the best-trained RF model using the global framework was used to detect stress conditions individually. We have found that the classification power of our trained RF models is equal to or higher than 99% for each subject of both datasets based on conventional evaluation metrics (e.g., accuracy, precision, recall, and F1 score). In addition, we combined both datasets to validate the generalization of the proposed global stress framework. For the combined dataset, we achieved the highest classifier accuracy equal to or higher than 97% for each subject. This result shows that including more subjects in the global training, which introduces more variability in our training dataset, does not reduce the performance of the global stress model on individual testing. This result should be seen as a stepping-stone approach to developing a global stress framework.

The high accuracy of the proposed global stress framework raises some concerns about the possibility of overfitting. Figure 7 compares the accuracy for three training methods using the WESAD dataset. The person-specific approach (e.g., specific training and testing) provides the highest stress prediction (blue line in Figure 7), but this method lacks generalization prediction. The proposed hybrid method with global training and individual testing has similar prediction power to the person-specific model. On the contrary, the accuracy of the leave-one-out approach, in which the training dataset does not include any data related to the tested subject, is highly dependent on the tested subject, ranging from 0.3143 to 0.949. Although these results could be considered the most realistic/generalized model, the individual performance may be deficient for some individuals such as Subject 14, lacking prediction power. However, following the authors in Ref. [ 22], the prediction model can provide successful results through incorporating person-specific data into the global training dataset. In Ref. [ 22], the authors discussed a potential calibration model to add these data. Whereas our proposed global stress framework is similar to the one proposed in Ref. [ 22] in terms of adding person-specific data into the training dataset, the main difference between both works is the number of features used. One of our objectives was to minimize this number of features, reducing the complexity of the model and the training time.

During this research study, we observed that the selected features using the mRMR method depend on the dataset, being different from the three tested cases (i.e., SWELL, WESAD, and combined validations). Additionally, since our model is a random forest (i.e., an ensemble of decision trees), one can use relative feature importance to evaluate the weight of each feature in the trained RF to predict stress. Figure 8 shows the importance scores of the mRMR-selected features for the three datasets. The metric used to measure the feature importance is the popular Gini importance, which calculates the number of instances a feature is used to decide a split in all trees in proportion to the number of samples it had a role in splitting [ 43]. In other words, the relative importance scores provide a measurement of the utility of each feature in the design of the decision tree. The more a feature is used to make predictions within the decision trees, the greater its relative importance. Whereas five of the eight features are the same in the SWELL and WESAD datasets (HR, HR_SQRT, MEAN_RR_SQRT, MEAN_RR_LOG, and MEDIAN_RR), Figure 8a,b demonstrate that their importance in the trained models are different. This result may be related to the difference between subjects and the cause of stress. Future work should be focused on further analyzing these results. On the other hand, the combined dataset ( Figure 8c) has the most unique subset of features (KURT, VLF, MEAN_REL_RR, HR_HF, pNN25, KURT_REL_RR, TP, and MEDIAN_REL_RR_LOG). Nonetheless, some of these features are engineered features. For example, the 'HR & HF' feature is highly correlated to the 'HR' one. Although the selected eight features of the combined dataset are totally different from the SWELL and WESAD datasets, these eight features also provide a high classifier model when trained using the SWELL and WESAD datasets (see Figure 9). The corresponding tables of these results are Tables S4 and S5 in the supplemental document. Note that the performance of the globally trained RF model is still high; the accuracy of detecting stress events is 100% for each subject in both datasets.

