A chain mediation model reveals the association between metacognition and quality of life in hematologic tumor patients.

Introduction

Hematologic malignancies constitute a spectrum of malignant diseases originating from or secondary to the hematopoietic system, encompassing conditions such as leukemia, lymphoma, and multiple myeloma. They stand as the fourth most prevalent tumors on a global scale 1 . Owing to their heightened malignancy, rapid disease progression, arduous treatment regimens, and heightened recurrence rates, individuals diagnosed with hematologic tumors, along with their families, endure a profound dual burden of both physical and psychological distress. In contrast to the attention bestowed upon solid tumors, the focus on patients grappling with hematologic malignancies remains notably insufficient 2 . Empirical studies underscore that those afflicted by hematologic tumors confront conspicuous emotional challenges, encompassing psychological distress (anxiety and depression) and fear, thereby adversely affecting their overall quality of life (QoL) 3 . Various investigations consistently underscore a significant correlation between the fear of disease progression (FoP) and the QoL, associating elevated levels of this fear with diminished levels of patient well-being 4 , 5 . A comprehensive systematic review addressing the QoL in hematologic tumor patients accentuates anxiety and depression as pivotal influencing factors 6 . Notably, anxiety and depression emerge as prevalent psychological issues within the broader context of cancer patients 7 , wherein metacognition (MC) emerges as a pivotal driving force for negative emotions, directly influencing the onset and progression of anxiety and depression 8 . In conclusion, MC emerges as a potentially intricate factor closely intertwined with the QoL in hematologic tumor patients, thereby underscoring the imperative to delve into the intrinsic connection between the two phenomena.

Metacognition and quality of life.

QoL encompasses a comprehensive evaluation of a patient’s post-illness living conditions, including physical, familial, and emotional dimensions 9 . The conceptual model of QoL emphasizes that this evaluation includes both objective factors and the subjective experience of current life 10 . While there has been extensive research on QoL in hematologic tumor patients, prior studies predominantly concentrated on factors related to disease treatment and surface-level psychological elements associated with life quality, neglecting the profound metacognitive factors that drive psychological processes 6 . The primary treatment period for hematologic tumor patients spans 3 to 18 months after diagnosis. During this time, complications, side effects, and various problems become more pronounced, and patients develop a deeper understanding of their disease. Consequently, patients’ QoL is impaired, and their FoP remains high and stable 6 , 11 . The concept of MC, introduced by Wells et al. based on the Self-Regulatory Executive Function model (S-REF) in 1996, represents higher-order cognitive processes that monitor or control individual cognitive functions 12 , thereby exerting a regulatory and guiding influence on individual emotional development 13 . MC encompasses four dimensions: metacognitive knowledge, metacognitive experiences, metacognitive goals, and metacognitive strategies. Inappropriate combinations of individual knowledge, experiences, goals, and strategies can result in the formation of unhelpful thinking patterns, leading to emotional distress 14 . According to this model, metacognitive beliefs influence one’s self-evaluation of thoughts and feelings, as well as subjective judgments about environmental safety and appropriate responses to the current environment, aligning with the QoL model 15 . A systematic review has also shown that MC is associated with QoL 16 . However, the direct impact of MC on QoL and the mechanisms through which this effect occurs remain unclear. Therefore, a more in-depth exploration of the relationship between these two variables is imperative for elucidating the underlying mechanisms. Hypothesis 1 posited in this study suggests that MC may exert a direct influence on QoL.

The mediating role of psychological distress.

The intricate mechanism through which MC influences QoL necessitates further exploration, with potential mediation by psychological distress (PD), that is anxiety and depression. Hematologic tumor patients, grappling with significant trauma, often display symptoms like tension, distress, heightened panic sensations, low mood, diminished interest in activities, and a inclination toward a pessimistic attitude regarding their illness 17 . Anxiety and depression not only directly manifest as adverse emotional states but also impede effective communication with family members, fostering ineffective coping mechanisms and establishing a detrimental cycle contributing to a deterioration in the patient’s QoL 3 18 .

