Introduction
Elevated blood pressure (BP), particularly high systolic BP, is the most significant risk factor for premature death worldwide.1 Data from Chinese national surveys among adults aged 35–75 years indicate low hypertension control rates, with fewer than one in twelve adults with hypertension achieving target BP levels.2 The clinical complexity of hypertension is compounded by its frequent coexistence with comorbidities,3 resulting in the wide inclusion of hypertension in multimorbidity indices.4 These intersecting health burdens have been shown to reduce quality of life and functional capacity while exacerbating poor hypertension control and mortality risk.5,6 The management of hypertension and its accompanying comorbidities in patients with duplicative and fragmented care often engenders treatment burden. This has been defined as the cumulative “work” of patienthood, encompassing attending medical appointments, undergoing diagnostic procedures, receiving therapeutic regimens, self-monitoring, and making lifestyle modifications, alongside their psychosocial impact on functioning and well-being.7–9 These aspects of burden are often exacerbated by intensified treatment.9
Effective hypertension management necessitates sustained, multifaceted self-care, incorporating dietary changes, smoking cessation, moderation of alcohol consumption, physical activity, self-monitoring, and medication.10–12 While robust evidence substantiates the effectiveness of self-care in achieving BP control,13,14 the asymptomatic nature of hypertension may tend to undermine patients’ adherence to symptom-driven treatment strategies.15,16 The reciprocal relationship between treatment burden and self-care has been posited, with empirical studies demonstrating that escalating treatment burdens often correlate with poorer compliance across pharmacological, exercise, and dietary domains.17–20 Meanwhile, intensified self-care regimens may be conversely linked to a rise in perceived treatment burden.21 Nevertheless, evidence from population studies regarding the temporal relationship between treatment burden and self-care is largely scanty, with limited understanding of the extent to which such relationship may influence BP control.
The present study aimed to explore the temporal relationship between treatment burden and self-care through cross-lagged panel analysis, while further examining their longitudinal impact on systolic BP levels and hypertension control through mediation analysis.
Materials and Methods
Study Design and Participants
We conducted a prospective observational cohort study within a network of 33 community health centres (CHCs) managed by a tertiary-level hospital in Shenzhen, southern China. These CHCs function as primary care extensions of tertiary hospitals, delivering standardised, free-of-charge national basic public health (BPH) services in the community.22,23 All hypertensive patients enrolled in the BPH programme from 2017 onwards were considered eligible for follow-up assessments. We employed a three-wave longitudinal design, ie, an initial enrolment assessment of treatment burden and self-care (T1), an interim follow-up evaluation of these measures after approximately 11 months (T2), and the final follow-up measurement of BP (T3; an approximate 14-month post-T2 observation) to capture any delayed clinical effects.
Measurement of Treatment Burden
Treatment burden was measured using the 15-item Treatment Burden Questionnaire (TBQ), an instrument originally developed in French.24 The tool was subsequently translated into English and validated among patients with long-term conditions.25 A Mandarin Chinese version of the TBQ instrument (TBQ_AU1.0_cmn-CN_RC) was developed by our team, commissioned by the Mapi Research Trust, following a standard forward and backward translation procedure. Our work adhered to the item structure of the English version without substantive modifications, additions, or omissions.26 Linguistic validation was conducted by a review panel consisting of two senior general practice (GP) physicians and ten primary care patients with multimorbidity. Cultural differences in language usage were carefully examined, with minor adaptations made to optimise cultural relevance to the Chinese healthcare context while maintaining translation equivalence. Component matrix yielded from the factor analysis accounted for 71.3% of the total variance. Psychometric evaluation revealed excellent internal consistency, as evidenced by a Cronbach’s α coefficient of 0.884, complemented by strong test–retest reliability with intraclass correlation coefficients (ICC) ranging from 0.725 to 0.846 across all individual items.26 These validation results substantiate the use of TBQ as a reliable and valid tool for measuring treatment burden in Chinese patients. Consistent with the original scoring interpretation of TBQ, a higher score reflects greater perceived treatment burden.
