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The effect of positive mental well-being on patient reported outcome (PRO): finding from a cross-sectional multi-disease study in China

Health and Quality of Life Outcomes volume 22, Article number: 100 (2024) Cite this article

Abstract

Purpose

This study aims to investigate the potential impact of positive mental well-being on responses of patient-reported outcome measures (PROMs), such as EQ-5D-5L.

Methods

This study utilized the data collected in a cross-sectional study in a sample consisted of individuals with different health conditions. Spearman's rank correlations were employed to investigate the relationship between the responses to the dimensions of EQ-5D-5L and the Short Warwick-Edinburgh Mental Well-being Scale (SWEMWBS). A binary logistic regression analysis and ordered logistic multivariable regression were utilized to examine how SWEMWBS scores could impact the responses to EQ-5D-5L dimensions, while controlling for variables such as age, gender, education level, health conditions, caring experience, and data collection methods. The effects of SWEMWBS on EQ-VAS and utility values were also examined.

Results

One thousand nine individuals participated in the survey. Spearman's rank correlation revealed that all dimensions of EQ-5D-5L, except for the anxiety/depression dimension, exhibited weak correlations with all dimensions of SWEMWBS. Binary logistic regression and ordered logistic multivariable regression indicated that age, SWEMWBS scores, gender, health conditions, data collection methods, and caring experience significantly influenced the likelihood of reporting problems in EQ-5D-5L responses. Notably, better SWEMWBS outcomes increased the likelihood of reporting no or fewer problems across all EQ-5D-5L dimensions. Spearman's rank correlation suggested a moderate or strong positive correlation between SWEMWBS scores and EQ-5D-5L utility values and EQ-VAS. The results of multiple linear regression analysis revealed that SWEMWBS scores, health conditions, caring experience, and data collection methods were significantly associated with EQ-5D utility values and EQ-VAS.

Conclusions

Individuals with better positive mental well-being results are more likely to report better results in PROMs like EQ-5D-5L. Future study is needed to understand the thought process and to explore strategies to cope with the response heterogeneity that led by the status of mental well-being.

Introduction

Traditional clinical measures like morbidity, mortality, and physiological outcomes illustrate the physiological benefits of treatment. The patient perspective provides a more comprehensive interpretation and assessment of the benefits of treatment under investigation [1, 2]. Patient-reported outcomes (PROs) are outcomes that are reported directly by patients, without any interpretation by clinicians or others [3]. These outcomes can range from simple symptoms such as fatigue, anxiety, and pain, to more complex concepts like health-related quality of life (HRQoL) and its related domains [4]. The utilization of PROs serves various purposes, including assessing intervention effectiveness, evaluating treatment outcomes, monitoring disease progression, and facilitating shared decision-making between patients and healthcare providers [1, 5,6,7,8], etc. PROs are often measured in absolute terms, such as a patient's rating of the severity of pain. The instruments used to measure PROs are referred to patient-reported outcome measures (PROMs). These measures can be used to obtain data on various aspects of a patient's condition, such as physiological functional status, HRQoL or health utility, caring experience, and symptoms arising from the patient's condition or therapy for it [1, 9]. The use of PROMs has become increasingly important with the shift towards a patient-centered, holistic approach to healthcare [10]. PROMs provide unique information on the effect of the disease and treatment from the patients' perspective, which can supplement conventional clinical measures like blood tests, functional tests, and imaging [11]. Moreover, PROMs have been increasingly used as primary or secondary endpoints in clinical studies [12,13,14,15].

Although PROMs have shown success in both clinical research and practice, there are certain limitations that hinder their widespread application. One of the main reasons for this is their susceptibility to subjective factors, making it uncertain how they produce varying levels and scores [5]. PROMs primarily rely on patients' self-assessments, which can be influenced by their personal perspectives, emotions, subjective awareness, etc., which lead to variation in the PROM results. When physicians utilize PROM results to monitor patients' conditions and treatment effectiveness, this variation can bias their judgment [16]. Previous studies have indicated that PROMs can potentially hinder patient-clinician relationships and lack clinically meaningful information [17,18,19]. These negative perceptions can undermine the potential of utilizing PRO data to support clinical decision-making and enhance patient-centered care [16]. Therefore, it is crucial to comprehend the factors that may influence how patients respond to PROMs.

As one of the factors that may influence the outcome of PROMs, positive mental well-being encompasses an optimal state of psychological functioning and an overall sense of wellness [20]. It involves experiencing positive emotions, including emotional stability, optimism, confidence, and more. Furthermore, it encompasses actively engaging in life, nurturing healthy relationships, achieving personal growth, and feeling in control of one's own life [21, 22]. Positive mental well-being surpasses the mere absence of mental health issues and instead signifies the presence of positive psychological functioning. This state empowers individuals to effectively handle daily stressors, maintain fulfilling relationships, and thrive in society. It may influence the responses to PROMs in several ways including improving subjective experiences [23], enhancing disease prognosis [24], increasing treatment adherence, and improving quality of life directly [23, 25,26,27,28]. Individuals with positive mental well-being tend to report higher satisfaction with their overall health conditions and experience a better quality of life. Conversely, patients with poor mental health may exhibit lower PROM scores due to factors such as reduced ability to cope with symptoms or treatment side effects. Yet, there lacks studies that systematically examine how positive mental well-being could affect the response of PROM. Short Warwick–Edinburgh Mental Well-Being Scale (SWEMWBS) is the only available PROM, which has been translated in simplified Chinese and validated in China, that measures positive mental well-being. EQ-5D-5L has been widely validated and used in China and other countries. In this study, we aimed to examine the relationship between positive mental well-being and EQ-5D-5L, which has been widely used as a PROM in different settings [29]. We quantitatively analyzed the effect of SWEMWBS scores on responding to EQ-5D-5L responses after controlling for other demographic factors. We hypothesized that respondents with better positive mental well-being are more likely to report better results for PROMs like EQ-5D-5L. We also explored the influence of demographic factors including health conditions, caring experience, and data collection methods on PROMs like EQ-5D-5L.

