INTRODUCTION
Hypertension represents a major global health challenge, affecting approximately 1.3 billion adults worldwide, with particularly high prevalence (59.2%) and alarmingly low control rates (14.6%) among older populations in China.1) This condition remains a leading modifiable risk factor for cardiovascular morbidity and mortality, with evidence supporting systolic blood pressure (SBP) targets below 130 mmHg for optimal cardiovascular and cognitive protection in high-risk older patients.2)
The clinical significance of hypertension is further compounded by its frequent comorbidity with chronic pain, which affects over 300 million individuals in China3) and demonstrates substantial overlap with hypertensive populations, with co-occurrence rates ranging from 44.3%4) to 86.5%5) across different clinical samples. This coexistence suggests potential pathophysiological interactions mediated through both neuroendocrine and psychological mechanisms.
From a pathophysiological point of view, existing studies suggest that a bidirectional link between hypertension and pain. Pain stimulates the sympathetic nervous system, leading to an increase in peripheral vascular resistance, heart rate and cardiac output, all of which contribute to an increase in blood pressure (BP). In addition, pain activates the hypothalamic-pituitary-adrenal axis, leading to the release of adrenocorticotropic hormone (ACTH) and catecholamines, which can further exacerbate elevated BP.6) Although hypertension has been shown to reduce pain perception by activating downstream inhibitory pathways via pressure receptors,7) the phenomenon known as “hypertension-related hyperalgesia” is typically observed only in response to acute pain. Patients with chronic pain often show reduced pressure reflex sensitivity, leading to increased BP fluctuations.8)
Despite these established physiological connections, the critical relationship between chronic pain and uncontrolled BP remains unexplored. There are many ways to measure chronic pain, one of which is the Body Pain Dimension of the SF-36,9) which we define separately as pain-related quality of life (pQOL). The pQOL quantifies pain's impact on daily function and was classified into four grades using a quartile grouping scheme: Q1 (0–25 points) represents pain that severely interferes with life, Q2 (26–50 points) is moderately interfering, Q3 (51–75 points) is mildly interfering, and Q4 (76–100 points) indicates that the pain is virtually non-interfering.
Poor medication adherence may be a putative risk factor for uncontrolled BP.10) Older hypertensives with chronic pain face compounded burdens: impaired physical fitness,11) diminished health-related QOL, and exacerbated negative self-perceptions of aging (SPA).12) Critically, SPA—one's beliefs about aging—not only correlates with QOL13) but also predicts medication adherence.14) Meanwhile, poor QOL independently undermines medication adherence,15) suggesting complex pathways between pQOL and BP. However, whether and how SPA and medication adherence mediate this relationship remains unknown, leaving a key psychological mechanism unaddressed in pain-hypertension management.
Thus, this study aimed to investigate the relationship between pQOL and uncontrolled BP in older hypertensive individuals and explore whether the relationship was mediated by SPA and medication adherence—a novel exploration of psychological-behavioral pathways in pain-hypertension association.
MATERIALS AND METHODS
Study Design and Setting
This survey was conducted across 15 urban community clinics and 22 rural village clinics in Suzhou, China. The present study is a cross-sectional investigation in which relevant data were collected through a questionnaire, without the implementation of any interventions. The study adhered to the principles outlined in the Declaration of Helsinki and received ethical approval by the Ethics Committee of Changshu No.1 People’s Hospital (No. 20160929). All participants signed informed consent forms before filling out the questionnaire.
Participants
We determined the required sample size based on the events per variable criterion, aiming for events per variable ≥10. With 23 candidate variables and an estimated event rate of 45%–55%, the minimum required sample size was 511; we enrolled 627 participants to account for potential attrition while maintaining adequate statistical power.16) During the data cleaning process, five samples were found to contain missing data, and after initial analysis, it was confirmed that these were completely random. Therefore, we chose to remove these samples containing missing data, and 622 complete data samples were used for the final analysis. Considering the small amount of missing data, the deletion process did not significantly affect the statistical efficacy of the results or the conclusions of the analyses. After obtaining informed consent, trained investigators invited patients to participate, ensuring accurate data collection and communication. For illiterate participants, questions were read aloud, and responses were recorded. A convenience sampling method was employed to select older hypertensive patients who met the inclusion criteria. The inclusion criteria were: (1) age ≥60 years; (2) diagnosed by a qualified healthcare provider with hypertension, SBP ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg, or using antihypertensive drugs (AHD)17); (3) able to communicate; (4) voluntary participation and informed consent; (5) a score for bodily pain >0 and <100. Patients who met the following conditions were excluded: (1) dementia or cognitive impairment; (2) malignancy-related pain (including active cancer, metastatic cancer and those undergoing anti-tumor therapy); and (3) pain of other clear etiologies (fracture within 3 months, active infection, severe osteoporosis with history of fracture, postoperative pain within 6 months).
