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ORIGINAL RESEARCH
published: 20 July 2015
doi: 10.3389/fnagi.2015.00139
Edited by:
Manuel Menéndez-González,
Hospital Álvarez Buylla, Spain
Reviewed by:
Paul Gerson Unschuld,
University of Zürich, Switzerland
Rena Li,
Roskamp Institute, USA
*Correspondence:
Jonas J. de Paula,
Faculdade de Medicina, Instituto
Nacional de Ciências e Tecnologia
e em Medicina Molecular,
Universidade Federal de Minas
Gerais, Avenue Alfredo Balena 190,
Belo Horizonte, Minas Gerais
30130-100, Brazil
jonasjardim@gmail.com
Received: 08 February 2015
Accepted: 06 July 2015
Published: 20 July 2015
Citation:
de Paula JJ, Diniz BS, Bicalho MA,
Albuquerque MR, Nicolato R,
de Moraes EN, Romano-Silva MA
and Malloy-Diniz LF (2015) Specific
cognitive functions and depressive
symptoms as predictors of activities
of daily living in older adults with
heterogeneous cognitive
backgrounds.
Front. Aging Neurosci. 7:139.
doi: 10.3389/fnagi.2015.00139
Specific cognitive functions and
depressive symptoms as predictors
of activities of daily living in older
adults with heterogeneous cognitive
backgrounds
Jonas J. de Paula1,2*, Breno S. Diniz 1,3 , Maria A. Bicalho1,4 ,
Maicon Rodrigues Albuquerque1,5, Rodrigo Nicolato1,3 , Edgar N. de Moraes1,4 ,
Marco A. Romano-Silva1,3 and Leandro F. Malloy-Diniz1,3
1Faculdade de Medicina, Instituto Nacional de Ciências e Tecnologia e em Medicina Molecular, Universidade Federal de
Minas Gerais, Belo Horizonte, Brazil, 2Department of Psychology, Faculdade de Ciências Médicas de Minas Gerais,
Belo Horizonte, Brazil, 3Department of Mental Health, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo
Horizonte, Brazil, 4Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais,
Belo Horizonte, Brazil, 5Department of Physical Education, Universidade Federal de Viçosa, Viçosa, Brazil
Cognitive functioning influences activities of daily living (ADL). However, studies reporting
the association between ADL and neuropsychological performance show inconsistent
results regarding what specific cognitive domains are related to each specific functional
domains. Additionally, whether depressive symptoms are associated with a worse
functional performance in older adults is still under explored. We investigated if specific
cognitive domains and depressive symptoms would affect different aspects of ADL.
Participants were 274 older adults (96 normal aging participants, 85 patients with mild
cognitive impairment, and 93 patients probable with mild Alzheimer’s disease dementia)
with low formal education (∼4 years). Measures of ADL included three complexity levels:
Self-care, Instrumental-Domestic, and Instrumental-Complex. The specific cognitive
functions were evaluated through a factorial strategy resulting in four cognitive domains:
Executive Functions, Language/Semantic Memory, Episodic Memory, and Visuospatial
Abilities. The Geriatric Depression Scale measured depressive symptoms. Multiple linear
regression analysis showed executive functions and episodic memory as significant
predictors of Instrumental-Domestic ADL, and executive functions, episodic memory
and language/semantic memory as predictors of Instrumental-Complex ADL (22 and
28% of explained variance, respectively). Ordinal regression analysis showed the
influence of specific cognitive functions and depressive symptoms on each one of
the instrumental ADL. We observed a heterogeneous pattern of association with
explained variance ranging from 22 to 38%. Different instrumental ADL had specific
cognitive predictors and depressive symptoms were predictive of ADL involving social
contact. Our results suggest a specific pattern of influence depending on the specific
instrumental daily living activity.
Keywords: activities of daily living, functional performance, neuropsychological assessment, depression,
dementia, mild cognitive impairment, executive functions
Frontiers in Aging Neuroscience | www.frontiersin.org 1July 2015 | Volume 7 | Article 139
de Paula et al. Cognitive functions, depression, and ADL
Introduction
Cognitive and functional impairments are hallmarks of cognitive
disorders and defining features of mild cognitive impairment
(MCI) and dementia. In MCI, the cognitive deficits do not impair
the capacity to live independently, in contrast to individuals with
dementia that present pronounced functional deficits, such as the
ones observed in Alzheimer’s disease (AD; (Pereira et al., 2010;
Brown et al., 2011;Seelye et al., 2013). The most usual form to
assess functional performance in older adults is the investigation
of activities of daily living (ADL), common activities performed
by the majority of older adults in a specific cultural setting
(Lawton and Brody, 1969).
Prior studies have investigated the relationship between
cognitive and functional performance in older adults with
MCI or AD. Longitudinal changes in cognition are related
to longitudinal changes in ADL (Farias et al., 2009). In a
comprehensive review, Royall et al. (2007) showed a weak to
moderate association between global cognitive measures and
functional impairment. However, their results are heterogeneous
with cognitive features responding for 0 to 80% of the variance in
functional performance (mean of 21% with a SD of 20%; Gold,
2012). Methodological differences and sample characteristics
might explain part of this excessive variability.
