Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis.

BACKGROUND: Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. OBJECTIVE: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. METHODS: Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. RESULTS: Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (ß=-93.61, P<.001), increased sleep variability (ß=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: ß=0.55, P=.001; sleep offset: ß=1.12, P<.001; M10 onset: ß=0.73, P=.003; HR acrophase: ß=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (ß of PHQ-8 × spring = -31.51, P=.002) and summer (ß of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (ß of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer. CONCLUSIONS: Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.

Social dysfunction relates to shifts within socioaffective brain systems among Schizophrenia and Alzheimer's disease patients.

Social dysfunction represents one of the most common signs of neuropsychiatric disorders, such as Schizophrenia (SZ) and Alzheimer's disease (AD). Perturbed socioaffective neural processing is crucially implicated in SZ/AD and generally linked to social dysfunction. Yet, transdiagnostic properties of social dysfunction and its neurobiological underpinnings remain unknown. As part of the European PRISM project, we examined whether social dysfunction maps onto shifts within socioaffective brain systems across SZ and AD patients. We probed coupling of social dysfunction with socioaffective neural processing, as indexed by an implicit facial emotional processing fMRI task, across SZ (N = 46), AD (N = 40) and two age-matched healthy control (HC) groups (N = 26 HC-younger and N = 27 HC-older). Behavioural (i.e., social withdrawal, interpersonal dysfunction, diminished prosocial or recreational activity) and subjective (i.e., feelings of loneliness) aspects of social dysfunction were assessed using the Social Functioning Scale and De Jong-Gierveld loneliness questionnaire, respectively. Across SZ/AD/HC participants, more severe behavioural social dysfunction related to hyperactivity within fronto-parieto-limbic brain systems in response to sad emotions (P = 0.0078), along with hypoactivity of these brain systems in response to happy emotions (P = 0.0418). Such relationships were not found for subjective experiences of social dysfunction. These effects were independent of diagnosis, and not confounded by clinical and sociodemographic factors. In conclusion, behavioural aspects of social dysfunction across SZ/AD/HC participants are associated with shifts within fronto-parieto-limbic brain systems. These findings pinpoint altered socioaffective neural processing as a putative marker for social dysfunction, and could aid personalized care initiatives grounded in social behaviour.

The Metabolome-Wide Signature of Major Depressive Disorder.

Major Depressive Disorder (MDD) is an often-chronic condition with substantial molecular alterations and pathway dysregulations involved. Single metabolite, pathway and targeted metabolomics platforms have indeed revealed several metabolic alterations in depression including energy metabolism, neurotransmission and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulation in depression and reveal previously untargeted mechanisms. Here we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive depression clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline and 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at the 6-year follow-up. Metabolites consistently associated with MDD status or depression severity on both occasions were examined in Mendelian randomization (MR) analysis using metabolite (N=14,000) and MDD (N=800,000) GWAS results. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Six years later, 34 out of the 79 metabolite associations were subsequently replicated. Downregulated metabolites were enriched with long-chain monounsaturated (P=6.7e-07) and saturated (P=3.2e-05) fatty acids and upregulated metabolites with lysophospholipids (P=3.4e-4). Adding BMI to the models changed results only marginally. MR analyses showed that genetically-predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression (severity) indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.

Genetic structure of major depression symptoms across clinical and community cohorts.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and aetiological subtypes. There are several challenges to integrating symptom data from genetically-informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. We conducted genome-wide association studies of major depressive symptoms in three clinical cohorts that were enriched for affected participants (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for missing data patterns in the community cohorts (use of Depression and Anhedonia as gating symptoms). The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analysing genetic association data.

The association between adiposity and atypical energy-related symptoms of depression: A role for metabolic dysregulations.

