Establishing the criterion validity of self-report measures of adherence in hemodialysis through associations with clinical biomarkers: A systematic review and meta-analysis

Helena Sousa; Oscar Ribeiro; Elísio Costa; Alan Jay Christensen; Daniela Figueiredo

doi:10.1371/journal.pone.0276163

Abstract

Accurate measurement of adherence is crucial to rigorously evaluate interventions aimed at improving this outcome in patients undergoing in-center hemodialysis. Previous research has shown great variability in non-adherence rates between studies, mainly due to the use of different direct (e.g., clinical biomarkers) and indirect (e.g., questionnaires) measures. Although self-reported adherence in hemodialysis has been widely explored, it is still unclear which is the most accurate questionnaire to assess this outcome; therefore, the question of how to optimize adherence measurement in research and clinical practice has emerged as a key issue that needs to be addressed. This systematic review and meta-analysis aimed to explore the criterion validity of self-report measures of adherence in hemodialysis established through the association between test scores and clinical biomarkers (the criterion measure). The protocol was registered in PROSPERO (2021 CRD42021267550). The last search was performed on March 29^th, 2022, on Web of Science (all databases included), Scopus, CINHAL, APA PsycInfo, and MEDLINE/PubMed. Twenty-nine primary studies were included, and thirty-eight associations were analyzed. The Hunter-Schmidt’s meta-analysis was computed for the associations with more than two studies (n = 20). The results showed that six associations were large (16%), 11 were medium (29%) and the remaining were of small strength. The test scores from the End-Stage Renal Disease Adherence Questionnaire (range: 0.212<r_c <0.319) and the Dialysis Diet and Fluid Non-Adherence Questionnaire (range: 0.206<r_c <0.359) had medium to large strength associations with interdialytic weight gain, serum phosphorus, and potassium levels, indicating that these questionnaires have reasonable concurrent criterion validity to measure fluid control and adherence to dietary restrictions in patients receiving hemodialysis. The available data did not allow exploring the criterion validity of the test scores in relation to hemodialysis attendance (skipping and/or shortening sessions). These results suggest that the decision to use one questionnaire over another must be made with caution, as researchers need to consider the characteristics of the sample and the objectives of the study. Given that direct and indirect methods have their advantages and disadvantages, the combination of adherence measures in hemodialysis is recommended to accurately assess this complex and multidimensional outcome.

Citation: Sousa H, Ribeiro O, Costa E, Christensen AJ, Figueiredo D (2022) Establishing the criterion validity of self-report measures of adherence in hemodialysis through associations with clinical biomarkers: A systematic review and meta-analysis. PLoS ONE 17(10): e0276163. https://doi.org/10.1371/journal.pone.0276163

Editor: Justyna Gołębiewska, Medical University of Gdansk, POLAND

Received: April 12, 2022; Accepted: October 1, 2022; Published: October 18, 2022

Copyright: © 2022 Sousa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by the national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., within CINTESIS R&D Unit (UIDB/4255/2020 and UIDP/4255/2020) and within the scope of the project RISE (LA/P/0053/2020), and by the project POCI-01-0145-FEDER-030228, funded by FEDER, through COMPETE2020- Programa Operacional Competitividade e Internacionalização (POCI), and by national funds (OE), through FCT/MCTES. Additionally, Helena Sousa has a PhD grant scholarship (reference number DFA/BD/4821/2020) financed by FCT (Fundação para a Ciência e Tecnologia) through FSE (Fundo Social Europeu).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Worldwide, approximately 89% of patients with kidney failure are being treated with hemodialysis; however, treatment success largely depends on patients’ adherence to various health behaviors that are crucial to quality of life and survival [1, 2]. Adherence in hemodialysis has been defined as the extent to which the patient’s behavior corresponds to the medical recommendations agreed with dialysis care professionals [3]. In the context of in-center maintenance hemodialysis, patients need to travel to a dialysis center three to four times a week to receive treatment without missing or shortening sessions, follow strict dietary and fluid restrictions, polypharmacy prescriptions, practice regular exercise, care for the vascular access, and attend follow-up consults [3–5]. Consequently, this renal replacement therapy has been described as one of the most burdensome treatment regimens, and low adherence to all these health behaviors and lifestyle changes is currently considered a major public health problem [6].

However, great variability in non-adherence rates has been reported [7–13]. For instance, the management of dietary restrictions has been pointed out as one of the most challenging behaviors with non-adherence rates ranging from 1.2 to 82.4%, immediately followed by medication intake (1.2 to 81%), and fluid control (3.4 to 74%) [7–12]. Research has also evidenced that non-adherence to in-center hemodialysis sessions varies widely, as 8 to 31% of patients choose to withdraw, withhold, or discontinue treatment [13], whereas 30 to 86% fail to attend dialysis sessions as prescribed [14]. This discrepancy has been mainly attributed to the lack of a gold standard assessment of adherence, as several direct (e.g., biochemical and physiological markers) and indirect (e.g., questionnaires) measures have been used to assess the different domains of adherence in hemodialysis [15].

Regarding direct assessment, studies have relied on clinical biomarkers such as interdialytic weight gain (IDWG), pre-dialysis serum potassium, and/or phosphorous levels as objective measures of fluid and dietary non-adherence, and medication intake [16]. However, the lack of universally accepted cutoff values for each indicator makes comparisons between studies difficult [15, 17]. In turn, self-report measures have been increasingly applied in research and renal care settings, since they are generally inexpensive, flexible, easy to interpret, timesaving, and allow for wide use [18]. The ability to understand patients’ perceptions and explore the reasons for non-adherence is another advantage of indirect measurement [19]. In this context, several questionnaires have been used, namely the End-Stage Renal Disease Adherence Questionnaire (ESRD-AQ), the Dialysis Diet and Fluid Non-Adherence Questionnaire (DDFQ), the Renal Adherence Behavior Questionnaire (RABQ), and other researcher designed/modified instruments [15]; however, their characteristics vary substantially in terms of domain assessed, single or multiple items, and recall period [20]. Furthermore, some lack the necessary validity studies and, therefore, it is still unclear which is the most appropriate to assess self-reported adherence in hemodialysis [9, 11].

To broaden the understanding of the psychometric properties of self-report measures of adherence in hemodialysis, this systematic review and meta-analysis explored their criterion validity established through the association between test scores and clinical biomarkers (the criterion measure). There are two forms of criterion validity: concurrent (an index of the degree to which a test score is related to some criterion measure obtained approximately at the same time, i.e., concurrently) and predictive validity (the degree to which a test score predicts some criterion measure obtained at a future time, i.e., how accurately scores on the test predict some criterion measure) [21, 22]. The use of biochemical data as a criterion measure was based on previous research describing that the criterion validity of self-reported adherence is established through comparison with clinical and biological outcomes, which has been explored in other chronic diseases such as HIV, hypertension, and diabetes [18, 20].

Having this knowledge is of utmost importance, as optimizing the measurement of adherence in hemodialysis is crucial for several reasons. For research, validity is one of the most important psychometric qualities of an instrument, being a synonym for its accuracy [21]. The use of valid measures is also necessary for targeting and rigorously evaluating the growing number of randomized clinical trials that aim to maximize adherence in patients undergoing maintenance hemodialysis [10]. In addition, improving adherence reporting in this population is crucial to support evidence-based public health decision-making, as policymakers often rely on prevalence rates and prediction studies to identify high-risk patients and fund the development of innovative interventions [18].

Methods

The findings were reported using the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statements [23]. The protocol for this study was registered on PROSPERO (2021 CRD42021267550) (see S1 File). The performance of meta-analyses is an addendum to the previously registered protocol. For more information regarding the PRISMA 2020 checklist, see S1 Checklist.