Gold (2012) discuss some of the methodological issues. The
definition and the type of ADL investigated varies between
studies. Some studies focuses on a unitary construct of ADL,
in contrast with several evidences from the literature of a
multidimensional construct involving activities of different levels
of complexity (Thomas et al., 1998;Niti et al., 2007;Gold, 2012;
de Paula et al., 2014). Beyond the usual distinction between Basic
(BADL) and Instrumental (IADL) activities, some studies found
different latent structures in ADL questionnaires and scales.
Thomas et al. (1998) reported a multidimensional structure for
BADL and IADL combined, proposing its interpretation based
on levels of complexity (basic, intermediate, and complex). Niti
et al. (2007) found a bifactorial structure for IADL (“physical”
and “cognitive”) in a sample of Asian older adults with a
significant influence of specific cultural aspects on participants’
responses. In a sample of low educated older adults from Brazil,
de Paula et al. (2014) observed a different factorial solution
with a “Domestic” IADL component and a “Complex” IADL
component, in addition to a third component classified as BADL.
Therefore, ADL may be a multidimensional construct with
different components varying according to sample characteristics
(Bootsma-van der Wiel et al., 2001;Cabrero-García and López-
Pina, 2008;Fieo et al., 2011). Such differences might account for
some of the high heterogeneity between studies reported in Royall
et al. (2007). This emphasizes the importance of investigating
specificities in ADL structure (Gold, 2012).
Methodological difficulties include instrument bias. Sikkes
et al. (2009) reviewed the literature concerning the different
measures of IADL focusing on its psychometric properties. Their
results suggest that most of the scales adopted in clinical and
research settings still lack of psychometric studies, reducing its
validity and reliability for the functional assessment, although
great effort is being dispend on this matter (Fieo et al., 2011).
The source of information (e.g., patient vs. caregiver) is also
not consensual in studies including cases of dementia, which
is of extreme importance to the use and interpretation of
scales measurement (Farias et al., 2005). Bootsma-van der Wiel
et al. (2001) also highlight the conceptual difference between
a “can do” or an “actually do” score in specific activities.
Additionally, there is no consensus in the literature indicating
scales and questionnaires as adequate methods to functional
level measurement. There are more ecological measures of
ADL, which involve the observation of the patient’s behavior
on real life or simulated settings (a more precise estimation
of functional performance; Chaytor and Schmitter-Edgecombe,
2003). However, these procedures also have limitations such
as the higher costs of execution and the inherent complexity
of the assessment, and might not be well suited for most
of the clinical and research settings. The suitability of these
ecological measures for studies involving cognitive performance
is controversial. There are studies showing a stronger association
of cognitive measures with ecological measures of ADL (Burton
et al., 2006;Tam et al., 2008). However, the opposite pattern
was also demonstrated with the relation between cognitive
performance with ADL being stronger for ADL measured by
scales/questionnaires (Cahn-Weiner et al., 2002; Mariani et al.,
2008;de Paula et al., 2014). The cognitive processes assessed
by the neuropsychological tests used in these studies might be
associated with ADL in different and more specific ways (Gold,
2012).
Taking specific cognitive domains as predictors of functional
performance most of the studies report associations with
executive functions (Royall et al., 2007;Pereira et al., 2008;
de Paula and Malloy-Diniz, 2013). This complex cognitive
construct usually shows the strongest correlations with functional
performance. Executive functions involve planning, initiation,
monitoring, inhibition, and flexibility of goal-oriented behavior
(Diamond, 2013). These specific aspects of executive functions
may contribute differently to the ADL (Jefferson et al.,
2006). However, other cognitive functions may contribute
to specific aspects of ADL. Cognitive measures of spatial
processing predicted participants’ performance in an ecological
measure of visuospatial abilities in which the subject had to
estimate distances, positions, and directions in a “real-life”
setting developed by Farley et al. (2011). Activities demanding
communicative skills were related to semantic process and
language (Razani et al., 2011). Schmitter-Edgecombe et al.
(2009) investigated different aspects of episodic memory and its
association with ADL reporting significant associations of specific
memory components with specific functional components. In
this sense, although most of the studies have focused on executive
functions or global cognitive measures, different kinds of ADL
may depend on different cognitive abilities.
Depressive symptoms can also impair functional performance
in older adults, usually in more complex activities (Bombin et al.,
2012;Tomita and Burns, 2013;Zahodne et al., 2013;Park et al.,
2014). Our group reported a weak association between depressive
symptoms and functional performance in older adults with low
formal education diagnosed with MCI or AD (de Paula and
Malloy-Diniz, 2013). A stronger association was recently reported
Frontiers in Aging Neuroscience | www.frontiersin.org 2July 2015 | Volume 7 | Article 139
de Paula et al. Cognitive functions, depression, and ADL
in a similar sample (Assis et al., 2014). Cahn-Weiner et al.