Methods

Study design and participants

This study utilized the data collected in a cross-sectional online survey and face-to-face survey in China. The online survey data was obtained from the E-QALY project conducted in China [30, 31]. The goal of the E-QALY project is to develop a new quality-of-life measure. A sample of 500 individuals with different health conditions (including asthma, arthritis, heart conditions, stroke, emphysema, thyroid disorder, bronchitis, liver, cancer, diabetes, epilepsy, blood pressure, Irritable Bowel Syndrome (IBS), Chronic Hepatitis B (CHB), HIV/AIDS, disability, depression, generalized anxiety disorder (GAD), and healthy individuals (defined as a visual analog score on health of > 80)) were recruited between April and July 2019 through Accent, a UK-based online survey company. All participants were aged 18 years or older and consented to complete the survey. The study was approved by the Ethics Committee of the University of Sheffield, United Kingdom (Approval letter number 025524), and the IRB of Jinan University, China (Approval letter number JNUKY-2020–001). Written informed consent was obtained from all participants prior to their participation in the online survey. Participants were first provided with an overview of the research purpose and asked to report their health condition. Eligible participants also provided background information such as age, gender, education level, and whether they were carers (carers were defined as those who provided care for friends or family members because they were sick, disabled, or elderly). Subsequently, participants were asked to complete the core survey, which included the E-QALY candidate items, EQ-5D descriptive systems (including EQ-5D-3L and EQ-5D-5L), EQ-VAS (completed once), and SWEMWBS. This study utilized the background information, EQ-5D-5L, EQ-VAS, and SWEMWBS data collected in the psychometric survey in China. The order in which participants completed the EQ-5D-5L and SWEMWBS was randomized, with half of the sample starting with SWEMWBS and the other half starting with EQ-5D-5L. All of 500 participants were included in this study: 231 individuals reported themselves with one or more physical conditions (including asthma, arthritis, heart conditions, stroke, emphysema, thyroid disorder, bronchitis, liver, cancer, diabetes, epilepsy, blood pressure, IBS, CHB, HIV/AIDS, disability, and other physical condition), 21 individuals with one or two mental condition (depression and GAD), 108 individuals with both physical and mental conditions, 140 individuals being healthy. No missing data was allowed in the online survey and all data were kept for analysis.

The face-to-face survey replicated the online survey setting except that participants were recruited in person and one version of the questionnaire was used with a fixed order of E-QALY candidate items, SWEMWBS, and EQ-5D-5L (including EQ-VAS). 550 patients with lung cancer, diabetes, depression, and schizophrenia were recruited from the Provincial Hospital of Guizhou, the No.2 Hospital affiliated with Guizhou University of Traditional Chinese Medicine, Yuqing Caring Psychiatric Hospital and Fenggang Caring Psychiatric Hospital. Healthy participants were recruited in different counties of Guizhou Province. The survey was conducted using a paper and pencil by 10 trained undergraduate students from Guizhou Medical University. The face-to-face data was collected between December 2021 and September 2022. All respondents were encouraged to complete the survey independently. Interviewers were instructed not to interpret the items for the respondents. Respondents were informed that they could skip questions they did not understand or preferred not to answer. The face-to-face study was approved by the Ethics Committee of Guizhou Medical University (Approval letter number 2021–101). Written informed consent was obtained from all participants before administering the survey. Respondents without reporting specific illnesses, yet who reported themselves not being healthy (n = 15), or missing EQ-5D-5L data (n = 4), SWEMWBS data (n = 18), and carer status (n = 4), were excluded. A total of 509 participants were included in this study: 101 individuals reported themselves with depression, 118 individuals with diabetes, 81 individuals with lung cancer, 112 individuals with schizophrenia, and 97 individuals were healthy.

Since both surveys were self-reported, we combined them for analysis. Patients with certain condition that met our inclusion criteria were recruited and, in the survey, a question about their other health condition was asked. Notably, we merged the disease groups into three categories: with physical conditions (including asthma, arthritis, heart conditions, stroke, emphysema, thyroid disorder, bronchitis, liver, cancer, diabetes, epilepsy, blood pressure, IBS, CHB, HIV/AIDS, disability, and other physical condition), with mental conditions (including GAD, depression, and schizophrenia), and with both mental and physical conditions.

Instruments

The EQ-5D-5L is a widely used generic PROM [32], which consists of a five-item health descriptive system and a visual analog scale (EQ-VAS) [33, 34]. The descriptive system contains five dimensions: mobility, self-care, usual activities, pain/discomfort, anxiety/depression, and five response levels in each dimension: 1 = no problems, 2 = slight problems, 3 = moderate problems, 4 = severe problems, and 5 = unable/extreme problems. The EQ-VAS is a 20-cm-long vertical health thermometer recording the current self-rated health of respondents that ranges from 0 (‘The worst health state you can imagine’) to 100 (‘The best health state’). We applied the value set of China developed by Luo et al. to calculate EQ-5D-5L utility values [35].

SWEMWBS is a widely used tool for monitoring mental well-being in populations, offering the advantage of robust measurement properties and brevity [36, 37]. In comparison to the 14-item Warwick-Edinburgh Well-Being Scale (WEMWBS), SWEMWBS provides superior interval scaling [36,37,38]. The Simplified Chinese translation of SWEMWBS was derived from the developer of WEMWBS and translated by Dong et al. [39]. It has been utilized in the Chinese general population as well as in hospitalized patients with mental illness [40,41,42]. SWEMWBS is a self-report instrument consisting of seven questions, each representing a specific aspect of mental well-being: feeling optimistic about the future, feeling useful, feeling relaxed, dealing with problems well, thinking clearly, feeling close to other people, and being able to make up one's mind about things. Each question offers five response options: 1 = none of the time, 2 = rarely, 3 = some of the time, 4 = often, and 5 = all of the time [37]. The raw level summary score is transformed into metric total scores using the SWEMWBS conversion Table [37].

Notably, the response levels reversed between SWEMWBS and EQ-5D-5L on item level, with a lower response indicating worse results for SWEMWBS but better results for EQ-5D-5L. On an aggregate level, a higher score suggests better results for both EQ-5D-5L utility values, EQ-VAS, and SWEMWBS overall score. In addition, the recall periods differed as EQ-5D-5L uses ‘today’ and SWEMWBS uses ‘over the past two weeks’.