Measures
This cross-sectional survey was conducted in multiple clinics in Suzhou, China, using self-administered questionnaires to collect socio-demographic and clinical data, self-reported pQOL, SPA, and medication adherence.
Exposure: Assessment of chronic pQOL
Chronic pQOL was assessed using the body pain dimension of the SF-36, a valid and reliable tool for evaluating bodily pain as part of overall health status.9) The Cronbach's alpha coefficient on the SF-36 scale for our sample was 0.74. The 2-item SF-36 bodily pain scale (BPS) measured pain intensity on a 6-point scale from “none” to “very severe,” and its impact on work on a 5-point scale from “not at all” to “extremely.” Responses were recoded and summed to calculate a raw scale score, which was then transformed to a 0–100 scale.18) We used the full SF-36 scale, which follows the official score conversion that a high score represents a better quality of life. Specific entries and scoring rules for pQOL can be found in Supplement A. All questionnaires were completed with the assistance of trained staff in a quiet clinic to minimize exposure measurement bias. Higher scores indicated less bodily pain. In this study, pQOL was classified into four grades using a quartile grouping scheme: Q1 (0–25 points) represents pain that severely interferes with life, Q2 (26–50 points) is moderately interfering, Q3 (51–75 points) is mildly interfering, and Q4 (76–100 points) indicates that the pain is virtually non-interfering. This classification corresponds exactly to the four-level pain classification system “none/mild/moderate/severe” commonly used in clinical practice. In particular, it should be noted that higher pQOL scores indicate less negative impact of pain on life.
Outcome: Uncontrolled BP
We deliberately framed our analysis around uncontrolled BP rather than BP control to align with clinical decision-making thresholds and enhance translational relevance for identifying high-risk patients. BP was measured using a calibrated Omron sphygmomanometer (Omron, Kyoto, Japan), with the arm positioned at heart level after 5 minutes of rest in a sitting position. Two measurements were taken 30–60 seconds apart, and a third measurement was taken if there was a significant difference. The average of the two or three measurements was used as the final BP value.19) SBP <140 mmHg was classified as “controlled” and ≥140 mmHg as “uncontrolled”; DBP <90 mmHg was classified as “controlled” and ≥90 mmHg as “uncontrolled”; SBP <140 mmHg and DBP <90 mmHg was classified as “controlled”, the rest were classified as “uncontrolled.”20)
Predictor: Self-perceptions of aging
The 32-item Aging Perceptions Questionnaire (APQ), developed by Barker et al.21) in 2007, was used to assess SPA across seven dimensions: timeline chronic, timeline cyclical, consequences (positive and negative), control (positive and negative), and emotional representations. These dimensions cover perceptions of aging as a continuous or cyclical process, beliefs about aging’s impact, control over aging, and emotional reactions, particularly negative ones. The Chinese version of the APQ (α=0.884) is reliable for assessing SPA in Chinese older adults. The test-retest reliability was considered good, as all intraclass correlation coefficients exceeded 0.4.22) Each dimension's score was based on the mean of relevant items, with higher scores indicating stronger endorsement of the perception.
Predictor: Medication adherence to AHD
Self-reported medication adherence was evaluated using the Chinese version of the 8-item Morisky Medication Adherence Scale (C-MMAS-8), including seven items with yes/no response options and one item with a 5-point Likert scale response option.23) Scores from all eight items were summed to produce an overall adherence score ranging from zero to eight, with higher scores indicating better adherence.23) The C-MMAS-8 showed good internal consistency (Cronbach's α=0.77) and test-retest reliability (r=0.88, p<0.001).24) This study focused solely on adherence to AHD. Adherence levels were categorized as high, medium, or low based on MMAS scores of 8, 6 to <8, and <6, respectively.25) While C-MMAS-8 scores were available for all participants (n=622), electronic medication records (EMR) showed significant missingness (n=196), including the number and frequency of AHDs taken. This reflects real-world challenges: self-medication with herbal therapies, fragmented EMR systems, and delays in prescription updates. However, our conclusions rely on C-MMAS-8 as a patient-centered adherence metric.