The common-sense model asserts that cognitive and emotional responses interact, creating a feedback loop intertwined with coping strategies. In the absence of an effective feedback loop, a patient's psychological distress is prone to gradual intensification 19 . A systematic review indicates a negative correlation between psychological distress levels in cancer patients and their QoL, implying that heightened severity of PD correlates with a decline in overall QoL 20 . Maria et al.'s study revealed a positive correlation between MC levels and various dimensions, as well as the total score of PD in cancer patients 21 , aligning with Butow et al.’s findings in a survey of breast cancer patients 22 . These research findings suggest an association among MC, PD, and QoL, although this relationship remains unclear in hematologic tumor patients. Thus, based on this, Hypothesis 2 is posited in this study, proposing that PD may function as a mediating factor between MC and QoL.

The mediating role of fear of disease progression.

FoP entails concerns about the various biological, social, and psychological consequences of disease progression or the fear of disease recurrence. FoP is deemed an appropriate response to the tangible threats associated with diagnosis, treatment, and the disease course, falling within the spectrum between normal and dysfunctional reactions. When FoP surpasses a certain threshold, it can impact a patient’s disease coping, QoL, or social functioning 23 . FoP has emerged as a prevalent psychological issues for patients with hematologic tumors 24 , 25 . Unaddressed or ineffectively coped FoP can lead to enduring adverse effects on a patient’s psychological, physical, and QoL dimensions 26 .

A survey by Walburg et al. discovered a FoP incidence of 44.4% among lymphoma survivors 27 . A research report focusing on young hematologic tumor patients revealed that 97.5% of them expressed concerns about disease progression 28 . In a study examining the relationship between FoP and QoL in the Chinese cancer population, the results indicated that the impact of FoP on QoL is independent 29 . International studies highlight a close association between MC and the occurrence and development of FoP, with MC significantly predicting FoP 30 , 31 . Therefore, this study posits Hypothesis 3, suggesting that FoP may act as a mediating factor between MC and QoL.

The chain mediating role of psychological distress and fear of disease progression.

The Self-Regulatory Executive Function model (S-REF), proposed by Wells in 1996 12 , posits that emotional disorders stem from maladaptive information processing, underscoring the crucial role of MC in cognitive restructuring. Within this model, MC emerges as a key factor. Concentrating on the process of cognitive change contributes to a profound understanding of emotional disorders among patients and plays a positive role in actively and effectively alleviating PD 32 , 33 . Clarifying the occurrence and development of maladaptive information processing directly influences the generation of PD, thereby assisting patients in coping better with their illness and improving their QoL.

The common-sense model elucidates the relationship between cognition and emotion, focusing on patients’ behavioral and emotional responses to stressors, forming the basis for providing effective coping behaviors and emotional responses 34 . Hematologic tumor patients, exposed to internal and external stimuli, undergo stress-induced emotional responses like anxiety, depression, and FoP, intensifying their psychological burden 35 . A systematic review on factors influencing FoP indicates an association between anxiety, depression, and FoP 36 . The various dimensions of QoL demonstrate varying degrees of correlation with FoP 37 .

Upon scrutinizing the aforementioned four variables, it becomes evident that alterations in MC have the potential to initiate a cascade of reactions, holding promise for the amelioration of patients’ QoL. Nevertheless, the absence of conclusive evidence from prior studies remains a challenge. Through the execution of this relational study, fresh insights have been gained, paving the way for the exploration of avenues to enhance patients’ QoL. Initiation from the metacognitive level could possibly yield insights into the impact of emotions on the QoL. Furthermore, there exists a relative dearth of MC-related research within the Chinese cancer population. In light of this, Hypothesis 4 is posited in this study, postulating that PD and FoP may act in tandem between MC and QoL, as elucidated by the mediating model in Fig. 1 . Figure 1 Hypothesized conceptual model of the chain mediation.

Methods

Study design

This study constituted a cross-sectional research initiative conducted between May and September 2023 at a large-scale tertiary Grade A comprehensive hospital in Henan Province, China.

Participants and procedure

This study focused on the collection of data from patients diagnosed with hematologic tumors. The inclusion criteria encompassed individuals who (i) met the diagnostic standards for hematologic tumors; (ii) had been diagnosed within the last 3 to 18 months; (iii) fell within the age range of 18 to 80 years; (iv) possessed an educational background of at least primary school level; and (v) voluntarily chose to participate in the study. Exclusion criteria involved individuals (i) concurrently diagnosed with other malignant tumors and (ii) with a history of or existing mental disorders or those currently undergoing treatment with psychotropic medications.

