Subjective Aging Difficulties among Older Adults with Subjective Cognitive Decline

Abstract

In older adults, subjective cognitive decline (SCD) may progress to an early stage of dementia. Yet, its association with subjective daily functional difficulties in aging is less well studied by experiences of mentally unhealthy days (MUDs). Employing a cross-sectional design approach, we analyzed the Behavioral Risk Factor Surveillance System dataset on 7429 older adults with SCD (aged 65 to >80, 45% males, 55% females) to explore SCD in instrumental daily activities of living (SCD-IADLs) and healthcare access mediation by MUDs and moderated mediation by age cohort, controlling gender and education. The bias-corrected percentile bootstrap with 5000 samplings revealed that MUDs partially mediate the relationship between SCD-IADLs and healthcare access, with a 28.2% mediating effect. Age cohort moderated the relationship between healthcare access and MUDs, MUDs and SCD-IADLs. Specifically, the predictive effects from healthcare access to MUDs and MUDs to SCD-IADLs were more profound in the 70–74 age cohort.

Keywords

affective reactivity chronic theory of aging depressive symptoms instrumental activities of daily living subjective aging difficulties

What this paper adds

• The study findings extend the chronic stress theory of aging that an individual’s affective reactivity to daily stressors, like lower healthcare access, may magnify IADL limitations in older adults with SCD.

• Our findings suggest an accentuation of sensitivity to functional difficulties at middle-old (70–74 years old) than at younger-old (65–69 years old) and older-old (>80 years old) age cohorts.

Applications of study findings

• Efforts to improve healthcare access for older-old adults would help reduce daily functional difficulties amongst older adults with SCD and the risk for frequent mental irritations.

• Future demand for services for older adults with SCD will rise substantially, requiring healthcare access for improved daily functioning.

Introduction

Subjective cognitive decline (SCD) is defined as an individual’s perceived concerns about increasing needs for support in activities based on typical mental functioning with aging (Ahn et al., 2020). If less well managed, SCD may progress to Alzheimer’s disease or an early stage of dementia (Reppermund et al., 2013; Rover et al., 2016; Stogmann et al., 2016; Taylor et al., 2018). Among individuals 45 years old and older living in the U.S. in 2015 and 2016, 11.29% experienced symptoms of SCD (Taylor et al., 2018). Half of this subpopulation (50.6%) reported SCD-related needs for support in instrumental activities of daily living (IADLs), like cooking, cleaning, taking medications, driving, or paying bills (Reppermund et al., 2013; Taylor et al., 2018). IADLs are advanced activities that require a higher level of cognitive resources than basic daily activities (Hall et al., 2011; Jekel et al., 2015). Deficits in highly cognitively demanding activities have been suggested as an early marker of early-stage dementia (Reppermund et al., 2013).

With access to health care services, SCD may be less prevalent (Office of Disease Prevention and Health Promotion, 2022). However, in a national U.S. sample, of the approximately 11% of older adults who experienced SCD, 54% had not discussed SCD with a health care professional (Wisniewski & Zelinski, 2018). Moreover, older adults with reduced health care coverage were more likely to experience SCD than those with more extensive coverage (Wisniewski & Zelinski, 2018). Although the association between health care and SCD has been confirmed, the underlying mechanism remains unclear, of which mentally unhealthy days may be a significant mediator. Additionally, little is known about the nature of SCD in IADLs.

Mentally Unhealthy Days at an Older Age

Chronic stress is associated with mentally unhealthy days (Sin et al., 2015). Older adults with SCD may experience reduced emotional affect associated with frequently challenged management of activities of daily living (i.e., household chores) than was the case at a younger age, contributing to their chronic distress (Almeida, 2005; Almeida et al., 2009; Fiske et al., 2009). How older adults manage their daily stressors may depend on how often they experience changes in emotional wellbeing when stressors occur compared to days with no stressors (Almeida et al., 2009). Some studies have examined individuals' appraisal of the stressors and variability in positive or negative affect (Charles et al., 2013; Mroczek et al., 2015; Sin et al., 2020) but not in population self-reporting subjective cognitive decline and mentally unhealthy days. However, evidence suggests that the average levels of negative affect people experience in responding to minor events in daily lives are predictors of long-term mental health outcomes (Charles et al., 2013; Hahn et al., 2014).