Statistical analyses

Data were analyzed using SPSS Version 22.0. We first described the sample characteristics and reported the distributions of the EQ-5D-5L dimension responses, SWEMWBS dimension responses, SWEMWBS scores, EQ-5D-5L utility values, and EQ-VAS. Means and standard deviations (SD) were used for describing continuous variables, while frequencies and percentages were used for describing categorical variables. The internal consistency of EQ-5D-5L and SWEMWBS was assessed using Cronbach’ s alpha [43], for which a value > 0.70 was considered acceptable [41, 43]. Further, the corrected item-total correlation (correlations between a specific item and the total score recalculated after omitting the specific item) were calculated by the Spearman's rank correlation coefficient, with a value of > 0.4 being considered good consistency [44]. The Spearman's rank correlations also were used to examine the correlation between the responses of EQ-5D-5L and SWEMWBS dimensions. Correlations were deemed as weak (0.10–0.29), moderate (0.30–0.49), and strong (≧0.5) [45,46,47]. Next, the binary logistic regression analysis (0 = no problem, 1 = slight/moderate/severe/unable/extreme problems) and ordered logistic multivariable regression were used to explore the association between SWEMWBS scores and EQ-5D-5L dimension responses controlling for age, gender, education level, health conditions, caring experience, and data collection methods. Next, the correlation between SWEMWBS scores and EQ-5D-5L utility values and EQ-VAS were also examined by the Spearman's rank correlations as their distributions were not normally distributed. The multiple linear regression analysis was used to analyze the influence of age, SWEMWBS scores, gender, education level, health conditions, caring experience, and data collection methods on EQ-5D-5L utility values and EQ-VAS. In the appendix, we supplemented the results of the ordered logistic multivariable regression analysis and the multiple linear regression analysis when the data from online survey and face-to-face survey were analyzed separately. Furthermore, we conducted an additional analysis exploring the associations between mental well-being, as measured by the SWEMWBS, and sociodemographic characteristics to verify the robustness of the results.

Results

The total sample consisted of 1,009 individuals, with 529 (52.2%) being female. Our study sample was evenly distributed across four age groups, with a mean age of 41.5 ± 14.82 years old. More than half of the participants had a college degree or higher education, accounting for 544 (53.7%) individuals. The participants encompassed four different health condition subgroups. This included 237 (23.4%) healthy individuals, 430 (42.4%) individuals with physical conditions, 234 (23.1%) with mental conditions, and 108 (10.7%) with both physical and mental conditions. Out of the total sample, 692 (68.3%) participants had no previous experience with caregiving. A summary of the characteristics of the entire sample can be found in Table 1.

Table 1 Sample characteristics
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The distributions of the EQ-5D-5L dimensions and SWEMWBS dimensions are depicted in Fig. 1. Regarding the EQ-5D-5L, the majority of respondents indicated 'no problems' in the dimensions of mobility, self-care, and usual activities, accounting for 73.6%, 79.4%, and 71.7% respectively. Levels 4 and 5 of the EQ-5D-5L had comparatively fewer responses. On the other hand, the SWEMWBS exhibited responses across all levels, with the highest percentage of respondents reporting 'often' in each dimension. In contrast, the 'none of the time' level was the least reported in each dimension.

Fig. 1
figure 1

Response distributions of EQ-5D-5L dimensions and SWEMWBS dimensions

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Figure 2 displays the distributions of the SWEMWBS scores, EQ-5D-5L utility values, and EQ-VAS. All of them showed a skewed distribution. Regarding the SWEMWBS scores, the mean score was 24.73 ± 5.71, ranging from 7.0 to 35.0. In addition, 10.7% of participants scored the highest possible score, 35 points. The mean of EQ-5D-5L utility was 0.86 ± 0.17. The utility values were greater than 0 except for one participant (utility value = -0.305) and 31.2% of participants had a utility value of 1. As for EQ-VAS, the mean score was 76.52 ± 18.96, ranging from 0 to 100. The majority of participants scored in the range of 68.10 to 90.00.

Fig. 2
figure 2

Distributions of SWEMWBS scores, EQ-5D-5L utilities, and EQ-VAS

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Good internal consistency was found for both instruments, as indicated by the high Cronbach’s α values (0.811 for EQ-5D-5L and 0.922 for SWEMWBS). The corrected item-total correlations were strong, ranging from 0.475 (anxiety/depression dimension) to 0.735 (usual activities dimension) for EQ-5D-5L, and from 0.737 (relaxed dimension) to 0.792 (problems dimension) for SWEMWBS (Table 2). For SWEMWBS, removing any single item did not result in a Cronbach’s α value higher than the overall α of 0.922, indicating good internal consistency across all dimensions. In contrast, for EQ-5D-5L, deleting the anxiety/depression dimension increased the overall Cronbach’s α, suggesting that this dimension may have lower internal consistency compared to other dimensions.

Table 3 presents Spearman's rank correlation coefficients between the EQ-5D-5L and SWEMWBS dimensions. The four dimensions of the EQ-5D-5L (mobility, self-care, usual activities, and pain/discomfort) exhibited weak correlations with all dimensions of the SWEMWBS, with correlation coefficients ranging from -0.297 to -0.001. The anxiety/depression dimension of the EQ-5D-5L showed a moderate correlation with several dimensions of the SWEMWBS: optimistic (ρ = -0.495), useful (ρ = -0.439), relaxed (ρ = -0.493), problems (ρ = -0.389), clearly (ρ = -0.336), close (ρ = -0.415), and mind (ρ = 0.392), respectively.

Table 2 The internal consistency of EQ-5D-5L and SWEMWBS
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Table 3 Spearman correlation coefficients between domain responses for the EQ-5D-5L and SWEMWBS
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The results of the binary logistic regression analysis are presented in Table 4. Among all controlled variables, education level had no effect over the likelihood of reporting problems for EQ-5D-5L dimensions except for the anxiety/depression dimension, other variables showed moderate to large effects. Specifically, older participants were found to have a higher likelihood of reporting problems in the mobility dimension (OR: 1.034; 95% CI: 1.017, 1.051), self-care dimension (OR: 1.051; 95%CI: 1.031, 1.072), and pain/discomfort (OR: 1.014; 95% CI: 1.000, 1.027). Male participants were more likely to report no problems than females in five dimensions of the EQ-5D-5L. Individuals with physical and/or mental conditions were more likely to report problems across all dimensions of the EQ-5D-5L compared to healthy individuals. The usual activities of the EQ-5D-5L exhibited the highest likelihood of problems (OR: 5.752 to 11.482), while the anxiety/depression dimension displayed the lowest likelihood of problems (OR: 3.875 to 10.594). Those with no prior caregiving experience and participants who underwent online interviews were more likely to report problems across all dimensions of the EQ-5D-5L compared to carers and participants interviewed face-to-face. Particularly, participants with higher SWEMWBS scores demonstrated a greater likelihood of reporting no problems across all dimensions of the EQ-5D-5L. The OR ranged from 0.837 (95%CI: 0.809, 0.866) for the anxiety/depression dimension to 0.920 (95%CI: 0.887, 0.955) for the self-care dimension of the EQ-5D-5L.