A questionnaire survey team was established, and its members underwent comprehensive training, which included the survey’s objectives, questionnaire content, and relevant precautions. All questionnaires were administered face-to-face, providing patients with the option to choose between completing a paper questionnaire or using the WeChat-based Wenjuanxing electronic questionnaire, based on their preference. Patients participating in the survey were required to either sign an informed consent form or provide consent through face-to-face communication before receiving the questionnaire. Any questions that arose during the completion process were promptly addressed through explanations. If, for any reason, a patient chose to terminate the process, their decision was respected, and paper questionnaires were collected on the spot or they exited the electronic form. Pursuant to recommendations for structural equation modeling 38 , we aimed to target a participant group exceeding 200. In our outreach efforts, we approached 250 caregivers, and 238 willingly participated in our study, with 7 paper questionnaires removed during organization. Finally, 231 questionnaires were included in the analysis, consisting of 158 paper questionnaires and 73 electronic questionnaires. This study received ethical approval from the ethics committee of the corresponding institute (Ethics number: ZUIRB2023-095) and adhered to the principles of the Helsinki Declaration.

Measurements

Sociodemographic and disease-related characteristics.

We collected sociodemographic data from patients, encompassing age, gender, marital status, education background, financial status, and working status. Patients’ diagnosis, duration of disease, and type of medical insurance were collected from the electronic health system.

Metacognition

MC was measured through the Chinese version of Meta-Cognitions Questionnaire-30 (MCQ-30) 39 . simplified the 65-item original meta-cognition questionnaire into 30 items 40 , that is, MCQ-30, contains a total of 5 domains: (1) Positive meta-cognitive beliefs, such as the items “Worry makes me more organized” and “Worry can help me solve problems”; (2) Negative meta-cognitive beliefs, such as “My worrying is driving me crazy”, and “Once I start worrying, I can’t stop”; (3) Cognitive self-confidence, such as “I feel that I can’t remember people’s names and what they said”; (4) The need to control thoughts, such as “Worrying makes me feel very uncomfortable”; (5) Cognitive self-awareness, such as “I often monitor what I am thinking”. Each dimension includes 6 items, using a 4-point Likert scale, ranging from 1 (disagree) to 4 (strongly agree). Higher scores on the meta-cognition questionnaire indicate that patients are more likely to develop mood disorders. The questionnaire demonstrated good reliability (Cronbach’s α = 0.897) and validity, with a confirmatory factor analysis showing a well-fitting five-factor structure and a correlation of 0.37 with the State-Trait Anxiety Inventory 39. In this study, the Cronbach’s α of MCQ-30 was 0.931, indicating a good internal reliability when used in patients with hematological tumors.

Fear of disease progression

FoP was assessed using the Chinese version of the Fear of Progression Questionnaire-Short Form (FoP-Q-SF) 41 . It contains a total of 12 items and 2 dimensions, namely physical health dimension and social and family health dimension. The physical health dimension mainly refers to patients’ fears about their own disease, such as the items “worry about disease progression” and “worry about some major treatments during the disease process.” The social and family health dimension refers to patients’ fears about social and family functions, such as “worry that it may affect future work” and “worry that I will not be able to pursue my hobbies in the future.” Each item is rated on a 5-point Likert scale, ranging from 1 to 5. The higher the score, the higher the patient’s FoP. A total score of ≥ 34 indicates a clinically meaningful level. The FoP-Q-SF demonstrated good reliability (Cronbach’s α = 0.883) and validity, with moderate correlations with anxiety (r = 0.693) and psychological functioning (r = −0.653), and confirmatory factor analysis showing good fit indices 41 . The Cronbach’s α coefficient of this scale measured in this study was 0.898.

Psychological distress

The Chinese version of Hospital Anxiety and Depression Scale (HADS) was used to assess PD in patients in general hospitals 42 . It consists of 14 items. The anxiety subscale includes 7 items such as “My heart is full of distress” and “I suddenly feel panic”. The depression subscale includes 7 items such as “I seem to feel my mood is gradually getting lower” items. The scores of the two subscales are divided into 0 to 7 as negative, 8 to 10 as mild, 11 to 14 as moderate, and 15 to 21 as severe. Cronbach’s α for the HADS was 0.879, with the total score showing correlations of 0.596 and 0.685 with the Self-rating Anxiety Scale and Self-rating Depression Scale, respectively. Exploratory factor analysis indicated that three factors explained 61.415% of the total variance 42 . In this study, the Cronbach’s α coefficients of the total scale, the anxiety subscale, and the depression subscale were 0.933, 0.885, and 0.892, respectively.