Environmental and contextual factors may influence an individual’s affective response. For instance, the stressor-reactivity path (Sin et al., 2020) reported that sociodemographic (i.e., age, education, gender), psychosocial, and health factors (i.e., healthcare access) modify how daily stressors affect daily wellbeing (Charles et al., 2013). Moreover, the risk for mentally unhealthy days was higher in a study involving adults 18–64 years old with poorer access to health care (Dobson-Brown, 2018). Older adults with SCD-IADL restrictions may vary in the frequency of mentally unhealthy days depending on the perceived level of severity contributed to by personal and environmental factors. Therefore, we speculate that healthcare access may be indirectly associated with older adults’ SCD-IADLs through the mediating effect of mentally unhealthy days.

Healthcare Access and SCD-IALDs

Older adults with poorer healthcare access are more likely to have SCD-IADLs (Gupta, 2021). Other adverse sociodemographic variables, like lower levels of education, membership in a minority group, and lower social and economic status, may speed up the deterioration of cognitive decline. As a result, older adults with SCD may be seen in a younger age group (45–54 years old) (Gupta, 2021), limiting daily activities earlier in the aging process. Older people with SCD who have no access to health care may lack support in adjusting to limited daily functioning (Gupta, 2021), increasing daily stressors. Past research examining emotional reactivity to daily stressors has shown how individuals with heightened negative affect, as a result of self-appraisal during increased daily stressors, experienced more frequent mentally unhealthy days (Charles et al., 2013; Hahn et al., 2014).

The relationship between healthcare access and SCD-IADLs may differ by age cohort: younger-old (65–74 years), middle-old (75–79 years), and older-old (≥80 years), depending on personal resources and sociodemographic factors. Educational and environmental factors, like level of education and access to health care, may influence an individual’s emotional reactivity to daily stressors as measured by mentally unhealthy days (Charles et al., 2013; Hahn et al., 2014; Sin et al., 2020). It is known that older-old adults (80 years and above) may use health care services differently than do younger-old adults (55–65 years old) (Fulmer et al., 2021). However, rates of hospitalization have decreased over time across all age groups (Tillmann et al., 2021), suggesting a need for research on age cohort moderation of the relationship between health care and SCD-IADLs with mentally unhealthy days as a mediator.

The Present Study

This study builds upon a scoping review on SCD profiling in daily activities using the International Classification of Functioning and Disability (ICF) (Komalasari et al., 2022). SCD effects on the body function and daily activities and participation can be explained within contextual factors. We aimed to test healthcare access as an environmental factor and MUDs as a personal factor influencing SCD-IADLs. We used a cross-sectional study to examine the relationship between healthcare access and SCD-IADLs, and the mediating role of mentally unhealthy days in that relationship. We also examined the moderating role of the age cohort on the relationship between healthcare access and SCD-IADLs. By convention, categorical sociodemographic variables are moderators, while experience continuum variables are mediators (Hayes & Andrew, 2014). We hypothesized that:

1. Mentally unhealthy days mediate the relationship between healthcare access and SCD-IADLs so that more frequent MUDs and lower healthcare access are associated with higher SCD-IADLs.

2. The age cohort moderates the indirect mediation effect of MUDs between healthcare access and SCD-IADLs with the predictive effect being stronger in the older-old age cohort.

Figure 1 presents the conceptual framework of the moderated mediation model of MUDs on healthcare access and SCD-IADLs, controlling gender and education. The profiling of SCD-IADLs among older adults with MUDs across age cohorts is important in understanding the health outcomes of older adults.

Figure 1.

The conceptual framework of the moderated mediation model of mentally unhealthy days on healthcare access and SCD-IADLs, controlling gender and education. SCD-IADLs (subjective cognitive decline in instrumental activities of daily living).

Methods

Design and Sample

This cross-sectional study utilized the 2019 Behavioral Risk Factor Surveillance Survey (BRFSS) data from the Center for Disease Prevention and Control (Centers for Disease Control and Prevention, 2019b), a large, nationally representative sample of 66,901 non-institutionalized U.S. adults aged 45 years or older. We analyzed data from 10,467 individuals ≥65 years old who reported having functional difficulties within the past year. We excluded 3038 cases with responses like “Don’t know/not sure” and “Refused” from the analysis, leaving 7429 cases for analysis.

Sociodemographic Characteristics of the Participants

The 7429 participants with SCD consisted of 3343 (45%) men and 4085 (55%) women. The age range were from 65 to ≥80 years, which included four age cohorts: 65–69 (22.3%), 70–74 (24.6%), 75–79 (19.8%), and ≥80 (33.2%). Most of the participants completed high school (93.9%). Of the participants, 2130 (28.7%) had a depressive disorder, with an average number of mentally unhealthy days of 5.54 (SD± 9.40). Most of the participants had health care coverage (97.6%), access to multiple health professionals (95.1%), and did not have financial difficulties seeing a doctor when needed (93.1%).

