Idiographic bidirectional associations of stressfulness of events and negative affect in daily life as indicators for mental health: An experience sampling study

Evidence suggests that complex micro-dynamics occurring in daily life underly the development of mental distress. We aimed to (1) study the cross-lagged association between stressful events and negative affect (NA), (2) show that there is substantial between-person variability in idiographic associations and (3) show that idiographic associations are indicative of mental health. Experience sampling study assessing perceived stressfulness of events (PSE) and NA four times per day for 2 weeks in a non-clinical convenience sample (N = 70, mean age = 22.9, 61% female, 69% German). Bivariate vector autoregressive model implemented in dynamic structural equation modelling to model the associations between stressful events and NA and obtain idiographic associations. Stressfulness of events and NA were significantly reciprocally associated. Autocorrelations and cross-lagged associations from PSE to NA showed substantial variability and were significantly related with trait measures of depression, anxiety, well-being, and perceived stress. Contrary to expectations, cross-lagged associations from NA to stressfulness of events were not related to trait mental health. The approach outlined in this article is useful for studying idiographic dynamics in daily life. The findings increase our understanding of micro-dynamics underlying mental health and individual differences in these processes.<br/

Examining a sentiment algorithm on session patient records in an eating disorder treatment setting:a preliminary study

Background: Clinicians collect session therapy notes within patient session records. Session records contain valuable information about patients’ treatment progress. Sentiment analysis is a tool to extract emotional tones and states from text input and could be used to evaluate patients’ sentiment during treatment over time. This preliminary study aims to investigate the validity of automated sentiment analysis on session patient records within an eating disorder (ED) treatment context against the performance of human raters.Methods: A total of 460 patient session records from eight participants diagnosed with an ED were evaluated on their overall sentiment by an automated sentiment analysis and two human raters separately. The inter-rater agreement (IRR) between the automated analysis and human raters and IRR among the human raters was analyzed by calculating the intra-class correlation (ICC) under a continuous interpretation and weighted Cohen’s kappa under a categorical interpretation. Furthermore, differences regarding positive and negative matches between the human raters and the automated analysis were examined in closer detail.Results: The ICC showed a moderate automated-human agreement (ICC = 0.55), and the weighted Cohen’s kappa showed a fair automated-human (k = 0.29) and substantial human-human agreement (k = 0.68) for the evaluation of overall sentiment. Furthermore, the automated analysis lacked words specific to an ED context.Discussion/conclusion: The automated sentiment analysis performed worse in discerning sentiment from session patient records compared to human raters and cannot be used within practice in its current state if the benchmark is considered adequate enough. Nevertheless, the automated sentiment analysis does show potential in extracting sentiment from session records. The automated analysis should be further developed by including context-specific ED words, and a more solid benchmark, such as patients’ own mood, should be established to compare the performance of the automated analysis to

Exploring the Benefits and Acceptance of Blended Positive Psychotherapy as an Adjunctive Treatment for Clients with Residual Depressive Symptoms:A Mixed-Method Study

A proof-of-concept study was conducted to explore the acceptability and potential benefits of a blended positive psychotherapy intervention for clients with residual depressive symptoms. A single-arm pilot study was conducted in 2022 and 2023 with 24 Dutch adults experiencing residual depressive symptoms after treatment. Clients who had recently received an evidence-based treatment for depressive disorder were approached to participate in this study through opportunity sampling. The intervention consisted of nine sessions with a therapist and a six-week self-guided digital positive psychology intervention. Acceptability was examined using semi-structured interviews (n = 15). Participants filled out questionnaires pre- (n = 21), mid- (n = 14) and post-intervention (n = 8). Potential benefits were assessed in terms of changes in mental well-being (MHC-SF), depression (PHQ-9) and personal recovery (QPR). Quantitative data and qualitative data were analysed using linear mixed-effects models and framework analysis, respectively. The analyses were primarily based on Sekhon’s theoretical framework of acceptability. Linear mixed-effects analyses showed changes over time in most mental health indicators, including mental well-being (Hedge’s g = 1.58), depression (g = 1.43) and personal recovery (g = 1.96). Most of the interviewed participants considered blended positive psychotherapy a valuable adjunctive treatment; it connected well with their wish to become more positive in their daily life without ignoring difficult experiences. For some participants, shifting towards a positive treatment approach was difficult, resulting in early dropout. This study’s findings suggest that blended positive psychotherapy is acceptable to most people with residual depressive symptoms after treatment. Its impact is yet to be established in larger samples of studies involving more robust designs.</p

The effects of positive psychology interventions on well-being and distress in patients with cardiovascular diseases: A systematic review and Meta-analysis

ObjectivePositive psychology interventions (PPIs) have been found to be effective for psychiatric and somatic disorders. However, a systematic review and meta-analysis of studies examining the effectiveness of PPIs for patients with cardiovascular disease (CVD) is lacking. This systematic review and meta-analysis aims to synthesize studies examining the effectiveness of PPIs and to examine their effects on mental well-being and distress using meta-analyses.MethodsThis study was preregistered on OSF (https://osf.io/95sjg/). A systematic search was performed in PsycINFO, PubMed and Scopus. Studies were included if they examined the effectiveness of PPIs on well-being for patients with CVD. Quality assessment was based on the Cochrane tool for assessing risk of bias. Three-level mixed-effects meta-regression models were used to analyze effect sizes of randomized controlled trials (RCTs).ResultsTwenty studies with 1222 participants were included, of which 15 were RCTs. Included studies showed high variability in study and intervention characteristics. Meta-analyses showed significant effects for mental well-being (β = 0.33) and distress (β = 0.34) at post-intervention and the effects were still significant at follow-up. Five of the 15 RCTs were classified as having fair quality, while the remaining had low quality.ConclusionThese results suggest that PPIs are effective in improving well-being and distress in patients with CVD and could therefore be a valuable addition for clinical practice. However, there is a need for more rigorous studies that are adequately powered and that help us understand what PPIs are most effective for which patient