Table 4 Binary logistic regression analysis with EQ-5D-5L dimensions as dependent variables
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In general, the results of the ordered logistic regression model (Table 5) were similar to the results of the binary logistic regression model (Table 4). Age, gender, health conditions, data collection methods, and caring experience showed significant effects on the likelihood of reporting problems for EQ-5D-5L responses. Among which, health conditions have the most obvious effect. Specifically, participants with physical and/or mental conditions reported more severe problems across all dimensions of the EQ-5D-5L, e.g., the OR ranged between 5.680 and 10.740 for the usual activities dimension and ranged between 4.007 and 6.567 for the anxiety/depression dimension. In terms of the SWEMWBS scores, a significant, negative association was observed between SWEMWBS scores and EQ-5D-5L responses, that is, the higher the SWEMWBS score, the less likelihood of reporting problems for EQ-5D-5L, with an OR ranging between 0.807 (95%CI: 0.782, 0.831) for the anxiety/depression dimension and 0.910 (95%CI: 0.878, 0.944) for the self-care dimension. When the data from online survey and face-to-face survey are analyzed separately, the effect of SWEMWBS scores on responses to EQ-5D-5L dimension responses is consistent (Appendix Table 7).

Table 5 Ordered logistic multivariable regression analysis with EQ-5D-5L dimensions as dependent variables
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The relationship between the SWEMWBS scores and EQ-5D-5L utility values and EQ-VAS is shown in Fig. 3. The Spearman’s rank correlation coefficients between the SWEMWBS scores and EQ-5D-5L utility values and EQ-VAS were 0.412 (P < 0.01) and 0.515 (P < 0.01), respectively, suggesting a moderate or strong positive correlation. Table 6 shows the results of multiple linear regression analysis. Age is not significantly associated with EQ-5D utility values and EQ-VAS; in contrast, gender, education level, SWEMWBS scores, health conditions, caring experience, and data collection methods were associated with the values. Males had a weak positive correlation with EQ-5D-5L utility values (β: 0.035; 95%CI: 0.017, 0.053); The SWEMWBS scores had a significant positive correlation with EQ-5D-5L utility values (β: 0.011; 95%CI: 0.010, 0.013) and EQ-VAS (β: 1.583; 95%CI: 1.391, 1.775). Appendix Table 8 shows a similar result with a positive correlation between SWEMWBS scores and utility values and EQ-VAS when the data from online survey and face-to-face survey are analyzed separately. The physical and/or mental conditions had a weak negative correlation with EQ-5D-5L utility values (β: -0.110; 95%CI: -0.135, -0.086; β: -0.096; 95%CI: -0.124, -0.067; β: -0.124; 95%CI: -0.159, -0.088; respectively) and had a stronger negative correlation with EQ-VAS (β: -7.433; 95%CI: -10.216, -4.650; β: -9.424; 95%CI: -12.664, -6.183;β: -8.724; 95%CI: -12.746, -4.703; respectively). The data collection methods had a weak positive correlation with EQ-5D-5L utility scores (β: -0.047; 95%CI: -0.076, -0.017), while caring experience had a stronger positive correlation with EQ-VAS (β: 2.854; 95%CI: 0.499, 5.209). The associations between SWEMWBS scores and sociodemographic characteristics is shown in appendix Table 9. Age, gender and caring experience had a positive correlation with SWEMWBS scores, while health conditions and data collection methods were negatively associated with SWEMWBS scores.

Fig. 3
figure 3

The correlation between SWEMWBS scores and quality of life (EQ-5D-5L utilities and EQ-VAS)

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Table 6 Multiple linear regression analysis with EQ-5D-5L Utilities and EQ-VAS as dependent variables
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Discussion

PROMs have gained growing popularity in randomized clinical trials and clinical practice due to their noninvasive nature, patient-centered approach, and ease of accessibility [8, 48, 49]. Despite its increasing use and importance, there is a lack of understanding regarding the impact of participants' psychological factors on their responses to commonly used PROMs like EQ-5D-5L. By conducting a quantitative analysis to examine the effect of SWEMWBS scores on responses to EQ-5D-5L dimension responses, utility values, and EQ-VAS in a large sample with different health conditions after accounting for other demographic factors, we found that SWEMWBS score was statistically significant associated with EQ-5D-5L outcomes.

As we hypothesized, our finding suggests that positive mental well-being can indeed influence PROM results. The following mechanisms may explain the results. Firstly, positive mental well-being can impact how individuals perceive and report their health-related experiences and outcomes. Previous studies have reported that individuals with higher levels of positive mental well-being are more likely to report better overall health, improved quality of life, and greater satisfaction with their health outcomes [23, 26, 50,51,52,53]. In comparison, individuals with poor positive mental well-being may pay excessive attention to their bodily discomfort or symptoms, leading them to the report of more negative health outcomes, lower quality of life, and reduced satisfaction with their health [54, 55]. Secondly, positive mental well-being can also affect individuals’ decision-making and execution of health behaviors, and further influence the assessment of health-related experiences and outcomes. Previous studies suggested that people with positive mental health are more likely to adopt positive health behaviors such as regular exercise, healthy eating, smoking cessation, and being more involved in a variety of social, etc. [56], while those with poor mental health are more likely to adopt unhealthy behaviors such as drinking, smoking, irregular eating habits, and abuse of drugs and alcohol [57,58,59]. Additionally, positive mental well-being may influence physiology directly, perhaps by protecting against ill health and risk of disease. For example, studies have shown that positive mental well-being can consistently protect individuals against cardiovascular disease, and alter the time course of disease processes such as atherosclerosis [24, 28, 51, 60,61,62]. This direct effect on health may directly affect their responses to PROM results. These studies offer a comprehensive perspective on how mental health impacts health across multiple dimensions. However, while previous studies have widely discussed the benefits of positive mental well-being on health, direct validation of the relationship between specific PROMs (such as EQ-5D-5L) and positive mental well-being is scarce. Our study specifically focuses on how positive mental well-being influences patient-reported outcome measures (PROMs). Our study supports the positive correlation between SWEMWBS scores and EQ-5D-5L assessment results through concrete data, providing more specific guidance for clinical and public health practices. Moreover, Our research further reveals how positive well-being impacts the outcome of physical dimensions in PROMs, offering a more nuanced understanding.