Variables and Measurement

Dependent variable

Subjective cognitive decline in instrumental activities of daily living (SCD-IADLs) was defined as the level of deficits in IADLs due to the experience of SCD (Jessen et al., 2014). In this study, SCD-IADLs were assessed by the following question: “During the past twelve months, as a result of confusion or memory loss, how often have you given up day-to-day household activities or chores that you used to do, such as cooking, cleaning, taking medications, driving or paying bills?” (Centers for Disease Control, 2019a). Responses to this question are on a 5-point Likert scale, ranging from 1 to 5 (e.g., 1-always, 2-usually, 3-sometimes, 4-rarely, 5-never). The coded scores were reversed, where a higher frequency was assigned a higher score, indicating a more significant IADL deficit due to SCD.

Predictor Variable

Healthcare access

Healthcare access was assessed by self-report on three yes/no questions: (1) “Do you have any kind of health care coverage, including health insurance, prepaid plans such as Health Management Organization, or government plans, such as Medicare or Indian health service?” (2) “Do you have one person you think of as your personal doctor or health care provider?” and (3) “Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?” A “yes” response was coded one, and “no” was coded two. All answers to the three questions were summed up, and higher scores denoted a lower level of healthcare access (Centers for Disease Control and Prevention, 2019b).

Mediator variable

Mentally unhealthy days were the average number of days during the past 30 days an older adult’s mental health was not good (Centers for Disease Control, 2019a). It is a self-report continuous data ranging from 0 to 30. A higher number of reported days represents a higher number of poor mental health days.

Moderator variable

In the present study sample, the age of participants was subdivided into four cohorts: 65–69 (1660, 22.3%), 70–74 (1830, 24.6%), 75–79 (1470, 19.8%), and ≥80 (2469, 33.2%).

Covariates

Education and gender were included as covariates due to preliminary evidence demonstrating their associations with SCD. A previous study showed that male older adults with mild cognitive impairment experienced more difficulties than women in performing IADLs (Reppermund et al., 2013). Older people with less formal education were more likely to have SCD and related functional disabilities (Taylor et al., 2018). The current study assessed an education level (i.e., did not graduate high school, graduated high school, attended college or technical school, or graduated college or technical school). Gender was assessed as male and female.

Ethical Standards

This BRFSS dataset is open access by the CDC (CDC, 2019), which provides de-identified data for public utilization. Therefore, there is no requirement for Institutional Review Board approval by the author’s institution or affiliation for secondary data analysis studies that utilize publicly accessible data.

Data Analysis

All analyses were performed using IBM SPSS V.28. 0 (IBM, Armonk, New York, USA). We utilized multiple regression and the bias-corrected percentile bootstrap method for the study. The theoretical model was tested by estimating the 95% CI for mediation and moderating effects with 5000 bootstrap samples. If the 95% CI did not include 0, the statistics were statistically significant, indicating that health care access exerted a significant indirect effect on SCD-IADLs through mentally unhealthy days. The moderation variable (age cohort) was divided into four levels 65–69, 70–74, 75–79, ≥80 to illuminate the moderating effect (Preacher et al., 2007). The split-plot analysis method (Frey, 2018) was used to examine the moderation effect’s direction further and a diagram to explain the moderation effect (Cohen et al., 2013). The mediation and moderated models were tested with the PROCESS V.4.0 macro for SPSS (Hayes & Andrew, 2014). In the current study, we selected model 4 and model 59 to analyze the mediating and moderated mediation effects.

Results

Bivariate Correlation of Main Variables

The mean, SD/median (quartile₁, quartile₃), and correlation coefficient of each variable are shown in Table 1. SCD-IADLs were positively correlated with mentally unhealthy days (r = .215, p < .001), healthcare access (r = .076, p < .001), age cohort (65 to >80 years old) (r = −.053, p < .001), and gender (r = −.087, p < .001), and negatively correlated with education (r = −.159, p < .001). The age cohort was negatively correlated with mentally unhealthy days (r = −.156, p < .001) and healthcare access (r = −.050, p < .001) (see Table 1).

Table 1.

Spearman Correlation Coefficients of Variables among Participants.

Variable	M ± SD	M ± (P_25,27)	1	2	3	4	5	6
1. Mentally unhealthy days	5.54 ± 9.46		1
2. Healthcare access		3 (3.3)	.044*	1
3. SCD-IADLs		1 (1.2)	.215*	.076*	1
4. Gender		2 (1.2)	.162*	−.043*	.087*	1
5. Age cohort		3 (2.4)	−.156*	−.050*	.053*	.060*	1
6. Education		3 (2.4)	−.069*	−.073*	−.159*	−.088*	−.071*	1

*p < .001.

