Selected Publications

Persons living with HIV (PLWH) report experiencing disproportionally severe and chronic pain and worry. However, no objective biomarkers of these subjective experiences have been developed. To address the lack of objective measures and assist in treatment planning, the current study examined whether digital biomarkers of pain severity, pain chronicity, and worry could be developed using passive wearable sensors continuously monitoring movement. Results suggest that digital biomarkers can predict pain severity (r(35) = 0.690), pain chronicity (74.63% accuracy), and worry severity (r(65) = 0.642) with high precision, suggesting that objective digital biomarkers alone accurately capture internal symptom experiences in PLWH.
The British Journal of Psychiatry, 2019

Development of digital biomarkers holds promise for enabling scalable, time-sensitive, and cost-effective strategies to monitor symptom severity among those with major depressive disorder. The current study examined the use of passive movement and light data from wearable devices to assess depression severity in 15 patients with major depressive disorder. Using over one week of movement data, we were able to significantly assess depression severity with high precision for self-reported (r = 0.855, 95% CI 0.610 to 0.950, p = 4.95x10-5) and clinician-rated (r = 0.604, 95% CI 0.133 to 0.894, p = .017) symptom severity. Pending replication, the present data suggests that the use of passive wearable sensors to inform healthcare decisions holds considerable promise.
Journal of Nervous and Mental Disease, 2019

Current approaches to psychiatric assessment are resource-intensive, requiring time-consuming evaluation by a trained clinician. Development of digital biomarkers holds promise for enabling scalable, time-sensitive, and cost-effective assessment of both psychiatric diagnosis and symptom change. The present study aimed to identify robust digital biomarkers of diagnostic status and changes in symptom severity over ~2 weeks, through re-analysis of public-use actigraphy data collected in patients with major depressive or bipolar disorder and healthy controls. Results suggest that participants’ diagnostic group status (i.e., mood disorder, Q1 control) can be predicted with a high degree of accuracy (predicted correctly 89% of the time, kappa = 0.773), using features extracted from actigraphy data alone. Results also suggest that actigraphy data can be used to predict symptom change across ~2 weeks (r = 0.782, p = 1.04e-05). Through inclusion of digital biomarkers in our statistical model, which are generalizable to new samples, the results may be replicated by other research groups in order to validate and extend this work.
Nature Partner Journal (npj) Digital Medicine, 2019

Objective: Although recent research has begun to examine the impact of elevated anxiety on evolutionary fitness, no prior research has examined anxiety across a continuum. Such research is important as the effect of traits across a continuum on fertility hold important implications for the levels and distribution of the traits in later generations. Method: In a three-generational sample (N = 2,657) the linear and quadratic relationship between anxiety and the number of children, grandchildren, and great-grandchildren 15 years later was examined. Results: The findings suggested that anxiety had a positive quadratic relationship with the number of children, grandchildren, and great-grandchildren 15 years later. These relationships were not significantly moderated by sex. Moreover, most of the variance between anxiety and the number of great-grandchildren was explained by anxiety’s influence on the number of children and grandchildren, as opposed to anxiety having an independent direct impact on the number of great-grandchildren. Conclusion: These findings suggest that extreme values from the mean anxiety are associated with increased evolutionary fitness within the modern environment.
Journal of Psychiatric Research, 2018

Background: The Contrast Avoidance Model (CAM) suggests that worry increases and sustains negative emotion to prevent a negative emotional contrast (sharp upward shift in negative emotion) and to increase the probability of a positive contrast (shift toward positive emotion). Method: In Study 1, we experimentally validated momentary assessment items (N=25). In Study 2, participants with generalized anxiety disorder (GAD) (N=31) and controls (N=37) were prompted once per hour regarding their worry, thought valence, and arousal 10x/day for 8 days. Results: Higher worry duration, negative thought valence, and uncontrollable train of thoughts predicted feeling more keyed up concurrently and sustained anxious activation one hour later. More worry, feeling keyed up, and uncontrollable train of thoughts predicted lower likelihood of a negative emotional contrast in thought valence, and higher likelihood of a positive emotional contrast in thought valence one hour later. Conclusions: Findings support the prospective ecological validity of CAM.
Clinical Psychological Science, 2018