(2002) found an association of depressive symptoms with ADL
independent from cognitive functioning. Remission of depressive
symptoms is associated with improvement of ADL (Nyunt
et al., 2012). Nonetheless, there is evidence for the contrary,
suggesting that depressive symptoms are not associated with
IADL after controlling for cognitive symptoms (Reppermund
et al., 2011;Wadsworth et al., 2012). Then, whether depressive
symptoms affect functional performance by behavioral symptoms
(depressed mood, lack of pleasure, apathy, and vegetative
symptoms) or through cognitive impairment associated with
depression remains unclear.
Functional deficit is one of the hallmarks of MCI and AD in
older adults. Improving cognitive functions and mood symptoms
may result in gains in functional performance. Therefore, a better
understanding of how specific cognitive abilities and depressive
symptoms contribute to the performance of specific ADL could
be important to the development of tailored rehabilitation
programs to improve daily functioning in individuals with
cognitive disorders in a personalized way. The objective of
the present study is to assess how specific cognitive abilities
and symptoms of depression are associated to different aspects
of ADL.
Materials and Methods
Participants
We evaluated 274 older adults from a public outpatient clinic
specialized in cognitive disorders and frailty. The center usually
receives elderly patients referred from primary-care physicians
when they suspect of cognitive impairment, mental disorders,
or multiple chronic diseases. Patients usually have a very low
socioeconomic status and less than 4 years of formal education.
Participants’ sociodemographic characteristics are shown in
Tab l e 1 . A more detailed description of the typical profile of
patients assessed in this center was published elsewhere (Bicalho
et al., 2013;de Paula et al., 2013a).
The participants underwent a detailed clinical, cognitive,
and behavioral assessment for diagnostic purposes as described
below. During the geriatrician examination and the clinical
neuropsychological assessment, the patients underwent
cognitive, functional and behavioral assessment to determine
their cognitive status. Ninety-three participants were diagnosed
with mild Alzheimer’s disease dementia (AD), 85 patients were
diagnosed with amnestic MCI (MCI) and 96 older adults were
normal aging participants without clinical history, cognitive,
or functional status suggestive of dementia or AD. AD was
diagnosed by the NINCDS-ADRDA criteria for probable
dementia (McKhann et al., 1984). Only patients with mild
dementia, according to Clinical Dementia Rating (CDR; Morris,
1993), were invited for participation in this study. MCI diagnosis
was based on a modified version of the Mayo Clinic diagnostic
criteria (Petersen et al., 2001). Criteria for MCI was as follow:
(1) Subjective cognitive complaint, preferably corroborated by
an informant/caregiver.
TABLE 1 | Participants’ demographic profile.
Cognitive status Normal aging 35%
Mild cognitive impairment 31%
Alzheimer’s disease dementia 34%
Gender Male 39%
Female 61%
Depression1Present 30%
Absent 70%
Age 60–69 years 34%
70–79 years 43%
80+years 23%
Formal education Illiterate 12%
1–4 years 57%
5–8 years 13%
9+years 12%
12+years 6%
Occupations2Craft and related trades workers 13%
Elementary occupations 34%
Service and sale workers 22%
Others 31%
Retired? No 13%
Yes 87%
Marital status Married 53%
Divorced 12%
Single 9%
Widow 36%
1According to the Geriatric Depression Scale 15 cut-off (5/6).
2According to the International Labour Office (ILO, 2012).
(2) Objective impairment on specific cognitive measures of
the assessment battery for diagnosis according to Brazilian
norms and cut-off scores (Porto et al., 2003;Nitrini et al.,
2004). The cognitive battery includes the Verbal Learning
Test of the CERAD Neuropsychological Battery (Morris
et al., 1989), the memory test from the Brief Cognitive Battery
(Nitrini et al., 2007), and subscales of the Mattis Dementia
Rating Scale (Mattis, 1988).
(3) Normal global cognitive functioning (MMSE above the cut-
off for dementia and CDR <1).
(4) Preserved or minimal impairments in ADL assessed by a
clinical interview and the CDR.
(5) Not demented based on the DSM-IV-TR criteria (American
Psychiatric Association [APA], 2000).
All groups (i.e., AD, MCI, and control) were combined in a
unique heterogeneous sample. This strategy was adopted to
increase statistical power and to avoid detection loss of cognitive
processes that are likely to underlie functional performance
(Farias et al., 2009;Gold, 2012).
Cognitive, Functional, and Mood Assessment
We adopted an unstructured protocol of neuropsychological
tests designed for the assessment of older adults with low formal
Frontiers in Aging Neuroscience | www.frontiersin.org 3July 2015 | Volume 7 | Article 139
de Paula et al. Cognitive functions, depression, and ADL
TABLE 2 | Neuropsychological measures used to extract the four cognitive factors according to de Paula et al. (2013a).
