Selected Publications

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 variation within and across days than across weeks. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent efforts have examined this heterogeneity by studying MDD as a system where symptoms influence one another over time across individuals. In studying MDD symptom dynamics, however, most studies have made a strong assumption: that the symptom dynamics themselves are static and do not dynamically change over time. Nevertheless, there is a possibility that the individual MDD system dynamics change continuously across time. As a part of the Tracking Depression Study, funded by the National Institute of Mental Health and the National Institute of General Medical Sciences, participants (N = 105) completed ratings of MDD depressive symptoms three times a day for 90 days. In the current manuscript, we conducted time-varying vector autoregressive models to investigate the idiographic symptom networks of the entire sample of participants collected to date, and illustrate the finding with a case series of five persons with MDD. Aligned with prior research, the results indicate that there is high heterogeneity across persons, such that the individual network composition is unique from person to person. Moreover, the results suggest that for most persons, individual MDD symptom networks change rather dramatically across the 90 days. This is supported by the fact that 86% of individuals experienced at least one change in their most influential symptom with the median number of shifts being three over the 90 days. Additionally, the majority of individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day tracking period. Our findings offer further insight into short-term symptom dynamics, suggesting that the composition and driving factors of MDD are not only heterogeneous across persons but also within-persons across time.
Journal of Psychopathology and Clinical Science, 2024

Behav Res Ther, 2024

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 of speech-based suicidal ideation detection using mobile phones through cross-dataset experiments using four datasets with N=786 individuals experiencing major depressive disorder, auditory verbal hallucinations, persecutory thoughts, and students with suicidal thoughts. Our results show that machine and deep learning methods generalize poorly in many cases. Thus, we evaluate unsupervised domain adaptation (UDA) and semi-supervised domain adaptation (SSDA) to mitigate performance decreases owing to distribution shifts. While SSDA approaches showed superior performance, they are often ineffective, requiring large target datasets with limited labels for adversarial and contrastive training. Therefore, we propose sinusoidal similarity sub-sampling (S3), a method that selects optimal source subsets for the target domain by computing pair-wise scores using sinusoids. Compared to prior approaches, S3 does not use labeled target data or transform features. Fine-tuning using S3 improves the cross-dataset performance of deep models across the datasets, thus having implications in ubiquitous technology, mental health, and machine learning.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7(4), 2023

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 associated with long-term depression symptom variability. Participant information included (N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively-collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach, and (ii) an exhaustive feature inclusion approach. SHapley Additive exPlanations (SHAP) was used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability (r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively-collected wearable movement and sleep data in detecting long-term depression symptom variability.
Translational Psychiatry, 2023

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 STB, yet the complex etiologies and consequences of censorship for research within mainstream online communities render an incomplete picture of STB manifestation. Analyses targeting online written content of suicidal users in environments where fear of reproach is mitigated may provide novel insight into modern trends and signals of STB expression. Complete written content of N = 192 users, including n = 48 identified as potential suicide completers/highest-risk users (HRUs), on the pro-choice suicide forum, Sanctioned Suicide, was modeled using a combination of lexicon-based topic modeling (EMPATH) and exploratory network analysis techniques to characterize and highlight prominent aspects of censorship-free suicidal discourse. Modeling of over 2 million tokens across 37,136 forum posts found higher frequency of positive emotion and optimism among HRUs, emphasis on methods seeking and sharing behaviors, prominence of previously undocumented jargon, and semantics related to loneliness and life adversity. This natural language processing (NLP)- and network-driven exposé of online STB subculture uncovered trends that deserve further attention within suicidology as they may be able to bolster detection, intervention, and prevention of suicidal outcomes and exposures.
Digital Health, 2023

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 jointly optimizing patient outcomes. However, stepped care can be time inefficient, as some persons may respond poorly and be forced to suffer with their symptoms for prolonged periods. Machine learning can offer insight into optimal treatment paths and inform clinical recommendations for incident patients. Methods: As part of the Step-Dep trial, participants with SD were randomized to receive stepped care (N=96) or usual care (N=140). Machine learning was used to predict changes in depressive symptoms every three months over a year for each treatment group. Results: Tree-based models were effective in predicting PHQ-9 changes among patients who received stepped care (r=0.35–0.46, MAE=0.14–0.17) and usual care (r=0.34–0.49, MAE=0.15–0.18). Patients who received stepped care were more likely to reduce PHQ-9 scores if they had high PHQ-9 but low HADS-A scores at baseline, a low number of chronic illnesses, and an internal locus of control. Limitations: Models may suffer from potential overfitting due to sample size limitations. Conclusion: Our findings demonstrate the promise of machine learning for predicting changes in depressive symptoms for SD patients receiving different treatments. Trained models can intake incident patient information and predict outcomes to inform personalized care.
Journal of Affective Disorders, 2023

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 challenge to treatment can be financial, geographic, and stigmatic barriers which limit the accessibility of traditional assessment measures. Further, compounded by frequent misdiagnosis or delayed detection, there is a need for effective, accessible, and scalable approaches to identification and management. Leveraging advances in computing and the ubiquitous nature of personal mobile and wearable technology, this narrative review examines the utilization of passive sensor data as a screening and diagnostic tool for mental disorders. As an alternative to traditional screening measures, passive sensing offers a tool to overcome barriers which prevent many from seeking services. We critically assess the literature up to September 2023, exploring the use of passive data—such as heart rate variability, movement patterns, and geolocation—to predict mental health outcomes across a spectrum of disorders. Through a translational perspective, our review explores the state of passive sensing science, with special emphasis on the capacity for the science to be implemented in real-world clinical and general populations. To this aim, we consider study designs, including participant demographics, data collection methods, sensor modalities, outcome measures, and analytic modeling approaches. Our findings highlight overarching limitations in the field including 1) the use of smaller, specialized sample populations, and 2) the predominant use of Android operating systems, 3) a reliance on self-reported measures as proxies for mental health outcomes which ultimately limit the potential to provide robust mental health assessment in larger population samples. We suggest that further research incorporate larger and more diverse samples, inclusion of further smartphone operating systems, and additional clinical-related assessments to strengthen predictive models and maintain a clinician in the loop. Despite these limitations, passive sensing technologies like GPS, heart rate monitoring, and actigraphy offer promise for enhancing early detection and improving the diagnostic process for mental disorders. We conclude that careful consideration of translational factors in the design of future research will aid in enhancing the potential and scope of future passive sensing studies, ultimately enhancing mental health outcomes on a broad scale.
Biomedical Materials and Devices, 2023

