The Effect of Apathy and Depressive Syndromes on Functional Outcomes in Alzheimer’s Disease

Abstract

Background:

Alzheimer’s disease (AD) is the most common cause of dementia. Its initially characterized by progressive short-term memory loss followed by cross-domain cognitive decline in later stages resulting in significant functional deficits and loss of activities of daily living (ADLs) independence. Apathy and depression are frequent neuropsychiatric sequelae in AD, but their contribution to functional deficits is poorly understood.

Objective:

We aimed to quantitatively investigate if apathy and depressive symptoms predict ADLs in AD. We also wanted to fractionate apathy dimensions by factor-analyzing the apathy evaluation scale (AES) and then investigate the dimensions’ relation to ADLs.

Methods:

We recruited a sample of 115 patients with probable or possible AD and assessed them for depression, apathy, and ADLs alongside other measures. We hypothesized that apathy and depressive symptoms would predict ADLs and that AES items will load into cognitive, behavioral, and affective factors that would differentially relate to ADLs.

Results:

Our results indicated that apathy symptoms predict ADLs deficits. The AES items resolved into a three-factor solution but the manner of clustering diverged from that proposed by AES authors. When these factors were regressed simultaneously, only behavioral apathy predicted global ADLs. Distinguishing basic from instrumental ADLs showed that behavioral and cognitive apathy symptoms associate with ADLs deficits while affective symptoms do not.

Conclusions:

Our results highlight the influence of apathy on ADLs in AD. This has important implications for patient care considering the high prevalence of apathy in AD and other dementing illnesses.

Keywords

Activities of daily living Alzheimer’s disease apathy depression

In Alzheimer’s disease (AD) cognitive and neuropsychiatric fallouts are responsible for the significant loss of activities of daily living (ADLs) associated with the disease. AD is the most dominant cause of dementia, accounting for between 60% to 70% of all dementia cases worldwide [1, 2]. In South Africa AD is ranked within the top 50 causes of death while the country ranks 31st in AD prevalence (see [3] for a review). Life expectancy post diagnosis typically ranges within a decade [2] and those years are characterized by a gradual cognitive decline with consequent loss of independence and functionality in carrying out ADLs [4, 5]. An early onset variant of the disease is associated with an even more rapid and aggressive cognitive and functional decline [6 –8]. AD is likely to contribute an ever increasing and significant disease burden to South Africa and other countries across the world.

Understanding and managing AD’s associated cognitive, functional, and neuropsychiatric fallouts is currently one of the best interventions due to the limited effects and availability of pharmacological treatment options. Research indicates that Basic Activities of Daily Living (BADLs) such as those involving self-hygiene, dressing, eating/feeding and toileting and more complex Instrumental Activities of Daily Living (IADLs) such as managing finances, shopping, and medical adherence seem to be differentially affected along the temporal course of the disease process [5, 9]. For instance, IADLs tend to be vulnerable much earlier, and their loss is often proportionate to the patient’s cognitive status. The cognitive dysfunction associated with memory loss is linked to IADLs impairments observed in these early phases of the disease [10 –12]. The pattern of disruption in ADLs is often complicated by the presence of neuropsychiatric symptoms [14, 15]. These neuropsychiatric symptoms are thought to result from AD related pathological changes in distributed brain pathways involving diverse connections across the limbic circuitry, basal forebrain bundles, substantia nigra, hypothalamus and the brain stem [15 –20]. Presence of cognitive and neuropsychiatric symptoms in AD is also associated with accelerated AD progression, poor prognosis, higher levels of disability, caregiver distress, and early institutionalization [6 , 22].

Depression and apathy are the most frequent of the neuropsychiatric comorbidities associated with AD. In particular, depressive symptoms tend to dominate the early stages of the disease [14 , 23]. Approximately 50% of patients with AD develop depressive symptoms at some point during the course of illness [14 , 25]. There seem to be a bi-directional relationship between the cognitive impairment that characterizes AD and late life depression, and there may also be psychological drivers for depression risk in cognitively impaired individuals [10]. Apathy on the other hand is more frequent (and persistent) than depression at all levels of AD [20, 26] and is a reliable predictor of mild cognitive impairment progression to AD [20 , 28]. Apathy is marked by cognitive, emotional, and behavioral deficits that are thought to emerge from attenuated self-initiation and reduced motivational inputs into goal directed behavior [29]. Common symptoms include lack of planned action and/or effort, dysregulated emotional responsiveness to life events (flat or unchanging affect) and lack of care and concern about one’s impairments [30 –35]. Consequently, the presence of apathy often results in unsatisfactory execution of ADLs, poor treatment compliance, diminished quality of life and problematic interpersonal relationships, and other functional deficits [26 , 36–41]. Given that apathy is also associated with lower adherence to treatment, its presence has the potential to increase the burden of comorbidities while also complicating their therapy [42, 43].