Potential Confounders: Demographic and Clinical Data
The demographic data included age, marital status, education level, living conditions, and smoking status. Clinical information included the duration of hypertension and the number of chronic diseases, which was collected through self-report. Measure the patient's height and weight and calculate body mass index (BMI) as weight divided by the square of height. Comorbidity refers to the presence of one or more additional diseases or clinical conditions unrelated to BP.26)
Statistical Analysis
The analysis of data was performed by using SPSS version 27.0 and PROCESS macro version 4.0 (https://www.processmacro.org/) for Windows. The pQOL scores were divided into four groups based on quartile.
Continuous variables were represented as mean±standard deviation and compared using ANOVA; categorical variables were represented as numbers and percentages and compared with the chi-square test. Potential covariates of pQOL were controlled. Binary logistic regression was used to evaluate the relationship between pQOL and uncontrolled SBP and DBP, adjusting for relevant covariates. Model 1 involved logistic regression of pQOL quartiles on uncontrolled BP. Model 2 extended Model 1 by including additional covariates, such as BMI, education level, duration of hypertension, presence of comorbidities, adherence to AHD, SPA total score, and marital status. Logistic regression models estimated odds ratios (ORs) with 95% confidence intervals (CIs), where OR >1 indicated higher prevalence of uncontrolled BP. Spearman correlation analysis was used to further explore the relationships between pQOL, adherence to AHD, dimensions of SPA, and uncontrolled SBP and DBP.
The role of the negative control dimension of SPA and medication adherence to AHD in the relationship between pQOL, and uncontrolled SBP/DBP was analyzed, using 5,000 bootstrap samples to calculate CIs and determine significance. The independent variable was pQOL scores, with negative control scores as mediator 1, medication adherence scores as mediator 2, and uncontrolled SBP/DBP as the dependent variable. To facilitate the analysis of clinical significance, we report b and OR separately, while finally to facilitate the analysis of mediated effect sizes, we use calculated standardized data. This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. The completed STROBE checklist is provided as Supplement B.
RESULTS
In this study, there were a total of 627 data points. During the data cleaning process, five samples were found to contain missing data, and after initial analysis. Therefore, we chose to remove these samples containing missing data, and 622 complete data samples were used for the final analysis. The pQOL score of older hypertensive patients was 52.0±24.7. After dividing into quartiles, the scores from Q1 to Q4 were 18.3±3.8, 36.9±5.7, 66.4±8.4, and 86.5±3.6 (Table 1).
Status of Uncontrolled BP in the Population with Different Scores of pQOL
Regarding pQOL, both SBP and DBP showed significant differences across quartiles. In particular, it should be noted that higher pQOL scores indicate less negative impact of pain on life. The overall mean SBP was 137.6±16.8 mmHg, with Q3 having the highest value (142.5±17.4 mmHg). The mean DBP was 84.7±9.6 mmHg, with Q3 also having the highest value (86.6±9.5 mmHg). About half of the patients had uncontrolled SBP (52.6%), DBP (45.2%), and BP (55.8%). Q3 had the highest proportion of uncontrolled SBP (68.9%), DBP (62.3%), and BP (71.9%) compared to other quartiles. Except for Q4, higher pQOL generally correlated with higher rates of uncontrolled SBP, DBP and BP (Table 1).
Demographic and Clinical Characteristics
The demographic and clinical characteristics are summarized in Tables 1 and 2. The average age of participants was 69.1 years, with most being married (n=498, 80.1%) and having low education (n=481, 77.3%). Fewer participants lived alone (n=57, 9.2%) or smoked (n=191, 30.7%). The average BMI and hypertension duration were 23.6 kg/m2 and 9.5 years, respectively, and over half had comorbidities (n=508, 81.7%). Participants in higher pQOL quartiles had higher education levels and lower BMI. Although hypertension type and daily AHD intake frequency did not differ significantly across quartiles, those with shorter hypertension duration were more common in higher pQOL quartiles. Statistical differences were also found in comorbidities, medication adherence, and some SPA dimension scales across pQOL quartiles.
