Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 Jul 7;18(4):e70187. doi: 10.1111/aphw.70187

Optimal mind–body exercise for quality of life, anxiety, and depression in cancer patients and survivors: A pairwise, network, and dose–response meta‐analysis of randomized controlled trials

Huan Feng 1, Pingping Zhou 2, Zhengwei Xie 1, Yubo Wang 1, XiaoJun Wang 1,✉
PMCID: PMC13428334  PMID: 42412001

Abstract

Mind–body exercise improves psychological outcomes in cancer patients, but the comparative efficacy of different modalities remains unclear. This study integrates pairwise, network, and dose–response meta‐analytic approaches to simultaneously compare multiple mind–body exercise modalities and establish intervention‐specific optimal doses in cancer patients. We systematically searched PubMed, Embase, Web of Science, and Cochrane Library from inception to December 31, 2025 for randomized controlled trials examining mind–body exercise interventions (yoga, tai chi, qigong, pilates, and dance) in cancer patients. Primary outcomes included quality of life, depression, and anxiety. We performed pairwise meta‐analysis with effect sizes reported as Hedges' g and 95% confidence intervals (CIs), Bayesian network meta‐analysis establishing efficacy hierarchies using surface under the cumulative ranking curve (SUCRA) values, and dose–response meta‐analysis identifying optimal exercise volumes. We included 73 randomized controlled trials involving 5130 participants. Mind–body exercise significantly improved quality of life (Hedges' g = 0.56, 95% CI: 0.36 to 0.77), reduced depression (Hedges' g = −0.41, 95% CI: −0.65 to −0.17), and alleviated anxiety (Hedges' g = −0.89, 95% CI: −1.40 to −0.37). Network meta‐analysis revealed clear efficacy hierarchies: pilates ranked highest for quality of life (SUCRA = 84.80%), yoga for depression (SUCRA = 73.70%), and qigong for anxiety (SUCRA = 83.27%). Dose–response analysis identified an inverted U‐shaped relationship with an overall optimal dose of 490 MET‐min/week for quality of life (standardized mean difference, SMD = 0.904, 95% credible interval, CrI: 0.603 to 1.235), with intervention‐specific optimal doses ranging from 300 to 520 MET‐min/week. Mind–body exercise interventions effectively improve quality of life, reduce depression, and alleviate anxiety in cancer patients, with pilates, yoga, and qigong demonstrating superior efficacy across different outcomes, supporting evidence‐based, personalized prescription and integration as an accessible, complementary intervention for cancer‐related psychological distress.

Keywords: anxiety, cancer, depression, dose–response, mind–body exercise, network meta‐analysis, quality of life

INTRODUCTION

Cancer remains one of the most significant global public health challenges, with an estimated 23.6 million new cases diagnosed annually and approximately 10 million cancer‐related deaths worldwide (Global Burden of Disease Cancer, 2022). Beyond the physical burden, cancer patients face substantial psychological distress throughout their disease trajectory. Depression and anxiety are highly prevalent, affecting 21% and 18% of cancer patients respectively—rates notably higher than those observed in the general population (Linden et al., 2012; Mitchell et al., 2011). These psychological comorbidities are not merely transient emotional responses but constitute clinically significant conditions with important health consequences. Depression increases cancer‐specific mortality by 21% and all‐cause mortality by 24%, while both depression and anxiety significantly impair treatment adherence, intensify physical symptom burden, and substantially diminish quality of life (QoL) (Arrieta et al., 2013; Pitman et al., 2018; Walker et al., 2021; Wang et al., 2020). The psychological burden extends beyond active treatment, with long‐term survivors demonstrating persistently elevated rates of mental disorders compared to healthy controls (Mitchell et al., 2013). Given that approximately 40% of cancer patients report clinically significant psychological distress (Caruso et al., 2017), effective management of anxiety and depression represents an important and inadequately addressed clinical need. Reduced QoL represents an equally critical yet distinct dimension of the cancer burden. Cancer patients consistently report significantly lower QoL across physical, emotional, social, and functional domains compared with the general population (Peters et al., 2016). These QoL deficits are not confined to the active treatment period; systematic reviews have documented that clinically meaningful QoL impairments persist in long‐term cancer survivors five or more years after diagnosis (Brandenbarg et al., 2019; Mols et al., 2005). Importantly, QoL has been established as an independent prognostic factor for survival. A landmark meta‐analysis of individual patient data from 30 European Organization for Research and Treatment of Cancer (EORTC) clinical trials involving 10,108 patients demonstrated that baseline QoL parameters, particularly physical functioning, provided significant prognostic information beyond conventional sociodemographic and clinical variables (Quinten et al., 2009). These findings were subsequently validated across 46 clinical trials covering 17 cancer types (Lim et al., 2025). Therefore, identifying effective interventions to improve QoL constitutes a primary therapeutic objective in comprehensive cancer care.

Current treatment options for cancer‐related anxiety and depression are substantially limited by significant practical and clinical barriers. While pharmacological interventions, primarily selective serotonin reuptake inhibitors (SSRIs), and psychotherapy, such as cognitive behavioral therapy (CBT), constitute standard care (Grassi et al., 2023), their effectiveness is considerably compromised in practice. Antidepressants are associated with notable side effects including gastrointestinal disturbances, sexual dysfunction, and potential drug–drug interactions with chemotherapeutic agents, leading to suboptimal adherence (Chochinov, 2001; Smith, 2015). Moreover, access to adequate mental health care remains markedly inadequate: Only 24% of cancer patients with major depression receive minimally effective pharmacological treatment, and a mere 5% access mental health professionals (Walker et al., 2014). Psychotherapeutic services are substantially constrained by workforce shortages and long waiting times, rendering them inaccessible to the majority of patients who need them (Li et al., 2016). These persistent gaps underscore a clear need for evidence‐based, accessible, safe, and well‐tolerated alternative interventions.

Mind–body exercise—encompassing yoga, tai chi, qigong, pilates, and similar modalities—has emerged as a promising non‐pharmacological intervention that combines physical movement with mental focus, breath regulation, and meditative components (Bower & Irwin, 2016). Accumulating evidence demonstrates that mind–body exercise effectively reduces anxiety and depression, improves QoL, and alleviates cancer‐related fatigue (Gonzalez et al., 2021; Lin et al., 2011; Wayne et al., 2018). Recent network meta‐analyses have shown that mind–body exercise ranks highest among all exercise types for reducing anxiety, with a surface under the cumulative ranking curve (SUCRA) value of 89.6%, and produces significantly greater reductions in depression than conventional aerobic or resistance training, with a standardized mean difference (SMD) of −0.89 versus −0.39, in cancer populations (Soong et al., 2025; Wang et al., 2025). However, critical knowledge gaps impede optimal clinical implementation. First, while mind–body exercise is generally effective, it remains unclear which specific modalities (yoga versus tai chi versus qigong) are most beneficial for different outcomes, as most meta‐analyses have used pairwise comparisons that cannot simultaneously rank multiple interventions (Mishra et al., 2012; Wen et al., 2025). Second, the optimal dose–response relationship—frequency, duration, and intensity—remains poorly characterized; although one study suggested 390 MET‐min/week as optimal (Xiong et al., 2024), questions persist regarding minimum thresholds, non‐linear effects, and variation across cancer types or outcomes. Third, previous reviews have predominantly focused on single cancer types or specific treatment phases, limiting the generalizability of their findings across diverse cancer populations (Buffart et al., 2017; Speck et al., 2010). Integrating network and dose–response meta‐analytic approaches can address these limitations by simultaneously comparing multiple interventions and characterizing optimal dosing parameters (Caldwell et al., 2005; Crippa & Orsini, 2016; Jansen & Naci, 2013; Salanti et al., 2011).

Therefore, this study aims to comprehensively evaluate the comparative efficacy of different mind–body exercise modalities on QoL, anxiety, and depression in cancer patients and survivors through integrated pairwise, network, and dose–response meta‐analyses of randomized controlled trials (RCTs). We sought to (1) establish overall effectiveness versus controls; (2) compare and rank specific mind–body exercise types (yoga, tai chi, qigong, pilates, and dance); and (3) determine optimal dose–response relationships. This comprehensive synthesis will provide evidence‐based guidance for clinicians, patients, and policymakers to optimize psychological care in cancer populations.

METHODS

This systematic review and network meta‐analysis was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for network meta‐analysis (PRISMA‐NMA) (Hutton et al., 2015). The methodological procedures followed the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions (Cumpston et al., 2019). The study protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD420261303914.