Measurement of Self-Care
The assessment of hypertension self-care encompassed 5 behavioural domains derived from the literature.27,28 These domains included smoking, alcohol drinking, physical activity, daily diet, and medication adherence, each operationalised as a dichotomous variable (0=nonadherent vs 1=adherent) with equal weighting, in accordance with previously validated methodologies,28,29 to reflect overall adherence. Current smoking was defined as smoking ≥1 cigarette daily for at least 6 months (0=smoking; 1=nonsmoking), while regular drinking was defined as alcohol drinking for an equivalent of >25 g/day (men) or >15 g/day (women) of alcohol consumption, or habitual drinking on ≥4 days per week (0=drinking; 1=nondrinking). Physical activity adherence required ≥30 minutes of moderate-intensity aerobic exercise (eg, brisk walking or cycling) on 5 or more days weekly on average (0=physical inactivity; 1=physical activity). A healthy diet was defined through self-reported adherence to principles of moderate flavouring and avoidance of excessive salty, sweety, or oily foods, alongside maintenance of balanced meat and vegetable consumption (0=unhealthy diet; 1=healthy diet). Medication adherence evaluation incorporated components of self-reported medication taking on time and following prescribed dosages, with participants reporting adherence to both components across seven consecutive days classified as adherent (1) [vs nonadherent (0)]. Patients who had missing or incomplete profiles on medication adherence during the study period were excluded to ensure the homogeneity of the study cohort in terms of self-care adherence measurements. A composite self-care score was derived through summation across all 5 domains (range: 0–5), with higher scores indicating better self-care adherence.
Assessment of Blood Pressure and Hypertension Control
The presence of hypertension and coexisting diabetes at enrolment was ascertained by the attending GP physician according to the clinical guidelines. Standardised BP measurement procedures were conducted using routinely validated automated sphygmomanometers with participants in a seated position. Measurements were obtained from the arm with higher BP values, with the mean of two readings taken at 1–2 minute intervals recorded. Patients who demonstrated a systolic BP ≥140 mmHg through repeated clinical measurements at the final follow-up assessment (T3) were classified as having suboptimal hypertension control.30
Statistical Analysis
Generalised linear models using the analysis of covariance (ANCOVA) approach were used to assess sex-based differences in study variables among participants with and without coexisting diabetes. Reciprocal relationships between treatment burden and self-care across time were evaluated using cross-lagged panel models, in which spuriousness was tested by comparing cross-lagged correlations based on assumptions of synchronicity and stationarity.31 The paradigm of the cross-lagged correlations was depicted in Figure 1. The path coefficient β1 represents the cross-lagged effects from self-care at enrolment (T1) on treatment burden at interim follow-up (T2), while path β2 coefficient indicates the cross-lagged effects from treatment burden at T1 on self-care at T2. Pearson correlation coefficients were computed for standardised (z score transformed) treatment burden and self-care measures at T1 and T2, with covariate adjustment, yielding six pairwise associations. The cross-lagged path coefficients (β1 and β2) were estimated from the correlation matrix using maximum likelihood method. The model was fully saturated with two explicitly measured variables, allowing model fit evaluation to be omitted given its just-identified nature. Relationships between treatment burden and self-care examined using cross-lagged panel models (CLPM) were stratified by age (<60 vs ≥60 years) and presence of diabetes comorbidity (diabetics vs nondiabetics), with covariate adjustment. The between-group differences in path coefficients were assessed using Fisher’s z test.
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Figure 1 Cross-lagged panel model of treatment burden and self-care among study participants. Notes: β1, β2 = cross-lagged path coefficients; r1 = synchronous correlations; r2, r3 = auto-correlations. Models adjusted for age, sex, and presence of diabetes comorbidity. *P<0.001.
