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RESEARCH ARTICLE
Structural equation modelling of the role of
cognition in functional interference and
treatment nonadherence among
haemodialysis patients
Frederick H. F. ChanID
1
, Pearl Sim
1
, Phoebe X. H. LimID
1
, Xiaoli Zhu
1,2
, Jimmy Lee
1,3
,
Sabrina Haroon
4
, Titus Wai Leong Lau
4
, Allen Yan Lun Liu
5
, Behram A. Khan
6,7,8
, Jason C.
J. Choo
9,10
, Konstadina GrivaID
1
*
1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, 2Nursing
Services, National Healthcare Group Polyclinics, Singapore, Singapore, 3Institute of Mental Health,
Singapore, Singapore, 4Division of Nephrology, Department of Medicine, National University Hospital,
Singapore, Singapore, 5Khoo Teck Puat Hospital, Singapore, Singapore, 6Renal Health Services,
Singapore, Singapore, 7National University Health System, Singapore, Singapore, 8Duke-NUS Medical
School, Singapore, Singapore, 9National Kidney Foundation, Singapore, Singapore, 10 Department of
Renal Medicine, Singapore General Hospital, Singapore, Singapore
*konstadina.griva@ntu.edu.sg
Abstract
Background and objectives
Cognitive impairment is common in haemodialysis patients and associated with adverse
health outcomes. This may be due to cognitive impairments interfering with daily functioning
and self-care, but evidence is limited. This cross-sectional study aims to explore the interre-
lationships between cognition and functional outcomes in haemodialysis patients.
Methods
Haemodialysis patients completed measures of objective cognitive function (Montreal Cog-
nitive Assessment), everyday problem-solving skills (scenario-based task), and subjective
cognitive complaints (self-report). Participants also self-reported sociodemographic infor-
mation, functional interference, treatment nonadherence, and mood and fatigue symptoms.
Patients’ clinical data including comorbidities and lab results were extracted from medical
record. Structural equation modelling was performed.
Results
A total of 268 haemodialysis patients (mean age = 59.87 years; 42.5% female) partici-
pated. The final model showed satisfactory fit: CFI = 0.916, TLI = 0.905, RMSEA = 0.033
(90% confidence interval 0.024 to 0.041), SRMR = 0.066, χ
2
(493) = 618.573 (p<.001).
There was a negative association between objective cognitive function and subjective cog-
nitive complaints. Cognitive complaints were positively associated with both functional
interference and treatment nonadherence, whereas objective performance was not.
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OPEN ACCESS
Citation: Chan FHF, Sim P, Lim PXH, Zhu X, Lee J,
Haroon S, et al. (2024) Structural equation
modelling of the role of cognition in functional
interference and treatment nonadherence among
haemodialysis patients. PLoS ONE 19(10):
e0312039. https://doi.org/10.1371/journal.
pone.0312039
Editor: Henry H.L. Wu, Kolling Institute of Medical
Research, The University of Sydney, AUSTRALIA
Received: May 6, 2024
Accepted: September 30, 2024
Published: October 17, 2024
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0312039
Copyright: ©2024 Chan 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 Statement: All relevant data are
within the paper and its Supporting information
files.
Everyday problem-solving skills emerged as a distinct aspect of cognition not associated
with objective performance or subjective complaints, but had additive utility in predicting
functional interference.
Conclusions
Subjective cognitive complaints and everyday problem-solving skills appear to be stronger
predictors of functional variables compared to objective performance based on traditional
tests. Routine screening of everyday cognitive difficulties may allow for early identification of
dialysis patients at risk of cognitive impairment, functional interference, treatment nonadher-
ence, and poor clinical outcomes.
Introduction
End-stage renal disease (ESRD) is the most advanced stage of chronic kidney disease where
kidney function is irreversibly lost, necessitating dialysis or transplantation [1,2]. Cognitive
impairments (CIs) are common in ESRD patients receiving haemodialysis (HD) treatment,
with more than 70% exhibiting at least mild impairments in one or more domains such as
attention, memory, and executive function [3–6]. “Brain fog” has been a common complaint
among dialysis patients [7] and a popular topic of discussion in online patient forums [8]. CIs
in ESRD are associated with adverse health outcomes including dialysis withdrawal, hospitali-
sation, and mortality [3,9–11]. These associations are assumed to be due to CIs interfering
with daily functioning, decision-making, and self-management capabilities, however empirical
evidence is scarce.