Cognitive domain Test Test measures Reference
Executive functions Frontal assessment battery Total score Dubois et al. (2000)
Verbal fluency Animals Lezak et al. (2004)
Fruits
Letter “S”
Digit span forward Correct trials ×Span Kessels et al. (2008)
Digit span backward Correct Trials ×Span Kessels et al. (2008)
Language semantic memory TN-LIN (naming test) Nouns Malloy-Diniz et al. (2007a)
Actions
Professions
Episodic memory RAVLT Short term memory (A1) Malloy-Diniz et al. (2007b)
Immediate recall (A6)
Delayed recall (A7)
Sum of words
Recognition memory
Visuospatial abilities Stick design test Total score Baiyewu et al. (2004)
Clock drawing test Total score Shulman (2000)
Token test (short version) Visual attention De Renzi and Faglioni (1978)
Complex comprehension
TN-LIN, Naming test of the laboratory of neuropsychological investigations; RAVLT, Rey auditory-verbal learning test.
TABLE 3 | Participants’ description and group comparisons.
Measures NA (N=96) MCI (N=85) AD (N=93) F/χ2Comparisons2
M(SD) M(SD) M(SD)
Age 72.61 (7.76) 73.18 (8.46) 73.18 (8.46) 1.80 −
Education 5.22 (4.29) 4.71 (4.00) 4.82 (3.46) 0.48 −
GDS-15 4.33 (3.95) 2.94 (2.84) 3.82 (3.22) 3.81 −
Sex (% Female) 67% 60% 55% 2.591−
MMSE 25.75 (3.85) 23.52 (3.62) 20.59 (3.98) 42.58∗∗ NA >MCI >AD
Language/Semantic memory −0.34 (1.20) −0.76 (0.96) −1.69 (1.16) 27.28∗∗ NA =MCI >AD
Episodic memory −0.28 (0.80) −1.22 (0.72) −1.76 (0.65) 93.47∗∗ NA >MCI >AD
Visuospatial abilities −0.31 (1.08) −0.84 (1.04) −1.47 (1.05) 30.38∗∗ NA >MCI >AD
Executive functions −0.58 (1.34) −1.21 (1.05) −2.25 (1.09) 46.91∗∗ NA >MCI >AD
GADL self-care 9.94 (0.32) 9.99 (0.11) 9.78 (0.87) 4.01∗NA =MCI >AD
GADL domestic 7.68 (0.86) 7.41 (1.20) 5.74 (2.19) 19.61∗∗ NA =MCI >AD
GADL complex 7.55 (1.27) 6.91 (1.47) 4.35 (2.57) 59.61∗∗ NA =MCI >AD
GADL global score 25.16 (2.06) 24.03 (2.34) 19.88 (3.92) 75.03∗∗ NA =MCI >AD
*0.05, **<0.001. NA, Normal aging; MCI, Mild cognitive impairment; AD, Alzheimer’s disease; M, Mean; SD, Standard-deviation; GDS-15, Geriatric Depression Scale 15
items; GADL, General Activities of Daily Living Scale; 1Chi-Square test; 2One-Way ANOVA and Sidak’s post hoc test (p <0.05).
education. The tests were not included in patients’ diagnosis.
Cognitive composite factors were obtained through factor
analysis from our sample (statistical procedures detailed below).
This approach allows the assessment of different aspects of
cognitive functioning (here, the core domains recommended for
the MCI and AD diagnosis) with greater specificity. The protocol
comprised tests of executive functions (Frontal Assessment
Battery, Verbal Fluency tests, and Digit Span); language
and semantic memory (Laboratory of Neuropsychological
Investigations Naming Test – Nouns, Verbs and Professions,
Token Test verbal comprehension component); episodic
memory (components of learning, recognition, immediate, and
delayed recall of the Rey Auditory-Verbal Learning Test); and
visuospatial abilities (Clock Drawing Test, Stick Design Test,
and Token Test visual attention components). These tests are
valid and reliable for the assessment of older adults with a
low educational background (de Paula et al., 2013a). The test
measures are shown in Tab l e 2 .
The assessment of ADL occurred during the clinical and
neuropsychological assessment by an interview with participants’
caregivers. For this study, we used the ‘General Activities of Daily
Living Scale’ (GADL) (de Paula et al., 2014), a multidimensional
functional measure of BADL/IADL based on the Lawton and
Brody (1969)andKatz et al. (1970) indexes of ADL. The
GADL shows a hierarchical structure with a general score and
three components of more specific activities: a measure of
Frontiers in Aging Neuroscience | www.frontiersin.org 4July 2015 | Volume 7 | Article 139
de Paula et al. Cognitive functions, depression, and ADL
FIGURE 1 | Association between Cognitive Factors and General
Activities of Daily Living Scale (GADL). The correlations of cognitive
factors with functional performance were all significant (p<0.001).
Executive functions showed the strongest correlation with GADL total
score (r=0.478), followed by episodic memory (r=0.449),
language/semantic memory (r=0.410), and visuospatial abilities
(r=0.302). The number of dots in the scatterplot differs from the
sample size due to superposed values.
BADL (Self-care: ability to change clothes, use the toilet, use
the shower, transference from bed or chair, and feed itself)
and two components of IADL. Instrumental Domestic ADL
include ability to perform domestic chores, use the telephone,
prepare meals, and do the laundry. Instrumental Complex ADL
include ability to manage financial matters, shopping, adequate
use of medication, and go out alone using transportation.