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Following the establishment of the temporal relationship between treatment burden at T1 and self-care at T2, a causal mediation model was constructed to examine whether self-care at T2 may mediate the association of treatment burden at T1 with systolic BP and hypertension control at T3. We specified treatment burden at T1 as the predictor variable (X), self-care at T2 as the mediator (M), and BP outcomes as dependent variables (Y). The mediation analysis was conducted via a four-stage sequential approach: (1) demonstrating the total effect of X on Y (βTotal), ie, X→Y association; (2) establishing the effect of X on M (βMX), ie, X→M association; (3) determining the effect of M on Y (βYM.X), ie, M→Y association while controlling for X; and (4) quantifying the mediation proportion by dividing the indirect effect (βIndirect) by the total effect, ie, [(βMX × βYM.X)/βTotal] × 100%. CLPM analyses were performed using Mplus 8.3, while the mediation analyses were conducted using Stata 15.1 with adjustment for age, sex, and presence of coexisting diabetes. Statistical significance level was set at P<0.05.
In the sensitivity analysis, we applied a leave-one-out cross-validation approach in both CLPM and mediation models, in which self-care was restructured by systematically excluding each of the five original domains in turn, thereby creating modified composite scores comprising the sum of the remaining four domains (maximum score: 4 points). To illustrate, when leaving out the nonsmoking domain, the recalculated self-care score incorporated nondrinking, physical activity, healthy diet, and medication adherence. CLPM and mediation models were adjusted for age, sex, and the presence of coexisting diabetes, which maintained consistency with the primary analysis.
Results
The longitudinal cohort comprised 1718 hypertensive patients (54.4% male; mean age 54.6 ± 11.9 years), of whom 490 had coexisting diabetes. Table 1 summarises the mean levels of variables at T1, T2, and T3, stratified by sex and presence of diabetes comorbidity. After adjusting for age, women participants had significantly higher self-care scores at both T1 and T2 compared to men (P<0.001). Table 2 presents pair-wise Pearson’s correlations between T1 and T2 values for self-care and treatment burden in the total sample and across age groups and subjects with and without coexisting diabetes, with adjustment for covariates where appropriate. Most of the correlation coefficients were significant (P<0.05), except between T1 self-care and T2 treatment burden in the total sample, under 60s, and those without coexisting diabetes. We also observed no significant correlations between T2 treatment burden and T2 self-care in those aged less than 60 years and those with the absence of coexisting diabetes.
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Table 1 Treatment Burden, Self-Care, Systolic Blood Pressure, and Hypertension Control Among Study Participants by Age and Presence of Diabetes Comorbidity
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Table 2 Pearson’s Correlation Coefficients of Relationship Between Treatment Burden and Self-Care Among Study Participants
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The CLPM analysis showed that path coefficients for treatment burden at T1 on subsequent self-care at T2 in the total sample (β2 = −0.089, P<0.001), when adjusted for age, sex, and diabetes comorbidity, were significant and in the expected directions, suggesting that greater treatment burden was associated with poorer self-care adherence (Figure 1). This pattern persisted across age-stratified (β2 = −0.083 for under 60s and β2 = −0.113 for older participants; both P<0.001; Figure 2) and comorbidity-stratified (β2 = −0.103 for patients with coexisting diabetes and β2 = −0.085 for nondiabetic patients; both P<0.001; Figure 3) analyses. Notably, the path coefficients did not significantly differ by age (under 60s vs older participants: P=0.558) and presence of diabetes comorbidity (diabetics vs nondiabetics: P=0.733).
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Figure 2 Cross-lagged panel model of treatment burden and self-care stratified by age. Notes: β1, β2 = cross-lagged path coefficients; r1 = synchronous correlations; r2, r3 = auto-correlations. Models adjusted for sex and presence of diabetes comorbidity. *P<0.001. aBetween-group difference in path coefficients (under 60 years: −0.083 vs ≥60 years: −0.113; P=0.558).
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Figure 3 Cross-lagged panel model of treatment burden and self-care by presence of diabetes. Notes: β1, β2 = cross-lagged path coefficients; r1 = synchronous correlations; r2, r3 = auto-correlations. Models adjusted for age and sex. *P<0.001. aBetween-group difference in path coefficients (diabetics: −0.103 vs nondiabetics: −0.085; P=0.733).
