A network model of depressive and anxiety symptoms: a statistical evaluation.

BACKGROUND: Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks. METHODS: A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression. RESULTS: Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model. CONCLUSION: Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.

An overview of diagnostics and therapeutics using large language models.

There is an acute need for solutions to treat stress and trauma-related sequelae, and there are well-documented shortages of qualified human professionals. Artificial intelligence (AI) presents an opportunity to create advanced screening, diagnosis, and treatment solutions that relieve the burden on people and can provide just-in-time interventions. Large language models (LLMs), in particular, are promising given the role language plays in understanding and treating traumatic stress and other mental health conditions. In this article, we provide an overview of the state-of-the-art LLMs applications in diagnostic assessments, clinical note generation, and therapeutic support. We discuss the open research direction and challenges that need to be overcome to realize the full potential of deploying language models for use in clinical contexts. We highlight the need for increased representation in AI systems to ensure there are no disparities in access. Public datasets and models will help lead progress toward better models; however, privacy-preserving model training will be necessary for protecting patient data.

Barriers and engagement in breast cancer survivorship wellness activities.

PURPOSE: Breast cancer survivors may be at risk for increased rates of emotional distress and poorer quality of life. Survivorship care plans (SCPs) promoting wellness activities may support well-being; however, survivors may not receive or engage in their SCPs. This study aimed to assess receipt and participation in SCP activities as well as barriers to engagement amongst breast cancer survivors. METHODS: Breast cancer survivors (n = 187; 99% female, Mean age = 57.7) consented and completed self-reported assessments of SCP recommendations, engagement and interest in wellness activities, and potential barriers to engagement. RESULTS: A minority of participants recalled receiving an SCP (21%). The most physician recommended (62%) and completed (53%) activity was exercise. Interest in adding other wellness activities to the SCP was high, with reported interest levels of approximately 50% for several activities (e.g., mind body, nutrition, psychotherapy interventions). Fully half reported that having a physician-designed plan would influence participation in activities. The most common reported barriers to SCP activity engagement were lack of time (82%), work/school (65%), and lack of information (65%). CONCLUSION: Few survivors recalled receiving a formal SCP, and lack of information about wellness activities was a commonly reported barrier to participation. Interest in wellness activities was generally high and may indicate the need for more formal prescription or motivation enhancement techniques to promote SCP engagement. There may be a clinical need to emphasize SCP recommendations to enhance recall and increase engagement in wellness activities that may reduce psychological distress and improve quality of life.

Just in time crisis response: suicide alert system for telemedicine psychotherapy settings.

AbstractObjective: To design a Natural Language Processing (NLP) algorithm capable of detecting suicide content from patients' written communication to their therapists, to support rapid response and clinical decision making in telehealth settings. Method: A training dataset of therapy transcripts for 1,864 patients was established by detecting patient content endorsing suicidality using a proxy-model anchored on therapists' suicide prevention interventions; human expert raters then assessed the level of suicide risk endorsed by patients identified by the proxy-model (i.e., no risk, risk factors, ideation, method, or plan). A bag-of-words classification model was then iteratively built using the annotations from the expert raters to detect suicide risk level in 85,216 labeled patients' sentences from the training dataset. Results: The final NLP model identified risk-related content from non-risk content with good accuracy (AUC = 82.78). Conclusions: Risk for suicide could be reliably identified by the NLP algorithm. The risk detection model could assist telehealth clinicians in providing crisis resources in a timely manner. This modeling approach could also be applied to other psychotherapy research tasks to assist in the understanding of how the psychotherapy process unfolds for each patient and therapist.

Trajectories of grief: Comparing symptoms from the DSM-5 and ICD-11 diagnoses.

