Introduction
Heart failure (HF) remains a prevalent and challenging global condition characterized by multiple symptoms, such as fatigue, dyspnea, shortness of breath, edema, and limitations in exercise capacity [1]. In developed countries, the prevalence of HF among adults is 1–2% [2]. In China, the prevalence of HF is 1.3% in individuals aged 35 years and above, affecting approximately 8.9 million [3]. The illness trajectory of HF is typically marked by episodes of acute decompensation and gradual decline in functional capacity [4]. The prognosis for patients is generally poor, with ongoing functional deterioration, frequent hospitalisations, and significant reductions in quality of life (QoL) [5]. A systematic review and meta-analysis revealed that patients with HF in low- and middle-income countries had poor QoL [6]. QoL reflects an individual’s perception of how illness and treatment impact physical, mental, and social well-being and serves as a crucial indicator of treatment effectiveness and disease management. Furthermore, QoL is a predictor of illness prognosis in patients with HF, and poor QoL increases the risk of rehospitalization and all-cause death [7]. Therefore, enhancing the QoL of patients with HF, beginning with the identification of modifiable factors, is crucial for healthcare providers.
Fear of progression (FoP) refers to a reactive, conscious and non-neurotic fear triggered by potential biological, psychological, and social consequences or recurrence of a disease; it is notably prevalent among patients with various chronic illnesses [8, 9]. Long-term HF treatment can cause serious economic and psychosocial problems. FoP is a significant psychosocial concern in patients with HF, with 34.8% of patients experiencing dysfunctional FoP [10]. While a normal FoP level can be adaptive and motivational, excessive and prolonged fear may result in lower treatment compliance and a decrease in healthy behaviors, ultimately negatively impacting patients’ physical, mental, and social health status [9]. Studies have confirmed that elevated FoP levels are associated with decreased QoL among patients with cancer [11] and chronic conditions such as diabetes and multiple sclerosis [9]. However, research on the association between FoP and QoL in patients with HF remains limited. Only one study has indicated that FoP is associated with poor sleep quality in patients with HF [12]. Hence, the link between FoP and QoL in patients with HF and the underlying mechanisms through which FoP influences QoL are notable research gaps.
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Based on the Stress and Coping Theory by Lazarus and Folkman [13], the outcome of a stressful situation is determined by a person’s cognitive evaluation and coping processes. Fop is a stressor for patients with HF and potentially influences QoL through these processes. The theory involves two types of cognitive appraisal: primary and secondary. In primary appraisal, individuals evaluate the relevance or importance of a situation to their needs, determining whether it poses a threat or challenge to the self. If they perceive an inability to overcome a stressor, they appraise it as harmful and constituting a threat, such as psychological distress. In the secondary appraisal process, individuals evaluate their resources and coping strategies, such as resilience, which are crucial supplements to primary appraisals. Thus, we speculate that FoP (stress) influences QoL (outcome) through psychological distress (primary appraisal) and resilience (secondary appraisal).
The symptoms of anxiety and depression are collectively termed as psychological distress. Psychological distress, which encompasses symptoms of anxiety and depression, is common in patients with HF, affecting up to 51% with depression [14] and 55% with anxiety [15]. Experiencing psychological distress is known to affect patient compliance and outcome [14, 16, 17]. Reports indicate that psychological distress can significantly diminish QoL across physical health, mental health, social relationships, and overall well-being [14, 17]. Moreover, FoP is positively correlated with psychological distress, including anxiety and depression in patients with chronic illnesses [18, 19]. Patients with higher FoP experience more severe psychological distress in brain cancer than those with lower FoP [20]. Based on these findings, we speculate that psychological distress (including anxiety and depression) mediates the link between FoP and QoL in patients with HF.
