A Systematic Review and Network Meta-Analysis of Randomised-Controlled Wellbeing-Focused Interventions

A Systematic Review and Network Meta-Analysis of Randomised-Controlled Wellbeing-Focused Interventions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Systematic Review and Network Meta-Analysis of Randomised-Controlled Wellbeing-Focused Interventions Andrew Kemp, Lowri Wilkie, Zoe Fisher, Antonia Geidel, Isabel Goodall, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5905657/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Nature Human Behaviour → Version 1 posted You are reading this latest preprint version Abstract This network meta-analysis (NMA) synthesises 183 randomised controlled trials (RCTs) with 22,811 participants, providing a novel, comprehensive comparison of wellbeing interventions. Findings indicate that interventions integrating psychological techniques with physical activity, including ‘movement combined with psychological intervention’ and yoga, rank highest. Mindfulness, compassion-focused therapies, and ACT also demonstrated strong effects, highlighting shared characteristics of presence, acceptance, and self-compassion as beneficial. Physical activity performed comparably to single positive psychology interventions, underscoring multiple pathways to wellbeing. All interventions were significantly more effective than no treatment control groups, except for nature-based interventions which did not significantly outperform controls. However, nature-based interventions were characterised by small samples and a moderate-to-high risk of bias. Multiple sub-group and sensitivity analyses confirmed the robustness of these conclusions. To advance the field, future research needs to explore multi-component approaches that transcend disciplinary silos, embrace interconnected determinants, and ultimately foster sustainable, holistic wellbeing. Social science/Psychology Social science/Psychology/Human behaviour Figures Figure 1 Figure 2 Figure 3 Introduction Global health is facing numerous interrelated challenges including the rise of non-communicable diseases (Collaborators et al., 2015; Hale et al., 2018; Murray & Lopez, 1997a , 1997b ), health inequalities (Asaria et al, 2016 ; Kadel et al., 2022 ; Welch et al., 2013 ) and the threat of climate change (Clayton & Manning, 2018 ; Kjellstrom et al., 2007 ; Redshaw et al, 2013 ; Thomas et al, 2014 ). However, there is increasing recognition that jointly bolstering the wellbeing of individuals, communities, and the planet provides a pathway to addressing these pressing issues (Kemp & Edwards, 2022; Lomas, 2023; Steger, 2023). Health interventions have often exclusively focused on pain, suffering and disease. Yet, a wealth of research now emphasises the link between the promotion of wellbeing and enhanced public health (Diener et al., 2017 ; Diener & Chan, 2011 ), resilience (Cosco et al., 2017 ), prevention of mental disorders (Keyes et al., 2011 ), strengthening of social connections (Mehl et al., 2009 ) and even the cultivation of pro-environmental behaviours (Zawadzki et al., 2020 ). Therefore, the need for systematic and systemic implementation of interventions to improve wellbeing is essential. Substantial research in the field of wellbeing has centred on psychological interventions explicitly designed to enhance wellbeing through Positive Psychology Interventions (PPIs) (Agteren et al., 2021 ; Carr et al., 2021 ; Sin & Lyubomirsky, 2009 ; C. A. White et al., 2019 ). Yet despite evidence that factors like exercise, nutrition, and sleep significantly impact mental health (Kaneita et al., 2007 ; Moreno-Agostino et al., 2019 ; Wiese et al., 2018 ), physical health-focused interventions are often studied separately from psychological wellbeing interventions. Wellbeing science has also faced criticism for focusing too closely on the individual, with less consideration for the social and environmental contexts in which the individual is embedded (Yakushko 2021). In response to these criticisms, a need arose for a comprehensive framework to consolidate existing scholarly literature, multidisciplinary research, and diverse theories into an integrated model. A framework was required to consider the interaction between mind and body, potential physiological underpinnings of wellbeing and broader collective and environmental context beyond the individual. Meeting this need, the GENIAL theoretical framework was developed to offer a structured approach to understanding wellbeing by integrating multiple disciplinary perspectives. The GENIAL model is a theoretical, inter-disciplinary framework of the wellbeing literature (A. Kemp et al., 2017 ; A. H. Kemp & Fisher, 2022 ; Mead et al., 2019 , 2021 , 2023 ). The framework summarises the key determinants of wellbeing relating to the individual (the importance of both mind and body connection including emotional regulation, sense of meaning and purpose and healthy lifestyle behaviours), the community (including social connection, social cohesion and social capital) and the planet (connection to nature and sustainable practices). The GENIAL model also highlights the vagus nerve – the tenth cranial nerve of the parasympathetic nervous system which connects the brain to nearly every organ in the body (gut, heart, lungs) –as a structural link between physical and mental health (Wilkie et al., 2022 ). A large body of research has revealed the key domains of wellbeing and how these both impact and are impacted by vagal function, for example, positive emotions, physical health, social connectedness and time spent in nature (Bello et al., 2020; Kok et al., 2012; McEwan et al. 2021; Natarajan et al., 2020). We have summarised wellbeing as a connection to the self, others and planet (Kemp 2022 ). This holistic definition is supported by evidence demonstrating the efficacy of wellbeing-promoting interventions from across disciplinary domains including psychological interventions (Agteren et al., 2021 ), physical activity (Buecker et al., 2020 ), social support (Steffens et al., 2021 ), and nature-connection (Pritchard et al., 2020 ). Psychological wellbeing interventions typically encompass techniques such as cultivating gratitude, promoting acts of kindness, compassion, character strengths, mindfulness and acceptance and commitment therapy (White et al., 2019 ). Several pairwise meta-analyses have sought to assess the pooled effectiveness of psychological interventions in improving wellbeing outcomes (Agteren et al., 2021 ; Carr et al., 2021 ; Sin & Lyubomirsky, 2009 ; White et al., 2019 ). The findings have shown a range of results, with effect sizes spanning from r = .10 (White et al., 2019 ) to g = 0.39 (Carr et al., 2021 ). Notably, a meta-analysis highlighted that multi-component positive psychological interventions (with an effect size of g = .28) and mindfulness-based interventions (with an effect size of g = .42) had the strongest effects (Agteren et al., 2021 ). Generally, these interventions demonstrate effect sizes that fall within the small to medium range and are influenced by various factors, including the specific target population, the intensity of the intervention, and the mode of delivery (Agteren et al., 2021 ). However, psychological interventions comprise of only one subset of approaches to promote wellbeing and are typically studied in isolation from interventions from other disciplines. One example is evidence indicating that physical activity makes an important contribution to wellbeing. Meta-analyses have found a medium-sized effect of physical activity on subjective wellbeing (d = 0.360) (Buecker et al., 2020 ); leisure-time physical activity is also associated with positive affect ( r = 0.21) and life satisfaction ( r = 0.12) (Wiese et al., 2018 ). Moreover, recent developments in wellbeing science have shed light on the importance of interventions targeting groups and communities (Kern et al., 2020; Lomas et al., 2020), recognising that positive social ties are essential for wellbeing (Haslam et al., 2017; Kemp et al., 2017 ). Meta-analyses have reported moderate effect sizes ( g = 0.66) for social identification-building interventions on wellbeing (Steffens et al., 2021 ). There is also a growing interest in nature-based interventions as a means of promoting wellbeing (Berg et al., 2010 ; Wilkie et al., 2022 ; Kamioka et al., 2014 ). Epidemiological evidence indicates that proximity to and time spent in nature can have significant positive effects on mental health outcomes and even mortality rates (Bratman et al., 2015 ; Gascon et al., 2015 ; Hartig et al., 2014 ; Twohig-Bennett & Jones, 2018 ; White et al., 2012 ). Meta-analyses have also revealed significant effect sizes for the relationship between nature connectedness and both hedonic (r = .20) and eudemonic (r = .24) wellbeing (Pritchard et al., 2020 ). Connection to nature is also a trait that is also associated with pro-environmental behaviours (Richardson 2016) and nature conservation efforts (Hughes 2018), highlighting opportunities to promote wellbeing of the individual as well as the planet. There is now a need to synthesise and compare the efficacy of these widely accepted and prescribed wellbeing interventions from across disciplines and domains. Prior research has also been constrained by pairwise meta-analyses which only allows the comparison of two treatments at a time. The aim of the present study therefore is to conduct a systematic review and network meta-analysis (NMA) to investigate the comparative effectiveness of multiple wellbeing-focused interventions including psychological interventions, physical activity, social identity building and nature-based interventions in a single analysis. We also aim to determine whether interventions which target multiple domains (e.g., physical activity performed in nature or combined with a psychological intervention) are more effective than those with a single focus. To ensure the validity of our NMA, we have decided to focus exclusively on participants from the general population rather than clinical populations, underpinned by several key reasons: impact, relevance, and rigour. By focusing on the general population, our study targets a broad demographic, recognising that health exists along a continuum (Keyes, 2007 ), and that those in the general population can benefit from interventions designed to enhance wellbeing while preventing disease (Sundell et al, 2023 ). This focus also increases the generalisability and utility of our results, enhancing their relevance for policy-making and design of public health strategies aimed at improving health across the community. Lastly, the principle of transitivity, which is crucial for the integrity of NMA, requires that the only systematic differences between comparisons are the treatments being compared. Including clinical populations would therefore introduce variability due to the heterogeneous nature of their conditions, undermining this assumption. By concentrating on the general population, we mitigate these risks and maintain a clear and consistent framework for comparing the effectiveness of different treatments. Results Results of the search and included studies. The search returned 9105 unique studies, of which 183 RCTs including 22,811 adult participants were used in the final NMA. The PRISMA flowchart outlining the inclusion process, including the reasons for exclusion, is presented in Supplementary Information 1.2. The mean age of the participants was 38.30 years (range 18–82). Studies took place either at universities (39%%), workplaces (27%), communities (22%) or online (12%). 79% of studies were conducted in western countries. The most frequently reported countries were the USA (25%), China (8%), UK (7%), Australia (6%) and Spain (5%). A table summarising study characteristics in addition to a list of references can be found in Supplimentary Information 1.3 and 1.4, respectively. In terms of bias risk assessment, the randomisation process was found to have a low risk of bias in 81% of the studies (criteria 1.0). Only 24% of the studies had a low risk of bias on deviations from intended interventions (criteria 2.0), mainly due to insufficient information regarding trial protocols. A considerable portion of the studies (73%) were rated as having a low risk of bias due to missing outcome data (criteria 3.0), while only 33% conducted intention-to-treat analysis. Around 44% were deemed to have a low risk of bias in outcome measurement (criteria 4.0), and 31% demonstrated a low risk of bias in the selection of reported results (criteria 5.0), with many lacking information on a pre-specified analysis plan. Overall, 12 studies (7%) were classified as 'Low Risk,' 61 (33%) were categorized as having 'Some Concerns,' and 110 (60%) were classified as 'High Risk.' A summary table of Risk of Bias (RoB) classifications can be found in Supplementary Information 1.5. Network Geometry The most frequently reported active interventions included Mindfulness-Based Interventions (n = 72), Exercise (n = 33) and Combined Theoretical Psychological Interventions (n = 25). Table 1 describes the node labels and the number of study arms included for each intervention. Some studies (n = 66) or study arms (n = 34) were excluded from the NMA following data extraction. For example, on occasion, interventions being compared across multiple arms of the same RCT were not distinct enough to be classified as separate nodes (for example, full versus partial interventions or three good things v gratitude intervention). In these instances, intervention arms which most closely fitted the other interventions in an existing node were included while others were excluded. For example, for Single PPIs node, ‘three good things’ or ‘character strength’ intervention arms were chosen over less standard PPIs such as ‘three