Search strategy

We systematically searched four bibliographic databases (Embase, PubMed, Web of Science, and Cochrane Library) from inception through December 31, 2025. The search framework integrated MeSH terms and text words across four domains: (1) oncological conditions; (2) mind–body movement practices (yoga, tai chi, qigong, pilates, and dance); (3) psychological outcomes (anxiety, depression, QoL); and (4) randomized trial identifiers. Complete search keywords are provided in Table S1.1. Additional retrieval strategies included backward and forward citation tracking via Google Scholar, manual examination of reference lists from relevant systematic reviews, and querying trial registries (ClinicalTrials.gov and WHO ICTRP) for unpublished studies. Authors were contacted for missing data when needed. No language restrictions were imposed during the database search to ensure comprehensive retrieval; however, only studies published in English were included during screening.

Eligibility criteria

Studies were selected according to predefined inclusion criteria based on the Population, Intervention, Comparison, Outcomes, and Study design (PICOS) framework (Table 1). Detailed operational definitions of each mind–body exercise modality and control condition are provided in Table S1.2.

TABLE 1.

Inclusion criteria according to the PICOS framework.

Component Criteria
Population (P) Adults (≥18 years) with any histologically confirmed cancer diagnosis, at any disease stage, receiving active treatment or in survivorship phase
Intervention (I) Mind–body movement interventions including yoga, tai chi, qigong, pilates, dance therapy; intervention duration ≥4 weeks; any frequency or intensity; supervised, semi‐supervised, or home‐based delivery. Mind–body exercise was required to be the only systematic difference between the intervention and control groups
Comparison (C) Usual care, waitlist control, or no intervention
Outcomes (O) Primary: Quality of life (EORTC QLQ‐C30, FACT‐G, SF‐36). Secondary: Anxiety (HADS‐A, GAD‐7); depression (HADS‐D, CES‐D); cancer‐related fatigue; sleep quality
Study design (S) Randomized controlled trials published in peer‐reviewed journals
Open in a new tab

Abbreviations: CES‐D, Center for Epidemiologic Studies Depression Scale; EORTC QLQ‐C30, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire‐Core 30; FACT‐G, Functional Assessment of Cancer Therapy‐General; GAD‐7, Generalized Anxiety Disorder‐7; HADS‐A, Hospital Anxiety and Depression Scale‐Anxiety; HADS‐D, Hospital Anxiety and Depression Scale‐Depression; SF‐36, Short Form‐36 Health Survey.

Study selection

Two reviewers (H.F. and Z.W.X.) independently screened all retrieved records by title and abstract to identify potentially relevant studies. Following this preliminary phase, two independent reviewers (Z.W.X. and Y.B.W.) performed full‐text evaluation of selected articles against the predefined inclusion criteria. Disagreements were resolved through consensus discussion or, when consensus could not be reached, adjudication by a third reviewer (P.P.Z.). The entire selection workflow was managed using EndNote 21 (Clarivate Analytics, Philadelphia, PA, USA) and is illustrated in a PRISMA flow diagram.

Data extraction

Two reviewers (Z.W.X. and Y.B.W.) independently extracted data from included studies using a standardized Excel spreadsheet (Microsoft Corporation, Redmond, WA, USA). A third reviewer (H.F.) verified and cross‐checked all extracted data for accuracy and completeness. The extracted information encompassed: (1) study characteristics (first author, publication year); (2) participant demographics (age, sex, clinical stage, cancer type, and treatment phase); (3) intervention parameters (type of mind–body exercise, frequency per week, session duration in minutes, total intervention weeks, cumulative intervention time, supervision mode); (4) control condition; (5) sample sizes for intervention and control groups; and (6) outcome measures and assessment instruments.

When essential data were missing or unclear, corresponding authors were contacted via email to request additional information. For studies reporting outcomes solely in graphical format, numerical data were digitally extracted using WebPlotDigitizer version 4.6 (Ankit Rohatgi, San Francisco, CA, USA).

Risk of bias and quality of evidence assessment

The methodological quality of included randomized controlled trials was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool (Sterne et al., 2019). Two independent reviewers (Z.W.X. and Y.B.W.) evaluated each study across five domains: (1) bias arising from the randomization process; (2) bias due to deviations from intended interventions; (3) bias due to missing outcome data; (4) bias in measurement of the outcome; and (5) bias in selection of the reported result. Each domain was rated as “low risk,” “some concerns,” or “high risk,” with an overall risk of bias judgment derived according to the RoB 2 algorithm. Inter‐rater reliability for the RoB 2 assessment was excellent (intraclass correlation coefficient = 0.98). Given the inherent challenges of blinding participants and personnel in exercise intervention trials, we supplemented the RoB 2 assessment with the Tool for the assEssment of Study qualiTy and reporting in EXercise (TESTEX) scale (Smart et al., 2015). The TESTEX comprises 15 items evaluating study quality (five items, maximum score 5) and reporting quality (10 items, maximum score 10), with higher scores indicating better methodological rigor. TESTEX scores were interpreted as follows: high quality (≥10 points), moderate quality (6–9 points), and low quality (<6 points). Inter‐rater reliability for the TESTEX scale was good (intraclass correlation coefficient = 0.89). Discrepancies in assessments were resolved through discussion or consultation with a third reviewer (H.F.).

The overall quality of evidence for each outcome was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach (Guyatt et al., 2008). Evidence quality was assessed across five domains: risk of bias, inconsistency, indirectness, imprecision, and publication bias. The quality of evidence was categorized as high, moderate, low, or very low.

Data synthesis and statistical analysis

Pairwise meta‐analysis

Pairwise meta‐analyses were conducted using a frequentist random‐effects model to pool treatment effects across studies comparing mind–body exercise interventions with control conditions. Pairwise comparisons were stratified by control type (non‐active vs. active control). As only one study (Odynets et al., 2019) provided a head‐to‐head active‐control comparison, its results are reported descriptively in Table S4, while the main‐text pairwise analyses include non‐active control comparisons only. Hedges' g was used as the SMD to account for small sample sizes and allow comparability across different outcome scales. Effect sizes were interpreted as small (0.2–0.5), moderate (0.5–0.8), or large (>0.8) according to established conventions.

Statistical heterogeneity was assessed using I 2 and τ 2 statistics, with I 2 values of 25%, 50%, and 75% representing low, moderate, and high heterogeneity, respectively (Higgins et al., 2003). Pre‐specified subgroup analyses were performed to explore potential moderators of treatment effects based on (1) sex; (2) cancer type; (3) clinical stage; (4) intervention duration, categorized as short‐term (<8 weeks), medium‐term (8–12 weeks), and long‐term (>12 weeks); (5) weekly exercise volume, defined as exercise frequency multiplied by session duration, categorized per American College of Sports Medicine (ACSM) guidelines (Campbell et al., 2019): low (<90 min/week), moderate (90–150 min/week), and high (>150 min/week); and (6) total exercise volume, defined as weekly exercise volume multiplied by intervention duration in weeks, categorized into tertiles. Between‐subgroup heterogeneity was evaluated using Cochran's Q test (p < .05). Additionally, univariate meta‐regression analyses were conducted to examine whether continuous intervention parameters (intervention duration, exercise frequency, session duration, weekly exercise volume, and total exercise volume) moderated treatment effects. All analyses were performed using Stata 17.0 (StataCorp, College Station, TX, USA) and R 4.3.1 with the meta and metafor packages. Statistical significance was set at p < .05.

Network meta‐analysis

Bayesian network meta‐analysis was conducted using the BUGSnet package (Version 1.1.0) (Béliveau et al., 2019) in R 4.3.1 to compare the relative efficacy of five mind–body exercise modalities (yoga, pilates, tai chi, qigong, and dance) on QoL, anxiety, and depression outcomes. For continuous outcomes, we used a normal likelihood with identity link function and arm‐level data to estimate SMDs with 95% credible intervals (CrIs). Model selection between fixed‐effect and random‐effects models was based on the deviance information criterion (DIC) (Spiegelhalter et al., 2002), with the model yielding lower DIC values being preferred. Non‐informative prior distributions were specified for treatment effects (normal distribution with mean 0 and variance 10,000) and between‐study heterogeneity (uniform distribution from 0 to 2). Markov Chain Monte Carlo simulations were run with three chains, each with 20,000 iterations (5000 burn‐in, 15,000 retained). Convergence was assessed using the Gelman–Rubin diagnostic (R‐hat < 1.05) and visual inspection of trace plots.

The consistency assumption was evaluated by comparing DIC between consistency and inconsistency (node‐splitting) models (difference <5 points considered acceptable), and global inconsistency was assessed using the design‐by‐treatment interaction model. Transitivity was evaluated by comparing the distribution of potential effect modifiers (cancer type, clinical stage, treatment status) across treatment comparisons. Treatment rankings were estimated using surface under the cumulative ranking curve (SUCRA) probabilities (Salanti et al., 2011), with values closer to 100% indicating a higher probability of being the best treatment. SUCRA values were visualized using radar plots to facilitate comparison across interventions. Statistical significance was determined when the 95% CrI did not include zero. Publication bias and small‐study effects were assessed using comparison‐adjusted funnel plots (Chaimani et al., 2013), where asymmetry was evaluated through visual inspection and statistical testing.