Associations between pQOL and Uncontrolled BP
Table 3 shows the results of pQOL on uncontrolled SBP and DBP in Model 1. Taking Q1 as reference, after adjusting for covariates (p<0.05), including BMI, education, duration of hypertension, comorbidities, medication adherence, SPA total score, negative control and marital status, the total effect of pQOL and uncontrolled SBP remained statistically significant (Q2, Q3, Q4: OR=2.52, 4.52, 3.05; p=0.006, <0.001, 0.004, respectively); the total effect of pQOL and uncontrolled DBP was statistically significant (Q2, Q3, Q4; OR=6.78, 14.19, 9.29; p<0.001, <0.001, <0.001, respectively); the total effect of pQOL and uncontrolled BP was statistically significant (Q2, Q3, Q4; OR=2.77, 5.50, 3.45; p=0.002, <0.001, 0.001, respectively).
Correlations of pQOL, Medication Adherence to AHD, Negative Control of SPA, and Uncontrolled BP
Table 4 shows that pQOL negatively correlated with medication adherence to AHD (ρ=-0.105, p<0.01) and negative control (ρ=-0.136, p<0.001), but positively correlated with uncontrolled SBP (ρ=0.342, p<0.001), DBP (ρ=0.356, p<0.001), and BP (ρ=0.330, p<0.001). Medication adherence negatively correlated with uncontrolled SBP (ρ=-0.186, p<0.001), DBP (ρ=-0.176, p<0.001) and BP (ρ=-0.213, p<0.001), while positively correlating with negative control (ρ=0.140, p<0.001). Negative control negatively correlated with uncontrolled SBP (ρ=-0.174, p<0.001), DBP (ρ=-0.085, p<0.05), and BP (ρ=-0.115, p<0.001). Although these results were statistically significant (all p<0.05), some correlations (e.g., ρ=-0.085) may have a more limited impact on clinical practice.27,28)
Mediating Effect of Medication Adherence to AHD, Negative Control of SPA
Negative control, pQOL, medication adherence to AHD, and uncontrolled BP were significantly correlated, which met the statistical requirements for the further mediating effect analysis of negative control and medication adherence to AHD. The results of regression analysis, test of intermediary effect and bootstrap test are shown in Tables 5, 6, and Fig. 1. For SBP, pQOL significantly predicted uncontrolled SBP (OR=1.02, p<0.001). When negative control and medication adherence were included, pQOL predicted negative control (b=-0.007, p<0.001), and negative control predicted both medication adherence (b=0.51, p<0.001) and uncontrolled SBP (OR=0.67, p<0.01). Medication adherence also negatively predicted uncontrolled SBP (OR=0.84, p<0.001). The direct effect of pQOL on uncontrolled SBP was reduced (0.246→0.212), indicating significant mediation by negative control and the chain mediation through medication adherence.
For DBP, pQOL significantly predicted uncontrolled DBP (OR=1.03, p<0.001). The inclusion of negative control and medication adherence revealed similar patterns, with negative control predicting medication adherence (b=0.51, p<0.001). The direct effect of pQOL on uncontrolled DBP was reduced (0.093→0.039), indicating significant chain mediation through negative control and medication adherence.
Table 6 and Fig. 1A demonstrate that negative control and medication adherence significantly mediate the relationship between pQOL and uncontrolled SBP, with a total mediation effect of 0.212. This includes two indirect effects: path 1 (pQOL→negative control→uncontrolled SBP, 0.198) and path 3 (pQOL→negative control→medication adherence→uncontrolled SBP, 0.017). Fig. 1B shows a similar chain mediation for uncontrolled DBP, with a total mediation effect of 0.039, where the significant indirect effect is path 3 (pQOL→negative control→medication adherence→uncontrolled DBP, 0.018). It is worth noting that the R2 values of the model ranged from 0.05 to 0.12, indicating a poor fit.
DISCUSSION
This study revealed a significant association between higher pQOL scores and greater prevalence of uncontrolled BP in older hypertensive patients, mediated partially through negative self-perceptions of aging and medication adherence. Our findings present several important implications for clinical practice and future research.