funny things’ or ‘gratitude visit’. When an intervention did not fit in to any node categories and there were not enough studies to create a new, distinct node, the arm or full study was excluded from NMA, for example ‘Mindful-compassion art-based therapy’. Following assessment of transitivity and local inconsistency, further nodes (e.g. SOCIAL, MEMS and CBT) had to be excluded from NMA. Additionally, some nodes had to be re-defined using a tighter description to reduce heterogeneity. See Supplementary Information 2.2 for detailed rationale of network geometry adaptions made based on initial transitivity assessments. >>INSERT TABLE 1 ABOUT HERE Table 1 Summary of final intervention nodes Node Label Intervention Brief Description N study arms included C No Intervention Control - includes passive control (e.g., sit still), no intervention and wait list and treatment as usual 162 MIND Mindfulness-based approaches 72 EX Physical Activity 33 COMB Multi-Theoretical Psychological intervention (e.g. combination of CBT, PPI and Mindfulness). Clear psychological paradigms combined into one. 25 PPI Single positive psychology intervention (e.g. three good things, character strengths or best possible self) 20 COMPAS Compassion focused therapy 17 ED Educational program or resources e.g. Psychoeducation or health behaviour education 15 YOGA Yoga 14 MPPI Multi-Component PPI 8 ACT Acceptance and commitment therapy 5 NAT Nature Interventions 4 Physical movement combined with a psychological intervention (excludes yoga) 3 The final network (Fig. 1 ) contained 183 studies, 28 direct comparisons, 38 indirect comparisons and 12 interventions. The network was well connected and had only one subnetwork. Acceptance and commitment therapy (ACT) and nature-based interventions (NAT) were not very strongly attached to the network as they were compared to control conditions only. >>INSERT FIG 1 ABOUT HERE Transitivity and Consistency Transitivity: visual inspection of the distribution of potential effect modifiers (Supplementary Information 2.3.) indicated that some characteristics (e.g. setting, intensity, delivery, format) were distributed differently across comparisons in the network (for example in some comparisons, all of the interventions were brief, and in others they were all long in intensity). However, overall, the variations in the distribution of effects were not substantial, prompting us to proceed with a statistical examination of inconsistency. Assessment of local inconsistency: in the final assessment of local inconsistency using the node splitting method (SIDE) (see Supplementary Information 2.4), no comparisons were statistically significant, indicating no inconsistency between direct and indirect estimates. Assessment of global inconsistency: The analysis found that there was inconsistency in the network (tau² = 0.114; tau = 0.338; I² = 74.2%, Q = 65.59, p < 0.0001), meaning the results varied across different comparisons. However, when a random effects model was used (allowing full design-by-treatment interactions), the inconsistency dropped and was no longer statistically significant (Q = 16.53, df = 27, p = 0.942). This suggests that the random-effects model helped account for inconsistency between studies in the network. Network meta-analysis results All interventions, except for nature-based interventions were statistically significant and therefore more effective than the control condition. A forest plot of the overall network estimates for each intervention are presented in Fig. 2 . A league table of both network and pairwise comparison estimates are presented in Table 2 . >>INSERT FIG 2 ABOUT HERE >>INSERT TABLE 2 ABOUT HERE Table 2 League table of network v direct evidence Upper right triangle displays effect size (and confidence intervals) estimates based on direct evidence; bottom left triangle displays network estimates. Bold text represents significant at p < 0.05. Treatment ACT C COMB COMPAS ED EX EXPSY MIND MPPI NAT PPI YOGA ACT ACT 0.39 ( 0.03, 0.75) . . . . . . . . . . C 0.39 ( 0.03, 0.75) C -0.40 (-0.58,-0.22) -0.47 (-0.67,-0.26) -0.11 (-0.87, 0.65) -0.38 (-0.56,-0.20) -0.83 (-1.72, 0.07) -0.45 (-0.55,-0.34) -0.31 (-0.59,-0.03) -0.05 (-0.47, 0.38) -0.40 (-0.59,-0.22) -0.44 (-0.70,-0.18) COMB -0.01 (-0.40, 0.39) -0.40 (-0.56,-0.23) COMB . 0.11 (-0.32, 0.55) -0.15 (-0.91, 0.61) . 0.03 (-0.58, 0.63) . . . . COMPAS -0.05 (-0.46, 0.35) -0.45 (-0.63,-0.26) -0.05 (-0.30, 0.20) COMPAS . 0.07 (-0.67, 0.81) . 0.07 (-0.73, 0.87) -0.08 (-0.89, 0.73) . -0.17 (-0.71, 0.37) . ED 0.13 (-0.28, 0.55) -0.26 (-0.47,-0.04) 0.14 (-0.10, 0.39) 0.19 (-0.10, 0.47) ED -0.27 (-0.63, 0.09) . -0.09 (-0.44, 0.26) . . -0.01 (-0.92, 0.91) . EX -0.03 (-0.42, 0.36) -0.42 (-0.57,-0.26) -0.02 (-0.23, 0.19) 0.03 (-0.21, 0.26) -0.16 (-0.39, 0.07) EX -0.32 (-0.80, 0.16) -0.13 (-1.02, 0.76) . . . -0.17 (-0.68, 0.34) EXPSY -0.34 (-0.93, 0.24) -0.73 (-1.20,-0.27) -0.34 (-0.82, 0.15) -0.29 (-0.79, 0.21) -0.48 (-0.98, 0.02) -0.32 (-0.77, 0.13) EXPSY 0.17 (-0.72, 1.05) . . . . MIND -0.05 (-0.42, 0.32) -0.44 (-0.54,-0.35) -0.05 (-0.23, 0.14) 0.00 (-0.21, 0.21) -0.19 (-0.41, 0.03) -0.03 (-0.20, 0.15) 0.29 (-0.18, 0.76) MIND . . 0.16 (-0.47, 0.79) 0.04 (-0.65, 0.73) MPPI 0.08 (-0.36, 0.53) -0.31 (-0.58,-0.04) 0.09 (-0.23, 0.40) 0.14 (-0.18, 0.46) -0.05 (-0.40, 0.29) 0.11 (-0.20, 0.42) 0.43 (-0.11, 0.96) 0.13 (-0.15, 0.42) MPPI . -0.18 (-0.90, 0.55) . NAT 0.34 (-0.21, 0.90) -0.05 (-0.47, 0.38) 0.35 (-0.10, 0.80) 0.40 (-0.06, 0.86) 0.21 (-0.27, 0.69) 0.37 (-0.08, 0.82) 0.69 ( 0.06, 1.31) 0.40 (-0.04, 0.83) 0.26 (-0.24, 0.76) NAT . . PPI -0.01 (-0.41, 0.38) -0.41 (-0.58,-0.23) -0.01 (-0.25, 0.23) 0.04 (-0.21, 0.29) -0.15 (-0.42, 0.12) 0.01 (-0.22, 0.24) 0.33 (-0.17, 0.82) 0.04 (-0.16, 0.23) -0.10 (-0.41, 0.21) -0.36 (-0.82, 0.10) PPI . YOGA -0.10 (-0.53, 0.33) -0.49 (-0.73,-0.26) -0.10 (-0.38, 0.19) -0.05 (-0.35, 0.25) -0.24 (-0.55, 0.08) -0.08 (-0.34, 0.19) 0.24 (-0.27, 0.75) -0.05 (-0.30, 0.20) -0.18 (-0.54, 0.17) -0.45 (-0.93, 0.04) -0.09 (-0.38, 0.20) YOGA Treatment Ranking According to P-score estimates (Fig. 3 ), physical movement combined with psychological intervention (EXPSY), yoga (YOGA), mindfulness (MIND), compassion-based interventions (COMPAS), physical activity (EX), single PPIs (PPI), multi-theoretical psychological interventions (COMB) and acceptance and commitment therapy (ACT) were ranked as the most effective treatments. >>INSERT FIG 3 ABOUT HERE Additional analyses Sub-group analysis found no statistically significant differences in the pooled effect of all active interventions (combined) versus inactive control across delivery mode of intervention (in-person, online platform, self-guided instructions, or live online video conferencing), intervention format (individual v group), setting of intervention (university, workplace, community or online) or country in which study took place (Western v non-Western). A statistically significant subgroup difference ( p = 0.001) was found for intensity of intervention (brief, short, medium, or long), whereby medium length interventions (5–8 weeks) resulted in the largest effect size estimate (0.57 [0.69; 0.45], I 2 = 81.9%) across all active interventions versus control. Full results for all sub-group analyses are presented in Supplementary Information 3. Sensitivity analysis using pairwise comparisons of all active interventions (combined) versus control also found no statistically significant subgroup differences in type of wellbeing outcome measure used (SWB, resilience, mindfulness, or positive affect), waitlist control (waitlist v no intervention control), or risk of bias (low, medium, or high). Full NMA analyses were also conducted (see Supplementary Information 4 for full sensitivity analysis results) using three alternative models; 1) excluding studies with high risk of bias 2) using subjective wellbeing as only outcome measure and 3) excluding small studies (defined as studies with included arms totalling an N size smaller than the lower quartile of included studies: N = 45). Exercise with psychological intervention (EXPSY), yoga (YOGA) mindfulness (MIND) and compassion (COMPAS) were consistently ranked in the top five interventions across all sensitivity analyses. Physical activity (EX), single PPIs, combined psychological interventions (COMB) and psychol/health education (ED) all remained significantly more effective than controls across all sensitivity analyses. In the SWB model, findings were comparable to the main NMA, most treatment rankings remained constant, and the same interventions remained significantly more effective than controls. In the low-medium risk of bias model, multi-component PPIs were no longer significant compared to controls (SMD = 0.36, CI -0.17: 0.89). When small studies were excluded from NMA, the findings were also consistent with the main model. CT was the only substantially inconsistent intervention across sensitivity analyses. ACT was ranked as more effective in the low-medium bias model (SMD = 0.51, rank = 5th ) and when subjective wellbeing (SWB) was used as the only outcome measure (SMD = 0.50, rank = 2nd ). ACT was no longer significantly different to control in the model that excluded small N studies (SMD = 0.22, rank = 10th ). The likely causes of discrepancies are explored in discussion. Certainty of evidence Regarding certainty of evidence, 71% of comparisons were rated as moderate, 26% of comparisons were rated as low and 3% comparisons were rated as very low. The certainty of evidence for each network estimate is reported in Supplementary Information 5. There is no clear trend regarding which intervention comparisons contain low or very low certainty of evidence, suggesting bias is moderately evenly distributed. Publication Bias The funnel plot of comparisons using control as reference had asymmetry towards a positive effect size as standard error (SE) increased (Supplementary Information 6). Using Egger's test, a significant relationship was observed between the standard error and bias (t(171) = 3.66, p = 0.0003). This suggests that smaller studies (less precise estimates with higher SE), may be published more frequently if they report positive results, whilst studies with negative or null findings may be less likely to be published. Discussion This network meta-analysis (NMA) represents an innovative effort to advance the understanding of the relative impacts of wellbeing interventions – a topic of significant research interest and debate (Bolier et al., 2013 ; Carr et al., 2021 ; Hendricks et al., 2020; Lim & Tierney, 2022 ; Seligman et al., 2005 ; Sin & Lyubomirsky, 2009 ; Weiss et al., 2016 ). By integrating data from 183 RCTs, this study provides a robust comparative evaluation of wellbeing-focused interventions across multiple domains. Leveraging the strengths of NMA, our approach moved beyond traditional pairwise meta-analyses to incorporate indirect and direct head-to-head comparisons, enabling a comprehensive understanding of intervention effectiveness. Our analysis synthesised RCT evidence on psychological interventions, physical activity, and nature-based interventions into a single unified analytical framework. Among the interventions evaluated, ‘movement combined with psychological intervention’ ranked as the most effective, while mindfulness, yoga, and compassion-focused interventions were also highly effective. Physical activity demonstrated comparable effectiveness to single positive psychology interventions and acceptance and commitment therapy. The combination of physical activity and psychological intervention shows significant promise in promoting wellbeing. This node included interventions such as awe walks, (Sturm, 2022); meditation combined with brisk walking, (Edwards & Loprinzi, 2019); and walking groups with positive psychology-focused coaching (Lee et al., 2019). However, it's important to note that this conclusion is drawn from a limited pool of evidence, as the node consisted of only three studies. Among these, two were deemed to have a high risk of bias, and the confidence interval for the pooled effect size was wide (SMD = 0.73, CI 0.27–1.20). Despite these limitations, this combination consistently ranked as a top intervention across all sensitivity analyses. Additionally, most comparisons involving this intervention were rated as moderate in certainty according to CINEMA ratings. While we had expected to find studies reporting on the impacts of diverse forms of exercise combined with psychological interventions, studies were limited to walking only, highlighting a need for further research on this topic. Future research should prioritise further investigation of the potential synergistic benefits of combining psychological and physical activity interventions using rigorous RCT methods to examine the most effective combinations. Our findings reinforce the robustness of mindfulness-based interventions for improving wellbeing. Mindfulness consistently maintained a prominent position, ranking among the top four interventions in all sensitivity analyses. The narrow confidence interval of the pooled estimate (SMD = 0.44, 0.35; 0.54), supported by an extensive body of direct evidence (n = 73), reinforces consensus that mindfulness is a consistently effective intervention for enhancing wellbeing within the general population (Khoury et al., 2013 , 2015 ; Sedlmeier et al., 2012 ). Compassion-focused interventions, which also share overlapping characteristics with mindfulness (Gilbert, 2009 , 2014 ), ranked fourth place in effectiveness and remained consistent across most sensitivity analyses. Yoga also consistently featured among the top three most effective interventions across all analyses. It is noteworthy that mindfulness and yoga share common techniques; most forms of yoga share common elements, including controlled breathwork (pranayama), physical postures (asanas) and