In recent research, psychological disorders have been increasingly defined as complex dynamic systems in which symptoms are interconnected and influence each other, thereby forming symptom networks. This paradigm shift calls for the analysis and interpretation of relationships between symptoms that are complex, potentially non-linear, and dynamic. Dynamic Time Warping (DTW) is used to measure similarity in temporal sequences, and has recently been found effective in modelling psychopathology symptom networks.

We aim to demonstrate that DTW could also be used to model the network structure in Ecological Momentary Assessment (EMA) data.

355 participants of the Netherlands Study of Depression and Anxiety (NESDA), of which 100 with and 255 without current disorder, completed EMA assessments of 20 symptoms (e.g., feeling sad, tired, satisfied) five times a day for two weeks. DTW analysis was performed on the group level, comparing participants suffering from mood disorders to healthy controls. DTW distances were visualized as an undirected symptom network, in which we adjusted for the average symptom severity per item per person.

DTW analysis of close to half a million symptom scores yielded six symptom dimensions based on their aggregated similarity of changes over time within the participants. Surprisingly, negative affect symptom networks were found to be less strongly connected in those currently suffering from mood disorders than in controls, whereas the network density of (reverse-coded) positive affect symptoms was more closely connected in this group. This is contrary to the results of previous studies, where negative affect-related symptom networks of those with mood disorders were found to be more strongly interconnected.

DTW is a promising new technique for analyzing EMA data and modeling dynamic symptom networks at both the individual and group levels. Using EMA data, symptom networks and dimensions can be modeled with great structural and temporal detail. Incorporating the temporal symptom dynamics may highlight the importance of the independent trajectories of negative mood symptoms.

None Declared

Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity.

Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions.

Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms.

Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

Cognitive symptoms are common during and following episodes of depression. Little is known about the persistence of self-reported and performance-based cognition with depression and functional outcomes.

This is a secondary analysis of a prospective naturalistic observational clinical cohort study of individuals with recurrent major depressive disorder (MDD; N = 623). Participants completed app-based self-reported and performance-based cognitive function assessments alongside validated measures of depression, functional disability, and self-esteem every 3 months. Participants were followed-up for a maximum of 2-years. Multilevel hierarchically nested modelling was employed to explore between- and within-participant variation over time to identify whether persistent cognitive difficulties are related to levels of depression and functional impairment during follow-up.

508 individuals (81.5%) provided data (mean age: 46.6, s.d.: 15.6; 76.2% female). Increasing persistence of self-reported cognitive difficulty was associated with higher levels of depression and functional impairment throughout the follow-up. In comparison to low persistence of objective cognitive difficulty (<25% of timepoints), those with high persistence (>75% of timepoints) reported significantly higher levels of depression (B = 5.17, s.e. = 2.21, p = 0.019) and functional impairment (B = 4.82, s.e. = 1.79, p = 0.002) over time. Examination of the individual cognitive modules shows that persistently impaired executive function is associated with worse functioning, and poor processing speed is particularly important for worsened depressive symptoms.

We replicated previous findings of greater persistence of cognitive difficulty with increasing severity of depression and further demonstrate that these cognitive difficulties are associated with pervasive functional disability. Difficulties with cognition may be an indicator and target for further treatment input.

Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.

To describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.

RADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.

A total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses.

RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.

No significant relationships.

Previous studies have shown a negative impact of the COVID-19 pandemic and its associated sanitary measures on mental health, especially among adolescents and young adults. Such a context may raise many concerns about the COVID-19 pandemic long-term psychological effects. An analysis of administrative databases could be an alternative and complementary approach to medical interview-based epidemiological surveys to monitor the mental health of the population. We conducted a nationwide study to describe the consumption of anxiolytics, antidepressants and hypnotics during the first year of the COVID-19 pandemic, compared to the five previous years.

A historic cohort study was conducted by extracting and analysing data from the French health insurance database between 1 January 2015 and 28 February 2021. Individuals were classified into five age-based classes. Linear regression models were performed to assess the impact of the COVID-19 pandemic period on the number of drug consumers, in introducing an interaction term between time and COVID-19 period.

Since March 2020, in all five age groups and all three drug categories studied, the number of patients reimbursed weekly has increased compared to the period from January 2015 to February 2020. The youngest the patients, the more pronounced the magnitude.

Monitoring the consumption of psychiatric medications could be of great interest as reliable indicators are essential for planning public health strategies. A post-crisis policy including reliable monitoring of mental health must be anticipated.