Understanding the influence of mental well-being on PROM results is essential for accurately assessing and interpreting patient-reported data. Researchers and healthcare professionals can consider mental well-being as a potential confounding or moderating factor when analyzing PROs and make appropriate adjustments in their analyses to account for its impact. In our previous study, the anxiety/depression dimension of EQ-5D-5L loads on the same factor as all dimensions of the SWEMWBS [63]. Jan R Böhnke et al. also found overlap between the anxiety/depression dimension of EQ-5D-5L and the SWEMWBS [64], suggesting that they measure the same underlying structure. Notably, although the structure they measure is similar, they involve two different aspects. In EQ-5D, the anxiety/depression dimension focuses on negative emotions, where the best state "1" only indicates the absence of negative emotions, and cannot assess positive emotional states. On the other hand, the SWEMWBS focuses on primarily the positive aspects of well-being [65], assessing the degree of positive emotions. This encourages to explore the effect of the anxiety/depression dimension on the other four dimensions of EQ-5D. In other words, when persons exhibit more problems on the anxiety/depression dimension, it is also likely that they will report more severe problems in the other four dimensions of physical health. By understanding how negative mental health like anxiety/depression impacts the outcome of physical dimensions in PROMs, we can gain more comprehensive and valuable insights into response patterns for PROMs. Traditionally, medical interventions have primarily focused on the treatment and rehabilitation of physical illnesses, while mental health has often been overlooked. If the results are indeed true, then interventions that promote mental well-being could also improve the outcome of physical dimensions measured by PROMs, even though physical health may not be improved. This finding provides new perspectives and strategies for medical practice. For example, by providing psychological support, cognitive-behavioral therapy, psycho-education, and other interventions, we can help patients establish a positive mental state that improves the outcome of physical dimensions in PROMs. This is important for improving the overall health and quality of life of patients. Particularly, while our study shows the impact of mental well-being on EQ-5D, it does not directly generalize to all types of PROMs. In other words, it may not be a relevant confounding factor for all PROs. We will further explore its impact on other PROMs in future studies.

Another influential factor in our results is the method of data collection. Interestingly, participants in our online survey were expected to report fewer issues across EQ-5D-5L dimensions compared to those engaging in face-to-face survey, primarily due to the younger age of the former group. However, contrary to this expectation, our findings indicated the opposite; participants from the online survey reported more problems across all EQ-5D-5L dimensions. We hypothesized that the primary reasons for this outcome are twofold. First, there might be a selection bias. It can be argued that patients willing to complete a face-to-face survey are likely to have a relatively better state of health. Severe health conditions might render it nearly impossible for a study to collect PROM data from such individuals. Secondly, the presence of interviewers and the assistance they provide during face-to-face data collection could lead to socially desirable responses.

In addition to the factors that we explored in this study, various other factors could contribute to the variations of responses in PROMs, including demographic factors (such as age, gender, ethnicity, education level, and socioeconomic status), health literacy, clinical considerations (including disease severity, comorbidities, and treatment history), language barriers, and cultural beliefs. For example, individuals with lower health literacy might encounter challenges comprehending and responding to PROMs, which potentially influence their responses [66]. Patients from distinct cultural backgrounds could hold varying health-related beliefs and values [67], thereby impacting their PROMs responses. This also suggests that the findings of this study may not be generalizable to other populations. Furthermore, patients might offer responses they perceive as socially favorable, rather than accurately reflecting their genuine experiences or sentiments. These factors collectively contribute to the variability in response heterogeneity observed in PROM results.

Coping with response heterogeneity in patient-reported outcome (PRO) studies can be challenging. Nevertheless, there exist several strategies for addressing this response heterogeneity. These strategies could be implemented either during the data collection phase, e.g., the use of multiple measurement methods; the data analysis phase, such as the utilization of statistical analysis techniques, such as item response theory (IRT) or latent class analysis (LCA), as well as approaches like subgroup analysis, sensitivity analyses, and stratification and adjustment; and other strategies like qualitative research methods, sample size considerations, and interpretation and reporting [68,69,70]. In particular, we propose that some strategies could be employed to handle response heterogeneity stemming from variations in mental well-being results, in addition to the aforementioned methods. In the data collection process, the incorporation of multiple measurement methods (e.g., the use of Proxy, interviewer administered, and self-report modes) could offer diverse perspectives on patients' symptoms and psychological states, mitigating the impact of psychological factors; consistent measurement could aid in monitoring changes in patients' symptoms and psychological states, enabling prompt identification and rectification of psychological factor influence. In addition, building a trusting rapport, when using interviewer-assisted surveys, for example, can reduce anxiety and enhance response accuracy. However, this may introduce social desirability bias, where respondents give socially acceptable answers. To minimize this, emphasize anonymity, use neutral language, and pilot test different modes of administration. During data analysis, strategies like LCA can identify distinct subgroups with unique response patterns, and IRT can detect differential item functioning to see if specific items are more influenced by mental well-being. Including mental well-being as a covariate in regression models or conducting sensitivity analyses by excluding extreme cases can further control for this variability. While the strategies may focus on mental well-being response heterogeneity, they are not limited to this domain. In fact, many of the principles and techniques outlined here can be applied to address other forms of response heterogeneity. For example, the strategies for building trust and reducing bias in data collection can be adapted to assess physical health, quality of life, or other patient-reported outcome. Similarly, the quantitative strategies for data analysis can be tailored to the specific research question and data collected. Future studies should explore other strategies.

There are some limitations that need to be addressed. First, our data collection followed an international study protocol which did not require us to note down the sampling procedures in terms of, for example, the number of participants being approached, and number of participants refused to participate. In addition, we could not verify the disease status of the online participants. All the information has been self-reported. Also, a typical problem of using online survey, which we cannot verify in this study is that the responses could come from the caregiver instead of the patients themselves [71]. Conversely, for the face-to-face survey, the data might be subjected to socially desirable bias due to the presence of interviewers. Secondly, our study only considered a limited number of control variables, potentially overlooking other significant factors such as socioeconomic variables (e.g., income, occupation, social status), lifestyle elements (e.g., diet, exercise, smoking, alcohol consumption), and treatment-related factors (such as treatment plans and medication dosages), which could impact the outcomes. Most importantly, due to the cross-sectional nature of the study, we cannot definitively confirm the causal relationship between positive mental well-being and EQ-5D-5L results. In other words, the observed association between positive mental well-being and EQ-5D-5L responses can also be interpreted the other way around, meaning that better HRQoL captured by EQ-5D-5L could lead to better mental well-being measured by SWEMWBS. Or, both of these effects could coexist, and it is impossible to disentangle them in this study. For future research, employing qualitative methods could help establish a causal relationship between these two outcomes.

While our findings contribute to the understanding of the relationship between mental well-being and PRO responses, they are based on a single-country dataset using the EQ-5D-5L and SWEMWBS. As such, the results should not be considered generalizable to other contexts or PROMs. Future studies should explore these associations using different instruments and in diverse cultural settings to validate and extend our findings.