meditation (dhyana) (Pascoe et al, 2015). This again underscores the potential of integrated mind and body approaches in the promotion of psychological wellbeing. In addition, physical activity had a moderate effect size (SMD = 0.39) and consistently ranked among the top six interventions. Notably, its rank and effect size estimate were comparable to those of Positive Psychology Interventions (PPIs), suggesting that exercise may be similarly effective in improving wellbeing outcomes. These findings highlight that there are multiple pathways and room for a personalised approach to wellbeing promotion and that individuals may choose an intervention that aligns best with their preferences and needs. This also further emphasises the potential benefits in combining physical movement with psychology interventions. The effectiveness of Acceptance and Commitment Therapy (ACT) displayed variations in sensitivity analyses. There was only a limited number of studies (n = 5) reporting ACT outcomes. Notably, among these, one study reported no significant difference for ACT compared to a control group (Danitz, 2014) which increased the confidence interval of the pooled effect estimate. In this study, wellbeing was assessed using the Philadelphia Mindfulness Scale which contains two sub-scales reported separately. During data extraction, the decision was made in the present study to extract data from the awareness subscale, which is framed in a positive manner and assesses ability to be present to experiences e.g. "I notice the emotions that I am experiencing from moment to moment” whilst the acceptance subscale is negatively framed and assesses degree to which individuals engage in self-criticism e.g. "I am critical of myself for having irrational or inappropriate emotions” and thus did not align with our definition of wellbeing. Danitz (2014) found that ACT significantly improved acceptance subscale but not awareness. In addition, for our NMA, only the post-measure score was extracted, as opposed to a change in mean score, thus ACT appeared to have a less favourable result than the control group for awareness due to baseline differences. In sensitivity analyses, upon the exclusion of this study, the estimate for ACT increased substantially. Furthermore, the ACT node was poorly connected to the network, as studies only compared ACT with control arms, so nearly all comparisons were indirect estimates only. Despite these complexities, our confidence in the overall effect size estimate and ACT's ranking in our primary NMA model (Fig. 2 ) remains robust and is reinforced by the inclusion of a low-bias study on ACT (Viskovich, 2020), which featured a substantial participant sample (1162 participants) and aligned closely with our overall NMA pooled effect size (SMD = 0.37, CI 0.26; 0.49), affirming the reliability of our findings. Contrary to expectations, nature-based interventions were not significantly more effective than control conditions. This finding was based on a node containing four studies examining 1) a nature activity programme (observing and drawing nature), 2) horticultural therapy, 3) nature photography, and 4) time in nature without electronic devices. Notably, this node was weakly integrated within the broader network and relied heavily on indirect evidence. The overall quality of evidence ranged from moderate to high risk of bias, largely due to the inclusion of small-scale studies. Beyond these findings, wider literature suggests that emotional responses to nature vary with individuals’ personal relationship to the environment. While nature often promotes well-being, it can also heighten anxiety, particularly in the context of climate change (Pihkala, 2020 ). Crucially, research highlights that a deep connection to nature, rather than mere exposure is key to psychological well-being (Capaldi et al., 2014 ) and encourages pro-environmental behaviour (Richardson et al., 2019 ). Interventions should aim not just to increase time in nature but to actively foster nature connectedness as a psychological trait. Richardson et al. ( 2019 ) emphasise that this requires intentional engagement, including mindfulness, sensory immersion, awe-inspiring experiences, and reflective practices that highlight human-nature interdependence. Our network meta-analysis (NMA) also surprisingly indicated that multi-component Positive Psychology Interventions (MPPIs) had a smaller effect size and ranked lower in p-score than individual Positive Psychology Interventions (PPIs), with the Three Good Things (3GT) gratitude intervention being prominently featured within the single PPI category. This contradicts the findings of a large meta-analysis (Agteren et al, 2021 ) which favoured MPPIs over single PPIs. A potential explanation for this is that our analysis included a study which directly compared a PPI with a MPPI and reported no significant difference in their efficacy on wellbeing (Neumeier et al., 2017). The slightly larger effect size estimate for single PPIs in our analysis, however, should not necessarily imply a clinical preference for them over MPPIs. We found no statistically significant difference between single and multi-component PPIs, and they had overlapping confidence intervals, meaning contextual factors will play a vital role in clinical intervention selection (Ciarrochi et al., 2022 ). Originally, we aimed to include social identity building interventions in the NMA network, but our systematic literature review revealed significant clinical heterogeneity within this category, which impacted on our capacity to synthesise such studies into a single node of evidence. Social interventions identified from our search included a focus on diverse topics, including current events (Rattenbury et al., 1989), reminiscence (Yousefi et al., 2015 ) emotional peer support (Hirani et al, 2018 ), and sharing parenting stressors (Chesak et al., 2020 ), and comparisons consistently showed a higher average participant age than other intervention nodes, indicating demographic disparities, adversely impacting on the assumption of transitivity. Social identity building interventions were therefore excluded from our NMA. This decision was driven by our commitment to maintaining the validity and reliability of our findings, given the potential compromise in integrity due to the heterogeneity and demographic differences within this category. The strength of our NMA relies on the quality of the included RCTs, and thus our findings are impacted by limitations inherent across wellbeing intervention research including often high, individual study risk of bias. For example, only 33% of included studies employed intention-to-treat analyses, which preserves randomisation by analysing all participants regardless of adherence. To address these concerns, we took several measures to ensure the reliability of our conclusions such as including only RCT designs, peer reviewed research and valid measurement scales. Whilst the observed asymmetry in funnel plots warrants a need for caution, this doesn't conclusively prove publication bias, given that comprehensive sensitivity analyses, excluding studies with low sample sizes and high risk of bias, yielded consistent results with the primary analysis, indicating robust conclusions. While excluding grey literature increases methodological rigour, future meta-analyses may benefit from its inclusion to help mitigate potential publication bias. Additionally, in sensitivity analyses, no discrepancy was found between the different types of wellbeing measures used. This consistency implies that the choice of a particular category of wellbeing measure (positive affect, mindfulness, subjective wellbeing, resilience) didn’t impact the conclusions drawn from the analysis. Conclusion This network meta-analysis provides a comprehensive comparison of wellbeing interventions, revealing the superior effectiveness of approaches that integrate psychological techniques with physical activity, as seen in the top-ranked ‘movement combined with psychological intervention’ and the strong performance of yoga. Physical activity, acceptance and commitment therapy and single positive psychology interventions showed comparable efficacy, highlighting multiple pathways to wellbeing. Crucially, our conclusions are robust, as sensitivity analyses, excluding small and high-risk studies, yielded consistent results. The consistently strong effects of mindfulness, compassion-focused therapies, yoga, and ACT also highlight shared psychological mechanisms - presence, acceptance, and self-compassion - as key drivers of wellbeing. We define wellbeing as a dynamic interplay between connection to self (mind and body), connection to others (social integration), and connection to nature. To advance the field, research must move beyond single interventions and embrace multidisciplinary approaches that reflect how people might naturally engage with multiple pathways to wellbeing - including via health behaviours, psychological techniques, social relationships, and connecting to nature. A deeper understanding of how holistic, multi-component interventions enhance wellbeing is crucial for developing sustainable, real-world strategies. Methodology Selection of Studies and Data Extraction This systematic review and NMA was registered with PROSPERO (ID CRD42023403480). The database was developed through a systematic search of Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, PsycINFO and Scopus in March 2023, and was continually updated by lead author (LW). The search strategy contained key words and MeSH (Medical Subject Headings) standardised terms relating to the interventions being studied, randomised controlled trials and wellbeing (see Supplementary Information 1.1 for full search string). References were managed using Covidence ( https://www.covidence.org/ ). Titles and abstracts were screened independently by three reviewers (AG, IG, and SK). Those titles and abstracts which resulted in disagreements were automatically included for full-text screening. Reviewers also screened the full texts according to eligibility criteria and recorded reasons for exclusions. Disagreements during full text screening were resolved by discussion or by the first author (LW). Data was extracted using a custom form on Covidence which was then checked for accuracy by LW. The preferred choice of extraction for outcome measures were means and SDs. When possible, these were calculated using alternative reported statistics, or study authors were contacted (and followed up at least once) via email to request missing data. Studies were excluded if no response was received by time of data analysis. Methodological quality of included randomised control studies were assessed using the Revised Cochrane risk-of-bias tool for randomised trials (RoB 2) by at least one reviewer and was checked for accuracy by the first author. Any disagreements were resolved via discussion with wider review team. Eligibility Criteria Eligibility criteria were developed using the PICOS framework and are summarised in Table 3 . We included randomised controlled trials (both parallel and cluster) published in peer-reviewed academic journals. Participants had to be aged 18 years or older, and not described as having a diagnosable condition, disease or dysfunction or receiving medical treatment for a disease at the time of study. Studies had to deliver at least one intervention described as being a psychological intervention, physical activity intervention, social identity or social support intervention, a nature-based intervention or contain a combination of these. They could be either individual or group format and could be delivered face to face, online or hybrid. Every study arm was assessed independently against PICOS eligibility criteria (see Table 3 ). Interventions could either be compared to a second eligible intervention or a no intervention or wait list control group. Table 3 Summary of PICOS eligibility criteria Inclusion Criteria Exclusion Criteria Population Adults > 18 years old Participants under 18 years old. Participants described as having a specific condition, disease, dysfunction. Intervention Psychological interventions, physical activity, social support, or nature-based interventions. Can be online, in-person or hybrid. Pharmacological or drug treatment arms. Comparison A randomised controlled ‘eligible’ intervention or no intervention or a wait list control. No randomly assigned control condition. Outcome Wellbeing (primary outcome) Single item measures of wellbeing. Studies which solely define wellbeing as reduction of ill-being (e.g. reduced anxiety scores). Study Type Randomised controlled trials. Observational studies, conference abstracts, non-randomised trials or studies not published as full-length articles in peer-reviewed journals Outcome Measures The primary outcome for this study was psychological wellbeing, defined as the presence of positive or adaptive characteristics such as measures of subjective wellbeing, life satisfaction, happiness, positive affect, resilience, or flourishing. Acknowledging that these characteristics may vary, ‘category of wellbeing outcome measure used’ was included as a sensitivity analysis to determine that measures did not differ significantly or lead to different findings. Studies which solely measured ‘wellbeing’ as a reduction in ill-being (e.g. reduced depression/anxiety scores) were excluded. Common standardised wellbeing scales included Perceived Wellness Score (PWS); Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS); 36-Item Short-Form Health Survey (SF-36): Mental Component Subscale; World Health Organization Wellbeing Scale (WHO-5); PANAS -Positive Affect. This list of scales is non-exhaustive and other measures were included if they met inclusion criteria. Network Geometry and Nodes