This structure was determined by factor analysis on the same
sample of the present study and showed evidence of reliability
and validity (de Paula et al., 2014). We scored each activity
using a 3-point Lickert scale (dependent, partially dependent,
or independent of assistance to perform the activity). The
GADL subscores of Self-care (0–10), Instrumental-Domestic
(0–8), Instrumental-Complex (0–8), and the Global score (0–
26) represented the general ADL measures in our study. To
investigate the association of different cognitive functions with
specific measures of functional performance, we also used each
item of the scale (13 different ADL) independently.
We assessed the depressive symptoms with the Brazilian
version of the Geriatric Depression Scale-15 (GDS-15; Sheikh and
Yeasavage, 1986). A validation study conducted in Brazil attested
its sensitivity and specificity for the detection of depression
(Almeida and Almeida, 1999). However, since our focus was not
to identify patients with major depressive disorder, but to use
a dimensional measure of its symptoms, we used the GDS-15
total score in this research. The GDS use for depression diagnosis
in dementia is controversial. To reduce biases we selected only
patients with mild dementia for the AD group (CDR ≤1). Due to
participants’ low formal education, the examiner read the GDS
questions aloud to ensure the comprehension and validity of
patients’ report.
Statistical Procedures
Our four cognitive domains were extracted by factor analysis
(principal axis factoring with an oblique rotation of the
neuropsychological tests described in Cognitive Assessment)
Frontiers in Aging Neuroscience | www.frontiersin.org 5July 2015 | Volume 7 | Article 139
de Paula et al. Cognitive functions, depression, and ADL
FIGURE 2 | Association between depression and GADL scores. A weak
correlation was observed between depressive status and GADL total score
(r=−0.151, p=0.013). In the scatterplot, blue dots represent the participants
under the cut-off for depression according to the Geriatric Depression Scale and
green dots the participants above the cut-off for depression. The number of
dots in the scatterplot differs from the sample size due to superposed values.
from our sample. The procedures were described in detail
elsewhere (de Paula et al., 2013a). Briefly, the cognitive factors
were saved by a regression method and standardized (Z-Score)
based on the performance of our cognitively normal non-
depressed participants. The four factors were executive functions,
episodic memory, language/semantic memory, and visuospatial
Abilities. These factors showed high internal consistency and
reliability (Cronbach’s alpha >0.800 for all factors).
We carried out univariate analysis of variance (continuous
variables) or chi-square tests (categorical variables) to evaluate
baseline differences in sociodemographic, clinical, cognitive,
and ADL measures between the AD, MCI, and control groups.
Part of this data was previously published (de Paula et al.,
2013a). For a preliminary assessment of the relationship
between cognitive functioning, depressive level and ADL,
we correlated each measure with the GADL global score.
The influence of age, education, and gender in ADL was
investigated by linear regression models (forced entry)
containing each ADL measure as dependent variables. We
also explored the pattern of association between cognitive
performance and depressive symptoms through Pearson
correlations to evaluate if the contribution of cognitive
abilities and symptoms of depression to ADL performance is
independent.
We used multiple linear regressions with a forced entry model
to test whether the performance on specific cognitive domains
and the intensity of depressive symptoms could predict the
scores of the GADL components. Z-scores of executive functions,
episodic memory, language/semantic memory, and visuospatial
abilities, along with depressive symptoms (GDS-15 total score),
were entered as independent variables in the models. In addition,
we carried out ordinal regression analysis to assess whether the
cognitive factors and depressive level predict the performance on
each specific item of the GADL. Effect sizes were estimated by the
adjusted R2(linear regression) or Nagelkerke Pseudo-R2(ordinal
regression).
Results
Tab l e 3 shows the sociodemographic, clinical data, ADL,
and Z-scores for individual cognitive domains, according
to diagnosis. Group comparisons indicate no differences
in sociodemographic measures (p>0.05), but significant
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de Paula et al. Cognitive functions, depression, and ADL
TABLE 4 | Linear regression models of cognitive function and depressive symptoms as predictors of different ADL.
Fdf pR
2Predictors Standard βp
GADL: Global score
22.90 (5,268) <0.001 29% Executive functions 0.30 <0.001
Episodic memory 0.25 <0.001
Language/Semantic memory 0.18 0.011
Visuospatial abilities −0.13 0.084
Depressive Symptoms −0.08 0.140
GADL: Self-care score
1.37 (5,268) 0.234 <1% Executive functions 0.20 0.039
Episodic memory −0.02 0.780
Language/Semantic memory −0.01 0.931
Visuospatial abilities −0.14 0.116
Depressive symptoms −0.05 0.299
GADL: Instrumental-domestic score
14.39 (5,268) <0.001 19% Executive functions 0.33 <0.001
Episodic memory 0.18 0.009
Language/Semantic memory 0.09 0.211
Visuospatial abilities −0.10 0.237
Depressive symptoms −0.05 0.402
GADL: Instrumental-complex score
24.66 (5,268) <0.001 30% Executive functions 0.22 0.007
Episodic memory 0.28 <0.001
Language/Semantic memory 0.23 0.001
Visuospatial abilities −0.12 0.120
Depressive symptoms −0.08 0.108
ADL, Activities of daily living; GADL, General Activities of Daily Living Scale; df, Degrees of freedom.
differences in cognitive (p<0.01), and ADL (p<0.05) measures.