BACKGROUND: Adiposity has been shown to be linked with atypical energy-related symptoms (AES) of depression. We used genomics to separate the effect of adiposity from that of metabolic dysregulations to examine whether the link between obesity and AES is dependent on the presence of metabolic dysregulations. METHOD: Data were from NEO (n = 5734 individuals) and NESDA (n = 2238 individuals) cohorts, in which the Inventory of Depressive Symptomatology (IDS-SR30) was assessed. AES profile was based on four symptoms: increased appetite, increased weight, low energy level, and leaden paralysis. We estimated associations between AES and two genetic risk scores (GRS) indexing increasing total body fat with (metabolically unhealthy adiposity, GRS-MUA) and without (metabolically healthy adiposity, GRS-MHA) metabolic dysregulations. RESULTS: We validated that both GRS-MUA and GRS-MHA were associated with higher total body fat in NEO study, but divergently associated with biomarkers of metabolic health (e.g., fasting glucose and HDL-cholesterol) in both cohorts. In the pooled results, per standard deviation, GRS-MUA was specifically associated with a higher AES score (ß = 0.03, 95%CI: 0.01; 0.05), while there was no association between GRS-MHA and AES (ß = -0.01, 95%CI: -0.03; 0.01). CONCLUSION: These results suggest that the established link between adiposity and AES profile emerges in the presence of metabolic dysregulations, which may represent the connecting substrate between the two conditions.

Emotional faces processing in major depressive disorder and prediction of antidepressant treatment response: A NeuroPharm study.

BACKGROUND: Major depressive disorder (MDD) is a prevalent neuropsychiatric illness for which it is important to resolve underlying brain mechanisms. Current treatments are often unsuccessful, precipitating a need to identify predictive markers. AIM: We evaluated (1) alterations in brain responses to an emotional faces functional magnetic resonance imaging (fMRI) paradigm in individuals with MDD, compared to controls, (2) whether pretreatment brain responses predicted antidepressant treatment response, and (3) pre-post change in brain responses following treatment. METHODS: Eighty-nine medication-free, depressed individuals and 115 healthy controls completed the fMRI paradigm. Depressed individuals completed a nonrandomized, open-label, 8-week treatment with escitalopram, including the option to switch to duloxetine after 4 weeks. We examined patient-control group differences in regional fMRI responses at baseline, whether baseline fMRI responses predicted treatment response at 8 weeks, including early life stress moderating effects, and change in fMRI responses in 36 depressed individuals rescanned following 8 weeks of treatment. RESULTS: Task reaction time was 5% slower in patients. Multiple brain regions showed significant task-related responses, but we observed no statistically significant patient-control group differences (Cohen's d < 0.35). Patient pretreatment brain responses did not predict antidepressant treatment response (area under the curve of the receiver operator characteristic (AUC-ROC) < 0.6) and brain responses were not statistically significantly changed after treatment (Cohen's d < 0.33). CONCLUSION: This represents the largest prediction study to date examining emotional faces fMRI features as predictors of antidepressant treatment response. Brain response to this fMRI emotional faces paradigm did not distinguish depressed individuals from healthy controls, nor was it predictive of antidepressant treatment response.Clinical Trial Registration: Site: https://clinicaltrials.gov, Trial Number: NCT02869035, Trial Title: Treatment Outcome in Major Depressive Disorder.

Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study.

BACKGROUND: The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored. OBJECTIVE: We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. METHODS: Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse-Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants' location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. RESULTS: This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=-0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=-0.07, P<.001) the subsequent periodicity of mobility. CONCLUSIONS: Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.

Charting brain growth and aging at high spatial precision.

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.

The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones.