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 triage persons who may not benefit from other interventions. In the current study, we used machine learning to predict individual response to a web-based PPI, in order to investigate baseline prognostic indicators of likelihood of response (N = 120). Our models demonstrated moderate correlations (happiness: rTest = 0.30 ± 0.09; depressive symptoms: rTest = 0.39 ± 0.06), indicating that baseline features can predict changes in happiness and depressive symptoms at a 6-month follow-up. Thus, machine learning can be used to predict outcome changes from a web-based PPI and has important clinical implications for matching individuals to PPIs based on their individual characteristics.
Journal of Positive Psychology, 2023

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 gamut into a general representation of severity. To explore the contributions of passive sensing streams more precisely, associations of nine passive sensing-derived features with self-report responses to Center for Epidemiologic Studies Depression (CES-D) items were modeled. Using data from the NetHealth study on N=469 college students, this work generated mixed ordinal logistic regression models to summarize contributions of pulse, movement, and sleep data to depression symptom detection. Emphasizing the importance of the college context, wearable features displayed unique and complementary properties in their heterogeneously significant associations with CES-D items. This work provides conceptual and exploratory blueprints for a reductionist approach to modeling depression within passive sensing research.
Behaviour Research and Therapy, 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. Linear mixed-effect models were used to model changes in daytime and nighttime worry duration and frequency for 163 participants who completed a six-day worry postponement intervention. Ensemble-based machine learning regression and classification models were implemented to predict changes in worry across the intervention. Model feature importance was derived using SHapley Additive exPlanation (SHAP). Moderate predictive performance was obtained for predicting changes in daytime worry duration (test r2 = 0.221, AUC = 0.77) and nighttime worry frequency (test r2 = 0.164, AUC = 0.72), while poor predictive performance was obtained for nighttime worry duration and daytime worry frequency. Baseline levels of worry and subjective health complaints were most important in driving model predictions. A complete-case analysis was leveraged to analyze the present data, which was collected from participants that were Dutch and majority female. This study suggests that treatment response to a digital intervention for GAD can be accurately predicted using baseline characteristics. Particularly, this worry postponement intervention may be most beneficial for individuals with high baseline worry but fewer subjective health complaints. The present findings highlight the complexities of and need for further research into daily worry dynamics and the personalizable utility of digital interventions.
Journal of Affective Disorders, 2023

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 real-world effectiveness, especially in hard to reach rural communities, is limited. Accordingly, the current work seeks to test the hypothesis that online screening use is greater in rural communities with limited mental health resources. The study utilized a national, online, population-based cohort consisting of Microsoft Bing search engine users across 18 months in the United States (representing approximately one-third of all internet searches), in conjunction with user-matched data of completed online mental health screens for anxiety, bipolar, depression, and psychosis (N = 4354) through Mental Health America, a leading non-profit mental health organization in the United States. Rank regression modeling was leveraged to characterize U.S. county-level screen completion rates as a function of rurality, health-care availability, and sociodemographic variables. County-level rurality and mental health care availability alone explained 42% of the variance in MHA screen completion rate (R2 = 0.42, p < 5.0 × 10−6). The results suggested that online screening was more prominent in underserved rural communities, therefore presenting as important tools with which to bridge mental health-care gaps in rural, resource-deficient areas.
Journal of Psychiatric Research, 2023

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 abnormalities exist in both disorders, little is known of the precise nature of these changes longitudinally. Passively-collected longitudinal data from wearable sensors is well suited to characterize naturalistic features which may cross traditional diagnostic categories (e.g., highlighting behavioral markers not captured by self-report information). The present analyses utilized raw minute-level actigraphy data from three diagnostic groups: individuals with schizophrenia (N = 23), individuals with depression (N = 22), and controls (N = 32), respectively, to interrogate naturalistic behavioral differences between groups. Subjects’ week-long actigraphy data was processed without diagnostic labels via unsupervised machine learning clustering methods, in order to investigate the natural bounds of psychopathology. Further, actigraphic data was analyzed across time to determine timepoints influential in model outcomes. We find distinct actigraphic phenotypes, which differ between diagnostic groups, suggesting that unsupervised clustering of naturalistic data aligns with existing diagnostic constructs. Further, we found statistically significant inter-group differences, with depressed persons showing the highest behavioral variability. However, diagnostic group differences only consider biobehavioral trends captured by raw actigraphy information. Passively-collected movement information combined with unsupervised deep learning algorithms shows promise in identifying naturalistic phenotypes in individuals with mental health disorders, specifically in discriminating between MDD and schizophrenia.
Vol 316, Pages 132-139, 2022

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 outcomes are unknown. Using lag-ensemble machine learning and feature importance analyses, 888 affect-based ecological momentary assessments across N = 75 adolescents with AN (N = 44) and healthy controls (N = 31) were analysed to explore significance of past PA, measured through passively collected wrist-worn actigraphy, with subsequent self-report momentary affect change across 9 affect constructs. Among AN adolescents, later lags (≥2.5 h) were important in predicting change across negative emotions (hostility, sadness, fear, guilt). AN-specific model performance on held-out test data revealed the holistic “negative affect” construct as significantly predictable. Only joviality and self-assurance, both positively-valenced constructs, were significantly predictable among healthy-control-specific models. Results recapitulated previous findings regarding the importance of PA in negative ER for AN individuals. Moreover, PA was found to play a uniquely prominent role in predicting negative affect 4.5–6 h later among AN adolescents. Future research into the PA-ER dynamic will benefit from targeting specific negative emotions across greater temporal scales.
European Eating Disorders Review, 2022

