Nicholas C Jacobson

Dartmouth College · Biomedical Data Science and Psychiatry

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 bio...

Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research...

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 di...

BACKGROUND Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research...

The application of digital technology to psychiatry research is rapidly leading to new discoveries and capabilities in the field of mobile health. However, the increase in opportunities to passively collect vast amounts of detailed information on study participants coupled with advances in statistical techniques that enable machine learning models...

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Lang...

Wearable accelerometry (actigraphy) has provided valuable data for clinical insights since the 1970s and is increasingly important as wearable devices continue to become widespread. The effectiveness of actigraphy in research and clinical contexts is heavily dependent on the modeling architecture utilized. To address this, we developed the Pretrain...

Background Depressed individuals have both heightened negative self-views and reduced positive self-views. The self-referential encoding task (SRET) can capture depressed individuals’ self-schemas by asking them to endorse whether a word describes them or not. Digital interventions that target positive biases in depression can help improve positive...

Ecological momentary assessment (EMA) offers advantages over retrospective questionnaires by reducing recall bias and capturing rapid symptom dynamics, and it is increasingly used to measure depression symptoms. However, few depression symptom measures are validated for EMA use in the manner expected for traditional questionnaires. Therefore, the c...

Ecological momentary assessment (EMA) offers advantages over retrospective questionnaires by reducing recall bias and capturing rapid symptom dynamics, and it is increasingly used to measure depression symptoms. However, few depression symptom measures are validated for EMA use in the manner expected for traditional questionnaires. Therefore, the c...

Negative rumination and emotion regulation difficulties have been consistently linked with depression. Despite anhedonia—the lack of interest in pleasurable experiences—being a cardinal symptom of depression, emotion regulation of positive emotions, including dampening, are considered far less in the literature. Given that anhedonia may manifest th...

In recent years, large language models (LLMs), including ChatGPT, have exponentially grown in application. Given existing barriers to mental health services, alongside the capability of LLMs to generate therapeutic responses, LLMs have potential to serve as accessible precursors, adjuncts, or alternatives to traditional therapy. However, little is...

The presentation of major depressive disorder (MDD) can vary widely due to its heterogeneity, including inter- and intraindividual symptom variability, making MDD difficult to diagnose with standard measures in clinical settings. Prior work has demonstrated that passively collected actigraphy can be used to detect MDD at a disorder level; however,...

Background: Despite major strides in conceptualizing and modeling the multifaceted nature of suicidal thought and behavior (STB) over the past few decades, the overall predictability of STB has not improved. This may be partly due to the dynamic nature of suicidal ideation (SI), which often fluctuates over hours, yet is largely overlooked in studie...

Depression and anxiety frequently co-occur with opioid use disorder (OUD) yet are often overlooked in standard OUD treatments. This study evaluated the feasibility, acceptability, and preliminary effectiveness of a mobile application designed to address these symptoms in individuals receiving medications for OUD (MOUD). A randomized controlled tria...

Behavior-based pharmacological side effects, including those of benzodiazepines, are often assessed through self-reported surveys, introducing response bias. This study aimed to address this limitation by leveraging deep learning and wearables in a large-scale, naturalistic setting to detect and characterize benzodiazepine psychomotor effects objec...

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape pioneers a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Lang...

Introduction: Existing theories and empirical works link phone use with anxiety; however, most leverage subjective self-reports of phone use (e.g., validated questionnaires) that may not correspond well with true behavior. Moreover, most works linking phone use with anxiety do not interrogate associations within a temporal framework. Accordingly, t...

Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work derived 17 egocentric network features to capture dynamics of soc...

Background: Existing interventions for co-occurring depression and cannabis use often do not treat both disorders simultaneously and can result in higher rates of symptom relapse. Traditional in-person interventions are often difficult to obtain due to financial and time limitations, which may further prevent individuals with co-occurring depressio...

Introduction Effectiveness of psychotherapy for substance use disorder (SUD) in community settings is lower than in controlled trials. We explored whether audio recording sessions for patients to review is an acceptable and feasible approach to improving psychotherapy effectiveness by enhancing engagement, recall, and self-reflection between sessio...

Major depressive disorder (MDD) and borderline personality disorder (BPD) often co-occur, with 20 % of adults with MDD meeting criteria for BPD. While MDD is typically diagnosed by symptoms persisting for several weeks, research suggests a dynamic pattern of symptom changes occurring over shorter durations. Given the diagnostic focus on affective s...

Anhedonia and depressed mood are two cardinal symptoms of major depressive disorder (MDD). Prior work has demonstrated that cannabis consumers often endorse anhedonia and depressed mood, which may contribute to greater cannabis use (CU) over time. However, it is unclear (1) how the unique influence of anhedonia and depressed mood affect CU and (2)...

Background:Chatbots powered by generative AI (Gen-AI) hold promise for building highly personalized, effective mental health treatments at scale, while also addressing existing user engagement and retention issues common among digital therapeutics. We present the first RCT testing an expert-fine-tuned Gen-AI-powered chatbot, Therabot, for mental he...