Studies that profile functional decline in AD often fail to account for inter-relations among the crucial cognitive and neuropsychiatric variables discussed so far. For instance, apathy and depression are phenomenologically, clinically, and neurally distinct (e.g., [39, 44]), but there is also substantial overlap of key symptoms of the two disorders in AD. For instance, anhedonia, loss of interest, and reduced activity are key symptoms in both apathy and depression and can make a differential diagnosis difficult [39, 44]. To complicate matters even more, traditional assessment tools for depression generally contain items specific to apathy (e.g., the Hamilton depression rating scale [45]), with a possibility that patients with apathy may be misdiagnosed with depression [46, 47].

The individual and comorbid effects of apathy and depression on ADLs performance in AD patients is poorly understood [10 , 25]. A few studies have been done on this (e.g., [20], but little is known for instance on whether subdomains of apathy (behavioral, cognitive, and emotional) differentially associate with capacities for ADLs performance, or whether different ADLs are differentially affected by global apathy or its subdomains. This type of research is also hampered by the lack of conclusive quantitative evidence on the psychometric properties of assessments for apathy or its conceptualization [48].

In this study we had three aims. First, to investigate the predictive effect of depressive (evaluated using the Cornell Scale for Depression in Dementia (CSDD) [49]) and apathy symptoms (evaluated using the informant version of the Apathy Evaluation Scale (AES-I) [50]) on ADLs while controlling for effects of age, gender, and level of education. Second, considering the lack of consensus on the concept and subdomains of apathy, we also wanted to establish through factor analysis if AES-I items load into the three factors (cognitive, behavioral, and affective) suggested by Marin [50]. Some researchers have questioned the integrity of these apathy subdomains [51], and it is also possible that the profile of apathy may differ across clinical groups [52]. Third, we aimed to examine if the subdomains of apathy that we derive from the factor analysis in aim 2 would predict ADLs performance differentially. We also wanted to investigate how the subdomains related to basic and instrumental ADLs.

Basing on previous studies (e.g., [5 , 53]), we hypothesized that higher levels of apathy and depression would predict limited capacity for ADLs. We also predicted that AES items would generally load into three factors that relate to the cognitive, behavioral, and affective aspects of apathy and that these subdomains would differentially predict ADLs performance. However, we also expected differences with how some specific items would load to these factors. We also hypothesized that the apathy subdomains would relate differently to basic and instrumental ADLs. Understanding these associations might be crucial in treating apathy because these subdomains may have distinct associations with the patients’ level of functioning as well as underlying brain pathology [5].

METHODS

Design and setting

This cross-sectional study follows up on data collected from outpatients for the memory clinic at the Albertina and Walter Sisulu Institute of Ageing in Africa (IAA) in the department of Psychiatry and mental health at Groote Schuur hospital, Cape Town. The data reported in this study was collected between year 2014 and 2018.

Participants

A 115 patients diagnosed with probable (67.1%) or possible (32.9%) AD on a standard criteria [53] were purposively sampled from a pool of 500 outpatients on a first time visit to the IAA Memory Clinic. Data on patients with other forms of diagnoses or dementias were excluded. In addition, patients with missing or incomplete data on crucial demographic variables, and also on the neuropsychiatric measures reported in this study were excluded even when they had the requisite diagnosis of probable or possible AD. The diagnostic process [53] constituted a case conference consensus by a multidisciplinary memory clinic team made up of neuropsychologists, neurologists, psychiatrists, and medical registrars using data acquired from the patient’s history, and objective assessments including bloodwork and brain scan data (see full description under procedures). The age of the participants ranged from 42 years to 92 years (mean = 71 years, SD = 8.99). The sample comprised 38 (33%) males and 77 (67%) females(Table 1).

