Predicting stress in first-year college students using sleep data from wearable devices.

Consumer wearables have been successful at measuring sleep and may be useful in predicting changes in mental health measures such as stress. A key challenge remains in quantifying the relationship between sleep measures associated with physiologic stress and a user's experience of stress. Students from a public university enrolled in the Lived Experiences Measured Using Rings Study (LEMURS) provided continuous biometric data and answered weekly surveys during their first semester of college between October-December 2022. We analyzed weekly associations between estimated sleep measures and perceived stress for participants (N = 525). Through mixed-effects regression models, we identified consistent associations between perceived stress scores and average nightly total sleep time (TST), resting heart rate (RHR), heart rate variability (HRV), and respiratory rate (ARR). These effects persisted after controlling for gender and week of the semester. Specifically, for every additional hour of TST, the odds of experiencing moderate-to-high stress decreased by 0.617 or by 38.3% (p<0.01). For each 1 beat per minute increase in RHR, the odds of experiencing moderate-to-high stress increased by 1.036 or by 3.6% (p<0.01). For each 1 millisecond increase in HRV, the odds of experiencing moderate-to-high stress decreased by 0.988 or by 1.2% (p<0.05). For each additional breath per minute increase in ARR, the odds of experiencing moderate-to-high stress increased by 1.230 or by 23.0% (p<0.01). Consistent with previous research, participants who did not identify as male (i.e., female, nonbinary, and transgender participants) had significantly higher self-reported stress throughout the study. The week of the semester was also a significant predictor of stress. Sleep data from wearable devices may help us understand and to better predict stress, a strong signal of the ongoing mental health epidemic among college students.

Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch Data.

OBJECTIVE: Panic attacks are an impairing mental health problem that affects 11% of adults every year. Current criteria describe them as occurring without warning, despite evidence suggesting individuals can often identify attack triggers. We aimed to prospectively explore qualitative and quantitative factors associated with the onset of panic attacks. RESULTS: Of 87 participants, 95% retrospectively identified a trigger for their panic attacks. Worse individually reported mood and state-level mood, as indicated by Twitter ratings, were related to greater likelihood of next-day panic attack. In a subsample of participants who uploaded their wearable sensor data (n = 32), louder ambient noise and higher resting heart rate were related to greater likelihood of next-day panic attack. CONCLUSIONS: These promising results suggest that individuals who experience panic attacks may be able to anticipate their next attack which could be used to inform future prevention and intervention efforts.

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health.

Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning.

Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report assessments for pre-school aged children are often biased, and thus increase the need for objective mental health screening tools. Leveraging digital tools to identify the behavioral signature of childhood mental disorders may enable increased intervention at the time with the highest chance of long-term impact. We present data from 84 participants (4-8 years old, 50% diagnosed with anxiety, depression, and/or ADHD) collected during a battery of mood induction tasks using the ChAMP System. Unsupervised Kohonen Self-Organizing Maps (SOM) constructed from movement and audio features indicate that age did not tend to explain clusters as consistently as gender within task-specific and cross-task SOMs. Symptom prevalence and diagnostic status also showed some evidence of clustering. Case studies suggest that high impairment (>80th percentile symptom counts) and diagnostic subtypes (ADHD-Combined) may account for most behaviorally distinct children. Based on this same dataset, we also present results from supervised modeling for the binary classification of diagnoses. Our top performing models yield moderate but promising results (ROC AUC .6-.82, TPR .36-.71, Accuracy .62-.86) on par with our previous efforts for isolated behavioral tasks. Enhancing features, tuning model parameters, and incorporating additional wearable sensor data will continue to enable the rapid progression towards the discovery of digital phenotypes of childhood mental health.Clinical Relevance- This work advances the use of wearables for detecting childhood mental health disorders.

Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy.

Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid-gestational periods. However, there is a distinct clinical advantage in identifying individuals at risk for PE prior to conception, when a wider array of preventive interventions are available. In this work, we leverage machine learning techniques to identify potential pre-pregnancy biomarkers of PE in a sample of 80 women, 10 of whom were diagnosed with preterm preeclampsia during their subsequent pregnancy. We explore prospective biomarkers derived from hemodynamic, biophysical, and biochemical measurements and several modeling approaches. A support vector machine (SVM) optimized with stochastic gradient descent yields the highest overall performance with ROC AUC and detection rates up to .88 and .70, respectively on subject-wise cross validation. The best performing models leverage biophysical and hemodynamic biomarkers. While preliminary, these results indicate the promise of a machine learning based approach for detecting individuals who are at risk for developing preterm PE before they become pregnant. These efforts may inform gestational planning and care, reducing risk for adverse PE-related outcomes.Clinical Relevance- This work considers the development and optimization of pre-pregnancy biomarkers for improving the identification of preterm (early-onset) preeclampsia risk prior to conception.

