A nationwide study on concordance with multimodal treatment guidelines in bipolar disorder

Background: Most previous studies on concordance with treatment guidelines for bipolar disorder focused on pharmacotherapy. Few studies have included other treatment modalities. Aims: To study concordance with the Dutch guideline of various treatment modalities in outpatient treatment settings for patients with bipolar disorder and to identity factors associated with concordance. Methods: A nationwide non-interventional study using psychiatrists’ and patients’ surveys. Results: 839 patients with bipolar or schizoaffective disorder bipolar type were included. Concordance with the guideline was highest for participation of a psychiatrist in the treatment (98%) and for maintenance pharmacotherapy (96%), but lower for supportive treatment (73.5%), use of an emergency plan (70.6%), psychotherapy (52.2%), group psychoeducation (47.2%), and mood monitoring (47%). Presence of a written treatment plan, a more specialized treatment setting, more years of education, and diagnosis of bipolar I disorder versus bipolar II, bipolar NOS, or schizoaffective disorder were significantly associated with better concordance. Conclusion: In contrast to pharmacotherapy, psychosocial treatments are only implemented to a limited extend in everyday clinical practice in bipolar disorder. More effort is needed to implement non-pharmacological guideline recommendations for bipolar disorder

On the link between emotionally driven impulsivity and aggression:evidence from a validation study on the Dutch UPPS-P

The UPPS-P seems to be a promising instrument for measuring different domains of impulsivity in forensic psychiatric patients. Validation studies of the instrument however, have been conducted only in student groups. In this validation study, three groups completed the Dutch UPPS-P: healthy student (N = 94) and community (N = 134) samples and a forensic psychiatric sample (N = 73). The five-factor structure reported previously could only be substantiated in a confirmatory factor analysis over the combined groups but not in the subsamples. Subgroup sample sizes might be too small to allow such complex analyses. Internal consistency, as assessed by Cronbach’s alpha, was high on most subscale and sample combinations. In explaining aggression, especially the initial subscale negative urgency (NU) was related to elevated scores on self-reported aggression in the healthy samples (student and community). The current study is the second study that found a relationship between self-reported NU and aggression highlighting the importance of addressing this behavioural domain in aggression management therapy

Pharmacological prevention of postictal agitation after electroconvulsive therapy—A systematic review and meta-analysis

BackgroundPostictal agitation (PIA) after electroconvulsive therapy (ECT) is a serious clinical problem estimated to occur in 7–36% of patients and recur in 19–54% of patients. PIA has the potential to cause dangerous situations for the patient and staff members aside from the financial impact. To date, it is unclear which pharmacological interventions should be used in the management of PIA. This study aimed to systematically review the (preventative) pharmacological treatment options for PIA after ECT.MethodA systematic search was done in PubMed, EMBASE, PsycINFO, and Web of Science from inception until 10 November 2022. We included randomized trials with any pharmacological intervention or comparison and a predefined outcome measure on PIA. Studies that solely included patients with neurodegenerative disorders or stroke were excluded. Data quality was assessed with the RoB2 and GRADE. Meta-analysis was performed if possible. This study was registered on PROSPERO under CRD42021262323.ResultsWe screened 2,204 articles and included 14 studies. Dexmedetomidine was investigated in 10 studies. Alfentanil, lignocaine, esmolol, midazolam, propofol, ketamine, haloperidol, and diazepam were each studied in only one study. Meta-analysis revealed an OR of 0.45 (0.32–0.63), a moderate effect size, in favor of dexmedetomidine than placebo to prevent PIA with very low heterogeneity (I2 = 0%). The certainty of the evidence was moderate. The other interventions studied were all found to have low certainty of evidence.ConclusionFor clinical practice, we believe that our results indicate that dexmedetomidine should be considered for the prevention of PIA in patients that have previously experienced PIA

Oral contraceptives, depressive and insomnia symptoms in adult women with and without depression

