The relationship between psychosocial behavior and the quality of life of male gout patients in southwest china: a cross-sectional study based on an information-motivation-behavioral skills model

doi:10.21203/rs.3.rs-3132546/v1

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Research Article

The relationship between psychosocial behavior and the quality of life of male gout patients in southwest china: a cross-sectional study based on an information-motivation-behavioral skills model

https://doi.org/10.21203/rs.3.rs-3132546/v1

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Background

The health-related quality of life (HRQoL) of gout patients is poor, and studies have shown that providing psychosocial behaviors interventions can improve the HRQoL of gout patients, but there is no cross-sectional study on whether psychosocial behaviors affect the HRQoL of gout patients.

Methods

For this cross-sectional study, 225 male patients with gout were enrolled. HRQoL was assessed using the gout impact scale, and the gout knowledge questionnaire was used to assess patients´ degree of knowledge about the disease. The positive psycap questionnaire was used to assess positive psychological capacity and finally, the gout patient self-management assessment scale was used to assess self-management behaviors. Multiple linear regression was used to analyze the factors affecting HRQoL in gout patients.

Results

The overall mean gout impact scale score was 52.7 (maximum possible = 100). Factors associated with the total gout impact scale score were tophi (β = 4.746), pain intensity (β = 1.294), and resiliency (β=-0.559). In addition, demographic characteristics (education level, smoking and marital status), clinical characteristics (tophi, pain intensity, number of attacks over 6 months, and number of affected joints) and psychosocial behavior variables (resiliency, hope, disease treatment management, diet management) were associated with several dimensions of the gout impact scale.

Conclusions

The HRQoL of male gout patients in Southwest China was assessed as at a medium level. We found that clinical characteristics and psychosocial behaviors are important factors affecting the HRQoL in men with gout, and should be the focus for interventions.

Gout

Health-related quality of life

Gout impact scale

Positive PsyCap

Self-management

Gout is a metabolic rheumatic disease caused by the deposition of urate crystals in the joints, tendons and other tissues due to continuously increasing serum uric acid levels^[1]. Worldwide, the prevalence of gout ranges from < 1–6.8% and and is increasing annually^[1]. The Global Burden of Disease Study 2017 (GBD 2017) reported that the prevalence and incidence of gout in China increased by 6.88% and 6.16%, respectively, between 1990 and 2017^[2]. Although the incidence of gout is rising both globally and in China, the rate of change in Chinese men is greater than at global levels^[2]. Gout has had negative effects on the physical, psychological and social life aspects of patients^[3], with poor health-related quality of life (HRQoL) lower in male gout patients^[4]. There is evidence that HRQoL in gout patients is associated with demographic and clinical characteristics and unhealthy emotions^[3–5].

The information-motivation-behavioral skills model (IMB) is the classical theory of behavior change. The IMB indicates that information, motivation, and behavioral skills are the core elements of behavior building, behavior change needs to be based on disease-related knowledge or information, with individuals having positive psychology and motivation, as well as objective skills to establish improved behavior ^[6]. Recent studies have confirmed that IMB-based interventions can improve HRQoL under chronic conditions^{[7, 8]}. Doherty et al.^[9] also reported improved HRQoL of gout patients by providing information support interventions. However, to the best of our knowledge, current studies of HRQoL in gout patients have not considered the factors affecting information, motivation, and behavioral skills. Therefore, the present study aims to correspond the level of gout knowledge, psychological capital and gout self-management behavior to the three elements of the information, motivation and behavior of IBM, and analyze the relationship between psychosocial behavioral factors and the HRQoL of male gout patients from the perspective of IMB, in order to provide a basis for developing intervention programs to improve their HQoL.

Study design and participants

This study was a cross-sectional survey, following the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) principle.

The male gout participants were recruited from an outpatient clinic at a hospital in Southwest China between February and December 2021. Participants were included if (1) they were aged ≥18 years and fulfilled the 2015 Gout classification criteria suggested by the American College of Rheumatology/European League Against Rheumatism collaborative initiative^[10]; (2) they understood their disease status and volunteered to participate in this study; and (3) they were able to understand and complete the questionnaire. Participants who had other diseases, such as cognitive dysfunction or mental illness, were excluded from the study.

