Real-time assessment of positive and negative affective fluctuations and mood lability in a transdiagnostic sample of youth.

BACKGROUND: Emotional lability, defined as rapid and/or intense affect fluctuations, is associated with pediatric psychopathology. Although numerous studies have examined labile mood in clinical groups, few studies have used real-time assessments in a well-characterized transdiagnostic sample, and no prior study has included participants with disruptive mood dysregulation disorder (DMDD). The present study leverages ecological momentary assessment (EMA) to assess emotional lability in a transdiagnostic pediatric sample. METHODS: One hundred thirty participants ages 8-18 with primary diagnoses of DMDD, attention-deficit/hyperactivity disorder (ADHD), an anxiety disorder (ANX), or healthy volunteers completed a previously validated 1-week EMA protocol. Clinicians determined diagnoses based on semi-structured interviews and assessed levels of functional impairment. Participants reported momentary affective states and mood change. Composite scores of fluctuations in positive and negative affect were generated. Affect fluctuations were compared between diagnostic groups and tested for their association with functional impairment. RESULTS: Diagnostic groups differed in levels of negative and positive emotional lability. DMDD patients demonstrated the highest level of labile mood compared with other groups. Emotional lability was associated with global impairment in the whole sample. CONCLUSIONS: Both positive and negative emotional lability is salient in pediatric psychopathology and is associated with functional impairment, particularly in DMDD youth.

Three-Month Follow-up Study of Mental Health Outcomes After a National COVID-19 Lockdown: Comparing Patients With Mood or Anxiety Disorders Living in an Area With a Higher Versus Lower Infection Incidence.

Objective: The COVID-19 pandemic and the related containment measures can represent a traumatic experience, particularly for populations living in high incidence areas and individuals with mental disorders. The aim of this study was to prospectively examine posttraumatic stress disorder (PTSD), anxiety, and depressive symptoms since the end of the first COVID-19 pandemic wave and Italy's national lockdown in subjects with mood or anxiety disorders living in 2 regions with increasing pandemic incidence.Methods: 102 subjects with a DSM-5 anxiety or mood disorder were enrolled from June to July 2020 and assessed at baseline (T0) and after 3 months (T1) with the Impact of Event Scale-Revised, Patient Health Questionnaire-9, Generalized Anxiety Disorder 7-Item, and Work and Social Adjustment Scale. At T1, subjects were also assessed by means of the Trauma and Loss Spectrum Self-Report for PTSD.Results: At T0, subjects from the high COVID-19 incidence area showed higher levels of traumatic symptoms than those from the low COVID-19 incidence area (P < .001), with a decrease at T1 with respect to T0 (P = .001). Full or partial DSM-5 PTSD related to the COVID-19 pandemic emerged in 23 subjects (53.5%) from the high COVID-19 incidence area and in 9 (18.0%) from the low COVID-19 incidence area (P < .001).Conclusions: Subjects with mood or anxiety disorders presented relevant rates of PTSD, depressive, and anxiety symptoms in the aftermath of the lockdown, and in most cases these persisted after 3 months. The level of exposure to the pandemic emerged as a major risk factor for PTSD development. Further long-term studies are needed to follow up the course of traumatic burden.

Mood Reactive Disorders among COVID-19 Inpatients: Experience from a Monocentric Cohort.

OBJECTIVE: We aimed to investigate the presence and severity of depressive symptoms among coronavirus disease 2019 (COVID-19) inpatients and any possible changes after their discharge. SUBJECT AND METHODS: We collected data of patients admitted to the Infectious Disease Unit in Sassari, Italy, for COVID-19, from March 8 to May 8, 2020. The Beck Depression Inventory-II (BDI-II) was performed 1 week after admission (T0) and 1 week after discharge (T1). The cutoff point chosen to define the clinical significance of depressive symptoms was 20 (at least moderate). RESULTS: Forty-eight subjects were included. Mean age was 64.3 ± 17.6 years, and 32 (66.7%) were male. Most frequent comorbidities were cardiovascular diseases (19; 39.6%) and hypertension (17; 35.4%). When performing BDI-II at T0, 21 (43.7%) patients reported depressive symptoms at T0, according to the chosen cutoff point (BDI-II = 20). Eight (16.7%) patients had minimal symptoms. Mild mood disturbance and moderate and severe depressive symptoms were found in 24 (50%), 14 (29.2%), and 2 (4.2%) patients, respectively, at T0. The comparison of the BDI-II questionnaire at T0 with T1 showed a significant improvement in the total score (p < 0.0001), as well as in 4 out of the 5 selected questions of interest (p < 0.05). Univariate analysis showed that kidney failure and the death of a roommate were significantly associated with severity of mood disorders. CONCLUSION: Mood disturbances and depressive symptoms commonly occur among COVID-19 inpatients. Our results show that COVID-19 inpatients might be at higher risk for developing depressive reactive disorders and could benefit from an early psychological evaluation and strategies improving sleep quality.