BACKGROUND: Diagnostic criteria for prolonged grief have appeared in the Diagnostic and Statistical Manual of Mental Disorders ( DSM-5; persistent complex bereavement disorder, PCBD) and in the ICD-11 (prolonged grief disorder, PGD), and the question of which diagnosis is most clinically useful has been hotly debated. This study provides the first longitudinal comparison of PCBD and PGD in their ability to capture symptom change over time and their relation to long-term outcomes. METHODS: A community sample was recruited consisting of 282 individuals who had recently lost a spouse. Structured clinical interviews were conducted at 3, 14, and 25 months postloss for symptoms corresponding to PCBD and PGD criteria. Outcomes at 25 months included PCBD and PGD caseness, depression, global functioning, and interviewer ratings of participant suffering. RESULTS: PCBD and PGD trajectories determined by growth mixture modeling, each captured three primary outcomes: resilience, moderate-improving symptoms, and prolonged-stable symptoms. The PGD solution also identified trajectories of increasing and decreasing distress: prolonged-worsening and acute-recovering symptoms. Prediction of 25-month outcomes indicated differences conforming to the severity of PGD symptoms, and the prolonged-worsening trajectory was associated with the worst adjustment. CONCLUSIONS: PGD symptoms were more differentiated, better-captured psychopathology, and other outcomes and were more sensitive to change over time compared to PCBD.

Commentary on evidence in support of a grief-related condition as a DSM diagnosis.

The death of a loved one is one of life's greatest stressors. Most bereaved individuals experience a period of acute grief that diminishes in intensity as they adapt to the changes brought about by their loss. Over the past four decades, a growing body of research has focused on a form of prolonged grief that is painful and impairing. There is a substantial and growing evidence base that supports the validity and significance of a grief-related disorder, including the clinical value of being able to diagnose it and provide effective targeted treatment. ICD-11 will include a new diagnosis of prolonged grief disorder (PGD). DSM-5 called this condition persistent complex bereavement disorder (PCBD) and included it in Section III, signaling agreement that a diagnosis is warranted while further research is needed to determine the optimal criteria. Given the remaining uncertainties, reading this literature can be confusing. There is inconsistency in naming the condition (including complicated grief as well as PGD and PCBD) and lack of uniformity in identifying it, with respect to the optimal threshold and timeframe for distinguishing it from normal grief. As an introductory commentary for this Depression and Anxiety special edition on this form of grief, the authors discuss the history, commonalities, and key areas of variability in identifying this condition. We review the state of diagnostic criteria for DSM-5 and the current ICD-11 diagnostic guideline, highlighting the clinical relevance of making this diagnosis.

Understanding the impact of complicated grief on combat related posttraumatic stress disorder, guilt, suicide, and functional impairment in a clinical trial of post-9/11 service members and veterans.

BACKGROUND: Complicated grief (CG) is a bereavement-specific syndrome distinct from but commonly comorbid with posttraumatic stress disorder (PTSD). While bereavement is common among military personnel (Simon et al., 2018), there is little research on the impact of CG comorbidity on PTSD treatment outcomes. METHODS: To evaluate the impact of comorbid CG on PTSD treatment outcomes we analyzed data from a randomized trial comparing prolonged exposure, sertraline, and their combination in veterans with a primary diagnosis of combat-related PTSD (n = 194). Assessment of PTSD, trauma-related guilt, functional impairment, and suicidal ideation and behavior occurred at baseline and weeks 6, 12, and 24 during the 24-week trial. RESULTS: CG was associated with lower PTSD treatment response (odds ratio (OR) = 0.29, 95% confidence interval (CI) [0.12, 0.69], p = 0.005) and remission (OR = 0.28, 95% CI [0.11, 0.71], p = 0.007). Those with CG had greater severity of PTSD (p = 0.005) and trauma-related guilt (<0.001) at baseline and endpoint. In addition, those with CG were more likely to experience suicidal ideation during the study (CG: 35%, 14/40 vs. no CG 15%, 20/130; OR = 3.01, 95% CI [1.29, 7.02], p = 0.011). CONCLUSIONS: Comorbid CG is associated with elevated PTSD severity and independently associated with poorer endpoint treatment outcomes in veterans with combat-related PTSD, suggesting that screening and additional intervention for CG may be needed.

Two-way messaging therapy for depression and anxiety: longitudinal response trajectories.

