Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from behavior should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study.

While studies show links between smartphone data and affective symptoms, we lack clarity on the temporal scale, specificity (e.g., to depression vs. anxiety), and person-specific (vs. group-level) nature of these associations. We conducted a large-scale (n = 1013) smartphone-based passive sensing study to identify within- and between-person digital markers of depression and anxiety symptoms over time. Participants (74.6% female; M age = 40.9) downloaded the LifeSense app, which facilitated continuous passive data collection (e.g., GPS, app and device use, communication) across 16 weeks. Hierarchical linear regression models tested the within- and between-person associations of 2-week windows of passively sensed data with depression (PHQ-8) or generalized anxiety (GAD-7). We used a shifting window to understand the time scale at which sensed features relate to mental health symptoms, predicting symptoms 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Spending more time at home relative to one's average was an early signal of PHQ-8 severity (distal ß = 0.219, p = 0.012) and continued to relate to PHQ-8 at medial (ß = 0.198, p = 0.022) and proximal (ß = 0.183, p = 0.045) windows. In contrast, circadian movement was proximally related to (ß = -0.131, p = 0.035) but did not predict (distal ß = 0.034, p = 0.577; medial ß = -0.089, p = 0.138) PHQ-8. Distinct communication features (i.e., call/text or app-based messaging) related to PHQ-8 and GAD-7. Findings have implications for identifying novel treatment targets, personalizing digital mental health interventions, and enhancing traditional patient-provider interactions. Certain features (e.g., circadian movement) may represent correlates but not true prospective indicators of affective symptoms. Conversely, other features like home duration may be such early signals of intra-individual symptom change, indicating the potential utility of prophylactic intervention (e.g., behavioral activation) in response to person-specific increases in these signals.

Secondary analyses of sex differences in attention improvements across three clinical trials of a digital therapeutic in children, adolescents, and adults with ADHD.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) remains underdiagnosed and undertreated in girls. Inattentive symptoms, often predominant in girls with ADHD, represent a key driver of impairment and often persist into adulthood. AKL-T01 is a regulated digital therapeutic targeting inattention. We examined potential sex differences in the efficacy of AKL-T01 in three separate trials for 1) children, 2) adolescents, and 3) adults. METHODS: We conducted secondary analyses of clinical outcomes by sex in three AKL-T01 randomized clinical trials in ADHD (n1 = 180 children 30.6% female, M(SD) age = 9.71 (1.32); n2 = 146 adolescents; 41.1% female, M(SD) age = 14.34 (1.26); n3 = 153 adults; 69.9% female, M(SD) age = 39.86 (12.84)). Active treatment participants used AKL-T01 for 25 min/day over 4-6 weeks. Primary outcomes included change in attention on the Test of Variables of Attention (TOVA) and symptom change on the clinician-rated ADHD Rating Scale (ADHD-RS). To evaluate study hypotheses, we conducted a series of robust linear regressions of TOVA and ADHD-RS change scores by sex, adjusting for baseline scores. RESULTS: In children, girls demonstrated greater improvement in objective attention relative to boys following AKL-T01 (TOVA Attentional Composite Score; Cohen's d = .36 and Reaction Time Mean Half; Cohen's d = .54), but no significant sex differences in ADHD rating scale change. We did not observe significant sex differences in outcomes in the adolescent or adult trials. Limitations include binary sex categorization and slight study design variation across the three samples. CONCLUSION: AKL-T01 might notably improve attentional functioning in girls with ADHD relative to boys. Objective attention measures may be particularly important in the assessment of attentional improvement in childhood, given known gender biases in ADHD symptom reporting. We emphasize the importance of considering sex and gender-specific factors in ADHD treatment evaluation. TRIAL REGISTRATIONS: STARS ADHD CHILD: ClinicalTrials.gov ID NCT03649074; STARS ADHD ADOLESCENT: ClinicalTrials.gov ID NCT04897074; STARS ADHD ADULT: ClinicalTrials.gov ID NCT05183919.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Longitudinal associations of daily affective dynamics with depression, generalized anxiety, and social anxiety symptoms.