This study complied with the ethical guidelines of the 1975 Declaration of Helsinki, and ethical approval was obtained from the Medical Ethics Committee in 2020 (ID: 2020898). All participants were referred by a rheumatologist to participate in a 24-week self-management research project for patients with gout. Participants were first verbally informed about the research project, and if they showed interest, trained researchers informed participants of the purpose and the voluntary, anonymous nature of the study. All participants signed an informed consent form and they were asked to complete the questionnaire independently and were encouraged to seek help when needed.

The sample size of this study was estimated to be 5-10 times the number of variables according to the Kendall principle^[11]. There were 27 independent variables and dimensions in this study, and considering that 20% of the invalid questionnaires were included, the estimated sample size required was 169-338 cases.

Data collection

Health-related quality of life

The gout impact scale (GIS) was used to assess the HRQoL of the participants. The GIS compiled by Hirsch et al^[12] , and sinicized by Li et al.^[13]in 2019 and its reliability and validity verified for use to assess HRQoL in patients with acute and chronic gout. The Chinese version of the GIS consists of 3 subscales, with 24 entries. It includes five dimensions, as follows: gout concern overall, gout medication side effects, unmet gout treatment requirements, gout concern during attacks, and well-being during attacks^[13]. The total scale is the average score of each item, with a full score of 100. Higher scores indicate a greater influence of disease and lower HRQoL^[13]. The Cronbach's α of the GIS in this study was 0.900.

Gout knowledge level

The gout knowledge questionnaire (GKQ) was used to measure awareness in the participants. ThevGKQ was developed by Zhang et al.^[14]in 2011 and included 10 test questions with a total score of 0 to 10 points. Higher scores indicate a higher level of knowledge about the disease. The GKQ's Flesch‒Kincaid grade level is 4.7, and the Flesch reading ease is 81.4%^[14].

Positive psychological capacity

A positive psycap questionnaire (PPQ) was used to evaluate the participants' psychological capital(PsyCap). The PPQ was compiled by Luthans et al.^[15] in 2007, and Zhang et al.^[16]standardized and verified its reliability and validity. The Chinese version of the PPQ has four dimensions, namely, self-efficacy, resiliency, optimism and hope^[16]. PPQ scores ranged from 26 to 182, with higher scores representing more positive psychology^[16]. The Cronbach's α of the PPQ in this study was 0.942.

Self-management behaviors

The gout patient self-management assessment scale (GPSAS) was developed by Yao et al.^[17]in 2020, based on Lorig's self-management theory. It was verified that it possessed good reliability and validity in China. The scale includes the four dimensions disease treatment management, diet management, lifestyle management, and psychosocial management^[17]. The total GPSAS score was 41 to 205, with higher scores indicating better self-management behavior^[17]. The Cronbach's α of the GPSAS in this study was 0.923.

Demographic and clinical characteristics of the participants

Demographic variables that were assessed included age, body mass index (BMI), marital status, education level, employment status, per capita monthly family income, gout-related expenses in the past year, and smoking and drinking status. Clinical characteristics included duration of symptoms, family history of gout, comorbidities, serum uric acid (SUA) levels in the mot recent month, urate-lowering therapy (ULT), tophi, and pain [measured by a 10 cm horizontal visual analog scale (VAS) with a higher score indicating a higher level of pain], number of attacks over 6 months and the number of affected joints.

Statistical analysis

Data analysis was conducted using SPSS (version 25.0, IBM Corp). Suspicious errors, missing values, and outliers were filtered. The frequency and composition ratio of the categorical variables were selected, and the continuous variables underwent descriptive statistical analysis by using the median (interquartile range, IQR) or the mean ± standard deviation (SD). Independent sample t tests, one-way ANOVA and correlation were used to analyze the relationship of GIS or dimensions with other variables. Variables with P <0.1 in univariate analysis were selected for multilinear stepwise regression influencing factors influencing GIS or dimensions. The two-sided test level was α=0.05, with P≤0.05 indicating statistical significance.

The demographic and clinical characteristics of the patients are listed in Table 1(additional files). A total of 225 participants was included in this study, aged between 18 and 75 years, mean 40.44±12.64 years. The mean BMI was 26.07±3.26 kg/m^2,and SUA was 480.45±129.41 µmol/L. The average pain score was 5 points. Among the participants, 76.44% were married, 80.44% were in employment, 26.67% had tophi, and 46.67% had comorbidities.