Eligibility criteria

Following the PICOS structure [24, 25], studies were included based on the following criteria:

Participants: Studies that assessed adults with kidney failure (aged 18 years or older) undergoing in-center hemodialysis. Research reporting the outcomes of patients on home hemodialysis was not considered; studies that focused primarily on other renal replacement therapies, such as peritoneal dialysis or kidney transplantation, were also excluded. This decision was based on the results of previous research suggesting that in-center hemodialysis is the most demanding treatment modality with the highest non-adherence rates [26, 27]. No limits were applied for the length of time on in-center hemodialysis.
Exposure: Studies that exposed patients to self-report measures that aimed to assess one or more domains of adherence in hemodialysis, namely attendance (shortening and/or skipping dialysis sessions), dietary restrictions, fluid control, and medication intake [9, 10, 15]. Questionnaires developed by researchers specifically for their study purpose, visual analog scales, and/or measures without validation data in patients receiving hemodialysis were excluded.
Comparator: Studies assessing clinical biomarkers likely to be affected by the different domains of adherence in hemodialysis, according to the most recently published National Kidney Foundation’s guidelines (NKF) [3, 28, 29].
Outcomes: The criterion validity of self-reported adherence measures was established through the strength of the association between test scores and clinical biomarkers likely to be affected by patients’ adherence [18, 22]; therefore, only studies reporting the results of these associations were considered eligible.
Study design: Only prospective and cross-sectional studies with quantitative data were included.

Searches

Studies were identified by searching Scopus, Web of Science Core Collection, Current Contents Connect, Derwent Innovations Index, KCI-Korean Journal Database, Russian Science Citation Index, SciELO Citation Index, MEDLINE/PubMed, CINHAL, and APA PsycInfo. The following keywords were used interchangeably: (dialysis OR hemodialysis OR haemodialysis OR end-stage renal disease OR end-stage kidney disease OR renal failure OR renal replacement therapy) AND (adherence OR compliance OR self-management OR self-care) AND (self-report OR questionnaire OR measure OR scale OR instrument) AND (albumin OR potassium OR phosphorus OR phosphate OR interdialytic weight OR IDWG OR Kt/V OR calcium OR sodium OR creatinine OR biomarkers). The search strategy was based on the inclusion criteria, other reviews related to adherence in hemodialysis [26], and the most recently published NKF guidelines [3, 28, 29]. Limits were applied to the English language and research with adults. Other reviews, protocols, practice guidelines, or conference abstracts were not considered. Studies published before 2000 were also excluded; this time limit has been established in accordance with the release of the first NFK Dialysis Outcome Quality Initiative clinical practice guidelines for hemodialysis adequacy and nutritional management in chronic kidney disease [30, 31]. The reference lists of the identified studies and other reviews were hand-searched to ensure that all-important studies were included. The electronic search was performed between July 1^st and 14^th, 2021, and updated on March 29^th, 2022. For more information about the search process, see S1 Table.

Study selection and data collection

Studies were extracted from the databases and exported to Rayyan, a software designed to facilitate the study selection in reviews (https://www.rayyan.ai/). After manually removing duplicates, the unique studies were selected over three steps: (i) studies were labeled as included, excluded, or unclear, based on title and abstract; (ii) those studies labeled as unclear or as included were retrieved; and (iii) the full text was analyzed. Two authors [HS and DF] independently performed the eligibility assessment with an inter-rater agreement of 88.2%. Discrepancies were resolved by discussion and consensus, and by consulting with a third author [OR]. The following data were retrieved: study characteristics (date, design, and sample size), sample characteristics (age, sex, country, and length of time on dialysis), self-report measures of adherence and results, biomarkers likely to be affected by adherence and results, statistics used to explore the association between self-report measures and biomarkers, and the major results for this association.

Critical appraisal

The quality of the included studies was critically appraised using the Joanna Briggs Institute (JBI) Critical Appraisal Checklists for Analytical Cross-Sectional Studies. This checklist aims to assess the methodological quality of a study regarding the possibility of bias in its design, conduct, and analysis. Each item on these checklists is appraised as “yes”, “no”, “unclear” or “not applicable”. Two authors [HS and EC] conducted the appraisal with an IR agreement of 87.5%. Discrepancies were solved by discussion and consensus, and by consulting with a third author [DF].

Strategy for data synthesis

The validity coefficient was used as an effect size measure [22]. This refers to a correlation coefficient that provides a measure of the strength of the relationship between the test scores (i.e., self-report measures of adherence) and results of the other measure of interest (i.e., biomarkers) [22].

Hunter-Schmidt’s psychometric meta-analyses [32] using the random-effects model were computed for associations with more than two studies (k ≥ 2). The ‘bare-bone’ mean of r (r_c) corrected for sampling error was calculated by weighting each correlation coefficient with the respective sample size. Values of 0.12, 0.20, and 0.32 were interpreted as small, medium, and large [33]. Heterogeneity among studies was evaluated using both the Chi-squared test and the I-squared statistic. A value of < 0.10 was considered to indicate statistically significant heterogeneity between studies [34]. An I-squared value of 25% represents a small degree of heterogeneity, 50% a moderate degree, and 75% a large degree of heterogeneity [34]. All the analyses were performed using R version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria). The “meta” package with the function “metafor” was used to perform the meta-analyses.

Results

Study selection

Twenty-nine primary studies met the inclusion criteria for this systematic review and meta-analysis. Fig 1 presents the different stages of study selection including reasons for exclusion.

Download:

Fig 1. PRISMA 2020 flow diagram.

https://doi.org/10.1371/journal.pone.0276163.g001

Study characteristics

The studies were published from 2001 to 2021. Seventeen (59%) were published after (and including) 2015. All studies had a cross-sectional design. Pearson’s product-moment correlation (n = 16, 55%) and Spearman’s rank correlation (n = 7, 24%) were the most frequently used coefficients to explore the association between test scores and clinical biomarkers. Table 1 shows the characteristics of the included studies and major results.

Download:

Table 1. Characteristics of the primary studies (n = 29) and major results.

https://doi.org/10.1371/journal.pone.0276163.t001

Critical appraisal

All studies clearly defined the inclusion criteria and described the population in detail. The questionnaires used to measure adherence were valid and reliable; however, only fourteen studies (48%) reported reliability results using the study sample. Internal consistency was mostly assessed with Cronbach’s alpha [7, 8, 12, 38, 42, 45, 48, 49, 55, 58] while studies using the ESRD-AQ calculated the inter-class and/or the item-total scale correlation coefficient [9, 11, 41, 54].

Since the type of hemodialysis (conventional or on-line hemodiafiltration), the number of dialysis sessions (per week), the etiology of kidney failure, the type of vascular access, and the level of residual renal function can confound the results of clinical biomarkers [59–61], these data were considered in the critical appraisal of each study. In this sense, only two studies reported that patients were receiving on-line hemodiafiltration [11, 35] while the remaining did not present data regarding the type of hemodialysis. Four studies [11, 35, 40, 54] stated that patients received treatment three times a week for four hours per session, while in two studies [36, 53] most patients were dialyzed twice a week. Only five studies (17%) identified kidney failure etiology, reporting that diabetes was the most common cause, followed by hypertension and glomerulonephritis [9, 11, 38, 39, 58]. Only one study [35] presented information on vascular access, describing that most patients underwent treatment through an arteriovenous fistula. Regarding residual renal function, only four studies (14%) described these data and two of them found that renal urea clearance (KrU) was significantly associated with non-adherence [46, 48].

Lastly, four studies (14%) did not provide information on how/when clinical biomarkers were collected [39, 41, 45, 49]. It is important to emphasize that there was great variability in this regard, which may introduce bias, as some studies (n = 6, 21%) stated that data collection took place in the same week as the self-report measure was applied, while others averaged the clinical results of the last weeks before the questionnaire administration (n = 7, 24%). For more information on the critical appraisal of all studies see S2 Table; for a detailed description of confounders, see S3 Table.

Participants’ characteristics

This study comprised a total of 5093 patients with kidney failure (M = 176, SD = 126, range: 50–564) underdoing in-center hemodialysis for an average of 64.8 months (SD = 41.4, range: 40.3–66.4). Six studies [7, 8, 30, 31, 40, 41, 46] reported categorical data, suggesting that patients with more than three months on dialysis were included in their analysis. Most participants were men (54%), with an average of 58.5 years old (SD = 6.91, range: 40.3–66.4), and recruited from 19 different countries. Only 10 primary studies reported patients’ race/ethnicity [9, 12, 41, 42, 44, 47, 50, 55, 56, 58]; most participants in these studies were non-White (Table 1).