CIs are typically accompanied by functional interference because performance of everyday
activities (e.g., personal hygiene, managing finances, etc.) is dependent upon the integrity of
cognitive, motor, and sensory-perceptual skills [12]. In the context of ESRD, a specific aspect
of daily functioning that is of particular relevance to patients is self-care and treatment adher-
ence. HD patients are prescribed complex medical regimen that requires them to take daily
medications, follow strict dietary guidelines, control fluid intake, and attend thrice-weekly
dialysis sessions. These self-care activities are cognitively demanding and can be challenging
for those with CIs. Medication taking, for instance, requires multiple cognitive processes
including encoding and storage of health information (e.g., understanding the importance of
taking medicine), executive function (e.g., developing a plan to adhere), prospective memory
(e.g., remembering to take medicine on time), working memory (e.g., keeping the intention to
take medicines active while preparing to take it), and source monitoring (e.g., remembering
whether the medicine has been taken) [13].
Clearly cognition plays an essential role in HD patients’ daily functioning and ability to
maintain and optimise their health by following the prescribed medical regimen. However,
currently we lack a comprehensive picture of the interplay between aspects of cognition and
aspects of daily functioning in this population. Studies using neuropsychological tests, the gold
standard measure of cognition, have found positive associations between objective cognitive
ability and functional independence in dialysis patients [14]. Other studies have used self-
reported measures to assess subjective cognitive complaints (SCCs). More frequent SCCs in
HD patients have been found to be associated with greater functional impairment [15], and
worse treatment adherence indicated by self-reports and laboratory results [16,17]. Further-
more, scenario-based tasks that assess everyday problem-solving skills have been used. These
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Funding: This work was supported by the
Venerable Yen Pei-National Kidney Foundation
Research Fund, Singapore [grant number NKFRC/
2021/01/02]. KG received research funding from
National Kidney Foundation Singapore. The funding
sources had no role in the study design,
recruitment of patients, data collection, analysis,
interpretation of the results, writing of the
manuscript, or decision to submit the manuscript
for publication.
Competing interests: The authors have declared
that no competing interests exist.
tasks require participants to generate solutions in response to problem scenarios, and are
thought to reflect executive ability in real-world contexts [18]. Two studies found that everyday
problem-solving skills positively predicted medication adherence in kidney transplant recipi-
ents [19,20].
Despite these promising findings, a study that comprehensively examines the complex asso-
ciations between cognition and real-world functioning in HD patients is lacking. As such, the
current cross-sectional study aims to adopt structural equation modelling (SEM) analysis to
disentangle the interrelationships among multiple aspects of cognition (i.e., objective perfor-
mance, subjective complaint, and everyday problem-solving) and key outcomes (i.e., func-
tional interference, treatment nonadherence, and clinical endpoints) in HD patients. SEM is a
powerful and flexible statistical technique that integrates factor analysis, path analysis, and
regression into a single framework [21]. It simultaneously accounts for multiple direct and
indirect associations among a range of variables, hence allowing for the validation of complex
theoretical models using a unified approach [21]. With this advanced statistical technique, this
study will map key cognitive indicators of functional interference and nonadherence so that
future interventions and support strategies could be developed to target the cognitive chal-
lenges that interfere with these key endpoints.
The hypothesised model to be tested in the current study is shown in Fig 1. We hypothe-
sised that objective cognitive function and everyday problem-solving skills would covary
(hence the bidirectional arrow) since they are both considered indicators of distinct aspects
of cognitive abilities [18]. While objective tests reflect individuals’ cognitive performance in
optimal conditions, problem-solving tasks reflect individuals’ problem-solving ability in
everyday contexts. We also hypothesised that these two variables would directly contribute
to SCCs since individuals with worse cognitive performance may experience more cogni-
tive difficulties in daily lives, hence reporting more frequent SCCs [7,22]. Furthermore, the
three cognitive indicators were each hypothesised to have a direct effect on functional inter-
ference and treatment nonadherence based on previous evidence [14–17,19,20].
Fig 1. Hypothesised model of associations between cognition, functional interference, and treatment nonadherence.
https://doi.org/10.1371/journal.pone.0312039.g001
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Materials and methods
Participants
A convenience sample of HD patients was recruited from the National Kidney Foundation
Singapore (NKF) between May 19 and November 4 2022. Ten NKF dialysis centres were
selected to ensure geographical representation of dialysis centres across Singapore. The inclu-
sion criteria were: (1) 21 years of age or older, (2) an estimated glomerular filtration rate lower
than 15mL/min/1.73m
2
, (3) having undergone HD treatment for at least 3 months, and (4) flu-
ent in either English or Mandarin. The exclusion criteria were: (1) only fluent in dialects, (2)
unable to give consent due to psychiatric diagnoses or established diagnosis of dementia, or
(3) unable to complete survey due to visual or hearing impairments.
Procedure
The study protocol was approved by the Institutional Review Board of the Nanyang Techno-
logical University (NTU-IRB-2021-025). A list of eligible patients was provided by the nurse
managers of each dialysis centre. Study team members fluent in the patients’ preferred lan-
guage approached each patient during their dialysis sessions. After obtaining written consent,
the following instruments were administered. Upon completion, patients were given a cash
compensation.