BACKGROUND: Low average affect, measured using ecological momentary assessment (EMA), has been consistently linked with depression, generalized anxiety, and social anxiety, supporting trait-like negative affect as a shared underlying feature. However, while theoretical models of emotion regulation would also implicate greater variability in daily affect in these conditions, empirical evidence linking EMA of mood variability with affective disorders is mixed. We used multilevel modeling to test relationships of daily mood and mood variability with depression, generalized anxiety, and social anxiety symptoms. METHODS: Participants (N = 1004; 72.31 % female; Mage = 40.85) responded to EMA of mood 2-3×/day and completed measures of depression (PHQ-8), generalized anxiety (GAD-7), and social anxiety (SPIN) every three weeks. RESULTS: Lower mean affect predicted all symptoms at both the between-person (PHQ-8: ß = -0.486, p < 0.001; GAD-7: ß = -0.429, p < 0.001; SPIN: ß = -0.284, p < 0.001) and within-person (PHQ-8: ß = -0.219, p < 0.001; GAD-7: ß = -0.196, p < 0.001; SPIN: ß = -0.049, p < 0.001) levels. Similarly, at the between-person level, greater affective variability was linked with all three clinical symptoms (PHQ-8: ß = 0.617, p < 0.001; GAD-7: ß = 0.703, p < 0.001; SPIN: ß = 0.449, p < 0.001). However, within-person, affective variability related to depression (ß = 0.144, p < 0.001) and generalized anxiety (ß = 0.150, p < 0.001), but not social anxiety (ß = 0.006, p = 0.712). LIMITATIONS: The COVID-19 pandemic lockdown period occurred midway through the study. CONCLUSION: Findings point to common and specific emotion dynamics that characterize affective symptoms severity, with implications for affective monitoring in a clinical context.

Emotion-related impulsivity across transdiagnostic dimensions of psychopathology.

OBJECTIVE: Several dimensions have received attention for their potential role in explaining shared variance in transdiagnostic symptoms of psychopathology. We hypothesized emotion-related impulsivity, the trait-like tendency toward difficulty restraining responses to emotion, would relate to symptoms of psychopathology, with two separable dimensions of emotion-related impulsivity relating distinctly to internalizing and externalizing symptoms. METHOD: Across two studies, we tested hypotheses using structural equation models of emotion-related impulsivity and multiple indicators of internalizing, externalizing, and thought symptoms. RESULTS: In Study 1 (658 undergraduates), emotion-related impulsivity was highly correlated with the general psychopathology (p) factor. In study 2 (421 Mechanical Turk participants), models did not support a general p factor; however, we replicated the hypothesized associations of emotion-related impulsivity dimensions with internalizing and externalizing factors. Across both studies, forms of emotion-related impulsivity uniquely and differentially related to internalizing and externalizing symptoms. CONCLUSIONS: Findings indicate emotion-related impulsivity may help explain transdiagnostic dimensions of psychopathology, such as the p factor.

Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety.