With the recent growth in intensive longitudinal designs and corresponding demand for methods to analyze such data, there has never been a more pressing need for user-friendly analytic tools that can identify and estimate optimal time lags in intensive longitudinal data. Available standard exploratory methods to identify optimal time lags within univariate and multivariate multiple subject time series are greatly under-powered at the group (i.e., population) level. We describe a hybrid exploratory-confirmatory tool, referred to herein as the Differential Time-Varying Effect Model (DTVEM), which features a convenient user-accessible function to identify optimal time lags and estimate these lags within a state-space framework. Data from an empirical ecological momentary assessment study are used to demonstrate the utility of the proposed tool in identifying the optimal time lag for studying the linkages between nervousness and heart rate in a group of undergraduate students. Using a simulation study, we illustrate the effectiveness of DTVEM in identifying optimal lag structures in multiple-subject, time series data with missingness, as well as its strengths and limitations as a hybrid exploratory-confirmatory approach compared to other existing approaches.
Behavior Research Methods, 2018

Not only do anxiety and depression diagnoses tend to co-occur, but their symptoms are highly correlated. Although a plethora of research has examined longitudinal associations between anxiety and depression, these data have not yet been effectively synthesized. To address this need, the current study undertook a systematic review and meta-analysis of 66 studies involving 88,336 persons examining the prospective relationship between anxiety and depression at both symptom and disorder levels. Using mixed-effect models, results suggested that all types of anxiety symptoms predicted later depressive symptoms (r = .34), and all types of depressive symptoms predicted later anxiety symptoms (r = .31). Although anxiety symptoms more strongly predicted depressive symptoms than vice versa, the difference in effect size for this analysis was very small and likely not clinically meaningful. Additionally, all types of diagnosed anxiety disorders predicted all types of later depressive disorders (OR = 2.77), and all depressive disorders predicted later anxiety disorders (OR = 2.73). Most anxiety and depressive disorders predicted each other with similar degrees of strength, but depressive disorders more strongly predicted social anxiety disorder (OR = 6.05) and specific phobia (OR = 2.93) than vice versa. Contrary to conclusions of prior reviews, our findings suggest that depressive disorders may be prodromes for social and specific phobia, whereas other anxiety and depressive disorders are bidirectional risk factors for one another.
Psychological Bulletin, 2017

Background. Prior research has shown that anxiety symptoms predict later depression symptoms following bereavement. Nevertheless, no research has investigated mechanisms of the temporal relationship between anxiety and later depressive symptoms or examined the impact of depressive symptoms on later anxiety symptoms following bereavement. Methods. The current study examined perceived emotional social support as a possible mediator between anxiety and depressive symptoms in a bereaved sample of older adults (N =250). Anxiety and depressive symptoms were measured at Wave 1 (immediately after bereavement), social support was measured at Wave 2 (18 months after bereavement), and anxiety and depressive symptoms were also measured at Wave 3 (48 months after bereavement). Results. Using Bayesian structural equation models, when controlling for baseline depression, anxiety symptoms significantly positively predicted depressive symptoms 48 months later, Further, perceived emotional social support significantly mediated the relationship between anxiety symptoms and later depressive symptoms, such that anxiety symptoms significantly negatively predicted later emotional social support, and emotional social support significantly negatively predicted later depressive symptoms. Also, when controlling for baseline anxiety, depressive symptoms positively predicted anxiety symptoms 48 months later. However, low emotional social support failed to mediate this relationship. Conclusions. Low perceived emotional social support may be a mechanism by which anxiety symptoms predict depressive symptoms 48 months later for bereaved individuals.
Journal of Affective Disorders, 2017

This study sought to evaluate the current evolutionary adaptiveness of psychopathology by examining whether these disorders impact the quantity of offspring or the quality of the parent–child relationship across the life span. Using the National Comorbidity Survey, this study examined whether DSM–III–R anxiety, posttraumatic stress, depressive, bipolar, substance use, antisocial, and psychosis disorders predicted later fertility and the quality of parent–child relationships across the life span in a national sample (N = 8,098). Using latent variable and varying coefficient models, the results suggested that anxiety in males and bipolar pathology in males and females were associated with increased fertility at younger ages. The results suggested almost all other psychopathology was associated with decreased fertility in middle to late adulthood. The results further suggested that all types of psychopathology had negative impacts on the parent–child relationship quality (except for antisocial pathology in males). Nevertheless, for all disorders, the impact of psychopathology on both fertility and the parent–child relationship quality was affected by the age of the participant. The results also showed that anxiety pathology is associated with a high-quantity, low-quality parenting strategy followed by a low-quantity, low-quality parenting strategy. Further, the results suggest that bipolar pathology is associated with an early high-quantity and a continued low-quality parenting strategy. Posttraumatic stress, depression, substance use, antisocial personality, and psychosis pathology are each associated with a low-quantity, low-quality parenting strategy, particularly in mid to late adulthood. These findings suggest that the evolutionary impact of psychopathology depends on the developmental context.
Journal of Abnormal Psychology, 2016

Recent Publications

More Publications

(2019). Objective Digital Phenotypes of Worry Severity, Pain Severity, and Pain Chronicity in Persons Living with HIV. The British Journal of Psychiatry.