Self-reported adherence

Adherence to dietary restrictions (Table 1) and fluid control were equally the most evaluated outcomes (n = 15, 52%), followed by medication intake, especially phosphate binders (n = 9, 31%). Only four studies (17%) reported patients’ attendance to in-center hemodialysis sessions [7, 11, 37, 41]. Six different self-report measures were applied. The ESRD-AQ (n = 8, 28%) and the DDFQ (n = 8, 28%) were the most frequently used to assess dietary and fluid restrictions, while the Morisky Medication Adherence Scale (MMAS-4 and MMAS-8; n = 9, 21%) was the most applied to assess medication intake. Available data suggested that an average of 63.5% (SD = 22.2, range: 31.8–98.3) of patients were non-adherent to dietary recommendations, 61.7% to fluid restrictions (SD = 20.7, range: 20.5–86.3), 27.8% to medication intake (SD = 14.4, range: 10.7–56.7), and 11.6% (SD = 5.71, range: 8–19.2) to in-center hemodialysis sessions (skipping and/or shortening sessions). For a summary of each self-reported adherence measure analyzed in this study, see S4 Table.

Clinical biomarkers of treatment adherence

Eighteen different biomarkers were used to assess adherence. Serum phosphorus levels (SPO; n = 16, 55%), serum potassium levels (SK; n = 14, 48%), IDWG (n = 14, 48%), serum albumin levels (ALB; n = 6, 21%), Kt/V (n = 4, 14%), and blood pressure (BP diastolic and systolic; n = 3, 10%) were the most frequently collected parameters. SPO, SK, and ALB were used to assess adherence to diet, while SPO also measured phosphate binder’s intake. IDWG was mainly used to assess fluid control, while BP was collected to evaluate adherence to antihypertensives. Available data (Table 1) suggested that the mean SPO value was 5.20 mg/dl (SD = 0.59, range: 4.2–6.37), SK was 5.04 mg/dl (SD = 0.28, range: 4 .70–5.60), and ALB was 3.99 g/dl (SD = 0.26, range: 3.70–4.51). Patients also had an average IDWG of 2.54 kilograms (SD = 0.41, range: 1.42–4.20) with an average Kt/V of 1.56 (SD = 0.18, range: 1.31–1.7). Regarding physiological data, the mean value of systolic BP was 146 mmHg (SD = 14.9, range: 135–163), while the mean diastolic BP was 72.4 mmHg (SD = 72.4, range: 63.8–87.4).

Criterion validity

In the primary studies, the application of the questionnaires was completed at approximately the same time as the collection of laboratory results (for details, see S3 Table); therefore, it was not possible to report data on the predictive value of self-report measures (predictive validity) and only their concurrent criterion validity was analyzed (Table 2).

Download:

Table 2. Results of meta-analyses for concurrent criterion-related validity correlation coefficients between test scores and clinical biomarkers of adherence in hemodialysis.

https://doi.org/10.1371/journal.pone.0276163.t002

Thirty-eight associations between self-report measures and clinical biomarkers likely to be affected by patients’ adherence were analyzed. Individual meta-analyses were also performed for associations with more than two primary studies (n = 20). These refer to dietary restrictions, fluid control, polypharmacy intake, and overall non-adherence; however, the available data did not allow exploring the criterion validity of the test scores in relation to hemodialysis attendance.

In general, six associations were large (16%), 11 were medium (29%) and the rest were of small strength. A large statistically significant and moderately heterogeneous association was found between the ESRD-AQ and IDWG for fluid control (r_c = 0.319; 95% CI = 0.218–0.419; I² = 53%; k = 4; n = 677) [9, 11, 36, 53]. For the same outcome and biomarker, large statistically significant and heterogeneous associations were found with the DDFQ (r_c = 0.359; 95% CI = 0.219–0.499; I² = 89%; k = 6; n = 1823) [7, 38, 45, 48, 49, 57]. Regarding dietary restrictions, both the ESRD-AQ (r_c = 0.212 with SK; 95% CI = 0.113–0.309; I² = 47%; k = 4; n = 710) [9, 36, 50, 53] and the DDFQ (r_c = 0.206 with SPO; 95% CI = 0.034–0.378; I² = 91%; k = 6; n = 1754) [7, 8, 38, 45, 49, 57] showed medium-strength statistically significant associations with clinically relevant biomarkers of this type of adherence. To measure overall non-adherence, the results showed a medium association between the ESRD-AQ scores and SPO levels (r_s = 0.237; k = 1; n = 90) [54] and a large association with IDWG values (r_s = 0.533; k = 1; n = 90) [54]. Compared with other patient-reported outcome measures, the MMAS had the most medium to large-strength associations with medication adherence outcomes (SPO: 0.20 < r_c < 0.383; BP: 0.20 < r < 0.286) [35, 43, 44, 46, 47, 55, 58].

Discussion

This systematic review with Hunter-Schmidt’s psychometric meta-analyses reports the criterion validity of self-report measures of adherence in hemodialysis, through the strength of the association between test scores and clinical biomarkers (the criterion measure).

Overall, the results showed that both the ESRD-AQ and the DDFQ have reasonable concurrent criterion validity with biomarkers that measure adherence to fluid and dietary restrictions; therefore, the choice of the most appropriate tool may depend on the objectives of each study and/or characteristics of the sample. On the one hand, the ESRD-AQ allows for a more comprehensive assessment of treatment adherence, as it measures different domains (hemodialysis attendance, medication intake, fluid, and dietary restrictions) but also reports on reasons for non-adherence, and patients’ perceptions and understanding of medical recommendations [9, 11, 62]. However, the readability of this instrument, as well as the patients’ ability to understand each question, has been poorly evaluated in validation studies [9]. On the other hand, the DDFQ may be more intelligible due to its lower complexity [9, 57] and therefore more appropriate if the study sample has low literacy levels. Nonetheless, some concerns have been raised due to its modest psychometric qualities (moderate construct validity) and over-simplified design, as it consists of only four questions that assess frequency and degree of adherence to fluid and dietary restrictions [9, 57]. In comparison, the ESRD-AQ has good construct and content validity and excellent test-retest reliability [9], which evidences that this tool is valuable for researchers and clinicians working with patients with kidney failure requiring maintenance in-center hemodialysis [9, 11] (see S4 Table).

In addition, the results showed that IDWG had the strongest correlations with self-reported fluid control and overall non-adherence, suggesting that this indicator may be the most sensitive marker to measure these outcomes in this population. Considering the results of each primary study, stronger correlations were found when IDWG values were averaged six to 12 sessions before the questionnaire application [52–54] (see S3 Table), which allows us to hypothesize that this may be a reliable way to use this parameter to measure adherence in future research. However, studies need to control for residual urinary output and extrarenal fluid losses to increase the reliability of this measure [48, 49], which only occurred in four of the 29 primary studies included in this review.

Finally, it is important to note that the strength of associations between self-reported adherence and clinical biomarkers was highly variable and heterogeneous. These results were somewhat expected as research shows this pattern in other age-related chronic diseases such as hypertension and diabetes, two highly comorbid health conditions in patients with kidney failure. In the context of in-center hemodialysis, modest associations may not necessarily reflect the lack of precision of the questionnaires to assess adherence, but rather a limitation of the clinical biomarkers used as criterion measures. In this sense, direct measurement of potassium and phosphorus intake can be confounded by several clinical outcomes such as the prescribed dialysis dosage (e.g., number of treatments per week and minutes of each dialysis session) and type (e.g., conventional hemodialysis vs on-line hemodiafiltration), presence of co-morbid conditions (e.g., presence of diabetes mellitus), and/or prescribed medication (e.g., class of phosphate binders) [59, 63–66]. Still, most primary studies failed to consider the impact of these confounding factors in their analysis, which may contribute to heterogeneity and the small strength associations between biomarkers and self-reported adherence.