Background: Prior literature links passively sensed information about a person's location, movement, and communication with social anxiety. These findings hold promise for identifying novel treatment targets, informing clinical care, and personalizing digital mental health interventions. However, social anxiety symptoms are heterogeneous; to identify more precise targets and tailor treatments, there is a need for personal sensing studies aimed at understanding differential predictors of the distinct subdomains of social anxiety. Our objective was to conduct a large-scale smartphone-based sensing study of fear, avoidance, and physiological symptoms in the context of trait social anxiety over time. Methods: Participants (n = 1013; 74.6 % female; M age = 40.9) downloaded the LifeSense app, which collected continuous passive data (e.g., GPS, communication, app and device use) over 16 weeks. We tested a series of multilevel linear regression models to understand within- and between-person associations of 2-week windows of passively sensed smartphone data with fear, avoidance, and physiological distress on the self-reported Social Phobia Inventory (SPIN). A shifting sensor lag was applied to examine how smartphone features related to SPIN subdomains 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Results: A decrease in time visiting novel places was a strong between-person predictor of social avoidance over time (distal ß = -0.886, p = .002; medial ß = -0.647, p = .029; proximal ß = -0.818, p = .007). Reductions in call- and text-based communications were associated with social avoidance at both the between- (distal ß = -0.882, p = .002; medial ß = -0.932, p = .001; proximal ß = -0.918, p = .001) and within- (distal ß = -0.191, p = .046; medial ß = -0.213, p = .028) person levels, as well as between-person fear of social situations (distal ß = -0.860, p < .001; medial ß = -0.892, p < .001; proximal ß = -0.886, p < .001) over time. There were fewer significant associations of sensed data with physiological distress. Across the three subscales, smartphone data explained 9-12 % of the variance in social anxiety. Conclusion: Findings have implications for understanding how social anxiety manifests in daily life, and for personalizing treatments. For example, a signal that someone is likely to begin avoiding social situations may suggest a need for alternative types of exposure-based interventions compared to a signal that someone is likely to begin experiencing increased physiological distress. Our results suggest that as a prophylactic means of targeting social avoidance, it may be helpful to deploy interventions involving social exposures in response to decreases in time spent visiting novel places.

From formative design to service-ready therapeutic: A pragmatic approach to designing digital mental health interventions across domains.

As digital mental health interventions (DMHIs) proliferate, there is a growing need to understand the complexities of moving these tools from concept and design to service-ready products. We highlight five case studies from a center that specializes in the design and evaluation of digital mental health interventions to illustrate pragmatic approaches to the development of digital mental health interventions, and to make transparent some of the key decision points researchers encounter along the design-to-product pipeline. Case studies cover different key points in the design process and focus on partnership building, understanding the problem or opportunity, prototyping the product or service, and testing the product or service. We illustrate lessons learned and offer a series of questions researchers can use to navigate key decision points in the digital mental health intervention (DMHI) development process.

A text messaging intervention to support the mental health of young adults: User engagement and feedback from a field trial of an intervention prototype.

Background: Young adults have high rates of mental health conditions, but most do not want or cannot access treatment. By leveraging a medium that young adults routinely use, text messaging programs have potential to keep young adults engaged with content supporting self-management of mental health issues and can be delivered inexpensively at scale. We designed an intervention that imparts strategies for self-managing mental health symptoms through interactive text messaging dialogues and engages users through novelty and variety in strategies (from cognitive behavioral therapy, acceptance and commitment therapy, and positive psychology) and styles of interaction (e.g., prompts, peer stories, writing tasks). Methods: The aim of this mixed-methods study was to pilot 1- and 2-week versions of an interactive text messaging intervention among young adults (ages 18-25), and to obtain feedback to guide intervention refinements. Young adults were recruited via a mental health advocacy website and snowball sampling at a North American University. We used Wizard-of-Oz methods in which study staff sent messages based on a detailed script. Transcripts of interviews were subject to qualitative analysis to identify aspects of the program that need improvements, and to gather participant perspectives on possible solutions. Results: Forty-eight individuals ages 18-25 participated in the study (mean age: 22.0). 85 % responded to the program at least once. Among those who ever responded, they replied to messages on 85 % of days, and with engagement sustained over the study period. Participants endorsed the convenience of text messaging, the types of interactive dialogues, and the variety of content. They also identified needed improvements to message volume, scheduling, and content. Conclusions: Young adults showed high levels of engagement and satisfaction with a texting program supporting mental health self-management. The program may be improved through refining personalization, timing, and message volume, and extending content to support use over a longer timeframe. If shown to be effective in randomized trials, this program has potential to help address a substantial treatment gap in young adults' mental health.

