Personalized Interventions for Alcoholism and Anxiety
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Rutgers, The State University of New Jersey
Disqualifiers: Currently receiving counseling, others
No Placebo Group
Trial Summary
What is the purpose of this trial?Anxiety and anxiety-related disorders frequently co-occur with alcohol use problems resulting in an enormous humanitarian and economic cost to society. The proposed research will use digital technology to examine person-specific risk factors predicting problematic alcohol use in individuals vulnerable to anxiety and anxiety-related disorders and will use this information to design a personalized intervention for individuals seeking psychological treatment. Results from this research will integrate output from novel and innovative digital technology methods into psychotherapy, advancing research on personalized treatment and prevention efforts.
Will I have to stop taking my current medications?
The trial does not specify if you need to stop taking your current medications, but you must be on a stable dose of any psychiatric medication for the duration of the study.
What data supports the effectiveness of the treatment Digital Phenotyping, Digital Phenotyping, Personalized Digital Intervention for alcoholism and anxiety?
Research shows that digital phenotyping, which uses data from smartphones to track behavior, has been effective in predicting relapse in mental health conditions like schizophrenia and improving depression diagnostics. This suggests it could help tailor interventions for alcoholism and anxiety by providing real-time insights into patients' behaviors and symptoms.
12345Is the treatment using digital phenotyping and personalized digital intervention generally safe for humans?
The research articles provided do not contain specific safety data for digital phenotyping or personalized digital interventions, but they discuss general methods for detecting and managing adverse drug events, which are important for ensuring the safety of treatments.
678910How does the treatment in the 'Personalized Interventions for Alcoholism and Anxiety' trial differ from other treatments?
This treatment is unique because it focuses on personalized interventions, potentially using technology like digital phenotyping and machine learning to tailor the approach to individual needs, unlike traditional one-size-fits-all methods.
1112131415Eligibility Criteria
This trial is for adults aged 18-65 with anxiety or related disorders and problematic alcohol use, who are interested in telehealth psychotherapy. Participants must have a smartphone and live in the state where the principal investigator is licensed.Inclusion Criteria
I have access to a smartphone.
I am between 18 and 65 years old.
I experience significant anxiety or have an anxiety disorder.
+3 more
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-3 weeks
Treatment
Participants receive a personalized intervention using CBT skills or participate in control conditions for comparison
12 weeks
Weekly sessions
Follow-up
Participants are monitored for safety and effectiveness after treatment
3 weeks
Participant Groups
The study tests personalized interventions using digital technology to manage anxiety-related disorders and reduce problematic alcohol use. It compares cognitive behavioral therapy skills tailored to individuals versus standard supportive counseling.
3Treatment groups
Experimental Treatment
Active Control
Group I: Personalized intervention conditionExperimental Treatment1 Intervention
This experimental condition will test a data-driven, person-specific intervention using CBT skills.
Group II: Therapeutic control conditionActive Control1 Intervention
This control condition will provide an experimental comparison to test the process of personalization.
Group III: Tracking control conditionActive Control1 Intervention
This second control condition will provide an experimental comparison to test the effects of health-related tracking and therapeutic contact.
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Rutgers Robert Wood Johnson Medical SchoolPiscataway, NJ
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Who Is Running the Clinical Trial?
Rutgers, The State University of New JerseyLead Sponsor
University of WashingtonLead Sponsor
National Institute on Alcohol Abuse and Alcoholism (NIAAA)Collaborator
References
Smartphone sensor data estimate alcohol craving in a cohort of patients with alcohol-associated liver disease and alcohol use disorder. [2023]Sensors within smartphones, such as accelerometer and location, can describe longitudinal markers of behavior as represented through devices in a method called digital phenotyping. This study aimed to assess the feasibility of digital phenotyping for patients with alcohol-associated liver disease and alcohol use disorder, determine correlations between smartphone data and alcohol craving, and establish power assessment for future studies to prognosticate clinical outcomes.