Table 1

Demographic characteristics of the participants

Variable	N	%
Sex
Male	38	33
Female	77	67
Marital status
Married	52	45.2
Widowed	39	33.9
Divorced	12	10.4
Never married	12	10.4
Highest level of education
No Formal schooling	5	4.3
Primary school	43	37.4
Secondary School	43	37.4
^*Matric	10	8.7
College	9	7.8
University	5	4.3

*Matric is the 12th and final year of secondary schooling in South Africa.

Measures

Apathy Evaluation Scale. The AES [50] is the most widely used apathy measure. It is also well-validated and reliable. There are three versions to the scale, i.e., clinician (AES-C), informant (AES-I), and self (AES-S) rated versions. In this study we used the AES-I because studies show it is the most psychometrically robust relative to the other two [50]. The AES-I has 18 items which ask informants to evaluate patients on levels of activity, interest, and motivation on a four-point Likert-type scale. Scores can range from 18 to 72, with higher scores being indicative of more apathy. Scores above 37 points suggest the presence of significant apathy symptoms. Psychometric studies have reported the AES-I’s Cronbach’s alpha as ranging from 0.86 to 0.94. It has also been shown to be a valid assessment measure of apathy relative to other versions, r = 0.50, p = 0.001 (see [50] for a detailed review).

Cornell Scale for Depression in Dementia. The CSDD [49] is used to quantify and characterize depression in patients with dementing illnesses. The scale has 19 items each rated on a 4-point Likert-type scale and scores can range between 0–38. A score in the range of 10–17 suggests probable major depression while scores above this threshold indicate a definite depressive syndrome. Psychometric research on the CSDD suggests that it has good validity and inter-rater reliability, with one study reporting an internal consistency coefficient of 0.84, a Cronbach’s alpha of 0.6, and predictive validity of 0.75 [54]. CSDD has a moderate to excellent detection of geriatric depression relative to other depression scales [55, 56].

The Bristol Activities of Daily Living Scale. The BADLs [57] is a non-cognitive 20 item informant rated measure used to profile capacity for activities of daily living in AD patients. For this study we used a purposively and contextually modified 17 items version of the scale as part of a standardized protocol. The modification consisted of removing or altering items that did not relate well to the South African context (for instance, in South Africa eating using fingers is considered normal behavior but is pathologized in the original scale). The items are scored on a five point Likert scale. The lowest attainable score for the scale is 0 while the maximum score is 51, with higher scores indicative of more incapacity and reliance on others to meet daily needs. The BADLs has demonstrable good psychometric properties [58] and has been used in clinical research, hospital, and community clinic settings in South Africa [58, 59].

Procedure

The principal clientele of the memory clinic are referrals from other health care facilities with mostly a query of dementia. In relation to AD the majority of first-time patients seen at the clinic are often in the mild to moderate stages of the disease process. These are the patients who made up the sample for this study. Patients give informed consent in line with ethics guidelines approved by the Healthy Sciences Department at the University of Cape Town before they are clerked and scheduled for a brain scan. These ethics provisions are in line with guidelines from the Helsinki declaration on ethical principles for medical research involving human participants [60].

The diagnostic process [53] was based on data gathered from a four staged consultation process. Firstly, a medical/psychiatric registrar conducts an intake interview on the patient’s medical history, demographic and biographical information, premorbid functional state and current complaints, and collects blood and urine samples. In the second and third stages, which run concurrently, the patient undergoes a physical examination and then is assessed on a battery of neurocognitive tests while the companion is separated to complete a battery of informant measures including the AES-I, Neuropsychiatric Inventory and Caregiver Distress, Deterioration Cognitive Observee scale, the BADLs, and the CSDD. The neurocognitive battery contains tests for orientation, processing speed, memory, visuo-spatial processing, attention, visuo-constructional abilities, language, and executive functioning These tests have had widespread use in South Africa [61]. See Supplementary Tables 1A and 1B for the full battery of tests and scales.