Quality of life

QoL was measured using the Functional Assessment of Cancer Therapy scale (FACT-G) 43 , which includes four dimensions and 27 items. The physical health dimension focuses on physical problems, such as “I feel pain”; the social and family status dimension includes “I am satisfied with the way my family communicates with me”, and the emotional status dimension includes 6 related items such as “I feel sad”. The functional status dimension expresses the impact of the disease on the patient’s daily functions, such as “I can work” and “I can enjoy life.” All items in this scale are scored on a 5-point Likert scale, ranging from 0 to 4. The options include “not at all”, “a little bit”, “somewhat”, “quite” and “very much”. Individual items are scored in reverse order. A higher total score on the scale indicates a better QoL. The Chinese version of the FACT-G has shown good reliability (Cronbach’s α = 0.907) and validity (content validity index = 0.89) 44 . The Cronbach’s α coefficient was 0.877 in the current study.

Statistical analyses

Data analysis was performed using SPSS 25.0. Descriptive statistics, such as means and standard deviations (SD), were calculated for quantitative variables, while frequencies and percentages were determined for qualitative ones. An initial bivariate correlation analysis examined the relationships among MC, PD, FoP, and QoL. The choice of correlation method depended on the normality of the variables’ distribution, as determined by the Kolmogorov–Smirnov test. Pearson correlation analysis was employed for normally distributed variables, while Spearman correlation analysis was utilized for non-normally distributed ones.

Subsequently, mediation models were assessed using the SPSS PROCESS macro 4.1, tailored for intricate model testing. Within PROCESS, Model 6 was utilized for dual mediators. In this analysis, MC functioned as the independent variable, QoL or its sub-dimension (physical, social and family, emotional, and functional) as the dependent variable, and both PD and FoP acted as mediators. Indirect effects were ascertained through a bias-corrected bootstrapping procedure, with a significant mediation effect indicated if the 95% CI excluded 0. All predictors were standardized to mitigate multicollinearity issues 45 .

Results

Common method deviation test

We used the Harman single-factor test to check for common method bias 46 . The results showed that 24 factors had characteristic roots greater than 1, and the cumulative variance extracted from the first factor was 24.50%, less than 40%. Therefore, there are no serious common methodological biases in this study and it can be continued.

Descriptive statistics

Table 1 presents the demographic details of the participants alongside a univariate analysis of QoL scores across various characteristics. A total of 231 patients were included in the final analysis. The average age was 43.28 years (SD 14.29), with 201 patients aged below 60, accounting for 87.0%. Male patients made up more than half of the cohort (127/231). Over 4/5 of the patients were married. 76.6% of patients had an education level of high school/technical secondary school or below. Regarding employment status, only 9.1% (21/231) were currently employed. In terms of average monthly household income, over 2/3 earned less than 3,000 yuan. More than half of the patients were diagnosed with leukemia, followed by lymphoma at 27.3%. Approximately 2/3 of the patients had been diagnosed for less than 6 months. Table 1 Univariate analysis of quality of life and its sub-dimensions of patients with haematological malignancies with different characteristics (n = 231). Variables Group N % Age (year) < 60 201 87.0 ≥ 60 30 13.0 Sex Male 127 55.0 Female 104 45.0 Marital status Married 190 82.3 Unmarried/Divorced/Widowed 8 (3.4) 17.7 Education level High school/Technical secondary school and below 177 76.6 College degree or above 54 23.4 Working status Employed 21 (9.1) 9.1 Unemployed 210 90.9 Family monthly income per capita (RMB) < 3000 163 70.6 ≥ 3000 68 29.4 Diagnosis Leukemia 120 51.9 Lymphoma 63 27.3 Myeloma 21 9.1 Myelodysplastic Syndromes 25 10.8 Others 2 0.9 Duration of the disease (month) < 6 158 68.4 ≥ 12 73 31.6

