Social Isolation and Serious Mental Illness: The Role of Context-Aware Mobile Interventions.

Social isolation is a common problem faced by individuals with serious mental illness (SMI), and current intervention approaches have limited effectiveness. This paper presents a blended intervention approach, called mobile Social Interaction Therapy by Exposure (mSITE), to address social isolation in individuals with serious mental illness. The approach combines brief in-person cognitive-behavioral therapy (CBT) with context-triggered mobile CBT interventions that are personalized using mobile sensing data. Our approach targets social behavior and is the first context-aware intervention for improving social outcomes in serious mental illness.

A Smartphone Intervention for People With Serious Mental Illness: Fully Remote Randomized Controlled Trial of CORE.

BACKGROUND: People with serious mental illness (SMI) have significant unmet mental health needs. Development and testing of digital interventions that can alleviate the suffering of people with SMI is a public health priority. OBJECTIVE: The aim of this study is to conduct a fully remote randomized waitlist-controlled trial of CORE, a smartphone intervention that comprises daily exercises designed to promote reassessment of dysfunctional beliefs in multiple domains. METHODS: Individuals were recruited via the web using Google and Facebook advertisements. Enrolled participants were randomized into either active intervention or waitlist control groups. Participants completed the Beck Depression Inventory-Second Edition (BDI-II), Generalized Anxiety Disorder-7 (GAD-7), Hamilton Program for Schizophrenia Voices, Green Paranoid Thought Scale, Recovery Assessment Scale (RAS), Rosenberg Self-Esteem Scale (RSES), Friendship Scale, and Sheehan Disability Scale (SDS) at baseline (T1), 30-day (T2), and 60-day (T3) assessment points. Participants in the active group used CORE from T1 to T2, and participants in the waitlist group used CORE from T2 to T3. Both groups completed usability and accessibility measures after they concluded their intervention periods. RESULTS: Overall, 315 individuals from 45 states participated in this study. The sample comprised individuals with self-reported bipolar disorder (111/315, 35.2%), major depressive disorder (136/315, 43.2%), and schizophrenia or schizoaffective disorder (68/315, 21.6%) who displayed moderate to severe symptoms and disability levels at baseline. Participants rated CORE as highly usable and acceptable. Intent-to-treat analyses showed significant treatment×time interactions for the BDI-II (F1,313=13.38; P<.001), GAD-7 (F1,313=5.87; P=.01), RAS (F1,313=23.42; P<.001), RSES (F1,313=19.28; P<.001), and SDS (F1,313=10.73; P=.001). Large effects were observed for the BDI-II (d=0.58), RAS (d=0.61), and RSES (d=0.64); a moderate effect size was observed for the SDS (d=0.44), and a small effect size was observed for the GAD-7 (d=0.20). Similar changes in outcome measures were later observed in the waitlist control group participants following crossover after they received CORE (T2 to T3). Approximately 41.5% (64/154) of participants in the active group and 60.2% (97/161) of participants in the waitlist group were retained at T2, and 33.1% (51/154) of participants in the active group and 40.3% (65/161) of participants in the waitlist group were retained at T3. CONCLUSIONS: We successfully recruited, screened, randomized, treated, and assessed a geographically dispersed sample of participants with SMI entirely via the web, demonstrating that fully remote clinical trials are feasible in this population; however, study retention remains challenging. CORE showed promise as a usable, acceptable, and effective tool for reducing the severity of psychiatric symptoms and disability while improving recovery and self-esteem. Rapid adoption and real-world dissemination of evidence-based mobile health interventions such as CORE are needed if we are to shorten the science-to-service gap and address the significant unmet mental health needs of people with SMI during the COVID-19 pandemic and beyond. TRIAL REGISTRATION: ClinicalTrials.gov NCT04068467; https://clinicaltrials.gov/ct2/show/NCT04068467.

Mental Health and Behavior of College Students During the COVID-19 Pandemic: Longitudinal Mobile Smartphone and Ecological Momentary Assessment Study, Part II.