Anomaly detection to predict relapse risk in schizophrenia. [2021]The integration of technology in clinical care is growing rapidly and has become especially relevant during the global COVID-19 pandemic. Smartphone-based digital phenotyping, or the use of integrated sensors to identify patterns in behavior and symptomatology, has shown potential in detecting subtle moment-to-moment changes. These changes, often referred to as anomalies, represent significant deviations from an individual's baseline, may be useful in informing the risk of relapse in serious mental illness. Our investigation of smartphone-based anomaly detection resulted in 89% sensitivity and 75% specificity for predicting relapse in schizophrenia. These results demonstrate the potential of longitudinal collection of real-time behavior and symptomatology via smartphones and the clinical utility of individualized analysis. Future studies are necessary to explore how specificity can be improved, just-in-time adaptive interventions utilized, and clinical integration achieved.
Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. [2023]The use of data generated passively by personal electronic devices, such as smartphones, to measure human function in health and disease has generated significant research interest. Particularly in psychiatry, objective, continuous quantitation using patients' own devices may result in clinically useful markers that can be used to refine diagnostic processes, tailor treatment choices, improve condition monitoring for actionable outcomes, such as early signs of relapse, and develop new intervention models. If a principal goal for digital phenotyping is clinical improvement, research needs to attend now to factors that will help or hinder future clinical adoption. We identify four opportunities for research directed toward this goal: exploring intermediate outcomes and underlying disease mechanisms; focusing on purposes that are likely to be used in clinical practice; anticipating quality and safety barriers to adoption; and exploring the potential for digital personalized medicine arising from the integration of digital phenotyping and digital interventions. Clinical relevance also means explicitly addressing consumer needs, preferences, and acceptability as the ultimate users of digital phenotyping interventions. There is a risk that, without such considerations, the potential benefits of digital phenotyping are delayed or not realized because approaches that are feasible for application in healthcare, and the evidence required to support clinical commissioning, are not developed. Practical steps to accelerate this research agenda include the further development of digital phenotyping technology platforms focusing on scalability and equity, establishing shared data repositories and common data standards, and fostering multidisciplinary collaborations between clinical stakeholders (including patients), computer scientists, and researchers.
Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives. [2022]Depression is a serious medical condition and is a leading cause of disability worldwide. Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations, lack of objective assessments, and assessments that rely on patients' perceptions, memory, and recall. Digital phenotyping (DP), especially assessments conducted using mobile health technologies, has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes. DP includes two primary sources of digital data generated using ecological momentary assessments (EMA), assessments conducted in real-time, in subjects' natural environment. This includes active EMA, data that require active input by the subject, and passive EMA or passive sensing, data passively and automatically collected from subjects' personal digital devices. The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients' clinical status. Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status. These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients. Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines. The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations. A clinically-relevant model for incorporating DP in clinical setting is presented. This model, based on investigations conducted by our group, delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process. Benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.
Digital Phenotyping With Mobile and Wearable Devices: Advanced Symptom Measurement in Child and Adolescent Depression. [2020]With an estimated 75% of all mental disorders beginning in the first two decades of life,1 childhood and adolescence are crucial developmental periods to identify and intercept the unfolding of mental health problems, their relationships with physical health, and the multiple, interwoven connections to the surrounding environment.2 Because an individual's mental health is best conceptualized, captured, and treated by taking into account the network of physiological and social functions that constitute the context of individual experience, accessing and analyzing data on multiple health indicators simultaneously can accelerate prediction of disease progression. With the advent of new technologies, dense and extensive amounts of biopsychosocial readouts that can be translated into clinically relevant information have become available in real time, with the potential to revolutionize the practice of medicine. However, challenges to this more ecological and comprehensive approach to mental health measurement include the actual capacity of capturing, safely storing, and analyzing dense data sets (encompassing, for example, mood, cognitions, physical activity, sleep, social interactions) from multiple synchronized sources, and identifying which among multiple indicators ultimately prove useful to improve prediction of a deterioration in symptoms and of initiating early intervention. In this Translations article, we focus on digital phenotyping (DP), which relates to the capturing of the aforementioned relevant biopsychosocial data. This concept is rapidly growing and gaining relevance to child and adolescent psychiatry, and is connected with overarching data science themes of "big data" (extremely large data sets, including data from electronic medical records, imaging, genomics, and patients' smartphones),3,4 in addition to "machine learning" (the science of getting computers to act without being explicitly programmed)5 and "precision medicine" (the practice of custom tailoring treatments to a patient's disease processes),6 which have all received attention in this journal. We will describe principles and current applications of DP, together with its potential to facilitate improved outcomes and its limits, using depression in children and adolescents as an illustrative example.