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Mediation analyses, controlling for age, sex, and diabetes comorbidity, revealed that self-care at T2 partially mediated (10.7%) the longitudinal association between treatment burden at T1 and systolic BP at T3 (βIndirect = 0.024, P<0.001; βTotal = 0.226, P<0.001; Figure 4). This mediation operates through a negative association between treatment burden at T1 and self-care at T2 (βMX = −0.010, P<0.001), coupled with a stronger inverse relationship between self-care at T2 and systolic BP at T3 (βYM.X = −2.294, P<0.001). Similarly, self-care partially mediated (11.1%) the pathway between treatment burden at T1 and hypertension control at T3 (βIndirect = −0.001, P<0.001; βTotal = −0.009, P<0.001; Figure 5), operating through a negative association between treatment burden at T1 and self-care at T2 (βMX = −0.011, P<0.001), coupled with a stronger relationship between self-care at T2 and hypertension control at T3 (βYM.X = 0.106, P<0.001).
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Figure 4 Mediating effect of self-care on the relationship between treatment burden and systolic blood pressure. Note: *P<0.001.
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Figure 5 Mediating effect of self-care on the relationship between treatment burden and hypertension control. Note: *P<0.001.
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In the leave-one-out sensitivity analysis revealed stable path coefficients from treatment burden at T1 to self-care at T2 across all subgroups, indicating robust temporal relationships unaffected by self-care domain exclusion (Supplementary Table 1). Mediation analysis showed consistently modest effect magnitudes and significance levels, with self-care at T2 accounting for 7.0–8.9% of the total effect of treatment burden at T1 on systolic BP levels and hypertension control at T3 across subgroups, suggesting that no single behavioural domain exerts disproportionate influence within the self-care construct (Supplementary Tables 2 and 3).
Discussion
This longitudinal study elucidated the temporal associations between treatment burden and self-care in a sample of Chinese hypertensive patients using cross-lagged path analysis—a robust statistical approach for determining causal relationships. Results demonstrated that increased treatment burden significantly predicted reduced self-care levels. This pattern was consistent across age (<60/≥60 years) and diabetes comorbidity (diabetic/nondiabetic) subgroups. These patterns influenced systolic BP levels and hypertension control in the subsequent 14 months, with self-care accounting for 10.7% and 11.1% of the total effect of increased treatment burden on elevated systolic BP levels and reduced hypertension control (both P<0.001), respectively.
Treatment burden has been identified as a risk factor compromising self-care capacity among patients with chronic conditions. Evidence from American primary care settings suggests that both cumulative and task-specific treatment burdens predict poorer adherence to therapeutic regimens.20 While such correlational findings are informative, they remain inadequate for establishing causal relationships. Our CLPM analysis advances these findings by establishing temporal precedence–higher treatment burden predicts later self-care decline.20,32 It may be possible that when treatment benefits were not immediately apparent, patients may experience increasingly onerous burden, thereby diminishing one’s emotional engagement and undermining their long-term motivation to maintain health-monitoring routine. Notably, though between-group differences were non-significant, the association between treatment burden and self-care was marginally stronger in older adults and those with coexisting diabetes. In elderly populations, this likely reflects the compounding effects of age-related functional decline,33 polypharmacy,34 and multimorbidity-induced therapeutic complexity.35 In those with concurrent diabetes, competing disease management priorities appear to exacerbate the challenges of maintaining hypertension self-care. These observations underscore the need for mixed-methods investigations to further understand the mechanisms driving these relationships in the process evaluation.36
The mediation pathway identified in our study substantiates the Cumulative Complexity Model, demonstrating how intensified treatment burden worsens the workload-capacity imbalances, triggering breakdowns in self-care capacity and driving patient complexity.37 Our secondary hypothesis suggests that excessive treatment burdens may exceed patients’ cognitive resources, impairing both self-care implementation and task prioritisation, ultimately compromising BP control. While self-care partially mediated these associations (9.3–11.1%), these modest effects warrant cautious clinical interpretation. The direct pathway established in our study revealed that each 1-unit decrease in treatment burden yielded a 0.20 mmHg systolic BP reduction (βDirect = 0.202, P<0.001), implying that treatment burden alleviation that achieves a clinically meaningful BP reduction threshold (eg, a 10 mmHg reduction in systolic BP, corresponding to 20% fewer major cardiovascular events and 13% lower all-cause mortality38) is likely to provide widely applicable health benefits. These findings collectively position treatment burden reduction as a viable intervention strategy for optimising BP management.