The network nodes were defined following discussion with research team members including clinicians with expertise on which interventions could logically be clustered together, based on both their underpinning theory and delivery in practice. Statistical Analysis Transitivity: The assumption of transitivity requires all interventions to be jointly randomizable. If this assumption holds, common comparisons should not vary significantly on key characteristics, or the validity of the indirect comparisons will be questionable (for example the ‘A’ in ‘A v B’ should not differ significantly to the ‘A’ in ‘A v C’, otherwise the indirect ‘B v C’ comparison will be invalid). To assess transitivity, we created a table of important characteristics (study setting, intervention intensity, delivery mode) to examine whether potential effect modifiers were similarly distributed across the comparisons. Variability from different study populations, interventions, and outcomes makes it difficult to ascertain that the treatments are being compared under equivalent conditions. The inclusion of heterogeneous interventions necessitate even more stringent control of population differences. Hence, we opted to use a general population sample, to ensure more uniformity and reliable comparisons. Pairwise Meta-Analyses: We conducted pairwise meta-analyses for all direct comparisons using a random-effects model. Homogeneity of effect sizes were estimated using Tau² and Higgins I² values. Standardised mean differences (SMDs) were reported with 95% confidence intervals. p values (alpha threshold = 0.05) were used to determine whether the effect sizes for each direct comparison were significant. Network Meta-Analysis: A random-effect NMA was conducted to estimate a single summary effect for each node in the network. Global inconsistency was assessed using the Q statistic, based on design-by-treatment interaction model (Higgins et al., 2012 ). Local inconsistency was assessed by comparing direct estimates to indirect estimates using the node splitting method (SIDE) (whereby p < 1.0 indicated statistically significant inconsistency). Treatments were ranked using P-scores, these range from 0 to 1 and can be interpreted as an average degree of certainty for a treatment to be better than the other treatments in the network (Rücker & Schwarzer, 2015 ). Small study effects were assessed using comparison-adjusted funnel plots, which report each study’s effect estimate against their reversed standard error. Asymmetry in the plot suggests that larger effects tend to be systematically found in smaller studies. Additional Analyses: six sub-group analyses were conducted on pre-specified potential effect modifiers. Analyses were first conducted using pairwise meta-analysis of all active interventions versus control. These were sub-grouped by mode of delivery (e.g. in-person, online, live video conferencing or instructions only); treatment format (individual or group); intensity (length) of intervention; setting of intervention (university, workplace, community or online); country (Western or non-Western) and age of participants. In addition, four sensitivity analyses were conducted to compare whether pooled effect of all interventions versus control differed depending on 1) type of outcome measure used; 2) whether control was a waitlist or no intervention; 3) risk of bias and 4) included study size. Additional sensitivity analyses were conducted using full NMA analyses of three alternative models; 1) excluding studies with high risk of bias; 2) using subjective wellbeing as the only outcome measure and 3) excluding small studies. Confidence in NMA: The risk of bias across studies was assessed with Confidence in Network Meta-Analysis (CINeMA) for NMA (Papakonstantinou et al., 2020). CINeMA considers six domains that impact confidence in the NMA results: 1. Within-study bias; 2. Reporting bias; 3. Indirectness; 4. Imprecision; 5. Heterogeneity; 6. Incoherence. Each treatment comparison was assessed as having “no concerns,” “some concerns,” or “major concerns” in each of the six domains. Then, judgments across the domains were summarized into a single confidence rating (high, moderate, low, or very low). Declarations Data Availability Statement The dataset used in this study is publicly available on the OSF repository at https://osf.io/nz59j/?view_only=30f14278418f454e8c6ee297493f2c39. Code Availability Statement The code used for this study is available on OSF: https://osf.io/nz59j/?view_only=30f14278418f454e8c6ee297493f2c39. The R script includes all steps for data processing, analysis, and visualisation. References Agteren, J. van, Iasiello, M., Lo, L., Bartholomaeus, J., Kopsaftis, Z., Carey, M., & Kyrios, M. (2021). 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Meta-analytic evidence for a robust and positive association between individuals pro-environmental behaviors and their subjective wellbeing. Environmental Research Letters , 15 (12), 123007. https://doi.org/10.1088/1748-9326/abc4ae Additional Declarations Yes there is potential Competing Interest. The first author declares a potential conflict of interest relating to their work as a yoga and mindfulness instructor. While this professional experience offers practical insights into these practices, it did not influence the design, execution, or interpretation of the research presented in this manuscript. 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Kemp","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYFACHgbGBiDFjyp6gAgtkg0kazFAVYRHC38D7wHGGRXb8ozPnzH8XPDHRp6B/fADZp4zuLVIHOBLYNxw5nax2Y0cY+mZbWmGDTxpBsw8N/C46wCPAePDttuJ227wbpDmbTgMdGUOAzPPB9w65MFa/t1O3Nx/dvNvnj//7Rv43+DXYgDSsrHhduIGhtxt0jxsBxIbJEC24HGY4WEeg4Mzjt0ulriR/82aty05uU3imcHBOXi8L3e8x/BhT83tPP7+Y8m3ef7Y2fbzJz988OYYHu8zQ+IgAS7AxkAgImEggaCKUTAKRsEoGLkAANwLVJwF4A4YAAAAAElFTkSuQmCC","orcid":"","institution":"Swansea University","correspondingAuthor":true,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Kemp","suffix":""},{"id":414858871,"identity":"eccf4fb9-46d0-4e03-9f74-f86252aff433","order_by":1,"name":"Lowri Wilkie","email":"","orcid":"https://orcid.org/0000-0001-8446-4926","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lowri","middleName":"","lastName":"Wilkie","suffix":""},{"id":414858872,"identity":"1084c0f8-59ee-4e96-a0aa-a36ae7dbbfed","order_by":2,"name":"Zoe Fisher","email":"","orcid":"","institution":"Swansea Bay University Health Board","correspondingAuthor":false,"prefix":"","firstName":"Zoe","middleName":"","lastName":"Fisher","suffix":""},{"id":414858873,"identity":"847f1418-9d8a-4411-bc38-eb2be5d24356","order_by":3,"name":"Antonia Geidel","email":"","orcid":"","institution":"Swansea University","correspondingAuthor":false,"prefix":"","firstName":"Antonia","middleName":"","lastName":"Geidel","suffix":""},{"id":414858874,"identity":"f6045df7-7adf-476a-a696-eb16f6cc9d6b","order_by":4,"name":"Isabel Goodall","email":"","orcid":"","institution":"Swansea University","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Goodall","suffix":""},{"id":414858875,"identity":"9bfcc79a-4219-4c10-8902-3e03d2ba2e02","order_by":5,"name":"Shannon Kamil","email":"","orcid":"","institution":"Swansea University","correspondingAuthor":false,"prefix":"","firstName":"Shannon","middleName":"","lastName":"Kamil","suffix":""},{"id":414858876,"identity":"e8566d1a-df47-407b-9060-9329358edf99","order_by":6,"name":"Elen Davies","email":"","orcid":"","institution":"Swansea University","correspondingAuthor":false,"prefix":"","firstName":"Elen","middleName":"","lastName":"Davies","suffix":""}],"badges":[],"createdAt":"2025-01-26 09:50:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5905657/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5905657/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41562-025-02369-1","type":"published","date":"2026-01-02T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79319207,"identity":"1a396de6-75c4-4b59-8e9a-0481ec2ad439","added_by":"auto","created_at":"2025-03-27 04:14:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141941,"visible":true,"origin":"","legend":"\u003cp\u003eEvidence network diagram: thickness of edge represents number of direct comparisons and size of node represents number of studies reporting the intervention.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5905657/v1/e28e162a4b66d7fb9a8d6086.png"},{"id":79319208,"identity":"3cdf1369-fb14-4998-ad6a-4ca0ec48f3ff","added_by":"auto","created_at":"2025-03-27 04:14:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197829,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot presenting overall NMA estimated effect sizes (SMD), 95% confidence intervals, N of comparisons for each treatment, P-score ranking and number of participants.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5905657/v1/03aa29b82a2d8ca5590f59eb.png"},{"id":79319211,"identity":"e2441e09-9602-4e20-807a-b38a9cb393f7","added_by":"auto","created_at":"2025-03-27 04:14:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84740,"visible":true,"origin":"","legend":"\u003cp\u003eInterventions ranked according to P-scores\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5905657/v1/e903c27f6fe6e2e349f58c10.png"},{"id":99419421,"identity":"eed5ace4-8938-4519-967a-617597f8be70","added_by":"auto","created_at":"2026-01-03 08:08:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1500913,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5905657/v1/17bce309-f337-47ee-b54f-64ea4589e07a.pdf"},{"id":79319220,"identity":"f3473563-f8cc-4077-956f-038d9805afab","added_by":"auto","created_at":"2025-03-27 04:14:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1355417,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"CURRENTNMASUPP.docx","url":"https://assets-eu.researchsquare.com/files/rs-5905657/v1/9dfaa478307d19042fb2a5ad.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nThe first author declares a potential conflict of interest relating to their work as a yoga and mindfulness instructor. While this professional experience offers practical insights into these practices, it did not influence the design, execution, or interpretation of the research presented in this manuscript.","formattedTitle":"A Systematic Review and Network Meta-Analysis of Randomised-Controlled Wellbeing-Focused Interventions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal health is facing numerous interrelated challenges including the rise of non-communicable diseases (Collaborators et al., 2015; Hale et al., 2018; Murray \u0026amp; Lopez, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1997a\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1997b\u003c/span\u003e), health inequalities (Asaria et al, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kadel et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Welch et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and the threat of climate change (Clayton \u0026amp; Manning, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kjellstrom et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Redshaw et al, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Thomas et al, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, there is increasing recognition that jointly bolstering the wellbeing of individuals, communities, and the planet provides a pathway to addressing these pressing issues (Kemp \u0026amp; Edwards, 2022; Lomas, 2023; Steger, 2023). Health interventions have often exclusively focused on pain, suffering and disease. Yet, a wealth of research now emphasises the link between the promotion of wellbeing and enhanced public health (Diener et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Diener \u0026amp; Chan, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), resilience (Cosco et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), prevention of mental disorders (Keyes et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), strengthening of social connections (Mehl et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and even the cultivation of pro-environmental behaviours (Zawadzki et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, the need for systematic and systemic implementation of interventions to improve wellbeing is essential.