Resilience refers to an individual’s ability to adjust and cope with challenges, trauma, or illness to preserve normal physiological and psychological functions [21]. A systematic review indicated that resilience helps navigate challenging situations, fosters positive emotions, and supports the maintenance or restoration of normal life functions in patients with cardiovascular diseases [22]. Resilience has been identified as a significant predictor of QoL in patients with HF [23]. Moreover, Tian [24] showed that FoP in patients with hematological malignancies is inversely associated with resilience. Resilience may indirectly protect QoL by reducing internal energy depletion caused by FoP, in addition to its direct positive effect on QoL [25]. Therefore, we propose that resilience mediates the association between FoP and QoL in patients with HF. The Risk-Protective Model of Resilience developed by Garmezy et al. [26] proposes that resilience may relieve the adverse effects of risk factors on health outcomes. Studies have found that resilience function as a buffer against the negative effects of risk factors, such as anxiety and depression, on health [22, 27]. Additionally, psychological distress is negatively correlated with resilience in patients with HF [28, 29]. Thus, we hypothesize that psychological distress and resilience act as serial mediators in the link between FoP and QoL in patients with HF.
Following the Stress and Coping Theory, we operationalize FoP as stress, psychological distress as primary appraisal, resilience as secondary appraisal, and QoL as an outcome. The hypothesized conceptual model of the multiple mediation is shown in Fig. 1. The following hypotheses are tested: Hypothesis 1: FoP has a negative and direct effect on the QoL (physical and mental QoL) of patients with HF. Hypothesis 2: Psychological distress serves as a mediator linking FoP and QoL (physical and mental QoL) in patients with HF. Hypothesis 3. Resilience serves as a mediator linking FoP and QoL (physical and mental QoL) in patients with HF. Hypothesis 4: Psychological distress and resilience sequentially mediate the relationship between FoP and QoL (physical and mental QoL) in patients with HF. Exploring the mediating mechanisms could not only deepen the understanding of the link between FoP and QoL but also clarify ways to improve the QoL of patients with HF.
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Fig. 1
Hypothesized conceptual model of multiple mediation
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Methods
Design
Using convenience sampling, we conducted a multicenter cross-sectional survey between March and December 2023 across four tertiary hospitals in Henan Province, Central China.
Participants
Participants were eligible if they fulfilled the following criteria: (1) aged 18 years or older and (2) was diagnosed with HF classified under New York Heart Association (NYHA) classes II–IV. Patients with acute HF; life-threatening illness; or unable to understand the questionnaire were not considered.
G*Power 3.1 was used to determine the sample size. The minimum sample size was 175, with a moderate effect size of 0.15, significance level of 0.05, and statistical power of 0.90 [30]. Recognizing potentially incomplete questionnaire responses, we increased the sample size by 20%, targeting 210 participants. Ultimately, 277 participants were considered adequate. This study used the STROBE checklist for observational research.
Procedures
Data were gathered by six trained graduate nursing students using structured self-report questionnaires. Before the questionnaire survey, all patients provided informed consent. Anonymity was ensured by using the participants’ initials for recording and coding. Assistance was provided if participants encountered difficulties; assistants read the questions aloud and recorded the participants’ responses impartially. Standard explanatory language was used to clarify the questions, with additional explanations offered as needed to ensure comprehensive understanding. After survey completion, the assistants reviewed the responses to ensure completeness and asked the participants to address any unanswered questions. Surveys with more than 10% unanswered questions or evident response patterns were eliminated. Of the 310 eligible patients, 12 declined to participate due to disinterest, 10 were unable to understand the questionnaire, and 11 failed to finish the survey. Thus, 277 patients were finally included in the analysis, indicating a response rate of 89.35%.
Measures
Demographic and clinical characteristics
Participant characteristics were collected using a standard questionnaire. The demographic characteristics included age, sex, residence, educational background, monthly income, marital status, job status, and living conditions. The clinical features encompassed body mass index (BMI), HF duration, re-hospitalization in six months, and NYHA class, which were retrieved from patient records.
The Chinese version of the fear of progression questionnaire-short form (FoP-Q-SF)
An individual’s FoP was measured using the Chinese version of the Fear of Progression Questionnaire-Short Form (FoP-Q-SF) [31], which was adapted from the work of Mehnert [32]. The questionnaire includes 12 items covering two dimensions: fear of physiological health and fear of social and family issues. Items are evaluated using a 5-point Likert scale, with 1 signifying “never” and 5 signifying “always.” Total scores vary between 12 and 60, with higher scores reflecting higher levels of FoP. Scores above 34 indicate psychological dysfunction associated with the fear of disease progression. In this study, the Cronbach’s alpha for the FoP-Q-SF was 0.878.