Empirical evidence confirms that enhanced self-care practices directly lower systolic BP.39–41 This relationship is corroborated by our study findings and aligns with the Individual and Family Self-Management Theory,42 which emphasises that effective BP control depends on patients’ self-management capabilities and sustained engagement with treatment regimens that derive from positive reinforcement mechanisms and emotional responses.43 Modifiable behavioural patterns have been estimated to account for up to 40% of premature deaths.44 However, the pivotal role of self-care and health-promoting behaviours in hypertension management remains undervalued in daily practice. Significant barriers persist across multiple levels, encompassing individual psychological constraints (eg, low self-efficacy and outcome expectancy), familial interactions, boarder social determinants, and systemic healthcare challenges that collectively constrain self-care capacity.12,45 This situation is exacerbated when clinical interventions are intensified without proper consideration of treatment burden, leading to unsustainable adherence as patients may inevitably prioritise among competing demands,37 particularly in a multimorbidity context.46
Implications for Theory and Practice
Our study provides empirical evidence supporting the Cumulative Complexity Model (CCM),37 substantiating its theoretical framework through the identified “treatment burden → self-care → health outcomes” pathway. Our findings demonstrate that treatment burden detrimentally affects health outcomes both directly and indirectly through its deleterious impact on self-care capacity, thereby necessitating an expansion of the CCM to incorporate these parallel mechanistic routes. Therapeutic intensification, while clinically intended to improve outcomes, may inadvertently exacerbate treatment burden through increased workload demands, connecting the burdensome experience with erosion of patient capacity, which may subsequently worsen health outcomes.37 The feedback loop may impose mounting pressures on healthcare systems through escalating service utilisation and resource expenditure.
The established mediation pathway (treatment burden → self-care → health outcomes) reveals treatment burden as a progressive determinant of self-care capacity erosion, thereby elevating BP through disruptions in self-management activities. These findings may call for a reorientation of strategies towards prioritising treatment burden mitigation, eg, through regimen simplification and use of organisational strategies, on top of the existing efforts to enhance an individual’s self-care competencies and adherence in medication management and lifestyle changes. Examples of implementation may include restructure of clinical services, patient-centred prescribing practices, and individualised treatment intensity calibration to better support chronic disease management. Incorporating burden-sensitive care assessment tools, eg, the TBQ,25 into routine clinical metrics may enable identification of workload reduction opportunities while evaluating how care aligned with patient priorities ultimately influences health outcomes.47
Strengths and Weaknesses
Our study has several strengths. To the best of our knowledge, this investigation represents the first population-level quantitative analysis to establish the temporal relationship between treatment burden and self-care while evaluating their combined impact on systolic BP and hypertension control. The research benefits from a relatively large primary care cohort of hypertensive patients and assessment of multiple aspects of self-care. The use of a valid and internationally recognised instrument ensured rigorous measurement of treatment burden. Result consistency across patient subgroups strengthened the robustness of study findings. This study has some limitations that warrant consideration. First, the reliance on self-reported measures of treatment burden and self-care behaviours may be susceptible to recall and socio desirability bias. Second, by excluding patients who discontinued medication from the analysis, the study may have biased the sample toward more engaged individuals. Third, our findings from a Chinese cohort may have limited generalisability to geographically diverse populations given cross-national variations in healthcare system structures and sociocultural contexts. Crucially, the characteristically strong family support in the Chinese society–encompassing filial piety, shared care-giving, intergenerational relationship, and emotional engagement–may disproportionately mitigate treatment burden relative to the Western populations wherein such support networks are often less institutionalised. Last but not least, the mediating effect of self-care on the treatment burden-BP linkage was modest, enunciating the need for further qualitative studies to uncover additional factors across the full adult life course.
Conclusions
In conclusion, our study demonstrated that elevated treatment burden preceded poor self-care behaviours in a longitudinal primary care cohort of Chinese hypertensive patients using cross-lagged path analysis. Self-care was identified as a significant mediator in the temporal pathway linking treatment burden to both systolic BP levels and hypertension control. These findings provide novel insights into the temporal relationships between treatment burden, self-care, and hypertension outcomes, which may be an important clue to optimise hypertension management strategies.