Measures
Objective cognitive function. The Montreal Cognitive Assessment (MoCA) was used to
assess objective cognitive function [23]. The MoCA is a cognitive screening test that assesses
visuospatial and executive functions (i.e., Trail-Making Test part B, cube copy, clock drawing,
abstraction), attention (i.e., digit span forward and backward, vigilance, serial-7 subtraction),
short-term memory (i.e., delayed recall), language (i.e., naming, sentence repetition, verbal flu-
ency), and orientation (i.e., awareness of time and place) [23].
Everyday problem-solving skills. The Everyday Problem-Solving (EPS) task consists of
real-world problem scenarios where participants were asked to generate solutions. Six scenar-
ios that have been used in previously studies were used in the current study [19,24]. Three sce-
narios described general daily problems and the other three described health-specific
problems. For each scenario participants were asked to generate as many solutions as possible.
The number of safe and effective solutions generated by each patient was used as an indicator
of problem-solving skills [20].
Subjective cognitive complaints. SCCs were measured using the 33-item Patient’s
Assessment of Own Functioning Inventory (PAOFI) [25]. This measure assesses SCCs in four
domains: memory (10 items), language (nine items), motor/sensory-perceptual ability (five
items), and higher-level cognitive functions (nine items). Participants rated on a six-point
Likert scale from “almost never” to “almost always” [25]. Mean scores of the four subscales
were calculated, with a higher score indicating more frequent SCCs. Cronbach’s alpha was
0.87 for the memory subscale, 0.89 for the language subscale, 0.73 for the motor/sensory-per-
ceptual subscale, and 0.87 for the higher-level cognitive function subscale.
Functional interference. We assessed functional interference as a key dependent variable
of the current study. The Work and Social Adjustment Scale (WSAS) is a measure of self-per-
ceived functional interference in five domains (i.e., work, home management, social leisure
activities, private leisure activities, and social relationships) attributable to an identified prob-
lem [26]. The original five items were used but preface was reworded to be cognition-specific.
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Participants rated on a nine-point Likert scale ranging from 0 (i.e., “not at all impaired”) to 8
(i.e., “very severely impaired”). Cronbach’s alpha was 0.93 for WSAS.
Treatment nonadherence. Patients’ self-reported medication nonadherence was mea-
sured by the five-item Medication Adherence Report Scale (MARS-5 ©Professor Rob Horne),
which was rated on a five-point scale ranging from “never” to “always” [27]. A higher total
score indicates poorer medication adherence. Cronbach’s alpha was 0.77. The Dialysis Diet
and Fluid non-adherence Questionnaire (DDFQ) was also assessed. DDFQ is a four-item scale
that assesses frequency and degree of dietary and fluid nonadherence in dialysis patients [28].
Moreover, we collected patients’ interdialytic weight gain (IDWG) as a clinical indicator of
fluid adherence. Relative IDWG (i.e., the ratio of absolute IDWG to a patient’s dry weight) was
assessed prior to each dialysis session in the week of and the week before the survey date. The
IDWG values were averaged across the two weeks. Additionally, the latest lab assay results
(i.e., sodium [Na], potassium [K], calcium [Ca], phosphorus [PO4], and calcium-phosphorus
product [Ca×PO4]) were collected and used as indicators of dietary and medication
adherence.
Mood and fatigue symptoms. Patients’ mood symptoms were measured by the two-item
Patient Health Questionnaire (PHQ-2; α= 0.65) and the two-item Generalised Anxiety Disor-
der (GAD-2; α= 0.77). These two measures are brief screening tools of depression and anxiety
that have been used in dialysis patients [29,30]. Fatigue was measured using the one-item
vitality subscale from the Kidney Disease Quality of Life questionnaire [31–33]. Higher scores
indicate greater mood or fatigue symptoms.
Sociodemographic and clinical information. Self-reported demographic information
was collected, including age, gender, ethnicity, education, relationship status, and employment
status. Clinical information including primary kidney disease diagnosis, comorbidities, dura-
tion on HD, dialysis dose (Kt/V), and medication count, were extracted from patients’ medical
record.
Statistical analyses
The steps of conducting the SEM analysis involved (1) testing of the baseline measurement
model with confirmatory factor analysis (CFA), (2) specification of the structural model con-
taining all hypothesised paths, and (3) modification of the structural model [34–36]. These
steps were performed using the “lavaan” package with the WLSMV estimator in R 4.2.2 [37,
38]. To assess model fit, we used the Comparative Fit Index (CFI) [39], Tucker—Lewis Index
(TLI) [40], the Root Mean Square Error of Approximation (RMSEA) [41], the Standardised
Root Mean Square (SRMR) [21], and the Chi-square [21]. For CFI and TLI, values above 0.90
were considered to indicate adequate model fit, and values above 0.95 were considered to indi-
cate excellent fit [39,42–45]. RMSEA values lower than 0.06 and SRMR values lower than 0.08
were considered to indicate good fit [21].