BACKGROUND: Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected with the disease, while billions of people have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale behavioral and mental health changes; however, few studies have been able to track these changes with frequent, near real-time sampling or compare these changes to previous years of data for the same individuals. OBJECTIVE: By combining mobile phone sensing and self-reported mental health data in a cohort of college-aged students enrolled in a longitudinal study, we seek to understand the behavioral and mental health impacts associated with the COVID-19 pandemic, measured by interest across the United States in the search terms coronavirus and COVID fatigue. METHODS: Behaviors such as the number of locations visited, distance traveled, duration of phone use, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife mobile smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments, including the Patient Health Questionnaire-4. The participants were 217 undergraduate students. Differences in behaviors and self-reported mental health collected during the Spring 2020 term, as compared to previous terms in the same cohort, were modeled using mixed linear models. RESULTS: Linear mixed models demonstrated differences in phone use, sleep, sedentary time and number of locations visited associated with the COVID-19 pandemic. In further models, these behaviors were strongly associated with increased interest in COVID fatigue. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, phone use, sedentary time), both anxiety and depression (P<.001) were significantly associated with interest in COVID fatigue. Notably, these behavioral and mental health changes are consistent with those observed around the initial implementation of COVID-19 lockdowns in the spring of 2020. CONCLUSIONS: In the initial lockdown phase of the COVID-19 pandemic, people spent more time on their phones, were more sedentary, visited fewer locations, and exhibited increased symptoms of anxiety and depression. As the pandemic persisted through the spring, people continued to exhibit very similar changes in both mental health and behaviors. Although these large-scale shifts in mental health and behaviors are unsurprising, understanding them is critical in disrupting the negative consequences to mental health during the ongoing pandemic.

Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study.

BACKGROUND: The vast majority of people worldwide have been impacted by coronavirus disease (COVID-19). In addition to the millions of individuals who have been infected with the disease, billions of individuals have been asked or required by local and national governments to change their behavioral patterns. Previous research on epidemics or traumatic events suggests that this can lead to profound behavioral and mental health changes; however, researchers are rarely able to track these changes with frequent, near-real-time sampling or compare their findings to previous years of data for the same individuals. OBJECTIVE: By combining mobile phone sensing and self-reported mental health data among college students who have been participating in a longitudinal study for the past 2 years, we sought to answer two overarching questions. First, have the behaviors and mental health of the participants changed in response to the COVID-19 pandemic compared to previous time periods? Second, are these behavior and mental health changes associated with the relative news coverage of COVID-19 in the US media? METHODS: Behaviors such as the number of locations visited, distance traveled, duration of phone usage, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments of the Patient Health Questionnaire-4. The participants were 217 undergraduate students, with 178 (82.0%) students providing data during the Winter 2020 term. Differences in behaviors and self-reported mental health collected during the Winter 2020 term compared to previous terms in the same cohort were modeled using mixed linear models. RESULTS: During the first academic term impacted by COVID-19 (Winter 2020), individuals were more sedentary and reported increased anxiety and depression symptoms (P<.001) relative to previous academic terms and subsequent academic breaks. Interactions between the Winter 2020 term and the week of the academic term (linear and quadratic) were significant. In a mixed linear model, phone usage, number of locations visited, and week of the term were strongly associated with increased amount of COVID-19-related news. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, and phone usage), both anxiety (P<.001) and depression (P=.03) were significantly associated with COVID-19-related news. CONCLUSIONS: Compared with prior academic terms, individuals in the Winter 2020 term were more sedentary, anxious, and depressed. A wide variety of behaviors, including increased phone usage, decreased physical activity, and fewer locations visited, were associated with fluctuations in COVID-19 news reporting. While this large-scale shift in mental health and behavior is unsurprising, its characterization is particularly important to help guide the development of methods to reduce the impact of future catastrophic events on the mental health of the population.

Loneliness in the Daily Lives of People With Mood and Psychotic Disorders.