Data Sharing Statement
The datasets used and analysed during the current study are available from the last corresponding author (HHXW) upon reasonable request.
Ethics Statement
Ethics approval was granted from the School of Public Health Biomedical Research Ethics Review Committee at Sun Yat‐Sen University in accordance with the Declaration of Helsinki 2013.
Informed Consent Statement
All patients provided written consent. Data were anonymised in the dataset to protect patient privacy.
Acknowledgments
We wish to acknowledge the tremendous support of the Guangdong-provincial Primary Healthcare Association (GDPHA) for the liaison with all study sites. We also thank our research collaborators, frontline staff at primary care facilities and students from Guangzhou Medical University and Sun Yat‐Sen University who were involved in conducting fieldwork and data collection.
Funding
National Natural Science Foundation of China (grant 72061137002) and Health Commission of Guangdong Province (grant 202303281631424512). The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Disclosure
The authors declare that there are no conflicts of interest in this work.
References
1. Razo C, Welgan CA, Johnson CO, et al. Effects of elevated systolic blood pressure on ischemic heart disease: a burden of proof study. Nat Med. 2022;28(10):2056–2065. doi:10.1038/s41591-022-01974-1
2. Lu J, Lu Y, Wang X, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project). Lancet. 2017;390(10112):2549–2558. doi:10.1016/S0140-6736(17)32478-9
3. Brilleman SL, Purdy S, Salisbury C, Windmeijer F, Gravelle H, Hollinghurst S. Implications of comorbidity for primary care costs in the UK: a retrospective observational study. Br J Gen Pract. 2013;63(609):E274–E282. doi:10.3399/bjgp13X665242
4. Claudia D, Klaus B, Bartels DB. The measurement of multiple chronic diseases–a systematic review on existing multimorbidity indices. J Gerontol A Biol Sci Med Sci. 2011;63(3):301–311. doi:10.1093/gerona/glq208
5. Fortin M, Bravo G, Hudon C, et al. Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res. 2006;15(1):83–91. doi:10.1007/s11136-005-8661-z
6. Tooth L, Hockey R, Byles J, Dobson A. Weighted multimorbidity indexes predicted mortality, health service use, and health-related quality of life in older women. J Clin Epidemiol. 2008;61(2):151–159. doi:10.1016/j.jclinepi.2007.05.015
7. Brar V, Kandala NB, Terry A, Thind A. Predictors of multimorbidity (Defined as Diabetes + Hypertension) amongst males aged 15–54 in India: an urban/rural split analysis. Int J Public Health. 2024;69:1606660. doi:10.3389/ijph.2024.1606660
8. Sav A, Salehi A, Mair FS, McMillan SS. Measuring the burden of treatment for chronic disease: implications of a scoping review of the literature. BMC Med Res Methodol. 2017;17(1):140. doi:10.1186/s12874-017-0411-8
9. Eton DT, Ramalho de Oliveira D, Egginton JS, et al. Building a measurement framework of burden of treatment in complex patients with chronic conditions: a qualitative study. Patient Relat Outcome Meas. 2012;3:39–49. doi:10.2147/PROM.S34681
10. Lawless MT, Tieu M, Chan RJ, Hendriks JM, Kitson A. Instruments measuring self-care and self-management of chronic conditions by community-dwelling older adults: a scoping review. J Appl Gerontol. 2023;42(7):1687–1709. doi:10.1177/07334648231161929
11. World Health Organization. WHO Guideline on Self-Care Interventions for Health and Well-Being, 2022 Revision. Geneva: World Health Organization; 2022.