Bivariate correlation analysis

Following the Kolmogorov–Smirnov normality test, it was determined that the MC score, the total QoL score, and the functional dimension score exhibited a normal distribution. Consequently, Pearson correlation analysis was applied to these variables. Conversely, for variables that did not adhere to a normal distribution, Spearman correlation was utilized. Table 2 presents the results of Pearson or Spearman correlations, displaying the mean values, SD, and correlations among the variables. The QoL scores averaged 69.76 with a SD of 16.50. MC exhibited positive correlations with both PD ( r = 0.363, P < 0.01) and FoP ( r = 0.544, P < 0.01) and negative correlations with QoL ( r = − 0.399, P < 0.01) as well as its sub-dimensions except for functional domain. PD was positively correlated with FoP ( r = 0.492, P < 0.01) and negatively with QoL ( r = -0.589, P < 0.01) and its four dimensions. Additionally, FoP had a negative relationship with QoL ( r = − 0.540, P < 0.01) and its sub-dimensions except for social and family domain. Table 2 Descriptive statistics and correlations among variables (n = 231). Variables 1 2 3 4 5 6 7 8 1 MC 1.000 2 PD 0.363** 1.000 3 FoP 0.544** 0.492** 1.000 4 QoL –0.399** a –0.589** –0.540** 1.000 5 Physical domain –0.469** –0.438** –0.517** 0.692** 1.000 6 Social and family domain –0.149* –0.236** –0.129 0.636** 0.136* 1.000 7 Emotional domain –0.504** –0.537** –0.639** 0.702** 0.532** 0.268** 1.000 8 Functional domain –0.043 a –0.408** –0.271** 0.733** a 0.305** 0.443** 0.258** 1.000 Mean 69.35 13.90 33.66 69.76 17.46 20.52 16.40 15.39 SD 18.39 8.80 10.36 16.50 5.96 5.88 5.43 6.70 FoP fear of disease progression, MC metacognition, PD psychological distress, QoL quality of life, SD standard deviation. ** P < 0.05; ** P < 0.01. a indicates results using Pearson correlation analysis, while the others denote results using Spearman correlation analysis.

Mediation analysis

The mediation model involving MC, PD, FoP, and QoL, generated by PROCESS macro, is presented in Table 3 , Table 4 , and Fig. 2 . The mediation analysis results showed that MC was a positive predictor of PD ( β = 0.166, P < 0.001) and FoP ( β = 0.244, P < 0.001). However, the direct effect of MC on QoL was not statistically significant ( β = -0.085, P > 0.05). Additionally, PD exhibited a positive effect on FoP ( β = 0.414, P < 0.001). FoP negatively impacted QoL ( β = -0.469, P < 0.001), as shown in Table 3 . Table 3 Results of mediation analysis. Independent variables R 2 F β SE t 95% CI Dependent variable: Psychological distress 0.120 31.349*** Metacognition 0.166 0.030 5.599*** (0.108, 0.224) Dependent variable: Fear of disease progression 0.418 81.713*** Metacognition 0.244 0.030 8.040*** (0.184, 0.304) Psychological distress 0.414 0.063 6.531*** (0.289, 0.539) Dependent variable: Quality of life 0.438 58.900*** Metacognition −0.085 0.054 −1.568 (−0.191, 0.022) Psychological distress −0.761 0.108 −7.016*** (−0.974, -0.547) Fear of disease progression −0.469 0.104 −4.518*** (−0.674, -0.265) Dependent variable: Physical dimention 0.377 45.867*** Metacognition −0.090 0.021 −4.372*** (−0.130, -0.049) Psychological distress −0.133 0.041 −3.225*** (−0.214, -0.052) Fear of disease progression −0.164 0.040 −4.151*** (−0.242, -0.086) Dependent variable: Social and family dimention 0.065 5.232** Metacognition −0.005 0.025 −0.197 (−0.054, 0.044) Psychological distress −0.166 0.050 −3.332** (−0.264, -0.068) Fear of disease progression −0.0001 0.048 −0.002 (−0.094, 0.094) Dependent variable: Emotional dimention 0.502 76.166*** Metacognition −0.053 0.017 −3.164** (−0.086, -0.020) Psychological distress −0.189 0.034 −5.616*** (−0.255, -0.122) Fear of disease progression −0.200 0.032 −6.209*** (−0.263, -0.136) Dependent variable: Functional dimention 0.170 15.492*** Metacognition 0.063 0.027 2.357* (0.010, 0.115) Psychological distress −0.273 0.054 −5.108*** (−0.379, -0.168) Fear of disease progression −0.106 0.051 −2.063* (−0.207, -0.005) β Unstandardized coefficient, CI Confidence interval, SE Standard error. * P < 0.05, ** P < 0.01, *** P < 0.001. Table 4 Chain mediation effect test. Path Effect value Boot SE Boot LLCI Boot ULCI Significance Total effect −0.358 0.054 −0.465 −0.251 √ Direct effect −0.085 0.054 −0.191 0.022 – MC → PD → QoL −0.126 0.033 −0.198 −0.066 √ MC → FoP → QoL −0.115 0.032 −0.182 −0.057 √ MC → PD → FoP → QoL −0.032 0.012 −0.059 −0.014 √ FoP fear of disease progression, LLCI the lower limit of 95% confidence interval, MC metacognition, PD psychological distress, QoL quality of life, SE standard error, ULCI the upper limit of 95% confidence interval. Figure 2 The chain mediation model. *** indicates statistically significant (P < 0.001).