Dose–response meta‐analysis

Bayesian dose–response meta‐analysis was conducted using the MBNMAdose package in R 4.3.1 to examine the relationship between exercise dose and treatment effects. Exercise intensity was classified according to metabolic equivalent (MET) values derived from the 2024 Adult Compendium of Physical Activities (Herrmann et al., 2024), where 1 MET represents the ratio of working to resting metabolic rate, equivalent to an oxygen consumption of 3.5 ml/kg/min (Leon et al., 2011). Based on standardized activity classifications and published energy expenditure measurements (Ainsworth et al., 2000), MET values were assigned as follows: yoga (2.5 METs), pilates (2.8 METs), tai chi (3.0 METs), qigong (3.0 METs), and dance (3.5 METs). Exercise dose was calculated as MET‐minutes per week (MET‐min/week) by multiplying the MET value by session duration and weekly frequency. Multiple dose–response functions were fitted and compared, including Emax, restricted cubic spline with knots at the 10th, 50th, and 90th percentiles, quadratic polynomial, and non‐parametric monotonic models (Mawdsley et al., 2016). Model selection was based on deviance information criterion (DIC), between‐study standard deviation, number of model parameters, and residual deviance (Spiegelhalter et al., 2002). The model with the lowest DIC and optimal balance between fit and parsimony was selected. Non‐informative prior distributions were specified for dose–response parameters. MCMC simulations used three chains of 20,000 iterations with 10,000 burn‐in iterations (default MBNMAdose settings). Convergence was assessed via Gelman–Rubin diagnostic (R‐hat < 1.05) and visual inspection of trace plots. Dose–response curves were generated to visualize the relationship between exercise dose and outcomes, with optimal dose ranges identified based on the fitted models. All statistical analyses were conducted using R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

RESULTS

Study selection and characteristics

The systematic literature search identified 25,335 records from four electronic databases and 33 additional records through other sources, including citation searching, Google Scholar, and trial registries. After removing duplicates and screening based on eligibility criteria, 73 RCTs met inclusion criteria and were included in this network meta‐analysis. The detailed study selection process is presented in Figure 1.

FIGURE 1.

FIGURE 1

Open in a new tab

Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) flow diagram for included and excluded studies.

The 73 included studies comprised a total of 5130 participants with cancer, with 2536 participants in intervention groups and 2594 participants in control groups. Among the mind–body exercise interventions examined, yoga was the most frequently investigated modality, accounting for 52.1% of included studies, followed by qigong at 19.2%, tai chi at 12.3%, dance at 11.0%, and pilates at 4.1%. Breast cancer was the predominant malignancy investigated, accounting for 79.5% of studies, followed by mixed cancer populations, lung cancer, colorectal cancer, and other cancer types. Most studies included patients with early to locally advanced disease at clinical stages 0 to III. Regarding treatment phase, 38.4% of studies enrolled patients in the post‐treatment phase, 27.4% during active treatment, and 20.5% in survivorship. QoL was primarily assessed using the EORTC QLQ‐C30 and Functional Assessment of Cancer Therapy (FACT) scales. Depression was evaluated using the CES‐D, Beck Depression Inventory (BDI), and HADS‐D, while anxiety was measured using the HADS‐A, State–Trait Anxiety Inventory (STAI), and GAD‐7. The characteristics of all included studies and outcome measurement instruments are summarized in Table S2.

Risk of bias assessment

The risk of bias assessment using the Cochrane RoB 2 tool revealed that 15.1% of studies had low risk of bias, 24.7% raised some concerns, and 60.2% were rated as high risk. The primary sources of bias were deviations from intended interventions due to blinding difficulties inherent to behavioral interventions and measurement bias associated with self‐reported outcomes. The TESTEX scale assessment showed high methodological quality overall, with scores ranging from 8 to 15 points and a mean of 12.29 ± 1.83 points (Table S3). According to predefined criteria, 80.8% of studies were classified as high quality, 19.2% as moderate quality, and none as low quality. Detailed risk of bias assessments are provided in File S3, including Figure S1 and Table S3.

Pairwise meta‐analysis

Pairwise meta‐analyses comparing mind–body exercise interventions with non‐active control conditions demonstrated significant benefits across all three primary outcomes. Overall and intervention‐specific effect size distributions are shown in Figures S2 and S3. Mind–body exercise significantly improved QoL (k = 62, N = 4454; Hedges' g = 0.564, 95% CI: 0.358 to 0.771, p < .001), reduced depression (k = 43, N = 3234; Hedges' g = −0.409, 95% CI: −0.652 to −0.167, p < .01), and alleviated anxiety (k = 28, N = 2226; Hedges' g = −.885, 95% CI: −1.404 to −0.366, p < .01). Substantial heterogeneity was observed across all outcomes. For QoL, tai chi did not achieve statistical significance (Hedges' g = 0.748, 95% CI: −0.173 to 1.670). Regarding depression, pilates (g = −0.420, 95% CI: −0.993 to 0.153), dance (Hedges' g = −0.252, 95% CI: −0.845 to 0.342), and tai chi (Hedges' g = −0.025, 95% CI: −0.466 to 0.415) showed non‐significant effects. For anxiety, qigong (Hedges' g = −1.303, 95% CI: −3.278 to 0.672), tai chi (Hedges' g = −0.218, 95% CI: −0.450 to 0.015), and pilates (Hedges' g = −0.302, 95% CI: −1.022 to 0.417) did not reach statistical significance. Substantial heterogeneity was observed across most comparisons, with I 2 values ranging from 53.4% to 98.8%.

Subgroup analyses revealed that treatment effects remained relatively consistent across most moderator variables, including sex distribution, cancer type, clinical stage, intervention duration, and exercise volume. The magnitude and direction of effects showed stability across diverse patient populations and intervention protocols, suggesting robust therapeutic benefits of mind–body exercise interventions in cancer populations (Figure S4). Meta‐regression analyses revealed that none of the five intervention parameters significantly moderated treatment effects for QoL or depression (all p > .05). For anxiety, weekly exercise volume was a significant moderator (beta = −0.017, p = .011), with higher weekly volume associated with greater anxiety reduction. Full meta‐regression results are presented in Figure S5. GRADE evidence quality ranged from moderate certainty for QoL outcomes to low or very low certainty for depression and anxiety outcomes. Detailed forest plots are presented in Figures 2 and 3, with complete GRADE profiles provided in Table S5.

FIGURE 2.

FIGURE 2

Open in a new tab

Funnel plots and forest plots for quality of life, depression, and anxiety outcomes. Note: (a) Overall effects of mind–body exercise interventions on quality of life, depression, and anxiety compared with control conditions. (b) Intervention‐specific effects by modality (yoga, tai chi, qigong, pilates, and dance) on quality of life, depression, and anxiety. Effect sizes presented as Hedges g with 95% confidence interval (CI). Point size reflects precision (inverse of standard error). Trunk represents pooled effect estimate. For quality of life, positive values favor mind–body exercise; for depression and anxiety, negative values indicate symptom reduction favoring mind–body exercise. I 2, heterogeneity index; N, number of effect sizes; k, number of studies; TC, tai chi; QG, qigong; DC, dance.

FIGURE 3.

FIGURE 3

Open in a new tab

Subgroup analyses of mind–body exercise effects on anxiety, quality of life, and depression. Note: Forest plots showing subgroup effects stratified by sex, clinical stage, cancer type, intervention duration, and exercise volume for anxiety (left), quality of life (middle), and depression (right). Effect sizes presented as Hedges g with 95% CI. K, number of studies; p value indicates subgroup difference. Positive values favor mind–body exercise interventions.

Network meta‐analysis

Network meta‐analyses compared six nodes (yoga, tai chi, qigong, pilates, dance, and control) across three primary outcomes, generating 15 pairwise comparisons for each outcome. Random‐effects models were selected based on DIC values, and all MCMC chains achieved convergence with R‐hat values below 1.05. Global consistency was supported across all three outcomes: the consistency model yielded lower DIC values than the inconsistency model, with differences either substantially exceeding or falling within the acceptable threshold of 5 points. Detailed network plots, model selection criteria (Table S7), convergence diagnostics, and consistency assessments are provided in File S5, Figures S6–S11, and Tables S6 and S7.