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 national cohort (N = 264) to assess GAD symptom severity. Results: Using one week of movement data, machine learning models accurately predicted GAD symptoms across a continuum (r = 0.511) and accurately detected those individuals with elevated GAD symptoms (AUC = 0.892, 70.0% Sensitivity, 95.5% Specificity, Brier Score = 0.092). Those with a risk score at the 90th percentile or above had 21 times the odds of having elevated GAD symptoms compared to those with lower risk scores. The risk score was most strongly associated with irritability, worry controllability, and restlessness (individual rs > 0.5). The risk scores for GAD were also discriminant of major depressive disorder symptom severity (r = 0.190). Limitations: The current study examined the detection of GAD symptom severity rather than the prediction of GAD symptom severity across time. Furthermore, the instant sample of data did not include nighttime actigraphy, as participants were not asked to wear the actigraphs at night. Conclusions: These results suggest that artificial intelligence can effectively utilize wearable movement data collected in daily life to accurately infer risk of GAD symptoms.
Translational Psychiatry, 2022

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. DP is defined as the use of digital data to profile health information objectively. Aims: Four distinct yet interrelated goals underpin this study: (a) to identify and describe empirical studies examining the use of DP to study depression; (b) to describe the different methods and technology employed; © to integrate the evidence to ascertain the efficacy of digital data in the studying, diagnosis, and monitoring of depression; and (d) to describe DP definitions and digital mental health records terminology. Results: Overall, 118 studies were assessed as eligible. Considering the terms employed, “EMA”, “ESM”, and “DP” were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps’ information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales. Conclusions: Findings suggested links between a person’s digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition; and (b) expanding the literature to consider a person’s broader contextual and developmental circumstances in relation to their digital data/records.
BMC Psychiatry, 2022

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 to assess participants’ moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and self-reported assessment to further integrate the 2 methodologies. Objective: We investigated whether sparse movement-related sensor data can be used to train machine learning models that are able to infer states of individuals’ work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor data were only collected while the participants filled out the questionnaires on their smartphones. Methods: We trained personalized machine learning models on data from employees (N=158) who participated in a 3-week ecological momentary assessment study. Results: The results suggested that passive smartphone sensor data paired with personalized machine learning models can be used to infer individuals’ self-reported states at later measurement occasions. The mean R2 was approximately 0.31 (SD 0.29), and more than half of the participants (119158, 75.3%) had an R2 of ≥0.18. Accuracy was only slightly attenuated compared with earlier studies and ranged from 38.41% to 51.38%. Conclusions: Personalized machine learning models and temporally linked passive sensing data have the capability to infer a sizable proportion of variance in individuals’ daily self-reported states. Further research is needed to investigate factors that affect the accuracy and reliability of the inference.
Journal of Medical Internet Research (JMIR), 2022

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 large-scale studies using naturalistic, longitudinal, objective data have validated physical activity findings, and actigraphy data are well suited to address this task. Objectives To evaluate whether differences in physical movement exist among individuals treated with SSRIs compared with control participants and to identify the unique features of the movement of patients treated with SSRIs. Design, Setting, and Participants This cross-sectional study examines longitudinally collected wearable movement data within a cross-sectional sample of 7162 participants from the 2005-2006 National Health and Nutrition Examination Survey (NHANES), a nationally representative population-based sample of noninstitutionalized persons in the US having both medication information and passive movement data. Statistical analysis was performed from April 1, 2021, to February 1, 2022. Exposures The use of SSRIs (sertraline hydrochloride, escitalopram oxalate, fluoxetine hydrochloride, paroxetine hydrochloride, and citalopram hydrobromide), as reported by participants interviewed by NNHANES personnel, was the primary exposure, measured as a binary variable (taking an SSRI vs not taking an SSRI). Main Outcomes and Measures The primary outcome was the intensity of body movement as recorded by a piezoelectric accelerometer worn on the right hip for more than 1 week. Results Of the 7162 participants included in the study, the mean (SD) age was 33.7 (22.6) years, 266 (3.7%) were taking an SSRI, 3706 (51.7%) were female, 1934 (27.0%) were Black, 1823 (25.5%) were Mexican American, 210 (2.9%) were other Hispanic, 336 (4.7%) were other or multiracial, and 2859 (39.9%) were White (per the NHANES data collection protocol). A cross-validated, deep learning classifier was constructed that achieved fair performance predicting SSRI use (area under the curve, 0.67 [95% CI, 0.64-0.71] for the validation set and 0.66 [95% CI, 0.64-0.68] for the test set). To account for possible confounding by indication, we constructed a parallel model incorporating depression severity, finding only marginal performance improvement. When averaged across individuals and across 7 days, the results show less overall movement in the SSRI group (mean, 120.1 vertical acceleration counts/min [95% CI, 115.7-124.6 vertical acceleration counts/min]) compared with the non-SSRI control group (mean, 168.8 vertical acceleration counts/min [95% CI, 162.8-174.9 vertical acceleration counts/min]). Conclusions and Relevance This cross-sectional study found a moderate association between passive movement and SSRI use, as well as SSRI detection capacity of passive movement using time series deep learning models. The results support the use of passive sensors for exploration and characterization of psychotropic medication adverse effects.
JAMA Network Open, 2022

The current commentary is focused on the methods and conclusions drawn in a recent meta-analysis which evaluated the impact of standalone interventions in treating anxiety and depressive symptoms (Weisel et al., 2019). The current commentary discusses the large impact of methodological choices made to exclude transdiagnostic treatments and group heterogeneous treatments on study conclusions. Additionally, the current commentary evaluates these conclusions considering opposing from two additional meta-analytic findings. The current review concludes that more research is needed effects before drawing any definitive conclusions, but the current evidence base suggests that apps show promising early efficacy in treating anxiety and depressive symptoms.
Meta-Psychology, 2022