Experiences of coping with the first wave of COVID-19 epidemic in Philadelphia, PA: Mixed methods analysis of a cross-sectional survey of worries and symptoms of mood disorders.

Our objective was to describe how residents of Philadelphia, Pennsylvania, coped psychologically with the first wave of COVID-19 pandemic. In a cross-sectional design, we aimed to estimate the rates and correlates of anxiety and depression, examine how specific worries correlated with general anxiety and depression, and synthesize themes of "the most difficult experiences" shared by the respondents. We collected data through an on-line survey in a convenience sample of 1,293 adult residents of Philadelphia, PA between April 17 and July 3, 2020, inquiring about symptoms of anxiety and depression (via the Hospital Anxiety and Depression Scale), specific worries, open-ended narratives of "the most difficult experiences" (coded into themes), demographics, perceived sources of support, and general health. Anxiety was evident among 30 to 40% of participants and depression-about 10%. Factor analysis revealed two distinct, yet inter-related clusters of specific worries related to mood disorders: concern about "hardships" and "fear of infection". Regression analyses revealed that anxiety, depression, and fear of infection, but not concern about hardships, worsened over the course of the epidemic. "The most difficult experiences" characterized by loss of income, poor health of self or others, uncertainty, death of a relative or a friend, and struggle accessing food were each associated with some of the measures of worries and mood disorders. Respondents who believed they could rely on support of close personal network fared better psychologically than those who reported relying primarily on government and social services organizations. Thematic analysis revealed complex perceptions of the pandemic by the participants, giving clues to both positive and negative experiences that may have affected how they coped. Despite concerns about external validity, our observations are concordant with emerging evidence of psychological toll of the COVID-19 pandemic and measures employed to mitigate risk of infection.

Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review.

BACKGROUND: Mood disorders are commonly underrecognized and undertreated, as diagnosis is reliant on self-reporting and clinical assessments that are often not timely. Speech characteristics of those with mood disorders differs from healthy individuals. With the wide use of smartphones, and the emergence of machine learning approaches, smartphones can be used to monitor speech patterns to help the diagnosis and monitoring of mood disorders. OBJECTIVE: The aim of this review is to synthesize research on using speech patterns from smartphones to diagnose and monitor mood disorders. METHODS: Literature searches of major databases, Medline, PsycInfo, EMBASE, and CINAHL, initially identified 832 relevant articles using the search terms "mood disorders", "smartphone", "voice analysis", and their variants. Only 13 studies met inclusion criteria: use of a smartphone for capturing voice data, focus on diagnosing or monitoring a mood disorder(s), clinical populations recruited prospectively, and in the English language only. Articles were assessed by 2 reviewers, and data extracted included data type, classifiers used, methods of capture, and study results. Studies were analyzed using a narrative synthesis approach. RESULTS: Studies showed that voice data alone had reasonable accuracy in predicting mood states and mood fluctuations based on objectively monitored speech patterns. While a fusion of different sensor modalities revealed the highest accuracy (97.4%), nearly 80% of included studies were pilot trials or feasibility studies without control groups and had small sample sizes ranging from 1 to 73 participants. Studies were also carried out over short or varying timeframes and had significant heterogeneity of methods in terms of the types of audio data captured, environmental contexts, classifiers, and measures to control for privacy and ambient noise. CONCLUSIONS: Approaches that allow smartphone-based monitoring of speech patterns in mood disorders are rapidly growing. The current body of evidence supports the value of speech patterns to monitor, classify, and predict mood states in real time. However, many challenges remain around the robustness, cost-effectiveness, and acceptability of such an approach and further work is required to build on current research and reduce heterogeneity of methodologies as well as clinical evaluation of the benefits and risks of such approaches.

