Biomedical subjects

Pim Cuijpers

Publications and source records attributed to Pim Cuijpers.

2 recordsLinked to original sources

PubMed2026

Targeting depression before it starts: An updated systematic review and meta-analysis of preventive interventions in young adults from randomized controlled trials.

INTRODUCTION: Young adulthood is a high-risk period for major depressive disorder (MDD) onset yet is underrepresented in prevention research. This systematic review and meta-analysis examined the effectiveness of psychosocial preventive interventions in adults aged 18-25 years, focusing on symptom reduction, medium- and long-term effects, and MDD onset. METHODS: We conducted a PRISMA-compliant systematic review and meta-analysis (PROSPERO: CRD42024625481) of randomized controlled trials (RCTs) of psychosocial preventive interventions for depression in young adults (18-25) published up to April 15, 2025. Between-group symptom reduction was quantified using Hedges' g and pooled with random-effects models. Outcomes were analyzed at post-intervention, 6-, and 12-month follow-up. Effects on MDD onset were assessed via risk ratio (RR). Risk of bias was assessed with the RoB-2 tool. RESULTS: We included 65 interventions from 58 RCTs (N = 11,333; mean age 20.4; 61.6% female), with 92.8% rated high risk of bias. Interventions at post-test reduced depressive symptoms vs. controls (k = 65; g = -0.52; 95%CI:-0.70;-0.33; p < 0.01), with significant heterogeneity (I2 = 93.0%). Indicated, guided, and face-to-face interventions outperformed universal, selective, self-guided, and online interventions. Effects on symptomatology were highly heterogeneous and non-significant at 6- (k = 8) and 12-month (k = 4) follow-up. Four RCTs were identified evaluating MDD onset risk; pooled effects suggested a reduction in MDD onset (RR = 0.77; 95%CI:0.61;0.97; p < 0.01) though risk of bias was high and samples were highly selective. INTERPRETATION: Psychosocial preventive interventions modestly reduce depressive symptoms at post-test and may lower onset risk in highly selective populations. However, findings remain limited by high risk of bias, scarce data on the long-term sustainability of effects, and unclear mechanisms.

Adolescent↗

PubMed2026

Big data and psychiatry: advances, constraints and future directions.

Early work in psychiatry research, often involving single sites, small samples, and limited variables, has shifted to contemporary research involving multiple sites, large samples, and many variables. Such research raises important questions, including concerns about data quality and methodological rigor, uncertainty about its key lessons, issues regarding clinical relevance, and questions about how to optimize future advances. Here we consider these questions and concerns against the context of big data work on community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials. The development of large datasets allowing well-powered analyses is a major milestone, but sample size alone does not guarantee more precise estimates, and ongoing attention to the quality and rigor of big data collation and analysis is needed. Big data research has fostered trans-disciplinarity and given insights into mechanisms underlying psychiatric disorders, but also emphasizes the intricacy, heterogeneity and variability of such mechanisms, and the importance of triangulating between large-scale and small-scale research. The complexity of psychiatric phenotypes and psychobiological mechanisms contributes to the difficulty in bridging from big data to clinical application; big data research reinforces the importance of holding our diagnoses of psychiatric disorders lightly and providing explanations of these conditions humbly; and future work needs to be more attentive to clinical issues. There is enormous scope for further building databases relevant to psychiatry, but advances in conceptual models and asking the right questions are equally valuable. The full impact of big data, including artificial intelligence analyses, remains to be seen, but overenthusiastic support should be tempered by a better understanding of its strengths and limitations. At its best, such work will contribute in an iterative and integrative way to advancing our knowledge of psychiatric disorders and mental health.

Big data↗