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 internalization) in women at high risk for an ED, compared to waitlist control, as well as whether it would significantly reduce overall ED psychopathology, depression, and anxiety and prevent ED onset. Method: Women who screened as high risk for an ED were randomized (N = 700) to (1) chatbot based on the StudentBodies© program; or (2) waitlist control. Participants were followed for 6 months. Results: For weight/shape concerns, there was a significantly greater reduction in intervention versus control at 3- (d = −0.20; p = .03) and 6-m-follow-up (d = −0.19; p = .04). There were no differences in change in thin-ideal internalization. The intervention was associated with significantly greater reductions than control in overall ED psychopathology at 3- (d = −0.29; p = .003) but not 6-month follow-up. There were no differences in change in depression or anxiety. The odds of remaining nonclinical for EDs were significantly higher in intervention versus control at both 3- (OR = 2.37, 95% CI [1.37, 4.11]) and 6-month follow-ups (OR = 2.13, 95% CI [1.26, 3.59]). Discussion: Findings provide support for the use of a chatbot-based EDs prevention program in reducing weight/shape concerns through 6-month follow-up, as well as in reducing overall ED psychopathology, at least in the shorter-term. Results also suggest the intervention may reduce ED onset. Public Significance We found that a chatbot, or a computer program simulating human conversation, based on an established, cognitive-behavioral therapy-based eating disorders prevention program, was successful in reducing women’s concerns about weight and shape through 6-month follow-up and that it may actually reduce eating disorder onset. These findings are important because this intervention, which uses a rather simple text-based approach, can easily be disseminated in order to prevent these deadly illnesses.
International Journal of Eating Disorders, 2022

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 challenges in designing and refining a rule-based prevention chatbot program for EDs, targeted at adult women at risk for developing an ED. Methods: Participants were 2409 individuals who at least began to use an EDs prevention chatbot in response to social media advertising. Over 6 months, the research team reviewed up to 52,129 comments from these users to identify inappropriate responses that negatively impacted users’ experience and technical glitches. Problems identified by reviewers were then presented to the entire research team, who then generated possible solutions and implemented new responses. Results: The most common problem with the chatbot was a general limitation in understanding and responding appropriately to unanticipated user responses. We developed several workarounds to limit these problems while retaining some interactivity. Conclusions: Rule-based chatbots have the potential to reach large populations at low cost but are limited in understanding and responding appropriately to unanticipated user responses. They can be most effective in providing information and simple conversations. Workarounds can reduce conversation errors.
Journal of Medical Internet Research, 2022

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 learning models to predict future anxiety symptoms among a sample reporting clinical anxiety disorder symptoms. Participants (N = 32) with generalized anxiety disorder and/or social anxiety disorder (based on self-report) installed a smartphone application and completed ecological momentary assessment symptoms assessing their anxiety and avoidance symptoms hourly for the course of one week (T = 2007 assessments). During the same period, the smartphone app collected information about physiological activation (heart rate and heart rate variability), exposure to light, social contact, and GPS location. GPS locations were coded to reveal the type of location and the weather information. Personalized deep learning models using the smartphone sensor data were capable of predicting the majority of total variation in anxiety symptoms (R2 = 0.748) and predicting a large proportion of within-person variation at the hour-by-hour level (mean R2 = 0.385). These results suggest that personalized deep learning models using smartphone sensor data are capable of accurately predicting future anxiety disorder symptom changes.
Behaviour Research and Therapy, 2021

Journal of Medical Internet Research (JMIR), 2021

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, individuals are required to keep up to date with latest health guidelines about the virus, which may be confusing in an age of social-media disinformation and shifting guidelines. To tackle these factors, we developed Elena+, a smartphone-based and conversational agent (CA) delivered pandemic lifestyle care intervention. Methods: Elena+ utilizes varied intervention components to deliver a psychoeducation-focused coaching program on the topics of: COVID-19 information, physical activity, mental health (anxiety, loneliness, mental resources), sleep and diet and nutrition. Over 43 subtopics, a CA guides individuals through content and tracks progress over time, such as changes in health outcome assessments per topic, alongside user-set behavioral intentions and user-reported actual behaviors. Ratings of the usage experience, social demographics and the user profile are also captured. Elena+ is available for public download on iOS and Android devices in English, European Spanish and Latin American Spanish with future languages and launch countries planned, and no limits on planned recruitment. Panel data methods will be used to track user progress over time in subsequent analyses. The Elena+ intervention is open-source under the Apache 2 license (MobileCoach software) and the Creative Commons 4.0 license CC BY-NC-SA (intervention logic and content), allowing future collaborations; such as cultural adaptions, integration of new sensor-related features or the development of new topics. Discussion: Digital health applications offer a low-cost and scalable route to meet challenges to public health. As Elena+ was developed by an international and interdisciplinary team in a short time frame to meet the COVID-19 pandemic, empirical data are required to discern how effective such solutions can be in meeting real world, emergent health crises. Additionally, clustering Elena+ users based on characteristics and usage behaviors could help public health practitioners understand how population-level digital health interventions can reach at-risk and sub-populations.
Frontiers in Public Health, 2021

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 ensemble machine learning model on a previously published dataset of N = 83 graduate students in the Netherlands to predict work boredom and fatigue from passively collected smartphone app use information. Using time-based feature engineering and lagged variations of the data to train, validate, and test idiographic models, we evaluated the efficacy of a lagged-ensemble predictive paradigm on sparse temporal data. Moreover, we probed the relative importance of both derived app use variables and lags within this predictive framework. Results: The ability to predict fatigue and boredom trajectories from app use information was heterogeneous and highly person-specific. Idiographic modeling reflected moderate to high correlative capacity (r > 0.4) in 47% of participants for fatigue and 24% for boredom, with better overall performance in the fatigue prediction task. App use relating to duration, communication, and patterns of use frequency were among the most important features driving predictions across lags, with longer lags contributing more heavily to final ensemble predictions compared with shorter ones. Conclusion: A lag-specific ensemble predictive paradigm is a promising approach to leveraging high-dimensional app use behavioral data for the prediction of work fatigue and boredom. Future research will benefit from evaluating associations on densely collected data across longer time scales.
Computers in Human Behavior, 2021