Project

(2019). Using Digital Phenotyping to Accurately Detect Depression Severity. Journal of Nervous and Mental Disease.

(2019). Digital biomarkers of mood disorders and symptom change. Nature Partner Journal (npj) Digital Medicine.

PDF Code Dataset Project Source Document

(2018). The Effects of Worry in Daily Life: An Ecological Momentary Assessment Study Supporting the Tenets of the Contrast Avoidance Model. Clinical Psychological Science.

PDF Source Document

(2018). Handling missing data in the modeling of intensive longitudinal data. Structural Equation Modeling.

PDF Project Source Document

Recent & Upcoming Talks

Depression Variability Predicts Later Anxiety for Those with Depressive Disorders
Jun 1, 2018 1:00 PM

Recent Posts

Overview This post documents reproducible code accompanying the manuscript draft “Digital Biomarkers of Mood Disorders and Symptom Change” by Nicholas C. Jacobson, Hilary M. Weingardenm and Sabine Wilhelm (published in Nature Partner Journal (npj): Digital Medicine). This code uses machine learning to predict the diagnostic status and depressive symptom change in a a group of 23 patients with bipolar disorder or major depressive disorder and 32 non-disordered controls using actigraphy data.

CONTINUE READING

Loading in the Dataset This code will illustrate the R package (DTVEM) with simulated data available in the DTVEM package. Click here to download and install the DTVEM package. First load the DTVEM package. library(DTVEM) Next load the simulated data included in the DTVEM package, called exampledat1. data(exampledat1) Get a look at the file structure. head(exampledat1) ## Time X ID ## 1 1 -1.076422 1 ## 2 2 -1.904713 1 ## 3 3 1.

CONTINUE READING

Projects

Mood Triggers

This describes a smartphone application to help users figure out triggers of their anxiety and depression in daily life.

The Differential Time Varying Effect Model

This project describes a novel technique entitled the Differential Time-Varying Effect Model, which is a tool to explore lags in intensive longitudinal data.

Using Intensive Longitudinal Data to Study Affective Dynamics

Studying affective dynamics from intraindividual variability in intensive longitudinal data.

Teaching

I have taught the following courses at Pennsylvania State University:

  • PSYCH 238: Introduction to Personality Psychology
  • PSYCH 301W: Basic Research Methods in Psychology
  • PSYCH 481: Introduction to Clinical Psychology

Contact

Join Our Team

We are currently recruiting:

  1. Undergraduate research assistants
  2. Lab manager
  3. Graduate students
  4. Postdoctoral students
  5. Smartphone App Programmer





Specific Areas of Involvement for Undergraduate Research Assistants

The current lab has many foci concentrating around:

  1. Developing statistical methods for densely sampled data collection.
  2. The integration of smartphone-based and wearable data to draw inferences about a person’s mental health status.
  3. Creating new smartphone applications to assess and treat anxiety, depression, and substance abuse.

Concrete practical benefits to undergraduate research assistants:

  • Opportunity to learn advanced quantitative skills
  • Creation of mentored original work (i.e. potentially being a co-author or first author on journal publication submissions and conference submissions)
  • Development of a strong relationship with a faculty member for (1) letters of recommendations, and/or (2) professional references
  • Excellent exposure to the following fields: (1) mental health (i.e. psychiatry, clinical psychology), (2) data science (i.e. bioinformatics, statistics, information science), and (3) computer science (i.e. computer engineering).

  • Extensions of modern statistical methods. Current smartphone-based and wearable data collection are limited in their ability to draw strong conclusions about the timing of causal processes. In the lab, one of our foci is the creation and validation of new tools, including the timing of naturalistic and causal processes within intensive longitudinal data.

  • Recommended interests: statistics (e.g. the major in Mathematical Data Science, though this is certainly not required).