Analyzing text message linguistic features: Do people with depression communicate differently with their close and non-close contacts?

BACKGROUND: Relatively little is known about how communication changes as a function of depression severity and interpersonal closeness. We examined the linguistic features of outgoing text messages among individuals with depression and their close- and non-close contacts. METHODS: 419 participants were included in this 16-week-long observational study. Participants regularly completed the PHQ-8 and rated subjective closeness to their contacts. Text messages were processed to count frequencies of word usage in the LIWC 2015 libraries. A linear mixed modeling approach was used to estimate linguistic feature scores of outgoing text messages. RESULTS: Regardless of closeness, people with higher PHQ-8 scores tended to use more differentiation words. When texting with close contacts, individuals with higher PHQ-8 scores used more first-person singular, filler, sexual, anger, and negative emotion words. When texting with non-close contacts these participants used more conjunctions, tentative, and sadness-related words and fewer first-person plural words. CONCLUSION: Word classes used in text messages, when combined with symptom severity and subjective social closeness data, may be indicative of underlying interpersonal processes. These data may hold promise as potential treatment targets to address interpersonal drivers of depression.

Negative interpretation bias connects to real-world daily affect: A multistudy approach.

Negative interpretation bias, the tendency to appraise ambiguous stimuli as threatening, shapes our emotional lives. Various laboratory tasks, which differ in stimuli features and task procedures, can quantify negative interpretation bias. However, it is unknown whether these tasks globally predict individual differences in real-world negative (NA) and positive (PA) affect. Across two studies, we tested whether different lab-based negative interpretation bias tasks predict daily NA and PA, measured via mobile phone across months. To quantify negative interpretation bias, Study 1 (N = 69) used a verbal, self-referential task whereas Study 2 (N = 110) used a perceptual, emotional image task with faces and scenes. Across tasks, negative interpretation bias was linked to heightened daily NA. However, only negative interpretation bias in response to ambiguous faces was related to decreased daily PA. These results illustrate the ecological validity of negative interpretation bias tasks and highlight converging and unique relationships between distinct tasks and naturalistic emotion. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Identifying real-world affective correlates of cognitive risk factors for internalizing disorders.

Cognitive risk factors are key in the vulnerability for internalizing disorders. Cognitive risk factors modulate the way individuals process information from the environment which in turn impacts the day-to-day affective experience. In 296 young adults, we assessed two transdiagnostic, general risk factors-repetitive negative thinking (RNT) and anxiety sensitivity in a high-RNT subsample (N = 119). We also assessed disorderand content-specific risk factors including worry, rumination, and three facets of anxiety sensitivity (cognitive, social, physical). To determine the day-to-day affective experience, we used cell-phone-based ecological momentary assessment to assess the mean and variability of positive and negative affect (PA; NA) over 3-4 months. Two multilevel multivariate Bayesian models were used to predict PA and NA mean and variability from (1) general and (2) specific cognitive risk factors. Mean NA was a nonspecific correlate of cognitive risk across both models, while mean PA was most strongly related to RNT and rumination. NA variability was most strongly related to RNT, rumination, and the physiological facet of anxiety sensitivity. PA variability was a specific correlate of RNT. Results highlight that cognitive risk factors for internalizing disorders manifest in unique patterns of day-to-day emotional experience. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Situational uncertainty interacts with anxiety sensitivity and distress intolerance to predict anticipated worry and preparation for a hurricane.