Discovering Digital Biomarkers of Panic Attack Risk in Consumer Wearables Data.

Panic attacks are an impairing mental health problem that impacts more than one out of every 10 adults in the United States (US). Clinical guidelines suggest panic attacks occur without warning and their unexpected nature worsens their impact on quality of life. Individuals who experience panic attacks would benefit from advance warning of when an attack is likely to occur so that appropriate steps could be taken to manage or prevent it. Our recent work suggests that an individual's likelihood of experiencing a panic attack can be predicted by self-reported mood and community-level Twitter-derived mood the previous day. Prior work also suggests that physiological markers may indicate a pending panic attack. However, the ability of objective physiological, behavioral, and environmental measures collected via consumer wearable sensors (referred to as digital biomarkers) to predict next-day panic attacks has not yet been explored. To address this question, we consider data from 38 individuals who regularly experienced panic attacks recruited from across the US. Participants responded to daily questions about their panic attacks for 28 days and provided access to data from their Apple Watches. Mixed Regressions, with an autoregressive covariance structure were used to estimate the prevalence of a next-day panic attack Results indicate that digital biomarkers of ambient noise (louder) and resting heart rate (higher) are indicative of experiencing a panic attack the next day. These preliminary results suggest, for the first time, that panic attacks may be predictable from digital biomarkers, opening the door to improvements in how panic attacks are managed and to the development of new preventative interventions.Clinical Relevance- Objective data from consumer wearables may predict when an individual is at high risk for experiencing a next-day panic attack. This information could guide treatment decisions, help individuals manage their panic, and inform the development of new preventative interventions.

The Impact of Childhood Mental Health and Substance Use on Methylation Aging Into Adulthood.

OBJECTIVE: To test whether childhood mental health symptoms, substance use, and early adversity accelerate the rate of DNA methylation (DNAm) aging from adolescence to adulthood. METHOD: DNAm was assayed from blood samples in 381 participants in both adolescence (mean [SD] age = 13.9 [1.6] years) and adulthood (mean [SD] age = 25.9 [2.7] years). Structured diagnostic interviews were completed with participants and their parents at multiple childhood observations (1,950 total) to assess symptoms of common mental health disorders (attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder, anxiety, and depression) and common types of substance use (alcohol, cannabis, nicotine) and early adversities. RESULTS: Neither childhood mental health symptoms nor substance use variables were associated with DNAm aging cross-sectionally. In contrast, the following mental health symptoms and substance variables were associated with accelerated DNAm aging from adolescence to adulthood: depressive symptoms (b = 0.314, SE = 0.127, p = .014), internalizing symptoms (b = 0.108, SE = 0.049, p = .029), weekly cannabis use (b =1.665, SE = 0.591, p = .005), and years of weekly cannabis use (b = 0.718, SE = 0.283, p = .012). In models testing all individual variables simultaneously, the combined effect of the variables was equivalent to a potential difference of 3.17 to 3.76 years in DNAm aging. A final model tested a variable assessing cumulative exposure to mental health symptoms, substance use, and early adversities. This cumulative variable was strongly associated with accelerated aging (b = 0.126, SE = 0.044, p = .005). CONCLUSION: Mental health symptoms and substance use accelerated DNAm aging into adulthood in a manner consistent with a shared risk mechanism.

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health.

Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

A large clinical trial to improve well-being during the transition to college using wearables: The lived experiences measured using rings study.

INTRODUCTION: The transition to college is a period of elevated risk for a range of mental health conditions. Although colleges and universities strive to provide mental health support to their students, the high demand for these services makes it difficult to provide scalable, cost-effective solutions. OBJECTIVE: To address these issues, the present study aims to compare the efficacy of three different treatments using a large cohort of 600 students transitioning to college. Interventions were selected based on their potential for generalizability and cost-effectiveness on college campuses. METHODS: The study is a Phase II parallel-group, four-arm, randomized controlled trial with 1:1 allocation that will assign 600 participants to one (n = 150 per condition) of four arms: 1) group-based therapy, 2) physical activity program, 3) nature experiences, or 4) weekly assessment condition as a control group. Physiological data will be collected from all participants using a wearable device to develop algorithmic mental and physical health functioning predictions. Once recruitment is complete, modeling strategies will be used to evaluate the outcomes and effectiveness of each intervention. DISCUSSION: The findings of this study will provide evidence as to the benefits of implementing scalable and proactive interventions using technology with the goal of improving the well-being and success of new college students.