The mediation analysis results revealed a total mediation effect of PD and FoP on the relationship between MC and QoL. The effect values of three indirect paths (MC → PD → QoL, MC → FoP → QoL, MC → PD → FoP → QoL) were −0.126, −0.115 and −0.032, respectively. Their 95% CIs did not encompass 0 ([−0.198, −0.066], [−0.182, −0.057], [−0.059, −0.014]), indicating the statistical significance of these pathways. Thus, PD played an intermediary role between MC and QoL (supporting Hypothesis 2), FoP acted as a mediator between MC and QoL (supporting Hypothesis 3), and PD and FoP had a chain mediating effect between MC and QoL (supporting Hypothesis 4) (see Table 4 , Fig. 2 ).

To further explore MC affecting QoL through PD and FoP, this study tested the mediating effects of the four dimensions of QoL (physical, social and family, emotional, and functional). The analysis results, as presented in Table 3 and Fig. 3 , revealed significant and negative impacts of MC, PD, and FoP on the physical domain of QoL ( P < 0.001). Regarding the social and family domain, only PD exhibited a significant negative impact ( P < 0.01). For the emotional domain, MC, PD, and FoP had significant negative impacts ( P < 0.01), and for the functional domain, MC, PD, and FoP showed significant and negative impacts ( P < 0.05). Figure 3 Chain mediation model of the four dimensions of quality of life: a Physical domain; b Social and family domain; c Emotional domain; d Functional domain. * P < 0.05, ** P < 0.01; *** P < 0.001.

Table 5 shows the mediating role of PD and FoP between MC and various QoL domains. The direct effect of MC on these dimensions was significant, except for the social and family domain. In the physical domain, three indirect paths were identified: MC → PD → physical domain, MC → FoP → physical domain, and MC → PD → FoP → physical domain, with 95% CIs of [−0.045, −0.006], [−0.065, −0.018], and [−0.022, −0.004], all excluding 0. Therefore, MC could affect the physical domain through the mediation of PD and FoP. For the social and family domain, only the path “MC → PD → social and family domain” was significant (95% CI [-0.049, -0.009]). In the emotional domain, three paths were significant: MC → PD → emotional domain, MC → FoP → emotional domain, and MC → PD → FoP → emotional domain, with 95% CIs of [−0.054, −0.014], [−0.070, −0.029], and [−0.024, −0.007], all excluding 0. Therefore, MC affected the emotional well-being of blood cancer patients through the mediation of PD and FoP. In the functional domain, three pathways were observed: MC → PD → functional domain, MC → FoP → functional domain, and MC → PD → FoP → functional domain, with 95% CIs of [−0.073, −0.022], [−0.052, −0.004], and [−0.016, −0.001], all excluding 0. Thus, MC influenced the functional aspect of QoL for blood cancer patients through the mediation of PD and FoP. To sum up, all four dimensions of QoL conformed to the chain mediation model, except for the social and family domain. Table 5 Testing the mediating effects of the four dimensions of quality of life (physical domain, social and family domain, emotional domain, functional domain). Path Effect value Boot SE Boot LLCI Boot ULCI Significance Direct effect MC → Physical domain −0.090 0.021 −0.130 -0.049 √ MC → Social and family domain −0.005 0.025 −0.054 0.044 − MC → Emotional domain −0.053 0.017 −0.086 -0.020 √ MC → Functional domain 0.063 0.027 0.010 0.115 √ Indirect effect MC → PD → Physical domain −0.022 0.010 −0.045 -0.006 √ MC → FoP → Physical domain −0.040 0.012 −0.065 -0.018 √ MC → PD → FoP → Physical domain −0.011 0.004 −0.022 -0.004 √ MC → PD → Social and family domain −0.028 0.010 −0.049 -0.009 √ MC → FoP → Social and family domain 0.000 0.013 −0.025 0.025 – MC → PD → FoP → Social and family domain 0.000 0.004 −0.007 0.008 – MC → PD → Emotional domain −0.031 0.010 −0.054 -0.014 √ MC → FoP → Emotional domain −0.049 0.011 −0.070 -0.029 √ MC → PD → FoP → Emotional domain −0.014 0.004 −0.024 -0.007 √ MC → PD → Functional domain −0.045 0.013 −0.073 -0.022 √ MC → FoP → Functional domain −0.026 0.012 −0.052 -0.004 √ MC → PD → FoP → Functional domain −0.007 0.004 −0.016 -0.001 √ Total effect MC → Physical domain −0.163 0.019 −0.199 -0.126 √ MC → Social and family domain −0.033 0.021 −0.074 0.009 – MC → Emotional domain −0.147 0.017 −0.180 -0.113 √ MC → Functional domain −0.016 0.024 −0.063 0.032 – FoP fear of disease progression, LLCI the lower limit of 95% confidence interval, MC metacognition, PD psychological distress, QoL quality of life, SE standard error, ULCI , the upper limit of 95% confidence interval.

