Lauren Alexandra Rutter

Background: Social media use among older adults continues to grow. Many people use social media to establish meaningful social ties and discuss their mental health. Depression in midlife and older adults is a critical public health concern, yet scalable, sensitive methods for early detection remain limited. Natural language processing offers new opportunities to examine sentiment and mental health through online language in typically understudied populations. Objective: This study examined whether sentiment and cognitive features of naturalistic social media language are associated with depression diagnosis and symptom severity among midlife and older adults. Methods: We constructed cohorts of adults aged 50 years and older from Reddit (n=688) and Bluesky (n=210). Participants who had ever had depression and never been diagnosed were recruited via a self-report survey on Prolific that collected depression history, age of diagnosis, current symptoms, and social media handles. Public posts were preprocessed and analyzed using a validated rule-based sentiment analysis (Valence Aware Dictionary and Sentiment Reasoner). Sentiment measures were aggregated at the user level and compared by depression status within and across platforms. Results: On Bluesky, users with a history of depression exhibited lower Valence Aware Dictionary and Sentiment Reasoner compound sentiment compared to users who had never been diagnosed (ρ=−0.16; P <.001). On Reddit, current depression severity showed a small association with lower compound sentiment, and although the bootstrap CI excluded zero, the association did not remain statistically significant after correction for multiple comparisons ( P >.05). These findings indicate a platform-specific relationship between depression status and online sentiment. Conclusions: Negative sentiment expressed on Bluesky was associated with depression history (ever diagnosed vs never diagnosed) among midlife and older adults. On Reddit, current depression severity showed a small association with negative sentiment that did not remain statistically significant after correction for multiple comparisons. Our findings suggest that sentiment-based language features may capture modest differences in online expression related to depression. Future work with larger samples and longitudinal symptom assessment is needed to determine whether sentiment reliably tracks with depression-related symptoms over time.

Objective: Processes of change in cognitive behavioral therapies (CBTs) remain frequently debated, perhaps due to methodological difficulties in conducting good process research. We investigated two contrasting emotion regulation strategies, cognitive reappraisal and expressive suppression, as processes of change in CBT disaggregating within- from between-person changes.Method: We examined whether week-to-week changes in cognitive reappraisal and expressive suppression (Emotion Regulation Questionnaire) predicted subsequent changes in distress (Kessler-6 Psychological Distress Scale) and well-being (WHO-5 Well-being Index) pooling two trials of a guided self-help CBT acceptance and commitment therapy. We used a random-intercept cross-lagged panel model to disaggregate between- vs. within-person changes. Participants (N = 274) were 82% female, 80% non-Hispanic White, and mean age 37 years (SD = 13). Results: At the within-person level, week-to-week changes in reappraisal were consistently associated with subsequent changes in distress, but less consistently with well-being. Changes in expressive suppression were less consistently associated with subsequent changes in distress or well-being. The associations between reappraisal and distress were robust to different model specifications, including detrending. Conclusions: Cognitive reappraisal may be involved in the process of change in CBTs, including so-called “third-wave” CBTs, which have traditionally de-emphasized its importance.Keywords: cognitive behavioral therapy; emotion regulation; mechanisms; digital mental health interventions; guided self-help

Introduction: Posttraumatic stress disorder (PTSD) frequently co-occurs with other mood and anxiety disorders. The contrast avoidance (CA) model proposes that individuals maintain anxiety to avoid large negative emotional shifts following aversive events. Although CA has been studied primarily in generalized anxiety disorder, its role in posttraumatic stress symptoms (PTSSs) remains largely unexplored. This study examined the association between CA, PTSSs, and other anxiety disorder vulnerability constructs.Methods: Participants (N = 1,186) completed an online survey assessing PTSSs, CA, and established PTSD risk factors. Model-based random forests identified the strongest predictors of PTSS severity. Bootstrapped AIC was used to identify significant two-way interactions, which were subsequently examined using simple slopes analyses.Results: CA significantly interacted with threat beliefs (b = 0.77, p < .001), such that threat beliefs were more strongly associated with PTSSs at higher levels of CA. Contrast avoidance also interacted with trust beliefs (b = –0.59, p < .001), indicating that greater trust in others attenuated the association between CA and PTSSs. A borderline interaction emerged between CA and behavioral avoidance (b = –0.46, p = .058). Model-based recursive partitioning suggested that cognitive vulnerabilities, particularly threat beliefs, amplified the effects of CA more consistently than affective or behavioral risk factors.Conclusions: Contrast avoidance is an important correlate of PTSSs, particularly when paired with cognitive vulnerabilities. Future longitudinal and experimental research should clarify causal mechanisms and evaluate whether interventions targeting CA and maladaptive trauma-related cognitions can reduce PTSS severity.