In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remains unidentified. Introducing additional screening tools may facilitate the diagnostic process.

This study aims to examine whether Experience Sampling Method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from non-depressed individuals. In addition, the added value of actigraphy-based measures was examined.

We used data from two samples to develop and validate prediction models. The development dataset included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and non-depressed individuals (n=82). The validation dataset included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and non-depressed individuals (n=27). Backward stepwise logistic regression analyses were applied to build the prediction models. The performance of the models was assessed with the goodness of fit indices, calibration curves, and discriminative ability (AUC, the area under the receiver operating characteristic curve).

In the development dataset, the discriminative ability was good for the actigraphy model (AUC=0.790) and excellent for the ESM (AUC=0.991) and combined-domains model (AUC=0.993). In the validation dataset, the discriminative ability was reasonable for the actigraphy model (AUC=0.648) and excellent for the ESM (AUC=0.891) and combined-domains model (AUC=0.892).

ESM is a good diagnostic predictor and is easy to calculate, and, therefore, holds promise for implementation in clinical practice. Actigraphy shows no added value to ESM as a diagnostic predictor, but might still be useful when active monitoring with ESM is not feasible.

No significant relationships.

Dietary interventions did not prevent depression onset nor reduced depressive symptoms in a large multi-center randomized controlled depression prevention study (MooDFOOD) involving overweight adults with subsyndromal depressive symptoms. We conducted follow-up analyses to investigate whether dietary interventions differ in their effects on depressive symptom profiles (mood/cognition; somatic; atypical, energy-related).

Baseline, 3-, 6-, and 12-month follow-up data from MooDFOOD were used (n = 933). Participants received (1) placebo supplements, (2) food-related behavioral activation (F-BA) therapy with placebo supplements, (3) multi-nutrient supplements (omega-3 fatty acids and a multi-vitamin), or (4) F-BA therapy with multi-nutrient supplements. Depressive symptom profiles were based on the Inventory of Depressive Symptomatology.

F-BA therapy was significantly associated with decreased severity of the somatic (B = −0.03, p = 0.014, d = −0.10) and energy-related (B = −0.08, p = 0.001, d = −0.13), but not with the mood/cognition symptom profile, whereas multi-nutrient supplementation was significantly associated with increased severity of the mood/cognition (B = 0.05, p = 0.022, d = 0.09) and the energy-related (B = 0.07, p = 0.002, d = 0.12) but not with the somatic symptom profile.

Differentiating depressive symptom profiles indicated that food-related behavioral interventions are most beneficial to alleviate somatic symptoms and symptoms of the atypical, energy-related profile linked to an immuno-metabolic form of depression, although effect sizes were small. Multi-nutrient supplements are not indicated to reduce depressive symptom profiles. These findings show that attention to clinical heterogeneity in depression is of importance when studying dietary interventions.

There is increasing interest in day-to-day affect fluctuations of patients with depressive and anxiety disorders. Few studies have compared repeated assessments of positive affect (PA) and negative affect (NA) across diagnostic groups, and fluctuation patterns were not uniformly defined. The aim of this study is to compare affect fluctuations in patients with a current episode of depressive or anxiety disorder, in remitted patients and in controls, using affect instability as a core concept but also describing other measures of variability and adjusting for possible confounders.

Ecological momentary assessment (EMA) data were obtained from 365 participants of the Netherlands Study of Depression and Anxiety with current (n = 95), remitted (n = 178) or no (n = 92) DSM-IV defined depression/anxiety disorder. For 2 weeks, five times per day, participants filled-out items on PA and NA. Affect instability was calculated as the root mean square of successive differences (RMSSD). Tests on group differences in RMSSD, within-person variance, and autocorrelation were performed, controlling for mean affect levels.

Current depression/anxiety patients had the highest affect instability in both PA and NA, followed by remitters and then controls. Instability differences between groups remained significant when controlling for mean affect levels, but differences between current and remitted were no longer significant.

Patients with a current disorder have higher instability of NA and PA than remitted patients and controls. Especially with regard to NA, this could be interpreted as patients with a current disorder being more sensitive to internal and external stressors and having suboptimal affect regulation.

Although depression with anxious distress appears to be a clinically relevant subtype of Major Depressive Disorder (MDD), whether it involves specific pathophysiology remains unclear. Inflammation has been implicated, but not comprehensively studied. We examined within a large MDD sample whether anxious distress and related anxiety features are associated with differential basal inflammation and innate cytokine production capacity.