BACKGROUND AND HYPOTHESIS: Loneliness, the subjective experience of feeling alone, is associated with physical and psychological impairments. While there is an extensive literature linking loneliness to psychopathology, limited work has examined loneliness in daily life in those with serious mental illness. We hypothesized that trait and momentary loneliness would be transdiagnostic and relate to symptoms and measures of daily functioning. STUDY DESIGN: The current study utilized ecological momentary assessment and passive sensing to examine loneliness in those with schizophrenia (N = 59), bipolar disorder (N = 61), unipolar depression (N = 60), remitted unipolar depression (N = 51), and nonclinical comparisons (N = 82) to examine relationships of both trait and momentary loneliness to symptoms and social functioning in daily life. STUDY RESULTS: Findings suggest that both trait and momentary loneliness are higher in those with psychopathology (F(4,284) = 28.00, P < .001, ηp2 = 0.27), and that loneliness significantly relates to social functioning beyond negative symptoms and depression (ß = -0.44, t = 6.40, P < .001). Furthermore, passive sensing measures showed that greater movement (ß = -0.56, t = -3.29, P = .02) and phone calls (ß = -0.22, t = 12.79, P = .04), but not text messaging, were specifically related to decreased loneliness in daily life. Individuals higher in trait loneliness show stronger relationships between momentary loneliness and social context and emotions in everyday life. CONCLUSIONS: These findings provide further evidence pointing to the importance of loneliness transdiagnostically and its strong relation to social functioning. Furthermore, we show that passive sensing technology can be used to measure behaviors related to loneliness in daily life that may point to potential treatment implications or early detection markers of loneliness.

Capturing the College Experience: A Four-Year Mobile Sensing Study of Mental Health, Resilience and Behavior of College Students during the Pandemic.

Understanding the dynamics of mental health among undergraduate students across the college years is of critical importance, particularly during a global pandemic. In our study, we track two cohorts of first-year students at Dartmouth College for four years, both on and off campus, creating the longest longitudinal mobile sensing study to date. Using passive sensor data, surveys, and interviews, we capture changing behaviors before, during, and after the COVID-19 pandemic subsides. Our findings reveal the pandemic's impact on students' mental health, gender based behavioral differences, impact of changing living conditions and evidence of persistent behavioral patterns as the pandemic subsides. We observe that while some behaviors return to normal, others remain elevated. Tracking over 200 undergraduate students from high school to graduation, our study provides invaluable insights into changing behaviors, resilience and mental health in college life. Conducting a long-term study with frequent phone OS updates poses significant challenges for mobile sensing apps, data completeness and compliance. Our results offer new insights for Human-Computer Interaction researchers, educators and administrators regarding college life pressures. We also detail the public release of the de-identified College Experience Study dataset used in this paper and discuss a number of open research questions that could be studied using the public dataset.

Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.

Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent efforts have examined MDD heterogeneity byinvestigating how symptoms influence one another over time across individuals in a system; however, these efforts have assumed that symptom dynamics are static and do not dynamically change over time. Nevertheless, it is possible that individual MDD system dynamics change continuously across time. Participants (N = 105) completed ratings of MDD symptoms three times a day for 90 days, and we conducted time varying vector autoregressive models to investigate the idiographic symptom networks. We then illustrated this finding with a case series of five persons with MDD. Supporting prior research, results indicate there is high heterogeneity across persons as individual network composition is unique from person to person. In addition, for most persons, individual symptom networks change dramatically across the 90 days, as evidenced by 86% of individuals experiencing at least one change in their most influential symptom and the median number of shifts being 3 over the 90 days. Additionally, most individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day period. Our findings offer further insight into short-term symptom dynamics, suggesting that MDD is heterogeneous both across and within persons over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App.

MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being. We argue that integrating behavioral sensing in LLMs will likely lead to a new frontier in AI. In this Late-Breaking Work paper, we discuss the MindScape contextual journal App design that uses LLMs and behavioral sensing to generate contextual and personalized journaling prompts crafted to encourage self-reflection and emotional development. We also discuss the MindScape study of college students based on a preliminary user study and our upcoming study to assess the effectiveness of contextual AI journaling in promoting better well-being on college campuses. MindScape represents a new application class that embeds behavioral intelligence in AI.

The role of borderline personality disorder traits in predicting longitudinal variability of major depressive symptoms among a sample of depressed adults.