Limitations

The primary studies only allowed conclusions to be drawn about the concurrent validity of self-report measures, leaving their predictive potential unexplored. The evidence of this systematic review with Hunter-Schmidt’s psychometric meta-analyses is also based on studies with different sample sizes, which makes it difficult to compare results. In addition, the results are based on studies with important methodological limitations such as the low control of potential confounders. Lastly, the individual meta-analyses provided evidence from a small number of studies with considerable heterogeneity; therefore, results should be interpreted with caution and viewed as preliminary.

Conclusion

The question of how to optimize the measurement of adherence in hemodialysis emerges as a key issue that needs to be addressed, given the wide variability in non-adherence rates between studies. The results showed that the test scores of the ESRD-AQ and DDFQ have medium to large strength associations with IDWG, SPO, and SK values, indicating that these questionnaires have reasonable concurrent criterion validity to measure fluid control, adherence to dietary restrictions, and overall non-adherence in patients undergoing in-center hemodialysis. However, several small-strength and considerably heterogeneous associations were found; therefore, the decision to use one questionnaire over another must be made with caution and consider the characteristics of the sample and the objectives of the study. Given that direct and indirect methods have their advantages and disadvantages, the combination of adherence measures is recommended to accurately assess this complex and multidimensional outcome. Future studies are needed to develop and assess the reliability of composite measures to increase confidence in hemodialysis adherence outcomes.

Supporting information

S1 Checklist. PRISMA 2020 checklist.

https://doi.org/10.1371/journal.pone.0276163.s001

(PDF)

S1 File. Protocol registered in PROSPERO.

https://doi.org/10.1371/journal.pone.0276163.s002

(PDF)

S1 Table. Search strategy for all databases.

https://doi.org/10.1371/journal.pone.0276163.s003

(PDF)

S2 Table. Critical appraisal of the primary studies.

https://doi.org/10.1371/journal.pone.0276163.s004

(PDF)

S3 Table. Confounding factors in primary studies.

https://doi.org/10.1371/journal.pone.0276163.s005

(PDF)

S4 Table. Summary of each self-report adherence measure analyzed in this systematic review.

https://doi.org/10.1371/journal.pone.0276163.s006

(PDF)