12. Riegel B, Moser DK, Buck HG, et al. Self-care for the prevention and management of cardiovascular disease and stroke: a scientific statement for healthcare professionals from the American Heart Association. J Am Heart Assoc. 2017;6(9):e006997. doi:10.1161/JAHA.117.006997
13. Boulware LE, Ephraim PL, Hill-Briggs F, et al. Hypertension self-management in socially disadvantaged African Americans: the Achieving Blood Pressure Control Together (ACT) randomized comparative effectiveness trial. J Gen Intern Med. 2020;35(1):142–152. doi:10.1007/s11606-019-05396-7
14. Maciejewski ML, Bosworth HB, Olsen MK, et al. Do the benefits of participation in a hypertension self-management trial persist after patients resume usual care? Circ Cardiovasc Qual Outcomes. 2014;7(2):269–275. doi:10.1161/CIRCOUTCOMES.113.000309
15. Chen SL, Tsai JC, Lee WL. The impact of illness perception on adherence to therapeutic regimens of patients with hypertension in Taiwan. J Clin Nurs. 2009;18(15):2234–2244. doi:10.1111/j.1365-2702.2008.02706.x
16. Boehmer KR, Gionfriddo MR, Rodriguez-Gutierrez R, et al. Patient capacity and constraints in the experience of chronic disease: a qualitative systematic review and thematic synthesis. BMC Fam Pract. 2016;17(1):127. doi:10.1186/s12875-016-0525-9
17. Rogers EA, Abi H, Linzer M, Eton DT. Treatment burden in people with hypertension is correlated with patient experience with self-management. J Am Board Fam Med. 2021;34(6):1243–1245. doi:10.3122/jabfm.2021.06.210191
18. Gallacher K, May CR, Montori VM, Mair FS. Understanding patients’ experiences of treatment burden in chronic heart failure using normalization process theory. Ann Fam Med. 2011;9(3):235–243. doi:10.1370/afm.1249
19. Nordfonn OK, Morken IM, Bru LE, Husebø AML. Patients’ experience with heart failure treatment and self-care – a qualitative study exploring the burden of treatment. J Clin Nurs. 2019;28(9–10):1782–1793. doi:10.1111/jocn.14799
20. Schreiner N, DiGennaro S, Harwell C, Burant C, Daly B, Douglas S. Treatment burden as a predictor of self-management adherence within the primary care population. Appl Nurs Res. 2020;54:151301. doi:10.1016/j.apnr.2020.151301
21. Sawicki GS, Sellers DE, Robinson WM. High treatment burden in adults with cystic fibrosis: challenges to disease self-management. J Cyst Fibros. 2009;8(2):91–96. doi:10.1016/j.jcf.2008.09.007
22. Heath I, Wang HHX, Mercer SW, et al. International Perspectives. In: watt GCM, editor. The Exceptional Potential of General Practice: Making a Difference in Primary Care. London, UK: CRC Press; 2018:185–198. doi:10.1201/9780429427640-13
23. Wang HHX, Wang JJ. Chapter 40 developing primary care in China. In: Griffiths SM, Tang JL, Yeoh EK, editors. Routledge Handbook of Global Public Health in Asia. London, UK: Routledge; 2014:584–600. doi:10.4324/9781315818719.ch40
24. Tran VT, Montori VM, Eton DT, Baruch D, Falissard B, Ravaud P. Development and description of measurement properties of an instrument to assess treatment burden among patients with multiple chronic conditions. BMC Med. 2012;10:68. doi:10.1186/1741-7015-10-68
25. Tran VT, Harrington M, Montori VM, Barnes C, Wicks P, Ravaud P. Adaptation and validation of the Treatment Burden Questionnaire (TBQ) in English using an internet platform. BMC Med. 2014;12:109. doi:10.1186/1741-7015-12-109
26. Hu XJ, Wang HHX, Li YT, et al. Healthcare needs, experiences and treatment burden in primary care patients with multimorbidity: an evaluation of process of care from patients’ perspectives. Health Expect. 2022;25(1):203–213. doi:10.1111/hex.13363
27. Warren-Findlow J, Seymour RB, Brunner Huber LR. The association between self-efficacy and hypertension self-care activities among African American Adults. J Community Health. 2012;37(1):15–24. doi:10.1007/s10900-011-9410-6