BACKGROUND: Major depressive disorder (MDD) and borderline personality disorder (BPD) often co-occur, with 20 % of adults with MDD meeting criteria for BPD. While MDD is typically diagnosed by symptoms persisting for several weeks, research suggests a dynamic pattern of symptom changes occurring over shorter durations. Given the diagnostic focus on affective states in MDD and BPD, with BPD characterized by instability, we expected heightened instability of MDD symptoms among depressed adults with BPD traits. The current study examined whether BPD symptoms predicted instability in depression symptoms, measured by ecological momentary assessments (EMAs). METHODS: The sample included 207 adults with MDD (76 % White, 82 % women) recruited from across the United States. At the start of the study, participants completed a battery of mental health screens including BPD severity and neuroticism. Participants completed EMAs tracking their depression symptoms three times a day over a 90-day period. RESULTS: Using self-report scores assessing borderline personality disorder (BPD) traits along with neuroticism scores and sociodemographic data, Bayesian and frequentist linear regression models consistently indicated that BPD severity was not associated with depression symptom change through time. LIMITATIONS: Diagnostic sensitivity and specificity may be restricted by use of a self-report screening tool for capturing BPD severity. Additionally, this clinical sample of depressed adults lacks a comparison group to determine whether subclinical depressive symptoms present differently among individuals with BPD only. CONCLUSIONS: The unexpected findings shed light on the interplay between these disorders, emphasizing the need for further research to understand their association.

MoodCapture: Depression Detection Using In-the-Wild Smartphone Images.

MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 participants diagnosed with major depressive disorder for 90 days. Images are captured naturalistically while participants respond to the PHQ-8 depression survey question: "I have felt down, depressed, or hopeless". Our analysis explores important image attributes, such as angle, dominant colors, location, objects, and lighting. We show that a random forest trained with face landmarks can classify samples as depressed or non-depressed and predict raw PHQ-8 scores effectively. Our post-hoc analysis provides several insights through an ablation study, feature importance analysis, and bias assessment. Importantly, we evaluate user concerns about using MoodCapture to detect depression based on sharing photos, providing critical insights into privacy concerns that inform the future design of in-the-wild image-based mental health assessment tools.

From mood to use: Using ecological momentary assessments to examine how anhedonia and depressed mood impact cannabis use in a depressed sample.

Anhedonia and depressed mood are two cardinal symptoms of major depressive disorder (MDD). Prior work has demonstrated that cannabis consumers often endorse anhedonia and depressed mood, which may contribute to greater cannabis use (CU) over time. However, it is unclear (1) how the unique influence of anhedonia and depressed mood affect CU and (2) how these symptoms predict CU over more proximal periods of time, including the next day or week (rather than proceeding weeks or months). The current study used data collected from ecological momentary assessment (EMA) in a sample with MDD (N = 55) and employed mixed effects models to detect and predict weekly and daily CU from anhedonia and depressed mood over 90 days. Results indicated that anhedonia and depressed mood were significantly associated with CU, yet varied at daily and weekly scales. Moreover, these associations varied in both strength and directionality. In weekly models, less anhedonia and greater depressed mood were associated with greater CU, and directionality of associations were reversed in the models looking at any CU (compared to none). Findings provide evidence that anhedonia and depressed mood demonstrate complex associations with CU and emphasize leveraging EMA-based studies to understand these associations with more fine-grained detail.

Dissociation of Cognitive Effort-Based Decision Making and Its Associations With Symptoms, Cognition, and Everyday Life Function Across Schizophrenia, Bipolar Disorder, and Depression.