Data are from 1078 MDD patients from the Netherlands study of depression and anxiety. Besides the DSM-5 anxious distress specifier, we studied various dimensional anxiety scales (e.g. Inventory of Depressive Symptomatology anxiety arousal subscale [IDS-AA], Beck Anxiety Inventory [BAI], Mood and Anxiety Symptoms Questionnaire Anxious Arousal scale [MASQ-AA]). Basal inflammatory markers included C-reactive protein, interleukin (IL)-6 and tumor-necrosis factor (TNF)-α. Innate production capacity was assessed by 13 lipopolysaccharide (LPS)-stimulated inflammatory markers. Basal and LPS-stimulated inflammation index scores were created.

Basal inflammation was not associated with anxious distress in MDD patients (anxious distress prevalence 54.3%), except for modest positive associations for IDS-AA and BAI scores. However, anxious distress was associated with higher LPS-stimulated levels (interferon-ɣ, IL-2, IL-6, monocyte chemotactic protein (MCP)-1, macrophage inflammatory protein (MIP)-1α, MIP-1β, matrix metalloproteinase-2, TNF-α, TNF-β, LPS-stimulated index). Oher anxiety indicators (number of specifier items and anxiety diagnoses, IDS-AA, BAI, MASQ-AA) were also associated with increased innate production capacity.

Within a large MDD sample, the anxious distress specifier was associated with increased innate cytokine production capacity but not with basal inflammation. Results from dimensional anxiety indicators largely confirm these results. These findings provide new insight into the pathophysiology of anxious depression.

Studies investigating the link between depressive symptoms and inflammation have yielded inconsistent results, which may be due to two factors. First, studies differed regarding the specific inflammatory markers studied and covariates accounted for. Second, specific depressive symptoms may be differentially related to inflammation. We address both challenges using network psychometrics.

We estimated seven regularized Mixed Graphical Models in the Netherlands Study of Depression and Anxiety (NESDA) data (N = 2321) to explore shared variances among (1) depression severity, modeled via depression sum-score, nine DSM-5 symptoms, or 28 individual depressive symptoms; (2) inflammatory markers C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α); (3) before and after adjusting for sex, age, body mass index (BMI), exercise, smoking, alcohol, and chronic diseases.

The depression sum-score was related to both IL-6 and CRP before, and only to IL-6 after covariate adjustment. When modeling the DSM-5 symptoms and CRP in a conceptual replication of Jokela et al., CRP was associated with ‘sleep problems’, ‘energy level’, and ‘weight/appetite changes’; only the first two links survived covariate adjustment. In a conservative model with all 38 variables, symptoms and markers were unrelated. Following recent psychometric work, we re-estimated the full model without regularization: the depressive symptoms ‘insomnia’, ‘hypersomnia’, and ‘aches and pain’ showed unique positive relations to all inflammatory markers.

We found evidence for differential relations between markers, depressive symptoms, and covariates. Associations between symptoms and markers were attenuated after covariate adjustment; BMI and sex consistently showed strong relations with inflammatory markers.

Literature has shown that obesity, metabolic syndrome and inflammation are associated with depression, however, evidence suggests that these associations are specific to atypical depression. Which of the atypical symptoms are driving associations with obesity-related outcomes and inflammation is unknown. We evaluated associations between individual symptoms of depression (both atypical and non-atypical) and body mass index (BMI), metabolic syndrome components and inflammatory markers.

We included 808 persons with a current diagnosis of depression participating in the Netherlands Study of Depression and Anxiety (67% female, mean age 41.6 years). Depressive symptoms were derived from the Composite International Diagnostic Interview and the Inventory of Depressive Symptomatology. Univariable and multivariable regression analyses adjusting for sex, age, educational level, depression severity, current smoking, physical activity, anti-inflammatory medication use, and statin use were performed.

Increased appetite was positively associated with BMI, number of metabolic syndrome components, waist circumference, C-reactive protein and tumor necrosis factor-α. Decreased appetite was negatively associated with BMI and waist circumference. Psychomotor retardation was positively associated with BMI, high-density lipoprotein cholesterol and triglycerides, and insomnia with number of metabolic syndrome components.