The generalized anxiety disorder 7-item scale (GAD-7)
The Generalized Anxiety Disorder 7-item scale (GAD-7) compiled by Spitzer et al. [33] was adopted to measure anxiety symptoms over the past two weeks. The scale includes seven aspects with a point value of 0–3 (0 = none at all, 1 = a few days, 2 = more than seven days, and 3 = almost every day). Higher scores denote more severe anxiety. Total scores range from 0 to 21. The GAD-7 is reliable and valid for Chinese patients with cardiovascular diseases [34]. Scores ≥ 10 points indicate that patients have anxiety symptoms. In this study, the Cronbach’s alpha for the scale was 0.898.
The patient health questionnaire-9 (PHQ-9)
Depressive symptoms over the previous two weeks were measured using the Patient Health Questionnaire-9 (PHQ-9) [35]. The nine criteria of the Statistical Manual of Mental Disorders IV, which are used to diagnose depression, provide the basis for this measurement. A 4-point Likert scale is used to rate each item, with 0 indicating “not at all” and 3 indicating “almost every day.” Total scores range from 0 to 27, with higher numbers denoting more severe depression. This scale is reliable and valid for Chinese patients with cardiovascular diseases [36]. Total scores ≥ 10 points indicate that patients are depressed. In this study, the Cronbach’s alpha for the scale was 0.807.
The 10-item Connor–Davidson Resilience Scale
Resilience was assessed using the 10-item Connor–Davidson Resilience Scale, which was created by Campbell–Sills [37]. With items rated from 0 (“not true at all”) to 4 (“true almost all the time”), this scale employs a single dimension. Higher total scores indicate stronger resilience. Total scores range from 0 to 40. This scale shows strong internal consistency [38]. In this study, the Cronbach’s alpha for the scale was 0.899.
The 12-item short form health survey (SF-12)
Patient QoL was evaluated using the 12-item Short Form Health Survey (SF-12) [39]. The participants were asked questions such as “How would you assess your health in the last month?” Two dimensions and two composite scores—the physical component summary (PCS) and mental component summary (MCS), which indicate physical and mental QoL, respectively—were produced by scoring items on a 5- or 6-point Likert scale. The scores were normalized from 0 to 100, with higher values indicating better QoL. The PCS and MCS of SF-12 norms for the general Chinese population are 52.60 ± 5.76 and 50.23 ± 8.24, respectively [40]. In this study, the PCS and MCS had Cronbach’s alpha values of 0.817 and 0.805, respectively.
Ethical consideration
This study adhered to the guidelines outlined in the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Henan Provincial People’s Hospital (No. 2021207). Written informed consent was obtained from the participants.
Statistical analysis
SPSS (version 26.0) was used to analyze the data. Frequencies and percentages were used to describe clinical and demographic features, such as sex, education, and job status. Scale scores and other continuous variables were shown as means with standard deviations (SD). Differences in physical and mental QoL scores among demographic and clinical factors were evaluated using independent sample t-tests or one-way analysis of variance. Multiple group pairwise comparisons were performed using the Bonferroni post hoc test. Pearson’s correlation analysis was used to evaluate the correlations between variables. Hayes [41] SPSS PROCESS macro, specifically Model 6, was employed to explore multiple mediating effects. Four possible paths through which X affects Y were tested using a serial mediation model. These pathways involved one direct effect and three distinct indirect effects. In this model, resilience and psychological distress (anxiety and depression) operated as mediators between FoP, the independent variable; and QoL (physical and mental health dimensions), the dependent variable. Statistically significant factors found during the univariate analysis were included as control variables. Mediation effects were assessed using 5,000 bootstrap samples and were considered significant if the confidence interval (CI) did not include zero.