Regarding pairwise comparisons with control, pilates (SMD = 1.05, 95% CrI: 0.25 to 1.84), tai chi (SMD = 0.81, 95% CrI: 0.00 to 1.61), qigong (SMD = 0.68, 95% CrI: 0.19 to 1.16), and yoga (SMD = 0.52, 95% CrI: 0.17 to 0.87) significantly improved QoL; yoga significantly reduced depressive symptoms (SMD = −0.54, 95% CrI: −0.95 to −0.14); and qigong significantly reduced anxiety symptoms (SMD = −1.99, 95% CrI: −3.84 to −0.13). No significant differences were observed in the remaining pairwise comparisons among active interventions across all three outcomes. Complete league tables are presented in Figure 4. SUCRA rankings indicated that pilates (84.80%), tai chi (69.56%), and qigong (62.09%) ranked highest for QoL improvement; yoga (73.70%), qigong (67.38%), and pilates (61.42%) performed best for depression reduction; and qigong (83.27%), dance (68.69%), and yoga (55.29%) were most effective for anxiety management. Comparison‐adjusted funnel plots indicated potential publication bias, as shown in Figure S10.

FIGURE 4.

FIGURE 4

Open in a new tab

Network meta‐analysis results and surface under the cumulative ranking curve (SUCRA) rankings for mind–body exercise interventions. Note: League tables showing pairwise comparisons between mind–body exercise modalities for (a) quality of life, (b) depression, and (c) anxiety. Each cell presents the standardized mean difference (SMD) with 95% credible interval (CrI) comparing row intervention versus column intervention. Asterisks denote statistical significance (*p < .05, **p < .01, ***p < .001). (d) Radar plot displaying SUCRA values for each intervention across three outcomes. Larger areas indicate higher probability of being among the best treatments. QG, qigong; TC, tai chi; DC, dance; CON, control.

Dose–response meta‐analysis

Dose–response network meta‐analysis examined the relationship between exercise dose measured in MET‐minutes per week and QoL outcomes in cancer patients. Multiple dose–response functions were compared, including Emax, restricted cubic spline, quadratic polynomial, and unrelated mean effects models. Model selection based on DIC indicated that the quadratic polynomial model provided the best fit to the data (DIC = 633.4; Table S8).

The analysis revealed a significant inverted U‐shaped dose–response relationship between exercise dose and QoL improvement. Across all mind–body exercise interventions, the optimal dose was identified at 490 MET‐min/week, with an effect size of 0.904 (95% CrI: 0.603 to 1.235). Intervention‐specific optimal doses varied considerably: pilates demonstrated the largest effect at 340 MET‐min/week (SMD = 1.53, 95% CrI: 0.137 to 2.96), followed by tai chi at 300 MET‐min/week (SMD = 1.21, 95% CrI: 0.118 to 2.32), qigong at 480 MET‐min/week (SMD = 1.01, 95% CrI: 0.376 to 1.66), yoga at 520 MET‐min/week (SMD = 0.918, 95% CrI: 0.411 to 1.41), and dance at 350 MET‐min/week (SMD = 0.612, 95% CrI: −0.362 to 1.56). The dose–response curves demonstrated that QoL benefits increased with exercise dose up to the optimal threshold but plateaued or diminished beyond these optimal doses for most interventions, suggesting a possible ceiling effect, although this pattern should be interpreted cautiously given the limited number of studies at higher exercise volumes. Detailed dose–response curves and model comparison statistics are presented in Figures 5 and 6, with additional diagnostics provided in File S6, Figures S12–S20, and Tables S8 and S9.

FIGURE 5.

FIGURE 5

Open in a new tab

Dose–response relationship between mind–body exercise volume and quality of life. Note: Non‐linear dose–response curve showing relationship between exercise volume (MET‐min/week) and quality of life. Solid line represents posterior median; dashed lines represent 95% credible intervals.

FIGURE 6.

FIGURE 6

Open in a new tab

Intervention‐specific dose–response curves for mind–body exercise modalities. Note: Non‐linear dose–response curves showing relationship between exercise volume (MET‐min/week) and quality of life for each mind–body modality: dance (DC), pilates, qigong (QG), tai chi (TC), and yoga. Solid lines represent posterior median effect estimates; dashed lines represent 95% credible intervals.

DISCUSSION

Main findings

This study integrates pairwise, network, and dose–response meta‐analytic approaches within a single framework, providing both efficacy rankings and intervention‐specific optimal dose recommendations for mind–body exercise in cancer patients and survivors. Analyzing 73 RCTs involving 5130 participants, we identified clear efficacy hierarchies: pilates ranked highest for QoL (SUCRA = 84.80%), yoga for depression (SUCRA = 73.70%), and qigong for anxiety (SUCRA = 83.27%). Our dose–response analysis revealed an inverted U‐shaped relationship with optimal doses ranging from 300 to 520 MET‐min/week, with an overall optimal dose of 490 MET‐min/week (SMD = 0.904, 95% CrI: 0.603 to 1.235).

Comparison with previous studies

Previous research examined mind–body exercise as a single intervention category or individual modalities in isolation (Abu‐Odah et al., 2024; Chen et al., 2024; Xu et al., 2025). Our network meta‐analysis advances this literature by enabling direct, simultaneous comparisons, providing probabilistic rankings of relative efficacy. The superior ranking of pilates for QoL is consistent with recent evidence demonstrating its effectiveness in improving functional capacity, body awareness, and overall well‐being in cancer survivors through core stabilization and controlled movement (Abdul Razak et al., 2024; Pinto‐Carral et al., 2018). Yoga's highest ranking for depression aligns with a recent NMA showing yoga as the most effective exercise modality for reducing depressive symptoms in cancer patients (SUCRA = 74.9%) (Wu et al., 2025) and is further supported by a Bayesian dose–response meta‐analysis demonstrating significant improvements in depressive symptoms (Cheng et al., 2025). Qigong's top ranking for anxiety extends previous evidence showing its effectiveness in autonomic regulation and stress reduction (Wayne et al., 2018; Zeng et al., 2019) and is consistent with findings that mind–body exercises emphasizing meditative breathing components may be particularly effective for anxiety management (Sun et al., 2024). However, these rankings should be interpreted cautiously given the uneven distribution of studies across modalities: yoga dominated the evidence base (52.1% of studies), while pilates (4.1%) and dance (11.0%) were substantially underrepresented, resulting in wider credible intervals for less‐studied modalities. Our optimal dose of 490 MET‐min/week closely matches Xiong et al.'s (2024) finding of 390 MET‐min/week for mind–body exercise, strengthening confidence in the 400–500 MET‐min/week range. The inverted U‐shaped relationship challenges the “more is better” assumption (Han et al., 2024) and aligns with current ACSM and American Society of Clinical Oncology (ASCO) guidelines recommending 150–300 minutes weekly of moderate‐intensity activity for cancer survivors (Campbell et al., 2019; Ligibel et al., 2022). Importantly, our intervention‐specific optimal doses—pilates at 340 MET‐min/week (SMD = 1.53), tai chi at 300 MET‐min/week (SMD = 1.21), qigong at 480 MET‐min/week (SMD = 1.01), and yoga at 520 MET‐min/week (SMD = 0.918)—provide unprecedented precision for clinical prescription beyond generic exercise recommendations.

Mechanisms and clinical implications

Mind–body exercise interventions are characterized by the integration of physical movement with focused attention on breathing, achievement of a meditative or calm state of mind, and deep relaxation. Unlike conventional aerobic or resistance training, these modalities emphasize slow, deliberate movements combined with controlled breathing techniques and present‐moment awareness. The therapeutic effects likely operate through interconnected neuroendocrine, immunological, and neuroplastic mechanisms. Mind–body practices down‐regulate the hypothalamic–pituitary–adrenal axis through breath‐focused exercises and meditative states, reducing cortisol hypersecretion and restoring homeostasis (Sun et al., 2024), and suppress inflammatory pathways including nuclear factor‐kappa B signaling (Lee et al., 2025; Wang et al., 2020). The emphasis on diaphragmatic breathing and controlled respiration promotes parasympathetic activation, increasing heart rate variability—a biomarker of stress recovery (Dong et al., 2024; Weber et al., 2010). These practices also facilitate neuroplastic changes in emotion regulation circuits, particularly reducing amygdala hyperreactivity while strengthening prefrontal‐amygdala connectivity (Tang et al., 2015; Wheeler et al., 2017). The contemplative components cultivate present‐moment awareness and cognitive reappraisal skills that disrupt ruminative thought patterns (Johannsen et al., 2022), while movement components enhance functional capacity and reduce fatigue. This multi‐component nature explains superior efficacy versus purely physical exercise lacking contemplative elements (Kulchycki et al., 2024; Soong et al., 2025).

Clinically, our findings enable personalized prescription: pilates for QoL, yoga for depression, and qigong for anxiety. Based on the dose–response analysis for QoL specifically, the optimal weekly doses translate to practical exercise durations: pilates 340 MET‐min/week (approximately 120 min at 2.8 METs), tai chi 300 MET‐min/week (approximately 100 min at 3.0 METs), qigong 480 MET‐min/week (approximately 160 min at 3.0 METs), yoga 520 MET‐min/week (approximately 210 min at 2.5 METs), and dance 350 MET‐min/week (approximately 100 min at 3.5 METs). These dose recommendations, derived from QoL outcomes, can be readily translated into practical weekly exercise schedules: for example, optimal qigong prescription can be achieved through three sessions of approximately 40 min each, while optimal pilates requires three sessions of approximately 40 minutes each, making these interventions feasible for integration into cancer survivors' daily routines. Given that only 24% of cancer patients with depression receive adequate treatment and 5% access mental health professionals (Walker et al., 2014), scalable mind–body programs could substantially address the treatment gap. Remote delivery models demonstrate effectiveness during active treatment (Mao et al., 2022), though successful implementation requires provider education, referral pathways, and reimbursement mechanisms (Avancini et al., 2025; Low et al., 2024).