BACKGROUND: Telemedicine is a strategy for overcoming barriers to access evidence-based psychotherapy. Digital modalities that operate outside session-based treatment formats, such as ongoing two-way messaging, may further address these challenges. However, no study to date has established suitability criteria for this medium. METHODS: A large outpatient sample (n = 10,718) engaged in daily messaging with licensed clinicians from a telemedicine provider. Patients consisted of individuals from urban and rural settings in all 50 states of the US, who signed up to the telemedicine provider. Using a longitudinal design, symptoms changes were observed during a 12 week treatment course. Symptoms were assessed from baseline every three weeks using the Patient Health Questionnaire (PHQ-9) for depression, and the Generalized Anxiety Disorder (GAD-7) for anxiety. Demographics and engagement metrics, such as word count for both patients and therapists, were also assessed. Growth mixture modeling was used to tease apart symptoms trajectories, and identify predictors of treatment response. RESULTS: Two subpopulations had GAD-7 and PHQ-9 remission outcomes (Recovery and Acute Recovery, 30.7% of patients), while two others showed amelioration of symptoms (Depression and Anxiety Improvement, 36.9% of patients). Two subpopulations experienced no changes in symptoms (Chronic and Elevated Chronic, 32.4% of patients). Higher use of written communication, patient characteristics, and engagement metrics reliably distinguished patients with the greatest level of remission (Recovery and Acute Recovery groups). CONCLUSIONS: Remission of depression and anxiety symptoms was observed during delivery of psychotherapy through messaging. Improvement rates were consistent with face-to-face therapy, suggesting the suitability of two-way messaging psychotherapy delivery. Characteristics of improving patients were identified and could be used for treatment recommendation. These findings suggest the opportunity for further research, to directly compare messaging delivery with a control group of treatment as usual. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT03699488, Retrospectively Registered October 8, 2018.

Message Delivery for the Treatment of Posttraumatic Stress Disorder: Longitudinal Observational Study of Symptom Trajectories.

BACKGROUND: Individuals with posttraumatic stress disorder (PTSD) face symptoms that can hinder access to treatment, such as avoidance and guilt. Telemedicine offers a technological solution to increase access to mental health care and overcome barriers to treatment. Although an increasing body of literature focused on synchronous telehealth (eg, live video), no studies have examined the delivery of PTSD treatment via two-way multimedia messages (ie, texting or messaging). OBJECTIVE: The aim of this study was to conduct a longitudinal observation of treatment for PTSD delivered using two-way asynchronous messaging. We also sought to identify individual and treatment characteristics that could predict the observed outcome differences. METHODS: Outpatients diagnosed with PTSD (N=475) received interventions from licensed therapists, which were delivered via messaging once or more than once per day, 5 days a week for 12 weeks. PTSD symptoms were assessed every 3 weeks using the PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders-5. Trajectories of PTSD symptoms were identified using growth mixture modeling (GMM). Using logistic regression, the demographic, treatment, and messaging characteristics of patient groups that improved were compared with the characteristics of patient groups that did not improve. RESULTS: The GMM identified 4 trajectories of PTSD symptoms: moderate improvement (197/475, 41.4%), high symptoms (197/475, 41.4%), chronic symptoms (61/475, 12.9%), and acute improvement (20/475, 4.3%). Patients with a clinically significant reduction in PTSD symptoms (231/475, 48.6%) were more likely to communicate via video (odds ratio [OR] 1.01, 95% CI 1.01-1.05; P=.03), have a higher working alliance with their therapist (OR 1.03, 95% CI 1.01-1.05; P=.02), and be at their first treatment experience (OR 2.03, 95% CI 1.18-3.54; P=.01). Treatment adherence was associated with greater therapeutic alliance (OR 1.07, 95% CI 1.03-1.10; P<.001), education (OR 2.13, 95% CI 1.13-4.03; P=.02), and more patient-generated messages per week (OR 1.08, 95% CI 1.04-1.13; P<.001). CONCLUSIONS: Multimedia message delivery for PTSD treatment showed symptom-reduction rates similar to traditional forms of treatment delivery, suggesting further study of messaging as a treatment medium. Most patients completed an 8-week course, reflecting the acceptability of messaging interventions. Delivering treatment via two-way messaging offers increased opportunities for widespread access to mental health care.

Symptoms of persistent complex bereavement disorder, depression, and PTSD in a conjugally bereaved sample: a network analysis.