Results
Sample characteristics
Table 1 shows the patient characteristics and group differences in physical and mental QoL. The sample comprised 112 female and 165 male participants, and 67.5% of them were older than 60 years. Most participants (91.0%) did not complete high school. Most patients were married or cohabiting (90.6%) and unemployed (75.8%). Approximately half of the patients lived in rural areas (56.7%), and 66.4% had an income of less than 3,000 Renminbi (RMB) each month. A small proportion of patients (6.90%) lived alone. Clinically, nearly half of the patients (45.1%) had a BMI within the normal range. Most patients (82.7%) had been living with HF for longer than six months and readmitted for HF during the previous six months (70.0%). Half of the patients were categorized as NYHA class III (53.8%), while one-fifth were categorized as NYHA class IV (20.2%).
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Table 1
Comparison of physical QoL and mental QoL by sample characteristics (N = 277)
Characteristic | n | % | Physical QoL | Mental QoL | ||||
|---|---|---|---|---|---|---|---|---|
Mean (SD) | t/F | P | Mean (SD) | t/F | P | |||
Age, years | ||||||||
<60 | 90 | 32.5 | 54.21 (8.33) | 6.012 | < 0.001 | 46.38 (9.29) | -4.286 | < 0.001 |
≥ 60 | 187 | 67.5 | 47.97 (7.97) | 51.74 (9.97) | ||||
Sex | ||||||||
Male | 165 | 59.6 | 51.05 (8.21) | 2.504 | 0.013 | 49.88 (9.28) | -0.227 | 0.820 |
Female | 112 | 40.4 | 48.45 (8.93) | 50.17 (11.15) | ||||
Education | ||||||||
≤High school | 252 | 91.0 | 49.55 (8.44) | -2.825 | 0.005 | 50.01 (10.03) | 0.061 | 0.951 |
> High school | 25 | 9.0 | 54.57 (8.87) | 49.88 (10.49) | ||||
Residence | ||||||||
Rural | 157 | 56.7 | 50.46 (8.50) | 1.022 | 0.308 | 49.17 (9.98) | -1.569 | 0.118 |
Urban | 120 | 43.3 | 49.4 (8.70) | 51.08 (10.09) | ||||
Monthly income, RMB | ||||||||
<3,000 | 184 | 66.4 | 49.17 (8.48) | -2.274 | 0.024 | 50.01 (10.12) | 0.026 | 0.979 |
≥ 3,000 | 93 | 33.6 | 51.64 (8.60) | 49.98 (9.99) | ||||
Marital status | ||||||||
Single/divorced/widowed | 26 | 9.4 | 48.25 (7.99) | -1.091 | 0.276 | 50.16 (12.29) | 0.084 | 0.933 |
Married/Cohabiting | 251 | 90.6 | 50.18 (8.64) | 49.98 (9.82) | ||||
Job | ||||||||
Unemployed | 210 | 75.8 | 49.07 (8.23) | -3.256 | 0.001 | 50.08 (10.12) | 0.227 | 0.821 |
Employed | 67 | 24.2 | 52.92 (9.09) | 49.76 (9.94) | ||||
Living conditions | ||||||||
Living alone | 19 | 6.9 | 50.2 (9.45) | 0.106 | 0.915 | 52.97 (12.00) | 1.338 | 0.182 |
Living with family | 258 | 93.1 | 49.99 (8.54) | 49.78 (9.89) | ||||
BMI | ||||||||
< 18.49a | 26 | 9.4 | 47.58 (5.69) | 2.990 | 0.031 | 49.97 (9.84) | 0.473 | 0.701 |
18.5–24.99b | 125 | 45.1 | 50.45 (8.53) | 50.52 (9.89) | ||||
25–29.99c | 89 | 32.1 | 49.21 (8.86) | 48.98 (10.78) | ||||
≥ 30d | 37 | 13.4 | 53.45 (9.07) | 50.70 (9.11) | ||||
Heart failure duration | ||||||||
< 6 months | 48 | 17.3 | 53.60 (8.38) | 3.247 | 0.001 | 46.51 (8.30) | -3.067 | 0.003 |
≥ 6 months | 229 | 82.7 | 49.25 (8.46) | 50.73 (10.25) | ||||
Re-hospitalization in six months | ||||||||
No | 83 | 30.0 | 51.62 (9.08) | 2.070 | 0.039 | 49.39 (9.08) | -0.660 | 0.510 |
Yes | 194 | 70.0 | 49.31 (8.29) | 50.26 (10.46) | ||||
NYHA class | ||||||||
IIe | 72 | 26.0 | 52.00 (9.02) | 2.704 | 0.069 | 51.68 (9.58) | 8.916 | < 0.001 |
IIIf | 149 | 53.8 | 49.22 (8.55) | 50.95 (10.11) | ||||
