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Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a debilitating condition characterized by persistent fatigue, impaired functioning, and substantial societal burden. Although cognitive behavioral therapy (CBT) is commonly used for CFS/ME, the effective components and delivery formats remain unclear. We systematically searched MEDLINE, Embase, PsyclNFO, Web of Science, the Cochrane Library, and major English and Chinese databases from inception to January 15, 2025, without language restrictions. Ultimately, a frequentist network meta-analysis (NMA) and component network meta-analysis (cNMA) of 16 randomized controlled trials were conducted to assess the associations between CBT components, delivery formats, and patient-important outcomes. At the treatment level, guided self-help CBT (mean difference [MD], -6.89; 95% CI, -9.56 to -4.22; moderate certainty) and individual CBT (MD, -4.81; 95% CI, -6.98 to -2.65; moderate certainty) probably reduced fatigue when measured immediately after treatment. At the component level, goal setting (incremental mean difference [iMD], -8.83; 95% CI, -16.92 to -0.74), third-wave components (iMD, -6.02; 95% CI, -11.33 to -0.72), and cognitive restructuring (iMD, -4.41; 95% CI, -8.83 to 0.01) were associated with reduced fatigue when measured immediately after treatment. Psychoeducation was potentially counterproductive (iMD, 5.23; 95% CI, 0.64 to 9.82). At the end of follow-up, cognitive restructuring was associated with reduced fatigue and depression, and improved physical function; goal setting was also associated with improved physical function. The combination of goal setting, third-wave components, and cognitive restructuring delivered via guided self-help may be an efficacious component-based intervention relative to the nonspecific treatment component (iMD, -16.33; 95% CI, -26.68 to -5.99). The effective CBT combination probably involves goal setting, third-wave components, and cognitive restructuring delivered via guided self-help, while psychoeducation may be detrimental. Future studies should prospectively examine core CBT components and their interactions, as well as incorporate measures of dose and intensity to guide more personalized and scalable interventions. CRD420251018083.

Cognitive-behaviour therapy (CBT) for panic disorder may consist of different combinations of several therapeutic components such as relaxation, breathing retraining, cognitive restructuring, interoceptive exposure and/or in vivo exposure. It is therefore important both theoretically and clinically to examine whether specific components of CBT or their combinations are superior to others in the treatment of panic disorder. Component network meta-analysis (NMA) is an extension of standard NMA that can be used to disentangle the treatment effects of different components included in composite interventions. We searched MEDLINE, EMBASE, PsycINFO and Cochrane Central, with supplementary searches of reference lists and clinical trial registries, for all randomized controlled trials comparing different CBT-based psychological therapies for panic disorder with each other or with control interventions. We applied component NMA to disentangle the treatment effects of different components included in these interventions. After reviewing 2526 references, we included 72 studies with 4064 participants. Interoceptive exposure and face-to-face setting were associated with better treatment efficacy and acceptability. Muscle relaxation and virtual-reality exposure were associated with significantly lower efficacy. Components such as breathing retraining and in vivo exposure appeared to improve treatment acceptability while having small effects on efficacy. The comparison of the most v. the least efficacious combination, both of which may be provided as ‘evidence-based CBT,’ yielded an odds ratio for the remission of 7.69 (95% credible interval: 1.75 to 33.33). Effective CBT packages for panic disorder would include face-to-face and interoceptive exposure components, while excluding muscle relaxation and virtual-reality exposure.

Background Component network meta-analysis (CNMA) enables disentangling individual component effects from multicomponent treatments. However, no established methods exist to quantify the contribution of evidence from constituent comparisons to the disentangled component effect estimates in CNMA, hindering the interpretability of results. Methods We proposed a leave-one-out algorithm to address this gap. The core approach iteratively excludes each constituent comparison (i.e., edge in the network), recomputes the variances of all component effects, and quantifies the precision leverage of each comparison based on the induced variance inflation. Contributions are assigned via a normalized matrix. We developed special rules to handle cases where exclusion renders component effects unidentifiable. The method also formally decomposes component estimates into direct and additive evidence sources. Its utility and validity were evaluated through implementation using hypothetical networks and a real-world dataset. Results The leave-one-out algorithm accurately identified pivotal evidence sources by capturing substantial variance fluctuations upon their exclusion. Contributions assigned via precision leverage effectively quantified the critical importance of comparisons isolating target components. Application to real-world data (66 comparisons, 21 components) also confirmed the method’s precision in discerning influential evidence within complex networks, and exhibited strong alignment with the parameter decomposition results. Crucially, validation revealed no inherent relationship exists between precision leverage and linear weighting. Conclusions The leave-one-out algorithm resolves a critical gap in CNMA methodology by providing a robust, variance-based framework for quantifying the contribution of constituent direct comparisons to component effect estimates. It reliably identifies pivotal evidence sources essential for component identifiability and precision across diverse network structures, enhancing the transparency and interpretability of evidence synthesis for complex interventions.