A school-based program to prevent depressive symptoms and strengthen well-being among pre-vocational students (Happy Lessons):protocol for a cluster randomized controlled trial and implementation study

BACKGROUND: Depression is one of the leading causes of illness and disability among young people. In the Netherlands, one in twelve Dutch adolescents has experienced depression in the last 12 months. Pre-vocational students are at higher risk for elevated depressive symptoms. Effective interventions, especially for this risk group, are therefore needed to prevent the onset of depression or mitigate the adverse long-term effects of depression. The aim of this study is to examine the effectiveness and implementation of a school-based program Happy Lessons (HL), that aims to prevent depression and promote well-being among pre-vocational students. METHODS: A cluster randomized controlled trial (RCT) with students randomized to HL or to care as usual will be conducted. Pre-vocational students in their first or second year (aged 12 to 14) will participate in the study. Subjects in both conditions will complete assessments at baseline and at 3- and 6-months follow-up. The primary outcome will be depressive symptoms using the Center for Epidemiologic Studies Depression Scale (CES-D) at 6-months follow-up. Secondary outcomes are well-being using the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) and life satisfaction (Cantril Ladder) measured at 6-months follow-up. Alongside the trial, an implementation study will be conducted to evaluate the implementation of HL, using both quantitative and qualitative methods (interviews, survey, and classroom observations). DISCUSSION: The results from both the RCT and implementation study will contribute to the limited evidence base on effective school-based interventions for the prevention of depression and promotion of well-being among pre-vocational adolescents. In addition, insights from the implementation study will aid identifying factors relevant for optimizing the future implementation and scale-up of HL to other schools and contexts. TRIAL REGISTRATION: This study was registered on 20 September 2021 in the Dutch Trial Register (NL9732). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-12321-3

Torn between living or dying—analyses of influencing factors on suicide ambivalence and its longitudinally impact on suicidal ideation and behavior in a high-risk sample

IntroductionFindings on the role of suicide ambivalence, an individual's wish to live (WL), and wish to die (WD) in the development of suicidality have been heterogenous. The main goal of this study was to examine associations of these constructs within the past week with sociodemographic factors and to longitudinally investigate their predictive power for suicidal ideation (SI) and suicide attempts (SA).MethodsN = 308 patients (54% female; M = 36.92 years, SD = 14.30), admitted to a psychiatric ward due to suicidality, were assessed for all constructs after admission, after six, nine, and 12 months. Data were analyzed with univariate fixed-effect models and lagged mixed-effect regression models.ResultsDecreased, WL increased post-baseline. Gender showed no significant link to ambivalence, WD, and WL. Ambivalence and WD correlated negatively with age and positively with depressiveness. More participants in a relationship showed a WL compared with single/divorced/widowed participants. More single participants or those in a relationship showed ambivalence than divorced/widowed participants. More single participants showed a WD than participants in a relationship/divorced/widowed. Longitudinally, ambivalence and WD predicted SI and SA.ConclusionThe findings underscore the importance of taking suicide ambivalence and WD into account in risk assessment and treatment

Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach

ObjectivesAnxiety and mood disorders greatly affect the quality of life for individuals worldwide. A substantial proportion of patients do not sufficiently improve during evidence-based treatments in mental healthcare. It remains challenging to predict which patients will or will not benefit. Moreover, the limited research available on predictors of treatment outcomes comes from efficacy RCTs with strict selection criteria which may limit generalizability to a real-world context. The current study evaluates the performance of different machine learning (ML) models in predicting non-improvement in an observational sample of patients treated in routine specialized mental healthcare.MethodsIn the current longitudinal exploratory prediction study diagnosis-related, sociodemographic, clinical and routinely collected patient-reported quantitative outcome measures were acquired during treatment as usual of 755 patients with a primary anxiety, depressive, obsessive compulsive or trauma-related disorder in a specialized outpatient mental healthcare center. ML algorithms were trained to predict non-response (&lt; 0.5 standard deviation improvement) in symptomatic distress 6 months after baseline. Different models were trained, including models with and without early change scores in psychopathology and well-being and models with a trimmed set of predictor variables. Performance of trained models was evaluated in a hold-out sample (30%) as a proxy for unseen data.ResultsML models without early change scores performed poorly in predicting six-month non-response in the hold-out sample with Area Under the Curves (AUCs) &lt; 0.63. Including early change scores slightly improved the models’ performance (AUC range: 0.68–0.73). Computationally-intensive ML models did not significantly outperform logistic regression (AUC: 0.69). Reduced prediction models performed similar to the full prediction models in both the models without (AUC: 0.58–0.62 vs. 0.58–0.63) and models with early change scores (AUC: 0.69–0.73 vs. 0.68–0.71). Across different ML algorithms, early change scores in psychopathology and well-being consistently emerged as important predictors for non-improvement.ConclusionAccurately predicting treatment outcomes in a mental healthcare context remains challenging. While advanced ML algorithms offer flexibility, they showed limited additional value compared to traditional logistic regression in this study. The current study confirmed the importance of taking early change scores in both psychopathology and well-being into account for predicting longer-term outcomes in symptomatic distress