Increased appetite – in the context of a depressive episode – was the only symptom that was associated with both metabolic as well as inflammatory markers, and could be a key feature of an immuno-metabolic form of depression. This immuno-metabolic depression should be considered in clinical trials evaluating effectiveness of compounds targeting metabolic and inflammatory pathways or lifestyle interventions.

Physical inactivity has been identified as a risk factor for depression and, less often, as a long-term consequence of depression. Underexplored is whether similar bi-directional longitudinal relationships are observed for anxiety disorders, particularly in relation to three distinct indicators of activity levels – sports participation, general physical activity and sedentary behavior.

Participants were from the Netherlands Study of Depression and Anxiety (NESDA; N = 2932, 18–65 years old; 57% current anxiety or depressive disorder, 21% remitted disorder, 22% healthy controls). At baseline, 2, 4, and 6 years, participants completed a diagnostic interview and self-report questionnaires assessing psychopathology symptom severity, physical activity indicators, and sociodemographic and health covariates.

Consistently across assessment waves, people with anxiety and/or depressive disorders had lower sports participation and general physical activity compared to healthy controls. Greater anxiety or depressive symptoms were associated with lower activity according to all three indicators. Over time, a diagnosis or greater symptom severity at one assessment was associated with poorer sports participation and general physical activity 2 years later. In the opposite direction, only low sports participation was associated with greater symptom severity and increased odds of disorder onset 2 years later. Stronger effects were observed for chronicity, with lower activity according to all indicators increasing the odds of disorder chronicity after 2 years.

Over time, there seems to a mutually reinforcing, bidirectional relationship between psychopathology and lower physical activity, particularly low sports participation. People with anxiety are as adversely affected as those with depression.

The heterogeneous aetiology of major depressive disorder (MDD) might affect the presentation of depressive symptoms across the lifespan. We examined to what extent a range of mood, cognitive, and somatic/vegetative depressive symptoms were differentially present depending on patient's age.

Data came from 1404 participants with current MDD (aged 18–88 years) from two cohort studies: the Netherlands Study of Depression and Anxiety (NESDA) and the Netherlands Study of Depression in Older Persons (NESDO). Associations between age (per 10 years) and 30 depressive symptoms as well as three symptom clusters (mood, cognitive, somatic/vegetative) were assessed using logistic and linear regression analyses.

Depression severity was found to be stable with increasing age. Nevertheless, 20 (67%) out of 30 symptoms were associated with age. Most clearly, with ageing there was more often early morning awakening [odds ratio (OR) 1.47, 95% confidence interval (CI) 1.36–1.60], reduced interest in sex (OR 1.42, 95% CI 1.31–1.53), and problems sleeping during the night (OR 1.33, 95% CI 1.24–1.43), whereas symptoms most strongly associated with younger age were interpersonal sensitivity (OR 0.72, 95% CI 0.66–0.79), feeling irritable (OR 0.73, 95% CI 0.67–0.79), and sleeping too much (OR 0.75, 95% CI 0.68–0.83). The sum score of somatic/vegetative symptoms was associated with older age (B = 0.23, p < 0.001), whereas the mood and cognitive sum scores were associated with younger age (B = −0.20, p < 0.001; B = −0.04, p = 0.004).

Depression severity was found to be stable across the lifespan, yet depressive symptoms tend to shift with age from being predominantly mood-related to being more somatic/vegetative. Due to the increasing somatic presentation of depression with age, diagnoses may be missed.

Clinical and aetiological heterogeneity have impeded our understanding of depression.

To evaluate differences in psychiatric and somatic course between people with depression subtypes that differed clinically (severity) and aetiologically (melancholic v. atypical).

Data from baseline, 2-, 4- and 6-year follow-up of The Netherlands Study of Depression and Anxiety were used, and included 600 controls and 648 people with major depressive disorder (subtypes: severe melancholic n = 308; severe atypical n = 167; moderate n = 173, established using latent class analysis).

Those with the moderate subtype had a significantly better psychiatric clinical course than the severe melancholic and atypical subtype groups. Suicidal thoughts and anxiety persisted longer in those with the melancholic subtype. The atypical subtype group continued to have the highest body mass index and highest prevalence of metabolic syndrome during follow-up, although differences between groups became less pronounced over time.

Course trajectories of depressive subtypes mostly ran parallel to each other, with baseline severity being the most important differentiator in course between groups.

To investigate neurophysiological parameters which possibly distinguish subtypes I and II of patients with a bipolar disorder, and contrast the findings with observations from a group of schizophrenic patients and a group of healthy controls.