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 interrogate the currently inconclusive nature of the compliance-outcome association within the mindfulness literature, the illustrative example leveraged a suite of machine learning techniques to highlight the unique affordances and perspectives of the predictive framework. Methods: Foundational information regarding facets of the machine learning analytical process, including model types, data preprocessing, feature engineering, validation, performance evaluation, and model introspection, was presented. With emphasis on providing details and justifications regarding modeling decisions along the way, the work systematically applied these introduced concepts to a real-world data example. This permitted an opportunity to build, introspect, and derive insight from a model tasked to explore dynamics underlying patient compliance to mindfulness exercises within a web-based delivery setting. Results: The constructed machine learning models suggested a moderate correlation of compliance with post-intervention reliable change in stress (r = 0.349 ± 0.018). Model introspection tools further revealed that a combination of both high consistency and high overall average compliance predicts a trend toward greater reduction in self-reported stress. Conclusions: Results of the illustrative study suggested that compliance, in pattern and absolute magnitude, is a significant contributor to online mindfulness therapy outcomes. Moreover, modeling efforts implicate machine learning as a uniquely beneficial paradigm with which to explore nuanced questions in the mindfulness research space.
Mindfulness, 2021

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 diary study, total observations = 7,504; sample 2: N = 2,104 in a 30 day daily diary study within a community sample in Germany; total observations = 28,090; sample 3: N = 245, ecological momentary assessment study within the United States from an outpatient psychiatry clinic completing five measurements per day for 21 days; total observations = 29,950), participants completed the Positive and Negative Affect Schedule. An exploratory multilevel factor analysis in sample 1 allowed for the simultaneous estimation of state factors (i.e., within-person factor analysis) and trait factors (i.e., between-person factor analysis). Confirmatory multilevel factor models examined the generalizability of the multilevel factor solutions to samples 2 and 3. Across all samples, the results suggested strong support for a two-factor solution for trait affect and a seven-factor solution for state emotion. Taken together, these results suggest that positive affect and negative affect can be used to describe differences across people, but at least seven differentiated emotions are experienced within persons across time.
Emotion, 2021

Background: The number of smartphone apps that focus on the prevention, diagnosis, and treatment of depression is increasing. A promising approach to increase the effectiveness of the apps while reducing the individual’s burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. JITAIs are designed to improve the effectiveness of the intervention and reduce the burden on the person using the intervention by providing the right type of support at the right time. The right type of support and the right time are determined by measuring the state of vulnerability and the state of receptivity, respectively. Objective: The aim of this study is to systematically assess the use of JITAI mechanisms in popular apps for individuals with depression. Methods: We systematically searched for apps addressing depression in the Apple App Store and Google Play Store, as well as in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. The relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, 2 authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, Google Scholar, IEEE Xplore, Web of Science, ACM Portal, and Science Direct), publications cited on the app’s website, information on the app’s website, and the app itself. All types of measurements (eg, open questions, closed questions, and device analytics) found in the apps were recorded and reviewed. Results: None of the 28 reviewed apps used JITAI mechanisms to tailor content to situations, states, or individuals. Of the 28 apps, 3 (11%) did not use any measurements, 20 (71%) exclusively used self-reports that were insufficient to leverage the full potential of the JITAIs, and the 5 (18%) apps using self-reports and passive measurements used them as progress or task indicators only. Although 34% (2368) of the reviewed publications investigated the effectiveness of the apps and 21% (1468) investigated their efficacy, no publication mentioned or evaluated JITAI mechanisms. Conclusions: Promising JITAI mechanisms have not yet been translated into mainstream depression apps. Although the wide range of passive measurements available from smartphones were rarely used, self-reported outcomes were used by 71% (2028) of the apps. However, in both cases, the measured outcomes were not used to tailor content and timing along a state of vulnerability or receptivity. Owing to this lack of tailoring to individual, state, or situation, we argue that the apps cannot be considered JITAIs. The lack of publications investigating whether JITAI mechanisms lead to an increase in the effectiveness or efficacy of the apps highlights the need for further research, especially in real-world apps
Journal of Medical Internet Research (JMIR), 2021

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 machine learning algorithms, may offer a promising way forward. Methods: We applied an ensemble machine learning model on a previously published dataset of adolescents on Instagram with a prior history of lifetime SI (N = 52) to predict SI within the past month. Using predictors that capture language use and activity within this SN, we evaluated the performance of our out-of-sample, cross-validated model against previous efforts and leveraged a model explainer to further probe relative predictor importance and subject-level phenomenology. Results: Linguistic and SN data predicted acute SI with an accuracy of 0.702 (sensitivity = 0.769, specificity = 0.654, AUC = 0.775). Model introspection showed a higher proportion of SN-derived predictors with substantial impact on prediction compared with linguistic predictors from structured interviews. Further analysis of subject-specific predictor importance uncovered potentially informative trends for future acute SI risk prediction. Conclusion: Application of ensemble learning methodologies to SN data for the prediction of acute SI may mitigate the complexities and modeling challenges of SI that exist within these time scales. Future work is needed on larger, more heterogeneous populations to fine-tune digital biomarkers and more robustly test external validity.
Internet Interventions, 2021

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 ≥ 5.7) GAD were randomly assigned to receive either three months of guided self-help cognitive-behavioral therapy (n = 117) or a waitlist control condition (n = 105). Results: Guided self-help participants recorded high program usage on average across all participants enrolled (M = 9.99 hours on the platform; SD = 20.87). Intent-to-treat analyses indicated that participants in the guided self-help condition experienced significantly greater reductions than participants in the waitlist condition on GAD symptom severity (d = -0.40), worry (d = -0.43), and depressive symptoms (d = -0.53). No usage variables predicted symptom change in the guided self-help condition. Participants on average reported that the program was moderately helpful, and a majority (82.1%) said they would recommend the program to a friend. Conclusions: Guided self-help appears to be a feasible and efficacious intervention for university students in India who meet clinical or subthreshold GAD criteria.
Psychotherapy, 2021

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 Positioning System (GPS) data, derived passively from a smartphone across seven days, to detect PTSD diagnostic status among a cohort (N = 185) of high-risk, previously traumatized women. Using daily time spent away and maximum distance traveled from home as a basis for model feature engineering, the results suggested that diagnostic group status can be predicted out-of-fold with high performance (AUC = 0.816, balanced sensitivity = 0.743, balanced specificity = 0.8, balanced accuracy = 0.771). Results further implicate the potential utility of GPS information as a digital biomarker of the PTSD behavioral repertoire. Future PTSD research will benefit from application of GPS data within larger, more diverse populations.
Scientific Reports, 2021