Clinical information on vascular risk factors like diabetes, heart disease, hypertension, claudication, hypercholesterolemia, atrial fibrillation, current smoking, alcohol and drug use and previous history of smoking, alcohol and drug use is also obtained. After these assessments the memory clinic interdisciplinary team of clinicians from neurology, neuropsychology, psychiatry, and geriatrics meet in a case conference to explore the differentials for diagnostic consensus based on the assessments as well as brain scan data. The team also formulates an intervention protocol. Although a definite diagnosis for the dementias is only possible on autopsy, the syndromic groups can be reliably profiled with a significant degree of accuracy based on the data gathered through the objective neuropsychiatric and neuropsychological assessments described above [62, 63].

A feedback session for the patient and the informant is then carried out to explain the assessments results and their implications for the patient and his family/caregiver. All the patient data is stored in the patients file at IAA and an electronic copy is also created and stored in a password protected database.

Statistical analysis

We ran bivariate correlations to evaluate associations across the demographic variables and depression, apathy, and ADLs scores. We then performed hierarchical multiple regressions to establish whether apathy and depression predict difficulties with carrying out ADLs after partialing out confounding variables (age, gender, level of education, and marital status). These variables have been shown to influence the severity and rate of cognitive fallouts in dementias. For instance, age has been shown to be the single most crucial risk factor for dementia [64]. Formal education seems to build cognitive reserve and aid well-versed life decision-making hence decelerating observable effects of the cognitive deterioration associated with dementing illnesses [65]. Marital status also seems to play a role in the cognitive status of the elderly in the context of AD. Some studies suggest that being married is associated with relatively lower cognitive fallouts, possibly because having a partner can provide supportive social and cognitive engagement [66]. For our analysis we blocked these potentially confounding variables together in a regression equation and entered apathy, depression, and age scores, testing for the value and significance of incremental sums of squares across steps.

We also performed an exploratory factor analysis to establish how AES-I items cluster and to establish if they resolved into a 3-factor solution as proposed by Marin [67]. The derived factors were then regressed on ADLs in a simultaneous regression equation. The regression models were checked for homoscedasticity, normality of residuals and other statistical assumptions. Lastly, we isolated basic (BADLs) and instrumental (IADLs) and investigated their individual relations to the three apathy subdomains. All the analyses were done using the statistical package for social sciences (SPSS) version 25. The significance was set at 0.05 alpha level.

RESULTS

Correlations of apathy, depression, and ADLs

We ran Pearson correlation analyses to investigate the relationships among our predictors (apathy and depressive symptoms) and the ability to perform ADLs (Table 2). The results show that apathy symptoms significantly correlate with the ability to carry out ADLs (0.288, p = 0.02). The association between depressive symptoms and ADLs performance is of borderline significance (0.188, p = 0.045). The correlation between apathy and depression is also significant (0.423, p = 0.00) (Table 4).

Table 2

Correlations matrix for apathy, depression, and ADLs

	1	2	3	4	5
1. Age
2. Sex	0.006
3. ADLs	0.173	–0.136
4. Depression	–0.185^*	0.110	0.188^*
5. Apathy	–0.104	0.023	0.288 ^**	0.423^**

^*Correlation is significant at the 0.05 level (2-tailed). ^**Correlation is significant at the 0.01 level (2-tailed). Significant statistics are in bold.

Predictors of capacity for ADLs

We performed a hierarchical regression analysis to see whether apathy (model 1) and depression (model 2) significantly predict capacity to execute ADLs. We included age (model 3), and then sex, level of education and marital status (model 4) in the regression equation. The results indicate that apathy accounts for 8.3% of the variation in ADLs performance. However, when we include depression, this value increases to 8.8%, indicating that depression accounts for only 0.5 % of the variance in ADLs performance. The inclusion of age (model 3) and sex, marital status & level of education (model 4) also increases the value to 20% implying that these variables account for an additional 11.2% of the total variance. The F change for model 1 is statistically significant (F(115, 1) = 10.244, p = 0.002). However, when other predictors are included in the equation the F change for the model is not statistically significant (F (115, 5) = 0.644, p = 0.667. The Durbin-Watson statistic is 1.921 showing that the assumption of independence of errors has not been violated (Tables 3 and 4).

