The Most Common Personalized Depression Treatment Debate Actually Isn't As Black Or White As You Might Think

Personalized Depression Treatment

Traditional therapy and medication don't work for a majority of people who are depressed. A customized treatment could be the answer.

Cue is a digital intervention platform that transforms passively acquired smartphone sensor data into personalized micro-interventions that improve mental health. We looked at the best-fitting personal ML models to each person using Shapley values, in order to understand their feature predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.

Predictors of Mood

Depression is a major cause of mental illness in the world.1 Yet only half of those affected receive treatment. To improve the outcomes, doctors must be able to recognize and treat patients with the highest probability of responding to certain treatments.

Personalized depression treatment is one method of doing this. By using mobile phone sensors as well as an artificial intelligence voice assistant, and other digital tools researchers at the University of Illinois Chicago (UIC) are working on new ways to predict which patients will benefit from the treatments they receive. With two grants awarded totaling over $10 million, they will use these techniques to determine the biological and behavioral factors that determine response to antidepressant medications and psychotherapy.

So far, the majority of research into predictors of depression treatment effectiveness has centered on clinical and sociodemographic characteristics. These include demographics such as gender, age, and education, and clinical characteristics such as symptom severity and comorbidities, as well as biological markers.

A few studies have utilized longitudinal data in order to determine mood among individuals. Few also take into account the fact that moods vary significantly between individuals. Therefore, it is essential to develop methods that allow for the recognition of the individual differences in mood predictors and the effects of treatment.

The team's new approach uses daily, in-person evaluations of mood and lifestyle variables using a smartphone app called AWARE, a cognitive evaluation with the BiAffect app and electroencephalography -- an imaging technique that monitors brain activity.  depression therapy  can then develop algorithms to identify patterns of behaviour and emotions that are unique to each individual.

In addition to these modalities the team also developed a machine-learning algorithm that models the dynamic variables that influence each person's mood. The algorithm combines these individual differences into a unique "digital phenotype" for each participant.

This digital phenotype was found to be associated with CAT-DI scores, which is a psychometrically validated scale for assessing severity of symptom. The correlation was weak however (Pearson r = 0,08; P-value adjusted by BH 3.55 10 03) and varied greatly among individuals.

Predictors of symptoms

Depression is a leading cause of disability around the world1, but it is often not properly diagnosed and treated. In addition an absence of effective interventions and stigma associated with depression disorders hinder many from seeking treatment.

To allow for individualized treatment to improve treatment, identifying the predictors of symptoms is important. The current methods for predicting symptoms rely heavily on clinical interviews, which are not reliable and only reveal a few features associated with depression.

Machine learning can enhance the accuracy of diagnosis and treatment for depression by combining continuous digital behavioral phenotypes gathered from smartphones with a valid mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes are able to provide a wide range of distinct behaviors and activities that are difficult to capture through interviews, and allow for continuous, high-resolution measurements.

The study included University of California Los Angeles (UCLA) students with mild to severe depressive symptoms participating in the Screening and Treatment for Anxiety and Depression (STAND) program29, which was developed under the UCLA Depression Grand Challenge. Participants were routed to online support or in-person clinical care according to the severity of their depression. Those with a CAT-DI score of 35 or 65 were assigned online support via an online peer coach, whereas those with a score of 75 were routed to clinics in-person for psychotherapy.

At baseline, participants provided an array of questions regarding their personal demographics and psychosocial characteristics. The questions included age, sex and education, financial status, marital status as well as whether they divorced or not, their current suicidal thoughts, intentions or attempts, as well as how often they drank. Participants also scored their level of depression severity on a scale ranging from 0-100 using the CAT-DI. The CAT-DI assessment was carried out every two weeks for participants who received online support, and weekly for those who received in-person care.

Predictors of Treatment Response

Research is focusing on personalization of treatment for depression. Many studies are focused on finding predictors that can help doctors determine the most effective medications for each person. In particular, pharmacogenetics identifies genetic variations that affect how the body's metabolism reacts to antidepressants. This allows doctors to select the medications that are most likely to be most effective for each patient, while minimizing the time and effort in trial-and-error treatments and eliminating any side effects that could otherwise hinder the progress of the patient.

Another promising approach is to develop predictive models that incorporate clinical data and neural imaging data. These models can be used to determine the best combination of variables that is predictive of a particular outcome, like whether or not a particular medication is likely to improve mood and symptoms. These models can be used to predict the patient's response to treatment, allowing doctors to maximize the effectiveness.

A new generation of studies uses machine learning methods, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of multiple variables and increase predictive accuracy. These models have been proven to be useful in the prediction of treatment outcomes like the response to antidepressants. These approaches are becoming more popular in psychiatry and will likely become the norm in the future treatment.

Research into depression's underlying mechanisms continues, in addition to ML-based predictive models. Recent findings suggest that depression is connected to the dysfunctions of specific neural networks. This theory suggests that the treatment for depression will be individualized focused on treatments that target these circuits in order to restore normal functioning.

Internet-based interventions are an option to achieve this. They can provide more customized and personalized experience for patients. For instance, one study found that a program on the internet was more effective than standard care in improving symptoms and providing an improved quality of life for patients suffering from MDD. A controlled study that was randomized to a personalized treatment for depression showed that a significant percentage of patients saw improvement over time as well as fewer side effects.

Predictors of side effects

A major challenge in personalized depression treatment involves identifying and predicting which antidepressant medications will cause the least amount of side effects or none at all. Many patients are prescribed a variety of medications before finding a medication that is effective and tolerated. Pharmacogenetics provides a novel and exciting way to select antidepressant medicines that are more effective and specific.

There are several predictors that can be used to determine the antidepressant to be prescribed, including genetic variations, phenotypes of patients such as ethnicity or gender, and comorbidities. However finding the most reliable and reliable predictors for a particular treatment will probably require randomized controlled trials of much larger samples than those typically enrolled in clinical trials. This is because it may be more difficult to detect the effects of moderators or interactions in trials that comprise only one episode per participant instead of multiple episodes spread over time.

Additionally, predicting a patient's response will likely require information about the severity of symptoms, comorbidities and the patient's subjective experience of tolerability and effectiveness. There are currently only a few easily assessable sociodemographic variables and clinical variables seem to be reliable in predicting the response to MDD. These include age, gender and race/ethnicity, BMI, SES and the presence of alexithymia.

There are many challenges to overcome in the application of pharmacogenetics for depression treatment. It is crucial to be able to comprehend and understand the definition of the genetic mechanisms that cause depression, as well as an understanding of an accurate indicator of the response to treatment. In addition, ethical issues such as privacy and the responsible use of personal genetic information must be considered carefully. In the long run, pharmacogenetics may provide an opportunity to reduce the stigma associated with mental health care and improve treatment outcomes for those struggling with depression. But, like any approach to psychiatry careful consideration and implementation is necessary. For now, it is recommended to provide patients with various depression medications that work and encourage them to talk openly with their physicians.