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 wearable sensor technology to predict long-term (17–18year) deterioration in generalized anxiety disorder and panic disorder symptoms from actigraphy data on daytime movement and nighttime sleeping patterns. As part of Midlife in the United States (MIDUS), a national longitudinal study of health and well-being, subjects (N = 265) (i) completed a phone-based interview that assessed generalized anxiety disorder and panic disorder symptoms at enrollment, (ii) participated in a one-week actigraphy study 9–14 years later, and (iii) completed a long-term follow-up, phone-based interview to quantify generalized anxiety disorder and panic disorder symptoms 17–18 years from initial enrollment. A deep auto- encoder paired with a multi-layered ensemble deep learning model was leveraged to predict whether participants experienced increased anxiety disorder symptoms across this 17–18 year period. Results: Out-of-sample cross-validated results suggested that wearable movement data could significantly predict which individuals would experience symptom deterioration (AUC = 0.696, CI [0.598, 0.793], 84.6% sensitivity, 52.7% specificity, balanced accuracy = 68.7%). Conclusions: Passive wearable actigraphy data could be utilized to predict long-term deterioration of anxiety disorder symptoms. Future studies should examine whether these methods could be implemented to prevent deterioration of anxiety disorder symptoms.
Journal of Affective Disorders, 2021

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 healthy controls) completed ecological momentary assessment (EMA) to rate a variety of negative (NE) and positive emotions (PE) 9 times per day for 8 days using a mobile phone application. 169 participants (97 anxious and depressed and 72 healthy controls) completed an online daily diary on their NE and PE for 50 days. Multilevel vector autoregressive models were run to compute NE and PE network densities in each data set. Results: In the EMA data set, both NE and PE network densities significantly predicted participants’ diagnostic status above and beyond demographics and the mean and standard deviation of NE and PE. Greater NE network density and lower PE network density were associated with anxiety and depression diagnoses. In the daily diary data set, NE and PE network densities did not significantly predict the diagnostic status. Conclusions: Greater inflexibility of NE and lower inflexibility of PE, indexed by emotion network density, are potential clinical markers of anxiety and depressive disorders when assessed at intra-daily levels as opposed to daily levels. Considering emotion network density, as well as the mean level and variability of emotions in daily life, may contribute to diagnostic prediction of anxiety and depressive disorders.
British Journal of Clinical Psychology, 2021

Background: 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 care settings. Methods: We approached the problem of predicting MDD and GAD using a novel machine learning pipeline to re-analyze data from an observational study. The pipeline constitutes an ensemble of algorithmically distinct machine learning methods, including deep learning. A sample of 4,184 undergraduate students completed the study, undergoing a general health screening and completing a psychiatric assessment for MDD and GAD. After explicitly excluding all psychiatric information, 59 biomedical and demographic features from the general health survey in addition to a set of engineered features were used for model training. Results: We assessed the model’s performance on a held-out test set and found an AUC of 0.73 (sensitivity: 0.66, specificity: 0.7) and 0.67 (sensitivity: 0.55, specificity: 0.7) for GAD, and MDD, respectively. Additionally, we used advanced techniques (SHAP values) to illuminate which features had the greatest impact on prediction for each disease. The top predictive features for MDD were being satisfied with living conditions and having public health insurance. The top predictive features for GAD were vaccinations being up to date and marijuana use. Conclusions: Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. By identifying biomarkers of GAD and MDD, these results may be used in future research to aid in the early detection of MDD and GAD.
Scientific Reports, 2020

While digital psychiatric interventions reduce treatment barriers, not all persons benefit from this type of treatment. Research is needed to preemptively identify who is likely to benefit from these digital treatments in order to redirect those people to a higher level of care. The current manuscript used an ensemble of machine learning methods to predict changes in major depressive and generalized anxiety disorder symptoms from pre to 9-month follow-up in a randomized controlled trial of a transdiagnostic digital intervention based on participants’ (N=632) pre-treatment data. The results suggested that baseline characteristics could accurately predict changes in depressive symptoms in both treatment groups (r=0.482, 95% CI[0.394, 0.561]; r=0.477, 95% CI[0.385, 0.560]) and anxiety symptoms in both treatment groups (r=0.569, 95% CI[0.491, 0.638]; r=0.548, 95% CI[0.464, 0.622]). These results suggest that machine learning models are capable of preemptively predicting a person’s responsiveness to digital treatments, which would enable personalized decision-making about which persons should be directed towards standalone digital interventions or towards blended stepped-care.
Psychiatry Research, 2020

Background: Sleep disruption is a common precursor to deterioration and relapse in people living with psychotic disorders. Understanding the temporal relationship between sleep and psychopathology is important for identifying and developing interventions which target key variables that contribute to relapse. Methods: We used a purpose-built digital platform to sample self-reported sleep and psychopathology variables over one year, in thirty-six individuals with schizophrenia. Once-daily measures of sleep duration and sleep quality, and fluctuations in psychopathology (positive and negative affect, cognition, and psychotic symptoms) were captured. We examined the temporal relationship between these variables using the Differential Time-Varying Effect (DTVEM) hybrid exploratory-confirmatory model. Results: Poorer sleep quality and shorter sleep duration maximally predicted deterioration in psychosis symptoms over the subsequent 1 – 8 and 1-12 days, respectively. These relationships were also mediated by negative affect and cognitive symptoms. Psychopathology variables also predicted sleep quality, but not sleep duration, and the effect sizes were smaller and of shorter lag duration. Conclusions: Reduced sleep duration and poorer sleep quality anticipate exacerbation of psychotic symptoms by approximately 1-2 weeks, and negative affect and cognitive symptoms mediate this relationship. We also observed a reciprocal relationship, that was of shorter duration and smaller magnitude. Sleep disturbance may play a causal role in symptom exacerbation and relapse, and represents an important and tractable target for intervention. It warrants greater attention as an early warning sign of deterioration, and low-burden, user-friendly digital tools may play a role in its early detection.
Journal of Medical Internet Research (JMIR): Formative Research, 2020