The total GIS score was between 14.58 and 94.79, with an average of 52.7±15.3, and each dimension score from high to low were Gout concern overall（81.4±17.3）, Gout medication side effects（66.6±21.3）, Gout concern during attack（61.8±21.1）, Unmet gout treatment need（46.8±16.1）, and Well-being during attack（38.1±23.4）. The average GKQ score of the participants was 6.12±2.10. The PPQ and its four dimensions were scored as 132.95±23.58, 35.6±7.1, 33.3±7.7, 32.0±6.2, and 32.1±6.5, respectively. The GPSAS and its four dimensions scored were 144.86±26.91, 49.1±10.8, 41.8±9.2, 28.1±7.9, and 25.9±5.8, respectively.

The results of the univariate analysis affecting the GIS or scores in each dimension are shown in Tables 1 and 2(additional files). Factors associated with the total GIS score included age (P=0.008), marital status (P=0.028), education level (P=0.038), BMI (P=0.031), SUA (P=0.042), symptom duration (P=0.010), number of attacks over half a year (P=0.003), number of affected joints (P=0.013), pain intensity (P<0.001), tophi (P=0.006), diet management (P=0.035), lifestyle management (P=0.005), psychosocial management (P=0.007), and resiliency (P<0.001). The following factors were associated with one or more of the five dimensions of the GIS. Demographic factors included age, marital status, employment status, BMI, smoking, and drinking. Clinical factors included symptom duration, SUA, number of attacks over half a year, number of affected joints, ULT, pain intensity, and tophi. Psychological behavioral characteristics included self-efficacy, resiliency, hope, disease treatment management, diet management, lifestyle management and psychosocial management. However, no relationships were found between per capita monthly family income, gout-related expenses in the past year, comorbidity, family history or GKQ scores with GIS or each GIS dimension score.

The results of multiple linear regression analysis are shown in Table 3. Pain intensity (β=0.255) with tophi (β=0.138) was positively correlated with the total GIS score, whereas resiliency (β=-0.282) was negatively associated with the total GIS score (R²=0.312, indicating that approximately 31.2% of the changes in the GIS could be explained by these variables). Hope (β=0.198) and tophi (β=0.178) were positively correlated with the dimension of gout concern overall, while the resiliency (β=-0.219) score was negatively associated with this dimension (R²= 0.191, indicating that an approximately 19.1% change in this dimension score can be explained by these variables). Pain intensity (β=0.142) was positively correlated with the dimension of gout medication side effects, while resiliency (β=-0.220) was negatively correlated with it (R²=0.101, indicating that approximately 10.1% of the change in this dimension score can be explained by these variables). The number of affected joints (β=0.240), education level (β=0.235), and number of attacks over half a year (β=0.155) were all positively correlated with the dimension of unmet gout treatment need, while smoking (β=-0.178) and disease treatment management (β=-0.178) were negatively correlated with this (R²=0.171, indicating that an approximately 17.1% change in this dimension score can be explained by these variables). The number of attacks over half a year (β=0.146), pain intensity (β=0.141), smoking status (β=0.131), and tophi (β=0.140) were all positively associated with the dimension of gout concern during attacks, and marital status (β=-0.168) and diet management (β=-0.138) were negatively associated (R²=0.178, indicating that a 17.8% change in this dimension score can be explained by these variables). Finally, pain intensity was positively correlated with the dimension of well-being during attacks (β=0.271), while resiliency (β= -0.325) was negatively correlated (R²=0.283, indicating that an approximately 28.3% change in this dimension score can be explained by these variables.

In the present study, we assessed the HRQoL in 225 Chinese male gout patients using the GIS. The participants had a GIS score of 52.7 ± 15.3, which represents a moderate disease level, slightly better than in the previous study of Zhou et al.^[18] (56.79 ± 15.45). It may be relevant for explaining this difference that the participants had different regional and demographic characteristics^[18]. The dimension with the highest participant score was gout concern overall (81.4 ± 17.3), meaning that thís dimension was the most influential, whereas the least influential dimension was well-being during attacks (38.1 ± 23.4). The ranking of the five dimensions found here is consistent with that reported by Zhou et al^[18]. We found that among the total GIS score and the five GIS dimensions, meaningful demographic variables included marital status, education level, and smoking; clinical characteristics included the number of attacks over half a year, the number of affected joints, pain intensity and tophi; and finally, social and behavioral variables included resiliency and hope, disease treatment management and diet management.