BACKGROUND: There is evidence for the impact of emotional intolerance on reactivity to stressors, but it is unknown whether the level of situational uncertainty may moderate this relationship. We examined whether situational uncertainty moderated the relationship between emotional intolerance and anticipated anxious responding to hurricane forecasts, considering three aspects of emotional tolerance: anxiety sensitivity, distress intolerance, and hurricane-specific distress intolerance. METHODS: Participants (N = 358) were Florida residents who experienced Hurricane Irma. Participants were presented with two hypothetical storm forecasts that varied in level of uncertainty: 5-day forecast (high uncertainty) and 3-day forecast (low uncertainty). Participants rated their anticipated worry and preparation for each forecast. RESULTS: Significant interactions between forecast uncertainty and both anxiety sensitivity and hurricane-specific distress intolerance emerged on anticipated worry, such that there was a stronger relationship in the high uncertainty condition. Forecast uncertainty also moderated the relationship between anxiety sensitivity and anticipated preparation in the same direction. There were no significant interactions between forecast uncertainty and distress intolerance on either anticipated worry or preparation. CONCLUSIONS: Specific aspects of emotional intolerance appear to have a stronger influence on anticipated worry and preparatory behavior in high uncertainty situations. These findings suggest that distinct emotional tolerance factors may influence these responses.

The association of language style matching in text messages with mood and anxiety symptoms.

Context: Impairment in social functioning is a feature and consequence of depression and anxiety disorders. For example, in depression, anhedonia and negative feelings about the self may impact relationships; in anxiety, fear of negative evaluation may interfere with getting close to others. It is unknown whether social impairment associated with depression and anxiety symptoms is reflected in day-to-day language exchanges with others, such as through reduced language style matching (LSM). Methods: Over 16 weeks, we collected text message data from 458 adults and evaluated differences in LSM between people with average scores above/below the clinical cutoff for depression, generalized anxiety, and social anxiety in text message conversations. Text message sentiment scores were computed across 73 Linguistic Inquiry and Word Count (LIWC) categories for each participant. T-tests were used to compare LSM across two groups (average scores above/below clinical cutoff) for each of the 3 diagnostic categories (depression, generalized anxiety, social anxiety), and each of the 73 LIWC categories, with correction for multiple comparisons. Results: We found reduced LSM of function words (namely, prepositions [t=-2.82, p=.032], articles [t=-5.26, p<.001], and auxiliary verbs [t=-2.64, p=.046]) in people with average scores above the clinical cutoff for generalized anxiety, and reduced LSM of prepositions (t=-4.26, p<.001) and articles (t=-3.39, p=.010) in people with average scores above the clinical cutoff for social anxiety. There were no significant differences in LSM of function words between people with average scores above and below the clinical cutoff for depression. Across all symptom categories, elevated affective psychopathology was associated with being more likely to style match on formality, including netspeak (generalized anxiety, t=5.77, p<.001; social anxiety, t=4.14, p<.001; depression, t=3.13, p=.021) and informal language (generalized anxiety, t=6.65, p<.001; social anxiety, t=5.14, p>.001; depression, t=3.20, p=.020).We also observed content-specific LSM differences across the three groups. Conclusions: Reduced LSM of function words among patients reporting elevated anxiety symptoms suggests that anxiety-related psychosocial difficulties may be perceptible in subtle cues from day-to-day language. Conversely, the absence of differences in the LSM of function words among people with average scores above and below the clinical cutoff for depression indicates a potentially distinct mechanism of social impairment. Implications: Results point to potential markers of psychosocial difficulties in daily conversations, particularly among those experiencing heightened anxiety symptoms. Future studies may consider the degree to which LSM is associated with self-reported psychosocial impairment, with the promise of informing cognitive-behavioral mechanisms and tailoring digital interventions for social skills.

Prospective associations of text-message-based sentiment with symptoms of depression, generalized anxiety, and social anxiety.