  • Specific opportunities: writing statistical software code, conducting Monte Carlo simulation studies, and writing manuscripts

  • Digital phenotyping/passive sensing of mental health. In recent years, wearable devices and smartphone sensors have been used to measure constructs related to psychopathology. The current work requires quantitative interests in applying machine learning to smartphone sensor data to determine whether some signals might be fundamentally related to psychopathology processes (e.g. whether we can determine if someone is experiencing an increase in anxiety based on their sleep patterns detected through smartphone accelerometers).

  • Specific opportunities: cleaning, organizing, and synthesizing data; literature reviews; coding; manuscript writing

  • Developing Apps for Mental Health. Very few persons have access to care for their mental health. A major focus of this lab is creating digital solutions to assess and treat mental health problems. The main mental health problems that we treat are anxiety and depressive disorders, although there are opportunities to develop treatments for other types of mental health problems. These include smartphone-based and web-based applications. To date, we have created applications with more than 50,000 installs, and, consequently, this work can have profound and wide impact on persons daily lives.

  • Specific opportunities: writing and altering app code (in Java for Android and/or Swift for iOS); working on the server-side code for data management; creating design prototypes; marketing and promotion of applications; potential opportunities for manuscript writing

Interested students should contact Nick directly (njacobson88@gmail.com).





Specific Areas of Involvement for Lab Manager

The lab manager will be integral to the functioning of the team. The lab manager will help facilitate all lab projects, supervise all undergraduate research assistants, coordinate all meetings, and conduct trainings. The lab manager will also work closely with Nick, the grad students, and post-docs. The lab manager may also have opportunities to conduct original research (see above). Given the broad exposure to a number of diverse projects, this will be excellent preparation for those interested in attending graduate school.

Interested persons should contact Nick directly (njacobson88@gmail.com)





Specific Areas of Involvement for Graduate Students

To be a graduate student, you must first gain admission to a graduate program within Dartmouth College. The lab focuses on problems which are highly interdisciplinary in nature, intersecting psychiatry/clinical psychology, computer science/computer engineering, and data science/statistics. Graduate programs of interest could include, but are not limited to, the following degree programs:

Graduate students in the lab will have the opportunity to choose and build upon their own areas of interest, as long as they align within the broader research areas of the lab. Graduate students will have the opportunity to be trained in paradigms of digital mental health and in the analysis of intensive longitudinal data related to the assessment of mental health in daily life. Graduate students will develop expertise that will prepare them for careers in academia and/or industry.

Graduate students will be expected to both participate in lab projects and develop their own independent areas of study. Graduate students will have the opportunity and be expected to participate fully in the research process, writing and submiting manuscripts, presenting posters or talks at research conferences, and writing graduate student fellowships and/or contributing to preliminary analyses to a full grant proposal. Graduate students will also be expected to mentor undergraduate students.

Graduate students will have access to existing lab data and have the opportunities to collect new original data. Interested students should contact Nick directly (njacobson88@gmail.com).



Specific Areas of Involvement for Postdoctoral Students

Postdoctoral students within the lab may be recruited through the National Institute on Drug Abuse (NIDA) T32 program. Postdoctoral students are expected to get involved in all areas of the lab, including: (1) digital mental health and (2) statistical analysis of longitudinal data. In addition to all of the lab interests specified above, a specific area of emphasis within the T32 will be addressing the complexities of co-occuring substance use and other mental health disorders. The fellow is expected to conduct research that will evaluate co-occuring disorders (i.e. substance use and mental disorders). Postdocs will be expected to develop project ideas, write grants, conduct analyses, mentor other lab members, and write and submit manuscripts.

Alternative funding opportunities may also be possible by applying for post-doctoral grants.

Interested persons should contact Nick directly (njacobson88@gmail.com).



Specific Areas of Involvement for Smartphone App Programmer

The lab is also hiring a smartphone app programmer. Anyone interested should have experience in programming applications in Java for Android and Swift for iOS. The programmer will also manage the remote data servers. The programmer will be responsible for creating and revising applications related to assessing and treating mental health problems.

Interested persons should contact Nick directly (njacobson88@gmail.com).





Collaborators

The lab tackles many interdisciplinary problems that are best addressed by team-science approaches, and we welcome the involvement of collaborators within both academia and industry. If you are interested in collaborating with the lab, please contact Nick directly (njacobson88@gmail.com).