Background Health and social care interventions are often complex and can be decomposed into multiple components. Multicomponent interventions are often evaluated in randomised controlled trials. Across trials, interventions often have components in common which are given alongside other components which differ across trials. Multicomponent interventions can be synthesised using component NMA (CNMA). CNMA is limited by the structure of the available evidence, but it is not always straightforward to visualise such complex evidence networks. The aim of this paper is to develop tools to visualise the structure of complex evidence networks to support CNMA. Methods We performed a citation review of two key CNMA methods papers to identify existing published CNMA analyses and reviewed how they graphically represent intervention complexity and comparisons across trials. Building on identified shortcomings of existing visualisation approaches, we propose three approaches to standardise visualising the data structure and/or availability of data: CNMA-UpSet plot, CNMA heat map, CNMA-circle plot. We use a motivating example to illustrate these plots. Results We identified 34 articles reporting CNMAs. A network diagram was the most common plot type used to visualise the data structure for CNMA (26/34 papers), but was unable to express the complex data structures and large number of components and potential combinations of components associated with CNMA. Therefore, we focused visualisation development around representing the data structure of a CNMA more completely. The CNMA-UpSet plot presents arm-level data and is suitable for networks with large numbers of components or combinations of components. Heat maps can be utilised to inform decisions about which pairwise interactions to consider for inclusion in a CNMA model. The CNMA-circle plot visualises the combinations of components which differ between trial arms and offers flexibility in presenting additional information such as the number of patients experiencing the outcome of interest in each arm. Conclusions As CNMA becomes more widely used for the evaluation of multicomponent interventions, the novel CNMA-specific visualisations presented in this paper, which improve on the limitations of existing visualisations, will be important to aid understanding of the complex data structure and facilitate interpretation of the CNMA results.

Background Component network meta-analysis (CNMA) decomposes the overall effect of a multicomponent intervention into the effects of its constituent components. It is important to quantify the contribution of each single studies (or comparisons) to the individual component effect obtained from the CNMA model. However, evidence for a single component is often distributed across comparisons of multicomponent interventions, making it difficult to trace graph‑theoretic based paths of evidence in a standard network plot. Methods We propose a two-stage algorithm to quantify evidence contributions in CNMA. First, as component-level evidence is not encoded as connected topological paths in the network of standard NMA, we introduce the concept of pseudo-paths. A pseudo‑path for a target component is defined as a set of directed edges whose linear combination—with non‑negative coefficients—yields a vector that isolates the effect of that component (i.e., equals 1 for the target component and 0 for all others). All pseudo-paths are identified by solving a non‑negative linear feasibility problem based on the CNMA design matrix . Second, we adapt the iterative logic of the shortest‑path approach to allocate evidence flow to these pseudo‑paths. Starting from the pseudo‑path with the fewest edges, we assign a flow on each edge is given by the corresponding absolute entry of the component-level hat matrix . After each allocation, the residual flows on the involved edges are updated, and the process repeats until all flow is exhausted. The algorithm generalizes the shortest‑path approach to an algebraic setting where paths are defined by linear combinations of edges with potentially fractional coefficients, and the flow is distributed proportionally to these coefficients, rather than equally as in standard NMA. We illustrated this approach using both a hypothetical example and real-world datasets. Results In both real-world data networks, the two-stage algorithm systematically identified and quantified the contributions of the pseudo-paths. The flow-weighted sum of pseudo-path–derived estimates matched exactly (within numerical tolerance) the overall component effect estimated by the CNMA model. This confirms that the proposed algorithm correctly decomposes and then recomposes the evidence structure that gives rise to the component effect estimate. Conclusions This study adapts the shortest‑path approach for use in CNMA, providing a quantitative method to trace evidence contributions to component‑level estimates. By introducing pseudo‑paths and a corresponding flow‑allocation algorithm, the method extends path‑based contribution analysis from standard NMA to the CNMA setting, enabling transparent decomposition of how evidence from multicomponent interventions synthesizes into component effects.