Previous studies^{[3, 5, 18]} reported that the effect of marital status on HRQoL in gout patients is inconsistent. Chandratre et al.^[3] found that married people had better HRQoL than single people. However, this was not the case for Chinese gout patients^{[5, 18]}. The present study found no statistically significant effect of marital status on the total GIS score, but further analysis suggested that married people had lower scores for gout concern during attacks than singles (β=-8.318), i.e. single people have more concerns during gout attacks than married people. During gout attacks, patients not only need to endure severe pain but also face changes in physical movement barriers, medical treatment, work, life and social interaction^{[19, 20]}. Patients with gout have an increased need for family and intimate relationship support^[19], while these needs in single people are less likely to be met. Therefore, healthcare providers should focus on single patients and assist them in predesigning responses to attacks of gout to mitigate their impact.

Previous studies have not found that education level is associated with HRQoL in gout patients, although it clearly does in other chronic diseases^{[21, 22]}. Interestingly, the present study found that the educational level of gout patients did have a significant impact on the GIS dimension of unmet gout treatment need (β = 1.795). This finding is not in agreement with some other studies^{[21, 22]}. A possible reason for this is that people with high educational levels may have access to better health-related knowledge and skills^{[23, 24]}, while information may have a negative impact on healthcare utilization^[25]. Therefore, health care providers are advised to guide gout patients to correctly access and utilize information resources to best meet their treatment needs.

The relationship between smoking and gout is also inconclusive^{[26, 27]}. Here, we found that smokers scored higher for the GIS dimension of gout concern during attacks (β = 5.792) but lower in the dimension of unmet gout treatment need (β=-6.024). Studies have shown that the effect of smoking on quality of life is related to the number of cigarettes smoked^[28], duration of the habit^[29], and age of the smokers^[30]. Currently, the effect of smoking on HRQoL and its mechanisms in gout patients still needs further investigation.

Clinical characteristics are important factors affecting HRQoL in gout patients. Those found to affect HRQoL in gout patients in the present study are similar to those found in previous studies^{[5, 31, 32]}. Clinical features associated with the total GIS score included tophi (β = 4.746) and pain intensity (β = 1.294). In addition, tophi also affects the two GIS dimensions of gout concern overall (β = 6.965, p = 0.010) and gout concern during attacks (β = 6.649). Tophi is a specific sign of chronic gout, which not only causes changes in body structure and joint movement restrictions^[33] but also exerts negative effects on psychological and social participation, and increases the burden to health care systems^[31]. Treatment to target (T2T) is an effective way to prevent and reduce tophi^[34]. Therefore, the implementation of standard treatment should be considered in the management of gout patients.

Studies^{[4, 5]} found that pain was a predictor of low HRQoL in the Chinese gout population. The correlation of pain with GIS and its different dimensions was also reported by Pao et al^[35]. Our study focused on the effect of the intensity of pain on gout patients. We found that pain intensity affected the total GIS score and the three dimensions of well-being during attacks (β = 2.108), gout medication side effects (β = 1.005) and gout concern during attacks (β = 0.989). As one of the indicators of the patient-reported outcome (PRO) of gout^[36], pain is the main symptom in acute gout attacks; it is also an important feature of chronic gout, which is also the most direct impact and the main cause of seeking medical treatment in gout patients^[36]. Moreover, pain carries a burden of treatment, decreased mobility, productivity and mood^[37]. Therefore, health care providers should take measures to actively control inflammation to reduce pain.

The two variables, the number of gout attacks and the number of affected joints, were not significantly associated with the GIS total score but were associated with some GIS dimensions. Gout attacks are one of the main reasons why patients seek medical treatment^[37], and they often present with severe joint swelling and pain, having serious impacts on daily life^{[19, 37]}. Frequent attacks and more joints affected mean that treatment is not satisfactory. While actively controlling acute attacks, medical staff should also pay attention to the standard treatment and T2T of gout to reduce the number of acute attacks and the number of affected joints and reduce the impact of the disease.

Resiliency is the ability of individuals to adapt to change in an appropriate and lasting manner, and their ability to choose the best way to address challenges in the face of adversity to maintain physical and mental health^[38]. Previous studies have found that psychological capital plays a protective role for maintaining the quality of life^[39]. We found that in all dimensions of the PPQ, psychological resiliency was negatively correlated with the total GIS score (β= -0.559), gout concern overall (β= -0.491), gout medication side effects (β= -0.608), and well-being during attacks (β= -0.984). this implies that the higher the level of resiliency, the less affected the gout-associated quality of life. People with high resiliency have a more positive adaptation^[38] and more willpower^[39] to more effectively accept and adapt to psychological impairment^[39] and manage the effects of the disease. Therefore, it is recommended that health care personnel conduct active psychological interventions to improve the level of resilience of gout patients to help them improve their quality of life. Hope refers to the individual's belief in achieving the goal and the power to adjust the path accordingly ^[39]. Hope is an important psychological and spiritual resource that can protect against perceived stress, and people with higher hope levels can adopt a more positive coping approach^{[40, 41]}. We found that hope was positively correlated with the score for the GIS dimension Gout concern overall (β = 0.532), meaning that hope has a negative impact on the quality of life of this dimension. This result differs from previous studies^{[40, 42]}, and the reasons for this phenomenon need to be further investigated.