References

1. Bossola M, Tazza L. Postdialysis fatigue: A frequent and debilitating symptom. Semin Dial. 2016;29(3):222–227. pmid:26806879
- View Article
- PubMed/NCBI
- Google Scholar
2. Himmelfarb J, Vanholder R, Mehrotra R, Tonelli M. The current and future landscape of dialysis. Nat Rev Nephrol. 2020;16(10):573–585. pmid:32733095
- View Article
- PubMed/NCBI
- Google Scholar
3. Ozen N, Cinar FI, Askin D, Mut D, Turker T. Nonadherence in Hemodialysis Patients and Related Factors: A Multicenter Study. J Nurs Res. 2019;27(4):e36. pmid:30720548
- View Article
- PubMed/NCBI
- Google Scholar
4. Lok CE, Huber TS, Lee T, Shenoy S, Yevzlin AS, Abreo K, et al. KDOQI Clinical Practice Guideline for Vascular Access: 2019 Update. Am J Kidney Dis. 2020;75(4 Suppl 2):S1–S164. pmid:32778223
- View Article
- PubMed/NCBI
- Google Scholar
5. Mallamaci F, Pisano A, Tripepi G. Physical activity in chronic kidney disease and the EXerCise Introduction to Enhance trial. Nephrol Dial Transplant. 2020;35(Suppl 2):ii18–ii22. pmid:32162664
- View Article
- PubMed/NCBI
- Google Scholar
6. GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–733. pmid:32061315
- View Article
- PubMed/NCBI
- Google Scholar
7. Ahrari S, Moshki M, Bahrami M. The Relationship Between Social Support and Adherence of Dietary and Fluids Restrictions among Hemodialysis Patients in Iran. J Caring Sci. 2014;3(1):11–19. pmid:25276744
- View Article
- PubMed/NCBI
- Google Scholar
8. Efe D, Kocaöz S. Adherence to diet and fluid restriction of individuals on hemodialysis treatment and affecting factors in Turkey. J Nurs Sci. 2015;12(2):113–123. pmid:25123654
- View Article
- PubMed/NCBI
- Google Scholar
9. Kim Y, Evangelista LS, Phillips LR, Pavlish C, Kopple JD. The End-Stage Renal Disease Adherence Questionnaire (ESRD-AQ): testing the psychometric properties in patients receiving in-center hemodialysis. Nephrol Nurs J. 2010;37(4):377–393. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3077091/ pmid:20830945
- View Article
- PubMed/NCBI
- Google Scholar
10. Murali KM, Mullan J, Roodenrys S, Hassan HC, Lambert K, Lonergan M. Strategies to improve dietary, fluid, dialysis or medication adherence in patients with end stage kidney disease on dialysis: A systematic review and meta-analysis of randomized intervention trials. PLoS One. 2019;14(1):e0211479. pmid:30695068
- View Article
- PubMed/NCBI
- Google Scholar
11. Poveda V, Amado L, Filgueiras M, Teixeira L, Miranda V, Santos-Silva A, et al. End-stage renal disease adherence questionnaire: translation and validation to the portuguese language. Ren Fail. 2016;38(10):1633–1638. pmid:27764984
- View Article
- PubMed/NCBI
- Google Scholar
12. Wileman V, Chilcot J, Norton S, Hughes L, Wellsted D, Farrington K. Choosing not to take phosphate binders: the role of dialysis patients’ medication beliefs. Nephron Clin Pract. 2011;119(3):c205–c213. pmid:21832846
- View Article
- PubMed/NCBI
- Google Scholar
13. Qazi HA, Chen H, Zhu M. Factors influencing dialysis withdrawal: a scoping review. BMC Nephrol. 2018;19(1):96. pmid:29699499
- View Article
- PubMed/NCBI
- Google Scholar
14. Mukakarangwa M, Chironda G, Nkurunziza A, Ngendahayo F, Bhengu B. Motivators and barriers of adherence to hemodialysis among patients with end-stage renal disease (ESRD) in Rwanda: A qualitative study. Intern J of Africa Nurs Scien. 2020;13:100221.
- View Article
- Google Scholar
15. Murali KM, Lonergan M. Breaking the adherence barriers: Strategies to improve treatment adherence in dialysis patients. Semin Dial. 2020;33(6):475–485. pmid:33034402
- View Article
- PubMed/NCBI
- Google Scholar
16. Tao WW, Tao XM, Wang Y, Bi SH. Psycho-social and educational interventions for enhancing adherence to dialysis in adults with end-stage renal disease: A meta-analysis. J Clin Nurs. 2020;29(15–16):2834–2848. pmid:32320513
- View Article
- PubMed/NCBI
- Google Scholar
17. Kaveh K, Kimmel PL. Compliance in hemodialysis patients: multidimensional measures in search of a gold standard. Am J Kidney Dis. 2001;37(2):244–266. pmid:11157365
- View Article
- PubMed/NCBI
- Google Scholar
18. Berg KM, Arnsten JH. Practical and conceptual challenges in measuring antiretroviral adherence. J Acquir Immune Defic Syndr. 2006;43 Suppl 1(Suppl 1):S79–S87. pmid:17133207
- View Article
- PubMed/NCBI
- Google Scholar
19. Garfield S, Clifford S, Eliasson L, Barber N, Willson A. Suitability of measures of self-reported medication adherence for routine clinical use: a systematic review. BMC Med Res Methodol. 2011;11:149. pmid:22050830
- View Article
- PubMed/NCBI
- Google Scholar
20. Stirratt MJ, Dunbar-Jacob J, Crane HM, Simoni JM, Czajkowski S, Hilliard ME, et al. Self-report measures of medication adherence behavior: recommendations on optimal use. Transl Behav Med. 2015;5(4):470–482. pmid:26622919
- View Article
- PubMed/NCBI
- Google Scholar
21. Boateng GO, Neilands TB, Frongillo EA, Melgar-Quiñonez HR, Young SL. Best practices for developing and validating scales for health, social, and behavioral research: A Primer. Front Public Health. 2018;6:149. pmid:29942800
- View Article
- PubMed/NCBI
- Google Scholar
22. Cohen RJ, Swerdlik M. Psychological testing and assessment: An introduction to tests and measurement. 9th ed. New York: McGraw Hill; 2018.
23. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. pmid:33782057
- View Article
- PubMed/NCBI
- Google Scholar
24. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700. pmid:19622552
- View Article
- PubMed/NCBI
- Google Scholar
25. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 2009; 6(7): e1000097. pmid:19621072
- View Article
- PubMed/NCBI
- Google Scholar
26. Lambert K, Mullan J, Mansfield K. An integrative review of the methodology and findings regarding dietary adherence in end stage kidney disease. BMC Nephrol. 2017;18(1):318. pmid:29061163
- View Article
- PubMed/NCBI
- Google Scholar
27. Wallace EL, Rosner MH, Alscher MD, Schmitt CP, Jain A, Tentori F, et al. Remote Patient Management for Home Dialysis Patients. Kidney Int Rep. 2017;2(6):1009–1017. pmid:29634048
- View Article
- PubMed/NCBI
- Google Scholar
28. Ikizler TA, Burrowes JD, Byham-Gray LD, Campbell KL, Carrero JJ, Chan W, et al. KDOQI Clinical Practice Guideline for Nutrition in CKD: 2020 Update. Am J Kidney Dis. 2020;76(3 Suppl 1):S1–S107. pmid:32829751
- View Article
- PubMed/NCBI
- Google Scholar
29. National Kidney Foundation. KDOQI clinical practice guideline for hemodialysis adequacy: 2015 update. Am J Kidney Dis. 2015;66(5):884–930. pmid:26498416
- View Article
- PubMed/NCBI
- Google Scholar
30. Levin N, Eknoyan G, Pipp M, Steinberg E. National Kidney Foundation: Dialysis Outcome Quality Initiative—development of methodology for clinical practice guidelines. Nephrol Dial Transplant. 1997;12(10):2060–2063. pmid:9351065
- View Article
- PubMed/NCBI
- Google Scholar
31. Kopple JD. National kidney foundation K/DOQI clinical practice guidelines for nutrition in chronic renal failure. Am J Kidney Dis. 2001;37(1 Suppl 2):S66–S70. pmid:11158865
- View Article
- PubMed/NCBI
- Google Scholar
32. Hunter J. E., & Schmidt F. L. (2004). Methods of Meta-Analysis: Correcting Error and Bias in Research Findings (2nd ed.). Thousand Oaks, CA: Sage.
33. Higgins J.P. & Green S. (2011). Cochrane handbook for systematic reviews of interventions (version 5.1.0). The Cochrane Collaboration. www.handbook.cochrane.org.
- View Article
- Google Scholar
34. Brydges CR. Effect size guidelines, sample size calculations, and statistical power in gerontology. Innov Aging. 2019;3(4):igz036. Published 2019 Sep 4. pmid:31528719
- View Article
- PubMed/NCBI
- Google Scholar
35. Amado L, Ferreira N, Miranda V, Meireles P, Povera V, Ferreira R, et al. Self-reported medication adherence in patients with end-stage kidney disease undergoing online-hemodiafiltration. J Ren Care. 2015;41(4):231–238. pmid:26768798
- View Article
- PubMed/NCBI
- Google Scholar
36. Antony E, James M, Roy A, George S. Assessment of treatment adherence and its predictors in maintenance hemodialysis patients. Asian J Res Nephr. 2020; 3(3):22–29. Available from: https://www.journalajrn.com/index.php/AJRN/article/view/30107/56490
- View Article
- Google Scholar
37. Anuja M, Ashok M V. Compliance to fluid and dietary restriction and treatment adherence among chronic hemodialysis patients in a tertiary care hospital, Puducherry. Int J Health Allied Sci. 2020;9:34–8.
- View Article
- Google Scholar
38. Chan YM, Zalilah MS, Hii SZ. Determinants of compliance behaviors among patients undergoing hemodialysis in Malaysia. PLoS One. 2012;7(8):e41362. pmid:22870215
- View Article
- PubMed/NCBI
- Google Scholar
39. Chao CT, Huang JW, COGENT (COhort of GEriatric Nephrology in NTUH) study group. Geriatric syndromes are potential determinants of the medication adherence status in prevalent dialysis patients. PeerJ. 2016;4:e2122. pmid:27326380
- View Article
- PubMed/NCBI
- Google Scholar
40. Chen C, Zheng J, Driessnack M, et al. Health literacy as predictors of fluid management in people receiving hemodialysis in China: A structural equation modeling analysis. Patient Educ Couns. 2021;104(5):1159–1167. pmid:33129627
- View Article
- PubMed/NCBI
- Google Scholar
41. Daniels GB, Robinson JR, Walker CA. Adherence to Treatment by African Americans Undergoing Hemodialysis. Nephrol Nurs J. 2018;45(6):561–568.Available from: https://pubmed.ncbi.nlm.nih.gov/30585711/ pmid:30585711
- View Article
- PubMed/NCBI
- Google Scholar
42. Fincham D, Kagee A, Moosa R. Dietary and fluid adherence among haemodialysis patients attending public sector hospitals in the Western Cape. South Afr J Clin Nutr. 2008;21(2):7–12.
- View Article
- Google Scholar
43. Ghimire S, Peterson GM, Castelino RL, Jose MD, Zaidi ST. Medication Regimen Complexity and Adherence in Haemodialysis Patients: An Exploratory Study. Am J Nephrol. 2016;43(5):318–324. pmid:27166159
- View Article
- PubMed/NCBI
- Google Scholar
44. Joson CG, Henry SL, Kim S, Cheung MY, Parab P, Abcar AC, et al. Patient-Reported Factors Associated With Poor Phosphorus Control in a Maintenance Hemodialysis Population. J Ren Nutr. 2016;26(3):141–148. pmid:26614738
- View Article
- PubMed/NCBI
- Google Scholar
45. Kara B, Caglar K, Kilic S. Nonadherence with diet and fluid restrictions and perceived social support in patients receiving hemodialysis. J Nurs Scholarsh. 2007;39(3):243–248. pmid:17760797
- View Article
- PubMed/NCBI
- Google Scholar
46. Katalinić L, Bašić-jukić N, Resić H, Vujičić B, Kudumija B, Ratković M, et al. Self-reported medication adherence correlates with phosphate levels, residual diuresis and nutritional status in hemodialysis patients: an international multicenter study. Acta Med Croatica. 2017; 3:151–155. Available from: https://hrcak.srce.hr/file/285693
