Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges.

In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children.

A Pilot Randomized Controlled Trial of the Family Assessment and Feedback Intervention (FAFI): Effects on Mental Health Literacy and Attitudinal Engagement with Health Supports and Services.

This randomized controlled trial tested the Family Assessment and Feedback Intervention (FAFI), a new intervention to enhance family engagement with emotional and behavioral health services. The FAFI is a guided conversation with families about results of their multidimensional assessment that is set in the context of motivational enhancement. It differs from other assessment-with-feedback interventions by extending the focus of assessment beyond the target child to parents and the family environment, addressing parental emotional and behavioral problems and competencies, spanning a broad range of children's and parents' strengths and difficulties, and being generalizable to many settings and practitioners. Participants were 81 families in primary care pediatrics. The FAFI was associated with a significant increase in parental mental health literacy and with an increase in parental attitudinal engagement with health supports and services that closely approached statistical significance (p = .052), while controlling for children's age and gender and family socioeconomic status.

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.

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 [...].

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.

Maternal Postpartum Depression Increases Vulnerability for Toddler Behavior Problems through Infant Cortisol Reactivity.

The current study examined the role of hypothalamic-pituitary-adrenal reactivity (a physiological indicator of stress) in early infancy as a mediator of the relationship between maternal postpartum depression and toddler behavior problems. Participants were 137 at-risk mothers and their children participating in a longitudinal study of intergenerational transmission of risk. Mothers' depression was measured five times during the infants' first 18 months. Infant cortisol was collected during a social stressor (the still-face paradigm) when infants were 6 months old, and mothers reported on toddlers' internalizing and externalizing symptoms at 18 months. Among this sample of high-risk mother-infant dyads, early postpartum depression predicted atypical infant cortisol reactivity at 6 months, which mediated the effect of maternal depression on increased toddler behavior problems. Clinical implications are discussed.

PTSD SYMPTOMS ACROSS PREGNANCY AND EARLY POSTPARTUM AMONG WOMEN WITH LIFETIME PTSD DIAGNOSIS.

BACKGROUND: Little is known about trajectories of PTSD symptoms across the peripartum period in women with trauma histories, specifically those who met lifetime PTSD diagnoses prior to pregnancy. The present study seeks to identify factors that influence PTSD symptom load across pregnancy and early postpartum, and study its impact on postpartum adaptation. METHOD: The current study is a secondary analysis on pregnant women with a Lifetime PTSD diagnosis (N = 319) derived from a larger community sample who were interviewed twice across pregnancy (28 and 35 weeks) and again at 6 weeks postpartum, assessing socioeconomic risks, mental health, past and ongoing trauma exposure, and adaptation to postpartum. RESULTS: Using trajectory analysis, first we examined the natural course of PTSD symptoms based on patterns across peripartum, and found four distinct trajectory groups. Second, we explored factors (demographic, historical, and gestational) that shape the PTSD symptom trajectories, and examined the impact of trajectory membership on maternal postpartum adaptation. We found that child abuse history, demographic risk, and lifetime PTSD symptom count increased pregnancy-onset PTSD risk, whereas gestational PTSD symptom trajectory was best predicted by interim trauma and labor anxiety. Women with the greatest PTSD symptom rise during pregnancy were most likely to suffer postpartum depression and reported greatest bonding impairment with their infants at 6 weeks postpartum. CONCLUSIONS: Screening for modifiable risks (interpersonal trauma exposure and labor anxiety) and /or PTSD symptom load during pregnancy appears critical to promote maternal wellbeing.

Longitudinal examination of infant baseline and reactivity cortisol from ages 7 to 16 months.

This study characterized the longitudinal evolution of HPA axis functioning from 7 to 16 months of age and identified individual and environmental factors that shape changes in HPA axis functioning over time. Participants were 167 mother-infant dyads drawn from a larger longitudinal study, recruited based on maternal history of being maltreated during childhood. Salivary cortisol levels were assessed before and after age-appropriate psychosocial stressors when infants were 7 and 16 months old. Maternal observed parenting and maternal reports of infant and environmental characteristics were obtained at 7 months and evaluated as predictors of changes in infant baseline cortisol and reactivity from 7 to 16 months. Results revealed that infants did not show a cortisol response at 7 months, but reactivity to psychosocial stress emerged by 16 months. Individual differences in cortisol baseline and reactivity levels over time were related to infant sex and maternal overcontrolling behaviors, underscoring the malleable and socially informed nature of early HPA axis functioning. Findings can inform prevention and intervention efforts to promote healthy stress regulation during infancy.

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

[This corrects the article DOI: 10.1371/journal.pone.0286218.].

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. PLAIN LANGUAGE SUMMARY: Using data from 381 participants in the Great Smoky Mountains Study, the authors examined whether childhood mental health symptoms, substance use, and early adversity accelerate biological aging, as measured by DNA methylation age, from adolescence to adulthood. Depressive symptoms and cannabis use were found to significantly accelerate biological aging. Models that tested the combined effect of mental health symptoms, substance use, and early adversity demonstrated that there was a shared effect across these types of childhood problems on accelerated aging.

The Two Fundamental Shapes of Sleep Heart Rate Dynamics and Their Connection to Mental Health in College Students.

Introduction: Wearable devices are rapidly improving our ability to observe health-related processes for extended durations in an unintrusive manner. In this study, we use wearable devices to understand how the shape of the heart rate curve during sleep relates to mental health. Methods: As part of the Lived Experiences Measured Using Rings Study (LEMURS), we collected heart rate measurements using the Oura ring (Gen3) for over 25,000 sleep periods and self-reported mental health indicators from roughly 600 first-year university students in the USA during the fall semester of 2022. Using clustering techniques, we find that the sleeping heart rate curves can be broadly separated into two categories that are mainly differentiated by how far along the sleep period the lowest heart rate is reached. Results: Sleep periods characterized by reaching the lowest heart rate later during sleep are also associated with shorter deep and REM sleep and longer light sleep, but not a difference in total sleep duration. Aggregating sleep periods at the individual level, we find that consistently reaching the lowest heart rate later during sleep is a significant predictor of (1) self-reported impairment due to anxiety or depression, (2) a prior mental health diagnosis, and (3) firsthand experience in traumatic events. This association is more pronounced among females. Conclusion: Our results show that the shape of the sleeping heart rate curve, which is only weakly correlated with descriptive statistics such as the average or the minimum heart rate, is a viable but mostly overlooked metric that can help quantify the relationship between sleep and mental health.

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.

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.

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.

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 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.

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.

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.