Table 3

Hierarchical regression model for the predictors of capacity for ADLs

				Change statistics
Model	R	R²	Adjusted R²	R² change	F change	Sig F change	Durbin Watson
1	0.288	0.083	0.075	0.083	10.244	0.002
2	0.297	0.088	0.072	0.005	0.646	0.423
3	0.417	0.174	0.120	0.086	2.225	0.057
4	0.447	0.200	0.105	0.025	0.644	0.667	1.941

Table 4

Hierarchical regression: ANOVA table

	ANOVA
Model	df	F	sig
1	1	10.244	0.002
2	2	5.429	0.006
3	7	3.225	0.004
4	12	2.118	0.022

The independent contribution of apathy in predicting ADLs is (β=0.256, p = 0.013) indicating that global apathy significantly predicts ability to carry out ADLs. On the contrary, depression does not significantly predict ADLs performance (β=0.164, p = 0.119). The potential confounding variables that we included in the regression equation did not significantly predict ADLs except for age (β=0.247, p = 0.018). Our matrix also shows that variance inflation factor (VIF) values are all below 10 indicating that the assumption of no multicollinearity has not been violated. The data also had a normal distribution (Fig. 1).

Fig. 1

Principal component factor analysis of the apathy evaluation scale.

We performed a principal component factor analysis of the AES to test for the 3 subdomains of apathy proposed by Marin [51]. We re-coded negatively worded items on the scale (see items 6, 10, and 11 in Table 5) for ease of analysis. The mean scores for the items are relatively similar indicating that they contribute similarly to apathy. The determinant of the correlation matrix is 9.731 E-5 which is bigger than 0.00001 indicating that there is no multicollinearity. We also computed the Kaiser Meyer Olkin statistic (KMO) which measures sampling adequacy. This measure is above 0.5 indicating that our sample size is adequate for a factor analysis to be performed (KMO = 0.89, p < 0.001).

Table 5

Descriptive statistics

AES items	Mean (N = 115)	Std. deviation
1. She is interested in things	2.21	1.04
2. She gets things done during the day	2.39	0.984
3. Getting things started on her own is important to her	2.28	1.20
4. She is interested in having new experiences	2.77	1.12
5. She is interested in learning new things	2.95	1.08
6. She puts little effort into anything	2.46	1.12
7. She approaches life with intensity	2.63	1.10
8. Seeing a job through to the end is important to her	2.23	1.21
9. She spends time doing things that interest her	2.48	1.11
10. Someone has to tell her what to do each day	2.38	1.25
11. She is less concerned about her problems than she should be	2.35	1.18
12. She has friends	2.38	1.10
13. Getting together with friends is important to her	2.70	1.19
14. When something good happens, she gets excited	1.83	.976
15. She has an accurate understanding of her problems	2.37	1.12
16. Getting things done during the day is important to her	2.30	1.12
17. She has initiative	2.68	1.19
18. She has motivation	2.61	1.13

We used the Keiser’s criterion for retaining factors with associated eigenvalues greater than 1. After extraction 3 factors were retained, with factor 1 having the highest variance (41.61%), while factor 2 and 3 had 8.4% and 6.24% of variance respectively. We also used the scree plot as our final guide on which factors to retain. The scree plot showed that the point inflection is at the fourth data point. Therefore, we retained the factors on the left of the data point excluding the point of inflection. Using the scree plot we then retained 3 factors. This was at par with the results of the Kaiser’s criterion. In reproduced correlations, we observed that there are 67 (43.0%) non redundant residuals with absolute values greater than 0.05 (Table 6).

Table 6

Eigenvalues

	Initial eigenvalues			Extraction sums of squared loadings			Rotation sums of squared loadings
Component	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %
1	7.490	41.61	41.61	7.49	41.61	41.61	4.14	23.013	23.01
2	1.525	8.47	50.08	1.52	8.47	50.08	3.70	20.57	43.58
3	1.124	6.24	56.327	1.124	6.24	56.32	2.29	12.73	56.32
4	0.999	5.55	61.87
5	0.894	4.96	66.84
6	0.873	4.85	71.69
7	0.766	4.25	75.95
8	0.648	3.60	79.55
9	0.607	3.37	82.92
10	0.540	3.00	85.93
11	0.524	2.91	88.84
12	0.415	2.30	91.14
13	0.357	1.98	93.12
14	0.331	1.83	94.96
15	0.317	1.76	96.72
16	0.225	1.25	97.98
17	0.205	1.13	99.11
18	0.159	0.88	100.00

Although our analysis showed that apathy items resolved into a three-factor solution, we had cross loadings of some items in two or three factors. To correct for this, we re-ran the factor analysis after deleting ‘problematic items’ and also excluding items that Marin [51] classified as unspecified “other” (Table 7). The factor loading after rotation suggests that factor one represents cognitive apathy, factor two represents behavioral apathy, and factor three represents affective apathy.