In a 7-year 11-wave study of low-SES adolescents (N = 856, age = 15.98), we compared multiple well-established transdiagnostic risk factors as predictors of first incidence of significant depressive, anxiety, and substance abuse symptoms across the transition from adolescence to adulthood. Risk factors included negative emotionality, emotion regula...

Mental health disorders—including depression, anxiety, trauma-related, and psychotic conditions—are pervasive and impairing, representing considerable challenges for both individual well-being and public health. Often the first challenges to treatment include financial, geographic, and stigmatic barriers, which limit the accessibility of traditiona...

Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent...

Speech-based diaries from mobile phones can capture paralinguistic patterns that help detect mental illness symptoms such as suicidal ideation. However, previous studies have primarily evaluated machine learning models on a single dataset, making their performance unknown under distribution shifts. In this paper, we investigate the generalizability...

Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associate...

Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using...

BACKGROUND Existing interventions for co-occurring depression and cannabis use often do not treat both disorders simultaneously and can result in higher rates of symptom relapse. Traditional in-person interventions are often difficult to obtain due to financial and time limitations, which may further prevent individuals with co-occurring depression...

Background The socially unattractive and stigmatizing nature of suicidal thought and behavior (STB) makes it especially susceptible to censorship across most modern digital communication platforms. The ubiquitous integration of technology with day-to-day life has presented an invaluable opportunity to leverage unprecedented amounts of data to study...

Positive psychology interventions (PPIs) are effective at increasing happiness and decreasing depressive symptoms. PPIs are often administered as self-guided web-based interventions, but not all persons benefit from web-based interventions. Therefore, it is important to identify whether someone is likely to benefit from web-based PPIs, in order to...

Objective: Transdiagnostic perspectives on the shared origins of mental illness posit that dysregulated emotion may represent a key driving force behind multiple forms of psychopathology, including substance use disorders. The present study examined whether a link between dysregulated emotion and trying illicit substances could be observed in chil...

Background: Subclinical depression (SD) is a mental health disorder characterized by minor depressive symptoms. Most SD patients are treated in the primary practice, but many respond poorly to treatment at the expense of provider resources. Stepped care approaches are appealing for tiering SD care to efficiently allocate scarce resources while joi...

Wearable technology enables unobtrusive collection of longitudinally dense data, allowing for continuous monitoring of physiology and behavior. These digital phenotypes, or device-based indicators, are frequently leveraged to study depression. However, they are usually considered alongside questionnaire sum-scores which collapse the symptomatic gam...

Introduction A variety of dimensions of psychopathology are observed in psychosis. However, the validation of clinical assessment scales, and their latent variable structure, is often derived from cross-sectional rather than longitudinal data, limiting our understanding of how variables interact and reinforce one another. Objectives Using experien...

ImportanceMajor Depressive Disorder (MDD), is a widespread and often debilitating mental health condition characterized by significant heterogeneity in symptom profiles, which poses a challenge for early detection. Changes in sleep and movement patterns associated with MDD carry valuable diagnostic information. This study explored the utility of wr...

Background: Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the preve...

Background: Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD. Objective: The aim is to examine patient enga...

Background: With a rapidly expanding gap between the need for and availability of mental health care, artificial intelligence (AI) presents a promising, scalable solution to mental health assessment and treatment. Given the novelty and inscrutable nature of such systems, exploratory measures aimed at understanding domain knowledge and potential bi...

Individuals with major depressive disorder (MDD) experience fewer positive and more negative emotions and use fewer positive words to describe themselves. Natural language processing (NLP) techniques have been used to predict depression, with pronoun and emotion usage being identified as important features. However, it is unclear how depressed indi...

Background: The socially unattractive and stigmatizing nature of suicidal thought and behavior (STB) makes it especially susceptible to censorship across most modern digital communication platforms. The ubiquitous integration of technology with day-to-day life has presented an invaluable opportunity to leverage unprecedented amounts of data to stud...

Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, the development of uniquely insightful digital markers can be used to improve STB risk detection and aid a globally ailing population. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work developed a social networ...

Body dysmorphic disorder (BDD) is common, severe, and often chronic. Cognitive behavioral therapy (CBT) is the first-line psychosocial treatment for BDD, with well-established efficacy. However, some patients do not improve with CBT, and little is known about how CBT confers its effects. Neurocognitive processes have been implicated in the etiology...

Introduction: Anxiety disorders are a prevalent and severe problem that are often developed early in life and can disrupt the daily lives of affected individuals for many years into adulthood. Given the persistent negative aspects of anxiety, accurate and early assessment is critical for long term outcomes. Currently, the most common method for an...

Background Individuals vary widely in emotional complexity (EC), the ways in which they represent and experience emotions. Emotional granularity, the degree to which individuals discriminate between emotions within positive or negative categories in daily experiences, is a widely studied form of EC linked to anxiety, depression, and personality pat...