It is important to mention that the idea of global training and individual testing for stress detection was recently published by Fauzi et al. in 2022 [ 29]. In that study, the authors investigated a stress detection method using 420 features extracted from ST, ACC, EDA, and BVP signals of the WESAD dataset. In Ref. [ 29], Fauzi et al. investigated three different learning schemes (i.e., individual, centralized, and federated) of a logistic regression (LR) algorithm. Whereas the individual learning approach uses each individual data instance for training and testing, the centralized learning scheme combines all users' data instances for training but is individually tested. The federated learning scheme operates similarly to the centralized one. The only difference between them is that the federated learning approach maintains the user's privacy, ensuring that each user's data never leave the user's device. Although our proposed global stress framework ( Figure 1) and the centralized learning scheme by Fauzi et al. are similar, the performance of the models is quite different. Fauzi et al. reported accuracy, precision, recall, and F1-score values ranging from 0.7475 to 1.00. Using our proposed system, we achieved higher performance matrices ranging from 0.99 to 1.00. Apart from the reported results, the main difference between our work and the recently published work by Fauzi et al. is the input features since they used a multimodal approach, combining features from multiple sensors and training their LR model without prior selection of them. In our work, we have been focused on selecting the top eight features with the highest classifying power to reduce the training time of the global stress platform.

In conclusion, in this study, we have proposed a global stress detection framework combining eight HRV features and an RF model with a classifying power of 99% or higher during individual testing using two benchmark datasets (i.e., WESAD and SWELL datasets). This study presents some limitations related to generalization, lack of diversity, and ability to track the cause of stress. The issues of generalization and lack of diversity are related to the fact that the used datasets contain information from a small number of individuals. Additionally, these measurements were recorded in a controlled laboratory setting, not mimicking a real-world setting or including a larger population. In addition, it is important to acknowledge that the used datasets may not be representative of the general population, since there is no information regarding the individuals' backgrounds (e.g., age, sex, race). Another potential limitation of this study is the unknown causes of stress, since stress response may differ based on its cause. The trained RF models have been only validated for three types of stress. Future work should be focused on applying the proposed global stress framework in real-world scenarios with a larger and broader population where people experience stress due to different causes. Such study will enable us to investigate the weight of the selected features and their clinical interpretation, aiming to disentangle the relationship between features and causes of stress. In this work, we selected HRV features from a chest sensor since the data are more accurate. Future work should also be focused on validating the proposed global stress framework using HRV features recorded on wristband devices instead of chest-based devices, since their applicability is broader.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s23115220/s1. A supplemental file provides supplementary information to show the performance metrics of the trained RF model for the SWELL, WESAD, and combined dataset using the global stress framework (e.g., global training but individual testing). The code is available in our GitHub [ 42]. Figure S1: Hyperparameter tuning-Evaluation of the model's accuracy versus the maximum depth metric for (a) SWELL, (b) WESAD, and (c) combined datasets. The validation accuracy is the mean of the validation score over the 15 folds, and the testing accuracy is the mean value of the individual accuracy for the different subjects. The prediction power of the model in the testing dataset is not penalized for increasing the tree depth, demonstrating that our model is not overfitted; Table S1: Performance metric of the trained RF model for the SWELL dataset using the global stress framework (e.g., global training but individual testing); Table S2: Performance metric of the trained RF model for the WESAD dataset using the global stress framework (e.g., global training but individual testing); Table S3: Performance metric of the trained RF model for the combined (SWELL + WESAD) dataset using the global stress framework (e.g., global training but individual testing); Table S4: Performance metric of the trained RF model for the SWELL dataset using the global stress framework and the eight selected features from the combined dataset. Table S5: Performance metric of the trained RF model for the WESAD dataset using the global stress framework and the eight selected features from the combined dataset.

Author Contributions

Conceptualization, K.D. and A.D.; methodology, K.D., B.B.-J. and A.D.; software, K.D.; validation, K.D., B.B.-J. and A.D.; investigation, K.D., B.B.-J. and A.D.; writing-original draft preparation, K.D. and A.D.; writing-review and editing, K.D., B.B.-J. and K.D.; visualization, K.D., B.B.-J. and A.D.; supervision, A.D.; project administration, A.D.; funding acquisition, A.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was partially funded by UMRF Ventures Professorship and the Herff College of Engineering at the University of Memphis.

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Acknowledgments

The authors thank Brian Bogue-Jimenez for discussing the methodology of the proposed global stress framework and verifying the correct implementation of the software code.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 8. Feature Importance Score for each selected feature in the trained RF models using ( a) SWELL, ( b) WESAD, and ( c) the combined dataset.