Table 7

Factor loading after rotation

	Orthogonal Rotation
AES items	1	2	3
1	0.545
2		0.610
4	0.810
5	0.828
6	0.675
7	0.586
9	0.479
10		0.760
11		0.792
12			0.868
13			0.789
14			0.463

To test the hypothesis that the individual apathy subdomains derived from the factor analysis exert differential challenges on ADLs, we performed a simultaneous regression analysis. The analysis indicates that our regression model explains 11.3% of the variation in ADLs performance. The F change was also statistically significant (F (115, 3) = 3.934, p = 0.004). In addition, the Durbin-Watson statistic was 2.111 suggesting that the assumption of independent of errors has been met. Although our model significantly predicted ADLs performance, not all of the predictors that were entered in the equations had a significant predictive effect. Behavioral apathy was the only subdomain of apathy that significantly predicted ADLs performance. (β=0.240, p = 0.011). Cognitive apathy (β=0.137, p = 0.225) and emotional apathy ((β=0.021, p = 0.845) did not significantly predict ADLs performance. The VIF values are all below 10 indicating that the assumption of no multicollinearity has not been violated.

Apathy subdomains and their relation to BADLs and IADLs

To investigate whether distinct sub-domains of apathy associate with different ADLs, we ran bivariate Pearson correlations between the apathy subdomains and basic (BADLs) and instrumental (IADLs) activities of daily living (57). Only behavioral apathy (0.229, p = 0.014) show significant association with BADLs, while both cognitive (0.256, p = 0.006) and behavioral (0.344, p≤0.001) are significantly related to IADLs (Table 8). On the other hand, emotional apathy is neither associated with the global ADLs (0.150, p = 0.110), BADLs (0.158, p = 0.092), or IADLs (0.115, p = 0.220).

Table 8

Relations between ADLs and domains of apathy

	Cognitive Apathy	Behavioral Apathy	Emotional Apathy
Total ADLs	0.243^**	0.329^**	0.150
BADLs	0.167	0.229^*	0.158
IADLs	0.256^**	0.344^**	0.115

N = 115. ^**Correlation is significant at the 0.01 level (2-tailed). ^*Correlation is significant at the 0.05 level (2-tailed). Significant correlations are in bold.

DISCUSSION

Effect of depressive and apathy symptoms on ADLs

We wanted to investigate the predictive effects of depressive and apathy symptoms on capacity for ADLs in patients with AD, controlling for effects of age, marital status, gender, and level of education [65, 66]. Our speculation was that global apathy and depression will independently predict ADLs performance. Our results partially confirmed this hypothesis. We found a significant unique contribution of apathy symptoms in predicting ADLs performance. Similarly depressive symptoms had borderline correlations with capacity for ADLs, but further regression modelling showed that their predictive power was weak. These results are consistent with prior research showing that apathy symptoms are good predictors of ADLs performance (e.g., [10 , 65]). This line of research suggests that the presence of apathy symptoms in patients with AD associates with attenuated ADLs, including deficits in executing basic daily routines such as maintaining hygiene or a good diet and problems with more complex instrumental ADLs such as financial budgeting, medical adherence, and shopping [20 , 38].

Some of the evidence suggests that in early stages of AD, patients with a comorbidity of apathy tend to have intact capacity to perform basic ADLs but struggle with carrying out the more complex instrumental ADLs [40]. These results are not surprising given that the ability to plan, initiate and have the motivation to carry out goal directed tasks requires executive inputs that are commonly compromised in apathy [32 , 53]. Consequently, global apathy often relates more to deficits in instrumental activities of daily living which tend to deteriorate in proportion with the progression of the disease [65, 40].

Although some studies associate depression with limited ADLs [10, 14], our results did not show this pattern. The weak predictive power of depressive symptoms for ADLs in our study may reflect the fact that these symptoms tend to dominate early stages of illness in AD when cognitive deficits are still mild to moderate and exert minimal impact on ADLs. Apathy symptoms on the other hand are prevalent across all AD stages, and some studies indicate they may dominate later stages of the disease when cognitive decline has a greater effect on ADLs [37]. Future studies would need to control for the possible influence of this temporal variability in cognitive and neuropsychiatric sequalae of AD.