Background: Chatbots could be a scalable solution that provides an interactive means of engaging users in behavioral health interventions driven by artificial intelligence. Although some chatbots have shown promising early efficacy results, there is limited information about how people use these chatbots. Understanding the usage patterns of chatbots for depression represents a crucial step toward improving chatbot design and providing information about the strengths and limitations of the chatbots. Objective: This study aims to understand how users engage and are redirected through a chatbot for depression (Tess) to provide design recommendations. Methods: Interactions of 354 users with the Tess depression modules were analyzed to understand chatbot usage across and within modules. Descriptive statistics were used to analyze participant flow through each depression module, including characters per message, completion rate, and time spent per module. Slide plots were also used to analyze the flow across and within modules. Results: Users sent a total of 6220 messages, with a total of 86,298 characters, and, on average, they engaged with Tess depression modules for 46 days. There was large heterogeneity in user engagement across different modules, which appeared to be affected by the length, complexity, content, and style of questions within the modules and the routing between modules. Conclusions: Overall, participants engaged with Tess; however, there was a heterogeneous usage pattern because of varying module designs. Major implications for future chatbot design and evaluation are discussed in the paper.
Journal of Medical Internet Research (JMIR): Formative Research, 2020

Introduction: Most people with psychiatric illnesses do not receive treatment for almost a decade after disorder onset. Online mental health screens reflect one mechanism designed to shorten this lag in help-seeking, yet there has been limited research on the effectiveness of screening tools in naturalistic settings. Material and methods: We examined a cohort of persons directed to a mental health screening tool via the Bing search engine (n = 126,060). We evaluated the impact of tool content on later searches for mental health selfreferences, self-diagnosis, care seeking, psychoactive medications, suicidal ideation, and suicidal intent. Website characteristics were evaluated by pairs of independent raters to ascertain screen type and content. These included the presence/absence of a suggestive diagnosis, a message on interpretability, as well as referrals to digital treatments, in-person treatments, and crisis services. Results: Using machine learning models, the results suggested that screen content predicted later searches with mental health self-references (AUC = 0.73), mental health self-diagnosis (AUC = 0.69), mental health care seeking (AUC = 0.61), psychoactive medications (AUC = 0.55), suicidal ideation (AUC = 0.58), and suicidal intent (AUC = 0.60). Cox-proportional hazards models suggested individuals utilizing tools with in-person care referral were significantly more likely to subsequently search for methods to actively end their life (HR = 1.727,p = 0.007). Discussion: Online screens may influence help-seeking behavior, suicidal ideation, and suicidal intent. Websites with referrals to in-person treatments could put persons at greater risk of active suicidal intent. Further evaluation using large-scale randomized controlled trials is needed.
Journal of Psychiatric Research, 2020

Introduction: Generalized anxiety disorder (GAD) is prevalent among college students. Smartphone-based interventions may be a low-cost treatment method. Method: College students with self-reported GAD were randomized to receive smartphone-based guided self-help (n = 50), or no treatment (n = 50). Post-treatment and six-month follow-up outcomes included the Depression Anxiety Stress Scales-Short Form Stress Subscale (DASS Stress), the Penn State Worry Questionnaire (PSWQ-11), and the State-Trait Anxiety Inventory-Trait (STAI-T), as well as diagnostic status assessed by the GAD-Questionnaire, 4th edition. Results: From pre- to post-treatment, participants who received guided self-help (vs. no treatment) experienced significantly greater reductions on the DASS Stress (d = -0.408) and a greater probability of remission from GAD (d = -0.445). There was no significant between-group difference in change on the PSWQ-11 (d = -0.208) or STAI-T (d = -0.114). From post to six-month follow-up there was no significant loss of gains on DASS Stress scores (d = -0.141) and of those who had remitted, 78.6% remained remitted. Yet rates of remitted participants no longer differed significantly between conditions at follow-up (d = -0.229). Conclusion: Smartphone-based interventions may be efficacious in treating some aspects of GAD. Methods for improving symptom reduction and long-term outcome are discussed.
Psychotherapy Research, 2020

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 has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression (N = 31). Using a combination of nomothetic and idiographically-weighted machine learning models, the results suggest that depressed mood can be accurately predicted from hour to hour with an average correlation between out of sample predicted depressed mood levels and observed depressed mood of 0.587, CI [0.552, 0.621]. This suggests that passively collected smartphone data can accurately predict future depressed mood among a sample reporting clinical levels of depression. If replicated in other samples, this modeling framework may allow just-in-time adaptive interventions to treat depression as it changes in the context of daily life.
Sensors, 2020

Researchers have increasingly begun to use consumer wearables or wrist-worn smartwatches and fitness monitors for measurement of cardiovascular psychophysiological processes related to mental and physical health outcomes. These devices have strong appeal because they allow for continuous, scalable, unobtrusive, and ecologically valid data collection of cardiac activity in “big data” studies. However, replicability and reproducibility may be hampered moving forward due to the lack of standardization of data collection and processing procedures and inconsistent reporting of technological factors (e.g., device type, firmware versions, sampling rate), biobehavioral variables (e.g., body mass index, wrist dominance and circumference), and participant demographic characteristics, such as skin tone, that may influence heart rate measurement. These limitations introduce unnecessary noise into measurement, which can cloud interpretation and generalizability of findings. This paper provides a brief overview of research using commercial wearable devices to measure heart rate, reviews literature on device accuracy, and outlines the challenges that non-standardized reporting pose for the field. We also discuss study design, technological, biobehavioral, and demographic factors that can impact the accuracy of the passive sensing of heart rate measurements, and provide guidelines and corresponding handouts for future study data collection and design, data cleaning and processing, analysis, and reporting that may help ameliorate some of these barriers and inconsistencies in the literature.
npj Digital Medicine, 2020