Limitations

Several limitations warrant acknowledgment. First, substantial heterogeneity was observed across most comparisons (I 2 = 53.4–98.8%), reflecting variability in participant characteristics, intervention protocols, instructor qualifications, and outcome measurement instruments. While we conducted extensive subgroup analyses to explore heterogeneity sources, residual unexplained variation persists. Meta‐regression analyses were limited by insufficient studies within modality‐specific subgroups (k < 10), precluding reliable moderator analyses at the intervention level. Second, 60.2% of included studies had a high risk of bias, primarily due to inadequate participant and personnel blinding—an inherent challenge in behavioral intervention research (Boutron et al., 2004) and reliance on self‐reported outcomes introduces potential measurement bias, as patients' awareness of treatment allocation may influence symptom reporting. Third, dose calculations relied on standardized MET values from published compendia that may not precisely capture individual‐level energy expenditure variations influenced by fitness level, age, body composition, and practice proficiency. Furthermore, the dose–response analysis was conducted exclusively for QoL outcomes; the optimal dose–response relationships for depression and anxiety remain uncharacterized and warrant investigation in future studies. Fourth, included trials predominantly enrolled breast cancer patients (79.5%) with early‐to‐intermediate stage disease (Stages 0–III), limiting generalizability to other cancer types—particularly hematological malignancies and male‐predominant cancers such as prostate cancer—and to patients with advanced or metastatic disease. Additionally, the distribution of studies across mind–body modalities was highly uneven: yoga dominated the evidence base (52.1% of studies), while dance therapy and pilates were substantially underrepresented (11.0% and 4.1%, respectively). This imbalance introduces uncertainty in network meta‐analysis rankings, as treatment effect estimates for less‐studied modalities rely more heavily on indirect comparisons and have wider credible intervals. The high SUCRA rankings of less‐studied modalities such as pilates (4.1% of studies) and dance (11.0%) should be interpreted cautiously given their reliance on limited direct comparisons and wider credible intervals.

CONCLUSIONS

This comprehensive network meta‐analysis provides robust evidence that mind–body exercise interventions effectively improve QoL, reduce depression, and alleviate anxiety in cancer patients and survivors. Through systematic comparison of multiple mind–body modalities, we established efficacy hierarchies with pilates demonstrating superior benefits for QoL, yoga for depression, and qigong for anxiety. Our dose–response analysis identified optimal exercise doses of 300–520 MET‐min/week for QoL depending on modality, with inverted U‐shaped relationships suggesting potential plateau effects beyond these thresholds, though data sparsity at higher doses may contribute to this pattern. The overall optimal dose of 490 MET‐min/week translates to feasible weekly practice schedules ranging from 100 to 210 min depending on the specific modality selected. These findings enable evidence‐based, personalized exercise prescription tailored to individual patient priorities and symptom profiles. Given the substantial treatment gap in psycho‐oncological care, with only 24% of cancer patients with depression receiving adequate pharmacological treatment, mind–body exercise programs represent an accessible, scalable, and cost‐effective intervention that can be integrated as a complementary intervention for cancer‐related psychological distress. Healthcare providers should discuss these evidence‐based exercise options with cancer patients to facilitate shared decision‐making and support the integration of mind–body exercise into comprehensive cancer survivorship care plans.

CONFLICT OF INTEREST STATEMENT

The authors have no relevant financial or non‐financial interests to disclose.

ETHICS STATEMENT

Not applicable.

Supporting information

Table S1. Supplementary Material: Search Strategy

Table S2. Characteristics of Included Studies

Figure S1. Risk of Bias (ROB2)

Figure S2. Orchard Plot and Caterpillar Plot for Mind‐Body Exercise Effects

Figure S3. Subgroup Analysis by Intervention Type: Orchard Plot and Caterpillar Plot

Figure S4. Subgroup Analysis of Mind‐Body Exercise Effects by Patient and Intervention Characteristics

Table S5. GRADE Evidence Profile for Mind–Body Exercise Interventions

Figure S6. Network Plots for Quality of Life, Depression, and Anxiety Outcomes

Figure S7. Comparison Funnel Plots for Model Selection in Bayesian Network Meta‐Analysis

Figure S8. Consistency Assessment in Network Meta‐Analysis

Figure S9. Forest Plot of Treatment Effects Compared with Control Group

Figure S10. Comparison‐Adjusted Funnel Plot for Assessment of Publication Bias

Table S7. Consistency and UME Model Fit Comparison

Figure S13. Node‐splitting analysis (density plot).

Figure S14. “Split” NMA of overall exercise.

Figure S15. “Split” NMA of different exercise agents.

Table S8. Model Selection for Dose‐Response Network Meta‐Analysis

Figure S16. Deviance plot at overall exercise

Figure S17. Deviance plots at treatment‐level.

Figure S18. Fit plots at overall exercise level.

Figure S19. Fit plots at agent‐level.

Figure S20. Ranking of effectiveness of different exercise

APHW-18-0-s001.docx (5.3MB, docx)

Feng, H. , Zhou, P. , Xie, Z. , Wang, Y. , & Wang, X. (2026). Optimal mind–body exercise for quality of life, anxiety, and depression in cancer patients and survivors: A pairwise, network, and dose–response meta‐analysis of randomized controlled trials. Applied Psychology: Health and Well‐Being, 18(4), e70187. 10.1111/aphw.70187

Funding information This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. 20232165).