Model development
Measurement model. The measurement model was first constructed, with a total of eight
latent variables, each measured by multiple indicator variables. Three latent variables were
constructed to reflect different aspects of cognition, namely “objective cognitive function”,
“everyday problem-solving skills”, and “subjective cognitive complaints”. These three latent
variables were indicated by the subscale/subdomain scores in the MoCA, EPS, and PAOFI,
respectively. Two latent variables were constructed for the dependent variables, including
“functional interference” measured by the five WSAS items, and “treatment nonadherence”
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measured by self-report (i.e., MARS-5 and DDFQ), and physiological and biochemical param-
eters (i.e., IDWG, Na, K, Ca, PO4, and Ca×PO4).
Following previous SEM studies of cognitive function in patients with chronic disease [20,
34,46], we constructed three additional latent variables in order to account for the confound-
ing effects sociodemographic, clinical, and psychological factors in the hypothesised relation-
ships. Specifically, a “sociodemographic” latent variable (indicated by age and years of
education) and a “comorbidity” latent variable (indicated by presence of diabetes, hyperten-
sion, hyperlipidaemia, and cardiovascular disease) were constructed because these are estab-
lished risk or protective factors of CIs in HD patients [47–49]. A “mood and fatigue
symptoms” latent variable (indicated by PHQ-2, GAD-2, and KDQOL vitality item) was also
specified because these symptoms can exacerbate SCCs [22] and are associated with beha-
vioural and clinical outcomes in ESRD patients [50].
A CFA was first conducted to verify the measurement quality of the eight latent variables.
The fit indices for the baseline measurement model were as follows: CFI = 0.839, TLI = 0.820,
RMSEA = 0.043 (90% confidence interval 0.036 to 0.049), SRMR = 0.078, χ
2
(566) = 810.166 (p
<.001), which suggested unsatisfactory model fit. All indicator variables had significant factor
loadings on their corresponding latent variables except for two adherence indicators (i.e., Na
and Ca). These two indicators were therefore removed from the model. However, fit indices
for the revised measurement model were still in the unacceptable range, CFI = 0.853,
TLI = 0.835, RMSEA = 0.043 (90% confidence interval 0.036 to 0.050), SRMR = 0.078, χ
2
(499)
= 719.322 (p<.001).
Modification indices suggested inclusion of error covariance between PO4 and Ca×PO4 in
the model. These two variables were indeed highly correlated with each other, r= 0.95, p<
.001. There is evidence that calcium and phosphorus are controlled by similar regulatory
mechanisms [51], and high levels of serum phosphorus can combine with calcium to form cal-
cium-phosphorus product [52]. It may therefore be theoretically justifiable to allow the resid-
ual terms of these two variables to freely covary [21]. We also added error covariance among
DDFQ items due to the similar wordings of these questions, and we added error covariance
between relative IDWG and the two fluid items in DDFQ as they are both thought to reflect
fluid adherence. Following this modification, the final measurement model showed acceptable
fit with the exception of Chi-square, CFI = 0.917, TLI = 0.905, RMSEA = 0.033 (90% confi-
dence interval 0.024 to 0.041), SRMR = 0.065, χ
2
(492) = 616.607 (p<.001). However, Chi-
square test is sensitive to sample size with larger samples decreasing the pvalue; the significant
Chi-square was therefore not used as a basis for model rejection.
In the final measurement model, all factor loadings of the latent variables were at a signifi-
cance level of p<.05. Detailed description of the final measurement model, including the
latent and indicator variables, as well as their measurement, interpretation, mean values, per-
centage, and factor loadings, are presented in Table 1.
Structural model. A structural model was then constructed to examine the hypothesised
regression paths between the latent variables specified in Fig 1. Objective cognitive function
and EPS skills were hypothesised to covary with each other, and to have negative effects on
SCCs. These three cognitive indicators were also hypothesised to have direct effects on func-
tional interference and treatment nonadherence. However, the regression path from objective
cognitive function to treatment nonadherence was considered optional because, despite theo-
retical assumptions, extant empirical evidence in the context of kidney disease suggest no asso-
ciation between these two variables [19,20].
In addition, regression paths were added from sociodemographic factors, comorbidity, and
mood and fatigue symptoms, to each of the five latent variables in Fig 1 (i.e., objective cogni-
tive function, everyday problem-solving skills, subjective cognitive complaints, functional
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Table 1. Description of latent variables and indicator variables of the final measurement model.