Population Characteristics
In this study, the pQOL scores for older hypertensive patients were 52.0±24.7, similar to a Turkish study (55.1±22.2).29) After dividing participants into quartiles, those with higher pQOL scores were more likely to be well-educated and have a lower BMI. An Indonesia study found that BMI was associated with QOL.30) Higher education improves access to health information and decision-making,31) while lower education is linked to disability-related back pain.32,33) Overweight individuals face increased risks for insulin resistance, metabolic disturbances, and poorer health-related QOL.34) Additionally, those with a shorter hypertension duration tended to have higher pQOL scores, possibly due to fewer migraine headaches.35) The data showed that more than 80% of patients in the high pQOL quartile had comorbidities (Q2, Q3, Q4; 80.5%, 100%, 100%) compared with only 40.1% in Q1. Comorbidities complicate hypertension management, making uncontrolled BP rates increase.36) The management of comorbid conditions in hypertensive patients may involve analgesic use or physical activity for pain relief, potentially influencing overall treatment outcomes,37) but may be less vigilant about BP or adherence to antihypertensive medications.
Relationship between pQOL and Uncontrolled BP
Our analysis showed a significant trend between pQOL and greater prevalence of uncontrolled BP. The highest proportion of uncontrolled SBP and DBP was found in Q3 (68.9% and 62.3%, respectively), with higher quartiles generally correlating with more uncontrolled BP. After adjusting for various factors, logistic regression revealed that higher pQOL was associated with significantly greater prevalence of uncontrolled BP—SBP (Q2, Q3, Q4; OR=2.52, 4.52, 3.05), DBP (Q2, Q3, Q4; OR=6.78, 14.19, 9.29), and BP (Q2, Q3, Q4; OR=2.77, 5.50, 3.45). Medication adherence significantly reduced the odds of uncontrolled BP—SBP (OR=0.84), DBP (OR=0.82), and BP (OR=0.80) (p<0.001), highlighting its crucial role in BP management.14) The correlations of pQOL and uncontrolled BP were inconsistent with our original research hypothesis, partly due to the inclusion of various types of pain in the study.38) Here are some possible explanations. First, high pQOL scores may reflect neuroadaptive changes in chronic pain patients, where persistent activation of descending pain inhibitory pathways elevates sensory thresholds—not only reducing pain perception7) but also blunting receptor sensitivity8) and awareness of hypertensive symptoms. This sensory adaptation could result in underestimation of blood pressure severity and reduced treatment vigilance. The phenomenon underscores the need for integrated monitoring in pain-adapted patients, as pain relief alone does not ensure cardiovascular protection. Second, the high comorbidity burden in patients with elevated pQOL scores (100% in Q3–Q4) likely contributes to therapeutic complexity, where analgesic use for pain management may divert attention from antihypertensive treatment adherence.35) Analgesic use for pain management may contribute to therapeutic distraction, potentially compromising antihypertensive medication adherence and blood pressure monitoring.
Mediating Effect Analysis
We found that the mediating role of the negative control dimension of SPA, as well as its chain mediation through medication adherence to AHD, is significant in the relationship between pQOL and uncontrolled SBP. Furthermore, the chain mediation of negative control→medication adherence to AHD is significant in the relationship between pQOL and uncontrolled DBP.
The term "control" in SPA refers to older people's beliefs about managing their own experience of aging, which are based on older people's self-perceived management skills. Individuals who hold a "negative control" perspective are likely to have lower self-perceived competence and poorer management skills, which may lead to poorer medication adherence and, consequently, be associated with higher prevalence of uncontrolled BP. Negative control is one of the dimensions of aging self-perception, which includes four items. (1) The delay in movement caused by aging is something I cannot control. (2) It's not up to me to decide whether I have mobility issues in my later years. (3) As I grow older, I cannot control whether I will lose vitality or passion for life. (4) The impact of aging on my social life is beyond my control. Items are rated on a 5-point scale ranging from strongly disagree to strongly agree. The negative control of SPA is associated with worse control over negative experiences and outcomes. Patients with higher scores believe that aging cannot be controlled through one's own behavior.20,21)
For uncontrolled SBP, the mediating effect of negative control is significant among the influences of pQOL on uncontrolled SBP, accounting for 80.49% of the total effect. The pQOL scores have a significant negative predictive effect on negative control scores, and negative control scores negatively predict uncontrolled SBP. Patients with a good pQOL tend to have lower scores in negative control, indicating better control over negative experiences and outcomes, one of which is more likely to be uncontrolled SBP. This is consistent with a study by Tsai et al.,39) which found a significant negative correlation between chronic pain and perceived control beliefs about pain. Those who believe they can control their SBP through personal behavior may not view uncontrolled SBP as a distinct clinical condition. Consequently, they may manage hypertension alongside other conditions, which can disrupt effective hypertension self-management and lead to greater prevalence of uncontrolled SBP.40)
Additionally, the chain-mediating effect of negative control→medication adherence to AHD is significant among the influences of pQOL on SBP, accounting for 6.91% of the total effect. Negative control scores positively predict medication adherence to AHD scores, indicating that those who believe they can control their SBP through personal behavior may have poor medication adherence to AHD. They may think that medication is only necessary when the SBP is high. A meta-analysis of 94 studies targeting various diseases found consistent evidence that better compliance is associated with a greater understanding of the necessity of treatment.41) Some studies suggest that patients who believe they lack control over their BP tend to have higher medication adherence.42,43) This belief might be shaped by their evaluation of disease severity and potential outcomes. For instance, patients aware of their high stroke risk might become more diligent about managing their BP, leading to better medication adherence.44) Conversely, patients who perceive themselves as having high control over their BP might demonstrate poorer compliance.45) Multiple studies have shown that medication adherence plays a specific and critical role in hypertension control.46-48) As a result, patients with high pQOL have higher prevalence of uncontrolled SBP.