BACKGROUND: Complicated and persistent grief reactions afflict approximately 10% of bereaved individuals and are associated with severe disruptions of functioning. These maladaptive patterns were defined in Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) as persistent complex bereavement disorder (PCBD), but its criteria remain debated. The condition has been studied using network analysis, showing potential for an improved understanding of PCBD. However, previous studies were limited to self-report and primarily originated from a single archival dataset. To overcome these limitations, we collected structured clinical interview data from a community sample of newly conjugally bereaved individuals (N = 305). METHODS: Gaussian graphical models (GGM) were estimated from PCBD symptoms diagnosed at 3, 14, and 25 months after the loss. A directed acyclic graph (DAG) was generated from initial PCBD symptoms, and comorbidity networks with DSM-5 symptoms of major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) were analyzed 1 year post-loss. RESULTS: In the GGM, symptoms from the social/identity PCBD symptoms cluster (i.e. role confusion, meaninglessness, and loneliness) tended to be central in the network at all assessments. In the DAG, yearning activated a cascade of PCBD symptoms, suggesting how symptoms lead into psychopathological configurations. In the comorbidity networks, PCBD and depressive symptoms formed separate communities, while PTSD symptoms divided in heterogeneous clusters. CONCLUSIONS: The network approach offered insights regarding the core symptoms of PCBD and the role of persistent yearnings. Findings are discussed regarding both clinical and theoretical implications that will serve as a step toward a more integrated understanding of PCBD.

Mood and Personality Characteristics are Associated with Metamemory Knowledge Accuracy in a Community-Based Cohort of Older Adults.

OBJECTIVES: Emerging work reveals the neuroanatomic changes that compromise metacognition; however, little is known about the impact of premorbid factors. Research suggests that psychological variables influence the perception of cognition, but whether they influence the accuracy of those perceptions (i.e., metacognition) has not been directly examined. PARTICIPANTS AND METHODS: Using Latent Class Analysis (LCA), we tested for discrete personality (NEOFFI) and mood (STAI, BDI-II, and GDS) classes among a community-based cohort of 151 older adults, enrolled in the NKI-Rockland study. Metamemory was calculated by comparing subjective memory ratings (modified Cognitive Failures Questionnaire) to objective memory (Rey Auditory Verbal Learning Test) to determine the degree to which individuals were overconfident, underconfident, or accurate in their self-assessment. A generalized linear model was used to examine whether metamemory differed across the emergent classes. A one sample t test was used to determine whether the metamemory scores of the emergent classes were statistically significantly different from zero, that is, over or under confident. RESULTS: Two discrete classes emerged in the LCA: Class 1 was characterized predominantly by high extraversion and conscientiousness and low neuroticism and anxiety; Class 2 was characterized predominantly by low extraversion and conscientiousness and high neuroticism and anxiety. Metamemory differed significantly as a function of Class Membership (F(4,151)=5.42; p<.001), with Class 1 demonstrating accurate metamemory (M=0.21; SD=1.31) and Class 2 demonstrating under-confidence (M=-0.59; SD=1.39) in their memory. CONCLUSIONS: The significant association between psychological factors and metamemory knowledge accuracy suggests that such characteristics may be important to consider in the conceptualization, assessment, and treatment of metacognitive disturbances. (JINS, 2018, 24, 498-510).

At-home, telehealth-supported ketamine treatment for depression: Findings from longitudinal, machine learning and symptom network analysis of real-world data.

