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Purpose The study of mental wellbeing requires reliable, valid, and practical measurement tools. One of the most widely used measures of mental wellbeing is the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS). Our aim was to examine the psychometric properties of SWEMWBS (a brief seven-item version) in a ‘real-world’ population sample of young people. Methods We used data from the 2017 School Health Research Network Student Health and Wellbeing Survey, completed by 103,971 students in years 7 to 11 from 193 secondary schools in Wales. We first estimated polychoric correlation matrices for the whole sample and by school year, and undertook a principal components analysis to check for configural invariance. Subsequently, we used a multiple-groups structural equation model with successively greater constraints to test measurement invariance. To examine external construct validity, we calculated correlations between the SWEMWBS score and four covariates: life satisfaction, somatisation, school pressure and bullying victimisation. Results Parallel analysis suggested that extraction of one factor was appropriate both overall and in each year group. Inspection of standardised loadings suggested that four items had progressively stronger correlations with the factor as students are older, but change in fit indices between models suggested that loadings and thresholds, but not residual variances, were invariant by age group. SWEMWBS scores were moderately correlated with measures of life satisfaction and somatisation, and weakly to moderately correlated with school pressure and bullying victimisation. Conclusions This study adds to the growing evidence that SWEMWBS is appropriate for measuring mental wellbeing in young people and suggests that SWEMWBS is appropriate for tracking the development of wellbeing across adolescence.

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 Previous systematic reviews of weight management programmes (WMPs) have not been able to account for heterogeneity of effectiveness within programmes using top‐down behavioural change taxonomies. This could be due to overlapping causal pathways to effectiveness (or lack of effectiveness) in these complex interventions. Qualitative comparative analysis (QCA) can help identify these overlapping pathways. Methods Using trials of adult WMPs with dietary and physical activity components identified from a previous systematic review, we selected the 10 most and 10 least effective interventions by amount of weight loss at 12 months compared to minimal treatment. Using intervention components suggested by synthesis of studies of programme user views, we labelled interventions as to the presence of these components and, using qualitative comparative analysis, developed pathways of component combinations that created the conditions sufficient for interventions to be most effective and least effective. Results Informed by the synthesis of views studies, we constructed 3 truth tables relating to quality of the user‐provider relationship; perceived high need for guidance from providers; and quality of the relationship between peers in weight management programmes. We found effective interventions were characterized by opportunities to develop supportive relationships with providers or peers, directive provider‐led goal setting and components perceived to foster self‐regulation. Conclusions Although QCA is an inductive method, this innovative approach has enabled the identification of potentially critical aspects of WMPs, such as the nature of relationships within them, which were previously not considered to be as important as more concrete content such as dietary focus.

Strong arguments have been made for early intervention for child problems, stating that early is more effective than later, as the brain is more malleable, and costs are lower. However, there is scant evidence from trials to support this hypothesis, which we therefore tested in two well-powered, state-of-the-art meta-analyses with complementary strengths: (a) Individual participant data (IPD) meta-analysis of European trials of Incredible Years parenting intervention (k = 13, n = 1696; age = 2-11); (b) Larger, trial-level robust variance estimation meta-analysis of a wider range of parenting programs (k = 156, n = 13,378, Mage = 2-10) for reducing disruptive behavior. Both analyses found no evidence that intervention earlier in childhood was more effective; programs targeted at a narrower age range were no more effective than general ones.

Network meta-analysis (NMA) methods allow simultaneous comparison of multiple interventions. Public health interventions are typically complex, being composed of multiple components. Methodological choices are therefore necessary when planning NMAs of complex interventions (eg, ‘splitting’ vs ‘lumping’; intervention- vs component-level analysis). This node-making activity is often poorly reported and has received limited attention within methodological guidelines. This review aims to describe the use of NMA methods in this context and investigate the elements involved in node-making to develop a typology. We conducted a systematic review to identify application of NMA methods to complex interventions applicable to public health (health promotion and primary prevention). We extracted the characteristics of the included reviews and networks, and the statistical tools applied therein. Thematic network analysis of text extracts related to node-making within the included reviews was applied to develop a typology of elements potentially involved in the formation of network nodes. We included 89 reviews and 102 networks applied to complex interventions relevant to public health. Included reviews were distributed across a breadth of topics including universal, selective, and indicated primary prevention, and involved outcomes including both physical and mental health. Almost all reviews applied standard NMA models, while just six reported additive component NMA models. Network nodes were formed by grouping similar interventions or intervention types (65/102), comparing named interventions (6/102), defining nodes as combinations of intervention components (26/102), or using an underlying component classification to group interventions into nodes (5/102). We developed a typology of node-making elements, identifying seven sets of potential considerations (Approach, Ask, Aim, Appraise, Apply, Adapt, and Assess). NMA methods are applied widely to evaluate complex interventions across health promotion and primary prevention. The proposed typology of these elements can inform future networks and facilitate improved reporting of methodological steps. Public health interventions — such as programs to prevent smoking or increase physical activity — are often complex and made up of many parts (‘components’). Deciding between multiple different interventions, or understanding which combination of intervention components works best, can require methods such as “network meta-analysis” (NMA). How to group interventions or components for NMA is known as ‘node-making’ but it is not clear what node-making approaches are currently used in NMA of public health interventions. In this paper, we reviewed 102 networks, identified from 89 reviews, and have summarized the range of methods used, with particular focus on the steps taken by reviewers to reach their node-making decisions. We arranged the identified node-making elements into seven categories, creating a classification which might be used to help researchers make decisions and report their methods in the future. •Network meta-analysis (NMA) methods are applied in reviews across public health.•A systematic review of reviews applying NMA to complex public health interventions.•Node-making for complex intervention NMAs lacks guidance and is variably reported.•A typology of the elements involved in node-making for NMA is proposed.•The typology provides a framework for reviewers to consider when planning NMAs.