Sixty-six volunteers underwent a MRI scan to determine the number and location of white matter lesions (WSL). A electrophysiological registration was made while all volunteers performed a auditory ‘oddball’ task, and the amplitude of the resulting P300 wave was compared.

Earlier reports of higher numbers of WSL in bipolar disorder were not replicated in this study. Subtypes I and II showed a different P300 amplitude and subtype I resembled the results of the schizophrenia group.

Bipolar patients in remission have a functional brain disorder that is expressed by a change in physiological response to external stimuli.

The nosological status of generalized anxiety disorder (GAD) versus dysthymic disorder (DD) has been questioned. The aim of this study was to examine qualitative differences within (co-morbid) GAD and DD symptomatology.

Latent class analysis was applied to anxious and depressive symptomatology of respondents from three population-based studies (2007 Australian National Survey of Mental Health and Wellbeing; National Comorbidity Survey Replication; and Netherlands Mental Health Survey and Incidence Study-2; together known as the Triple study) and respondents from a multi-site naturalistic cohort [Netherlands Study of Depression and Anxiety (NESDA)]. Sociodemographics and clinical characteristics of each class were examined.

A three-class (Triple study) and two-class (NESDA) model best fitted the data, reflecting mainly different levels of severity of symptoms. In the Triple study, no division into a predominantly GAD or DD co-morbidity subtype emerged. Likewise, in spite of the presence of pure GAD and DD cases in the NESDA sample, latent class analysis did not identify specific anxiety or depressive profiles in the NESDA study. Next, sociodemographics and clinical characteristics of each class were examined. Classes only differed in levels of severity.

The absence of qualitative differences in anxious or depressive symptomatology in empirically derived classes questions the differentiation between GAD and DD.

Identifying depressive subtypes is an important tool in reducing the heterogeneity of major depressive disorder. However, few studies have examined the stability of putative subtypes of depression over time.

The sample included 488 persons from the Netherlands Study of Depression and Anxiety (NESDA) who had major depressive disorder at baseline and at the 2-year follow-up assessment. A latent transition analysis (LTA) was applied to examine the stability of depressive subtypes across time-points. Differences in demographic, clinical, psychosocial and health correlates between subtypes were evaluated in a subsample of persons with stable subtypes.

Three subtypes were identified at each time-point: a moderate subtype (prevalence T0 39%, T1 42%), a severe typical subtype (T0 30%, T1 25%), and a severe atypical subtype (T0 31%, T1 34%). The LTA showed 76% stability across the 2-year follow-up, with the greatest stability in the severe atypical class (79%). Analyses of correlates in the stable subtypes showed a predominance of women and more overweight and obesity in the severe atypical subtype, and a greater number of negative life events and higher neuroticism and functioning scores in the severe typical subtype.

Subtypes of major depressive disorder were found to be stable across a 2-year follow-up and to have distinct determinants, supporting the notion that the identified subtypes are clinically meaningful.

Current classification of unipolar depression reflects the idea that prognosis is essential. However, do DSM categories of major depressive disorder (MDD), dysthymic disorder (Dysth) and double depression (DD=MDD+Dysth) indeed adequately represent clinically relevant course trajectories of unipolar depression? Our aim was to test DSM categories (MDD, Dysth and DD) in comparison with empirically derived prognostic categories, using a prospectively followed cohort of depressed patients.

A large sample (n=804) of out-patients with unipolar depression were derived from a prospective cohort study, the Netherlands Study of Depression and Anxiety (NESDA). Using latent class growth analysis (LCGA), empirically derived 2-year course trajectories were constructed. These were compared with DSM diagnoses and a wider set of putative predictors for class membership.

Five course trajectories were identified, ranging from mild severity and rapid remission to high severity and chronic course trajectory. Contrary to expectations, more than 50% of Dysth and DD were allocated to classes with favorable course trajectories, suggesting that current DSM categories do not adequately represent course trajectories. The class with the most favorable course trajectory differed on several characteristics from other classes (younger age, more females, less childhood adversity, less somatic illnesses, lower neuroticism, higher extraversion). Older age, earlier age of onset and lower extraversion predicted poorest course trajectory.

MDD, Dysth and DD did not adequately match empirically derived course trajectories for unipolar depression. For the future classification of unipolar depression, it may be wise to retain the larger, heterogeneous category of unipolar depression, adopting cross-cutting dimensions of severity and duration to further characterize patients.