Associations of preschool reactive bed-sharing with sociodemographic factors, sleep disturbance, and psychopathology.

OBJECTIVE: To advance understanding of early childhood bed-sharing and its clinical significance, we examined reactive bed-sharing rates, sociodemographic correlates, persistence, and concurrent and longitudinal associations with sleep disturbances and psychopathology. METHODS: Data from a representative cohort of 917 children (mean age 3.8 years) recruited from primary pediatric clinics in a Southeastern city for a preschool anxiety study were used. Sociodemographics and diagnostic classifications for sleep disturbances and psychopathology were obtained using the Preschool Age Psychiatric Assessment (PAPA), a structured diagnostic interview administered to caregivers. A subsample of 187 children was re-assessed approximately 24.7 months after the initial PAPA interview. RESULTS: Reactive bed-sharing was reported by 38.4% of parents, 22.9% nightly and 15.5% weekly, and declined with age. At follow-up, 48.9% of nightly bed-sharers and 88.7% of weekly bed-sharers were no longer bed-sharing. Sociodemographics associated with nightly bed-sharing were Black and (combined) American Indian, Alaska Native and Asian race and ethnicity, low income and parent education less than high school. Concurrently, bed-sharing nightly was associated with separation anxiety and sleep terrors; bed-sharing weekly was associated with sleep terrors and difficulty staying asleep. No longitudinal associations were found between reactive bed-sharing and sleep disturbances or psychopathology after controlling for sociodemographics, baseline status of the outcome and time between interviews. CONCLUSIONS: Reactive bed-sharing is relatively common among preschoolers, varies significantly by sociodemographic factors, declines during the preschool years and is more persistent among nightly than weekly bed-sharers. Reactive bed-sharing may be an indicator of sleep disturbances and/or anxiety but there is no evidence that bed-sharing is an antecedent or consequence of sleep disturbances or psychopathology.

Parental supervision positively impacts children's economic prospects two decades later: A prospective longitudinal study.

IMPORTANCE: Upward income mobility is associated with better health outcomes and reduced stress. However, opportunities are unequally distributed, particularly so for those in rural communities and whose family have lower educational attainment. OBJECTIVE: To test the impact of parental supervision on their children's income two decades later adjusting for parental economic and educational status. DESIGN: This study is a longitudinal, representative cohort study. From 1993-2000, annual assessments of 1,420 children were completed until age 16, then followed up at age 35, 2018-2021, for further assessment. Models tested direct effects of parental supervision on child income, and indirect effects via child educational attainment. SETTING: This study is an ongoing longitudinal population-based study of families in 11 predominately rural counties of the Southeastern U.S. PARTICIPANTS: About 8% of the residents and sample are African American and fewer than 1% are Hispanic. American Indians make up 4% of the population in study but were oversampled to make up 25% of the sample. 49% of the 1,420 participants are female. MAIN OUTCOMES AND MEASURES: 1258 children and parents were assessed for sex, race/ethnicity, household income, parent educational attainment, family structure, child behavioral problems, and parental supervision. The children were followed up at age 35 to assess their household income and educational attainment. RESULTS: Parental educational attainment, income, and family structure were strongly associated with their children's household income at age 35 (e.g., r = .392, p < .05). Parental supervision of the child was associated with increased household income for the child at age 35, adjusting for SES of the family of origin. Children of parents who did not engage in adequate supervision earned approximately $14,000 less/year (i.e., ~13% of the sample's median household income) than those who did. The association of parental supervision and child income at 35 was mediated by the child's educational attainment. CONCLUSION AND RELEVANCE: This study suggests adequate parental supervision during early adolescence is associated with children's economic prospects two decades later, in part by improving their educational prospects. This is particularly important in areas such as rural Southeast U.S.

Discovering Digital Biomarkers of Panic Attack Risk in Consumer Wearables Data.