We also think that the link between depression and ADLs deficits in other studies might reflect in part the symptom overlap between apathy and depression on symptoms like anhedonia, loss of interest, and reduced level of activity that may occur in some clinical samples [44 , 47]. Traditional depression instruments often treat apathy as a symptom of depression too, and some contain apathy-specific items. For example, the CSDD [49] has items that evaluate loss of interest and reduced activity. Other studies do not have control on the extraneous variables we controlled for in this study.

Apathy subdomains and AES-I factor structure

Our other aim was to factorize apathy symptoms on the AES-I and investigate the relations of emerging factors with ADLs. This line of enquiry is necessary given the lack of consensus on the integrity of apathy dimensions and their neural and functional correlates [71]. Results from our principal component factor analysis indicated that AES-I items indeed exhibit a three-factor solution that can be categorized as affective, behavioral, and cognitive apathy [51]. However, some of the items did not load into these factors in the manner that was proposed by Marin [51]. Given that apathy symptoms can arise from damage to a myriad of cognitive, affective, and motoric systems that sustain goal directed behavior, it is understandable that the profile of these symptoms might vary across disorders in reflection of underlying neural pathology. Below we discuss the AES factor structure in relation to our findings, and then we move on to discuss how this structure associates with capacity for ADLs for our sample.

Affective apathy factor. Marin (1991) suggested that two of the 18 AES items load into the affective apathy sub-domain (item 7 [S/he approaches life with intensity], and item 14 [When something good happens, he/she gets excited.]) However, in our principal component analysis only item 14 loaded into the affective subdomain of apathy, which is understandable because the item describes the connection between an experience and its related/associated emotion(s). On the contrary, item 7 did not load into this subdomain but instead loaded into the cognitive cluster. One possible explanation could be that item 7 has both emotional and cognitive dimensions. The statement can refer to whether the patient approaches life with enthusiasm (emotional) or approaches life with energy and motivation (cognitive). Separating emotional from cognitive inputs into motivated behavior is always difficult, especially in relation to the type of behavior sampled by the item. Our principal component matrix also loaded item 12 (S/he has friends) and item 13 (Getting together with friends is important to her/him) into the affective subdomain of apathy. Getting together with friends and placing importance on the process have affective components in that both processes may associate with emotional fulfilment. They may reflect the emotional importance attached to having friends and getting together with them. This may explain why the items can conceptually relate to the affective apathy factor.

Behavioral apathy factor. The authors of the AES [50] proposed that 5 items in the scale load into the behavioral subdomain of apathy. Our analysis showed that only 2 of those items loaded into the factor (Item 2 [s/he gets things done during the day] and item 10 [someone has to tell him/her what to do each day]). Contrary to Marin’s proposition item 6 [s/he puts little effort into anything], item 9 [s/he spends time doing things that interest him/ her] and item 12 [s/he has friends] did not load into this factor. Items 9 and 6 loaded into the cognitive apathy subdomain. For item 9 this is possibly because the characteristic manifestation of cognitive apathy is attenuation of interest [34, 43]. Item 6 can also be understood in the context of cognition in that putting effort initially requires a cognitive motivational drive which is then translated into a behavior.

Cognitive apathy factor. Of the 8 items that Marin [50] proposed constitute cognitive apathy, only 3 loaded into this cluster on our analysis (Item 1 [s/he is interested in things], item 4 [s/he is interested in having new experiences] and item 5 [s/he is interested in learning new things]). These items collectively tap into ‘interest’ which is one of the indices that characterize cognitive apathy (see, [43]). Additional items loading into this factor on our component matrix are items 6, 7, and 9 which we have already discussed. Although Marin [50] suggested that item 3 (getting things started on his/her own is important to him/her), item 8 (seeing a job through to the end is important to her/him), and item 16 (getting things done during the day is important to her/him), as cognitive apathy indices, they did not load into any of the subdomains of apathy. These three items are conceptually similar. They seem to be asking the respondents two things. For instance, “seeing a job through to the end ... ” and “important to her/him” can be understood to be two questions. The first part refers to behavioral apathy and the second part relates more to cognitive apathy. It is possible that such a line of questioning can pose interpretational ambiguity to the respondents.