DATA AVAILABILITY STATEMENT

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

REFERENCES

  1. Abdul Razak, N. , Azhar, Z. I. , Ismail, Z. , Mohd Azman, Z. A. , Abdul Manap, S. A. , Ramli, N. , & Chee Mun, C. (2024). Impact of pilates exercise on quality of life, functional capacity, cancer‐related fatigue, depression and salivary cortisol of colorectal cancer survivors: A quasi‐experimental study. Asian Pacific Journal of Cancer Prevention, 25(8), 2895–2904. 10.31557/APJCP.2024.25.8.2895 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Abu‐Odah, H. , Wang, M. , Su, J. J. , Collard‐Stokes, G. , Sheffield, D. , & Molassiotis, A. (2024). Effectiveness of dance movement therapy and dance movement interventions on cancer patients' health‐related outcomes: A systematic review and meta‐analysis. Supportive Care in Cancer, 32(4), 235. 10.1007/s00520-024-08431-4 [DOI] [PubMed] [Google Scholar]
  3. Ainsworth, B. E. , Haskell, W. L. , Whitt, M. C. , Irwin, M. L. , Swartz, A. M. , Strath, S. J. , O'Brien, W. L. , Bassett, D. R. Jr. , Schmitz, K. H. , Emplaincourt, P. O. , Jacobs, D. R. Jr. , & Leon, A. S. (2000). Compendium of physical activities: An update of activity codes and MET intensities. Medicine and Science in Sports and Exercise, 32(9 Suppl), S498–S504. 10.1097/00005768-200009001-00009 [DOI] [PubMed] [Google Scholar]
  4. Arrieta, Ó. , Angulo, L. P. , Núñez‐Valencia, C. , Dorantes‐Gallareta, Y. , Macedo, E. O. , Martínez‐López, D. , Alvarado, S. , Corona‐Cruz, J.‐F. , & Oñate‐Ocaña, L. F. (2013). Association of Depression and Anxiety on quality of life, treatment adherence, and prognosis in patients with advanced non‐small cell lung cancer. Annals of Surgical Oncology, 20(6), 1941–1948. 10.1245/s10434-012-2793-5 [DOI] [PubMed] [Google Scholar]
  5. Avancini, A. , Borsati, A. , Toniolo, L. , Ciurnelli, C. , Belluomini, L. , Budolfsen, T. , Lillelund, C. , Milella, M. , Quist, M. , & Pilotto, S. (2025). Physical activity guidelines in oncology: A systematic review of the current recommendations. Critical Reviews in Oncology/Hematology, 210, 104718. 10.1016/j.critrevonc.2025.104718 [DOI] [PubMed] [Google Scholar]
  6. Béliveau, A. , Boyne, D. J. , Slater, J. , Brenner, D. , & Arora, P. (2019). BUGSnet: An R package to facilitate the conduct and reporting of Bayesian network meta‐analyses. BMC Medical Research Methodology, 19(1), 196. 10.1186/s12874-019-0829-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Boutron, I. , Tubach, F. , Giraudeau, B. , & Ravaud, P. (2004). Blinding was judged more difficult to achieve and maintain in nonpharmacologic than pharmacologic trials. Journal of Clinical Epidemiology, 57(6), 543–550. 10.1016/j.jclinepi.2003.12.010 [DOI] [PubMed] [Google Scholar]
  8. Bower, J. E. , & Irwin, M. R. (2016). Mind‐body therapies and control of inflammatory biology: A descriptive review. Brain, Behavior, and Immunity, 51, 1–11. 10.1016/j.bbi.2015.06.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Brandenbarg, D. , Maass, S. W. M. C. , Geerse, O. P. , Stegmann, M. E. , Handberg, C. , Schroevers, M. J. , & Duijts, S. F. A. (2019). A systematic review on the prevalence of symptoms of depression, anxiety and distress in long‐term cancer survivors: Implications for primary care. European Journal of Cancer Care, 28(3), e13086. 10.1111/ecc.13086 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Buffart, L. M. , Kalter, J. , Sweegers, M. G. , Courneya, K. S. , Newton, R. U. , Aaronson, N. K. , Jacobsen, P. B. , May, A. M. , Galvão, D. A. , Chinapaw, M. J. , Steindorf, K. , Irwin, M. L. , Stuiver, M. M. , Hayes, S. , Griffith, K. A. , Lucia, A. , Mesters, I. , van Weert, E. , Knoop, H. , … Brug, J. (2017). Effects and moderators of exercise on quality of life and physical function in patients with cancer: An individual patient data meta‐analysis of 34 RCTs. Cancer Treatment Reviews, 52, 91–104. 10.1016/j.ctrv.2016.11.010 [DOI] [PubMed] [Google Scholar]
  11. Caldwell, D. M. , Ades, A. E. , & Higgins, J. P. (2005). Simultaneous comparison of multiple treatments: Combining direct and indirect evidence. BMJ, 331(7521), 897–900. 10.1136/bmj.331.7521.897 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Campbell, K. L. , Winters‐stone, K. M. , Wiskemann, J. , May, A. M. , Schwartz, A. L. , Courneya, K. S. , Zucker, D. S. , Matthews, C. E. , Ligibel, J. A. , Gerber, L. H. , Morris, G. S. , Patel, A. V. , Hue, T. F. , Perna, F. M. , & Schmitz, K. H. (2019). Exercise guidelines for cancer survivors: Consensus statement from international multidisciplinary roundtable. Medicine & Science in Sports & Exercise, 51(11), 2375–2390. 10.1249/mss.0000000000002116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Caruso, R. , Nanni, M. G. , Riba, M. B. , Sabato, S. , & Grassi, L. (2017). The burden of psychosocial morbidity related to cancer: Patient and family issues. International Review of Psychiatry, 29(5), 389–402. 10.1080/09540261.2017.1288090 [DOI] [PubMed] [Google Scholar]
  14. Chaimani, A. , Higgins, J. P. T. , Mavridis, D. , Spyridonos, P. , & Salanti, G. (2013). Graphical tools for network meta‐analysis in STATA. PLoS ONE, 8(10), e76654. 10.1371/journal.pone.0076654 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Chen, Y. , Tang, Y. , & Zhou, Z. (2024). The effects of tai Chi and Baduanjin on breast cancer patients: Systematic review and meta‐analysis of randomized controlled trials [systematic review] [Systematic Review]. Frontiers in Oncology, 14. 10.3389/fonc.2024.1434087 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cheng, H. , Zhou, Z. , Wang, H. , & Hang, X. (2025). Optimizing yoga interventions for mental health in cancer survivors—A quantitative assessment of dose, duration, and subgroup effects: A Bayesian and dose‐response meta‐analysis. Psycho‐Oncology, 34(8), e70222. 10.1002/pon.70222 [DOI] [PubMed] [Google Scholar]
  17. Chochinov, H. M. (2001). Depression in cancer patients. The Lancet Oncology, 2(8), 499–505. 10.1016/S1470-2045(01)00456-9 [DOI] [PubMed] [Google Scholar]
  18. Crippa, A. , & Orsini, N. (2016). Dose‐response meta‐analysis of differences in means. BMC Medical Research Methodology, 16(1), 91. 10.1186/s12874-016-0189-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Cumpston, M. , Li, T. , Page, M. J. , Chandler, J. , Welch, V. A. , Higgins, J. P. , & Thomas, J. (2019). Updated guidance for trusted systematic reviews: A new edition of the Cochrane handbook for systematic reviews of interventions. Cochrane Database of Systematic Reviews, 10(10), Ed000142. 10.1002/14651858.Ed000142 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Dong, J. , Chi, J. , & Wang, D. (2024). Effects of mind–body exercise on physical ability, mental health and quality of life in stroke patients: A systematic review and meta‐analysis [Systematic Review]. Frontiers in Public Health, 12. 2024. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1432510 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Global Burden of Disease Cancer, C . (2022). Cancer incidence, mortality, years of life lost, years lived with disability, and disability‐adjusted life years for 29 cancer groups from 2010 to 2019: A systematic analysis for the global burden of disease study 2019. JAMA Oncology, 8(3), 420–444. 10.1001/jamaoncol.2021.6987 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Gonzalez, M. , Pascoe, M. C. , Yang, G. , de Manincor, M. , Grant, S. , Lacey, J. , Firth, J. , & Sarris, J. (2021). Yoga for depression and anxiety symptoms in people with cancer: A systematic review and meta‐analysis. Psycho‐Oncology, 30(8), 1196–1208. 10.1002/pon.5671 [DOI] [PubMed] [Google Scholar]
  23. Grassi, L. , Caruso, R. , Riba, M. B. , Lloyd‐Williams, M. , Kissane, D. , Rodin, G. , McFarland, D. , Campos‐Ródenas, R. , Zachariae, R. , Santini, D. , & Ripamonti, C. I. (2023). Anxiety and depression in adult cancer patients: ESMO clinical practice guideline. ESMO Open, 8(2), 101155. 10.1016/j.esmoop.2023.101155 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Guyatt, G. H. , Oxman, A. D. , Vist, G. E. , Kunz, R. , Falck‐Ytter, Y. , Alonso‐Coello, P. , & Schünemann, H. J. (2008). GRADE: An emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 336(7650), 924–926. 10.1136/bmj.39489.470347.AD [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Han, B. , Zhang, P. , Zhao, H. , Zhang, X. , Gao, H. , Dong, J. , Zeng, L. , Pi, P. , & Pei, J. (2024). Effects of exercise interventions on quality of life in patients with breast cancer: A systematic review and network meta‐analysis. Psycho‐Oncology, 33(7), e6370. 10.1002/pon.6370 [DOI] [PubMed] [Google Scholar]
  26. Herrmann, S. D. , Willis, E. A. , Ainsworth, B. E. , Barreira, T. V. , Hastert, M. , Kracht, C. L. , Schuna, J. Jr. , Cai, Z. , Quan, M. , & Tudor‐Locke, C. (2024). 2024 adult compendium of physical activities: A third update of the energy costs of human activities. Journal of Sport and Health Science, 13(1), 6–12. 10.1016/j.jshs.2023.10.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Higgins, J. P. T. , Thompson, S. G. , Deeks, J. J. , & Altman, D. G. (2003). Measuring inconsistency in meta‐analyses. BMJ, 327(7414), 557–560. 10.1136/bmj.327.7414.557 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hutton, B. , Salanti, G. , Caldwell, D. M. , Chaimani, A. , Schmid, C. H. , Cameron, C. , Ioannidis, J. P. , Straus, S. , Thorlund, K. , Jansen, J. P. , Mulrow, C. , Catalá‐López, F. , Gøtzsche, P. C. , Dickersin, K. , Boutron, I. , Altman, D. G. , & Moher, D. (2015). The PRISMA extension statement for reporting of systematic reviews incorporating network meta‐analyses of health care interventions: Checklist and explanations. Annals of Internal Medicine, 162(11), 777–784. 10.7326/m14-2385 [DOI] [PubMed] [Google Scholar]
  29. Jansen, J. P. , & Naci, H. (2013). Is network meta‐analysis as valid as standard pairwise meta‐analysis? It all depends on the distribution of effect modifiers. BMC Medicine, 11(1), 159. 10.1186/1741-7015-11-159 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Johannsen, M. , Nissen, E. R. , Lundorff, M. , & O'Toole, M. S. (2022). Mediators of acceptance and mindfulness‐based therapies for anxiety and depression: A systematic review and meta‐analysis. Clinical Psychology Review, 94, 102156. 10.1016/j.cpr.2022.102156 [DOI] [PubMed] [Google Scholar]
  31. Kulchycki, M. , Halder, H. R. , Askin, N. , Rabbani, R. , Schulte, F. , Jeyaraman, M. M. , Sung, L. , Louis, D. , Lix, L. , Garland, A. , Mahar, A. L. , Abou‐Setta, A. , & Oberoi, S. (2024). Aerobic physical activity and depression among patients with cancer: A systematic review and meta‐analysis. JAMA Network Open, 7(10), e2437964. 10.1001/jamanetworkopen.2024.37964 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lee, S. C. , Tsai, P. H. , Yu, K. H. , & Chan, T. M. (2025). Effects of mind‐body interventions on immune and neuroendocrine functions: A systematic review and meta‐analysis of randomized controlled trials. Healthcare (Basel), 13(8). 10.3390/healthcare13080952 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Leon, A. , Whitt‐Glover, M. , Vezina, J. , Greer, J. , Tudor‐Locke, C. , Bassett, D. , Meckes, N. , Herrmann, S. , Haskell, W. , & Ainsworth, B. (2011). 2011 compendium of physical activities: A second update of codes and MET values. Medicine & Science in Sports & Exercise, 43(8), 1575–1581. 10.1249/MSS.0b013e31821ece12 [DOI] [PubMed] [Google Scholar]
  34. Li, M. , Kennedy, E. B. , Byrne, N. , Gérin‐Lajoie, C. , Katz, M. R. , Keshavarz, H. , Sellick, S. , & Green, E. (2016). Management of Depression in patients with cancer: A clinical practice guideline. Journal of Oncology Practice, 12(8), 747–756. 10.1200/JOP.2016.011072 [DOI] [PubMed] [Google Scholar]
  35. Ligibel, J. A. , Bohlke, K. , May, A. M. , Clinton, S. K. , Demark‐Wahnefried, W. , Gilchrist, S. C. , Irwin, M. L. , Late, M. , Mansfield, S. , Marshall, T. F. , Meyerhardt, J. A. , Thomson, C. A. , Wood, W. A. , & Alfano, C. M. (2022). Exercise, diet, and weight management during cancer treatment: ASCO guideline. Journal of Clinical Oncology, 40(22), 2491–2507. 10.1200/jco.22.00687 [DOI] [PubMed] [Google Scholar]