Panic attacks are an impairing mental health problem that impacts more than one out of every 10 adults in the United States (US). Clinical guidelines suggest panic attacks occur without warning and their unexpected nature worsens their impact on quality of life. Individuals who experience panic attacks would benefit from advance warning of when an attack is likely to occur so that appropriate steps could be taken to manage or prevent it. Our recent work suggests that an individual's likelihood of experiencing a panic attack can be predicted by self-reported mood and community-level Twitter-derived mood the previous day. Prior work also suggests that physiological markers may indicate a pending panic attack. However, the ability of objective physiological, behavioral, and environmental measures to predict next-day panic attacks has not yet been explored. To address this question, we consider data from 38 individuals who regularly experienced panic attacks recruited from across the US. Participants responded to daily questions about their panic attacks for 28 days and provided access to data from their Apple Watches. Results indicate that objective measures of ambient noise (louder) and resting heart rate (higher) are related to the likelihood of experiencing a panic attack the next day. These preliminary results suggest, for the first time, that panic attacks may be predictable from data passively collected by consumer wearable devices, opening the door to improvements in how panic attacks are managed and to the development of new preventative interventions. Clinical Relevance: Objective data from consumer wearables may predict when an individual is at high risk for experiencing a next-day panic attack. This information could guide treatment decisions, help individuals manage their panic, and inform the development of new preventative interventions.

Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy.

Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid-gestational periods. However, there is a distinct clinical advantage in identifying individuals at risk for PE prior to conception, when a wider array of preventive interventions are available. In this work, we leverage machine learning techniques to identify potential pre-pregnancy biomarkers of PE in a sample of 80 women, 10 of whom were diagnosed with preterm preeclampsia during their subsequent pregnancy. We explore biomarkers derived from hemodynamic, biophysical, and biochemical measurements and several modeling approaches. A support vector machine (SVM) optimized with stochastic gradient descent yields the highest overall performance with ROC AUC and detection rates up to .88 and .70, respectively on subject-wise cross validation. The best performing models leverage biophysical and hemodynamic biomarkers. While preliminary, these results indicate the promise of a machine learning based approach for detecting individuals who are at risk for developing preterm PE before they become pregnant. These efforts may inform gestational planning and care, reducing risk for adverse PE-related outcomes.

UVM KID Study: Identifying Multimodal Features and Optimizing Wearable Instrumentation to Detect Child Anxiety.

Anxiety and depression, collectively known as internalizing disorders, begin as early as the preschool years and impact nearly 1 out of every 5 children. Left undiagnosed and untreated, childhood internalizing disorders predict later health problems including substance abuse, development of comorbid psychopathology, increased risk for suicide, and substantial functional impairment. Current diagnostic procedures require access to clinical experts, take considerable time to complete, and inherently assume that child symptoms are observable by caregivers. Multi-modal wearable sensors may enable development of rapid point-of-care diagnostics that address these challenges. Building on our prior work, here we present an assessment battery for the development of a digital phenotype for internalizing disorders in young children and an early feasibility case study of multi-modal wearable sensor data from two participants, one of whom has been clinically diagnosed with an internalizing disorder. Results lend support that sacral movement responses and R-R interval during a short stress-induction task may facilitate child diagnosis. Multi-modal sensors measuring movement and surface biopotentials of the chest and trapezius are also shown to have significant redundancy, introducing the potential for sensor optimization moving forward. Future work aims to further optimize sensor placement, signals, features, and assessments to enable deployment in clinical practice. Clinical Relevance- This work considers the development and optimization of technologies for improving the identification of children with internalizing disorders.

Advancing Digital Medicine with Wearables in the Wild.

This editorial provides a concise overview of the use and importance of wearables in the emerging field of digital medicine [...].

Early adversities accelerate epigenetic aging into adulthood: a 10-year, within-subject analysis.

BACKGROUND: Longitudinal studies are needed to clarify whether early adversities are associated with advanced methylation age or if they actually accelerate methylation aging. This study test whether different dimensions of childhood adversity accelerate biological aging from childhood to adulthood, and, if so, via which mechanisms. METHODS: 381 participants provided one blood sample in childhood (average age 15.0; SD = 2.3) and another in young adulthood (average age 23.1; SD = 2.8). Participants and their parents provided a median of 6 childhood assessments (total = 1,950 childhood observations), reporting exposures to different types of adversity dimensions (i.e. threat, material deprivation, loss, unpredictability). The blood samples were assayed to estimate DNA methylation age in both childhood and adulthood and also change in methylation age across this period. RESULTS: Cross-sectional associations between the childhood adversity dimensions and childhood measures of methylation age were non-significant. In contrast, multiple adversity dimensions were associated with accelerated within-person change in methylation age from adolescence to young adulthood. These associations attenuated in model testing all dimensions at the same time. Accelerated aging increased with increasing number of childhood adversities: Individuals with highest number of adversities experienced 2+ additional years of methylation aging compared to those with no exposure to childhood adversities. The association between total childhood adversity exposure and accelerated aging was partially explained by childhood depressive symptoms, but not anxiety or behavioral symptoms. CONCLUSIONS: Early adversities accelerate epigenetic aging long after they occur, in proportion to the total number of such experiences, and in a manner consistent with a shared effect that crosses multiple early dimensions of risk.