IVg | 56 | 20.2 | 49.51 (7.81) | 45.30 (9.25) | ||||
Patients with poor physical QoL were more likely to be over 60 years old (p < 0.001), be female (p = 0.013), have less than a high school education (p = 0.005), earning under 3,000 RMB per month (p = 0.024), be unemployed (p = 0.001), have a BMI < 18.49 (compared to those with a BMI ≥ 30) (p = 0.045), living with HF for more than six months (p = 0.001), have readmissions within six months (p = 0.039). Conversely, patients younger than 60 years old (p < 0.001), those with NYHA class IV (p = 0.001), and with HF for less than six months (p = 0.003) had poorer mental QoL.
Scale scores and correlation coefficients for the study variables
The correlations between the variables are displayed in Table 2. The mean scores for FoP, anxiety, depression, resilience, physical QoL, and mental QoL were 23.91 (7.66), 4.01 (3.94), 5.18 (3.72), 25.48 (5.81), 50.00 (8.59), and 50.00 (10.06), respectively. Physical QoL was inversely correlated with depression (r = -0.118, p < 0.05) and positively correlated with resilience (r = 0.183, p < 0.01). Mental QoL was negatively correlated with FoP (r = - 0.544, p < 0.01), anxiety (r = -0.670, p < 0.01), and depression (r = - 0.620, p < 0.01) and positively correlated with resilience (r = 0.446, p < 0.01). FoP was positively correlated with anxiety (r = 0.572, p < 0.01) and depression (r = 0.495, p < 0.01) and negatively correlated with resilience (r = - 0.291, p < 0.01). Anxiety (r = - 0.264, p < 0.01) and depression (r = - 0.285, p < 0.01) were negatively correlated with resilience.
Table 2
Correlation coefficients of the study variables (N = 277)
Variable | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
1. FoP | 1 | |||
2. Anxiety | 0.572** | 1 | ||
3. Depression | 0.495** | 0.736** | 1 | |
4. Resilience | -0.291** | -0.264** | -0.285** | 1 |
5. Physical QoL | -0.040 | -0.005 | -0.118* | 0.183** |
6. Mental QoL | -0.544** | -0.670** | -0.620** | 0.446** |
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Mediating effects
Figures 2, 3 and 4 depict the multiple mediation models used to examine the link between FoP and QoL in patients with HF. The mediation model considered significant factors from the univariate analyses of physical QoL, namely age, sex, education, monthly income, job, BMI, HF duration, and re-hospitalization in six months. The total effect of FoP on physical QoL was statistically significant (effect = -0.123; 95% CI, -0.236 to -0.009). For the mediators of anxiety and resilience, the total indirect effect of FoP on physical QoL was not significant (effect = -0.014; 95% CI, -0.092 to 0.066). For the mediators of depression and resilience, the total indirect effect of FoP on physical QoL was significant (effect = -0.084; 95% CI, -0.160 to -0.018). Two pathways explained the indirect impact of FoP on physical QoL: (i) resilience independently (effect = -0.030; 95% CI, -0.070 to -0.006), and (ii) depression and resilience serially (effect = -0.014; 95% CI, -0.034 to -0.003). These pathways accounted for 24.4%, and 11.4% of the total effect, respectively. The effect of FoP on physical QoL through depression alone was not significant (effect = -0.040; 95% CI, -0.110 to 0.021), and the direct effect of Fop on physical QoL was also not significant (effect = -0.042; 95% CI, -0.172 to 0.088). The details are presented in Table 3.