Background Non-pharmacological interventions have a myriad of available intervention options and contain multiple components. Whether specific components of non-pharmacological interventions or combinations are superior to others remains unclear. The main aim of this study is to compare the effects of different combinations of non-pharmacological interventions and their specific components on health-related outcomes in adults with subjective cognitive decline. Methods PubMed, Embase, Cochrane, CINAHL, PsycINFO, CENTRAL, Web of Science, and China’s two largest databases, CNKI and Wanfang, were searched from inception to 22nd, January 2023. Randomized controlled trials using non-pharmacological interventions and reporting health outcomes in adults with subjective cognitive decline were included. Two independent reviewers screened studies, extracted data, and assessed risk of bias. Component network meta-analysis was conducted employing an additive component model for network meta-analysis. This study followed the PRISMA reporting guideline and the PRISMA checklist is presented in Additional file 2. Results A total of 39 trials with 2959 patients were included (range of mean ages, 58.79–77.41 years). Resistance exercise might be the optimal intervention for reducing memory complaints in adults with subjective cognitive decline; the surface under the cumulative ranking p score was 0.888, followed by balance exercise ( p  = 0.859), aerobic exercise ( p  = 0.832), and cognitive interventions ( p  = 0.618). Music therapy, cognitive training, transcranial direct current stimulation, mindfulness therapy, and balance exercises might be the most effective intervention components for improving global cognitive function (iSMD, 0.83; 95% CI, 0.36 to 1.29), language (iSMD, 0.31; 95% CI, 0.24 to 0.38), ability to perform activities of daily living (iSMD, 0.55; 95% CI, 0.21 to 0.89), physical health (iSMD, 3.29; 95% CI, 2.57 to 4.00), and anxiety relief (iSMD, 0.71; 95% CI, 0.26 to 1.16), respectively. Conclusions The form of physical activity performed appears to be more beneficial than cognitive interventions in reducing subjective memory complaints for adults with subjective cognitive decline, and this difference was reflected in resistance, aerobic, and balance exercises. Randomized clinical trials with high-quality and large-scale are warranted to validate the findings. Trial registration PROSPERO registry number. CRD42022355363.

Obesity is a chronic complex disease associated with increased risks of developing several serious and potentially life-threatening conditions. It is a growing global health issue. Pharmacological treatment is an option for patients living with overweight or obesity. Digital technology may be leveraged to support patients with weight loss in the community, but it is unclear which of the multiple digital options are important for success. This systematic review and component network meta-analysis aimed to identify components of digital support for weight loss interventions that are most likely to be effective in supporting patients to achieve weight loss goals. We searched MEDLINE, Embase, APA PsycInfo, and Cochrane Central Register of Controlled Trials from inception to November 2023 for randomized controlled trials using any weight loss intervention with digital components and assessing weight loss outcomes in adults with BMI ≥25 kg/m (≥23 kg/m for Asian populations). Eligible trials were prioritized for synthesis based on intervention relevance and duration, and the target population. Trial arms with substantial face-to-face elements were deprioritized. Prioritized trials were assessed for quality using the Cochrane Risk of Bias Tool v1. We conducted intervention component analysis to identify key digital intervention features and a coding framework. All prioritized trial arms were coded using this framework and were included in component network meta-analysis. Searches identified 6528 reports, of which 119 were included. After prioritization, 151 trial arms from 68 trials were included in the synthesis. Nine common digital components were identified from the 151 trial arms: provision of information or education, goal setting, provision of feedback, peer support, reminders, challenges or competitions, contact with a specialist, self-monitoring, and incentives or rewards. Of these, 3 components were identified as \"best bets\" because they were consistently and numerically, but not usually significantly, most likely to be associated with weight loss at 6 and 12 months. These were patient information, contact with a specialist, and incentives or rewards. An exploratory model combining these 3 components was significantly associated with successful weight loss at 6 months (-2.52 kg, 95% CI -4.15 to -0.88) and 12 months (-2.11 kg, 95% CI -4.25 to 0.01). No trial arms used this specific combination of components. Our findings indicate that the design of digital interventions to support weight loss should be carefully crafted around core components. On their own, no single digital component could be considered essential for success, but a combination of information, specialist contact, and incentives warrants further examination. PROSPERO CRD42023493254; https://tinyurl.com/ysyj8j8s.