Latent Variable Indicator Variable Measurement/Example Item Interpretation of Higher
Values
Mean (SD)
/N(%)
Factor
Loading
Objective Cognitive
Function
Visuospatial/
Executive
Trail-making test part B; cube copy; clock drawing;
abstraction
Better visuospatial and
executive functions
4.07 (1.58) 0.65
Attention Digit span forward and backward; vigilance; serial-7
subtraction
Better attention ability 4.76 (1.47) 0.65
Memory Delayed recall Better memory 2.34 (1.89) 0.52
Language Naming; sentence repetition; verbal fluency Better language function 4.38 (1.03) 0.52
Orientation Awareness of time and place Better orientation 5.71 (0.54) 0.23
Everyday Problem-
Solving Skills
General Problem "Now let’s say that one evening you go to the refrigerator
and you notice that it is not cold inside, but rather, it’s
warm. What would you do?"
Better problem-solving skills for
general problems
7.59 (3.91) 0.75
Health Problem "You accidentally took the wrong combination of
medication. What do you do?"
Better problem-solving skills for
health-related problems
4.3 (2.27) 0.84
Subjective Cognitive
Complaints
Memory "How often do you lose things or have trouble
remembering where they are?"
More frequent memory
complaints
2.16 (0.81) 0.74
Language "How often do you have difficulty thinking of the names of
things?"
More frequent language
complaints
2.06 (0.86) 0.81
Motor/Sensory-
Perceptual
“How often do you have difficulty feeling things with your
right hand?”
More frequent complaints
about motor/sensory-
perceptual abilities
1.96 (0.9) 0.71
Higher-Level
Cognitive
“How often do you have difficulty finding your way about?” More frequent complaints
about higher-level cognitive
functions
1.74 (0.76) 0.93
Functional
Interference
Work "Because of my cognitive difficulties, my ability to work is
impaired."
Greater impact of cognitive
difficulties on work ability
1.39 (1.96) 0.80
Home
Management
"Because of my cognitive difficulties, my home
management (cleaning, tidying, shopping, cooking, looking
after home or children, paying bills) is impaired."
Greater impact of cognitive
difficulties on home
management
1.22 (1.96) 0.85
Social Activities "Because of my cognitive difficulties, my social leisure
activities (with other people, such as parties, bars, clubs,
outings, visits, dating, home entertainment) are impaired."
Greater impact of cognitive
difficulties on social leisure
activities
1.33 (2.09) 0.89
Leisure Activities "Because of my cognitive difficulties, my private leisure
activities (done alone, such as reading, gardening,
collecting, sewing, walking alone) are impaired."
Greater impact of cognitive
difficulties on private leisure
activities
1.23 (1.94) 0.87
Relationships "Because of my cognitive difficulties, my ability to form and
maintain close relationships with others, including those I
live with, is impaired."
Greater impact of cognitive
difficulties on social
relationships
1.00 (1.69) 0.81
Treatment
Nonadherence
MARS-5 "I forget to take them." Poorer medication adherence 7.29 (2.80) 0.60
DDFQ-1 "How many days during the past 14 days didn’t you follow
your diet guidelines?"
More frequent diet
nonadherence
2.65 (3.93) 0.33
DDFQ-2 "To what degree did you deviate from your diet guidelines?" Greater deviation from diet
guidelines
0.99 (0.93) 0.59
DDFQ-3 "How many days during the past 14 days didn’t you follow
your fluid guidelines?"
More frequent fluid
nonadherence
2.66 (3.99) 0.41
DDFQ-4 "To what degree did you deviate from your fluid
guidelines?"
Greater deviation from fluid
guidelines
1.03 (0.95) 0.64
K Latest available laboratory results Higher serum potassium 4.76 (0.64) 0.15
PO4 Higher serum phosphorus 4.66 (1.17) 0.20
Ca×PO4 Higher calcium-phosphorus
product
42.79
(11.36)
0.19
IDWG Relative interdialytic weight gain averaged across the week
of and the week before the survey date
Higher relative interdialytic
weight gain
3.34 (1.06) 0.13
(Continued)
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interference, treatment nonadherence), in order to account for their potential confounding
effects [22,47,53–55]. The structural model was tested and respecified until a final model was
determined by weighing model fit indices and model parsimony (i.e., a simpler model with
fewer parameters is favoured over more complex models provided the models fit the data simi-
larly well).
Results
Sample characteristics
We approached a total of 459 HD patients in NKF dialysis centres. Ninety patients were
excluded due to ineligibility. Within the remaining 369 eligible patients, 268 consented to par-
ticipate (response rate 72.6%). The main reasons for rejection were lack of interest and feeling
unwell. Therefore, 268 patients were included in final analyses. Table 2 reports the sociodemo-
graphic and clinical profiles of the sample. The mean age of the sample was 59.87 (SD = 11.72).
Patients on average had been on HD for 78.85 (SD = 62.80) months.
