It's Time To Forget Personalized Depression Treatment: 10 Reasons Why You Don't Need It
Personalized Depression Treatment
For many people gripped by depression, traditional therapy and medications are not effective. The individual approach to treatment could be the answer.
Cue is a digital intervention platform that transforms passively acquired smartphone sensor data into personalized micro-interventions to improve mental health. We parsed the best-fit personalized ML models for each subject using Shapley values to identify their predictors of feature and reveal distinct features that are able to change mood as time passes.
Predictors of Mood
Depression is a leading cause of mental illness across the world.1 Yet, only half of those suffering from the condition receive treatment. To improve the outcomes, doctors must be able to recognize and treat patients most likely to benefit from certain treatments.
Personalized depression treatment can help. Researchers at the University of Illinois Chicago are developing new methods for predicting which patients will benefit most from specific treatments. They make use of sensors on mobile phones, a voice assistant with artificial intelligence, and other digital tools. With two grants totaling more than $10 million, they will employ these techniques to determine the biological and behavioral factors that determine responses to antidepressant medications as well as psychotherapy.
The majority of research to date has focused on clinical and sociodemographic characteristics. These include demographics such as age, gender and education, and clinical characteristics such as symptom severity, comorbidities and biological markers.
While many of these variables can be predicted from the information available in medical records, few studies have used longitudinal data to determine the causes of mood among individuals. depression treatment options take into account the fact that moods vary significantly between individuals. Therefore, it is crucial to create methods that allow the identification of the individual differences in mood predictors 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. This enables the team to create algorithms that can detect different patterns of behavior and emotion that vary between individuals.
The team also created an algorithm for machine learning to create dynamic predictors for each person's depression mood. The algorithm combines these personal differences into a unique "digital phenotype" for each participant.
The digital phenotype was associated with CAT DI scores, a psychometrically validated symptom severity scale. However the correlation was tinny (Pearson's r = 0.08, adjusted BH-adjusted P-value of 3.55 1003) and varied widely across individuals.
Predictors of Symptoms
Depression is one of the world's leading causes of disability1 but is often not properly diagnosed and treated. In addition the absence of effective interventions and stigma associated with depressive disorders stop many people from seeking help.
To facilitate personalized treatment to improve treatment, identifying the patterns that can predict symptoms is essential. Current prediction methods rely heavily on clinical interviews, which are unreliable and only reveal a few features associated with depression.
Using machine learning to integrate continuous digital behavioral phenotypes of a person captured by sensors on smartphones and a validated online tracker of mental health (the Computerized Adaptive Testing Depression Inventory, CAT-DI) along with other indicators of symptom severity could increase the accuracy of diagnostics and the effectiveness of treatment for depression. Digital phenotypes permit continuous, high-resolution measurements. They also capture a variety of unique behaviors and activity patterns that are difficult to document using interviews.
The study comprised University of California Los Angeles students with moderate to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were sent online for assistance or medical care depending on the degree of their depression. Participants with a CAT-DI score of 35 65 were assigned online support with a peer coach, while those who scored 75 patients were referred to in-person clinics for psychotherapy.
Participants were asked a series of questions at the beginning of the study regarding their psychosocial and demographic characteristics as well as their socioeconomic status. The questions included age, sex and education as well as financial status, marital status, whether they were divorced or not, the frequency of suicidal ideas, intent or attempts, and the frequency with which they consumed alcohol. Participants also scored their level of depression symptom severity on a scale ranging from 0-100 using the CAT-DI. CAT-DI assessments were conducted every week for those that received online support, and every week for those who received in-person support.
Predictors of Treatment Response
The development of a personalized depression treatment is currently a research priority, and many studies aim at identifying predictors that will allow clinicians to identify the most effective medications for each individual. In particular, pharmacogenetics identifies genetic variations that affect how the body's metabolism reacts to antidepressants. This allows doctors select medications that are most likely to work for every patient, minimizing the amount of time and effort required for trial-and error treatments and avoid any negative side effects.
Another promising approach is to build prediction models combining clinical data and neural imaging data. These models can be used to identify which variables are most predictive of a specific outcome, such as whether a medication will improve symptoms or mood. These models can also be used to predict the patient's response to a treatment they are currently receiving, allowing doctors to maximize the effectiveness of treatment currently being administered.
A new generation of machines employs machine learning techniques like supervised and classification algorithms such as regularized logistic regression, and tree-based methods to combine the effects of several variables and improve predictive accuracy. These models have proven to be useful for the prediction of treatment outcomes like the response to antidepressants. These models are getting more popular in psychiatry, and it is likely that they will become the standard for the future of clinical practice.
In addition to prediction models based on ML, research into the mechanisms behind depression is continuing. Recent research suggests that the disorder is associated with dysfunctions in specific neural circuits. This theory suggests that an individualized treatment for depression will be based upon targeted therapies that restore normal functioning to these circuits.
One method to achieve this is through internet-delivered interventions that offer a more personalized and customized experience for patients. One study found that a web-based program improved symptoms and provided a better quality of life for MDD patients. A controlled study that was randomized to a customized treatment for depression found that a substantial percentage of patients experienced sustained improvement and fewer side negative effects.
Predictors of adverse effects
In the treatment of depression, a major challenge is predicting and identifying which antidepressant medication will have no or minimal side negative effects. Many patients take a trial-and-error approach, using several medications prescribed before finding one that is effective and tolerable. Pharmacogenetics is an exciting new way to take an efficient and specific method of selecting antidepressant therapies.
Many predictors can be used to determine the best antidepressant to prescribe, including gene variants, phenotypes of patients (e.g. gender, sex or ethnicity) and the presence of comorbidities. However, identifying the most reliable and valid predictive factors for a specific treatment will probably require randomized controlled trials with considerably larger samples than those typically enrolled in clinical trials. This is due to the fact that it can be more difficult to detect moderators or interactions in trials that contain only one episode per participant instead of multiple episodes over time.
Furthermore, predicting a patient's response will likely require information on the comorbidities, symptoms profiles and the patient's subjective experience of tolerability and effectiveness. There are currently only a few easily measurable sociodemographic variables as well as clinical variables are reliable in predicting the response to MDD. These include age, gender and race/ethnicity, SES, BMI and the presence of alexithymia.
There are many challenges to overcome when it comes to the use of pharmacogenetics to treat depression. It is crucial to have a clear understanding and definition of the genetic mechanisms that cause depression, as well as an accurate definition of an accurate predictor of treatment response. In addition, ethical concerns such as privacy and the ethical use of personal genetic information, must be considered carefully. In the long run pharmacogenetics can be a way to lessen the stigma associated with mental health treatment and improve the outcomes of those suffering with depression. But, like all approaches to psychiatry, careful consideration and planning is essential. For now, it is recommended to provide patients with an array of depression medications that are effective and encourage patients to openly talk with their doctor.