BACKGROUND: Most smartphones and wearables are currently equipped with location sensing (using GPS and mobile network information), which enables continuous location tracking of their users. Several studies have reported that various mobility metrics, as well as home stay, that is, the amount of time an individual spends at home in a day, are associated with symptom severity in people with major depressive disorder (MDD). Owing to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with MDD symptoms. OBJECTIVE: The objective of this study is to examine the relationship between the overall severity of depressive symptoms, as assessed by the 8-item Patient Health Questionnaire, and median daily home stay over the 2 weeks preceding the completion of a questionnaire in individuals with MDD. METHODS: We used questionnaire and geolocation data of 164 participants with MDD collected in the observational Remote Assessment of Disease and Relapse-Major Depressive Disorder study. The participants were recruited from three study sites: King's College London in the United Kingdom (109/164, 66.5%); Vrije Universiteit Medisch Centrum in Amsterdam, the Netherlands (17/164, 10.4%); and Centro de Investigación Biomédica en Red in Barcelona, Spain (38/164, 23.2%). We used a linear regression model and a resampling technique (n=100 draws) to investigate the relationship between home stay and the overall severity of MDD symptoms. Participant age at enrollment, gender, occupational status, and geolocation data quality metrics were included in the model as additional explanatory variables. The 95% 2-sided CIs were used to evaluate the significance of model variables. RESULTS: Participant age and severity of MDD symptoms were found to be significantly related to home stay, with older (95% CI 0.161-0.325) and more severely affected individuals (95% CI 0.015-0.184) spending more time at home. The association between home stay and symptoms severity appeared to be stronger on weekdays (95% CI 0.023-0.178, median 0.098; home stay: 25th-75th percentiles 17.8-22.8, median 20.9 hours a day) than on weekends (95% CI -0.079 to 0.149, median 0.052; home stay: 25th-75th percentiles 19.7-23.5, median 22.3 hours a day). Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay (employed participants: 25th-75th percentiles 16.1-22.1, median 19.7 hours a day; unemployed participants: 25th-75th percentiles 20.4-23.5, median 22.6 hours a day). CONCLUSIONS: Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future studies. In addition, they illustrate that passive sensing of individuals with depression is feasible and could provide clinically relevant information to monitor the course of illness in patients with MDD.

Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study.

BACKGROUND: Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. OBJECTIVE: The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). METHODS: Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. RESULTS: We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. CONCLUSIONS: We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.

An integrative study of five biological clocks in somatic and mental health.

Biological clocks have been developed at different molecular levels and were found to be more advanced in the presence of somatic illness and mental disorders. However, it is unclear whether different biological clocks reflect similar aging processes and determinants. In ~3000 subjects, we examined whether five biological clocks (telomere length, epigenetic, transcriptomic, proteomic, and metabolomic clocks) were interrelated and associated to somatic and mental health determinants. Correlations between biological aging indicators were small (all r < 0.2), indicating little overlap. The most consistent associations of advanced biological aging were found for male sex, higher body mass index (BMI), metabolic syndrome, smoking, and depression. As compared to the individual clocks, a composite index of all five clocks showed most pronounced associations with health determinants. The large effect sizes of the composite index and the low correlation between biological aging indicators suggest that one's biological age is best reflected by combining aging measures from multiple cellular levels.

Anxiety disorders.

Anxiety disorders form the most common group of mental disorders and generally start before or in early adulthood. Core features include excessive fear and anxiety or avoidance of perceived threats that are persistent and impairing. Anxiety disorders involve dysfunction in brain circuits that respond to danger. Risk for anxiety disorders is influenced by genetic factors, environmental factors, and their epigenetic relations. Anxiety disorders are often comorbid with one another and with other mental disorders, especially depression, as well as with somatic disorders. Such comorbidity generally signifies more severe symptoms, greater clinical burden, and greater treatment difficulty. Reducing the large burden of disease from anxiety disorders in individuals and worldwide can be best achieved by timely, accurate disease detection and adequate treatment administration, scaling up of treatments when needed. Evidence-based psychotherapy (particularly cognitive behavioural therapy) and psychoactive medications (particularly serotonergic compounds) are both effective, facilitating patients' choices in therapeutic decisions. Although promising, no enduring preventive measures are available, and, along with frequent therapy resistance, clinical needs remain unaddressed. Ongoing research efforts tackle these problems, and future efforts should seek individualised, more effective approaches for treatment with precision medicine.

Suicide ideation versus suicide attempt: Examining overlapping and differential determinants in a large cohort of patients with depression and/or anxiety.