  36. Lim, L. , Machingura, A. , Taye, M. , Pe, M. , Coens, C. , Martinelli, F. , Alanya, A. , Antunes, S. , Tu, D. , Basch, E. , Ringash, J. , Brandberg, Y. , Groenvold, M. , Eggermont, A. , Cardoso, F. , Van Meerbeeck, J. , Koller, M. , Van der Graaf, W. T. A. , Taphoorn, M. J. B. , … Musoro, J. (2025). Prognostic value of baseline EORTC QLQ‐C30 scores for overall survival across 46 clinical trials covering 17 cancer types: A validation study. EClinicalMedicine, 82, 103153. 10.1016/j.eclinm.2025.103153 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lin, K.‐Y. , Hu, Y.‐T. , Chang, K.‐J. , Lin, H.‐F. , & Tsauo, J.‐Y. (2011). Effects of yoga on psychological health, quality of life, and physical health of patients with cancer: A meta‐analysis. Evidence‐Based Complementary and Alternative Medicine, 2011(1), 659876. 10.1155/2011/659876 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Linden, W. , Vodermaier, A. , Mackenzie, R. , & Greig, D. (2012). Anxiety and depression after cancer diagnosis: Prevalence rates by cancer type, gender, and age. Journal of Affective Disorders, 141(2–3), 343–351. 10.1016/j.jad.2012.03.025 [DOI] [PubMed] [Google Scholar]
  39. Low, S. L. K. , Ho, G. F. , Liu, B. , Koh, E.‐S. , Fei, Y. , Teo, C. S. , & Zhu, X. (2024). Exploring Guolin qigong (mind‐body exercise) for improving cancer related fatigue in cancer survivors: A mixed method randomized controlled trial protocol. Integrative Cancer Therapies, 23, 15347354241252698. 10.1177/15347354241252698 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Mao, J. J. , Ismaila, N. , Bao, T. , Barton, D. , Ben‐Arye, E. , Garland, E. L. , Greenlee, H. , Leblanc, T. , Lee, R. T. , Lopez, A. M. , Loprinzi, C. , Lyman, G. H. , MacLeod, J. , Master, V. A. , Ramchandran, K. , Wagner, L. I. , Walker, E. M. , Bruner, D. W. , Witt, C. M. , & Bruera, E. (2022). Integrative medicine for pain Management in Oncology: Society for Integrative Oncology–ASCO guideline. Journal of Clinical Oncology, 40(34), 3998–4024. 10.1200/JCO.22.01357 [DOI] [PubMed] [Google Scholar]
  41. Mawdsley, D. , Bennetts, M. , Dias, S. , Boucher, M. , & Welton, N. J. (2016). Model‐based network meta‐analysis: A framework for evidence synthesis of clinical trial data. CPT: Pharmacometrics & Systems Pharmacology, 5(8), 393–401. 10.1002/psp4.12091 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Mishra, S. I. , Scherer, R. W. , Snyder, C. , Geigle, P. M. , Berlanstein, D. R. , & Topaloglu, O. (2012). Exercise interventions on health‐related quality of life for people with cancer during active treatment. Cochrane Database of Systematic Reviews, 2012, Cd008465. 10.1002/14651858.CD008465.pub2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Mitchell, A. J. , Chan, M. , Bhatti, H. , Halton, M. , Grassi, L. , Johansen, C. , & Meader, N. (2011). Prevalence of depression, anxiety, and adjustment disorder in oncological, haematological, and palliative‐care settings: A meta‐analysis of 94 interview‐based studies. The Lancet Oncology, 12(2), 160–174. 10.1016/S1470-2045(11)70002-X [DOI] [PubMed] [Google Scholar]
  44. Mitchell, A. J. , Ferguson, D. W. , Gill, J. , Paul, J. , & Symonds, P. (2013). Depression and anxiety in long‐term cancer survivors compared with spouses and healthy controls: A systematic review and meta‐analysis. The Lancet Oncology, 14(8), 721–732. 10.1016/S1470-2045(13)70244-4 [DOI] [PubMed] [Google Scholar]
  45. Mols, F. , Vingerhoets, A. J. , Coebergh, J. W. , & van de Poll‐Franse, L. V. (2005). Quality of life among long‐term breast cancer survivors: A systematic review. European Journal of Cancer, 41(17), 2613–2619. 10.1016/j.ejca.2005.05.017 [DOI] [PubMed] [Google Scholar]
  46. Odynets, T. , Briskin, Y. , & Todorova, V. (2019). Effects of different exercise interventions on quality of life in breast cancer patients: A randomized controlled trial. Integrative Cancer Therapies, 18, 1534735419880598. 10.1177/1534735419880598 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Peters, E. , Mendoza Schulz, L. , & Reuss‐Borst, M. (2016). Quality of life after cancer—How the extent of impairment is influenced by patient characteristics. BMC Cancer, 16(1), 787. 10.1186/s12885-016-2822-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Pinto‐Carral, A. , Molina, A. J. , de Pedro, A. , & Ayan, C. (2018). Pilates for women with breast cancer: A systematic review and meta‐analysis. Complementary Therapies in Medicine, 41, 130–140. 10.1016/j.ctim.2018.09.011 [DOI] [PubMed] [Google Scholar]
  49. Pitman, A. , Suleman, S. , Hyde, N. , & Hodgkiss, A. (2018). Depression and anxiety in patients with cancer. BMJ, 361, k1415. 10.1136/bmj.k1415 [DOI] [PubMed] [Google Scholar]
  50. Quinten, C. , Coens, C. , Mauer, M. , Comte, S. , Sprangers, M. A. G. , Cleeland, C. , Osoba, D. , Bjordal, K. , & Bottomley, A. (2009). Baseline quality of life as a prognostic indicator of survival: A meta‐analysis of individual patient data from EORTC clinical trials. The Lancet Oncology, 10(9), 865–871. 10.1016/S1470-2045(09)70200-1 [DOI] [PubMed] [Google Scholar]
  51. Salanti, G. , Ades, A. E. , & Ioannidis, J. P. (2011). Graphical methods and numerical summaries for presenting results from multiple‐treatment meta‐analysis: An overview and tutorial. Journal of Clinical Epidemiology, 64(2), 163–171. 10.1016/j.jclinepi.2010.03.016 [DOI] [PubMed] [Google Scholar]
  52. Smart, N. A. , Waldron, M. , Ismail, H. , Giallauria, F. , Vigorito, C. , Cornelissen, V. , & Dieberg, G. (2015). Validation of a new tool for the assessment of study quality and reporting in exercise training studies: TESTEX. JBI Evidence Implementation, 13(1). 10.1097/XEB.00000000000000020 [DOI] [PubMed] [Google Scholar]
  53. Smith, H. R. (2015). Depression in cancer patients: Pathogenesis, implications and treatment (review). Oncology Letters, 9(4), 1509–1514. 10.3892/ol.2015.2944 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Soong, R. Y. , Low, C. E. , Ong, V. , Sim, I. , Lee, C. , Lee, F. , Chew, L. , Yau, C. E. , Lee, A. R. Y. B. , & Chen, M. Z. (2025). Exercise interventions for depression, anxiety, and quality of life in older adults with cancer: A systematic review and meta‐analysis. JAMA Network Open, 8(2), e2457859. 10.1001/jamanetworkopen.2024.57859 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Speck, R. M. , Courneya, K. S. , Mâsse, L. C. , Duval, S. , & Schmitz, K. H. (2010). An update of controlled physical activity trials in cancer survivors: A systematic review and meta‐analysis. Journal of Cancer Survivorship, 4(2), 87–100. 10.1007/s11764-009-0110-5 [DOI] [PubMed] [Google Scholar]
  56. Spiegelhalter, D. J. , Best, N. G. , Carlin, B. P. , & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B: Statistical Methodology, 64(4), 583–639. 10.1111/1467-9868.00353 [DOI] [Google Scholar]
  57. Sterne, J. A. C. , Savović, J. , Page, M. J. , Elbers, R. G. , Blencowe, N. S. , Boutron, I. , Cates, C. J. , Cheng, H.‐Y. , Corbett, M. S. , Eldridge, S. M. , Emberson, J. R. , Hernán, M. A. , Hopewell, S. , Hróbjartsson, A. , Junqueira, D. R. , Jüni, P. , Kirkham, J. J. , Lasserson, T. , Li, T. , … Higgins, J. P. T. (2019). RoB 2: A revised tool for assessing risk of bias in randomised trials. BMJ, 366, l4898. 10.1136/bmj.l4898 [DOI] [PubMed] [Google Scholar]
  58. Sun, F. , Li, L. , Wen, X. , Xue, Y. , & Yin, J. (2024). The effect of tai Chi/qigong on depression and anxiety symptoms in adults with cancer: A systematic review and meta‐regression. Complementary Therapies in Clinical Practice, 56, 101850. 10.1016/j.ctcp.2024.101850 [DOI] [PubMed] [Google Scholar]
  59. Tang, Y.‐Y. , Hölzel, B. K. , & Posner, M. I. (2015). The neuroscience of mindfulness meditation. Nature Reviews Neuroscience, 16(4), 213–225. 10.1038/nrn3916 [DOI] [PubMed] [Google Scholar]
  60. Walker, J. , Hansen, C. H. , Martin, P. , Symeonides, S. , Ramessur, R. , Murray, G. , & Sharpe, M. (2014). Prevalence, associations, and adequacy of treatment of major depression in patients with cancer: A cross‐sectional analysis of routinely collected clinical data. The Lancet Psychiatry, 1(5), 343–350. 10.1016/S2215-0366(14)70313-X [DOI] [PubMed] [Google Scholar]
  61. Walker, J. , Mulick, A. , Magill, N. , Symeonides, S. , Gourley, C. , Burke, K. , Belot, A. , Quartagno, M. , van Niekerk, M. , Toynbee, M. , Frost, C. , & Sharpe, M. (2021). Major depression and survival in people with cancer. Psychosomatic Medicine, 83(5), 410–416. 10.1097/PSY.0000000000000942 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Wang, Y. , Wang, Y. , Cui, Y. , Su, W. , & Xue, Y. (2025). Comparative efficacy of exercise interventions for managing cancer‐related symptoms and enhancing quality of life: A systematic review and network meta‐analysis. European Journal of Oncology Nursing, 79, 103022. 10.1016/j.ejon.2025.103022 [DOI] [PubMed] [Google Scholar]
  63. Wang, Y.‐H. , Li, J.‐Q. , Shi, J.‐F. , Que, J.‐Y. , Liu, J.‐J. , Lappin, J. M. , Leung, J. , Ravindran, A. V. , Chen, W.‐Q. , Qiao, Y.‐L. , Shi, J. , Lu, L. , & Bao, Y.‐P. (2020). Depression and anxiety in relation to cancer incidence and mortality: A systematic review and meta‐analysis of cohort studies. Molecular Psychiatry, 25(7), 1487–1499. 10.1038/s41380-019-0595-x [DOI] [PubMed] [Google Scholar]
  64. Wayne, P. M. , Lee, M. S. , Novakowski, J. , Osypiuk, K. , Ligibel, J. , Carlson, L. E. , & Song, R. (2018). Tai Chi and Qigong for cancer‐related symptoms and quality of life: A systematic review and meta‐analysis. Journal of Cancer Survivorship, 12(2), 256–267. 10.1007/s11764-017-0665-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Weber, C. S. , Thayer, J. F. , Rudat, M. , Wirtz, P. H. , Zimmermann‐Viehoff, F. , Thomas, A. , Perschel, F. H. , Arck, P. C. , & Deter, H. C. (2010). Low vagal tone is associated with impaired post stress recovery of cardiovascular, endocrine, and immune markers. European Journal of Applied Physiology, 109(2), 201–211. 10.1007/s00421-009-1341-x [DOI] [PubMed] [Google Scholar]
  66. Wen, J. , Hui, S. S. , Chin, E. C. , Yang, Y. , & Sit, C. H. (2025). The effectiveness of mind‐body exercise on health‐related quality of life and mental health during and after breast cancer treatment: An umbrella review of meta‐analyses for randomized controlled trials. Worldviews on Evidence‐Based Nursing, 22(2), e70008. 10.1111/wvn.70008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Wheeler, M. S. , Arnkoff, D. B. , & Glass, C. R. (2017). The neuroscience of mindfulness: How mindfulness alters the brain and facilitates emotion regulation. Mindfulness, 8(6), 1471–1487. 10.1007/s12671-017-0742-x [DOI] [Google Scholar]
  68. Wu, P. , Qian, M. , Chen, X. , Zhu, M. , & Liu, J. (2025). Impact of different exercise types on depression and anxiety in individuals with cancer: A systematic review and network meta‐analysis of randomized controlled trials. Journal of Psychosomatic Research, 192, 112107. 10.1016/j.jpsychores.2025.112107 [DOI] [PubMed] [Google Scholar]
  69. Xiong, Z. , Yuan, Y. , Yang, Y. , Qiu, B. , Bai, Y. , Wang, T. , Wang, J. , Zhang, L. , & Li, Y. (2024). Optimal exercise dose‐response improves health‐related quality of life in cancer survivors: A systematic review and Bayesian network meta‐analysis of RCTs. Frontiers in Oncology, 14, 1510578. 10.3389/fonc.2024.1510578 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Xu, J. , Li, H. , Sze, D. M.‐y. , Chan, V. W. S. , & Yang, A. W. H. (2025). Effectiveness of qigong and tai Chi for quality of life in patients with cancer: An umbrella review and meta‐analysis. BMC Complementary Medicine and Therapies, 25(1), 141. 10.1186/s12906-025-04875-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Zeng, Y. , Xie, X. , & Cheng, A. S. K. (2019). Qigong or tai Chi in cancer care: An updated systematic review and meta‐analysis. Current Oncology Reports, 21(6), 48. 10.1007/s11912-019-0786-2 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. Supplementary Material: Search Strategy