- View Article
- Google Scholar
47. Kauric-Klein Z. Predictors of nonadherence with blood pressure regimens in hemodialysis. Patient Prefer Adherence. 2013;7:973–980. pmid:24101864
- View Article
- PubMed/NCBI
- Google Scholar
48. Khalil AA, Darawad M, Al Gamal E, Hamdan-Mansour AM, Abed MA. Predictors of dietary and fluid non-adherence in Jordanian patients with end-stage renal disease receiving haemodialysis: a cross-sectional study. J Clin Nurs. 2013;22(1–2):127–136. pmid:22686337
- View Article
- PubMed/NCBI
- Google Scholar
49. Kugler C, Maeding I, Russell CL. Non-adherence in patients on chronic hemodialysis: an international comparison study. J Nephrol. 2011;24(3):366–375. pmid:20954134
- View Article
- PubMed/NCBI
- Google Scholar
50. Lim JH, Chinna K, Khosla P, Karupaiah T, Daud ZAM. Understanding How Nutrition Literacy Links to Dietary Adherence in Patients Undergoing Maintenance Hemodialysis: A Theoretical Exploration Using Partial Least Squares Structural Equation Modeling. Int J Environ Res Public Health. 2020;17(20):7479. pmid:33066603
- View Article
- PubMed/NCBI
- Google Scholar
51. Mellon L, Regan D, Curtis R. Factors influencing adherence among Irish haemodialysis patients. Patient Educ Couns. 2013;92(1):88–93. pmid:23481215
- View Article
- PubMed/NCBI
- Google Scholar
52. Mollaoğlu M, Kayataş M. Disability is associated with nonadherence to diet and fluid restrictions in end-stage renal disease patients undergoing maintenance hemodialysis. Int Urol Nephrol. 2015;47(11):1863–1870. pmid:26399715
- View Article
- PubMed/NCBI
- Google Scholar
53. Naalweh KS, Barakat MA, Sweileh MW, Al-Jabi SW, Sweileh WM, Zyoud SH. Treatment adherence and perception in patients on maintenance hemodialysis: a cross—sectional study from Palestine. BMC Nephrol. 2017;18(1):178. pmid:28558719
- View Article
- PubMed/NCBI
- Google Scholar
54. Ok E, Kutlu Y. The Effect of Motivational Interviewing on Adherence to Treatment and Quality of Life in Chronic Hemodialysis Patients: A Randomized Controlled Trial. Clinical Nursing Research. Intern J Car Scien. 2020;12(1):423–429. pmid:33225724
- View Article
- PubMed/NCBI
- Google Scholar
55. Umeukeje EM, Merighi JR, Browne T, Victoroff JN, Umanath K, Lewis JB, et al. Self-Motivation Is Associated With Phosphorus Control in End-Stage Renal Disease. J Ren Nutr. 2015;25(5):433–439. pmid:25912398
- View Article
- PubMed/NCBI
- Google Scholar
56. Umeukeje EM, Merighi JR, Browne T, Wild M, Alsmaan H, Umanath K, et al. Health care providers’ support of patients’ autonomy, phosphate medication adherence, race and gender in end stage renal disease. J Behav Med. 2016;39(6):1104–1114. pmid:27167227
- View Article
- PubMed/NCBI
- Google Scholar
57. Vlaminck H, Maes B, Jacobs A, Reyntjens S, Evers G. The dialysis diet and fluid non-adherence questionnaire: validity testing of a self-report instrument for clinical practice. J Clin Nurs. 2001;10(5):707–715. pmid:11822521
- View Article
- PubMed/NCBI
- Google Scholar
58. Wileman V, Farrington K, Wellsted D, Almond M, Davenport A, Chilcot J. Medication beliefs are associated with phosphate binder non-adherence in hyperphosphatemic haemodialysis patients. Br J Health Psychol. 2015;20(3):563–578. pmid:25209368
- View Article
- PubMed/NCBI
- Google Scholar
59. Blankestijn PJ, Grooteman MP, Nube MJ, Bots ML. Clinical evidence on haemodiafiltration. Nephrol Dial Transplant. 2018;33(suppl_3):iii53–iii58. pmid:30281128
- View Article
- PubMed/NCBI
- Google Scholar
60. Fotheringham J, Smith MT, Froissart M, Kronenberg F, Stenvinkel P, Floege J, et al. Hospitalization and mortality following non-attendance for hemodialysis according to dialysis day of the week: a European cohort study. BMC Nephrol. 2020;21(1):218. pmid:32517695
- View Article
- PubMed/NCBI
- Google Scholar
61. Lousa I, Reis F, Beirão I, Alves R, Belo L, Santos-Silva A. New Potential Biomarkers for Chronic Kidney Disease Management-A Review of the Literature. Int J Mol Sci. 2020;22(1):43. pmid:33375198
- View Article
- PubMed/NCBI
- Google Scholar
62. Kim Y, Evangelista LS. Development and cultural adaptation of the Spanish version of the End Stage Renal Disease Adherence Questionnaire (SESRD-AQ). Nephrol Nurs J. 2013;40(6):493–507. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4447134/ pmid:24579396
- View Article
- PubMed/NCBI
- Google Scholar
63. Barreto FC, Barreto DV, Massy ZA, Drüeke TB. Strategies for Phosphate Control in Patients With CKD. Kidney Int Rep. 2019;4(8):1043–1056. pmid:31440695
- View Article
- PubMed/NCBI
- Google Scholar
64. Chan AHY, Horne R, Hankins M, Chisari C. The Medication Adherence Report Scale: A measurement tool for eliciting patients’ reports of nonadherence. Br J Clin Pharmacol. 2020;86(7):1281–1288. pmid:31823381
- View Article
- PubMed/NCBI
- Google Scholar
65. Janežič A, Locatelli I, Kos M. Criterion validity of 8-item Morisky Medication Adherence Scale in patients with asthma. PLoS One. 2017;12(11):e0187835. pmid:29190693
- View Article
- PubMed/NCBI
- Google Scholar
66. Marienne J, Laville SM, Caillard P, Batteux B, Gras-Champel V, Masmoudi K, et al. Evaluation of changes over time in the drug burden and medication regimen complexity in ESRD patients before and after renal transplantation. Kidney Int Rep. 2020;6(1):128–137. pmid:33426392
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Bossola M, Tazza L. Postdialysis fatigue: A frequent and debilitating symptom. Semin Dial. 2016;29(3):222–227. pmid:26806879
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Himmelfarb J, Vanholder R, Mehrotra R, Tonelli M. The current and future landscape of dialysis. Nat Rev Nephrol. 2020;16(10):573–585. pmid:32733095
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Ozen N, Cinar FI, Askin D, Mut D, Turker T. Nonadherence in Hemodialysis Patients and Related Factors: A Multicenter Study. J Nurs Res. 2019;27(4):e36. pmid:30720548
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Lok CE, Huber TS, Lee T, Shenoy S, Yevzlin AS, Abreo K, et al. KDOQI Clinical Practice Guideline for Vascular Access: 2019 Update. Am J Kidney Dis. 2020;75(4 Suppl 2):S1–S164. pmid:32778223
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Mallamaci F, Pisano A, Tripepi G. Physical activity in chronic kidney disease and the EXerCise Introduction to Enhance trial. Nephrol Dial Transplant. 2020;35(Suppl 2):ii18–ii22. pmid:32162664
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–733. pmid:32061315
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Ahrari S, Moshki M, Bahrami M. The Relationship Between Social Support and Adherence of Dietary and Fluids Restrictions among Hemodialysis Patients in Iran. J Caring Sci. 2014;3(1):11–19. pmid:25276744
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Efe D, Kocaöz S. Adherence to diet and fluid restriction of individuals on hemodialysis treatment and affecting factors in Turkey. J Nurs Sci. 2015;12(2):113–123. pmid:25123654
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Kim Y, Evangelista LS, Phillips LR, Pavlish C, Kopple JD. The End-Stage Renal Disease Adherence Questionnaire (ESRD-AQ): testing the psychometric properties in patients receiving in-center hemodialysis. Nephrol Nurs J. 2010;37(4):377–393. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3077091/ pmid:20830945
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref10] 10. Murali KM, Mullan J, Roodenrys S, Hassan HC, Lambert K, Lonergan M. Strategies to improve dietary, fluid, dialysis or medication adherence in patients with end stage kidney disease on dialysis: A systematic review and meta-analysis of randomized intervention trials. PLoS One. 2019;14(1):e0211479. pmid:30695068
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref11] 11. Poveda V, Amado L, Filgueiras M, Teixeira L, Miranda V, Santos-Silva A, et al. End-stage renal disease adherence questionnaire: translation and validation to the portuguese language. Ren Fail. 2016;38(10):1633–1638. pmid:27764984
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref12] 12. Wileman V, Chilcot J, Norton S, Hughes L, Wellsted D, Farrington K. Choosing not to take phosphate binders: the role of dialysis patients’ medication beliefs. Nephron Clin Pract. 2011;119(3):c205–c213. pmid:21832846
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref13] 13. Qazi HA, Chen H, Zhu M. Factors influencing dialysis withdrawal: a scoping review. BMC Nephrol. 2018;19(1):96. pmid:29699499
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref14] 14. Mukakarangwa M, Chironda G, Nkurunziza A, Ngendahayo F, Bhengu B. Motivators and barriers of adherence to hemodialysis among patients with end-stage renal disease (ESRD) in Rwanda: A qualitative study. Intern J of Africa Nurs Scien. 2020;13:100221.
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref15] 15. Murali KM, Lonergan M. Breaking the adherence barriers: Strategies to improve treatment adherence in dialysis patients. Semin Dial. 2020;33(6):475–485. pmid:33034402
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref16] 16. Tao WW, Tao XM, Wang Y, Bi SH. Psycho-social and educational interventions for enhancing adherence to dialysis in adults with end-stage renal disease: A meta-analysis. J Clin Nurs. 2020;29(15–16):2834–2848. pmid:32320513
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref17] 17. Kaveh K, Kimmel PL. Compliance in hemodialysis patients: multidimensional measures in search of a gold standard. Am J Kidney Dis. 2001;37(2):244–266. pmid:11157365
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref18] 18. Berg KM, Arnsten JH. Practical and conceptual challenges in measuring antiretroviral adherence. J Acquir Immune Defic Syndr. 2006;43 Suppl 1(Suppl 1):S79–S87. pmid:17133207
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref19] 19. Garfield S, Clifford S, Eliasson L, Barber N, Willson A. Suitability of measures of self-reported medication adherence for routine clinical use: a systematic review. BMC Med Res Methodol. 2011;11:149. pmid:22050830
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref20] 20. Stirratt MJ, Dunbar-Jacob J, Crane HM, Simoni JM, Czajkowski S, Hilliard ME, et al. Self-report measures of medication adherence behavior: recommendations on optimal use. Transl Behav Med. 2015;5(4):470–482. pmid:26622919
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref21] 21. Boateng GO, Neilands TB, Frongillo EA, Melgar-Quiñonez HR, Young SL. Best practices for developing and validating scales for health, social, and behavioral research: A Primer. Front Public Health. 2018;6:149. pmid:29942800
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref22] 22. Cohen RJ, Swerdlik M. Psychological testing and assessment: An introduction to tests and measurement. 9th ed. New York: McGraw Hill; 2018.