Conclusion

Better SWEMWBS scores could lead to better EQ-5D-5L results, highlighting the effect of positive mental well-being on the responses of PROMs like EQ-5D-5L. More research is needed to better understand the mechanism of mental well-being on PRO.

Data availability

Data are available from the corresponding author (hym18370966170@163.com) on reasonable request.

References

  1. Black N. Patient reported outcome measures could help transform healthcare. BMJ (Clinical research ed). 2013;346: f167.

    PubMed  Google Scholar 

  2. Rathert C, Huddleston N, Pak Y. Acute care patients discuss the patient role in patient safety. Health Care Manage Rev. 2011;36(2):134–44.

    Article  PubMed  Google Scholar 

  3. Bouazza YB, Chiairi I, El Kharbouchi O, De Backer L, Vanhoutte G, Janssens A, et al. Patient-reported outcome measures (PROMs) in the management of lung cancer: A systematic review. Lung Cancer (Amsterdam, Netherlands). 2017;113:140–51.

    Article  PubMed  Google Scholar 

  4. U.S. Department of Health and Human Services FDA Center. Guidance for industry: patient-reported outcome measures: use in medical product development to support labeling claims: draft guidance. Health Qual Life Outcomes. 2006;4:79.

  5. Weinfurt KP, Reeve BB. Patient-reported outcome measures in clinical research. JAMA. 2022;328(5):472–3.

    Article  PubMed  Google Scholar 

  6. Zhou H, Yao M, Gu X, Liu M, Zeng R, Li Q, et al. Application of patient-reported outcome measurements in clinical trials in China. JAMA Netw Open. 2022;5(5): e2211644.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Doward LC, Gnanasakthy A, Baker MG. Patient reported outcomes: looking beyond the label claim. Health Qual Life Outcomes. 2010;8:89.

    Article  PubMed  PubMed Central  Google Scholar 

  8. The LN. Patient-reported outcomes in the spotlight. Lancet Neurolo. 2019;18(11):981.

    Article  Google Scholar 

  9. Algurén B, Coenen M, Malm D, Fridlund B, Mårtensson J, Årestedt K. A scoping review and mapping exercise comparing the content of patient-reported outcome measures (PROMs) across heart disease-specific scales. J Patient Reported outcomes. 2020;4(1):7.

    Article  Google Scholar 

  10. Øvretveit J, Zubkoff L, Nelson EC, Frampton S, Knudsen JL, Zimlichman E. Using patient-reported outcome measurement to improve patient care. International journal for quality in health care : journal of the International Society for Quality in Health Care. 2017;29(6):874–9.

    Article  PubMed  Google Scholar 

  11. Mercieca-Bebber R, King MT, Calvert MJ, Stockler MR, Friedlander M. The importance of patient-reported outcomes in clinical trials and strategies for future optimization. Patient related outcome measures. 2018;9:353–67.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kotecha D, Bunting KV, Gill SK, Mehta S, Stanbury M, Jones JC, et al. Effect of digoxin vs bisoprolol for heart rate control in atrial fibrillation on patient-reported quality of life: The RATE-AF randomized clinical Trial. JAMA. 2020;324(24):2497–508.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jia Y, Wen J, Qureshi R, Ehrhardt S, Celentano DD, Wei X, et al. Effect of redundant clinical trials from mainland China evaluating statins in patients with coronary artery disease: cross sectional study. BMJ (Clinical research ed). 2021;372: n48.

    PubMed  Google Scholar 

  14. Kochar B, Martin CF, Kappelman MD, Spiegel BM, Chen W, Sandler RS, et al. Evaluation of gastrointestinal patient reported outcomes measurement information system (GI-PROMIS) Symptom scales in subjects with inflammatory bowel Diseases. Am J Gastroenterol. 2018;113(1):72–9.

    Article  PubMed  Google Scholar 

  15. Santosa KB, Qi J, Kim HM, Hamill JB, Wilkins EG, Pusic AL. Long-term patient-reported outcomes in postmastectomy breast reconstruction. JAMA Surg. 2018;153(10):891–9.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Campbell R, Ju A, King MT, Rutherford C. Perceived benefits and limitations of using patient-reported outcome measures in clinical practice with individual patients: a systematic review of qualitative studies. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation. 2022;31(6):1597–620.

    Article  PubMed  Google Scholar 

  17. Chen J, Ou L, Hollis SJ. A systematic review of the impact of routine collection of patient reported outcome measures on patients, providers and health organisations in an oncologic setting. BMC Health Serv Res. 2013;13: 211.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Valderas JM, Kotzeva A, Espallargues M, Guyatt G, Ferrans CE, Halyard MY, et al. The impact of measuring patient-reported outcomes in clinical practice: a systematic review of the literature. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation. 2008;17(2):179–93.

    Article  CAS  PubMed  Google Scholar 

  19. Ishaque S, Karnon J, Chen G, Nair R, Salter AB. A systematic review of randomised controlled trials evaluating the use of patient-reported outcome measures (PROMs). Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation. 2019;28(3):567–92.

    Article  CAS  PubMed  Google Scholar 

  20. Schönfeld P, Brailovskaia J, Bieda A, Zhang XC, Margraf J. The effects of daily stress on positive and negative mental health: Mediation through self-efficacy. International journal of clinical and health psychology : IJCHP. 2016;16(1):1–10.

    Article  PubMed  Google Scholar 

  21. Seligman ME, Csikszentmihalyi M. Positive psychology. An introduction. The American psychologist. 2000;55(1):5–14.

    Article  CAS  PubMed  Google Scholar 

  22. Sheldon KM, King L. Why positive psychology is necessary. Am Psychol. 2001;56(3):216–7.

    Article  CAS  PubMed  Google Scholar 

  23. Keyes CL. Promoting and protecting mental health as flourishing: a complementary strategy for improving national mental health. Am Psychol. 2007;62(2):95–108.

    Article  PubMed  Google Scholar 

  24. Luo YH, Xia W, He XL, Zhang JP, Li HCW. Psychological interventions for enhancing resilience in parents of children with cancer: a systematic review and meta-analysis. Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer. 2021;29(11):7101–10.

    Article  PubMed  Google Scholar 

  25. Finck C, Barradas S, Zenger M, Hinz A. Quality of life in breast cancer patients: Associations with optimism and social support. International journal of clinical and health psychology : IJCHP. 2018;18(1):27–34.

    Article  PubMed  Google Scholar 

  26. Matzka M, Köck-Hódi S, Jahn P, Mayer H. Relationship among symptom clusters, quality of life, and treatment-specific optimism in patients with cancer. Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer. 2018;26(8):2685–93.