Discussion

This study explores how MC levels influence the QoL in blood cancer patients, with a focus on the mediating roles of PD and FoP. The results indicate that while MC does not directly impact QoL, PD and FoP serve as effective mediating pathways through which MC influences the QoL.

The research supports Hypothesis 1, revealing a negative correlation between MC and QoL, which aligns with existing literature suggesting that MC offers a new perspective for exploring QoL 47 . Despite this, the direct impact of MC on QoL is not statistically significant. MC primarily affects emotional disorders, but its direct influence on QoL is unlikely due to the multifaceted nature of psychosomatic factors involved in QoL 31 , 48 .

Further analysis indicates that MC has a significant negative predictive effect on physiological, emotional, and functional dimensions of QoL, but not on the social-family dimension. The finding that MC negatively impacts the physiological dimension of QoL is consistent with previous research indicating that negative metacognitive beliefs are associated with severe physical symptoms in patients with mental illness 49 , which partially overlap with those of cancer patients. These symptoms include statements like “I am not mentally well” and “I feel pain”, suggesting more severe negative metacognitive beliefs correlate with more severe physical symptoms. Regarding the emotional dimension, previous studies indicate that metacognitive beliefs contribute to variations in patient emotional well-being after controlling for demographic and disease-related factors 50 . This study confirms the significant role of MC in the psychological well-being of blood cancer patients, suggesting that assessing MC levels can aid in emotional management and coping. MC levels can also predict the functional dimension of QoL. This may be because negative metacognitive beliefs lead to patients’ inability to control their worries, making it difficult for them to face the disease correctly, which seriously affects their normal work and life 51 . However, as a higher-order cognitive process, MC may not directly impact common social and family issues faced by patients 52 . Contrary to this, research on adolescent emotional regulation suggests that MC predicts social and school-related QoL. The disparity may stem from population differences, emphasizing the need for further exploration of the MC-QoL relationship 53 .

While MC does not directly impact overall QoL, it influences QoL indirectly through three distinct pathways. The first pathway involves PD acting as a mediator between MC and QoL. Blood cancer patients often experience heightened PD due to factors such as severe physical symptoms, rapid disease progression, and an uncertain prognosis 54 , 55 . MC contributes to the development and perpetuation of PD by predicting its manifestation 56 . Elevated levels of negative MC among patients correlate with an increased perceptions of pressure from environmental, familial, or treatment-related factors, hindering the adoption of positive coping strategies 8. The MC model posits that repetitive negative thinking beliefs are most predictive of changes in PD 57 , consequently adversely affecting QoL. Our findings indicate a significant negative impact of PD on QoL, consistent with previous studies 58 , 59 . PD significantly mediates the impact of MC on the four dimensions of QoL, emphasizing the importance of cognitive interventions to improve MC levels and alleviate PD 60 . These interventions may provide new insights for alleviating psychological distress and improving QoL for blood cancer patients.