Parenting programs for reducing disruptive child behavior are built on two main perspectives: relationship enhancement (i.e., unconditional sensitivity diminishes disruptiveness) and behavior management (i.e., conditional rewards diminish disruptiveness). Two meta-analyses (156 and 41 RCTs; Ntotal = 15,768; Mchildage = 1-11 years) tested the theoretical model that integrating relationship enhancement with behavior management is superior to behavior management alone. The integrative approach showed no overall superiority. Relative to behavior management, the integrative approach was superior in treatment settings, but inferior in prevention settings (Meta-analysis 1). The integrative approach and behavior management approach did not have differential sustained effects up to 3 years after the program (Meta-analysis 2). Findings argue against current practice to implement the same parenting programs in treatment and prevention settings.

To investigate the application of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach and the quality of evidence ratings in systematic reviews of complex interventions. This study examined all 40 systematic reviews published in three Cochrane Review Groups from 2013 to May 2014: Cochrane Developmental, Psychosocial and Learning Problems Group (CDPLPG); Cochrane Public Health Group (CPHG); and Cochrane Depression, Anxiety, and Neurosis Group (CCDAN). The reviews were coded and classified into “complex” (n = 24) and “simple” (n = 16) intervention review groups based on the predefined complexity dimensions from the extant literature mapped into the PICOTS framework. All the data were analyzed in these two groups to help identify specific patterns of the GRADE ratings in the reviews of complex interventions. Outcomes of complex intervention reviews had higher proportions of “very low” quality of evidence ratings compared with those of simple intervention reviews (37.5% vs. 9.1% for the primary benefit outcomes) and were more frequently downgraded for inconsistency, performance bias, and study design. None of the outcomes of complex intervention reviews (0%) were given “high” GRADE ratings. Results suggest that the GRADE assessment may not adequately describe the evidence base of complex interventions.

To explore user perspectives on applying the Grading of Recommendations Assessment, Development and Evaluation (GRADE) to systematic reviews of complex interventions. Thirty-three authors of recent (2013 onward) systematic reviews were contacted regarding their perspectives on using GRADE from three Cochrane review groups: Cochrane Developmental, Psychosocial, and Learning Problems Group; Cochrane Public Health Group; and Cochrane Depression, Anxiety, and Neurosis Group. Framework Analysis was applied to the data to identify the challenges in applying GRADE and suggestions for its extension, that is, adaptation. These two themes were cross-compared between the groups of answers from “simple” vs. “complex” intervention review authors to identify the specific perspectives on using GRADE in reviews of complex interventions. Specific challenges were identified in applying GRADE to reviews of complex interventions. These were related to the assessment of nonrandomized studies and performance bias in GRADE. Authors perceived these challenges to contribute to frequent downgrading of the “best evidence possible” for complex interventions. Meanwhile, GRADE was found to lack an analytic approach to enable adequate evidence synthesis and assessment of intervention implementation elements. Users suggest that the GRADE guidance be extended to address-specific considerations for complex interventions.

To enhance equity in clinical and epidemiological research, it is crucial to understand researcher motivations for conducting equity-relevant studies. Therefore, we evaluated author motivations in a randomly selected sample of equity-relevant observational studies published during the COVID-19 pandemic. We searched MEDLINE for studies from 2020 to 2022, resulting in 16,828 references. We randomly selected 320 studies purposefully sampled across income setting (high vs low–middle-income), COVID-19 topic (vs non–COVID-19), and focus on populations experiencing inequities. Of those, 206 explicitly mentioned motivations which we analyzed thematically. We used discourse analysis to investigate the reasons behind emerging motivations. We identified the following motivations: (1) examining health disparities, (2) tackling social determinants to improve access, and (3) addressing knowledge gaps in health equity. Discourse analysis showed motivations stem from commitments to social justice and recognizing the importance of highlighting it in research. Other discourses included aspiring to improve health-care efficiency, wanting to understand cause-effect relationships, and seeking to contribute to an equitable evidence base. Understanding researchers' motivations for assessing health equity can aid in developing guidance that tailors to their needs. We will consider these motivations in developing and sharing equity guidance to better meet researchers' needs.

Background: During the COVID-19 pandemic, it has been necessary to deliver mental health care using technologies (e-mental health). But there have been difficulties in its application. Quantitative systematic reviews such as meta-analysis doesn't allow us to fully identify and properly describe this subject. Thus, our study has two main objectives: a) \"to determine what evidence is available for synchronous e-mental health implementation\"; and b) \"to develop a framework informed by a realist analysis for the implementation of synchronous e-mental health\". Methods: We will search MEDLINE, EBM Reviews, PsycINFO, EMBASE, SCOPUS, CINAHL Complete, and Web of Science databases from 1st January 2015 to September 2020, with no language restriction. A systematic review with a narrative description and a realist synthesis will be conducted. Primary studies relating to adults with common mental health problems using any type of mobile mental health intervention that includes a synchronic component and communication with a mental health professional will be included. For the analysis, we will make a realist synthesis of the systematic reviews, using a grounded theory approach with an emergent approach to synthesize the information, prioritizing the systematic reviews with a lower risk of bias in the AMSTAR-2 tool. The realist synthesis will be based on the interpretation, integration, and inference of the evaluated elements and the generation of hypotheses to better understand the implementation process of synchronous e-mental health. Finally, we will present the overall assessment in a Summary of Qualitative Findings table. Conclusion: Our results will allow a better understanding of the facilitator and limitations in implementing e-mental health.