Background: The coronavirus disease (COVID-19) has led to dramatic changes worldwide in people’s everyday lives. To combat the pandemic, many governments have implemented social distancing, quarantine, and stay-at-home orders. There is limited research on the impact of such extreme measures on mental health. Objective: The goal of this study was to examine whether stay-at-home orders produced differential changes in mental health symptoms using internet search queries on a national scale. Methods: In the United States, individual states vary in their adoption of measures to reduce the spread of COVID-19; as of March 23, 2020, 11 of the 50 states had issued stay-at-home orders. The staggered rollout of stay-at-home measures across the United States allows us to investigate whether these measures impact mental health by exploring variations in mental health search queries across the states. This paper examines the changes in mental health search queries on Google between March 16-23, 2020, across each state and Washington, DC. Specifically, this paper examines differential changes in mental health searches based on patterns of search activity following issuance of stay-at-home orders in these states compared to all other states. The participants were all the people who searched mental health terms in Google between March 16-23. Between March 16-23, 11 states underwent stay-at-home orders to prevent the transmission of COVID-19. Outcomes included search terms measuring anxiety, depression, obsessive-compulsive, negative thoughts, irritability, fatigue, anhedonia, concentration, insomnia, and suicidal ideation. Results: Analyzing over 10 million search queries using generalized additive mixed models, the results suggested that the implementation of stay-at-home orders are associated with a significant flattening of the curve for searches for suicidal ideation, anxiety, negative thoughts, and sleep disturbances, with the most prominent flattening associated with suicidal ideation and anxiety. Conclusions: These results suggest that, despite decreased social contact, mental health search queries increased rapidly prior to the issuance of stay-at-home orders, and these changes dissipated following the announcement and enactment of these orders. Although more research is needed to examine sustained effects, these results suggest mental health symptoms were associated with an immediate leveling off following the issuance of stay-at-home orders.
JMIR Mental Health, 2020

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 under-reported and under-recognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform healthcare decisions, offers a possible method of addressing this assessment barrier. Objective: To determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset. Methods: In this study, participants (N = 59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an application which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) across two weeks. Next this passive sensor data was used to form digital biomarkers which were paired with machine learning models to predict participants’ social anxiety symptom severity. Results: The results suggested that this passive sensor data could be utilized to accurately predict participants’ social anxiety symptom severity (r = 0.702 between predicted and observed symptom severity), and demonstrated discriminant validity between depression, negative affect, and positive affect. Conclusions: These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.
Journal of Medicial Internet Research (JMIR), 2020

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 to process such information has raised novel ethical dilemmas regarding researchers’ duties to: (i) monitor adverse events and intervene accordingly; (ii) obtain fully informed, voluntary consent; (iii) protect the privacy of participants; and (iv) increase the transparency of powerful, machine learning models to ensure they can be applied ethically and fairly in psychiatric care. This review highlights emerging ethical challenges and unresolved ethical questions in mobile health research and provides recommendations on how mobile health researchers can address these issues in practice. Ultimately, the hope is that this review will facilitate continued discussion on how to achieve best practice in mobile health research within psychiatry.
Bulletin of the World Health Organization, 2020

Our field has come a long way in establishing cognitive behavioral therapy as the empirically-supported treatment of choice for a wide range of mental and behavioral health problems. Nevertheless, most individuals with mental disorders do not receive any care at all, and those who do often have difficulty accessing care that is consistently high in quality. Addressing these issues is complex and costly and thus progress has been slow. We are entering an exciting stage in which emerging technologies might offer novel solutions to the treatment gap. This paper discusses a number of technology-enabled solutions to our field’s challenges, including internet-based and smartphone-based cognitive behavioral therapy. Nevertheless, we must remain attentive to potential pitfalls of these emerging technologies. The manuscript incorporates suggestions for how the field may approach these potential pitfalls and provides a vision for how we might develop powerful, scalable, precisely timed, personalized interventions to enhance global mental health.
Behavior Therapy, 2019

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

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(2024). Depressive Symptoms as a Heterogeneous and Constantly Evolving Dynamical System: Idiographic Depressive Symptom Networks of Rapid Symptom Changes among Persons with Major Depressive Disorder. Journal of Psychopathology and Clinical Science.

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(2024). Implementing precision methods in personalizing psychological therapies: Barriers and possible ways forward. Behav Res Ther.

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(2023). Detecting Major Depressive Disorder Presence Using Passively-Collected Wearable Movement Data in a Nationally-Representative Sample. Psychiatry Research.

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(2023). Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7(4).

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(2023). Behind the Screen: A Narrative Review of Advancements in Mobile and Wearable Passive Sensing for Mental Health Assessment. Biomedical Materials and Devices.


(2023). Predicting Individual Response to a Web-Based Positive Psychology Intervention: A Machine Learning Approach. Journal of Positive Psychology.

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(2023). Depression deconstructed: Wearables and passive digital phenotyping for analyzing individual symptoms. Behaviour Research and Therapy.

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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.


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.



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.


I currently teach the following courses at Dartmouth College:

  • QBS 126: Analysis of Densely Collected Longitudinal Data
  • QBS 270: Quantitative Biomedical Sciences Journal Club

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


Join Our Team

We are currently recruiting:

  1. Undergraduate research assistants
  2. Graduate students
  3. Postdoctoral Scholars

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.

Research assistants do not need to be interested in participating in all projects. For example, someone with a major of the psychology and the brain sciences may be interested in that involve digital mental health, but they are not interested in projects that involve any coding/syntax writing. Alternatively, someone in computer science may be more interested in the creation of a digital platform, and they would not be required to have an interest in mental health or statistics.

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 (

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 (

Specific Areas of Involvement for Postdoctoral Students


Dr. Jacobson is seeking applicants for a postdoctoral research fellow position in the Center for Technology and Behavioral Health within the Geisel School of Medicine at Dartmouth College in Hanover, New Hampshire. The position start date is flexible, with options to start immediately or in Summer of 2023. Persons with doctorates in clinical psychology, counseling psychology, or psychiatry are desired.

The postdoctoral research fellow will be primarily working on a project related to the use of personalized digital phenotyping based on passively collected data from smartphones and wearable sensors to predict rapid symptom changes in major depressive disorder. The position is funded by the National Institute of Mental Health.

Interested applicants should contact Dr. Jacobson directly at

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

#Interested persons should contact Nick directly (


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 (