BACKGROUND: Improving safe and effective access to ketamine therapy is of high priority given the growing burden of mental illness. Telehealth-supported administration of sublingual ketamine is being explored toward this goal. METHODS: In this longitudinal study, moderately-to-severely depressed patients received four doses of ketamine at home over four weeks within a supportive digital health context. Treatment was structured to resemble methods of therapeutic psychedelic trials. Patients receiving a second course of treatment were also examined. Symptoms were assessed using the Patient Health Questionnaire (PHQ-9) for depression. We conducted preregistered machine learning and symptom network analyses to investigate outcomes (osf.io/v2rpx). RESULTS: A sample of 11,441 patients was analyzed, demonstrating a modal antidepressant response from both non-severe (n = 6384, 55.8 %) and severe (n = 2070, 18.1 %) baseline depression levels. Adverse events were detected in 3.0-4.8 % of participants and predominantly neurologic or psychiatric in nature. A second course of treatment helped extend improvements in patients who responded favorably to initial treatment. Improvement was most strongly predicted by lower depression scores and age at baseline. Symptoms of Depressed mood and Anhedonia sustained depression despite ongoing treatment. LIMITATIONS: This study was limited by the absence of comparison or control groups and lack of a fixed-dose procedure for ketamine administration. CONCLUSIONS: At-home, telehealth-supported ketamine administration was largely safe, well-tolerated, and associated with improvement in patients with depression. Strategies for combining psychedelic-oriented therapies with rigorous telehealth models, as explored here, may uniquely address barriers to mental health treatment.

Linguistic markers of anxiety and depression in Somatic Symptom and Related Disorders: Observational study of a digital intervention.

BACKGROUND: Somatic Symptom and Related Disorders (SSRD), including chronic pain, result in frequent primary care visits, depression and anxiety symptoms, and diminished quality of life. Treatment access remains limited due to structural barriers and functional impairment. Digital delivery offers to improve access and enables transcript analysis via Natural Language Processing (NLP) to inform treatment. Therefore, we investigated asynchronous message-delivered SSRD treatment, and used NLP methods to identify symptom reduction markers from emotional valence. METHODS: 173 individuals diagnosed with SSRD received interventions from licensed therapists via messaging 5 days/week for 8 weeks. Depression and anxiety symptoms were measured with the PHQ-9 and GAD-7 from baseline every three weeks. Symptoms trajectories were identified using unsupervised random forest clustering. Emotional valence expressed and use of emotional words were extracted from patients' de-identified transcripts, respectively using VADER and NCR Lexicon. Valence differences were examined using logistic regression. RESULTS: Two subpopulations were identified showing symptoms Improvement (n = 72; 41.62 %) and non-response (n = 101; 58.38 %). Improvement patients expressed more positive valence in the first week of treatment (OR = 1.84, CI: 1.12-3.02; p = .015) and were less likely to express negative valence by the end of treatment (OR = 0.05; CI: 0.30-0.83; p = .008). Non-response patients used more negative valence words, including pain. LIMITATIONS: Findings were derived from observational data obtained during an ecological intervention, without the inclusion of a control group. CONCLUSIONS: NLP identified linguistic markers distinguishing changes in anxiety and depression symptoms over treatment. Digital interventions offer new forms of delivery and provide the opportunity to automatically collect data for linguistic analysis.

Physical activity may buffer against depression and promote resilience after major life stressors.

As many individuals experience potentially traumatic or stressful life events, understanding factors that are likely to promote resilience is imperative. Given the demonstrated efficacy of exercise for depression treatment, we examined if exercise buffers against the risk of developing psychiatric symptoms following life stressors. 1405 participants (61% female) from a longitudinal panel cohort experienced disability onset (43%), bereavement (26%), heart attack (20%), divorce (11%), and job loss (3%). They reported time spent exercising and depressive symptoms (Center for Epidemiologic Studies Depression scale) across three time points collected in two-year intervals: T0 (pre-stressor), T1 (acutely post-stressor), and T2 (post-stressor). Participants were classified in previously identified heterogeneous depression trajectories pre- to post-life stressor: resilient (69%), emerging (11.5%), chronic (10%), and improving (9.5%). Multinomial logistic regression found that more T0 exercise predicted likelihood of classification in resilient versus other groups (all p<.02). Controlling for covariates, only the higher likelihood of classification in resilient versus improving remained (p=.03). Follow-up repeated measures general linear model (GLM) assessed whether trajectory was associated with exercise at each time, controlling for covariates. GLM indicated significant within-subjects effects for time (p=.016, partial η2=.003) and time*trajectory (p=.020, partial η2=.005) on exercise and significant between-subjects effects of trajectory (p<.001, partial η2=.016) and all covariates. The resilient group showed consistent high exercise levels. The improving group had consistent moderate exercise. The emerging and chronic groups were associated with lower exercise post-stressor. Pre-stressor exercise may buffer against depression and ongoing exercise may be associated with lower depression levels following a major life stressor.