OBJECTIVE: Individuals with a depressive and/or anxiety disorder are known to have an elevated risk of suicide. However, these diagnoses alone are insufficient at differentiating patients with suicide ideation that attempt suicide from those that do not. Few studies examined such differences in an ideation-to-action framework. Using this framework, extensive multivariate testing was performed to examine differences between suicidal patients with and without a suicide attempt. METHOD: Data were from 1576 respondents with a depressive and/or anxiety disorder, participating in the Netherlands Study of Depression and Anxiety. Logistic regression analyses were used to analyze associations between sociodemographic, clinical, personality, and psychosocial risk factors and suicide ideation and attempt. RESULTS: Patients with suicide ideation could be uniquely distinguished from non-suicidal patients by more years of education, presence of a depressive disorder (vs anxiety disorder) and higher introversion. Patients with suicide ideation and a past suicide attempt could be uniquely distinguished from non-suicidal patients by a younger age of onset, a lifetime alcohol use disorder, more external locus of control and lower levels of social support. Within the group of patients with suicide ideation, patients with a suicide attempt were more likely to have childhood trauma and lower education, and be of non-Western descent than patients with suicide ideation and no past attempt. CONCLUSION: This study found that although various clinical, personality and psychosocial characteristics distinguish patients with suicide ideation from non-suicidal patients, many of these often-cited factors do not distinguish patients with a suicide attempt from those who only think about suicide. However, childhood trauma, lower education and non-Western descent could aid in detecting suicide attempt risk among patients with suicide ideation.

Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19.

BACKGROUND: In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed. OBJECTIVE: We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)-base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19. METHODS: We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background. RESULTS: We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more. CONCLUSIONS: RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.

Experienced Burden of and Adherence to Smartphone-Based Ecological Momentary Assessment in Persons with Affective Disorders.

(1) Background: The use of smartphone-based ecological momentary assessment (EMA) questionnaires in affective disorder research has rapidly increased. Though, a thorough understanding of experienced burden of and adherence to EMA is crucial in determining the usefulness of EMA. (2) Methods: Persons with current affective disorders (n = 100), remitted persons (n = 190), and healthy controls (n = 94) participated in a smartphone-based EMA two-week monitoring period. Our primary outcomes were (momentary) perceived burden of and adherence to EMA. (3) Results: In the whole sample, lower positive and higher negative affect were associated with slightly higher levels of perceived momentary burden (B = -0.23 [95%CI = -0.27-0.19], B = 0.30 [95%CI = 0.24-0.37], respectively). The persons with current affective disorders reported slightly higher levels of experienced momentary burden (Mdn = 1.98 [IQR = 1.28-2.57]), than the remitted persons (Mdn = 1.64 [IQR = 1.11-2.24]) and healthy controls (Mdn = 1.28 [IQR = 1.04-1.92]). Nevertheless, the persons with current affective disorders still showed very high adherence rates (Mdn = 94.3% [IQR = 87.9-97.1]), at rates on a par with the remitted persons (Mdn = 94.3% [IQR = 90.0-97.1]) and healthy controls (Mdn = 94.3% [IQR = 90.0-98.6]). (4) Discussion: Frequent momentary questionnaires of mental well-being are slightly more burdensome to the persons with current affective disorders, but this does not seem to have a negative impact on adherence. Their high rate of adherence to EMA-which was similar to that in remitted persons and healthy controls -suggests that it is feasible to apply (short-duration) EMA.

A clinical staging approach to improving diagnostics in anxiety disorders: Is it the way to go?

BACKGROUND: Clinical staging is a paradigm in which stages of disease progression are identified; these, in turn, have prognostic value. A staging model that enables the prediction of long-term course in anxiety disorders is currently unavailable but much needed as course trajectories are highly heterogenic. This study therefore tailored a heuristic staging model to anxiety disorders and assessed its validity. METHODS: A clinical staging model was tailored to anxiety disorders, distinguishing nine stages of disease progression varying from subclinical stages (0, 1A, 1B) to clinical stages (2A-4B). At-risk subjects and subjects with anxiety disorders (n = 2352) from the longitudinal Netherlands Study of Depression and Anxiety were assigned to these nine stages. The model's validity was assessed by comparing baseline (construct validity) and 2-year, 4-year and 6-year follow-up (predictive validity) differences in anxiety severity measures across stages. Differences in depression severity and disability were assessed as secondary outcome measures. RESULTS: Results showed that the anxiety disorder staging model has construct and predictive validity. At baseline, differences in anxiety severity, social avoidance behaviors, agoraphobic avoidance behaviors, worrying, depressive symptoms and levels of disability existed across all stages (all p-values < 0.001). Over time, these differences between stages remained present until the 6-year follow-up. Differences across stages followed a linear trend in all analyses: higher stages were characterized by the worst outcomes. Regarding the stages, subjects with psychiatric comorbidity (stages 2B, 3B, 4B) showed a deteriorated course compared with those without comorbidity (stages 2A, 3A, 4A). CONCLUSION: A clinical staging tool would be useful in clinical practice to predict disease course in anxiety disorders.