Summary on the AES factor structure. In summary, our analysis indicates that although Marin’s conceptualization of apathy is dominant in research (and in some instances in clinical practice), the discrepancies between Marin’s categorization of AES symptoms and results from our analysis suggest that there can be other alternative conceptualizations. These results support suggestions advanced by other researchers who argue that apathy cannot be defined as a disorder of motivation per se (see [32, 34]). It is also possible that our conceptualization of apathy differed somewhat from Marin’s conceptualization on the basis of psychometric reasons. For instance, to our knowledge Marin’s conceptualization was largely based on clinical evidence rather than on robust statistical conclusions.

Apathy sub-domains and ADLs

When we simultaneously regressed our derived apathy subdomains to predicted global ADLs performance, only behavioral apathy symptoms significantly predicted ADLs deficits. These results are plausible given that apathy is primarily a disorder of goal directed behavior. Some studies report a similar trend. For instance, Levy and Dubois [31] suggest that behavioral apathy involves difficulties with activating thoughts and initiating motor programs that are required to complete an action. Similarly, given that evidence suggests that behavioral apathy and incapacity for ADLs (especially IADLs) is associated with fronto-parietal disturbances [12], we expected this subdomain to be related with global ADLs performance.

We, however, did not expect both cognitive and affective apathy to be poor predictors of global ADLs. Some studies have shown that the manifestation of cognitive apathy in particular is often associated with executive impairments which include ADLs, especially IADL [9, 53]. In line with this position, we found that when we further fractionated ADLs into basic or instrumental sub-categories, behavioral apathy symptoms correlated with deficits in both basic and instrumental ADLs while cognitive symptoms only associated with loss of instrumental ADLs. These results are plausible considering that basic ADLs rely on capacities for overt behavioral outputs while instrument ADLs rely more on higher order cognitive functions such as those involved in planning and problem solving [9, 53]. The crucial point from these results is that fractionating apathy symptoms can potentially give important insights into the role of apathy symptoms in functional decline and disruption of ADLs.

Conclusion

Results from our study suggest that the presence of apathy in AD patients has implications on their ability to carry out day to day tasks. Although depressive symptoms may also uniquely contribute to the attenuation of function in these patients, their effects seem to be diluted by the presence of apathy symptoms. In light of our results, isolating apathy symptoms from depressive symptoms in traditional depression instruments before relating them to functional correlates is crucial. So too is fractionating apathy itself into its subsyndromes. Considering that the concept of apathy encompasses deficits in emotional, cognitive, or behavioral inputs into goal directed behavior, deficits in any of these domains can produce dysfunction. This approach is useful because as our study shows, subdomains of apathy differentially predict ADL performance. For instance, while behavioral apathy is associated with deficits in ADLs, the influence of cognitive apathy symptoms emerges when the ADLs are sub-divided into basic and instrumental activities. Future research should note these differential effects. We also envisage replicating this study using larger samples or heterogeneous samples in order to dilute the effect of the disease process on variables. A potential limitation in this study is that we did not impose a strict control for disease (AD) severity, which is known to associate with defective ADLs performance and other variables in our study [69 , 71]. It is possible that the association we found between our neuropsychiatric outcomes and ADLs might be compounded by AD severity. However, sample is made up of patients on their first-time consultation with the memory clinic and usually experiencing mild to moderate symptoms. Subsequent studies should aim to exert stricter control for duration and severity of illness.

AUTHOR CONTRIBUTIONS

Progress Njomboro (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Writing – original draft; Writing – review & editing); Tlholego Lekhutlile (Data curation; Formal analysis; Writing – original draft).

Footnotes

ACKNOWLEDGMENTS

We would like to acknowledge members of the Albertina and Walter Sisulu Institute of Ageing in Africa (IAA) and members of its memory clinic for assisting in the data collection process.

We would also want to acknowledge that the work in this manuscript was part of a dissertation that is archived with the University of Cape Town Library [].

FUNDING

This work was supported by Walter Sisulu Institute of Ageing in Africa (IAA).

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

The data supporting the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

The supplementary material is available in the electronic version of this article: .

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