In addition to PD, MC affects QoL through FoP, enriching the S-REF model. Improved FoP levels are closely associated with reduced negative MC and intrusive thoughts in patients 61 . Elevated negative MC, often accompanying severe physical symptoms in blood cancer patients, leads to unfavorable perceptions of physiological conditions and negative coping, increasing FoP and directly affecting the physiological dimension of QoL 30 . Negative MC exacerbates patients' concerns about disease progression or recurrence, resulting in long-term sadness, tension, and despair, thereby influencing the emotional dimension of QoL. Inappropriate self-perceptions directly impact FoP, leading to challenges such as sleep issues and social role loss, impairing functional status 62 . Therefore, MC, through FoP, impacts the functional dimension of QoL. Blood cancers are prone to recurrence and severe symptoms, with elevated FoP primarily stemming from perceptions of physical symptoms and loss of family roles in self-awareness, having minimal impact on family support. Thus, MC’s influence through FoP on the social and family dimension is not significant. These findings suggest that targeted interventions focusing on FoP and specific psychological therapies may be more effective in enhancing health-related QoL for patients 63 .

MC also indirectly impacts QoL through a chain-mediated effect involving PD and FoP. The effects of this chain mediation are smaller than the individual effects of PD or FoP alone. Recent research in patients with anxiety disorders indicates that cognitive interventions based on MC can improve PD and FoP, thereby influencing QoL 64 . A study on the relationship between QoL and FoP in lymphoma patients emphasizes that recent cancer diagnoses and depression-related pessimistic emotions may lead to FoP, which in turn adversely affects QoL 65 . The indirect effects of MC through the chain mediation of PD and FoP on the four dimensions of QoL are significant, excluding the social-family dimension. As an individual's cognitive awareness, MC can regulate PD through monitoring and control of cognitive processes, with the severity of PD closely related to the level of FoP 66 , 67 . Inappropriately adjusting the metacognitive levels of blood cancer patients exacerbates their psychological distress. Prolonged PD intensifies FoP, leading to poor mental well-being, despair, and loss of enjoyment in life, thereby influencing the patients’ physical, emotional states, and functional status. Blood cancer patients find it challenging to establish a complete social network after the onset of the disease, with most support coming from friends and family. While concerns about cognitive changes can significantly impact their physical, emotional, and functional well-being, the impact on the social-family situation is minimal 68 .

This study presents several limitations. Firstly, its cross-sectional design precludes the establishment of causal relationships between variables. Future research endeavors should consider employing longitudinal methods to provide a more nuanced exploration of causation. Secondly, the reliance on self-reporting for data collection introduces potential biases. Subsequent studies are encouraged to employ more objective and diverse data collection methods. Additionally, while the current study focused on examining the impact of MC on the QoL of Chinese blood cancer patients, specifically considering PD and FoP, there are still numerous unexplored factors in this context. The disease course data collected in this study were graded rather than specific numerical values, so the disease course was not included as a control variable. Future research is encouraged to investigate additional mediating or moderating variables and to control for sociodemographic and clinical factors to enhance the understanding of the underlying mechanisms at play. Lastly, this study conducted a sample survey in one hospital in central China, indicating certain limitations in sample representation. To address this, future research should encompass more regions for large-sample comparisons and tracking studies. Moreover, the consistency of the prediction model established in this study with other cancer populations (such as solid cancer) requires further verification.

Conclusion

This study investigates the influence of MC on the QoL of blood tumor patients and examines the underlying mechanisms. The findings indicate that MC does not have a direct and significant impact on the QoL of blood tumor patients. However, it exerts an indirect influence by affecting the overall QoL and its specific dimensions—physical, emotional, and functional—through the pathways of PD and FoP. Moreover, the study reveals a chain mediating effect where MC influences all four dimensions of QoL through the interconnected roles of PD and FoP, with the exception of the social and family domain. Consequently, this research serves as a valuable theoretical framework for guiding interventions aimed at enhancing the QoL of blood tumor patients.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author contributions

C.Y.C, P.P.C. led the conception, design of the survey, and writing of the manuscript. M.Q.Z., F.Y. coordinated the research. F.Y., Y.R.W., T.L., Z.F.C., J.J.Y, P, C., Z.X.Z., T.F.F. supported the training and supervision of data collection and led the data analysis. C.P.L., T.F.F., Z.F.C. checked the quality of data. P.P.C., M.Q.Z. performed critical review of the manuscript. All co-authors substantially contributed to the interpretation of the results and to the preparation of the manuscript.

Data availability

Data and analytical methods in this study are available from the corresponding author upon reasonable request.

Competing interests

The authors declare no competing interests.

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