Personalized depression treatment without medication Treatment

Traditional therapy and medication do not work for many people suffering from depression. A customized treatment may be the answer.

Cue is an intervention platform for digital devices that transforms passively acquired sensor data from smartphones into customized micro-interventions designed to improve mental health. We analyzed the best-fitting personalized ML models to each person, using Shapley values to discover their characteristic predictors. The results revealed distinct characteristics that changed mood in a predictable manner over time.

Predictors of Mood

Depression is a leading cause of mental illness in the world.1 Yet the majority of people suffering from the condition receive treatment. In order to improve outcomes, healthcare professionals must be able to identify and treat patients with the highest probability of responding to specific treatments.

Personalized depression treatment is one method of doing this. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will gain the most from certain treatments. They make use of mobile phone sensors, a voice assistant with artificial intelligence, and other digital tools. Two grants totaling more than $10 million will be used to discover biological and behavioral factors that predict response.

The majority of research done to so far has focused on sociodemographic and clinical characteristics. These include factors that affect the demographics like age, sex and education, clinical characteristics such as symptoms severity and comorbidities and biological markers such as neuroimaging and genetic variation.

While many of these variables can be predicted from information in medical records, few studies have utilized longitudinal data to determine the factors that influence mood in people. Few studies also take into account the fact that moods can be very different between individuals. Therefore, it is critical to create methods that allow the determination of different mood predictors for each person and treatment effects.

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. The team is able to develop algorithms to detect patterns of behavior and emotions that are unique to each person.

In addition to these methods, the team also developed a machine-learning algorithm that models the dynamic predictors of each person's depressed mood. The algorithm blends the individual characteristics to create a unique "digital genotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, a psychometrically validated scale for assessing severity of symptom. However, the correlation was weak (Pearson's r = 0.08, BH-adjusted P-value of 3.55 x 10-03) and varied widely across individuals.

Predictors of symptoms

Depression is the most common cause of disability around the world1, however, it is often untreated and misdiagnosed. In addition the absence of effective interventions and stigma associated with depression disorders hinder many individuals from seeking help.

To help with personalized alternative treatment for depression and anxiety, it is essential to identify the factors that predict symptoms. The current methods for predicting symptoms rely heavily on clinical interviews, which are unreliable and only detect a few features associated with depression.

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

The study involved University of California Los Angeles students with mild to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and depression treatment tms program29, which was developed as part of the UCLA Depression Grand Challenge. Participants were sent online for support or to clinical treatment depending on the severity of their depression. Participants with a CAT-DI score of 35 or 65 were assigned to online support via the help of a peer coach. those with a score of 75 patients were referred to psychotherapy in-person.

Participants were asked a series questions at the beginning of the study regarding their psychosocial and demographic characteristics as well as their socioeconomic status. The questions covered education, age, sex and gender and financial status, marital status and whether they were divorced or not, their current suicidal thoughts, intent or attempts, as well as the frequency with which they consumed alcohol. Participants also rated their degree of depression severity on a 0-100 scale using the CAT-DI. CAT-DI assessments were conducted every other week for participants that received online support, and every week for those who received in-person treatment.

Predictors of Treatment Response

The development of a personalized depression treatment is currently a top research topic and many studies aim to identify predictors that enable clinicians to determine the most effective drugs for each person. Pharmacogenetics in particular identifies genetic variations that determine how the body's metabolism reacts to drugs. This enables doctors to choose medications that are likely to work best for each patient, minimizing the time and effort in trial-and-error procedures and avoiding side effects that might otherwise hinder progress.

Another option is to develop predictive models that incorporate clinical data and neural imaging data. These models can be used to identify which variables are most predictive of a specific outcome, like whether a medication will improve symptoms or mood. These models can also be used to predict the response of a patient to treatment that is already in place which allows doctors to maximize the effectiveness of their current treatment.

A new generation of studies utilizes machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based techniques) to blend the effects of several variables and improve the accuracy of predictive. These models have shown to be effective in forecasting treatment outcomes, such as the response to antidepressants. These methods are becoming popular in psychiatry and it is expected that they will become the norm for the future of clinical practice.

In addition to ML-based prediction models research into the mechanisms behind depression is continuing. Recent research suggests that depression is linked to the malfunctions of certain neural networks. This theory suggests that an individualized treatment for depression will depend on targeted treatments that restore normal function to these circuits.

One method of doing this is to use internet-based interventions that offer a more individualized and tailored experience for patients. A study showed that an internet-based program helped improve symptoms and led to a better quality life for MDD patients. Additionally, a randomized controlled trial of a personalized treatment for depression demonstrated steady improvement and decreased adverse effects in a large percentage of participants.

Predictors of adverse effects

A major challenge in personalized depression treatment is predicting the antidepressant medications that will have minimal or no side effects. Many patients have a trial-and error approach, with various medications prescribed until they find one that is safe and effective. Pharmacogenetics provides a novel and exciting method to choose antidepressant drugs that are more effective and specific.

A variety of predictors are available to determine which antidepressant is best to prescribe, including genetic variants, patient phenotypes (e.g., sex or ethnicity) and co-morbidities. To determine the most reliable and accurate predictors of a specific treatment, random controlled trials with larger sample sizes will be required. This is due to the fact that it can be more difficult to detect interactions or moderators in trials that only include one episode per person rather than multiple episodes over time.

Additionally, the prediction of a patient's reaction to a particular medication will also likely require information on symptoms and comorbidities in addition to the patient's previous experience of its tolerability and effectiveness. Currently, only a few easily assessable sociodemographic variables and clinical variables are consistently associated with response to MDD. These include gender, age, race/ethnicity, SES, BMI and the presence of alexithymia.

The application of pharmacogenetics in depression treatment is still in its beginning stages and there are many hurdles to overcome. First is a thorough understanding of the underlying genetic mechanisms is required as well as an understanding of what constitutes a reliable predictor for treatment response. Ethics like privacy, and the ethical use of genetic information must also be considered. In the long run the use of pharmacogenetics could provide an opportunity to reduce the stigma associated with mental health care and improve treatment outcomes for those struggling with depression. But, like any other psychiatric treatment, careful consideration and implementation is essential. For now, the best option is to provide patients with various effective depression and anxiety treatment near me medication options and encourage them to talk freely with their doctors about their concerns and experiences.