[ref23] 23. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. pmid:33782057
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref24] 24. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700. pmid:19622552
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref25] 25. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 2009; 6(7): e1000097. pmid:19621072
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref26] 26. Lambert K, Mullan J, Mansfield K. An integrative review of the methodology and findings regarding dietary adherence in end stage kidney disease. BMC Nephrol. 2017;18(1):318. pmid:29061163
View Article
PubMed/NCBI
Google Scholar

[98] View Article

[99] PubMed/NCBI

[100] Google Scholar

[ref27] 27. Wallace EL, Rosner MH, Alscher MD, Schmitt CP, Jain A, Tentori F, et al. Remote Patient Management for Home Dialysis Patients. Kidney Int Rep. 2017;2(6):1009–1017. pmid:29634048
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref28] 28. Ikizler TA, Burrowes JD, Byham-Gray LD, Campbell KL, Carrero JJ, Chan W, et al. KDOQI Clinical Practice Guideline for Nutrition in CKD: 2020 Update. Am J Kidney Dis. 2020;76(3 Suppl 1):S1–S107. pmid:32829751
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref29] 29. National Kidney Foundation. KDOQI clinical practice guideline for hemodialysis adequacy: 2015 update. Am J Kidney Dis. 2015;66(5):884–930. pmid:26498416
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref30] 30. Levin N, Eknoyan G, Pipp M, Steinberg E. National Kidney Foundation: Dialysis Outcome Quality Initiative—development of methodology for clinical practice guidelines. Nephrol Dial Transplant. 1997;12(10):2060–2063. pmid:9351065
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref31] 31. Kopple JD. National kidney foundation K/DOQI clinical practice guidelines for nutrition in chronic renal failure. Am J Kidney Dis. 2001;37(1 Suppl 2):S66–S70. pmid:11158865
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref32] 32. Hunter J. E., & Schmidt F. L. (2004). Methods of Meta-Analysis: Correcting Error and Bias in Research Findings (2nd ed.). Thousand Oaks, CA: Sage.

[ref33] 33. Higgins J.P. & Green S. (2011). Cochrane handbook for systematic reviews of interventions (version 5.1.0). The Cochrane Collaboration. www.handbook.cochrane.org.
View Article
Google Scholar

[123] View Article

[124] Google Scholar

[ref34] 34. Brydges CR. Effect size guidelines, sample size calculations, and statistical power in gerontology. Innov Aging. 2019;3(4):igz036. Published 2019 Sep 4. pmid:31528719
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref35] 35. Amado L, Ferreira N, Miranda V, Meireles P, Povera V, Ferreira R, et al. Self-reported medication adherence in patients with end-stage kidney disease undergoing online-hemodiafiltration. J Ren Care. 2015;41(4):231–238. pmid:26768798
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref36] 36. Antony E, James M, Roy A, George S. Assessment of treatment adherence and its predictors in maintenance hemodialysis patients. Asian J Res Nephr. 2020; 3(3):22–29. Available from: https://www.journalajrn.com/index.php/AJRN/article/view/30107/56490
View Article
Google Scholar

[134] View Article

[135] Google Scholar

[ref37] 37. Anuja M, Ashok M V. Compliance to fluid and dietary restriction and treatment adherence among chronic hemodialysis patients in a tertiary care hospital, Puducherry. Int J Health Allied Sci. 2020;9:34–8.
View Article
Google Scholar

[137] View Article

[138] Google Scholar

[ref38] 38. Chan YM, Zalilah MS, Hii SZ. Determinants of compliance behaviors among patients undergoing hemodialysis in Malaysia. PLoS One. 2012;7(8):e41362. pmid:22870215
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref39] 39. Chao CT, Huang JW, COGENT (COhort of GEriatric Nephrology in NTUH) study group. Geriatric syndromes are potential determinants of the medication adherence status in prevalent dialysis patients. PeerJ. 2016;4:e2122. pmid:27326380
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref40] 40. Chen C, Zheng J, Driessnack M, et al. Health literacy as predictors of fluid management in people receiving hemodialysis in China: A structural equation modeling analysis. Patient Educ Couns. 2021;104(5):1159–1167. pmid:33129627
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref41] 41. Daniels GB, Robinson JR, Walker CA. Adherence to Treatment by African Americans Undergoing Hemodialysis. Nephrol Nurs J. 2018;45(6):561–568.Available from: https://pubmed.ncbi.nlm.nih.gov/30585711/ pmid:30585711
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref42] 42. Fincham D, Kagee A, Moosa R. Dietary and fluid adherence among haemodialysis patients attending public sector hospitals in the Western Cape. South Afr J Clin Nutr. 2008;21(2):7–12.
View Article
Google Scholar

[156] View Article

[157] Google Scholar

[ref43] 43. Ghimire S, Peterson GM, Castelino RL, Jose MD, Zaidi ST. Medication Regimen Complexity and Adherence in Haemodialysis Patients: An Exploratory Study. Am J Nephrol. 2016;43(5):318–324. pmid:27166159
View Article
PubMed/NCBI
Google Scholar

[159] View Article

[160] PubMed/NCBI

[161] Google Scholar

[ref44] 44. Joson CG, Henry SL, Kim S, Cheung MY, Parab P, Abcar AC, et al. Patient-Reported Factors Associated With Poor Phosphorus Control in a Maintenance Hemodialysis Population. J Ren Nutr. 2016;26(3):141–148. pmid:26614738
View Article
PubMed/NCBI
Google Scholar

[163] View Article

[164] PubMed/NCBI

[165] Google Scholar

[ref45] 45. Kara B, Caglar K, Kilic S. Nonadherence with diet and fluid restrictions and perceived social support in patients receiving hemodialysis. J Nurs Scholarsh. 2007;39(3):243–248. pmid:17760797
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref46] 46. Katalinić L, Bašić-jukić N, Resić H, Vujičić B, Kudumija B, Ratković M, et al. Self-reported medication adherence correlates with phosphate levels, residual diuresis and nutritional status in hemodialysis patients: an international multicenter study. Acta Med Croatica. 2017; 3:151–155. Available from: https://hrcak.srce.hr/file/285693
View Article
Google Scholar

[171] View Article

[172] Google Scholar

[ref47] 47. Kauric-Klein Z. Predictors of nonadherence with blood pressure regimens in hemodialysis. Patient Prefer Adherence. 2013;7:973–980. pmid:24101864
View Article
PubMed/NCBI
Google Scholar

