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70
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Introduction
Additional affiliations
February 2019 - present
May 2018 - January 2019
May 2009 - July 2011
Education
August 2011 - May 2018
August 2005 - May 2009
Publications
Publications (70)
This study explores the capability of passive digital sensor data from smartphones and smartwatches to predict self-reported ecological momentary assessments (EMA) of affect, motivation, interest, and pleasure in activities in an unseen test sample. Using data from 245 depressed participants with high-to-low anhedonia (195 train, 50 test) generatin...
Interpersonal psychotherapy (IPT) and antidepressant medications are both first-line interventions for adult depression, but their relative efficacy in the long term and on outcome measures other than depressive symptomatology is unknown. Individual participant data (IPD) meta-analyses can provide more precise effect estimates than conventional met...
The experience-sampling method (ESM) captures psychological experiences over time and in everyday contexts, thereby offering exciting potential for collecting more temporally fine-grained and ecologically valid data for psychological research. Given that rapid methodological developments make it increasingly difficult for novice ESM researchers to...
Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual's behavior. These measures involve the near-continuous and unobtrusive collection of data from smartphones without requiring active input from participants. For example, GPS sensors are used to deter...
This study examines the relationship between self-reported and physiologically measured sleep quality in individuals with depression and its impact on neurocognitive performance. Using data from 249 participants with medium to high depression monitored over 13 weeks, sleep quality was assessed via retrospective self-report and physiological measure...
Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual's behavior. These measures involve the near-continuous and unobtrusive collection of data from smartphones without requiring active input from participants. For example, GPS sensors are used to deter...
The experience sampling method (ESM) enables data collection from everyday life,improving assessment of cognitive, affective, and behavioral constructs as they vary between- and within people over time and context. However, the issues of how we best can measure constructs using ESM remains largely unknown as existing measurement theory, which was d...
Researchers at the Department of Veterans Affairs (VA) have studied interventions for posttraumatic stress disorder and co-occurring conditions in both traditional and digital formats. One such empirically supported intervention is web skills training in affective and interpersonal regulation (webSTAIR), a coached, 10-module web program based on ST...
The experience sampling method (ESM) captures psychological experiences over time and in everyday contexts, thereby offering exciting potential for collecting more temporally fine-grained and ecologically valid data for psychological research. Given that rapid methodological developments make it increasingly difficult for novice ESM researchers to...
Understanding how and for whom cognitive-behavioral therapies work is central to the development and improvement of mental health interventions. Suboptimal quantification of the active elements of cognitive-behavioral therapies has hampered progress in elucidating mechanisms of change. To advance process research on cognitive-behavioral therapies,...
Background
Cognitive therapy and behavioural activation are both widely applied and effective psychotherapies for depression, but it is unclear which works best for whom. Individual participant data (IPD) meta-analysis allows for examining moderators at the participant level and can provide more precise effect estimates than conventional meta-analy...
Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to mindfulness-based cognitive therapy (MBCT). Using previously published dat...
Importance:
Socioeconomic factors are associated with the prevalence of depression, but their associations with prognosis are unknown. Understanding this association would aid in the clinical management of depression.
Objective:
To determine whether employment status, financial strain, housing status, and educational attainment inform prognosis...
IMPORTANCE: Depression is a major cause of disability worldwide. Although empirically supported treatments are available, there is scarce evidence on how to effectively personalize psychological treatment selection.
OBJECTIVE: To compare the clinical effectiveness and cost-effectiveness of 2 treatment selection strategies: stepped care and stratifi...
Background:
Subgrouping methods have the potential to support treatment decision making for patients with depression. Such approaches have not been used to study the continued course of depression or likelihood of relapse following treatment.
Method:
Data from individual participants of seven randomised controlled trials were analysed. Latent pr...
Objective
: To investigate associations between major life events and prognosis independent of treatment type: 1) after adjusting for clinical prognostic factors and socio-demographics; 2) among patients with depressive episodes at least six-months long; and 3) patients with a first life-time depressive episode.
Methods
: Six RCTs of adults seekin...