    Article  PubMed  Google Scholar 

  27. Müller R, Gertz KJ, Molton IR, Terrill AL, Bombardier CH, Ehde DM, et al. Effects of a tailored positive psychology intervention on well-being and pain in individuals with chronic pain and a physical disability: A feasibility trial. Clin J Pain. 2016;32(1):32–44.

    Article  PubMed  Google Scholar 

  28. Boehm JK, Kubzansky LD. The heart’s content: the association between positive psychological well-being and cardiovascular health. Psychol Bull. 2012;138(4):655–91.

    Article  PubMed  Google Scholar 

  29. Wang A, Rand K, Yang Z, Brooks R, Busschbach J. The remarkably frequent use of EQ-5D in non-economic research. Eur J Health Econ. 2022;23(6):1007–14.

    Article  PubMed  Google Scholar 

  30. Weng G, Hong Y, Luo N, Mukuria C, Jiang J, Yang Z, et al. Comparing EQ-5D-3L and EQ-5D-5L in measuring the HRQoL burden of 4 health conditions in China. Eur J Health Econ. 2023;24(2):197–207.

    Article  PubMed  Google Scholar 

  31. Peasgood T, Mukuria C, Brazier J, Marten O, Kreimeier S, Luo N, et al. Developing a new generic health and wellbeing measure: psychometric survey results for the EQ-HWB. Value Health. 2022;25(4):525–33.

    Article  PubMed  Google Scholar 

  32. Kwon JY, Cuthbertson L, Sawatzky R. The use of generic patient-reported outcome measures in emergency department surveys: discriminant validity evidence for the Veterans RAND 12-Item Health Survey and the EQ-5D. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. 2022;25(12):1939–46.

    Article  PubMed  Google Scholar 

  33. Brooks R. EuroQol: the current state of play. Health Policy. 1996;37(1):53–72.

    Article  CAS  PubMed  Google Scholar 

  34. EuroQol Group. EuroQol--a new facility for the measurement of health-related quality of life. Health policy (Amsterdam, Netherlands). 1990;16(3):199–208.

  35. Luo N, Liu G, Li M, Guan H, Jin X, Rand-Hendriksen K. Estimating an EQ-5D-5L Value Set for China. Value Health. 2017;20(4):662–9.

    Article  PubMed  Google Scholar 

  36. Ng Fat L, Scholes S, Boniface S, Mindell J, Stewart-Brown S. Evaluating and establishing national norms for mental wellbeing using the short Warwick-Edinburgh Mental Well-being Scale (SWEMWBS): findings from the Health Survey for England. Qual Life Res. 2017;26(5):1129–44.

    Article  PubMed  Google Scholar 

  37. Stewart-Brown S, Tennant A, Tennant R, Platt S, Parkinson J, Weich S. Internal construct validity of the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS): a Rasch analysis using data from the Scottish Health Education Population Survey. Health Qual Life Outcomes. 2009;7: 15.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Melendez-Torres GJ, Hewitt G, Hallingberg B, Anthony R, Collishaw S, Hall J, et al. Measurement invariance properties and external construct validity of the short Warwick-Edinburgh mental wellbeing scale in a large national sample of secondary school students in Wales. Health Qual Life Outcomes. 2019;17(1):139.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dong A, Chen X, Zhu L, Shi L, Cai Y, Shi B, et al. Translation and validation of a Chinese version of the Warwick-Edinburgh mental well-being scale with undergraduate nursing trainees. J Psychiatr Ment Health Nurs. 2016;23(9–10):554–60.

    Article  CAS  PubMed  Google Scholar 

  40. Ng SS, Lo AW, Leung TK, Chan FS, Wong AT, Lam RW, et al. Translation and validation of the Chinese version of the short Warwick-Edinburgh mental well-being scale for patients with mental illness in Hong Kong. East Asian Arch Psychiatry. 2014;24(1):3–9.

    CAS  PubMed  Google Scholar 

  41. Sun Y, Luk TT, Wang MP, Shen C, Ho SY, Viswanath K, et al. The reliability and validity of the Chinese Short Warwick-Edinburgh mental well-being scale in the general population of Hong Kong. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation. 2019;28(10):2813–20.

    Article  PubMed  Google Scholar 

  42. Fung SF. Psychometric evaluation of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) with Chinese University Students. Health Qual Life Outcomes. 2019;17(1):46.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Terwee CB, Bot SD, de Boer MR, van der Windt DA, Knol DL, Dekker J, et al. Quality criteria were proposed for measurement properties of health status questionnaires. J Clin Epidemiol. 2007;60(1):34–42.

    Article  PubMed  Google Scholar 

  44. Ferchiou A, Todorov L, Lajnef M, Baudin G, Pignon B, Richard JR, et al. Schizotypal Personality Questionnaire-Brief - Likert format: Factor structure analysis in general population in France. L’Encephale. 2017;43(6):558–63.

    Article  CAS  PubMed  Google Scholar 

  45. Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi medical journal : the journal of Medical Association of Malawi. 2012;24(3):69–71.

    CAS  PubMed  Google Scholar 

  46. Overholser BR, Sowinski KM. Biostatistics primer: part 2. Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition. 2008;23(1):76–84.

    Article  PubMed  Google Scholar 

  47. Schober P, Boer C, Schwarte LA. Correlation Coefficients: Appropriate Use and Interpretation. Anesth Analg. 2018;126(5):1763–8.

    Article  PubMed  Google Scholar 

  48. Snyder CF, Aaronson NK. Use of patient-reported outcomes in clinical practice. Lancet (London, England). 2009;374(9687):369–70.

    Article  PubMed  Google Scholar 

  49. Rotenstein LS, Huckman RS, Wagle NW. Making patients and doctors happier - the potential of patient-reported outcomes. N Engl J Med. 2017;377(14):1309–12.

    Article  PubMed  Google Scholar 

  50. Keyes CL. Mental illness and/or mental health? Investigating axioms of the complete state model of health. J Consult Clin Psychol. 2005;73(3):539–48.

    Article  PubMed  Google Scholar 

  51. Gison A, Rizza F, Bonassi S, Donati V, Giaquinto S. Effects of dispositional optimism on quality of life, emotional distress and disability in Parkinson’s disease outpatients under rehabilitation. Funct Neurol. 2015;30(2):105–11.