Natural language processing for mental health interventions: a systematic review and research framework.

Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP's potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients' clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers' characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness.

Association of Health Care Work With Anxiety and Depression During the COVID-19 Pandemic: Structural Topic Modeling Study.

BACKGROUND: Stressors for health care workers (HCWs) during the COVID-19 pandemic have been manifold, with high levels of depression and anxiety alongside gaps in care. Identifying the factors most tied to HCWs' psychological challenges is crucial to addressing HCWs' mental health needs effectively, now and for future large-scale events. OBJECTIVE: In this study, we used natural language processing methods to examine deidentified psychotherapy transcripts from telemedicine treatment during the initial wave of COVID-19 in the United States. Psychotherapy was delivered by licensed therapists while HCWs were managing increased clinical demands and elevated hospitalization rates, in addition to population-level social distancing measures and infection risks. Our goal was to identify specific concerns emerging in treatment for HCWs and to compare differences with matched non-HCW patients from the general population. METHODS: We conducted a case-control study with a sample of 820 HCWs and 820 non-HCW matched controls who received digitally delivered psychotherapy in 49 US states in the spring of 2020 during the first US wave of the COVID-19 pandemic. Depression was measured during the initial assessment using the Patient Health Questionnaire-9, and anxiety was measured using the General Anxiety Disorder-7 questionnaire. Structural topic models (STMs) were used to determine treatment topics from deidentified transcripts from the first 3 weeks of treatment. STM effect estimators were also used to examine topic prevalence in patients with moderate to severe anxiety and depression. RESULTS: The median treatment enrollment date was April 15, 2020 (IQR March 31 to April 27, 2020) for HCWs and April 19, 2020 (IQR April 5 to April 27, 2020) for matched controls. STM analysis of deidentified transcripts identified 4 treatment topics centered on health care and 5 on mental health for HCWs. For controls, 3 STM topics on pandemic-related disruptions and 5 on mental health were identified. Several STM treatment topics were significantly associated with moderate to severe anxiety and depression, including working on the hospital unit (topic prevalence 0.035, 95% CI 0.022-0.048; P<.001), mood disturbances (prevalence 0.014, 95% CI 0.002-0.026; P=.03), and sleep disturbances (prevalence 0.016, 95% CI 0.002-0.030; P=.02). No significant associations emerged between pandemic-related topics and moderate to severe anxiety and depression for non-HCW controls. CONCLUSIONS: The study provides large-scale quantitative evidence that during the initial wave of the COVID-19 pandemic, HCWs faced unique work-related challenges and stressors associated with anxiety and depression, which required dedicated treatment efforts. The study further demonstrates how natural language processing methods have the potential to surface clinically relevant markers of distress while preserving patient privacy.

Heterogeneity of posttraumatic stress, depression, and fear of cancer recurrence in breast cancer survivors: a latent class analysis.

PURPOSE: Breast cancer survivors may demonstrate elevated psychological distress, which can also hinder adherence to survivorship care plans. Our goal was to study heterogeneity of behavioral health and functioning in breast cancer survivors, and identify both risk and protective factors to improve targets for wellness interventions. METHODS: Breast cancer survivors (n = 187) consented to complete self-reported psychological measures and to access their medical records. Latent class analysis (LCA) was used to classify heterogeneous subpopulations based on levels of depression, post-traumatic stress, fear of cancer recurrence, cancer-related pain, and fatigue. Multinomial logistic regression and auxiliary analysis in a 3-step modeling conditional approach was used to identify characteristics of the group based on demographics, treatment history and characteristics, and current medication prescriptions. RESULTS: Three subpopulations of breast cancer survivors were identified from the LCA: a modal Resilient group (48.2%, n = 90), a Moderate Symptoms group (34%, n = 65), and an Elevated Symptoms group (n = 17%, n = 32) with clinically-relevant impairment. Results from the logistic regression indicated that individuals in the Elevated Symptoms group were less likely to have a family history of breast cancer; they were more likely to be closer to time of diagnosis and younger, have received chemotherapy and psychotropic prescriptions, and have higher BMI. Survivors in the Elevated Symptoms group were also less likely to be prescribed estrogen inhibitors than the Moderate Symptoms group. CONCLUSIONS: This study identified subgroups of breast cancer survivors based on behavioral, psychological, and treatment-related characteristics, with implications for targeted monitoring and survivorship care plans. IMPLICATIONS FOR CANCER SURVIVORS: Results showed the majority of cancer survivors were resilient, with minimal psychological distress. Results also suggest the importance of paying special attention to younger patients getting chemotherapy, especially those without a family history of breast cancer.