Fig. 2
Multiple mediation model of depression and resilience linking FoP and physical QoL in patients with HF (N = 277). Note: FoP, fear of progression; QoL, quality of life; HF, heart failure Dotted line means a non-significant path *p < 0.05, **p < 0.01, ***p < 0.001
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Fig. 3
Multiple mediation model of anxiety and resilience linking FoP and mental QoL in patients with HF (N = 277). Note: FoP, fear of progression; QoL, quality of life; HF, heart failure *p < 0.05, **p < 0.01, ***p < 0.001
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Fig. 4
Multiple mediation model of depression and resilience linking FoP and mental QoL in patients with HF (N = 277). Note: FoP, fear of progression; QoL, quality of life; HF, heart failure *p < 0.05, **p < 0.01, ***p < 0.001
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Table 3
Multiple mediation effects of depression and resilience on FoP and physical QoL
Effect | SE | LLCI | ULCI | |
|---|---|---|---|---|
Total effect | -0.123 | 0.058 | -0.236 | -0.009 |
Direct effect | -0.042 | 0.066 | -0.172 | 0.088 |
Total indirect effect | -0.084 | 0.036 | -0.160 | -0.018 |
FoP → Depression → Physical QoL | -0.040 | 0.034 | -0.110 | 0.021 |
FoP → Resilience → Physical QoL | -0.030 | 0.016 | -0.070 | -0.006 |
FoP → Depression → Resilience → Physical QoL | -0.014 | 0.008 | -0.034 | -0.003 |
Similarly, the mediation model considered significant factors from the univariate analyses of mental QoL, namely age, HF duration, and NYHA class. The total effect of FoP on mental QoL was statistically significant (effect = -0.491; 95% CI, -0.592 to -0.390). For the mediators of anxiety and resilience, the total indirect effect of FoP on mental QoL was significant (effect = -0.358; 95% CI, -0.440 to -0.283). Three pathways explained the indirect impact of FoP on mental QoL: (i) anxiety independently (effect = -0.285; 95% CI, -0.361 to -0.217), (ii) resilience independently (effect = -0.052; 95% CI, -0.098 to -0.019), and (iii) anxiety and resilience serially (effect = -0.021; 95% CI, -0.044 to -0.004); these pathways accounted for 58.0%, 10.6%, and 4.3% of the total effect, respectively. Meanwhile, the direct influence of FoP on mental QoL was significant (effect = -0.142; 95% CI, -0.240 to -0.043). The details are presented in Table 4.
Table 4
Multiple mediation effects of anxiety and resilience on FoP and mental QoL
Effect | SE | LLCI | ULCI | |
|---|---|---|---|---|
Total effect | -0.491 | 0.051 | -0.592 | -0.390 |
Direct effect | -0.142 | 0.050 | -0.240 | -0.043 |
Total indirect effect | -0.358 | 0.040 | -0.440 | -0.283 |
FoP → Anxiety → Mental QoL | -0.285 | 0.036 | -0.361 | -0.217 |
FoP → Resilience → Mental QoL | -0.052 | 0.020 | -0.098 | -0.019 |
FoP → Anxiety → Resilience → Mental QoL | -0.021 | 0.010 | -0.044 | -0.004 |
For the mediators of depression and resilience, the total indirect impact of FoP on mental QoL was significant (effect = -0.278; 95% CI, -0.357 to -0.210). FoP indirectly affected mental QoL through: (i) depression independently (effect = -0.207; 95% CI, -0.276 to -0.153), (ii) resilience independently (effect = -0.048; 95% CI, -0.091 to -0.016), and (iii) depression and resilience serially (effect = -0.022; 95% CI, -0.045 to -0.008); these pathways accounted for 42.2%, 9.8%, and 4.5% of the total effect, respectively. Meanwhile, the direct effect of FoP on mental QoL was significant (effect = -0.224; 95% CI, -0.321 to -0.127). The details are presented in Table 5.