Studies have confirmed the benefit of self-management interventions in improving the quality of life of patients with chronic diseases^{[43, 44]}. The present study found that in all GPSAS dimensions, only disease treatment management was negatively correlated with unmet gout treatment need (β= -0.265), and diet management was negatively correlated with gout concern during attacks (β= -0.377). This implies that active disease treatment and diet management can help to improve the HRQoL in gout patients. Such results are similar to those of Quon et al.^[44]. Hence, the present study supports a relationship between self-management behaviors and the HRQoL of gout patients, suggesting that the HRQoL can be improved through aggressive self-management interventions.

Although existing studies confirm that the level of disease knowledge is closely related to quality of life^[45], the present study did not confirm this association, which requires further investigation.

Limitations

This study has some deficiencies. First, it is a cross-sectional study that may be limited in explaining causality. Second, we did not recruit healthy controls synchronously, weakening the strength of the conclusions. Again, we did not follow up with the patients due to time and manpower limitations.

In the present study, we based on the IMB model and considered the relationship of psychosocial behaviors with the quality of life of patients with gout. In conclusion, the HRQoL of male gout patients in Southwest China was moderately affected. Demographic factors, clinical characteristics, and psychosocial and behavioral characteristics can influence HRQoL in gout patients. The findings suggest that healthcare providers should intervene to actively control clinical symptoms, improve psychological resilience, and improve self-management behaviors in order to improve the quality of life of patients with gout and reduce its impact.

HRQoL: health-related quality of life

GIS:gout impact scale

IMB:information-motivation-behavioral skills model (IMB)

GKQ:gout knowledge questionnaire

PPQ:positive psycap questionnaire

PsyCap:psychological capital

GPSAS: gout patient self-management assessment scale

SUA: serum uric acid

ULT: urate-lowering therapy

Ethics approval and consent to participate

The data used in our research is secondary data. The data from the self-management programs for gout patients already obtained the ethical approval and informed consent and was approved by research ethics committees of West China hospital (ID: 2020898).

Consent for publication

Not Applicable.

Availability of data and materials

Data used in the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that there is no conflict of interest.

Funding

This study was supported by the SCST (Science and Technology of Sichuan Province) (Grant No.2018FZ0110)

Authors' contributions

YW and HC was responsible for the conception and design of the study, planning, and drafted, the manuscript. YW, BC carried out the statistical analysis. YW, YLC, XG and LM participated in the initiation implementation and data collection. HC and HL helped in revising the manuscript, and all authors reviewed the final manuscript.

Acknowledgements

The authors acknowledge gout patients who were involved in this study for providing feedback and data.The authors would like to express their gratitude to EditSprings (https://www.editsprings.cn ) for the expert linguistic services provided.

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Table 1 is available in the Supplementary Files section.

Table 2 Levels of GKQ, PPQ, GPSAS, and univariate analysis of GIS or each GIS dimension (N=225)