Network outcome analysis identifies difficulty initiating sleep as a primary target for prevention of depression: a 6-year prospective study.

STUDY OBJECTIVES: Major depressive disorder (MDD) is the leading cause of disability worldwide. Its high recurrence rate calls for prevention of first-onset MDD. Although meta-analysis suggested insomnia as the strongest modifiable risk factor, previous studies insufficiently addressed that insomnia might also occur as a residual symptom of unassessed prior depression, or as a comorbid complaint secondary to other depression risks. METHODS: In total, 768 participants from the Netherlands Study of Depression and Anxiety who were free from current and lifetime MDD were followed-up for four repeated assessments, spanning 6 years in total. We performed separate Cox proportional hazard analyses to evaluate whether baseline insomnia severity, short-sleep duration, and individual insomnia complaints prospectively predicted first-onset MDD during follow-up. The novel method of network outcome analysis (NOA) allowed us to sort out whether there is any direct predictive value of individual insomnia complaints among several other complaints that are associated with insomnia. RESULTS: Over 6-year follow-up, 141 (18.4%) were diagnosed with first-onset MDD. Insomnia severity but not sleep duration predicted first-onset MDD (HR = 1.11, 95% CI: 1.07-1.15), and this was driven solely by the insomnia complaint difficulty initiating sleep (DIS) (HR = 1.10, 95% CI: 1.04-1.16). NOA likewise identified DIS only to directly predict first-onset MDD, independent of four other associated depression complaints. CONCLUSIONS: We showed prospectively that DIS is a risk factor for first-onset MDD. Among the different other insomnia symptoms, the specific treatment of DIS might be the most sensible target to combat the global burden of depression through prevention.

Longitudinal course of suicidal ideation and predictors of its persistence - A NESDA study.

BACKGROUND: Prior research indicates that the factors that trigger suicidal ideation may differ from those that maintain it, but studies into the maintenance of suicidal ideation remain scarce. Our aim was to assess the longitudinal course of suicidal ideation, and to identify predictors of persistent suicidal ideation. METHODS: We used data from the Netherlands Study of Depression and Anxiety (NESDA). We performed a linear mixed-effects growth model analysis (n = 230 with current suicidal ideation at baseline) to assess the course of suicidal ideation over time (baseline through 2-, 4-, 6- and 9-year follow-up). We used logistic regression analysis (n = 195) to test whether factors previously associated with the incidence of suicidal ideation in the literature (insomnia, hopelessness, loneliness, borderline personality traits, childhood trauma, negative life events) also predict persistence of suicidal ideation (i.e., reporting ideation at two consecutive assessment points, 6- and 9-years). We controlled for socio-demographics, clinical diagnosis and severity, medication use, and suicide attempt history. RESULTS: Suicidal ideation decreased over time, and this decrease became slower with increasing time, with the majority of symptom reductions occurring in the first two years of follow-up. More severe insomnia and hopelessness were associated with increased odds of persistent suicidal ideation, and hopelessness was a significant mediator of the relationship between insomnia and persistent suicidal ideation. LIMITATIONS: Findings may not generalize to those with more severe suicidal ideation due to dropout of those with the worst clinical profile. CONCLUSIONS: Targeting insomnia and hopelessness in treatment may be particularly important to prevent the persistence of suicidal ideation.