\u003c/p\u003e \u003cp\u003eSubstantial research in the field of wellbeing has centred on psychological interventions explicitly designed to enhance wellbeing through Positive Psychology Interventions (PPIs) (Agteren et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Carr et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sin \u0026amp; Lyubomirsky, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; C. A. White et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Yet despite evidence that factors like exercise, nutrition, and sleep significantly impact mental health (Kaneita et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Moreno-Agostino et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wiese et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), physical health-focused interventions are often studied separately from psychological wellbeing interventions. Wellbeing science has also faced criticism for focusing too closely on the individual, with less consideration for the social and environmental contexts in which the individual is embedded (Yakushko 2021). In response to these criticisms, a need arose for a comprehensive framework to consolidate existing scholarly literature, multidisciplinary research, and diverse theories into an integrated model. A framework was required to consider the interaction between mind and body, potential physiological underpinnings of wellbeing and broader collective and environmental context beyond the individual. Meeting this need, the GENIAL theoretical framework was developed to offer a structured approach to understanding wellbeing by integrating multiple disciplinary perspectives. The GENIAL model is a theoretical, inter-disciplinary framework of the wellbeing literature (A. Kemp et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; A. H. Kemp \u0026amp; Fisher, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mead et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The framework summarises the key determinants of wellbeing relating to the individual (the importance of both mind and body connection including emotional regulation, sense of meaning and purpose and healthy lifestyle behaviours), the community (including social connection, social cohesion and social capital) and the planet (connection to nature and sustainable practices). The GENIAL model also highlights the vagus nerve \u0026ndash; the tenth cranial nerve of the parasympathetic nervous system which connects the brain to nearly every organ in the body (gut, heart, lungs) \u0026ndash;as a structural link between physical and mental health (Wilkie et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A large body of research has revealed the key domains of wellbeing and how these both impact and are impacted by vagal function, for example, positive emotions, physical health, social connectedness and time spent in nature (Bello et al., 2020; Kok et al., 2012; McEwan et al. 2021; Natarajan et al., 2020). We have summarised wellbeing as a connection to the self, others and planet (Kemp \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This holistic definition is supported by evidence demonstrating the efficacy of wellbeing-promoting interventions from across disciplinary domains including psychological interventions (Agteren et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), physical activity (Buecker et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), social support (Steffens et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and nature-connection (Pritchard et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePsychological wellbeing interventions typically encompass techniques such as cultivating gratitude, promoting acts of kindness, compassion, character strengths, mindfulness and acceptance and commitment therapy (White et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Several pairwise meta-analyses have sought to assess the pooled effectiveness of psychological interventions in improving wellbeing outcomes (Agteren et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Carr et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sin \u0026amp; Lyubomirsky, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; White et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The findings have shown a range of results, with effect sizes spanning from \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.10 (White et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to g\u0026thinsp;=\u0026thinsp;0.39 (Carr et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, a meta-analysis highlighted that multi-component positive psychological interventions (with an effect size of \u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.28) and mindfulness-based interventions (with an effect size of \u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.42) had the strongest effects (Agteren et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Generally, these interventions demonstrate effect sizes that fall within the small to medium range and are influenced by various factors, including the specific target population, the intensity of the intervention, and the mode of delivery (Agteren et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, psychological interventions comprise of only one subset of approaches to promote wellbeing and are typically studied in isolation from interventions from other disciplines. One example is evidence indicating that physical activity makes an important contribution to wellbeing. Meta-analyses have found a medium-sized effect of physical activity on subjective wellbeing \u003cem\u003e(d\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.360) (Buecker et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); leisure-time physical activity is also associated with positive affect (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.21) and life satisfaction (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12) (Wiese et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, recent developments in wellbeing science have shed light on the importance of interventions targeting groups and communities (Kern et al., 2020; Lomas et al., 2020), recognising that positive social ties are essential for wellbeing (Haslam et al., 2017; Kemp et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Meta-analyses have reported moderate effect sizes (\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66) for social identification-building interventions on wellbeing (Steffens et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There is also a growing interest in nature-based interventions as a means of promoting wellbeing (Berg et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wilkie et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kamioka et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Epidemiological evidence indicates that proximity to and time spent in nature can have significant positive effects on mental health outcomes and even mortality rates (Bratman et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gascon et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hartig et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Twohig-Bennett \u0026amp; Jones, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; White et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Meta-analyses have also revealed significant effect sizes for the relationship between nature connectedness and both hedonic (r\u0026thinsp;=\u0026thinsp;.20) and eudemonic (r\u0026thinsp;=\u0026thinsp;.24) wellbeing (Pritchard et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Connection to nature is also a trait that is also associated with pro-environmental behaviours (Richardson 2016) and nature conservation efforts (Hughes 2018), highlighting opportunities to promote wellbeing of the individual as well as the planet.\u003c/p\u003e \u003cp\u003eThere is now a need to synthesise and compare the efficacy of these widely accepted and prescribed wellbeing interventions from across disciplines and domains. Prior research has also been constrained by pairwise meta-analyses which only allows the comparison of two treatments at a time. The aim of the present study therefore is to conduct a systematic review and network meta-analysis (NMA) to investigate the comparative effectiveness of multiple wellbeing-focused interventions including psychological interventions, physical activity, social identity building and nature-based interventions in a single analysis. We also aim to determine whether interventions which target multiple domains (e.g., physical activity performed in nature or combined with a psychological intervention) are more effective than those with a single focus. To ensure the validity of our NMA, we have decided to focus exclusively on participants from the general population rather than clinical populations, underpinned by several key reasons: impact, relevance, and rigour. By focusing on the general population, our study targets a broad demographic, recognising that health exists along a continuum (Keyes, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and that those in the general population can benefit from interventions designed to enhance wellbeing while preventing disease (Sundell et al, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This focus also increases the generalisability and utility of our results, enhancing their relevance for policy-making and design of public health strategies aimed at improving health across the community. Lastly, the principle of transitivity, which is crucial for the integrity of NMA, requires that the only systematic differences between comparisons are the treatments being compared. Including clinical populations would therefore introduce variability due to the heterogeneous nature of their conditions, undermining this assumption. By concentrating on the general population, we mitigate these risks and maintain a clear and consistent framework for comparing the effectiveness of different treatments.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eResults of the search and included studies.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe search returned 9105 unique studies, of which 183 RCTs including 22,811 adult participants were used in the final NMA. The PRISMA flowchart outlining the inclusion process, including the reasons for exclusion, is presented in Supplementary Information 1.2. The mean age of the participants was 38.30 years (range 18\u0026ndash;82). Studies took place either at universities (39%%), workplaces (27%), communities (22%) or online (12%). 79% of studies were conducted in western countries. The most frequently reported countries were the USA (25%), China (8%), UK (7%), Australia (6%) and Spain (5%). A table summarising study characteristics in addition to a list of references can be found in Supplimentary Information 1.3 and 1.4, respectively.\u003c/p\u003e \u003cp\u003eIn terms of bias risk assessment, the randomisation process was found to have a low risk of bias in 81% of the studies (criteria 1.0). Only 24% of the studies had a low risk of bias on deviations from intended interventions (criteria 2.0), mainly due to insufficient information regarding trial protocols. A considerable portion of the studies (73%) were rated as having a low risk of bias due to missing outcome data (criteria 3.0), while only 33% conducted intention-to-treat analysis. Around 44% were deemed to have a low risk of bias in outcome measurement (criteria 4.0), and 31% demonstrated a low risk of bias in the selection of reported results (criteria 5.0), with many lacking information on a pre-specified analysis plan. Overall, 12 studies (7%) were classified as 'Low Risk,' 61 (33%) were categorized as having 'Some Concerns,' and 110 (60%) were classified as 'High Risk.' A summary table of Risk of Bias (RoB) classifications can be found in Supplementary Information 1.5.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Geometry\u003c/h2\u003e \u003cp\u003eThe most frequently reported active interventions included Mindfulness-Based Interventions (n\u0026thinsp;=\u0026thinsp;72), Exercise (n\u0026thinsp;=\u0026thinsp;33) and Combined Theoretical Psychological Interventions (n\u0026thinsp;=\u0026thinsp;25). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the node labels and the number of study arms included for each intervention.\u003c/p\u003e \u003cp\u003eSome studies (n\u0026thinsp;=\u0026thinsp;66) or study arms (n\u0026thinsp;=\u0026thinsp;34) were excluded from the NMA following data extraction. For example, on occasion, interventions being compared across multiple arms of the same RCT were not distinct enough to be classified as separate nodes (for example, full versus partial interventions or three good things v gratitude intervention). In these instances, intervention arms which most closely fitted the other interventions in an existing node were included while others were excluded. For example, for Single PPIs node, \u0026lsquo;three good things\u0026rsquo; or \u0026lsquo;character strength\u0026rsquo; intervention arms were chosen over less standard PPIs such as \u0026lsquo;three funny things\u0026rsquo; or \u0026lsquo;gratitude visit\u0026rsquo;. When an intervention did not fit in to any node categories and there were not enough studies to create a new, distinct node, the arm or full study was excluded from NMA, for example \u0026lsquo;Mindful-compassion art-based therapy\u0026rsquo;.\u003c/p\u003e \u003cp\u003eFollowing assessment of transitivity and local inconsistency, further nodes (e.g. SOCIAL, MEMS and CBT) had to be excluded from NMA. Additionally, some nodes had to be re-defined using a tighter description to reduce heterogeneity. See Supplementary Information 2.2 for detailed rationale of network geometry adaptions made based on initial transitivity assessments.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026gt;INSERT TABLE 1 ABOUT HERE\u003c/strong\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of final intervention nodes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode Label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntervention Brief Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN study arms included\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Intervention Control - includes passive control (e.g., sit still), no intervention and wait list and treatment as usual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMindfulness-based approaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-Theoretical Psychological intervention (e.g. combination of CBT, PPI and Mindfulness). Clear psychological paradigms combined into one.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle positive psychology intervention (e.g. three good things, character strengths or best possible self)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOMPAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompassion focused therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducational program or resources e.g. Psychoeducation or health behaviour education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYOGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYoga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-Component PPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcceptance and commitment therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNature Interventions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical movement combined with a psychological intervention (excludes yoga)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe final network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) contained 183 studies, 28 direct comparisons, 38 indirect comparisons and 12 interventions. The network was well connected and had only one subnetwork. Acceptance and commitment therapy (ACT) and nature-based interventions (NAT) were not very strongly attached to the network as they were compared to control conditions only.