The normal aging group outperformed both MCI and AD groups
in cognitive measures, except for language/semantic memory
compared with MCI patients. MCI patients showed higher scores
than AD patients in all cognitive measures. Differences in ADL
occurred only between AD and the other groups.
Correlations between each cognitive factor, depressive
symptoms and the global score of the GADL are show in
Figures 1 and 2. All correlations between cognitive measures and
ADL were significant (p<0.001) and moderate, but we found
only a weak correlation between depressive symptoms and the
functional measure (r=−0.151, p=0.013). The correlations
between depressive symptoms and cognitive performance were
significant for executive functions (r=−0.192, p=0.001), but
we found no association with episodic memory (r=−0.019,
p=0.753), language/semantic memory (r=−0.085, p=0.162)
and visuospatial abilities (r=−0.039, p=0.525). The influence
of sociodemographic factors (age, education and gender) on ADL
performance was not significant: GADL Complex (F=1.31,
p=0.272), GADL Domestic (F=1.13, p=0.336), GADL
Self-care (F=1.56, p=0.200), and the global score (F=2.40,
p=0.068).
Tab l e 4 shows the predictors of GADL subscales scores.
For the global score of the GADL the model was significant
(F=22.90, p<0.001, R2=0.29) and contained as predictors
executive functions (p<0.001), episodic memory (p<0.001),
and language/semantic memory (p=0.011). The Self-Care
model was not significant (F=1.37, p=0.234, R2<0.01).
The model for Instrumental-Complex ADL was significant
(F=24.66, p<0.001, R2=0.30) and contained as predictors
executive functions (p=0.007), episodic memory (p<0.001),
and language/semantic memory (p=0.001). The model
for Instrumental-Domestic was also significant (F=14.39,
p<0.001, R2=0.19) and involved executive functions
(p<0.001) and episodic memory (p-0.009) as significant
predictors.
Tab l e s 5 and 6show the role of specific cognitive factors
and depressive symptoms as predictors of specific IADL. Since
the cognitive factors and depressive symptoms were unrelated
to GADL Self-Care scores, the analyses were carried out
for Instrumental-Domestic and Instrumental-Complex activities
only.
All Instrumental-Domestic activities were associated with
executive functions (p<0.05) and, except for the independence
in doing personal laundry, with episodic memory (p<0.05).
Language/semantic memory was related only to the correct
use of the telephone (p=0.014). Visuospatial abilities were
not significantly related to any Domestic ADL (all p>0.05).
Depressive symptoms were predictors of doing personal laundry
(p=0.025) and difficulties using the telephone (p=0.003). The
effect sizes for the comparisons were moderate-large, ranging
from 22 to 28% of explained variance.
Episodic memory was a significant predictor of all
Instrumental-Complex ADL (p<0.05). Executive Functions
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de Paula et al. Cognitive functions, depression, and ADL
TABLE 5 | Ordinal regression analysis of cognitive functions and depressive symptoms as predictors of Instrumental-Domestic activities of daily living.
χ2df pR
2Predictors Est. SE p
Do simple domestic chores
48.70 5 <0.001 26% Executive functions −0.74 0.22 0.001
Episodic memory −0.74 0.28 0.008
Language/Semantic memory −0.24 0.19 0.207
Visuospatial abilities 0.49 0.25 0.065
Depressive symptoms 0.06 0.05 0.224
Do personal laundry
45.91 5 <0.001 24% Executive functions −0.72 0.20 <0.001
Episodic memory −0.61 0.25 0.015
Language/Semantic memory −0.08 0.18 0.649
Visuospatial abilities 0.29 0.23 0.209
Depressive symptoms 0.11 0.05 0.025
Use the telephone
52.97 5 <0.001 28% Executive functions −0.57 0.20 0.005
Episodic memory −0.37 0.26 0.147
Language/Semantic memory −0.45 0.18 0.014
Visuospatial abilities 0.14 0.24 0.558
Depressive symptoms 0.15 0.05 0.003
Prepare meals
42.53 5 <0.001 22% Executive functions −0.64 0.19 0.001
Episodic memory −0.47 0.24 0.046
Language/Semantic memory −0.01 0.17 0.978
Visuospatial abilities 0.01 0.22 0.950
Depressive symptoms 0.02 0.05 0.668
χ2, Chi-Square test; df, Degrees of freedom; R2, Nagelkerke pseudo R-Square; Est., Ordinal logistic regression model estimate; SE, Standard error.
followed a similar pattern (p<0.05), but was not predictive
of the individual’s ability to manage finances (p=0.089).
Language/Semantic Memory was a significant predictor of
independence in performing simple shopping (p<0.001) and
managing finances (p=0.008). The Visuospatial abilities were
a significant predictor of the ability to go out alone and use
transportation (p=0.012). Depressive symptoms predicted the
ability to shop (p=0.030), handle financial matters (p=0.016),
and go out alone using transportation (p<0.001), but not the
medication management (p=0.162). The effect sizes of these
models were large, ranging from 30 to 38% of explained variance.