Final structural model
The fit indices for the full structural model were as follows: CFI = 0.917, TLI = 0.905,
RMSEA = 0.033 (90% confidence interval 0.024 to 0.041), SRMR = 0.065, χ
2
(492) = 616.607 (p
<.001). The path between objective cognitive function and treatment nonadherence was
indeed not statistically significant, consistent with prior work [19,20]. We removed this path
while retaining all other paths and reran the structural model, with fit indices as follows:
CFI = 0.916, TLI = 0.905, RMSEA = 0.033 (90% confidence interval 0.024 to 0.041),
SRMR = 0.066, χ
2
(493) = 618.573 (p<.001). A chi-squared difference test showed that this
Table 1. (Continued)
Latent Variable Indicator Variable Measurement/Example Item Interpretation of Higher
Values
Mean (SD)
/N(%)
Factor
Loading
Sociodemographic Age Years of age Older age 59.87
(11.72)
0.59
Education Years of full-time education Higher education level 9.59 (3.56) -0.61
Comorbidity Diabetes Medical record Presence of diabetes 145
(54.3%)
0.69
Hypertension Presence of hypertension 232
(86.9%)
0.58
Hyperlipidaemia Presence of hyperlipidaemia 143
(53.6%)
0.64
Cardiovascular
disease
Presence of cardiovascular
disease
141
(52.8%)
0.47
Mood & Fatigue
Symptoms
Depression "Over the last 2 weeks, how often have you been bothered
by the following problems? Little interest or pleasure in
doing things."
Higher depressive symptoms 1.10 (1.46) 0.82
Anxiety "Over the last 2 weeks, how often have you been bothered
by the following problems? Feeling nervous, anxious, or on
edge."
Higher anxious symptoms 1.06 (1.51) 0.84
Fatigue "How much of the time during the past 4 weeks did you
have a lot of energy?"
Higher fatigue symptoms 3.18 (1.45) 0.43
Notes. SD = Standard deviation. N= Sample size. MARS = Medication Adherence Report Scale. DDFQ = Dialysis Diet and Fluid non-adherence Questionnaire.
K = Serum potassium. PO4 = Serum phosphorus. Ca×PO4 = Calcium-phosphorus product. IDWG = Interdialytic weight gain.
https://doi.org/10.1371/journal.pone.0312039.t001
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trimmed model, which was also the more parsimonious model, could explain the observed
data equally well compared to the full structural model without a significant loss in data-model
fit (p= .101).
The final trimmed model is presented in Fig 2. For conciseness, only regression paths that
were statistically significant were presented in the figure. The model revealed that objective
cognitive performance negatively predicted SCCs, but was not associated with EPS skills, func-
tional interference, or treatment nonadherence. SCCs were positively associated with both
functional interference and treatment nonadherence, suggesting indirect effects of objective
performance on these dependent variables through SCCs. On the other hand, EPS skills were
found to be unrelated to either objective performance or SCCs, but had a negative association
with functional interference, suggesting that individuals with worse EPS skills had more severe
functional interference. Sociodemographic factors (i.e., age and years of education) were asso-
ciated with objective cognitive function, EPS skills, and treatment nonadherence, whereas
Table 2. Sample characteristics (N = 268).
Mean (SD) / N(%)
Sociodemographic
Gender
Male 154 (57.5%)
Female 114 (42.5%)
Age (years) 59.87 (11.72)
Range 26–84
Ethnicity
Chinese 151 (56.3%)
Malay 80 (29.9%)
Indian or others 37 (13.8%)
Years of education 9.59 (3.56)
Relationship status
In a relationship 182 (67.9%)
Not in a relationship 86 (32.1%)
Work status
Working 76 (28.5%)
Not working 191 (71.5%)
Clinical
Primary diagnosis
Diabetic nephropathy 122 (45.5%)
Glomerulonephritis 49 (18.3%)
Hypertension 36 (13.4%)
IgA nephropathy 12 (4.5%)
Others/uncertain aetiology 49 (18.3%)
Presence of diabetes 145 (54.3%)
Presence of hypertension 232 (86.9%)
Presence of hyperlipidaemia 143 (53.6%)
Presence of cardiovascular disease 141 (52.8%)
Duration on HD (months) 78.85 (62.80)
Medication count 12.76 (4.25)
Kt/V 1.60 (0.24)
Notes. SD = Standard deviation. N= Sample size. HD = Haemodialysis. Kt/V = Dialysis dose.
https://doi.org/10.1371/journal.pone.0312039.t002
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mood and fatigue symptoms were predictive of SCCs and functional interference. Comorbid-
ity, however, was not associated with any other latent variables, and was therefore not pre-
sented in Fig 2. The final model explained 65.7% of the variance in functional interference
(indicated by an R
2
of 0.657), and 63.3% of the variance in treatment nonadherence (indicated
by an R
2
of 0.633).