Table 5
Multiple mediation effects of depression and resilience on FoP and mental QoL
Effect | SE | LLCI | ULCI | |
|---|---|---|---|---|
Total effect | -0.491 | 0.051 | -0.592 | -0.390 |
Direct effect | -0.224 | 0.049 | -0.321 | -0.127 |
Total Indirect effect | -0.278 | 0.037 | -0.357 | -0.210 |
FoP → Depression → Mental QoL | -0.207 | 0.031 | -0.276 | -0.153 |
FoP → Resilience → Mental QoL | -0.048 | 0.019 | -0.091 | -0.016 |
FoP → Depression → Resilience → Mental QoL | -0.022 | 0.009 | -0.045 | -0.008 |
Discussion
This study examined the multiple mediating roles of psychological distress and resilience in the relationship between FoP and QoL in adults with HF. Our findings revealed that FoP negatively affected physical QoL through two indirect paths: resilience, and psychological distress-depression and resilience in serial. Additionally, FoP negatively affected mental QoL through three indirect paths: psychological distress, resilience, and psychological distress and resilience in serial. Patients with a higher FoP tended to experience more severe anxiety or depression and lower resilience, which, in turn, were related to impaired QoL.
In the current study, FoP was found to have a negative association with physical and mental QoL. This result supports Hypothesis 1. Similarly, Hu et al. [42] reported a negative predictive relationship between higher FoP and lower QoL in patients with inflammatory bowel disease. FoP is common among patients with cardiac conditions [43]. Clarke et al. [43] identified that the FoP of patients with cardiac conditions encompasses fears related to death, health, interpersonal relationships, treatment, seeking help, role responsibilities, and physical activity, which often manifest as avoidance, hyperawareness, and misattribution of symptoms. Such cognitive outcomes are influenced by patients’ perceptions of their conditions [44]. Excessive fear and stress can reduce adherence to treatment, impair social functioning, increase treatment costs, and lead to adverse health outcomes (e.g., poor QoL) [42, 45]. Therefore, it is imperative to prioritize QoL in patients with HF, particularly those exhibiting elevated levels of FoP.
This study showed that psychological distress (anxiety and depression) mediated the relationship between FoP and mental QoL in patients with HF. Patients with higher FoP levels experienced more severe psychological anguish, which further lowered their mental QoL. Thus, Hypothesis 2 is partially supported. The Self-regulatory Executive Function Model suggests that emotional distress, such as anxiety and depression, is influenced by cognitive processes [46]. Elevated FoP, focused on the damage, threat, and challenge from environmental situations, may amplify perceptions of threat and uncertainty, thereby exacerbating psychological distress [20]. Previous study has shown that fear of social and health elements as a result of illness development is linked to a higher likelihood of experiencing psychological symptoms, such as anxiety and depression [47]. Furthermore, evidence has revealed that anxiety and depression symptoms are linked to poor mental QoL in adults with HF [14, 17]. Hence, psychological distress is crucial for understanding the link between FoP and mental QoL in patients with HF. Interventions aimed at alleviating psychological distress may lessen the negative effects of FoP on the mental QoL of patients with HF.
This study found that resilience mediated the relationship between FoP and physical and mental QoL in patients with HF. Higher levels of FoP may be alleviated by stronger resilience, leading to better QoL. Thus, Hypothesis 3 is supported. These findings are consistent with those of Tian et al. [24] who found that resilience mediates the connection between FoP and health outcomes in patients with hematological malignancies. The Risk-Protective Model of Resilience [26] states that resilience can mitigate the detrimental effects of a risk factor (e.g., FoP) on health. Additionally, individuals with stronger resilience are more likely to participate in functional metacognition and better comprehend their condition, ultimately leading to decreased FoP [48]. Therefore, resilience is vital to the connection between FoP and QoL in patients with HF. Hence, we can improve the QoL of patients by promoting their level of resilience and reducing their sense of disease-related fear in patients with HF.