Generalized anxiety disorder (GAD) is a highly prevalent and burdensome mental illness, characterized by prolonged periods of debilitating anxiety and worry, often associated with somatic symptoms. GAD is poorly understood, heterogenous in presentation, underdiagnosed, and often missed entirely. Current assessments like the GAD-7 and GAD-Q IV are w...

BACKGROUND Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD. OBJECTIVE The aim is to examine patient engageme...

Mental health and substance use disorders are highly prevalent and are the leading cause of global disease burden worldwide. Nevertheless, most persons with mental health or substance use disorders do not receive care. Prominent barriers to traditional care include (1) stigma, (2) lack of culturally competent treatments, (3) prohibitively high cost...

Two distinct literatures have evolved to study within-person changes in affect over time. One literature has examined affect dynamics with millisecond-level resolution under controlled laboratory conditions, and the second literature has captured affective dynamics across much longer timescales (e.g., hours or days) within the relatively uncontroll...

Many young individuals at risk for eating disorders spend time on social media and frequently search for information related to their body image concerns. In a large randomized study, we demonstrated that a guided chat-based intervention could reduce weight and shape concerns and eating disorder pathology. The goal of the current study was to deter...

Social network analysis (SNA) is an increasingly popular and effective tool for modeling psychological phenomena. Through application to the personality literature, social networks, in conjunction with passive, non-invasive sensing technologies, have begun to offer powerful insight into personality state variability. Resultant constructions of soci...

The current manuscript is a commentary on “Mobile phone–based interventions for mental health: A systematic meta-review of 14 meta-analyses of randomized controlled trials”. Although embedded within a nuanced discussion, one of the primary conclusions readers have taken from the meta-analysis was “we failed to find convincing evidence in support of...

Mental health disorders are highly prevalent, yet few persons receive access to treatment; this is compounded in rural areas where mental health services are limited. The proliferation of online mental health screening tools are considered a key strategy to increase identification, diagnosis, and treatment of mental illness. However, research on re...

Major depressive disorder (MDD) is the leading cause of global disease burden. Diagnostically, major depressive episodes are conceptualized as a series of individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, among those meeting criteria for MDD, symptoms are highly variable, showing greater variatio...

Digital Therapeutics for Mental Health and Addiction: The State of the Science and Vision for the Future presents the foundations of digital therapeutics with a broad audience in mind, ranging from bioengineers and computer scientists to those in psychology, psychiatry and social work. Sections cover cutting-edge advancements in the field, offering...

According to psychological flexibility theory, fully experiencing one's emotions, even when they involve negative reactions, can enhance psychological well-being. In pursuit of this possibility, procedures capable of disentangling reaction intensities from reaction durations, in response to affective images, were developed and variations of this pa...

Objective 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 respons...

Objective: Anorexia nervosa (AN) is commonly experienced alongside difficulties of emotion regulation (ER). Previous works identified physical activity (PA) as a mechanism for AN sufferers to achieve desired affective states, with evidence towards mitigation of negative affect. However, temporal associations of PA with specific emotional state out...

Background 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. Method The current study used passive movement data collected within a large n...

Introduction Despite existing work examining the effectiveness of smartphone digital interventions for schizophrenia at the group level, response to digital treatments is highly variable and requires more research to determine which persons are most likely to benefit from a digital intervention. Materials and methods The current work utilized data...

An amendment to this paper has been published and can be accessed via the original article.

Introduction Schizophrenia and Major Depressive Disorder (MDD) are highly burdensome mental disorders, with significant cost to both individuals and society. Despite these disorders representing distinct clinical categories, they are each heterogenous in their symptom profiles, with considerable transdiagnostic features. Although movement and sleep...

Anxiety disorders are a prevalent and severe problem that are often developed early in life and can disrupt the daily lives of affected individuals for many years into adulthood. Given the persistent negative aspects of anxiety, accurate and early assessment is critical for long term outcomes. Currently, the most common method for anxiety assessmen...

Background This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression...

Objective The prevalence of suicide in the United States has seen an increasing trend and is responsible for 1.6% of all mortality nationwide. Although suicide has the potential to broadly impact the entire population, it has a substantially increased prevalence in persons with epilepsy (PWE), despite many of these individuals consistently seeing a...

BACKGROUND The impacts of the coronavirus (COVID-19) pandemic on mental health across the world and in the United States have been well documented. However, there is limited research examining the long-term effects of the pandemic on mental health, particularly in relation to pervasive policies such as statewide mask mandates as well as political p...

Background: The impacts of the coronavirus (COVID-19) pandemic on mental health across the world and in the United States have been well documented. However, there is limited research examining the long-term effects of the pandemic on mental health, particularly in relation to pervasive policies such as statewide mask mandates as well as political...

Background: This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depressio...

Background Cannabis misuse in young adults is a major public health concern. An important predictor of continued use is cannabis craving. Due to the time-varying nature of cravings, brief momentary interventions delivered while cravings are elevated may improve the use of strategies to cope with cravings and reduce cannabis use. Objective The goal...