INTRODUCTION: Neuropsychiatric symptoms frequently precede Alzheimer's disease and related dementias, yet risk prediction models typically rely on baseline depression severity or diagnostic status. We examined whether instability in depressive symptoms predicted incident mild cognitive impairment (MCI) or dementia. METHODS: Data were drawn from 11,951 older adults enrolled across 42 US Alzheimer's Disease Research Centers and followed for up to 19 years. Depressive symptoms were assessed using the Geriatric Depression Scale and characterized using rule‐based groupings and latent class trajectories. The Structured Life‐Course Modeling Approach (SLCMA) was used to compare four competing temporal hypotheses of risk. Cox models adjusted for demographic factors and apolipoprotein E (APOE) ε4 status. RESULTS: Fluctuating depressive symptom trajectories were associated with increased risk of incident cognitive impairment. Symptom instability showed the lowest prediction error in exploratory SLCMA analyses. DISCUSSION: Instability in depressive symptoms may represent an early and clinically accessible risk marker for MCI and dementia.

Background: Anxiety and mood disorders, characterized by elevated negative affect (NA) and cognitive impairments, are highly prevalent among college students. Within-person (WP) NA variability, which captures moment-to-moment fluctuations in NA, provides unique insights into emotional processes that are not reflected in mean NA levels. Cognitive variability, particularly reaction time (RT) inconsistency, is increasingly recognized as a sensitive marker of cognitive health and functional integrity. Although prior research links NA to cognitive variability, the short-term dynamics of these associations in naturalistic settings remain understudied. College students provide an ideal population for examining these dynamics using ecological momentary assessment (EMA). Objective: This study investigated the association between WP NA and RT inconsistency, hypothesizing that higher WP fluctuations in NA would predict increased RT inconsistency. We also examined the moderating roles of practice effects and covariates, including neuroticism, insomnia, and sex. Methods: Using EMA, 99 university students completed morning and evening assessments over 14 days, including a cognitive task measuring RT inconsistency (standard deviation in trial-level RT) and self-reported NA. Multilevel modeling was used to assess WP fluctuations in NA and their impact on RT inconsistency, accounting for time (session number), between-person differences in NA, and covariates such as sleep problems, neuroticism, age, sex, and use of a touch device. Results: WP fluctuations in NA significantly predicted increased RT inconsistency (exp(β)=1.022, 95% CI 1.008‐1.037; P =.007), supporting the hypothesis that NA variability disrupts cognitive performance. Male students exhibited lower RT inconsistency than female students, with a small effect size (exp(β)=0.824, 95% CI 0.694‐0.977; P =.049). Finally, EMA sessions were inversely associated with RT inconsistency, with a stronger effect up to session 3 (exp(β)=0.930, 95% CI 0.879‐0.985; P =.03) than after session 3 (exp(β)=0.986, 95% CI=0.979‐0.992; P <.001), indicating practice effects. Conclusions: Momentary fluctuations in NA influence cognitive variability, particularly in the early stages of repeated cognitive tasks, underscoring the role of emotional processes in cognitive performance. Practice effects and individual differences, such as sex and insomnia, influence these associations. These findings highlight the use of EMA for understanding cognitive-affective processes and suggest potential intervention targets, such as addressing NA, to improve cognitive functioning in emotionally vulnerable populations like college students.

Introduction: Accurate recognition of others’ emotional facial expressions, referred to as emotion sensitivity (ES), is a central component of nonverbal communication. Previous research suggests that personality traits such as neuroticism and extraversion may shape nonverbal emotion decoding but findings have been mixed. The present study examined whether these traits predict performance on the Belmont Emotion Sensitivity Test (BEST), a task designed to measure subtle discrimination of anger, fear, and happiness while minimizing response bias. Method: A sample of 289 college students completed self-report measures of neuroticism and extraversion, as well as the Belmont Emotion Sensitivity Test (BEST), which assesses the ability to discriminate subtle differences in facial expressions of fear, anger, and happiness. Depression, anxiety, and stress levels were also measured. Results: Female participants reported higher levels of neuroticism ( t = 4.39, p < .001; d = .59) and extraversion ( t = 2.33, p = .02; d = .33) t han male participants. Neuroticism severity was positively correlated with depression, anxiety, and stress. Extraversion was negatively correlated with depression, anxiety, and stress (range of r s = .55−.68, p < .001). Multiple regressions showed that neither neuroticism nor extraversion significantly predicted ES scores for any emotion category. Discussion: Although women reported higher levels of neuroticism and extraversion, these traits did not predict performance on a validated emotion sensitivity task. These findings suggest that, in this nonclinical young adult sample, neuroticism and extraversion were not robust predictors of performance on this measure of emotion sensitivity. Implications are discussed for theories linking personality, psychopathology, and nonverbal emotion perception.