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026gt;INSERT FIG 1 ABOUT HERE\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eTransitivity and Consistency\u003c/h3\u003e\n\u003cp\u003eTransitivity: visual inspection of the distribution of potential effect modifiers (Supplementary Information 2.3.) indicated that some characteristics (e.g. setting, intensity, delivery, format) were distributed differently across comparisons in the network (for example in some comparisons, all of the interventions were brief, and in others they were all long in intensity). However, overall, the variations in the distribution of effects were not substantial, prompting us to proceed with a statistical examination of inconsistency.\u003c/p\u003e \u003cp\u003eAssessment of local inconsistency: in the final assessment of local inconsistency using the node splitting method (SIDE) (see Supplementary Information 2.4), no comparisons were statistically significant, indicating no inconsistency between direct and indirect estimates.\u003c/p\u003e \u003cp\u003eAssessment of global inconsistency: The analysis found that there was inconsistency in the network (tau\u0026sup2; = 0.114; tau\u0026thinsp;=\u0026thinsp;0.338; I\u0026sup2; = 74.2%, Q\u0026thinsp;=\u0026thinsp;65.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), meaning the results varied across different comparisons. However, when a random effects model was used (allowing full design-by-treatment interactions), the inconsistency dropped and was no longer statistically significant (Q\u0026thinsp;=\u0026thinsp;16.53, df\u0026thinsp;=\u0026thinsp;27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.942). This suggests that the random-effects model helped account for inconsistency between studies in the network.\u003c/p\u003e\n\u003ch3\u003eNetwork meta-analysis results\u003c/h3\u003e\n\u003cp\u003eAll interventions, except for nature-based interventions were statistically significant and therefore more effective than the control condition. A forest plot of the overall network estimates for each intervention are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A league table of both network and pairwise comparison estimates are presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026gt;INSERT FIG 2 ABOUT HERE\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026gt;INSERT TABLE 2 ABOUT HERE\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLeague table of network v direct evidence Upper right triangle displays effect size (and confidence intervals) estimates based on direct evidence; bottom left triangle displays network estimates. Bold text represents significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOMB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOMPAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eED\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEXPSY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMIND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMPPI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePPI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eYOGA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e( 0.03, 0.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e( 0.03, 0.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.40\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(-0.58,-0.22)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.47\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(-0.67,-0.26)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003cp\u003e(-0.87, 0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.38\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(-0.56,-0.20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.83\u003c/p\u003e \u003cp\u003e(-1.72, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.45\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(-0.55,-0.34)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-0.31\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(-0.59,-0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003cp\u003e(-0.47, 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e-0.40\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(-0.59,-0.22)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e-0.44\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(-0.70,-0.18)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003cp\u003e(-0.40, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.40\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(-0.56,-0.23)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e 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align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003cp\u003e(-0.10, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003cp\u003e(-0.63, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003cp\u003e(-0.44, 0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003cp\u003e(-0.92, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e 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\u003cp\u003e(-0.11, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003cp\u003e(-0.15, 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMPPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003cp\u003e(-0.90, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003cp\u003e(-0.21, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003cp\u003e(-0.47, 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003cp\u003e(-0.10, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003cp\u003e(-0.06, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003cp\u003e(-0.27, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003cp\u003e(-0.08, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e( 0.06, 1.31)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003cp\u003e(-0.04, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003cp\u003e(-0.24, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003cp\u003e(-0.41, 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.41\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(-0.58,-0.23)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003cp\u003e(-0.25, 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003cp\u003e(-0.21, 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003cp\u003e(-0.42, 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e(-0.22, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003cp\u003e(-0.17, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003cp\u003e(-0.16, 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003cp\u003e(-0.41, 0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003cp\u003e(-0.82, 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYOGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003cp\u003e(-0.53, 0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.49\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(-0.73,-0.26)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003cp\u003e(-0.38, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003cp\u003e(-0.35, 0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003cp\u003e(-0.55, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003cp\u003e(-0.34, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003cp\u003e(-0.27, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003cp\u003e(-0.30, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003cp\u003e(-0.54, 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003cp\u003e(-0.93, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003cp\u003e(-0.38, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eYOGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eTreatment Ranking\u003c/h3\u003e\n\u003cp\u003eAccording to P-score estimates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), physical movement combined with psychological intervention (EXPSY), yoga (YOGA), mindfulness (MIND), compassion-based interventions (COMPAS), physical activity (EX), single PPIs (PPI), multi-theoretical psychological interventions (COMB) and acceptance and commitment therapy (ACT) were ranked as the most effective treatments.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026gt;INSERT FIG 3 ABOUT HERE\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eAdditional analyses\u003c/h3\u003e\n\u003cp\u003eSub-group analysis found no statistically significant differences in the pooled effect of all active interventions (combined) versus inactive control across delivery mode of intervention (in-person, online platform, self-guided instructions, or live online video conferencing), intervention format (individual v group), setting of intervention (university, workplace, community or online) or country in which study took place (Western v non-Western). A statistically significant subgroup difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) was found for intensity of intervention (brief, short, medium, or long), whereby medium length interventions (5\u0026ndash;8 weeks) resulted in the largest effect size estimate (0.57 [0.69; 0.45], \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2 =\u003c/sup\u003e 81.9%) across all active interventions versus control. Full results for all sub-group analyses are presented in Supplementary Information 3.\u003c/p\u003e \u003cp\u003eSensitivity analysis using pairwise comparisons of all active interventions (combined) versus control also found no statistically significant subgroup differences in type of wellbeing outcome measure used (SWB, resilience, mindfulness, or positive affect), waitlist control (waitlist v no intervention control), or risk of bias (low, medium, or high).\u003c/p\u003e \u003cp\u003eFull NMA analyses were also conducted (see Supplementary Information 4 for full sensitivity analysis results) using three alternative models; 1) excluding studies with high risk of bias 2) using subjective wellbeing as only outcome measure and 3) excluding small studies (defined as studies with included arms totalling an N size smaller than the lower quartile of included studies: N\u0026thinsp;=\u0026thinsp;45).\u003c/p\u003e \u003cp\u003eExercise with psychological intervention (EXPSY), yoga (YOGA) mindfulness (MIND) and compassion (COMPAS) were consistently ranked in the top five interventions across all sensitivity analyses. Physical activity (EX), single PPIs, combined psychological interventions (COMB) and psychol/health education (ED) all remained significantly more effective than controls across all sensitivity analyses.\u003c/p\u003e \u003cp\u003eIn the SWB model, findings were comparable to the main NMA, most treatment rankings remained constant, and the same interventions remained significantly more effective than controls. In the low-medium risk of bias model, multi-component PPIs were no longer significant compared to controls (SMD\u0026thinsp;=\u0026thinsp;0.36, CI -0.17: 0.89). When small studies were excluded from NMA, the findings were also consistent with the main model. CT was the only substantially inconsistent intervention across sensitivity analyses. ACT was ranked as more effective in the low-medium bias model (SMD\u0026thinsp;=\u0026thinsp;0.51, rank\u0026thinsp;=\u0026thinsp;5th ) and when subjective wellbeing (SWB) was used as the only outcome measure (SMD\u0026thinsp;=\u0026thinsp;0.50, rank\u0026thinsp;=\u0026thinsp;2nd ). ACT was no longer significantly different to control in the model that excluded small N studies (SMD\u0026thinsp;=\u0026thinsp;0.22, rank\u0026thinsp;=\u0026thinsp;10th ). The likely causes of discrepancies are explored in discussion.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCertainty of evidence\u003c/h2\u003e \u003cp\u003eRegarding certainty of evidence, 71% of comparisons were rated as moderate, 26% of comparisons were rated as low and 3% comparisons were rated as very low. The certainty of evidence for each network estimate is reported in Supplementary Information 5. There is no clear trend regarding which intervention comparisons contain low or very low certainty of evidence, suggesting bias is moderately evenly distributed.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePublication Bias\u003c/h3\u003e\n\u003cp\u003eThe funnel plot of comparisons using control as reference had asymmetry towards a positive effect size as standard error (SE) increased (Supplementary Information 6). Using Egger's test, a significant relationship was observed between the standard error and bias (t(171)\u0026thinsp;=\u0026thinsp;3.66, p\u0026thinsp;=\u0026thinsp;0.0003). This suggests that smaller studies (less precise estimates with higher SE), may be published more frequently if they report positive results, whilst studies with negative or null findings may be less likely to be published.