OBJECTIVE: Language patterns may elucidate mechanisms of mental health conditions. To inform underlying theory and risk models, we evaluated prospective associations between in vivo text messaging language and differential symptoms of depression, generalized anxiety, and social anxiety. METHODS: Over 16 weeks, we collected outgoing text messages from 335 adults. Using Linguistic Inquiry and Word Count (LIWC), NRC Emotion Lexicon, and previously established depression and stress dictionaries, we evaluated the degree to which language features predict symptoms of depression, generalized anxiety, or social anxiety the following week using hierarchical linear models. To isolate the specificity of language effects, we also controlled for the effects of the two other symptom types. RESULTS: We found significant relationships of language features, including personal pronouns, negative emotion, cognitive and biological processes, and informal language, with common mental health conditions, including depression, generalized anxiety, and social anxiety (ps < .05). There was substantial overlap between language features and the three mental health outcomes. However, after controlling for other symptoms in the models, depressive symptoms were uniquely negatively associated with language about anticipation, trust, social processes, and affiliation (ßs: -.10 to -.09, ps < .05), whereas generalized anxiety symptoms were positively linked with these same language features (ßs: .12-.13, ps < .001). Social anxiety symptoms were uniquely associated with anger, sexual language, and swearing (ßs: .12-.13, ps < .05). CONCLUSION: Language that confers both common (e.g., personal pronouns and negative emotion) and specific (e.g., affiliation, anticipation, trust, and anger) risk for affective disorders is perceptible in prior week text messages, holding promise for understanding cognitive-behavioral mechanisms and tailoring digital interventions.

Insights Into Needs and Preferences for Mental Health Support on Social Media and Through Mobile Apps Among Black Male University Students: Exploratory Qualitative Study.

BACKGROUND: Black college-aged men are less likely than their peers to use formal, therapeutic in-person services for mental health concerns. As the use of mobile technologies and social media platforms is steadily increasing, it is important to conduct work that examines the future utility of digital tools and technologies to improve access to and uptake of mental health services for Black men and Black men in college. OBJECTIVE: The aim of this study was to identify and understand college-attending Black men's needs and preferences for using digital health technologies and social media for stress and mental health symptom management. METHODS: Interviews were conducted with Black male students (N=11) from 2 racially diverse universities in the Midwestern United States. Participants were asked questions related to their current mental health needs and interest in using social media platforms and mobile-based apps for their mental health concerns. A thematic analysis was conducted. RESULTS: Four themes emerged from the data: current stress relief strategies, technology-based support needs and preferences (subthemes: mobile-based support and social media-based support), resource information dissemination considerations (subthemes: information-learning expectations and preferences and information-sharing preferences and behaviors), and technology-based mental health support design considerations (subtheme: relatability and representation). Participants were interested in using social media and digital technologies for their mental health concerns and needs, for example, phone notifications and visual-based mental health advertisements that promote awareness. Relatability in the context of representation was emphasized as a key factor for participants interested in using digital mental health tools. Examples of methods for increasing relatability included having tools disseminated by minority-serving organizations and including components explicitly portraying Black men engaging in mental health support strategies. The men also discussed wanting to receive recommendations for stress relief that have been proven successful, particularly for Black men. CONCLUSIONS: The findings from this study provide insights into design and dissemination considerations for future work geared toward developing mental health messaging and digital interventions for young Black men.

A longitudinal investigation of COVID-19 pandemic experiences and mental health among university students.