Variables	Description Mean±SD	Total GIS		Gout concern overall		Gout medication side effects		Unmet gout treatment need		Gout concern during attack		Well-being during attack
Variables	Description Mean±SD	r	p	r	p	r	p	r	p	r	p	r	p
GKQ	6.1±2.1	0.05	0.480	0.10	0.143	0.05	0.449	0.05	0.449	-0.03	0.679	0.03	0.693
PPQ	133.0±23.6	-0.16	0.018	0.04	0.570	-0.16	0.014	-0.16	0.014	-0.07	0.323	-0.19	0.004
Self efficacy	35.6±7.1	-0.09	0.177	0.04	0.586	-0.11	0.090	-0.11	0.090	-0.08	0.209	-0.10	0.143
Resiliency	33.3±7.7	-0.33	＜0.001	-0.11	0.090	-0.24	＜0.001	-0.24	＜0.001	-0.19	0.005	-0.33	＜0.001
Optimism	32.0±6.2	-0.03	0.674	0.10	0.132	-0.06	0.377	-0.06	0.377	0.06	0.412	-0.08	0.208
Hope	32.1±6.5	-0.06	0.398	0.14	0.040	-0.13	0.048	-0.13	0.048	0.02	0.753	-0.11	0.095
GPSAS	144.9±26.9	-0.16	0.014	0.04	0.561	-0.09	0.181	-0.09	0.181	-0.03	0.666	-0.18	0.006
Disease treatment management	49.1±10.8	-0.06	0.399	0.13	0.062	-0.07	0.289	-0.07	0.289	0.07	0.308	-0.09	0.175
Diet management	41.8±9.2	-0.14	0.035	0.02	0.739	-0.10	0.120	-0.10	0.120	-0.05	0.499	-0.14	0.034
Lifestyle management	28.1±7.9	-0.19	0.005	-0.08	0.216	0.04	0.571	0.04	0.571	-0.12	0.085	-0.19	0.004
Psychosocial management	25.9±5.8	-0.18	0.007	0.03	0.686	-0.17	0.010	-0.17	0.010	-0.03	0.631	-0.20	0.003

Note:GIS gout impact scale, GKQ gout knowledge questionnaire, PPQ positive psycap questionnaire, GPSAS gout patient self-management assessment scale, SD standard deviation

Table 3 Multiple linear regression of factors associated with GIS and GIS dimension scores

Predictor	B	SEE	Standardized coefficients (β)	t	p	95% CI	R²	Adjusted R²	F/P
GIS total
Pain（VAS）	1.294	0.327	0.255	3.957	＜0.001	0.653 ~ 1.935	0.312	0.259	F=5.894
Tophi	4.746	2.408	0.138	1.971	0.050	0.026 ~ 9.467			p＜0.001
Resiliency	-0.559	0.135	-0.282	-4.135	＜0.001	-0.824 ~ -0.294
Gout concern overall
Tophi	6.965	2.689	0.178	2.590	0.010	1.694 ~ 12.236	0.191	0.149	F=4.568
Resiliency	-0.491	0.175	-0.219	-2.801	0.006	-0.834 ~ -0.147			p＜0.001
Hope	0.532	0.225	0.198	2.363	0.019	0.091 ~ 0.973
Gout medication side effects
Pain（VAS）	1.005	0.460	0.142	2.187	0.030	0.104 ~ 1.906	0.101	0.071	F=3.464
Resiliency	-0.608	0.230	-0.220	-2.647	0.009	-1.058 ~ -0.158			p=0.002
Unmet gout treatment need
Educational level	2.657	0.734	0.235	3.622	＜0.001	1.211~4.103	0.171	0.152	F=9.050
Smoking status (reference: no)	-6.024	2.135	-0.178	-2.822	0.005	-10.231~-1.817			p＜0.001
Number of attacks over 6 months	1.629	0.721	0.155	2.260	0.025	0.208~3.050
Number of affected joints	3.007	0.881	0.240	3.413	0.001	1.271~4.744
Disease treatment management	-0.265	0.095	-0.178	-2.799	0.006	-0.452~-0.079
Gout concern during attack
Marital status (reference: single)	-8.318	3.186	-0.168	-2.610	0.010	-14.599~-2.038	0.178	0.155	F=7.848
Number of attacks over 6 months	2.010	0.955	0.146	2.104	0.037	0.127~3.893			p＜0.001
Pain（VAS）	0.989	0.469	0.141	2.110	0.036	0.065~1.912
Diet management	-0.377	0.171	-0.138	-2.210	0.028	-0.714~-0.041
Tophi	6.649	3.084	0.140	2.156	0.032	0.570~12.728
Smoking status (reference: no)	5.792	2.785	0.131	2.080	0.039	0.303~11.282
Well-being during attack
Pain（VAS）	2.108	0.502	0.271	4.202	＜0.001	1.125 ~ 3.092	0.283	0.235	F=5.924
Resiliency	-0.984	0.238	-0.325	-4.130	＜0.001	-1.451 ~ -0.517			p＜0.001

Note:GIS gout impact scale, SEE standard errors of estimation, VAS visual analog scale

No competing interests reported.

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The relationship between psychosocial behavior and the quality of life of male gout patients in southwest china: a cross-sectional study based on an information-motivation-behavioral skills model

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