28. Warren-Findlow J, Basalik DW, Dulin M, Tapp H, Kuhn L. Preliminary validation of the Hypertension Self-Care Activity Level Effects (H-SCALE) and clinical blood pressure among patients with hypertension. J Clin Hypertens. 2013;15(9):637–643. doi:10.1111/jch.12157
29. King A, Ringel JB, Safford MM, et al. Association between caregiver strain and self-care among caregivers with diabetes. JAMA Network Open. 2021;4(2):e2036676. doi:10.1001/jamanetworkopen.2020.36676
30. Brunström M, Ng N, Dahlström J, et al. Association of physician education and feedback on hypertension management with patient blood pressure and hypertension control. JAMA Network Open. 2020;3(1):e1918625. doi:10.1001/jamanetworkopen.2019.18625
31. Kivimäki M, Feldt T, Vahtera J, Nurmi JE. Sense of coherence and health: evidence from two cross-lagged longitudinal samples. Soc Sci Med. 2000;50(4):583–597. doi:10.1016/s0277-9536(99)00326-3
32. Schreiner N, Perazzo J, Digenarro S, Currie J, Daly B, Webel A. Examining the association between item specific treatment burden and adherence in people living with HIV. West J Nurs Res. 2020;42(7):495–502. doi:10.1177/0193945919880317
33. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–156. doi:10.1093/gerona/56.3.m146
34. Masnoon N, Shakib S, Kalisch-Ellett L, Caughey GE. What is polypharmacy? A systematic review of definitions. BMC Geriatr. 2017;17(1):230. doi:10.1186/s12877-017-0621-2
35. Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294(6):716–724. doi:10.1001/jama.294.6.716
36. May C, Finch T, Mair F, et al. Understanding the implementation of complex interventions in health care: the normalization process model. BMC Health Serv Res. 2007;7:148. doi:10.1186/1472-6963-7-148
37. Shippee ND, Shah ND, May CR, Mair FS, Montori VM. Cumulative complexity: a functional, patient-centered model of patient complexity can improve research and practice. J Clin Epidemiol. 2012;65(10):1041–1051. doi:10.1016/j.jclinepi.2012.05.005
38. Ettehad D, Emdin CA, Kiran A, et al. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet. 2016;387(10022):957–967. doi:10.1016/S0140-6736(15)01225-8
39. Chobanian AV, Bakris GL, Black HR, et al. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA. 2003;289(19):2560–2572. doi:10.1001/jama.289.19.2560
40. Qu Z, Parry M, Liu F, et al. Self-management and blood pressure control in China: a community-based multicentre cross-sectional study. BMJ Open. 2019;9(3):e025819. doi:10.1136/bmjopen-2018-025819
41. Nguyen QC, Waddell EN, Thomas JC, Huston SL, Kerker BD, Gwynn RC. Awareness, treatment, and control of hypertension and hypercholesterolemia among insured residents of New York City, 2004. Prev Chronic Dis. 2011;8(5):A109.
42. Ryan P, Sawin KJ. The individual and family self-management theory: background and perspectives on context, process, and outcomes. Nurs Outlook. 2009;57(4):217–225.e216. doi:10.1016/j.outlook.2008.10.004
43. Schulman-Green D, Jaser S, Martin F, et al. Processes of self-management in chronic illness. J Nurs Scholarsh. 2012;44(2):136–144. doi:10.1111/j.1547-5069.2012.01444.x
44. Dzau VJ, Mcclellan MB, Mcginnis JM, et al. Vital directions for health and health care: priorities from a national academy of medicine initiative. JAMA. 2017;317(14):1461–1470. doi:10.1001/jama.2017.1964
45. Wang HHX, Mercer SW. Understanding barriers to adherence to optimal treatment of elevated blood pressure and hypertension-insights from primary care. JAMA Network Open. 2021;4(12):e2138651. doi:10.1001/jamanetworkopen.2021.38651
46. Bayliss EA, Steiner JF, Fernald DH, Crane LA, Main DS. Descriptions of barriers to self-care by persons with comorbid chronic diseases. Ann Fam Med. 2003;1(1):15–21. doi:10.1370/afm.4
47. Tinetti ME, Naik AD, Dindo L, et al. Association of patient priorities-aligned decision-making with patient outcomes and ambulatory health care burden among older adults with multiple chronic conditions: a nonrandomized clinical trial. JAMA Intern Med. 2019;179(12):1688–1697. doi:10.1001/jamainternmed.2019.4235