Discussion
To the best of our knowledge, this is the first SEM study to explore the complex interrelation-
ships between multiple cognitive indicators and key functional and clinical variables in dialysis
patients. The final model revealed interesting pathways through which cognition was directly
and indirectly associated with functional interference and treatment nonadherence, highlight-
ing cognitive difficulties as an important barrier in this population to functional capacity, life
participation, treatment adherence, and disease management.
A key finding was that patients with more frequent SCCs also experienced more severe
daily interference and exhibited poorer treatment adherence. In contrast, objective cognitive
performance was only indirectly associated with these outcomes through SCCs. Indeed, it has
been suggested that objective CIs based on neuropsychological tests do not necessarily trans-
late to impairments in real-world functioning outside of test environment because some
patients may adopt strategies to compensate for everyday cognitive failure, which is typically
not allowed in standardised testing [56]. For some other patients, cognitive difficulties may be
too mild to be detected by objective tests, but are nevertheless problematic for everyday life
Fig 2. Final structural model. Standardised coefficients with standard errors are reported for regression paths. Standardised factor loadings are reported for paths
between indicator variables and latent variables. Nonsignificant paths are not presented in the figure.
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[57]. Consistent with our findings, Song et al. found that SCCs, but not objective performance,
predicted self-reported difficulties in performing activities of daily living in dialysis patients
[15].
Importantly, treatment nonadherence in the present study was measured by both self-
report and biochemical and physiological parameters. Inter-dialytic weight gain is typically
thought to be an indicator of fluid adherence and sodium intake, whereas potassium and phos-
phorus may indicate dietary adherence [55]. Serum phosphorus can also indicate patient
adherence to phosphate binders. Higher values of these clinical markers have been associated
with poor survival [55]. Our findings are consistent with a recent longitudinal study where a
reduction in SCCs over time was accompanied by a significant improvement in serum levels
of PO4 and Ca×PO4 in HD patients [17]. Notably, the observed association between SCCs and
nonadherence was significant even taking into account sociodemographic factors, comorbid-
ity, and mood and fatigue symptoms, suggesting that this effect cannot be explained by other
well-established determinants of nonadherence such as age and depression. Taken together,
SCCs appear to be a useful measure that can simultaneously red-flag functional interference,
nonadherence, and poor clinical outcomes in dialysis patients.
Another interesting finding was that patients with worse EPS skills were more likely to
experience functional interference. The EPS task used in this study required participants to
generate safe and effective solutions to the given scenarios. There is evidence in the cognitive
development literature that the ability to generate a number of alternative solutions is a good
indicator of problem-solving ability [58]. Patients who were unable to generate multiple solu-
tions in response to the EPS task scenarios may also be more likely to experience failure in
solving problems that arise in real-world contexts, which in the long term can impair indepen-
dence in various aspects of life.
Unexpectedly though, EPS task performance was not associated with treatment nonadher-
ence. This is inconsistent with two other studies where EPS skills were found to predict medi-
cation adherence in kidney transplantation recipients [19,20]. These two studies, however,
only assessed cognition using neuropsychological tests and the EPS task. It may be that SCCs
have a stronger association with adherence and therefore by accounting for its effect in our
model, the predictive value of EPS skills on adherence diminished. Indeed, the SCC measure
used in our study is comprehensive and covers multiple cognitive domains, whereas EPS skills
are dependent not just upon executive function, but also life experience, knowledge, and envi-
ronmental factors [59].
We found that patients with worse cognitive performance on objective tests also subjec-
tively reported more cognitive complaints. This observed association was modest, which is not
unexpected based on previous work in other populations [22,60]. Although objective tests and
subjective reports both intend to capture the same underlying construct (i.e., cognition), they
measure it using completely different methods and are influenced by different sets of predic-
tors. While objective tests assess performance at a single time point in distraction-free environ-
ments, self-reports are based on accumulative daily experience that may be more reflective of
longitudinal cognitive changes [60]. Objective and subjective cognition have therefore been
considered as distinct constructs that complement each other with different utility in clinical
and research settings. Assessing objective CIs can help establishing diagnosis of CIs and allow
for advance care planning, whereas assessing SCCs can help identifying aspects of CIs that
have the greatest impact on patients, hence allowing for a more patient-centred approach to
managing this debilitating complication [61].
The current study has several important clinical implications. First, cognitive indicators
that incorporate everyday scenarios (i.e., EPS and SCCs) appeared to be better predictors of
real-world functional and clinical outcomes in HD patients, compared to traditional cognitive
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tests. The associations of SCCs with underlying cognitive deficits and worse outcomes high-
light the potential of SCCs as a stand-alone patient-reported outcome measure with clinical
utility in dialysis settings that simultaneously signifies risks in multiple aspects of patients’
health and well-being. Indeed, SCCs are increasingly considered as a prodromal marker of
progression to dementia in Alzheimer’s disease research [62,63], and as a core patient-
reported outcome in populations such as cancer [64] and HIV patients [65]. To date, research
and clinical practice in the field of nephrology have been predominantly focusing on objective
cognition, with SCCs being understudied and underrecognised. The emphasis on objective
cognition is essential for diagnostic purposes, but hinders a comprehensive understanding of
cognitive well-being in this population. Previous studies determined the utility of SCC mea-
sures solely based on their ability to distinguish patients with and without objective CIs [66].
