Mondriaan GGZ

Identifying young service users whose depressive symptoms persist into adulthood is crucial to avert potential difficulties they may face when transitioning from Child and Adolescent Mental Healthcare Services (CAMHS) to Adult Mental Healthcare Services (AMHS). As depressive symptoms are diverse in severity and stability, it is important to objectively identify those who require continued support. A clinical cohort of 763 young people from eight European countries, reaching the upper age limit of their CAMHS, was studied to identify trajectories of self-reported depressive problems (ASEBA Youth Self-Report and Adult Self-Report) over a 24-month follow-up period, as well as associated risk factors for persistent depression and service use, using growth mixture modeling. Self-reported, parent-reported, and clinician-reported interviews and questionnaires were used to collect information on risk factors for persistent depression and service use. We identified a low, a decreasing, an increasing and a high trajectory of depressive symptoms. The high trajectory could be predicted with almost all risk factors for persistent depression (i.g. higher clinician-rated psychopathology, more suicidality, comorbidity and being a victim of bullying) and these young people were likely to transition to AMHS or require specialist mental health care at a later date. More importantly, young people whose depressive symptoms increase to similar levels as the group with persistent high-levels of depressive symptoms (the increasing trajectory) could not be properly distinguished from other young people based on risk factors, while their needed continuation of treatment (either in AMHS or CAMHS) was equally likely as the end of care.

The traditional methods of processing data have been found slow and unresponsive especially when large data is to be processed in a relatively short time. These challenges can, however, be addressed by Cloud-native data analytics platforms that are cloud architectures aimed at solving problems. The focus of this paper involves the raising of the speed and the efficiency of data processing through the deployment of Cloud-Native platforms. There is thus the use of distributed systems processing, parallel processing, and efficient query processing in handling the data. Indeed, few experiments were carried out to show that cloud-native solutions are more efficient than traditional methods in terms of time, CPU usage, and scalability. They indicate that the integrated cloud-native platforms enhance data analytics speed and effectiveness, which make them of great benefit to industries relying on analyzing data. Further research is suggested to investigate more optimization strategies and enhancements in constructing the data analytics pipelines with Cloud Network technologies.

The importance of Customer Experience in the current business world has prompted various organizations to look for ways of improving their interactions with the customers. This problem has been supplemented with the solution of real-time data analysis together with the use of cloud technology. This paper examines how real time data analytics that is supported by cloud can easily enhance customer experience through timely offering of services. The paper explores the use of live data in analysing the behaviour of the customer and explains the benefits of scalability and efficiency in cloud for data processing. This research, therefore, aims at exploring and analyzing various cases to explain how firms can use cloud-based real-time data analytics for enhancing the customer impact, satisfaction, and overall loyalty. These include integrating these technologies in order to help companies respond immediately to customers’ requirements while providing additional value to the whole customer experience. The conclusion can be drawn from this study that there is a need for firms to embrace the technologies in order to offer value to the ever demanding customer market.

ABSTRACT Posttraumatic stress disorder (PTSD) is prevalent in individuals with acquired brain injury (ABI). This study investigated the effectiveness and applicability of Eye Movement Desensitization and Reprocessing (EMDR) for PTSD in individuals with ABI. Data were collected using a non-concurrent multiple baseline single case experimental design (SCED), with a baseline, treatment, maintenance, and 3-month follow-up phase, across four cases. EMDR treatment was provided using a manualized standard EMDR protocol. The primary outcome was PTSD symptoms. Secondary outcomes were general mental health and cognitive functions. Visual analyses, TAU-U analyses, and analyses using the Reliable Change Index were performed. All four participants (two with TBI, two with stroke) showed a significant decrease in PTSD symptoms, which continued in maintenance and was retained at follow-up. The participants no longer fulfilled criteria for PTSD classification and showed reliable improvement in PTSD severity score post-treatment and at follow-up. No adverse events occurred and no adjustments in EMDR protocol were necessary. There was no consistent improvement in general mental health nor a consistent improvement in cognitive functioning. This study provided empirical support for the effectiveness and applicability of EMDR for PTSD in four participants with stroke or TBI.

We aimed to investigate the effect of TFT on voice-hearing in people with PTSD and psychosis using experience sampling method (ESM). A secondary aim was to explore how changes in voice-hearing are related to changes in PTSD. This is an adjunct longitudinal ESM study of a sub-group of participants (N = 39) from a randomised controlled trial that compared TFT to a waiting-list control group. Voice-hearing participants filled in 10 daily voice-hearing-related questionnaires for six consecutive days at baseline and post-treatment at pseudo-random times during the day. PTSD symptom severity was assessed at baseline and post-treatment. Multilevel linear regression was used to test the effect of TFT on voice-hearing and to analyse the relationship between changes in voice-hearing and changes in PTSD. The intention-to-treat analysis showed a significant interaction effect between time and treatment condition (p < .00001) with a small effect size (dppc2 = -0.27), indicating a larger decrease in voice-hearing in the TFT group than in the waiting-list control group. Also, a significant association was observed between changes in PTSD symptoms and changes in voice-hearing (p < .00001). Our findings tentatively suggest that, even when voices are not targeted directly, TFT for PTSD can alleviate distressing voices.