[174] View Article

[175] PubMed/NCBI

[176] Google Scholar

[ref48] 48. Khalil AA, Darawad M, Al Gamal E, Hamdan-Mansour AM, Abed MA. Predictors of dietary and fluid non-adherence in Jordanian patients with end-stage renal disease receiving haemodialysis: a cross-sectional study. J Clin Nurs. 2013;22(1–2):127–136. pmid:22686337
View Article
PubMed/NCBI
Google Scholar

[178] View Article

[179] PubMed/NCBI

[180] Google Scholar

[ref49] 49. Kugler C, Maeding I, Russell CL. Non-adherence in patients on chronic hemodialysis: an international comparison study. J Nephrol. 2011;24(3):366–375. pmid:20954134
View Article
PubMed/NCBI
Google Scholar

[182] View Article

[183] PubMed/NCBI

[184] Google Scholar

[ref50] 50. Lim JH, Chinna K, Khosla P, Karupaiah T, Daud ZAM. Understanding How Nutrition Literacy Links to Dietary Adherence in Patients Undergoing Maintenance Hemodialysis: A Theoretical Exploration Using Partial Least Squares Structural Equation Modeling. Int J Environ Res Public Health. 2020;17(20):7479. pmid:33066603
View Article
PubMed/NCBI
Google Scholar

[186] View Article

[187] PubMed/NCBI

[188] Google Scholar

[ref51] 51. Mellon L, Regan D, Curtis R. Factors influencing adherence among Irish haemodialysis patients. Patient Educ Couns. 2013;92(1):88–93. pmid:23481215
View Article
PubMed/NCBI
Google Scholar

[190] View Article

[191] PubMed/NCBI

[192] Google Scholar

[ref52] 52. Mollaoğlu M, Kayataş M. Disability is associated with nonadherence to diet and fluid restrictions in end-stage renal disease patients undergoing maintenance hemodialysis. Int Urol Nephrol. 2015;47(11):1863–1870. pmid:26399715
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref53] 53. Naalweh KS, Barakat MA, Sweileh MW, Al-Jabi SW, Sweileh WM, Zyoud SH. Treatment adherence and perception in patients on maintenance hemodialysis: a cross—sectional study from Palestine. BMC Nephrol. 2017;18(1):178. pmid:28558719
View Article
PubMed/NCBI
Google Scholar

[198] View Article

[199] PubMed/NCBI

[200] Google Scholar

[ref54] 54. Ok E, Kutlu Y. The Effect of Motivational Interviewing on Adherence to Treatment and Quality of Life in Chronic Hemodialysis Patients: A Randomized Controlled Trial. Clinical Nursing Research. Intern J Car Scien. 2020;12(1):423–429. pmid:33225724
View Article
PubMed/NCBI
Google Scholar

[202] View Article

[203] PubMed/NCBI

[204] Google Scholar

[ref55] 55. Umeukeje EM, Merighi JR, Browne T, Victoroff JN, Umanath K, Lewis JB, et al. Self-Motivation Is Associated With Phosphorus Control in End-Stage Renal Disease. J Ren Nutr. 2015;25(5):433–439. pmid:25912398
View Article
PubMed/NCBI
Google Scholar

[206] View Article

[207] PubMed/NCBI

[208] Google Scholar

[ref56] 56. Umeukeje EM, Merighi JR, Browne T, Wild M, Alsmaan H, Umanath K, et al. Health care providers’ support of patients’ autonomy, phosphate medication adherence, race and gender in end stage renal disease. J Behav Med. 2016;39(6):1104–1114. pmid:27167227
View Article
PubMed/NCBI
Google Scholar

[210] View Article

[211] PubMed/NCBI

[212] Google Scholar

[ref57] 57. Vlaminck H, Maes B, Jacobs A, Reyntjens S, Evers G. The dialysis diet and fluid non-adherence questionnaire: validity testing of a self-report instrument for clinical practice. J Clin Nurs. 2001;10(5):707–715. pmid:11822521
View Article
PubMed/NCBI
Google Scholar

[214] View Article

[215] PubMed/NCBI

[216] Google Scholar

[ref58] 58. Wileman V, Farrington K, Wellsted D, Almond M, Davenport A, Chilcot J. Medication beliefs are associated with phosphate binder non-adherence in hyperphosphatemic haemodialysis patients. Br J Health Psychol. 2015;20(3):563–578. pmid:25209368
View Article
PubMed/NCBI
Google Scholar

[218] View Article

[219] PubMed/NCBI

[220] Google Scholar

[ref59] 59. Blankestijn PJ, Grooteman MP, Nube MJ, Bots ML. Clinical evidence on haemodiafiltration. Nephrol Dial Transplant. 2018;33(suppl_3):iii53–iii58. pmid:30281128
View Article
PubMed/NCBI
Google Scholar

[222] View Article

[223] PubMed/NCBI

[224] Google Scholar

[ref60] 60. Fotheringham J, Smith MT, Froissart M, Kronenberg F, Stenvinkel P, Floege J, et al. Hospitalization and mortality following non-attendance for hemodialysis according to dialysis day of the week: a European cohort study. BMC Nephrol. 2020;21(1):218. pmid:32517695
View Article
PubMed/NCBI
Google Scholar

[226] View Article

[227] PubMed/NCBI

[228] Google Scholar

[ref61] 61. Lousa I, Reis F, Beirão I, Alves R, Belo L, Santos-Silva A. New Potential Biomarkers for Chronic Kidney Disease Management-A Review of the Literature. Int J Mol Sci. 2020;22(1):43. pmid:33375198
View Article
PubMed/NCBI
Google Scholar

[230] View Article

[231] PubMed/NCBI

[232] Google Scholar

[ref62] 62. Kim Y, Evangelista LS. Development and cultural adaptation of the Spanish version of the End Stage Renal Disease Adherence Questionnaire (SESRD-AQ). Nephrol Nurs J. 2013;40(6):493–507. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4447134/ pmid:24579396
View Article
PubMed/NCBI
Google Scholar

[234] View Article

[235] PubMed/NCBI

[236] Google Scholar

[ref63] 63. Barreto FC, Barreto DV, Massy ZA, Drüeke TB. Strategies for Phosphate Control in Patients With CKD. Kidney Int Rep. 2019;4(8):1043–1056. pmid:31440695
View Article
PubMed/NCBI
Google Scholar

[238] View Article

[239] PubMed/NCBI

[240] Google Scholar

[ref64] 64. Chan AHY, Horne R, Hankins M, Chisari C. The Medication Adherence Report Scale: A measurement tool for eliciting patients’ reports of nonadherence. Br J Clin Pharmacol. 2020;86(7):1281–1288. pmid:31823381
View Article
PubMed/NCBI
Google Scholar

[242] View Article

[243] PubMed/NCBI

[244] Google Scholar

[ref65] 65. Janežič A, Locatelli I, Kos M. Criterion validity of 8-item Morisky Medication Adherence Scale in patients with asthma. PLoS One. 2017;12(11):e0187835. pmid:29190693
View Article
PubMed/NCBI
Google Scholar

[246] View Article

[247] PubMed/NCBI

[248] Google Scholar

[ref66] 66. Marienne J, Laville SM, Caillard P, Batteux B, Gras-Champel V, Masmoudi K, et al. Evaluation of changes over time in the drug burden and medication regimen complexity in ESRD patients before and after renal transplantation. Kidney Int Rep. 2020;6(1):128–137. pmid:33426392
View Article
PubMed/NCBI
Google Scholar

[250] View Article

[251] PubMed/NCBI

[252] Google Scholar

Figures

Abstract

Introduction

Methods

Eligibility criteria

Searches

Study selection and data collection

Critical appraisal

Strategy for data synthesis

Results

Study selection

Study characteristics

Critical appraisal

Participants’ characteristics

Self-reported adherence

Clinical biomarkers of treatment adherence

Criterion validity

Discussion

Limitations

Conclusion

Supporting information

S1 Checklist. PRISMA 2020 checklist.

S1 File. Protocol registered in PROSPERO.

S1 Table. Search strategy for all databases.

S2 Table. Critical appraisal of the primary studies.

S3 Table. Confounding factors in primary studies.

S4 Table. Summary of each self-report adherence measure analyzed in this systematic review.

References