    PubMed  PubMed Central  Google Scholar 

  52. Diener E, Seligman ME. Very happy people. Psychol Sci. 2002;13(1):81–4.

    Article  PubMed  Google Scholar 

  53. Steptoe A, Deaton A, Stone AA. Subjective wellbeing, health, and ageing. Lancet (London, England). 2015;385(9968):640–8.

    Article  PubMed  Google Scholar 

  54. Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care. 2003;41(11):1284–92.

    Article  PubMed  Google Scholar 

  55. Kroenke K, Spitzer RL, Williams JB. The PHQ-15: validity of a new measure for evaluating the severity of somatic symptoms. Psychosom Med. 2002;64(2):258–66.

    Article  PubMed  Google Scholar 

  56. Lyubomirsky S, King L, Diener E. The benefits of frequent positive affect: does happiness lead to success? Psychol Bull. 2005;131(6):803–55.

    Article  PubMed  Google Scholar 

  57. Taylor SE, Lerner JS, Sage RM, Lehman BJ, Seeman TE. Early environment, emotions, responses to stress, and health. J Pers. 2004;72(6):1365–93.

    Article  PubMed  Google Scholar 

  58. Mojtabai R. National trends in mental health disability, 1997–2009. Am J Public Health. 2011;101(11):2156–63.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Linde JA, Jeffery RW, Levy RL, Sherwood NE, Utter J, Pronk NP, et al. Binge eating disorder, weight control self-efficacy, and depression in overweight men and women. International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity. 2004;28(3):418–25.

    Article  CAS  PubMed  Google Scholar 

  60. Steptoe A, Wardle J. Positive affect and biological function in everyday life. Neurobiol Aging. 2005;26 Suppl 1:108–12.

    Article  PubMed  Google Scholar 

  61. Tindle HA, Chang YF, Kuller LH, Manson JE, Robinson JG, Rosal MC, et al. Optimism, cynical hostility, and incident coronary heart disease and mortality in the Women’s Health Initiative. Circulation. 2009;120(8):656–62.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Kubzansky LD, Huffman JC, Boehm JK, Hernandez R, Kim ES, Koga HK, et al. Positive Psychological Well-Being and Cardiovascular Disease: JACC Health Promotion Series. J Am Coll Cardiol. 2018;72(12):1382–96.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Hong Y, Jiang X, Zhang T, Luo N, Yang Z. Examining the relationship between the Short Warwick-Edinburgh Mental Well-being Scale (SWEMWBS) and EQ-5D-5L and comparing their psychometric properties. Health Qual Life Outcomes. 2023;21(1):25.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Böhnke JR, Croudace TJ. Calibrating well-being, quality of life and common mental disorder items: psychometric epidemiology in public mental health research. Br J Psychiatry. 2016;209(2):162–8.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Tennant R, Hiller L, Fishwick R, Platt S, Joseph S, Weich S, et al. The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): development and UK validation. Health Qual Life Outcomes. 2007;5:63.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Jiang J, Hong Y, Zhang T, Yang Z, Lin T, Liang Z, et al. Comparing the measurement properties of the EQ-5D-5L and the EQ-5D-3L in hypertensive patients living in rural China. Qual Life Res. 2021;30(7):2045–60.

    Article  PubMed  Google Scholar 

  67. Luo N, Wang Y, How CH, Wong KY, Shen L, Tay EG, et al. Cross-cultural measurement equivalence of the EQ-5D-5L items for English-speaking Asians in Singapore. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation. 2015;24(6):1565–74.

    Article  CAS  PubMed  Google Scholar 

  68. Patrick DL, Burke LB, Gwaltney CJ, Leidy NK, Martin ML, Molsen E, et al. Content validity–establishing and reporting the evidence in newly developed patient-reported outcomes (PRO) instruments for medical product evaluation: ISPOR PRO good research practices task force report: part 1–eliciting concepts for a new PRO instrument. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. 2011;14(8):967–77.

    Article  PubMed  Google Scholar 

  69. Reeve BB, Hays RD, Bjorner JB, Cook KF, Crane PK, Teresi JA, et al. Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Med Care. 2007;45(5 Suppl 1):S22-31.

    Article  PubMed  Google Scholar 

  70. Hays RD, Morales LS, Reise SP. Item response theory and health outcomes measurement in the 21st century. Medical care. 2000;38(9 Suppl):Ii28-42.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Bowling A. Mode of questionnaire administration can have serious effects on data quality. J Public Health (Oxf). 2005;27(3):281–91.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank the EuroQol Research Foundation to fund this study (Grant number: 20180600R1, 353RA ) and the E-QALY team to provide support for this project.

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Authors and Affiliations

  1. Health Services Management School, Guizhou Medical University, Guiyang, 550002, People’s Republic of China

    Zhihao Yang

  2. Medical Psychiatry and Psychotherapy, Erasmus Medical Center, Rotterdam, 3015CN , the Netherlands

    Zhihao Yang

  3. Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

    Nan Luo

  4. The Third Affiliated Hospital of Sun Yat-Sen University, The Third Affiliated Hospital of Sun Yat-Sen University, 600, Tianhe RoadTianhe District, Guangzhou, 510630, People’s Republic of China

    Yanming Hong

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Contributions

Zhihao Yang, Nan Luo, and Yanming Hong contributed to data acquisition. Zhihao Yang and Yanming Hong analyzed the data and draft the manuscript. Nan Luo revised the manuscript critically for important intellectual content. Zhihao Yang and Yanming Hong contributed to the conception, design, and the interpretation of the results. All authors reviewed and approved the final version. Zhihao Yang and Yanming Hong is the study guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

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The online survey was approved by the Ethics Committee of the University of Sheffield, United Kingdom (Approval letter number 025524), and the IRB of Jinan University, China (Approval letter number JNUKY-2020–001). The face-to-face study was approved by the Ethics Committee of Guizhou Medical University (Approval letter number 2021–101). Written informed consent was obtained from all participants.

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Appendix

Appendix

See Table 7, Table 8 and Table 9.

Table 7 Ordered logistic multivariable regression analysis with EQ-5D-5L dimensions as dependent variables when the data from online survey and face-to-face survey are analyzed separately
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Table 8 Multiple linear regression analysis with EQ-5D-5L Utility scores and EQ-VAS as dependent variables when the data from online survey and face-to-face survey are analyzed separately
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Table 9 Multiple lineal regression analysis with SWEMWBS scores as dependent variables
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Yang, Z., Luo, N. & Hong, Y. The effect of positive mental well-being on patient reported outcome (PRO): finding from a cross-sectional multi-disease study in China. Health Qual Life Outcomes 22, 100 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12955-024-02314-2

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Health and Quality of Life Outcomes

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