As previous studies have shown that personality disorder (PD) assessment in older adults is often hampered because assessment tools are tailored toward younger adults, establishing the age-neutrality of novel tools is crucial. This study primarily aimed to evaluate the age-neutrality of the Level of Personality Functioning Brief Form (LPFS-BF 2.0) and the Personality Inventory for DSM-5 Modified + (PID-5-BF+M), using a sample of 254 community-dwelling adults. The analysis of Differential Item Functioning (DIF) demonstrated the age-neutrality of both instruments, with only 8.3% of LPFS-BF 2.0 items and 5.6% of PID-5-BF+M items exhibiting DIF. Differential Test Functioning (DTF) analyses revealed large DTF for the LPFS-BF 2.0 total score, indicating that age-specific norms might be necessary for this score. In summary, this study supports the use of these instruments in both older and younger adults, enhancing the assessment of PDs across the life span.

ObjectivesTo assess the effectiveness, cost-effectiveness and return on investment of individual placement and support (IPS) implemented through a reimbursement strategy on a nationwide scale compared with traditional vocational rehabilitation (TVR) regarding sustainable participation in competitive employment in individuals with severe mental illness receiving sickness or disability benefits.MethodsAn observational study was conducted using administrative data regarding all Dutch individuals receiving sickness or disability benefits in the period 2012–2019. Exact matching and difference-in-difference fixed-effect estimations were performed to handle the non-randomised nature of the data. The matched sample consisted of 863 IPS and 16 466 TVR participants. The primary effect measure was the proportion of individuals having worked for at least 48 hours per month in competitive employment (ie, for 12 hours or more per week); the proportion of individuals having worked in competitive employment for at least 1 hour per month was also evaluated. Cost-effectiveness and return on investment were assessed from the societal perspective (intervention, sickness/disability benefit and healthcare costs) and payer perspective (sickness/disability benefit costs).ResultsIPS led to a statistically significant higher probability of being competitively employed for at least 12 hours per week of 3.7% points (95% CI 0.8% to 6.7%) to 7.5% points (95% CI 3.8% to 11.3%) and of being competitively employed for at least 1 hour per month of 4.7% points (95% CI 1.6% to 7.7%) to 8.9% points (95% CI 5.2 to 12.6%) from 6 to 36 months after starting the intervention. From the societal and payer perspective, IPS was—on average—less costly and more effective than TVR and return-on-investment estimates showed that IPS was—on average—cost saving (eg, societal perspective: ∆C: −364 (95% CI −3977 to 3249); ∆E: 0.104 (95% CI 0.046 to 0.164); benefit–cost ratio: 2.1 (95% CI −14.8 to 19.1)), but the uncertainty surrounding these estimates was large.ConclusionsIPS implemented through a reimbursement strategy on a nationwide scale is more effective and potentially cost-effective than TVR in people with severe mental illness receiving sickness or disability benefits. Based on these results, the implementation of IPS by a wide scale reimbursement strategy could be promoted to enhance sustainable participation in competitive employment in these individuals. Future economic evaluations should strive for a more robust sample size and a long follow-up period.

The Personality Inventory for DSM-5 Brief Form + Modified (PID-5-BF+M) is a self-report questionnaire measuring maladaptive personality traits, as defined by the dimensional classifications of personality disorders in DSM-5 Section 3 and ICD-11. The instrument combines both classifications to capture six personality domains and 18 underlying personality facets, operationalized by two items each. This study examined the construct validity of this questionnaire in older adults, by examining the factor structure and the reliability of the domains and facets. Additionally, the study investigated the relationship between maladaptive personality traits and resilience, as measured by the Connor-Davidson Resilience Scale (CD-RISC). The PID-5-BF+M was administered to 251 older adults from the general population, 104 of the respondents also filled in the CD-RISC. The hierarchical factor structure of the PID-5-BF+M was corroborated in in older adults. Additionally, the domain and facet scales were found to be internally consistent. The correlations with the CD-RISC showed logical associations. The domain of Negative Affectivity and the facets Emotional Lability, Anxiety and Irresponsibility were negatively associated with resilience. Based on these results, this study supports the construct validity of the PID-5-BF+M in older adults. However, future research on the age-neutrality of the instrument is still needed.

Satisfying daily life functioning is essential in mental healthcare. Standard assessments focus on symptoms and are designed to detect underlying vulnerabilities. However, they offer insufficient insight into patterns of contextual variability and resilience. Consequently, interventions are planned using incomplete information. The experience-sampling method (ESM) is a structured moment-to-moment diary assessing the individual's affect, thoughts, perception and behaviour in the daily life context. ESM helps to understand variation in mental states (e.g., anxiety or sleeping problems) as adaptational processes in relation to contextual challenges (functional analysis). Although ESM has been extensively studied across psychological disorders, the adoption by mental health professionals and their patients remains limited because the 'how to' is unclear. This paper presents a practical guide for ESM application in routine clinical care. It integrates empirical knowledge with expert experiences and provides real-world examples and recommendations for successful implementation. The guide comprises how to engage and motivate patients and how to customize assessments to the patient's needs. It also includes instructions to interpret results and create an atmosphere of shared decision-making. Experience-sampling technology has merits for patients with various mental health complaints and across healthcare settings, although the exact use and implementation may vary depending on the individual case.