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...

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...

Background: Depressed individuals 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 usage and positive and negative emotions being identified as important features. However, it is unclear whether us...

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...

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...

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

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...

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...

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...

UNSTRUCTURED 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 craving, brief momentary interventions delivered while cravings are elevated may improve use of strategies to cope with cravings and reduce cannabis use. The goal of this manus...

Individuals with body dysmorphic disorder (BDD) suffer from distressing or impairing preoccupations with perceived imperfections in their appearance. This often-chronic condition is associated with significant functional impairment and elevated rates of psychiatric comorbidity and morbidity, including depression, substance use disorders, and suicid...

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...

Background: Sensors embedded in smartphones allow for the passive momentary quantification of people's states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor da...

Introduction: Across the U.S., the prevalence of opioid use disorder (OUD) and the rates of opioid overdoses have risen precipitously in recent years. Several effective medications for OUD (MOUD) exist and have been shown to be life-saving. A large volume of research has identified a confluence of factors that predict attrition and continued subst...

Importance: Selective serotonin reuptake inhibitors (SSRIs) are a common first-line treatment for some psychiatric disorders, including depression and anxiety; although they are generally well tolerated, SSRIs have known adverse effects, including movement problems, sleep disruption, and gastrointestinal problems (eg, nausea and upset stomach). No...

Previous research has demonstrated that adults with comorbid depressive and anxiety disorders are significantly more likely to show pathological use of drugs or alcohol. Few studies, however, have examined associations of this type in children. A better understanding of the relationships between affective disorders and substance experimentation in...

Transdiagnostic frameworks posit a causal link between emotion regulation (ER) ability and psychopathology. However, there is little supporting longitudinal evidence for such frameworks. Among N = 1,262 adolescents, we examined the prospective bidirectional relationship between ER and future pathological anxiety, depression, and substance dependenc...

Background Chatbots have the potential to provide cost-effective mental health prevention programs at scale and increase interactivity, ease of use, and accessibility of intervention programs. Objective The development of chatbot prevention for eating disorders (EDs) is still in its infancy. Our aim is to present examples of and solutions to chall...

Objective Prevention of eating disorders (EDs) is of high importance. However, digital programs with human moderation are unlikely to be disseminated widely. The aim of this study was to test whether a chatbot (i.e., computer program simulating human conversation) would significantly reduce ED risk factors (i.e., weight/shape concerns, thin-ideal i...

Background: The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of th...

Smartphones are capable of passively capturing persons’ social interactions, movement patterns, physiological activation, and physical environment. Nevertheless, little research has examined whether momentary anxiety symptoms can be accurately assessed using these methodologies. In this research, we utilize smartphone sensors and personalized deep...

[This corrects the article DOI: 10.3389/fpubh.2021.625640.].

BACKGROUND Sensors embedded in smartphones allow for the passive momentary quantification of people’s states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor data...

Objectives Machine learning models are a promising, yet underutilized tool within the mindfulness field. Accordingly, this work aimed to provide a practical introduction to key machine learning concepts through an illustrative investigation of the association between at-home mindfulness exercise compliance and stress reduction. To further interroga...

Background: The current COVID-19 coronavirus pandemic is an emergency on a global scale, with huge swathes of the population required to remain indoors for prolonged periods to tackle the virus. In this new context, individuals' health-promoting routines are under greater strain, contributing to poorer mental and physical health. Additionally, indi...

Background: There is an increasing number of smartphone applications (apps) focusing on prevention, treatment, and diagnosis of depression. A promising approach to increase the effectiveness while reducing the individual's burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. JITAIs are designed to improve the effectiveness o...

Intro As smartphone usage becomes increasingly prevalent in the workplace, the physical and psychological implications of this behavior warrant consideration. Recent research has investigated associations between workplace smartphone use and fatigue and boredom, yet findings are not conclusive. Methods To build off recent efforts, we applied an en...

BACKGROUND The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the...

Worldwide, there is roughly one mental health care provider for every 400 people with major depressive disorder (MDD). Without including other disorders, it would be impossible for everyone suffering from MDD to get clinical assistance. One step towards closing this gap may be the development of digital interventions. These can be delivered via sma...