For uncontrolled DBP, the chain mediating effect of negative control→medication adherence to AHD is significant among the influences of pQOL on uncontrolled DBP, accounting for 19.35% of the total effect. However, the mediating role of negative control in the impact of pQOL on DBP is not significant.
Although pain-related QOL showed significant direct effects on uncontrolled SBP (OR=1.02, p<0.001) and uncontrolled DBP (OR=1.03, p<0.001), its indirect effect through medication adherence was not statistically supported. This suggests that, the pain-BP association may operate through pathways other than adherence; the C-MMAS-8 scale might not capture adherence behaviors most relevant to pain interference; and limited variability in adherence scores (R2=0.05) reduced power to detect mediation.
The R2 values of the models ranged from 0.05 to 0.12, indicating a poor fit. The likely reason for this is that pain-hypertension modulation involves multisystem (neurological/endocrine/immune) interactions, and existing linear models may not be able to capture complex nonlinear relationships. In addition, the association between pQOL and uncontrolled BP observed in this study may be influenced by unmeasured confounders, including (1) neurobiological factors (e.g., autonomic function); (2) variations in medication use (e.g., dosage, duration, and frequency of use of nonsteroidal anti-inflammatory medications); and (3) socio-environmental factors (e.g., accessibility of healthcare resources). The limited explanatory power of the model (R2=0.05–0.12) suggests that clinical decisions need to be made in combination with other independent risk indicators rather than relying on a single pQOL score. Current results still provide preliminary support for the psychological-behavioral pathway hypothesis of the pain-hypertension association.
Limitations
First, the sampling method was not stratified random sampling, which means that bias may have been introduced, and participants may not be fully representative of the broader older hypertensive population. Second, because this was a cross-sectional survey with a limited sample size, we were unable to explain the causal relationship between pQOL and uncontrolled BP. In the future, we will conduct prospective observational or intervention studies with larger sample sizes to verify their relationship and the mediating role of SPA and AHD medication adherence. Third, although the measurement tools used in this study showed good reliability and validity, the self-report nature may introduce subjectivity. Future studies should incorporate professional assessments to provide a more comprehensive evaluation. Fourth, despite adjusting for socio-demographic covariates in the multivariate model, the mediating effect remained small. This may be attributed to residual confounding from uncollected or inadequately collected factors, which could be addressed with more detailed data in future studies. Fifth, the failure of this study to account for pain duration is an important methodological limitation that may have a substantial impact on our findings. This unmeasured temporal dimension creates a fundamental ambiguity in the interpretation of pQOL scores: Q1 patients (low pQOL) may include individuals experiencing acute or recent-onset pain that triggers sympathetic activation and elevated blood pressure, whereas quartiles with high pQOL may include long-term chronic pain adapters. Besides, comorbidities may be an important confounding factor that we have not circumvented and may exaggerate observed effects.
Conclusion
Higher pQOL scores were significantly associated with the prevalence of uncontrolled BP. In terms of SPA, the negative control dimension negatively mediated the relationship with uncontrolled SBP. Additionally, a chain mediation effect of negative control and medication adherence was observed, which influenced the relationship between pQOL and blood pressure in older hypertensive patients. This suggests that healthcare workers cannot ignore patients with good pQOL. Although their symptoms may not be prominent, they should still strengthen education on hypertension management, raise their awareness of hypertension, and urge them to take AHD.
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