Background: Worldwide, oral contraceptive (OC) use is a very common form of birth control, although it has been associated with symptoms of depression and insomnia. Insomnia is a risk factor for major depressive disorder (MDD) but may also be a symptom of the disorder. Despite the large number of women who use OC, it is yet unknown whether women with previous or current diagnosis of depression are more likely to experience more severe depressive and insomnia symptoms during concurrent OC use than women without diagnosis of depression. Aim: This study examined associations between OC use and concurrent symptoms of depression (including atypical depression) and insomnia as well as between OC and prevalences of concurrent dysthymia and MDD. Participants were adult women with and without a history of MDD or dysthymia. We hypothesized that OC use is associated with concurrent increased severity of depressive symptoms and insomnia symptoms, as well as with an increased prevalence of concurrent diagnoses of dysthymia and MDD. We also hypothesized that a history of MDD or dysthymia moderates the relationship between OC use and depressive and insomnia symptoms. Methods: Measurements from premenopausal adult women from the Netherlands Study of Depression and Anxiety (NESDA) were grouped, based on whether participants were using OC or naturally cycling (NC). OC use, timing and regularity of the menstrual cycle were assessed with a structured interview, self-reported symptoms of depression (including atypical depression), insomnia with validated questionnaires, and MDD and dysthymia with structured diagnostic interviews. Results: We included a total of 1301 measurements in women who reported OC use and 1913 measurements in NC women (mean age 35.6, 49.8% and 28.9% of measurements in women with a previous depression or current depression, respectively). Linear mixed models showed that overall, OC use was neither associated with more severe depressive symptoms (including atypical depressive symptoms), nor with higher prevalence of diagnoses of MDD or dysthymia. However, by disentangling the amalgamated overall effect, within-person estimates indicated increased depressive symptoms and depressive disorder prevalence during OC use, whereas between-person estimated indicated lower depressive symptoms and prevalence of depressive disorders. OC use was consistently associated with more severe concurrent insomnia symptoms, in the overall estimates as well as in the within-person and between-person estimates. Presence of current or previous MDD or dysthymia did not mod-erate the associations between OC use and depressive or insomnia symptoms. Discussion: The study findings showed consistent associations between OC use and more severe insomnia symptoms, but no consistent associations between OC and depressive symptoms or diagnoses. Instead, post-hoc analyses showed that associations between OC and depression differed between within-and between person -estimates. This indicates that, although OC shows no associations on the overall level, some individuals might experience OC-associated mood symptoms. Our findings underscore the importance of accounting for individual differences in experiences during OC use. Furthermore, it raises new questions about mechanisms underlying associations between OC, depression and insomnia.Stress-related psychiatric disorders across the life spa

Linked Markov sources: Modeling outcome-dependent social processes

Many social processes are adaptive in the sense that the process changes as a result of previous outcomes. Data on such processes may come in the form of categorical time series. First, the authors propose a class of Markov Source models that embody such adaptation. Second, the authors discuss new methods to evaluate the fit of such models. Third, the authors apply these models and methods to data on a social process that is a preeminent example of an adaptive process: (encoded) conversation as arises in structured interviews. © 2007 Sage Publications

Reducing suicides in mental healthcare: results from a 4-year follow-up implementation study in the Netherlands (SUPRANET)

ObjectiveIn 2016, the SUicide PRevention Action NETwork (SUPRANET) was launched. The SUPRANET intervention aims at better implementing the suicide prevention guideline. An implementation study was developed to evaluate the impact of SUPRANET over time on three outcomes: 1) suicides, 2) registration of suicide attempts, and 3) professionals’ knowledge and adherence to the guideline.MethodsThis study included 13 institutions, and used an uncontrolled longitudinal prospective design, collecting biannual data on a 2-level structure (institutional and team level). Suicides and suicide attempts were extracted from data systems. Professionals’ knowledge and adherence were measured using a self-report questionnaire. A three-step interrupted time series analysis (ITSA) was performed for the first two outcomes. Step 1 assessed whether institutions executed the SUPRANET intervention as intended. Step 2 examined if institutions complied with the four guideline recommendations. Based on steps 1 and 2, institutions were classified as below or above average and after that, included as moderators in step 3 to examine the effect of SUPRANET over time compared to the baseline. The third outcome was analyzed with a longitudinal multilevel regression analysis, and tested for moderation.ResultsAfter institutions were labeled based on their efforts and investments made (below average vs above average), we found no statistically significant difference in suicides (standardized mortality ratio) between the two groups relative to the baseline. Institutions labeled as above average did register significantly more suicide attempts directly after the start of the intervention (78.8 per 100,000 patients, p<0.001, 95%CI=(51.3 per 100,000, 106.4 per 100,000)), and as the study progressed, they continued to report a significantly greater improvement in the number of registered attempts compared with institutions assigned as below average (8.7 per 100,000 patients per half year, p=0.004, 95%CI=(3.3 per 100,000, 14.1 per 100,000)). Professionals working at institutions that invested more in the SUPRANET activities adhered significantly better to the guideline over time (b=1.39, 95%CI=(0.12,2.65), p=0.032).ConclusionInstitutions labeled as above average registered significantly more suicide attempts and also better adhered to the guideline compared with institutions that had performed less well. Although no convincing intervention effect on suicides was found within the study period, we do think that this network is potentially able to reduce suicides. Continuous investments and fully implementing as many guideline recommendations as possible are essential to achieve the biggest drop in suicides