Interestingly, depression and resilience mediated the relationship between FoP and physical QoL, while psychological distress and resilience were multiple serial mediators in the relationship between FoP and mental QoL in patients with HF, partially confirming Hypothesis 4. Patients with high FoP experienced more severe psychological distress, followed by a lower level of resilience and, hence, impaired QoL. In this study, a negative association was observed between psychological distress and resilience. This relationship can be attributed to psychological distress, such as depressive symptoms, which diminishes resilience and subsequently lowers the psychological well-being of patients with HF, as described in previous research [28, 29]. Resilience, the capacity to adapt well to challenges, plays a protective role in mitigating the adverse psychological effects (e.g., psychological distress) of stress (e.g., FoP) by fostering adaptive coping strategies, ultimately promoting psychological well-being [24, 25]. Hence, the pathway from psychological distress to resilience is an important bridge for the effects of FoP on QoL. Interventions targeting the reduction of psychological distress and promotion of resilience may be beneficial in enhancing the well-being of patients with HF.
However, the present findings did not confirm the assumption that psychological distress-anxiety and resilience mediate the link between FoP and physical QoL in patients with HF. This could be explained by the lack of a significant correlation between physical QoL, FoP and anxiety in this study. Another possibility is that other unexplored variables such as coping strategies, social support, and disease-specific factors may better explain the relationship between FoP and physical QoL. In addition, as HF represents an advanced stage of cardiovascular disease with severe and intractable symptoms, it may directly contribute to poor physical QoL regardless of anxiety. Longitudinal studies are thus required to explore the direct and causative links between FoP and physical QoL, as well as the mediators between physical QoL predictors and physical QoL.
Implications
The findings indicate that FoP directly and indirectly influences QoL through psychological distress and resilience. As determined from these, efforts to promote QoL should be integrated into comprehensive interventional strategies. Targeted interventions should involve the mitigation of FoP and psychological distress and the enhancement of resilience. First, healthcare professionals should assess the FoP levels of patients with HF when providing nursing care. A meta-analysis showed that cognitive behavioral therapy is generally effective in reducing FoP in patients with cancer [49]. Further research is required to verify the effectiveness of these interventions in patients with HF. These interventions, intended to mitigate FoP, may directly or indirectly promote QoL. Second, the finding indicated that psychological distress significantly contributed to QoL. Psychosocial interventions such as stress management interventions, cognitive behavioral therapy, and acceptance and commitment therapy have proven effective for managing symptoms related to anxiety and depression [50‐52]. Third, since resilience was a crucial mechanism through which FoP affected mental QoL, healthcare providers should focus on developing individuals’ resilience through resilience-enhancing interventions, such as spiritual care program [53]. Furthermore, given that psychological distress and resilience acted as serial mediators in the association between FoP and QoL, interventions that address both factors will be effective in improving QoL.
Limitations
This study has several limitations. First, the cross-sectional study design limited the capacity to determine causal relationships between the variables. A longitudinal or experimental design is required to better understand the time-based connections between these variables. Second, response biases might have been introduced when self-report measures were used. By integrating objective evaluations such as physiological markers with self-report measures, future research could strengthen the validity of the results. Third, the sample was primarily drawn from tertiary hospitals in a single region, and the use of convenience sampling in the study could have increased the likelihood of selection bias. These approaches restrict the applicability of our findings to different healthcare settings and geographical areas. Larger and more representative samples would be beneficial in future studies to provide thorough comparisons.
Conclusions
Patients with HF who experience a high FoP are more likely to have poor QoL. Psychological distress and resilience act as multiple mediators between FoP and QoL. Interventions aimed at reducing psychological distress and enhancing resilience may be beneficial in relieving the effects of FoP on the QoL of patients with HF.
Acknowledgements
The authors would like to thank the cardiovascular unit staff of the hospitals for their support with participant recruitment. The authors express the gratitude to the patients for their contribution to this study.
Clinical trial number
Not applicable.
Declarations
Ethics approval and consent to participate
This study was approved by the Medical Ethics Committee of the Henan Provincial People’s Hospital (No. 2021207). Written informed consent was obtained from the participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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