OBJECTIVES: Previous studies have established a link between the COVID-19 pandemic and poor mental health. They further suggest that young adults may be especially vulnerable to worsened mental health during the pandemic, but few studies have investigated which specific aspects of the COVID-19 experience affect psychological well-being over time. To better understand concrete predictors of poor mental health outcomes in this population, we identified several pandemic-related experiences and evaluated their effects on mental health symptoms (depression, anxiety, stress, alcohol, and substance use) in a sample of U.S. college students (N = 176). METHODS: Both mental health symptoms and pandemic-related experiences were evaluated at the start of quarantine (March/April 2020, Time 1) and the end of the Spring 2020 semester (May 2020, Time 2). Given the limited literature on specific predictors of mental health during a pandemic, we used elastic net regression, a novel analytic method that helps with variable selection when theoretical background is limited, to narrow our field of possible predictors. RESULTS: While mental health symptoms were elevated at both timepoints, there were no clinically significant changes from Time 1 to Time 2 and few differences between sociodemographic groups. Both disruption due to the pandemic (ß = .25, p = .021) and limited confidence in the federal government's response (ß = -.14, p = .038) were significant predictors of depression symptoms at the end of the semester, even when controlling for baseline depression. Further, predictions that the pandemic would continue to impact daily life further into the future were linked with pandemic stress response symptoms (ß = .15, p = .032) at Time 2, beyond the effects of baseline symptoms. Alcohol (ß = -.22, p = .024) and substance use (ß = -.26, p = .01) were associated with reduced adherence to COVID-19 guidelines. CONCLUSIONS: Our findings indicate that specific aspects of the pandemic experience may be influencing internalizing symptoms and alcohol/substance use in college students, pointing to potential avenues for targeted support and intervention. PRACTITIONER POINTS: A range of factors may influence university student mental health during the COVID-19 pandemic. Students who expect the pandemic will continue to impact daily life further into the future maybe more likely to report stress symptoms. Disruption due to the pandemic and limited confidence in the federal government's response may be associated with depression symptoms. Alcohol and substance use are associated with lower COVID-19 guideline adherence in university students.

A Spanish translation of the Hoarding Rating Scale: Differential item functioning and convergent validity.

Hoarding disorder (HD) involves extreme difficulties discarding possessions and significant clutter in living areas. Although hoarding occurs worldwide, cross-cultural research remains in nascent stages, hampered in part by a lack of validated measures in non-English languages. We aimed to validate a Spanish translation of the Hoarding Rating Scale (HRS), a widely used measure of core HD symptoms. Our sample (N=736) included participants responding in English (n=548; 45.4% female; 7.9% Latinx) or Spanish (n=188; 46.3% female; 79.9% Latinx) to questionnaires via Amazon's Mechanical Turk. An item response theory (IRT) approach was used to test differential item functioning (DIF) of the English and Spanish HRS. We also examined convergent validity of each language version with other HD. Initial comparisons revealed that hoarding symptoms were elevated in the Spanish-speaking sample compared to the English-speaking sample. DIF tests flagged the clutter item for potential bias (McFadden's ß=.069), but closer examination revealed that the impact was negligible. The Spanish HRS was significantly linked with other hoarding measures (Saving Inventory-Revised: ß=.497, p<.001; Obsessive-Compulsive Inventory-Revised-Hoarding Subscale: ß=.329, p=.008), controlling for mood, anxiety, stress, and non-hoarding OCD symptoms. However, the Spanish HRS was not significantly associated with Clutter Image Rating scores. Findings supported the utility of the HRS to measure of HD symptoms in Spanish speakers, though cross-linguistic assessment of clutter and the applicability of clinical cutoffs with Spanish-speaking samples merits further study.

The distribution of daily affect distinguishes internalizing and externalizing spectra and subfactors.

There has been increasing recognition that classically defined psychiatric disorders cluster hierarchically. However, the degree to which this hierarchical taxonomy manifests in the distribution of one's daily affective experience is unknown. In 462 young adults, we assessed psychiatric symptoms across internalizing and externalizing disorders and then used cell-phone-based ecological momentary assessment (EMA) to assess the distribution (mean, standard deviation, skew, kurtosis) of one's positive and negative affect over 3-4 months. Psychiatric symptoms were modeled using a higher-order factor model that estimated internalizing and externalizing spectra as well as specific disorders. Individualized factor loadings were extracted, and path models assessed associations between spectra and syndromes, and daily affect. Internalizing and externalizing spectra displayed broad differences in the distribution of affective experiences, while within the internalizing spectrum, syndromes loading onto fear and distress subfactors were associated with distinct patterns of affective experiences. (PsycInfo Database Record (c) 2021 APA, all rights reserved).