Impact of dimensions of early adversity on adult health and functioning: A 2-decade, longitudinal study.

Recent neurodevelopmental and evolutionary theories offer strong theoretical rationales and some empirical evidence to support the importance of specific dimensions of early adversity. However, studies have often been limited by omission of other adversity dimensions, singular outcomes, and short follow up durations. 1,420 participants in the community, Great Smoky Mountains Study, were assessed up to eight times between age 9 and 16 for four dimensions of early adversity: Threat, Material Deprivation, Unpredictability, and Loss (as well as a Cumulative Adversity measure). Participants were followed up to four times in adulthood (ages 19, 21, 25, and 30) to measure psychiatric disorders, substance disorder, and "real-world" functioning. Every childhood adversity dimension was associated with multiple adult psychiatric, substance, or functional outcomes when tested simultaneously in a multivariable analysis that accounted for other childhood adversities. There was evidence of differential impact of dimensions of adversity exposure on proximal outcomes (e.g., material deprivation and IQ) and even on distal outcomes (e.g., threat and emotional functioning). There were similar levels of prediction between the best set of individual adversity scales and a single cumulative adversity measure when considering distal outcomes. All dimensions of childhood adversity have lasting, pleiotropic effects, on adult health and functioning, but these dimensions may act via distinct proximal pathways.

Parental perception of mental health needs in young children.

BACKGROUND: There is evidence of unmet psychiatric needs in children under 6. These young children are dependent on their parents to identify their mental health needs. This study tested child and parent associations with parent perception of young child mental health need. METHOD: Parents of 917 children (aged 2-6 years) completed a diagnostic interview about their child assessing depression, anxiety, ODD/CD, ADHD, and impairment. Parents were surveyed about their own depression, anxiety, and asked about their psychiatric impairment. Parents were also asked whether they perceived their child as having a mental health need. RESULTS: Only 38.8% of children who met criteria for a diagnosis were perceived by their parents as having a need, similar to previously studied rates in school-aged children. Perception of need was associated with higher levels of symptoms and impairment. Thresholds for at least half of parents perceiving their child as having a need were relatively high: 19 or more symptoms, or 4 or more impairments. There was evidence of specificity: children with depressive disorders were more likely to be perceived as in need compared with other disorders. In terms of parent factors, more parental depressive symptoms were associated with higher perception of child need when the child had a diagnosis. Parental psychological impairment was associated with higher perception of need when the child had no diagnosis. CONCLUSIONS: Most preschool children that meet criteria for a psychiatric disorder are not perceived as needing help by their parents, which is dependent on both child and parent factors.

Correction: Personality trait predictors of adjustment during the COVID pandemic among college students.

[This corrects the article DOI: 10.1371/journal.pone.0248895.].

Personality trait predictors of adjustment during the COVID pandemic among college students.

Personality traits have been found to be related to a variety of health outcomes. The aim of this study was to examine how personality traits were associated with adjustment to the COVID pandemic in college students. The sample included 484 first-year university students (76% female) attending a northeastern university who completed the Big Five Inventory (BFI) personality assessment at the beginning of a semester that was disrupted by the COVID pandemic. Using a phone-based app, students completed daily ratings of mood, perceived stress levels, and engagement in a number of health promotion activities (exercise, mindfulness, adequate sleep, etc.) throughout the semester both before and after the onset of the pandemic (e.g., a within-person longitudinal design). Results, as expected, showed that mood and wellness indices generally declined during the COVID period, although stress levels actually decreased. Further, irrespective of COVID, improved mood, less perceived stress and greater participation in health promotion activities were significantly associated with a number of personality traits including neuroticism (lower), extraversion (higher), agreeableness (higher), and conscientiousness (higher). Of primary interest, mixed-effects models were used to test how major personality traits interacted with any changes in daily ratings from the pre-COVID to COVID period. Significant interactions terms were found suggesting differential impacts of the COVID epidemic for students with low versus high levels of particular traits. Higher levels of extraversion, for example, were found to be related to decreases in mood as the pandemic progressed in contrast to those with lower extraversion, for whom there was a slight increase in mood over time. These data support the conclusion that personality traits are related to mental health and can play a role in a person's ability to cope with major stressful events. Different traits may also be more adaptive to different types of stressors.