BACKGROUND: Anhedonia and amotivation are symptoms of many different mental health disorders that are frequently associated with functional disability, but it is not clear whether the same processes contribute to motivational impairments across disorders. This study focused on one possible factor, the willingness to exert cognitive effort, referred to as cognitive effort-cost decision making. METHODS: We examined performance on the deck choice task as a measure of cognitive effort-cost decision making, in which people choose to complete an easy task for a small monetary reward or a harder task for larger rewards, in 5 groups: healthy control (n = 80), schizophrenia/schizoaffective disorder (n = 50), bipolar disorder with psychosis (n = 58), current major depression (n = 60), and past major depression (n = 51). We examined cognitive effort-cost decision making in relation to clinician and self-reported motivation symptoms, working memory and cognitive control performance, and life function measured by ecological momentary assessment and passive sensing. RESULTS: We found a significant diagnostic group × reward interaction (F8,588 = 4.37, p < .001, ηp2 = 0.056). Compared with the healthy control group, the schizophrenia/schizoaffective and bipolar disorder groups, but not the current or past major depressive disorder groups, showed a reduced willingness to exert effort at the higher reward values. In the schizophrenia/schizoaffective and bipolar disorder groups, but not the major depressive disorder groups, reduced willingness to exert cognitive effort for higher rewards was associated with greater clinician-rated motivation impairments, worse working memory and cognitive control performance, and less engagement in goal-directed activities measured by ecological momentary assessment. CONCLUSIONS: These findings suggest that the mechanisms contributing to motivational impairments differ among individuals with psychosis spectrum disorders versus depression.

COVID Student Study: A Year in the Life of College Students during the COVID-19 Pandemic Through the Lens of Mobile Phone Sensing.

The COVID-19 pandemic continues to affect the daily life of college students, impacting their social life, education, stress levels and overall mental well-being. We study and assess behavioral changes of N=180 undergraduate college students one year prior to the pandemic as a baseline and then during the first year of the pandemic using mobile phone sensing and behavioral inference. We observe that certain groups of students experience the pandemic very differently. Furthermore, we explore the association of self-reported COVID-19 concern with students' behavior and mental health. We find that heightened COVID-19 concern is correlated with increased depression, anxiety and stress. We evaluate the performance of different deep learning models to classify student COVID-19 concerns with an AUROC and F1 score of 0.70 and 0.71, respectively. Our study spans a two-year period and provides a number of important insights into the life of college students during this period.

First-Gen Lens: Assessing Mental Health of First-Generation Students across Their First Year at College Using Mobile Sensing.

The transition from high school to college is a taxing time for young adults. New students arriving on campus navigate a myriad of challenges centered around adapting to new living situations, financial needs, academic pressures and social demands. First-year students need to gain new skills and strategies to cope with these new demands in order to make good decisions, ease their transition to independent living and ultimately succeed. In general, first-generation students are less prepared when they enter college in comparison to non-first-generation students. This presents additional challenges for first-generation students to overcome and be successful during their college years. We study first-year students through the lens of mobile phone sensing across their first year at college, including all academic terms and breaks. We collect longitudinal mobile sensing data for N=180 first-year college students, where 27 of the students are first-generation, representing 15% of the study cohort and representative of the number of first-generation students admitted each year at the study institution, Dartmouth College. We discuss risk factors, behavioral patterns and mental health of first-generation and non-first-generation students. We propose a deep learning model that accurately predicts the mental health of first-generation students by taking into account important distinguishing behavioral factors of first-generation students. Our study, which uses the StudentLife app, offers data-informed insights that could be used to identify struggling students and provide new forms of phone-based interventions with the goal of keeping students on track.

On the Transition of Social Interaction from In-Person to Online: Predicting Changes in Social Media Usage of College Students during the COVID-19 Pandemic based on Pre-COVID-19 On-Campus Colocation.

Pandemics significantly impact human daily life. People throughout the world adhere to safety protocols (e.g., social distancing and self-quarantining). As a result, they willingly keep distance from workplace, friends and even family. In such circumstances, in-person social interactions may be substituted with virtual ones via online channels, such as, Instagram and Snapchat. To get insights into this phenomenon, we study a group of undergraduate students before and after the start of COVID-19 pandemic. Specifically, we track N=102 undergraduate students on a small college campus prior to the pandemic using mobile sensing from phones and assign semantic labels to each location they visit on campus where they study, socialize and live. By leveraging their colocation network at these various semantically labeled places on campus, we find that colocations at certain places that possibly proxy higher in-person social interactions (e.g., dormitories, gyms and Greek houses) show significant predictive capability in identifying the individuals' change in social media usage during the pandemic period. We show that we can predict student's change in social media usage during COVID-19 with an F1 score of 0.73 purely from the in-person colocation data generated prior to the pandemic.