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis network meta-analysis (NMA) represents an innovative effort to advance the understanding of the relative impacts of wellbeing interventions \u0026ndash; a topic of significant research interest and debate (Bolier et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Carr et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hendricks et al., 2020; Lim \u0026amp; Tierney, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Seligman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sin \u0026amp; Lyubomirsky, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Weiss et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). By integrating data from 183 RCTs, this study provides a robust comparative evaluation of wellbeing-focused interventions across multiple domains. Leveraging the strengths of NMA, our approach moved beyond traditional pairwise meta-analyses to incorporate indirect and direct head-to-head comparisons, enabling a comprehensive understanding of intervention effectiveness. Our analysis synthesised RCT evidence on psychological interventions, physical activity, and nature-based interventions into a single unified analytical framework. Among the interventions evaluated, \u0026lsquo;movement combined with psychological intervention\u0026rsquo; ranked as the most effective, while mindfulness, yoga, and compassion-focused interventions were also highly effective. Physical activity demonstrated comparable effectiveness to single positive psychology interventions and acceptance and commitment therapy.\u003c/p\u003e \u003cp\u003eThe combination of physical activity and psychological intervention shows significant promise in promoting wellbeing. This node included interventions such as awe walks, (Sturm, 2022); meditation combined with brisk walking, (Edwards \u0026amp; Loprinzi, 2019); and walking groups with positive psychology-focused coaching (Lee et al., 2019). However, it's important to note that this conclusion is drawn from a limited pool of evidence, as the node consisted of only three studies. Among these, two were deemed to have a high risk of bias, and the confidence interval for the pooled effect size was wide (SMD\u0026thinsp;=\u0026thinsp;0.73, CI 0.27\u0026ndash;1.20). Despite these limitations, this combination consistently ranked as a top intervention across all sensitivity analyses. Additionally, most comparisons involving this intervention were rated as moderate in certainty according to CINEMA ratings. While we had expected to find studies reporting on the impacts of diverse forms of exercise combined with psychological interventions, studies were limited to walking only, highlighting a need for further research on this topic. Future research should prioritise further investigation of the potential synergistic benefits of combining psychological and physical activity interventions using rigorous RCT methods to examine the most effective combinations.\u003c/p\u003e \u003cp\u003eOur findings reinforce the robustness of mindfulness-based interventions for improving wellbeing. Mindfulness consistently maintained a prominent position, ranking among the top four interventions in all sensitivity analyses. The narrow confidence interval of the pooled estimate (SMD\u0026thinsp;=\u0026thinsp;0.44, 0.35; 0.54), supported by an extensive body of direct evidence (n\u0026thinsp;=\u0026thinsp;73), reinforces consensus that mindfulness is a consistently effective intervention for enhancing wellbeing within the general population (Khoury et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sedlmeier et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Compassion-focused interventions, which also share overlapping characteristics with mindfulness (Gilbert, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), ranked fourth place in effectiveness and remained consistent across most sensitivity analyses. Yoga also consistently featured among the top three most effective interventions across all analyses. It is noteworthy that mindfulness and yoga share common techniques; most forms of yoga share common elements, including controlled breathwork (pranayama), physical postures (asanas) and meditation (dhyana) (Pascoe et al, 2015). This again underscores the potential of integrated mind and body approaches in the promotion of psychological wellbeing.\u003c/p\u003e \u003cp\u003eIn addition, physical activity had a moderate effect size (SMD\u0026thinsp;=\u0026thinsp;0.39) and consistently ranked among the top six interventions. Notably, its rank and effect size estimate were comparable to those of Positive Psychology Interventions (PPIs), suggesting that exercise may be similarly effective in improving wellbeing outcomes. These findings highlight that there are multiple pathways and room for a personalised approach to wellbeing promotion and that individuals may choose an intervention that aligns best with their preferences and needs. This also further emphasises the potential benefits in combining physical movement with psychology interventions.\u003c/p\u003e \u003cp\u003eThe effectiveness of Acceptance and Commitment Therapy (ACT) displayed variations in sensitivity analyses. There was only a limited number of studies (n\u0026thinsp;=\u0026thinsp;5) reporting ACT outcomes. Notably, among these, one study reported no significant difference for ACT compared to a control group (Danitz, 2014) which increased the confidence interval of the pooled effect estimate. In this study, wellbeing was assessed using the Philadelphia Mindfulness Scale which contains two sub-scales reported separately. During data extraction, the decision was made in the present study to extract data from the awareness subscale, which is framed in a positive manner and assesses ability to be present to experiences e.g. \"I notice the emotions that I am experiencing from moment to moment\u0026rdquo; whilst the acceptance subscale is negatively framed and assesses degree to which individuals engage in self-criticism e.g. \"I am critical of myself for having irrational or inappropriate emotions\u0026rdquo; and thus did not align with our definition of wellbeing. Danitz (2014) found that ACT significantly improved acceptance subscale but not awareness. In addition, for our NMA, only the post-measure score was extracted, as opposed to a change in mean score, thus ACT appeared to have a less favourable result than the control group for awareness due to baseline differences. In sensitivity analyses, upon the exclusion of this study, the estimate for ACT increased substantially. Furthermore, the ACT node was poorly connected to the network, as studies only compared ACT with control arms, so nearly all comparisons were indirect estimates only. Despite these complexities, our confidence in the overall effect size estimate and ACT's ranking in our primary NMA model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) remains robust and is reinforced by the inclusion of a low-bias study on ACT (Viskovich, 2020), which featured a substantial participant sample (1162 participants) and aligned closely with our overall NMA pooled effect size (SMD\u0026thinsp;=\u0026thinsp;0.37, CI 0.26; 0.49), affirming the reliability of our findings.\u003c/p\u003e \u003cp\u003eContrary to expectations, nature-based interventions were not significantly more effective than control conditions. This finding was based on a node containing four studies examining 1) a nature activity programme (observing and drawing nature), 2) horticultural therapy, 3) nature photography, and 4) time in nature without electronic devices. Notably, this node was weakly integrated within the broader network and relied heavily on indirect evidence. The overall quality of evidence ranged from moderate to high risk of bias, largely due to the inclusion of small-scale studies. Beyond these findings, wider literature suggests that emotional responses to nature vary with individuals\u0026rsquo; personal relationship to the environment. While nature often promotes well-being, it can also heighten anxiety, particularly in the context of climate change (Pihkala, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Crucially, research highlights that a deep connection to nature, rather than mere exposure is key to psychological well-being (Capaldi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and encourages pro-environmental behaviour (Richardson et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Interventions should aim not just to increase time in nature but to actively foster nature connectedness as a psychological trait. Richardson et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) emphasise that this requires intentional engagement, including mindfulness, sensory immersion, awe-inspiring experiences, and reflective practices that highlight human-nature interdependence.\u003c/p\u003e \u003cp\u003eOur network meta-analysis (NMA) also surprisingly indicated that multi-component Positive Psychology Interventions (MPPIs) had a smaller effect size and ranked lower in p-score than individual Positive Psychology Interventions (PPIs), with the Three Good Things (3GT) gratitude intervention being prominently featured within the single PPI category. This contradicts the findings of a large meta-analysis (Agteren et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) which favoured MPPIs over single PPIs. A potential explanation for this is that our analysis included a study which directly compared a PPI with a MPPI and reported no significant difference in their efficacy on wellbeing (Neumeier et al., 2017). The slightly larger effect size estimate for single PPIs in our analysis, however, should not necessarily imply a clinical preference for them over MPPIs. We found no statistically significant difference between single and multi-component PPIs, and they had overlapping confidence intervals, meaning contextual factors will play a vital role in clinical intervention selection (Ciarrochi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Originally, we aimed to include social identity building interventions in the NMA network, but our systematic literature review revealed significant clinical heterogeneity within this category, which impacted on our capacity to synthesise such studies into a single node of evidence. Social interventions identified from our search included a focus on diverse topics, including current events (Rattenbury et al., 1989), reminiscence (Yousefi et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) emotional peer support (Hirani et al, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and sharing parenting stressors (Chesak et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and comparisons consistently showed a higher average participant age than other intervention nodes, indicating demographic disparities, adversely impacting on the assumption of transitivity. Social identity building interventions were therefore excluded from our NMA. This decision was driven by our commitment to maintaining the validity and reliability of our findings, given the potential compromise in integrity due to the heterogeneity and demographic differences within this category.\u003c/p\u003e \u003cp\u003eThe strength of our NMA relies on the quality of the included RCTs, and thus our findings are impacted by limitations inherent across wellbeing intervention research including often high, individual study risk of bias. For example, only 33% of included studies employed intention-to-treat analyses, which preserves randomisation by analysing all participants regardless of adherence. To address these concerns, we took several measures to ensure the reliability of our conclusions such as including only RCT designs, peer reviewed research and valid measurement scales. Whilst the observed asymmetry in funnel plots warrants a need for caution, this doesn't conclusively prove publication bias, given that comprehensive sensitivity analyses, excluding studies with low sample sizes and high risk of bias, yielded consistent results with the primary analysis, indicating robust conclusions. While excluding grey literature increases methodological rigour, future meta-analyses may benefit from its inclusion to help mitigate potential publication bias. Additionally, in sensitivity analyses, no discrepancy was found between the different types of wellbeing measures used. This consistency implies that the choice of a particular category of wellbeing measure (positive affect, mindfulness, subjective wellbeing, resilience) didn\u0026rsquo;t impact the conclusions drawn from the analysis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis network meta-analysis provides a comprehensive comparison of wellbeing interventions, revealing the superior effectiveness of approaches that integrate psychological techniques with physical activity, as seen in the top-ranked ‘movement combined with psychological intervention’ and the strong performance of yoga. Physical activity, acceptance and commitment therapy and single positive psychology interventions showed comparable efficacy, highlighting multiple pathways to wellbeing. Crucially, our conclusions are robust, as sensitivity analyses, excluding small and high-risk studies, yielded consistent results. The consistently strong effects of mindfulness, compassion-focused therapies, yoga, and ACT also highlight shared psychological mechanisms - presence, acceptance, and self-compassion - as key drivers of wellbeing. We define wellbeing as a dynamic interplay between connection to self (mind and body), connection to others (social integration), and connection to nature. To advance the field, research must move beyond single interventions and embrace multidisciplinary approaches that reflect how people might naturally engage with multiple pathways to wellbeing - including via health behaviours, psychological techniques, social relationships, and connecting to nature. A deeper understanding of how holistic, multi-component interventions enhance wellbeing is crucial for developing sustainable, real-world strategies.