Discussion
In the present study, we showed distinct cognitive domains
having a significant impact on ADL in older adults with a
wide range of cognitive deficits. Executive functioning and
Episodic Memory showed the strongest significant association
with functional performance. Language/Semantic Memory
contributed to complex aspects of ADL and visuospatial abilities
contributed only to a specific instrumental activity. Depressive
symptoms had a significant influence on more complex ADL
such as handling finances. Self-care ADL were not related to
cognitive performance. Executive functions showed only a
weak correlation with depressive symptoms. The results are
in agreement with previous studies and highlight the close
relationship between deficits in specific cognitive domains and
functional loss.
Episodic memory and executive functions were the most
important predictors of domestic ADL performance. The
execution of these activities requires skills related to the
identification and ordering of different steps necessary to achieve
the final goal (e.g., different steps to prepare a meal) or recalling
information after a period of time or in face of distractors (e.g.,
remembering what of house cleaning was already done and what
was not). These behaviors are intrinsically related to different
aspects of executive functioning and episodic memory.
Similar to our findings, previous studies showed executive
functions and episodic memory tests as significant predictors
of the ability to cook and to do household chores (Farias
et al., 2003;Matsuda and Saito, 2005;Mariani et al., 2008).
Jefferson et al. (2006) found association between verbal fluency
and food preparing, cognitive flexibility, and selective attention
with doing laundry, but no correlations between executive
functions and house cleaning. An interesting research using
extrapyramidal signs and structural brain imaging found that,
controlling these previous factors and sociodemographic aspects,
the performance in tests of memory and executive functions
was still associated with cooking, and specific measures of
executive functions with the ability to perform simple domestic
chores (Bennett et al., 2006). We found an association between
depressive symptoms with doing laundry. We hypothesize that
this might be a sample bias. Most of the older adults assessed
Frontiers in Aging Neuroscience | www.frontiersin.org 8July 2015 | Volume 7 | Article 139
de Paula et al. Cognitive functions, depression, and ADL
in our study had a very low socioeconomic level and usually
do not have laundry machines. Since they do the laundry
manually, the association with depressive symptoms may be due
to lack of energy or apathy since this activity is very physically
demanding.
We found significant associations between executive
functions, language/semantic memory, depressive symptoms,
and telephone use. The engagement of these specific cognitive
functions may reflect the necessity of communication to
perform this activity. The neuropsychological battery used
in this study included instruments related to expressive
language, comprehension, and access to semantic and
phonological lexicons. Therefore, we expected that ADL
related to communicative skills would be influenced by the
performance on these cognitive domains (Taler and Phillips,
2008;Razani et al., 2011). Farias et al. (2003), however, found
motor praxis as the only predictor of telephone use. Depressive
symptoms also influenced telephone use in our study. Social
isolation, a common characteristic of elderly persons with
depression (Corcoran et al., 2013), may reduce the individual
willingness to actively pursue contact with other people, leading
to impairments in this specific activity.
Two of the Complex-ADL activities involve management
of finances. Episodic memory and language/semantic memory
predicted financial management, while executive functions,
episodic memory, and language/semantic memory predicted
shopping ability. These are complex activities and involve
several cognitive processes (Marson et al., 2009). Sherod et al.
(2009) decomposed financial managing it in basic financial
skills, financial conceptual knowledge, financial transactions,
checkbook control, banking control, and financial judgment. The
authors identified the cognitive predictors of financial capacity
in the spectrum of normal aging, MCI, and AD using a specific
questionnaire for financial management and a comprehensive
battery of neuropsychological tests. Their findings suggest that
arithmetic skills (which relies on working memory) are the main
predictor of financial capacity. Jefferson et al. (2006)foundan
association between selective attention and the ability to shop
and to control finances. Tasks related to episodic memory, basic
math skills, and a test related to language/semantic memory
predicted financial control in Matsuda and Saito (2005)study.
Razani et al. (2011) evaluated skills related to the management
of money and found similar predictors to the present study:
how to write out checks was associated with language, control
the checkbook was related to executive functions, and shopping
ability was associated with memory and executive functions.
Motor praxis also might be related to financial management
(Farias et al., 2003). Additionally, we observed depressive
symptoms predicting worse performance in the management of
finances. This finding is in contrast to Farias et al. (2003)who
found no significant association between depressive symptoms
and management of finances.
TABLE 6 | Ordinal regression analysis of cognitive functions and depressive symptoms as predictors of Instrumental-Complex activities of daily living.