In the modern history of psychotherapy, understanding the individual patient and how to optimize treatment for each individual has been an important challenge. For the therapist, personalization often has meant deciding which treatments to offer based on clinical assessment and formulation, or deciding moment-to-moment what techniques to employ, gi...
UNSTRUCTURED
Many individuals in need of mental health services do not currently receive care. Scalable programs are needed to reduce the burden of mental illness among those without access to existing providers. Digital interventions present one avenue for increasing the reach of mental health services. These interventions often rely upon paraprof...
Many individuals in need of mental health services do not currently receive care. Scalable programs are needed to reduce the burden of mental illness among those without access to existing providers. Digital interventions present an avenue for increasing the reach of mental health services. These interventions often rely on paraprofessionals, or co...
PTSD treatment guidelines recommend several treatments with extensive empirical support, including Prolonged Exposure (PE), a trauma-focused treatment and Present-Centered Therapy (PCT), a non-trauma-focused therapy. Research to inform treatment selection has yielded inconsistent findings with single prognostic variables that are difficult to integ...
Aims
To determine whether age, gender and marital status are associated with prognosis for adults with depression who sought treatment in primary care.
Methods
Medline, Embase, PsycINFO and Cochrane Central were searched from inception to 1st December 2020 for randomised controlled trials (RCTs) of adults seeking treatment for depression from thei...
For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learnin...
We present methodological advances in understanding the effectiveness of personalized medicine models and supply easy-to-use open-source software. Personalized medicine involves the systematic use of individual patient characteristics to determine which treatment option is most likely to result in a better average outcome for the patient. Why is pe...
Background
Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety como...
Background
This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data.
Methods
Individual patient data from all six eligible randomised controlled trials were used to develop ( k = 3, n = 1722) and test ( k = 3, n = 918) nine models. Predictors inc...
Background
This study aimed to investigate general factors associated with prognosis regardless of the type of treatment received, for adults with depression in primary care.
Methods
We searched Medline, Embase, PsycINFO and Cochrane Central (inception to 12/01/2020) for RCTs that included the most commonly used comprehensive measure of depressive...
Background
Antidepressant medication and interpersonal psychotherapy (IPT) are both recommended interventions in depression treatment guidelines based on literature reviews and meta-analyses. However, ‘conventional’ meta-analyses comparing their efficacy are limited by their reliance on reported study-level information and a narrow focus on depress...
Dropout from psychotherapy is common and can have negative effects for patients, providers, and researchers. Better understanding when and why patients stop treatment early, as well as actionable factors contributing to dropout, has the potential to prevent it. Here, we examine dropout from a large randomized controlled trial of transdiagnostic ver...
Objective:
Depressed patients rate social support as important for prognosis, but evidence for a prognostic effect is lacking. We aimed to test the association between social support and prognosis independent of treatment type, and the severity of depression, and other clinical features indicating a more severe illness.
Methods:
Individual patie...
Background: Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety com...
AimsTo develop, validate, and compare the performance of nine models predicting post-treatment outcomes for depressed adults based on pre-treatment data. Methods
Individual patient data from all six eligible RCTs were used to develop (k=3, n=1722) and test (k=3, n=1136) nine models. Predictors included depressive and anxiety symptoms, social suppor...
In light of the well-established relationship between posttraumatic stress disorder (PTSD) and suicidal ideation (SI), there has been a push for treatments that simultaneously improve symptoms of PTSD and decrease SI. Using data from a randomized controlled hybrid implementation-effectiveness trial, the current study investigated the effectiveness...
Objective: This study aims at developing a treatment selection algorithm using a combination of machine learning and statistical inference to recommend patients’ optimal treatment based on their pre-treatment characteristics. Methods: A disorder-heterogeneous, naturalistic sample of N = 1,379 outpatients treated with either cognitive behavioral the...
Major depressive disorder is highly recurrent over the lifespan, even following successful pharmacological and/or psychological intervention. Here we investigate whether predictive modeling can be used to optimize treatment recommendations, when choosing between continuing maintenance antidepressant medication (mADM) treatment or switching to Mindf...