Machine yearning: How advances in computational methods lead to new insights about reactions to loss.

The loss of a loved one is a potentially traumatic event that can result in disparate outcomes and symptom patterns. Machine learning methods offer computational tools to probe this heterogeneity and understand grief psychopathology in its complexity. In this article, we examine the latest contributions to the scientific study of bereavement reactions garnered through the use of computational methods. We focus on findings originating from trajectory modeling studies, as well as the recent insights originating from the network analysis of prolonged grief symptoms. We also discuss applications of artificial intelligence for the accurate identification of major depression and post-traumatic stress, as examples for their potential applications to the study of loss reactions.

At-home, sublingual ketamine telehealth is a safe and effective treatment for moderate to severe anxiety and depression: Findings from a large, prospective, open-label effectiveness trial.

BACKGROUND: At-home Ketamine-assisted therapy (KAT) with psychosocial support and remote monitoring through telehealth platforms addresses access barriers, including the COVID-19 pandemic. Large-scale evaluation of this approach is needed for questions regarding safety and effectiveness for depression and anxiety. METHODS: In this prospective study, a large outpatient sample received KAT over four weeks through a telehealth provider. Symptoms were assessed using the Patient Health Questionnaire (PHQ-9) for depression, and the Generalized Anxiety Disorder scale (GAD-7) for anxiety. Demographics, adverse events, and patient-reported dissociation were also analyzed. Symptom trajectories were identified using Growth Mixture Modeling, along with outcome predictors. RESULTS: A sample of 1247 completed treatment with sufficient data, 62.8 % reported a 50 % or greater improvement on the PHQ-9, d = 1.61, and 62.9 % on the GAD-7, d = 1.56. Remission rates were 32.6 % for PHQ-9 and 31.3 % for GAD-7, with 0.9 % deteriorating on the PHQ-9, and 0.6 % on the GAD-7. Four patients left treatment early due to side effects or clinician disqualification, and two more due to adverse events. Three patient subpopulations emerged, characterized by Improvement (79.3 %), Chronic (11.4 %), and Delayed Improvement (9.3 %) for PHQ-9 and GAD-7. Endorsing side effects at Session 2 was associated with delayed symptom improvement, and Chronic patients were more likely than the other two groups to report dissociation at Session 4. CONCLUSION: At-home KAT response and remission rates indicated rapid and significant antidepressant and anxiolytic effects. Rates were consistent with laboratory- and clinic-administered ketamine treatment. Patient screening and remote monitoring maintained low levels of adverse events. Future research should assess durability of effects.

An observational study of Internet behaviours for adolescent females following sexual abuse.

Child sexual abuse (CSA) is associated with revictimization and sexual risk-taking behaviours. The Internet has increased the opportunities for teens to access sexually explicit imagery and has provided new avenues for victimization and exploitation. Online URL activity and offline psychosocial factors were assessed for 460 females aged 12-16 (CSA = 156; comparisons = 304) with sexual behaviours and Internet-initiated victimization assessed 2 years later. Females who experienced CSA did not use more pornography than comparisons but were at increased odds of being cyberbullied (odds ratio = 2.84, 95% confidence interval = 1.67-4.81). These females were also more likely to be represented in a high-risk latent profile characterized by heightened URL activity coupled with problematic psychosocial factors, which showed increased odds of being cyberbullied, receiving online sexual solicitations and heightened sexual activity. While Internet activity alone may not confer risk, results indicate a subset of teens who have experienced CSA for whom both online and offline factors contribute to problematic outcomes.