Introduction Online social networking data (SN) is a contextually and temporally rich data stream that has shown promise in the prediction of suicidal thought and behavior. Despite the clear advantages of this digital medium, predictive modeling of acute suicidal ideation (SI) currently remains underdeveloped. SN data, in conjunction with robust ma...

Researchers have held a long-standing debate regarding the validity of discrete emotions versus global affect. The current manuscript tries to integrate these perspectives by explicitly examining the structures of state emotions and trait affect across time. Across three samples (sample 1: N = 176 Unites States undergraduates in a 50 day daily diar...

Episodic Future Thinking (EFT), mental simulation of personally relevant and positive future events, may modulate delay discounting (DD) in cannabis users. Whether EFT impacts cannabis use, whether DD mediates this effect, and whether EFT can be enhanced by prompting future events across specific life domains is unknown. Active, adult cannabis user...

BACKGROUND: Online guided self-help may be an effective and scalable intervention for symptoms of generalized anxiety disorder (GAD) among university students in India. METHODS: Based on an online screen for GAD administered at four Indian universities, 222 students classified as having clinical (DSM-5 criteria) or subthreshold (GAD-Q-IV score ≥ 5....

Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is difficult. Fortunately, advances in mobile technology, passive sensing, and analytics offer promising avenues for research and development. The present study examined the ability to utilize Global Po...

Background Clinical reports from patients suffering from the novel coronavirus (COVID-19) reflect a high prevalence of sensory deprivation or loss pertaining to smell (dysosmia/anosmia) and/or taste (dysgeusia/ageusia). Given the importance of the senses to daily functioning and personal experience, the mental health consequences of these symptoms...

Background: Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected; billions have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scal...

Objectives: Using two intensive longitudinal data sets with different timescales (90 minutes, daily), we examined emotion network density, a metric of emotional inflexibility, as a predictor of clinical-level anxiety and depression. Design: Mobile-based intensive longitudinal assessments. Methods: 119 participants (61 anxious and depressed, 58...

BACKGROUND There is an increasing number of smartphone applications (apps) focusing on prevention, treatment, and diagnosis of depression. A promising approach to increase the effectiveness while reducing the individual’s burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. OBJECTIVE With this work, we systematically assess...

Background:Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected; billions have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale...

BACKGROUND Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected; billions have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale...

Objectives: Using two intensive longitudinal datasets with different timescales (90 minutes, daily), we examined emotion network density (a metric of emotional rigidity) as a predictor of clinical levels of anxiety and/or depression. Design: Mobile-based intensive longitudinal assessments. Methods: In study 1, 119 participants (61 anxious and depre...

BACKGROUND Chatbots have the potential to provide cost-effective mental health prevention programs at scale and increase interactivity, ease of use, and accessibility of intervention programs. OBJECTIVE The development of chatbot prevention for eating disorders (EDs) is still in its infancy. Our aim is to present examples of and solutions to chall...

About a third of college students struggle with anxiety, depression, or an eating disorder, and only 20-40% of college students with mental disorders receive treatment. Inadequacies in mental health care delivery result in prolonged illness, disease progression, poorer prognosis, and greater likelihood of relapse, highlighting the need for a new ap...

Background. Clinical reports from patients suffering from the novel coronavirus (COVID-19) reflect a high prevalence of sensory deprivation or loss pertaining to smell (dysosmia/anosmia) and/or taste (dysgeusia/ageusia). Given the importance of the senses to daily functioning and personal experience, the mental health consequences of these symptoms...

Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could foster the detection of GAD and MDD in primary ca...

Current assessment methods have enabled many advancements in the fields of psychology and psychiatry by standardizing diagnoses for treatment, prognosis and research. They have created a “common language” for both clinicians and researchers. Such assessments, like the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire,...

Background : Recent studies have demonstrated that passive smartphone and wearable sensor data collected throughout daily life can predict anxiety symptoms cross-sectionally. However, to date, no research has demonstrated the capacity for these digital biomarkers to predict long-term prognosis. Methods : We utilized deep learning models based on w...