Severe mental illness is associated with increased mortality and severe course of COVID-19.

OBJECTIVE: Psychiatric disorders have been associated with unfavourable outcome following respiratory infections. Whether this also applies to coronavirus disease 2019 (COVID-19) has been scarcely investigated. METHODS: Using the Danish administrative databases, we identified all patients with a positive real-time reverse transcription-polymerase chain reaction test for COVID-19 in Denmark up to and including 2 January 2021. Multivariable cox regression was used to calculate 30-day absolute risk and average risk ratio (ARR) for the composite end point of death from any cause and severe COVID-19 associated with psychiatric disorders, defined using both hospital diagnoses and redemption of psychotropic drugs. RESULTS: We included 144,321 patients with COVID-19. Compared with patients without psychiatric disorders, the standardized ARR of the composite outcome was significantly increased for patients with severe mental illness including schizophrenia spectrum disorders 2.43 (95% confidence interval [CI], 1.79-3.07), bipolar disorder 2.11 (95% CI, 1.25-2.97), unipolar depression 1.70 (95% CI, 1.38-2.02), and for patients who redeemed psychotropic drugs 1.70 (95% CI, 1.48-1.92). No association was found for patients with other psychiatric disorders 1.13 (95% CI, 0.86-1.38). Similar results were seen with the outcomes of death or severe COVID-19. Among the different psychiatric subgroups, patients with schizophrenia spectrum disorders had the highest 30-day absolute risk for the composite outcome 3.1% (95% CI, 2.3-3.9%), death 1.2% (95% CI, 0.4-2.0%) and severe COVID-19 2.7% (95% CI, 1.9-3.6%). CONCLUSION: Schizophrenia spectrum disorders, bipolar disorder, unipolar depression and psychotropic drug redemption are associated with unfavourable outcomes in patients with COVID-19.

Association of Psychiatric Disorders With Mortality Among Patients With COVID-19.

Importance: To date, the association of psychiatric diagnoses with mortality in patients infected with coronavirus disease 2019 (COVID-19) has not been evaluated. Objective: To assess whether a diagnosis of a schizophrenia spectrum disorder, mood disorder, or anxiety disorder is associated with mortality in patients with COVID-19. Design, Setting, and Participants: This retrospective cohort study assessed 7348 consecutive adult patients for 45 days following laboratory-confirmed COVID-19 between March 3 and May 31, 2020, in a large academic medical system in New York. The final date of follow-up was July 15, 2020. Patients without available medical records before testing were excluded. Exposures: Patients were categorized based on the following International Statistical Classification of Diseases, Tenth Revision, Clinical Modification diagnoses before their testing date: (1) schizophrenia spectrum disorders, (2) mood disorders, and (3) anxiety disorders. Patients with these diagnoses were compared with a reference group without psychiatric disorders. Main Outcomes and Measures: Mortality, defined as death or discharge to hospice within 45 days following a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test result. Results: Of the 26 540 patients tested, 7348 tested positive for SARS-CoV-2 (mean [SD] age, 54 [18.6] years; 3891 [53.0%] women). Of eligible patients with positive test results, 75 patients (1.0%) had a history of a schizophrenia spectrum illness, 564 (7.7%) had a history of a mood disorder, and 360 (4.9%) had a history of an anxiety disorder. After adjusting for demographic and medical risk factors, a premorbid diagnosis of a schizophrenia spectrum disorder was significantly associated with mortality (odds ratio [OR], 2.67; 95% CI, 1.48-4.80). Diagnoses of mood disorders (OR, 1.14; 95% CI, 0.87-1.49) and anxiety disorders (OR, 0.96; 95% CI, 0.65-1.41) were not associated with mortality after adjustment. In comparison with other risk factors, a diagnosis of schizophrenia ranked behind only age in strength of an association with mortality. Conclusions and Relevance: In this cohort study of adults with SARS-CoV-2-positive test results in a large New York medical system, adults with a schizophrenia spectrum disorder diagnosis were associated with an increased risk for mortality, but those with mood and anxiety disorders were not associated with a risk of mortality. These results suggest that schizophrenia spectrum disorders may be a risk factor for mortality in patients with COVID-19.