Table S2. Characteristics of Included Studies

Figure S1. Risk of Bias (ROB2)

Figure S2. Orchard Plot and Caterpillar Plot for Mind‐Body Exercise Effects

Figure S3. Subgroup Analysis by Intervention Type: Orchard Plot and Caterpillar Plot

Figure S4. Subgroup Analysis of Mind‐Body Exercise Effects by Patient and Intervention Characteristics

Table S5. GRADE Evidence Profile for Mind–Body Exercise Interventions

Figure S6. Network Plots for Quality of Life, Depression, and Anxiety Outcomes

Figure S7. Comparison Funnel Plots for Model Selection in Bayesian Network Meta‐Analysis

Figure S8. Consistency Assessment in Network Meta‐Analysis

Figure S9. Forest Plot of Treatment Effects Compared with Control Group

Figure S10. Comparison‐Adjusted Funnel Plot for Assessment of Publication Bias

Table S7. Consistency and UME Model Fit Comparison

Figure S13. Node‐splitting analysis (density plot).

Figure S14. “Split” NMA of overall exercise.

Figure S15. “Split” NMA of different exercise agents.

Table S8. Model Selection for Dose‐Response Network Meta‐Analysis

Figure S16. Deviance plot at overall exercise

Figure S17. Deviance plots at treatment‐level.

Figure S18. Fit plots at overall exercise level.

Figure S19. Fit plots at agent‐level.

Figure S20. Ranking of effectiveness of different exercise

APHW-18-0-s001.docx (5.3MB, docx)

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Articles from Applied Psychology. Health and Well-Being are provided here courtesy of Wiley

RESOURCES