χ2df pR
2Predictors Est. SE p
Manage finances
67.43 5 <0.001 30% Executive functions −0.30 0.18 0.089
Episodic memory −0.81 0.23 <0.001
Language/Semantic memory −0.43 0.16 0.008
Visuospatial abilities 0.05 0.20 0.801
Depressive symptoms 0.11 0.05 0.016
Shopping
91.15 5 <0.001 38% Executive functions −0.42 0.18 0.018
Episodic memory −0.95 0.24 <0.001
Language/Semantic memory −0.60 0.16 <0.001
Visuospatial abilities 0.31 0.21 0.139
Depressive symptoms 0.10 0.05 0.030
Use of medication
96.07 5 <0.001 38% Executive functions −0.71 0.18 <0.001
Episodic memory −0.99 0.22 <0.001
Language/Semantic memory −0.27 0.15 0.077
Visuospatial abilities 0.36 0.20 0.065
Depressive symptoms 0.06 0.04 0.162
Go out alone and use transports
75.93 5 <0.001 33% Executive functions −0.51 0.18 0.004
Episodic memory −0.72 0.22 0.001
Language/Semantic memory 0.23 0.20 0.252
Visuospatial abilities −0.40 0.16 0.012
Depressive symptoms 0.14 0.04 0.001
χ2, Chi-Square test; df, Degrees of freedom; R2, Nagelkerke pseudo R-Square; Est., Ordinal logistic regression model estimate; SE, Standard error.
Frontiers in Aging Neuroscience | www.frontiersin.org 9July 2015 | Volume 7 | Article 139
de Paula et al. Cognitive functions, depression, and ADL
The correct use of medications was associated with executive
functions and episodic memory in the present research in
accordance with previous studies (Maddigan et al., 2003;Sino
et al., 2014). A very common complaint by patients with
memory impairment is to forget when to take medications
or difficult to remember if he/she has already take it or not.
This emphasizes the importance of different aspects of the
episodic memory for the correct maintenance of medical care
routine (Matsuda and Saito, 2005). Complex medication routines
may demand more executive control (Maddigan et al., 2003).
A study reported that performance in executive functions tests
(including working memory) was a significant predictor of
medication use in older adults (Insel et al., 2006). However,
Jefferson et al. (2006) did not find any significant associations
in this direction. Compensatory strategies might explain these
discrepancies. Carlson et al. (2005) tested two objective measures
of medication use capacity (schedule and pillbox) and found
a significant association between memory performance and
the schedule strategy and between executive functions and the
pillbox.
We found executive functions, episodic memory, visuospatial
skills, and depressive symptoms as predictors of ADL related to
going out of home alone to distant locations using transportation.
Different aspects of executive functions such as planning,
cognitive flexibility, and selective attention were associated with
transportation in the study of Jeferson et al. (2006). Deficits in
these functions were associated with impairment in “visually”
dependent activities such as driving, orientation, and transport
use (Silva et al., 2009;Farley et al., 2011). However, despite the
expected relationship between visuospatial abilities and the ability
to travel long distances, few studies have found a significant
direct association between the neuropsychological performance
and its functional counterpart. Our findings provide a model in
which visuospatial and navigation abilities depend on executive
functions, episodic memory, and visuospatial skills. Matsuda and
Saito (2005) found similar results in the Japanese population.
Depressive symptoms also predicted performance in the ability
to travel. As in telephone use, social isolation might mediate
the association between depressive symptoms and functional
performance on this task.
In our view, the strengths of the current study are the
relatively large sample size, the heterogeneity of the participants
and the use of fine-grained cognitive and functional measures.
The use of cognitive factors validated for this population
instead of tests’ raw scores allow the construction of more
precise conceptual models and is easier to be generalized for
other settings, since the data is analyzed in the cognitive
construct level and can be represented by different cognitive
tests. However, the present results should be viewed in light
of its limitations. Although the neuropsychological measures
used comprise four specific cognitive domains, the protocol had
no specific measure of processing speed, a cognitive domain
related to functional performance and a potential mediator of
depression influence on daily functioning (Brown et al., 2013).
The executive functions factor adopted in this study may be
related to processing speed in our population since processing
speed and executive functions influence verbal fluency in an
independent way in older adults with low formal education (de
Paula et al., 2013b). Therefore, the relationship between the
executive function domain and functional performance could be
secondary to its processing speed component. Additional studies
including specific measures of processing speed should evaluate
its impact on ADL. Additionally, the present study has a cross-
sectional design, which limits the interpretation of our findings.
Future studies with a longitudinal design are necessary to assess
the impact of changes in specific cognitive functions on the
performance of specific ADL. Another aspect is the lack of an
ecological functional measure since our work relies on scales of
caregiver report, which may result in different results.
Conclusion
We found executive function and episodic memory as the
cognitive domains most frequently related to impairment in
general constructs of ADL. Nonetheless, language, semantic
memory, and visuospatial abilities may influence specific
functional aspects of ADL. The development of better predictive
models can aid to the development of tailored and personalized
rehabilitation programs to improve functional performance of
subjects with neurocognitive disorders.
Funding
This work was supported by the following grants: APQ-01972/12-
10, APQ-02755-10, APQ-04706-10, CBB-APQ-00075-09 from
FAPEMIG, and 573646/2008-2 from CNPq. The funders had no
role in study design, data collection, analysis, decision to publish,
or preparation of the manuscript.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
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Frontiers in Aging Neuroscience | www.frontiersin.org 12 July 2015 | Volume 7 | Article 139