Objectives: Depression is a noted risk factor for general cognitive decline and Alzheimer’s disease and related dementias. Evidence suggests that this may be due to its association with greater variability in performance across cognitive tasks, known as cognitive dispersion. The current study examines how depression and dispersion uniquely predict cognitive decline in a longitudinal study of cognitively normal (CN) older adults. Methods: Data were drawn from the Harvard Aging Brain Study. Participants were 287 individuals who were CN at baseline, contributing 1,547 observations across six longitudinal waves over 8.68 years. Depression was assessed using the Geriatric Depression Scale. Cognitive status was assessed via the Preclinical Alzheimer’s Cognitive Composite. Cognitive dispersion was defined as the intraindividual standard deviation of scores across five cognitive tasks that represent complementary dimensions of executive functioning and cognitive control. Results: Cross-sectional mixed-effects models revealed that higher depression was associated with lower baseline cognitive performance (β = −0.02, SE = 0.01, t(1482) = −4.01, p < .001), but depression was not related to dispersion. In combined longitudinal models including both depression and dispersion, higher average dispersion (between-persons) predicted lower cognitive performance (β = −0.38, SE = 0.11, t(272) = −3.45, p < .001) and faster decline (β = −0.07, SE = 0.03, t(258)=-2.34, p = .02). Depression was not a significant predictor of cognitive decline when modeled with dispersion. Discussion: Our findings highlight the importance of monitoring depression during clinic visits, as it is significantly related to concurrent cognitive performance in aging populations. Since dispersion and not depression predicted decline over time, cognitive dispersion may serve as a specific marker of future impairment risk.

BACKGROUND Social media use among older adults continues to grow. Many people use social media to establish meaningful social ties and discuss their mental health. Depression in midlife and older adults is a critical public health concern, yet scalable, sensitive methods for early detection remain limited. Natural language processing offers new opportunities to examine cognitive-affective processes through online language in typically understudied populations. OBJECTIVE This study examined whether emotional and cognitive features of naturalistic social media language are associated with depression diagnosis and symptom severity among midlife and older adults. METHODS We constructed cohorts of adults aged 50+ from Reddit (n = 688) and Bluesky (n = 210). Depressed and never-diagnosed control participants were recruited via a self-report survey on Prolific, which assessed depression history, age of diagnosis, current symptoms, and social media handles. Public posts were preprocessed and analyzed using a validated rule-based sentiment analysis approach to quantify emotional tone. Sentiment measures were aggregated at the user level and compared by depression status within and across platforms. RESULTS On Bluesky, depressed users exhibited significantly more negative emotional language compared to non-depressed users, as reflected by lower overall sentiment scores (ρ = -.16, P <.001). On Reddit, negative emotional language increased as depression severity increased (ρ = -.09, P= .03). These findings indicate a platform-specific relationship between depression status and expressed negative sentiment among older adults. CONCLUSIONS Negative emotional tone expressed on Bluesky is associated with depression status (ever diagnosed vs. never diagnosed) among midlife and older adults, while negative emotional tone expressed on Reddit is associated with depression severity, suggesting that both overall emotional tone and longitudinal affective patterns vary by platform and clinical context. These results highlight the value of sentiment-based language markers for detecting depression-related differences in online expression within emerging social media platforms and underscore the importance of considering platform context when leveraging digital language as a marker of mental health in aging populations. CLINICALTRIAL n/a

People with personality disorders (PDs) tend to experience invalidation in their daily life-others seem to disregard, reject, or punish their psychological experiences (e.g., emotions and self-concept). Our study aimed to (a) examine within-person covariation in day-to-day perceived invalidation and PD symptom severity and (b) explore minority stress as a form of invalidation-one that targets aspects of people's minoritized identities. We recruited 170 community adults and 339 undergraduate students, oversampling from sexual/gender and ethnic/racial minoritized groups, to complete daily surveys of invalidation, minority stress, and PD features over 2 weeks. We observed that daily invalidation (r = .35) and, to a smaller extent, minority stress (rs = .22-.23) had meaningful within-person correlations with same-day PD symptoms. Despite moderate between-person correlations (r_range = .28-.41) between invalidation and minority stress measures, they were more modestly associated on a within-person basis (r_range = .09-.11). In a multilevel multiple regression model, both invalidation and minority stress uniquely predicted daily PD symptoms, collectively accounting for approximately 20% of within-person outcome variation. Based on these results, we speculate that various forms of invalidation (i.e., those that target thoughts, emotions, self-concepts, and identities) interact with PD features in a vicious cycle, such that daily invalidation activates personality pathology, which evokes more invalidation, and so on. We encourage more recognition of identity-based invalidation and investigation into the ways that minority stress could advance interpersonal theories of personality pathology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).