We propose that SCCs should be treated as an outcome equally important as objective CIs,
and should be assessed in combination with neuropsychological tests where possible.
Second, everyday cognitive difficulties reflected by SCCs and the EPS task emerged as
potential modifiable risk factors for functional interference and treatment nonadherence.
There is evidence that functional impairment is present in 21 to 85% of ESRD patients [11],
and that optimal adherence is not achieved in as many as 80% of dialysis patients [55]. Our
study showed that these high rates may in part be explained by the cognitive burden experi-
enced by this population. It is therefore pivotal to develop and implement interventions that
improve everyday cognitive abilities and compensate for cognitive lapses in this population so
that the impacts of CIs on daily functioning and self-care can be mitigated. To date, there is a
lack of research on the feasibility and effectiveness of cognitive interventions for ERSD
patients. Future work in this area is needed to improve patient-centred care and to optimise
adaptation to dialysis initiation.
Several limitations warrant acknowledgement. First, it should be noted that this is a cross-
sectional observational study. Also, SEM is not inherently a causal method [21]. Caution is
therefore needed when interpreting the significant paths in our final model. Although the
directions of these associations have been hypothesised and tested, the study design and statis-
tical method do not permit the interpretation of a cause-and-effect sequence. Future longitudi-
nal studies and intervention research are needed to explore potential causal mechanisms and
further validate our observations. Second, the MoCA used in this study is only a brief screen-
ing tool for global cognition. No comprehensive neuropsychological battery or standard diag-
nostic test was conducted to determine objective cognitive function. This is because the tools
administered in this study were already very lengthy and required about 30–40 minutes for
each patient to complete. A more comprehensive neuropsychological assessment was therefore
not carried out considering patients’ response burden and potential fatigue. Third, SCCs and
functional interference were measured by self-report, which may be susceptible to recall bias.
Future studies that incorporate informant-reports of SCCs and more objective measures of
functional capacity may be needed. Finally, the study was conducted in a sample of HD
patients, which limited the generalisability to other kidney disease subgroups such as perito-
neal dialysis and kidney transplantation.
Conclusion
In summary, this study adopted the SEM technique to disentangle the complex associations
between cognition and key functional and clinical parameters in HD patients. Results revealed
the central role of SCCs in indicating not just underlying cognitive deficits but also functional
interference, treatment nonadherence, and suboptimal clinical outcomes. In contrast, EPS
skills were found to be associated with only functional interference but not nonadherence,
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whereas performance on traditional tests did not associate with any of these variables. It may
be important to screen for SCCs in dialysis patients which would allow for early identification
of the high-risk population, and subsequently early prevention or intervention strategies to
mitigate the consequences of CIs.
Supporting information
S1 Data.
(CSV)
Acknowledgments
We thank the nurse managers and staff of the dialysis centres of National Kidney Foundation
Singapore for facilitating the patient recruitment procedures and providing patients’ medical
data. We would also like to thank Kevin Tan, Shane Tan, Sze Ing Tan, and Nicole Tan for their
assistance in patient recruitment and data entry.
Author Contributions
Conceptualization: Konstadina Griva.
Data curation: Frederick H. F. Chan, Pearl Sim, Phoebe X. H. Lim, Behram A. Khan.
Formal analysis: Frederick H. F. Chan, Konstadina Griva.
Funding acquisition: Jimmy Lee, Sabrina Haroon, Titus Wai Leong Lau, Behram A. Khan,
Konstadina Griva.
Investigation: Frederick H. F. Chan.
Methodology: Frederick H. F. Chan, Jimmy Lee, Sabrina Haroon, Titus Wai Leong Lau, Beh-
ram A. Khan, Konstadina Griva.
Project administration: Frederick H. F. Chan, Pearl Sim, Phoebe X. H. Lim, Konstadina
Griva.
Supervision: Konstadina Griva.
Validation: Frederick H. F. Chan.
Visualization: Frederick H. F. Chan.
Writing – original draft: Frederick H. F. Chan.
Writing – review & editing: Frederick H. F. Chan, Pearl Sim, Phoebe X. H. Lim, Xiaoli Zhu,
Jimmy Lee, Sabrina Haroon, Titus Wai Leong Lau, Allen Yan Lun Liu, Behram A. Khan,
Jason C. J. Choo, Konstadina Griva.
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