Background : Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatmen...
Background: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment...
Objective:
Research on predictors of treatment outcome in depression has largely derived from randomized clinical trials involving strict standardization of treatments, stringent patient exclusion criteria, and careful selection and supervision of study clinicians. The extent to which findings from such studies generalize to naturalistic psychiatr...
Background
Psychotherapies for depression are equally effective on average, but individual responses vary widely. Outcomes can be improved by optimizing treatment selection using multivariate prediction models. A promising approach is the Personalized Advantage Index (PAI) that predicts the optimal treatment for a given individual and the magnitude...
Background : Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatmen...
BACKGROUND: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment...
Two core features of depression include depressed mood (heightened distress) and anhedonia (reduced pleasure). Despite their centrality to depression, studies have not examined their contribution to treatment outcomes in a randomized clinical trial providing mainstream treatments like antidepressant medications (ADM) and cognitive therapy (CT). We...
Background : Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatmen...
The cognitive and behavioral interventions can be as efficacious as antidepressant medications and more enduring, but some patients will be more likely to respond to one than the other. Recent work has focused on developing sophisticated selection algorithms using machine-learning approaches that answer the question, “What works best for whom?” Mor...
Objectives
Lithium and quetiapine are known to be effective treatments for bipolar disorder. However, little information is available to inform prediction of response to these medications. Machine‐learning methods can identify predictors of response by examining variables simultaneously. Further evaluation of models on a test sample can estimate ho...
Objective: We use a new variable selection procedure for treatment selection which generates treatment recommendations based on pre-treatment characteristics for adults with mild-to-moderate depression deciding between cognitive behavioral (CBT) versus psychodynamic therapy (PDT). Method: Data are drawn from a randomized comparison of CBT versus PD...
Background
Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophe...
Mental health researchers and clinicians have long sought answers to the question "What works for whom?" The goal of precision medicine is to provide evidence-based answers to this question. Treatment selection in depression aims to help each individual receive the treatment, among the available options, that is most likely to lead to a positive ou...
Background:
Dropout rates for effective therapies for posttraumatic stress disorder (PTSD) can be high, especially in practice settings. Although clinicians have intuitions regarding what treatment patients may complete, there are few systematic data to drive those judgments.
Methods:
A multivariable model of dropout risk was constructed with ra...
Individuals seeking treatment for mental health problems often have to choose between several different treatment options. For disorders like depression and PTSD, many of the available treatments have been found to be, on average, equally effective. Research on precision medicine aims to identify the most effective treatment for each patient. This...
Objective: Treatment selection could improve outcomes by helping individuals select their optimal intervention. We refine the Personalized Advantage Index approach to generate individual treatment recommendations based on pre-treatment characteristics for adults with depression deciding between cognitive behavioral (CBT) versus psychodynamic therap...
Objectives: Dropout rates for effective therapies for posttraumatic stress disorder (PTSD) can be high, especially in practice settings. Although clinicians have intuitions regarding what treatment patients may complete, there are few systematic data to drive those judgments.Methods: A multivariable model of dropout risk was constructed with random...
Objective: Current treatment guidelines for bipolar disorder (BD) recommend several medications as equally effective first-line mood stabilizers. However, knowledge of moderators in bipolar treatments can determine subsets of patients that would benefit from certain medications. Method: Data (N=482) were drawn from a randomized clinical trial (RCT)...
Introduction:
Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works best for the depressed individual. In this paper,...
In medical practice, when more than one treatment option is viable, there is
little systematic use of individual patient characteristics to estimate which
treatment option is most likely to result in a better outcome for the patient.
We introduce a new framework for using statistical models for personalized
medicine. Our framework exploits (1) data...
Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations.
To illustrate and test a new method for integrating predictive information to aid in treatment selection,...
Multivariate pattern analysis (MVPA) is a powerful tool for relating theories of cognitive function to the neural dynamics observed while people engage in cognitive tasks. Here, we use the Context Maintenance and Retrieval model of free recall (CMR; Polyn et al., 2009a) to interpret variability in the strength of task-specific patterns of distribut...