Background Network meta-analysis (NMA) allows estimating and ranking the effects of several interventions for a clinical condition. Component network meta-analysis (CNMA) is an extension of NMA which considers the individual components of multicomponent interventions. CNMA allows to “reconnect” a disconnected network with common components in subnetworks. An additive CNMA assumes that component effects are additive. This assumption can be relaxed by including interaction terms in the CNMA. Methods We evaluate a forward model selection strategy for component network meta-analysis to relax the additivity assumption that can be used in connected or disconnected networks. In addition, we describe a procedure to create disconnected networks in order to evaluate the properties of the model selection in connected and disconnected networks. We apply the methods to simulated data and a Cochrane review on interventions for postoperative nausea and vomiting in adults after general anaesthesia. Model performance is compared using average mean squared errors and coverage probabilities. Results CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. For disconnected networks, we recommend to use additive CNMA only if strong clinical arguments for additivity exist. Conclusions CNMA methods are feasible for connected networks but questionable for disconnected networks.

Objectives The present study tested the efficacy of parenting program components in reducing disruptive or delinquent child behaviors at first post-treatment for families with children in early versus middle childhood. Methods Eighty-five studies were identified, containing five parenting components (Psychoeducation [PE], Behavior management [BM], Relationship enhancement [RE], Parental self-management [SM], and Parent as a coach [PC]). Results For both early and middle childhood, four parenting program components were effective, namely (1) BM, (2) BM with RE, (3) BM with SM, and (4) BM with PE and RE and SM and PC. However, BM with RE and SM, as well as BM with PE and RE and SM, were effective during early childhood. BM with RE appeared to be the most beneficial intervention during early childhood, while BM was most effective during middle childhood. Conclusion The evidence highlights the need to implement different programmatic components developmentally, during early versus middle childhood.

Background: Probiotic and low fermentable oligosaccharide, disaccharide, monosaccharide, and polyol (FODMAP) diet are two commonly used management approaches for patients with irritable bowel syndrome (IBS). We aimed to evaluate the most effective combinations and components among different probiotics or low FODMAP diet through component network meta-analysis (NMA). Methods: We searched Embase, Ovid Medline, and Web of Science from inception to 21 January 2021. Randomized controlled trials (RCTs) examining the efficacy of probiotics and low FODMAP diet for IBS were included, with placebo, sham diet, or conventional treatments as controls. Binary outcomes were compared among treatments using the relative ratio (RR). A minimally contextualized framework recommended by the GRADE group was used to evaluate the certainty of evidence. The primary efficacy outcome was the relief of global IBS symptoms, and the secondary efficacy outcome was the reduction in IBS symptom scores or abdominal pain scores. Key Results: We included 76 RCTs (n = 8058) after screening 1940 articles. Eight RCTs were classified as low risk of bias. Standard network meta-analysis (NMA) showed that Lactobacillus (RR 1.74, 95% CI 1.22–2.48) and Bifidobacterium (RR 1.76, 95% CI 1.01–3.07) were the most effective for the primary efficacy outcome (high certainty evidence); component NMA showed that Bacillus (RR 5.67, 95% CI 1.88 to 17.08, p = 0.002) and Lactobacillus (RR 1.42, 95% CI 1.07 to 1.91, p = 0.017) were among the most effective components. The results of standard NMA and CNMA analysis of the improvement of overall IBS symptom scores or abdominal pain scores were consistent with this finding. Conclusion: Lactobacillus was the most effective component for the relief of IBS symptoms; Bifidobacterium and Bacillus were possibly effective and need further verification. Systematic Review Registration: website, identifier registration number.