ADEpedia-on-OHDSI: A next generation pharmacovigilance signal detection platform using the OHDSI common data model. [2020]Supplementing the Spontaneous Reporting System (SRS) with Electronic Health Record (EHR) data for adverse drug reaction detection could augment sample size, increase population heterogeneity and cross-validate results for pharmacovigilance research. The difference in the underlying data structures and terminologies between SRS and EHR data presents challenges when attempting to integrate the two into a single database. The Observational Health Data Sciences and Informatics (OHDSI) collaboration provides a Common Data Model (CDM) for organizing and standardizing EHR data to support large-scale observational studies. The objective of the study is to develop and evaluate an informatics platform known as ADEpedia-on-OHDSI, where spontaneous reporting data from FDA's Adverse Event Reporting System (FAERS) is converted into the OHDSI CDM format towards building a next generation pharmacovigilance signal detection platform.
Fusion of nonclinical and clinical data to predict human drug safety. [2013]Adverse drug reactions continue to be a major cause of morbidity in both patients receiving therapeutics and in drug R&D programs. Predicting and possibly eliminating these adverse events remains a high priority in industry, government agencies and healthcare systems. With small molecule candidates, the fusion of nonclinical and clinical data is essential in establishing an overall system that creates a true translational science approach. Several new advances are taking place that attempt to create a 'patient context' mechanism early in drug research and development and ultimately into the marketplace. This 'life-cycle' approach has as its core the development of human-oriented, nonclinical end points and the incorporation of clinical knowledge at the drug design stage. The next 5 years should witness an explosion of what the author views as druggable and safe chemical space, pharmacosafety molecular targets and the most important aspect, an understanding of unique susceptibilities in patients developing adverse drug reactions. Our current knowledge of clinical safety relies completely on pharmacovigilance data from approved and marketed drugs, with a few exceptions of drugs failing in clinical trials. Massive data repositories now and soon to be available via cloud computing should stimulate a major effort in expanding our view of clinical drug safety and its incorporation into early drug research and development.
Adverse drug events: identification and attribution. [2022]The definition of an adverse drug event should be tailored to one's purpose in examining the incident. Although the more specific of these definitions is required for scientific evaluation of the link between drug and event, other less stringent definitions are usually adequate for clinical purposes. Knowledge about the safety profile of a drug in humans is limited at the time of marketing. The mechanisms for supplementing safety data during postmarketing include (1) the Spontaneous Reporting System maintained by the Food and Drug Administration, (2) formal projects to assemble safety data on larger or more complex populations, and (3) formal projects designed to answer specific research questions. Judgments about attribution can be no better than the data that support them. The criteria applied by the clinician to the individual adverse drug experience to determine association differ from those required to establish causation based on epidemiologic evidence. In most situations, regulatory action on drug recall should be based on epidemiologic evidence. This article will discuss the choice of a definition for an adverse drug event, examine the extent and nature of the safety data assembled on a drug at the time it is marketed, propose the best methods for collecting additional information after marketing, and designate factors to be considered in judging a drug to be causally related to an adverse event.
Ambulatory care visits for treating adverse drug effects in the United States, 1995-2001. [2019]Adverse d[rug events (ADEs) are a well-recognized patient safety 4concern, but their magnitude is unknown. Ambulatory viisits for treating adverse drug effects (VADEs) as recordeed in national surveys offer an alternative way to estimatte the national prevalence of ADEs because each VA]DE indicates that an ADE occurred and was seriousenough to require care.
Post-market surveillance of consumer products: Framework for adverse event management. [2022]Analysis of spontaneous reports of adverse events is an important source of information that can be used to improve consumer products. Various agencies have adverse event reporting requirements and many companies collect such data directly from consumers. Nonetheless, a universal framework is absent that identifies and evaluates spontaneously reported adverse events, and, most important, assesses the potential association between exposure and adverse events. We are presenting a three-part framework: Phase I - Intake and Documentation of Original Incidents; Phase II - In Depth Review and Follow-up of Phase I Incidents (enhanced, tailored questionnaire); Phase III - Association Assessment. The basis for scoring the strength of association between exposure and adverse events requires assessment of standard factors of association including: temporality; biological, physiological, or pharmacological plausibility; results of de-challenge; results of re-challenge; and consideration of confounding factors. Scores tied to the answers to these questions are totaled for each incident to determine the strength of association between exposure and reported adverse event. We propose that consumer product companies come together to adopt such an association assessment framework to improve adverse event management, obtain maximum value from the data obtained, and use the knowledge derived to improve overall product safety for consumers.
Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention. [2023]Generalized anxiety disorder (GAD) is a prevalent mental health disorder that often goes untreated. A core aspect of GAD is worry, which is associated with negative health outcomes, accentuating a need for simple treatments for worry. The present study leveraged pretreatment individual differences to predict personalized treatment response to a digital intervention.
Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life. [2022]Generalized anxiety disorder (GAD) is a highly prevalent condition. Monitoring GAD symptoms requires substantial time, effort, and cost. The development of digital phenotypes of GAD may enable new scalable, timely, and inexpensive assessments of GAD symptoms.
The profound heterogeneity of substance use disorders: Implications for treatment development. [2022]A single treatment approach will never be sufficient to address the diversity of individuals with substance use disorders (SUDs). SUDs have historically defied definition through simple characterizations or models, and no single characterization has led to the development of broadly effective interventions. The range of dimensions of heterogeneity among individuals with SUDs, including severity, type of substance, and issues that frequently co-occur underscore that highly tailored approaches are needed. To approach personalized medicine for individuals with SUDs; two major developments are needed. First, given the diversity of individuals with SUDs, multivariate phenotyping approaches are needed to identify the particular features driving addictive processes in any individual. Second, a wider range of interventions that directly target core mechanisms of addiction and the problems that co-occur with them are needed. As clinicians cannot be expected to master the full range of interventions that may target these core processes, developing these so that they can be delivered easily, flexibly, and systematically via technology will facilitate our ability to truly tailor interventions to this highly complex and challenging population. One such technology-delivered intervention, computer-based training for cognitive behavioral therapy (CBT4CBT), is used as an example to illustrate a vision for the future of highly-tailored interventions for individuals with SUDs.
Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data. [2023]Just-in-time adaptive interventions (JITAIs), typically smartphone apps, learn to deliver therapeutic content when users need it. The challenge is to "push" content at algorithmically chosen moments without making users trigger it with effortful input. We trained a randomForest algorithm to predict heroin craving, cocaine craving, or stress (reported via smartphone app 3x/day) 90 min into the future, using 16 weeks of field data from 189 outpatients being treated for opioid-use disorder. We used only one form of continuous input (along with person-level demographic data), collected passively: an indicator of environmental exposures along the past 5 h of movement, as assessed by GPS. Our models achieved excellent overall accuracy-as high as 0.93 by the end of 16 weeks of tailoring-but this was driven mostly by correct predictions of absence. For predictions of presence, "believability" (positive predictive value, PPV) usually peaked in the high 0.70s toward the end of the 16 weeks. When the prediction target was more rare, PPV was lower. Our findings complement those of other investigators who use machine learning with more broadly based "digital phenotyping" inputs to predict or detect mental and behavioral events. When target events are comparatively subtle, like stress or drug craving, accurate detection or prediction probably needs effortful input from users, not passive monitoring alone. We discuss ways in which accuracy is difficult to achieve or even assess, and warn that high overall accuracy (including high specificity) can mask the abundance of false alarms that low PPV reveals.
Using technical innovations in clinical practice: the Drinker's Check-Up software program. [2019]Interest in assessing and treating a variety of psychological conditions with software programs is increasing rapidly. This article reviews a software program for problem drinkers entitled the Drinker's Check-Up (DCU) and illustrates its use with three patients. The DCU is based on the principles of brief motivational interventions and can be used as a stand-alone intervention by therapists without expertise in substance abuse or as a prelude to alcohol treatment services. It is the first software program to provide integrated assessment, feedback, and assistance with decision making for individuals experiencing problems with alcohol. Preliminary data from an ongoing clinical trial of the DCU as a stand-alone intervention indicate that it is an effective intervention for a wide range of problem drinkers.