Psychosocial factors, health behaviors and risk of cancer incidence:Testing interaction and effect modification in an individual participant data meta-analysis

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.</p

Psychosocial factors, health behaviors and risk of cancer incidence:Testing interaction and effect modification in an individual participant data meta-analysis

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.</p

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood.

BACKGROUND: Although major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare. METHODS: Ecological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1-10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period. RESULTS: Overall, four profiles were identified, which we labeled as: (1) "very negative and least variable mood" (n = 14) (2) "negative and moderate variable mood" (n = 204), (3) "positive and moderate variable mood" (n = 41), and (4) "negative and highest variable mood" (n = 28). The degree of emotional inertia was virtually identical across the profiles. CONCLUSIONS: The real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia

Examining the Theoretical Framework of Behavioral Activation for Major Depressive Disorder: Smartphone-Based Ecological Momentary Assessment Study.

BACKGROUND: Behavioral activation (BA), either as a stand-alone treatment or as part of cognitive behavioral therapy, has been shown to be effective for treating depression. The theoretical underpinnings of BA derive from Lewinsohn et al's theory of depression. The central premise of BA is that having patients engage in more pleasant activities leads to them experiencing more pleasure and elevates their mood, which, in turn, leads to further (behavioral) activation. However, there is a dearth of empirical evidence about the theoretical framework of BA. OBJECTIVE: This study aims to examine the assumed (temporal) associations of the 3 constructs in the theoretical framework of BA. METHODS: Data were collected as part of the "European Comparative Effectiveness Research on Internet-based Depression Treatment versus treatment-as-usual" trial among patients who were randomly assigned to receive blended cognitive behavioral therapy (bCBT). As part of bCBT, patients completed weekly assessments of their level of engagement in pleasant activities, the pleasure they experienced as a result of these activities, and their mood over the course of the treatment using a smartphone-based ecological momentary assessment (EMA) application. Longitudinal cross-lagged and cross-sectional associations of 240 patients were examined using random intercept cross-lagged panel models. RESULTS: The analyses did not reveal any statistically significant cross-lagged coefficients (all P>.05). Statistically significant cross-sectional positive associations between activities, pleasure, and mood levels were identified. Moreover, the levels of engagement in activities, pleasure, and mood slightly increased over the duration of the treatment. In addition, mood seemed to carry over, over time, while both levels of engagement in activities and pleasurable experiences did not. CONCLUSIONS: The results were partially in accordance with the theoretical framework of BA, insofar as the analyses revealed cross-sectional relationships between levels of engagement in activities, pleasurable experiences deriving from these activities, and enhanced mood. However, given that no statistically significant temporal relationships were revealed, no conclusions could be drawn about potential causality. A shorter measurement interval (eg, daily rather than weekly EMA reports) might be more attuned to detecting potential underlying temporal pathways. Future research should use an EMA methodology to further investigate temporal associations, based on theory and how treatments are presented to patients. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02542891, https://clinicaltrials.gov/ct2/show/NCT02542891; German Clinical Trials Register, DRKS00006866, https://tinyurl.com/ybja3xz7; Netherlands Trials Register, NTR4962, https://www.trialregister.nl/trial/4838; ClinicalTrials.Gov, NCT02389660, https://clinicaltrials.gov/ct2/show/NCT02389660; ClinicalTrials.gov, NCT02361684, https://clinicaltrials.gov/ct2/show/NCT02361684; ClinicalTrials.gov, NCT02449447, https://clinicaltrials.gov/ct2/show/NCT02449447; ClinicalTrials.gov, NCT02410616, https://clinicaltrials.gov/ct2/show/NCT02410616; ISRCTN registry, ISRCTN12388725, https://www.isrctn.com/ISRCTN12388725