\u003c/p\u003e "},{"header":"Methodology","content":"\u003ch2\u003eSelection of Studies and Data Extraction\u003c/h2\u003e\u003cp\u003e This systematic review and NMA was registered with PROSPERO (ID CRD42023403480). The database was developed through a systematic search of Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, PsycINFO and Scopus in March 2023, and was continually updated by lead author (LW). The search strategy contained key words and MeSH (Medical Subject Headings) standardised terms relating to the interventions being studied, randomised controlled trials and wellbeing (see Supplementary Information 1.1 for full search string). References were managed using Covidence (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.covidence.org/\u003c/span\u003e\u003cspan address=\"https://www.covidence.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Titles and abstracts were screened independently by three reviewers (AG, IG, and SK). Those titles and abstracts which resulted in disagreements were automatically included for full-text screening. Reviewers also screened the full texts according to eligibility criteria and recorded reasons for exclusions. Disagreements during full text screening were resolved by discussion or by the first author (LW). Data was extracted using a custom form on Covidence which was then checked for accuracy by LW. The preferred choice of extraction for outcome measures were means and SDs. When possible, these were calculated using alternative reported statistics, or study authors were contacted (and followed up at least once) via email to request missing data. Studies were excluded if no response was received by time of data analysis. Methodological quality of included randomised control studies were assessed using the Revised Cochrane risk-of-bias tool for randomised trials (RoB 2) by at least one reviewer and was checked for accuracy by the first author. Any disagreements were resolved via discussion with wider review team.\u003c/p\u003e\u003ch2\u003eEligibility Criteria\u003c/h2\u003e\u003cp\u003eEligibility criteria were developed using the PICOS framework and are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. We included randomised controlled trials (both parallel and cluster) published in peer-reviewed academic journals. Participants had to be aged 18 years or older, and not described as having a diagnosable condition, disease or dysfunction or receiving medical treatment for a disease at the time of study. Studies had to deliver at least one intervention described as being a psychological intervention, physical activity intervention, social identity or social support intervention, a nature-based intervention or contain a combination of these. They could be either individual or group format and could be delivered face to face, online or hybrid. Every study arm was assessed independently against PICOS eligibility criteria (see Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Interventions could either be compared to a second eligible intervention or a no intervention or wait list control group.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of PICOS eligibility criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclusion Criteria\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclusion Criteria\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdults \u0026gt; 18 years old\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipants under 18 years old.\u003c/p\u003e \u003cp\u003eParticipants described as having a specific condition, disease, dysfunction.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychological interventions, physical activity, social support, or nature-based interventions. Can be online, in-person or hybrid.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePharmacological or drug treatment arms.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA randomised controlled ‘eligible’ intervention or no intervention or a wait list control.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo randomly assigned control condition.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWellbeing (primary outcome)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle item measures of wellbeing. Studies which solely define wellbeing as reduction of ill-being (e.g. reduced anxiety scores).\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Type\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandomised controlled trials.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObservational studies, conference abstracts, non-randomised trials or studies not published as full-length articles in peer-reviewed journals\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eOutcome Measures\u003c/h2\u003e\u003cp\u003eThe primary outcome for this study was psychological wellbeing, defined as the presence of positive or adaptive characteristics such as measures of subjective wellbeing, life satisfaction, happiness, positive affect, resilience, or flourishing. Acknowledging that these characteristics may vary, ‘category of wellbeing outcome measure used’ was included as a sensitivity analysis to determine that measures did not differ significantly or lead to different findings. Studies which solely measured ‘wellbeing’ as a reduction in ill-being (e.g. reduced depression/anxiety scores) were excluded. Common standardised wellbeing scales included Perceived Wellness Score (PWS); Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS); 36-Item Short-Form Health Survey (SF-36): Mental Component Subscale; World Health Organization Wellbeing Scale (WHO-5); PANAS -Positive Affect. This list of scales is non-exhaustive and other measures were included if they met inclusion criteria.\u003c/p\u003e\u003ch2\u003eNetwork Geometry and Nodes\u003c/h2\u003e\u003cp\u003eThe network nodes were defined following discussion with research team members including clinicians with expertise on which interventions could logically be clustered together, based on both their underpinning theory and delivery in practice.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eTransitivity: The assumption of transitivity requires all interventions to be jointly randomizable. If this assumption holds, common comparisons should not vary significantly on key characteristics, or the validity of the indirect comparisons will be questionable (for example the ‘A’ in ‘A v B’ should not differ significantly to the ‘A’ in ‘A v C’, otherwise the indirect ‘B v C’ comparison will be invalid). To assess transitivity, we created a table of important characteristics (study setting, intervention intensity, delivery mode) to examine whether potential effect modifiers were similarly distributed across the comparisons. Variability from different study populations, interventions, and outcomes makes it difficult to ascertain that the treatments are being compared under equivalent conditions. The inclusion of heterogeneous interventions necessitate even more stringent control of population differences. Hence, we opted to use a general population sample, to ensure more uniformity and reliable comparisons.\u003c/p\u003e\u003cp\u003ePairwise Meta-Analyses: We conducted pairwise meta-analyses for all direct comparisons using a random-effects model. Homogeneity of effect sizes were estimated using Tau² and Higgins I² values. Standardised mean differences (SMDs) were reported with 95% confidence intervals. \u003cem\u003ep\u003c/em\u003e values (alpha threshold = 0.05) were used to determine whether the effect sizes for each direct comparison were significant.\u003c/p\u003e\u003cp\u003eNetwork Meta-Analysis: A random-effect NMA was conducted to estimate a single summary effect for each node in the network. Global inconsistency was assessed using the Q statistic, based on design-by-treatment interaction model (Higgins et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Local inconsistency was assessed by comparing direct estimates to indirect estimates using the node splitting method (SIDE) (whereby \u003cem\u003ep\u003c/em\u003e \u0026lt; 1.0 indicated statistically significant inconsistency). Treatments were ranked using P-scores, these range from 0 to 1 and can be interpreted as an average degree of certainty for a treatment to be better than the other treatments in the network (Rücker \u0026amp; Schwarzer, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSmall study effects were assessed using comparison-adjusted funnel plots, which report each study’s effect estimate against their reversed standard error. Asymmetry in the plot suggests that larger effects tend to be systematically found in smaller studies.\u003c/p\u003e\u003cp\u003eAdditional Analyses: six sub-group analyses were conducted on pre-specified potential effect modifiers. Analyses were first conducted using pairwise meta-analysis of all active interventions versus control. These were sub-grouped by mode of delivery (e.g. in-person, online, live video conferencing or instructions only); treatment format (individual or group); intensity (length) of intervention; setting of intervention (university, workplace, community or online); country (Western or non-Western) and age of participants. In addition, four sensitivity analyses were conducted to compare whether pooled effect of all interventions versus control differed depending on 1) type of outcome measure used; 2) whether control was a waitlist or no intervention; 3) risk of bias and 4) included study size. Additional sensitivity analyses were conducted using full NMA analyses of three alternative models; 1) excluding studies with high risk of bias; 2) using subjective wellbeing as the only outcome measure and 3) excluding small studies.\u003c/p\u003e\u003cp\u003eConfidence in NMA: The risk of bias across studies was assessed with Confidence in Network Meta-Analysis (CINeMA) for NMA (Papakonstantinou et al., 2020). CINeMA considers six domains that impact confidence in the NMA results: 1. Within-study bias; 2. Reporting bias; 3. Indirectness; 4. Imprecision; 5. Heterogeneity; 6. Incoherence. Each treatment comparison was assessed as having “no concerns,” “some concerns,” or “major concerns” in each of the six domains. Then, judgments across the domains were summarized into a single confidence rating (high, moderate, low, or very low).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used in this study is publicly available on the OSF repository at https://osf.io/nz59j/?view_only=30f14278418f454e8c6ee297493f2c39.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCode Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code used for this study is available on OSF: https://osf.io/nz59j/?view_only=30f14278418f454e8c6ee297493f2c39. The R script includes all steps for data processing, analysis, and visualisation. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgteren, J. van, Iasiello, M., Lo, L., Bartholomaeus, J., Kopsaftis, Z., Carey, M., \u0026amp; Kyrios, M. (2021). 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Meta-analytic evidence for a robust and positive association between individuals pro-environmental behaviors and their subjective wellbeing. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(12), 123007. https://doi.org/10.1088/1748-9326/abc4ae\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5905657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5905657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis network meta-analysis (NMA) synthesises 183 randomised controlled trials (RCTs) with 22,811 participants, providing a novel, comprehensive comparison of wellbeing interventions. Findings indicate that interventions integrating psychological techniques with physical activity, including \u0026lsquo;movement combined with psychological intervention\u0026rsquo; and yoga, rank highest. Mindfulness, compassion-focused therapies, and ACT also demonstrated strong effects, highlighting shared characteristics of presence, acceptance, and self-compassion as beneficial. Physical activity performed comparably to single positive psychology interventions, underscoring multiple pathways to wellbeing. All interventions were significantly more effective than no treatment control groups, except for nature-based interventions which did not significantly outperform controls. However, nature-based interventions were characterised by small samples and a moderate-to-high risk of bias. Multiple sub-group and sensitivity analyses confirmed the robustness of these conclusions. To advance the field, future research needs to explore multi-component approaches that transcend disciplinary silos, embrace interconnected determinants, and ultimately foster sustainable, holistic wellbeing.\u003c/p\u003e","manuscriptTitle":"A Systematic Review and Network Meta-Analysis of Randomised-Controlled Wellbeing-Focused Interventions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 04:14:34","doi":"10.21203/rs.3.rs-5905657/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-human-behaviour","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nathumbehav","sideBox":"Learn more about [Nature Human Behaviour](http://www.nature.com/nathumbehav/)","snPcode":"","submissionUrl":"","title":"Nature Human Behaviour","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f6b98f94-805a-4499-8ff9-26b8a40dd12d","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":44238450,"name":"Social science/Psychology"},{"id":44238451,"name":"Social science/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2026-01-03T08:08:40+00:00","versionOfRecord":{"articleIdentity":"rs-5905657","link":"https://doi.org/10.1038/s41562-025-02369-1","journal":{"identity":"nature-human-behaviour","isVorOnly":false,"title":"Nature Human Behaviour"},"publishedOn":"2026-01-02 05:00:00","publishedOnDateReadable":"January 2nd, 2026"},"versionCreatedAt":"2025-03-27 04:14:34","video":"","vorDoi":"10.1038/s41562-025-02369-1","vorDoiUrl":"https://doi.org/10.1038/s41562-025-02369-1","workflowStages":[]},"version":"v1","identity":"rs-5905657","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5905657","identity":"rs-5905657","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}