Figure 9. Performance metric of the trained RF model for the SWELL ( a) and WESAD ( b) dataset using the global stress framework and the eight selected features from the combined dataset.

Datasets

Subjects

Features

Total Instances Per Feature

Instances vs. Class

Modified Instances vs. Class

WESAD

15

65

135,650

71,640 baseline

94,704 no-stress

23,064 amusement

40,946 stress

40,946 stress

SWELL

22

67

391,638

212,400 no-stress

212,400 no-stress

110,943 interruption

179,238 stress

68,295 time pressure

Combined WEDAD and SWELL

37

63

527,288

220,184 stress

307,104 no-stress

Confusion Matrix

Actual Values

Positive

Negative

Predicted values

Positive

TP

FP

Features

Description [16]

Dataset

AMA Style

Dahal K, Bogue-Jimenez B, Doblas A. Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model. Sensors. 2023; 23(11):5220. https://doi.org/10.3390/s23115220

Chicago/Turabian Style

Dahal, Kamana, Brian Bogue-Jimenez, and Ana Doblas. 2023. "Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model" Sensors 23, no. 11: 5220. https://doi.org/10.3390/s23115220

APA Style

Dahal, K., Bogue-Jimenez, B., & Doblas, A. (2023). Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model. Sensors, 23(11), 5220. https://doi.org/10.3390/s23115220

# data[].attributes.attachment_link.title
Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model

# data[].attributes.attachment_link.description
Approximately 65% of the worldwide adult population has experienced stress, affecting their daily routine at least once in the past year. Stress becomes harmful when it occurs for too long or is continuous (i.e., chronic), interfering with our performance, attention, and concentration.

# data[].attributes.headline
For each technology, identify any possible negative aspects of utilization of that technology.

# data[].attributes.subject
For each technology, identify any possible negative aspects of utilization of that technology.

# data[].attributes.body
One of the main negatives that comes from this would be the risk of heightened anxiety over the fixation of results, causing someone to force themselves to feel a certain way, resulting in inaccurate data. This can cause a misdiagnosis because it could send a patient into an anxiety attack, causing a heart rate spike, and messing with information.

# data[].attributes.attachment_link.title
Photoplethysmography Enabled Wearable Devices and Stress Detection: A Scoping Review

# data[].attributes.attachment_link.description
Background: Mental and physical health are both important for overall health. Mental health includes emotional, psychological, and social well-being; however, it is often difficult to monitor remotely. The objective of this scoping review is to investigate studies that focus on mental health and stress detection and monitoring using PPG-based wearable sensors. Methods: A literature review for this scoping review was conducted using the PRISMA (Preferred Reporting Items for the Systematic Reviews and Meta-analyses) framework. A total of 290 studies were found in five medical databases (PubMed, Medline, Embase, CINAHL, and Web of Science). Studies were deemed eligible if non-invasive PPG-based wearables were worn on the wrist or ear to measure vital signs of the heart (heart rate, pulse transit time, pulse waves, blood pressure, and blood volume pressure) and analyzed the data qualitatively. Results: Twenty-three studies met the inclusion criteria, with four real-life studies, eighteen clinical studies, and one joint clinical and real-life study. Out of the twenty-three studies, seventeen were published as journal-based articles, and six were conference papers with full texts. Because most of the articles were concerned with physiological and psychological stress, we decided to only include those that focused on stress. In twelve of the twenty articles, a PPG-based sensor alone was used to monitor stress, while in the remaining eight papers, a PPG sensor was used in combination with other sensors. Conclusion: The growing demand for wearable devices for mental health monitoring is evident. However, there is still a significant amount of research required before wearable devices can be used easily and effectively for such monitoring. Although the results of this review indicate that mental health monitoring and stress detection using PPG is possible, there are still many limitations within the current literature, such as a lack of large and diverse studies and ground-truth methods, that need to be addressed before wearable devices can be globally useful to patients.