M ± SD: mean ± Standard Deviation; M ± (P_25,27): median (quartile, quartile).

1 follows normal distribution and is described as M ± SD.

2–6 does not follow normal distribution and is described as M ± (P_25,27).

Healthcare Access Mediation of SCD-IADLs by Mentally Unhealthy Days

Model 4 in the SPSS macro compiled by Hayes and Andrew (2014) was used to test the mediating effect of mentally unhealthy days on the relationship between healthcare access and SCD-IADLs, controlling for gender and education. As shown in Tables 2 and 3, healthcare access had a significant predictive effect on SCD-IADLs (β = .184, t = 5.52, p < .001), meaning a lower level of healthcare access corresponds to a higher level of SCD-IADL difficulties. The direct predictive effect of healthcare access was still significant when the mediating variable mentally unhealthy days was added. In the path from healthcare access on mentally unhealthy days, healthcare access had a significant positive predictive effect on the mentally unhealthy days (b = 2.30, t = 7.86, p < .001), signifying that mentally unhealthy days decrease as healthcare access increases. In the path from mentally unhealthy days to SCD-IADLs, the mentally unhealthy days also had a significant positive predictive effect on SCD-IADL (b = .022, t = 17.29, p < .001), indicating that as the mentally unhealthy days increase, difficulties in SCD-IADLs also increase.

Table 2.

Testing the Mediation Effect of Mentally Unhealthy Days Between Healthcare Access and SCD-IADLs among Older Adults with SCD in the U.S., 2019.

Predictors	Model 1 (SCD-IADLs)				Model 2 (MUDs)				Model 3 (SCD-IADLs)
	β	t	95% CI		β	t	95% CI		β	t	95% CI
			LL	UL			LL	UL			LL	UL
Healthcare access	.133	4.04*	.068	.197	2.30	7.86*	1.73	2.88	.184	5.52*	.119	.250
Mentally unhealthy days	.022	17.29*	.020	.025
Gender	.104	4.25*	.056	.153	2.67	12.27*	2.24	3.10	.164	6.62*	.116	.213
Education	−.153	−11.78*	−.179	−.128	−.825	−7.11*	−1.05	−.598	−.172	−12.99*	−.197	−.146
R ²	.072				.036				.035
F	144.41				93.62				89.34

*P < .001.

SCD-IADLs, subjective cognitive decline in instrumental activities of daily living; MUDs, mentally unhealthy days.

Table 3.

Total Effect, Direct Effect, and Mediation Effect of Mentally Unhealthy Days on Healthcare Access and SCD-IADLs.

Total effect	β	SE	BootCI
			LL	UL
Direct effect	.133	.033	.068	.197	72.28%
Indirect effect	.052	.008	.036	.069	28.2%

BootSE, bootstrap standard error; BootCI, bootstrap 95% confidence interval.

In addition, the upper and lower limits of the bootstrap 95%CI for the direct effect of healthcare access on SCD-ADL and the mediating effect of mentally unhealthy days on healthcare access and SCD-IADLs did not include 0 (Table 3), indicating that the mediating effect was statistically significant. The mediating effect value was .052, and the 95% CI was (.036–.069), which accounted for 28.2% of the total effect. This value showed that mentally unhealthy days partially mediated the relationship between healthcare access and SCD-IADLs. Our first hypothesis was fully supported such that the mentally unhealthy days mediated the relationship between healthcare access and SCD-IADLs. We also found that more frequent mentally unhealthy days and lower healthcare access were associated with more SCD-IADL limitations.

Age cohort moderation of the mediation effect of mentally unhealthy days in the relationship between healthcare access and SCD-IADLs

The PROCESS macro method (model 59) compiled by Hayes and Andrew (2014) was used to test the moderated mediation model while controlling for age and education (Tables 4 and 5). As shown in Table 4, dummy variables were created for the four age cohorts as a moderator, with age cohort 1 as a reference. Moderator 1 (W1) represented age cohort 2 versus 1, moderator 2 (W2) defined age cohort 3 versus 1, and moderator 3 (W3) represented age cohort 4 versus 1. After the age cohort was added to the model, the interaction term between healthcare access and W1 (age cohort 2 vs. 1) (β = 2.72, t = 3.55, p < .001) and between healthcare access and W2 (age cohort 3 vs. 1) (β = −4.86, t = −5.34, p < .001) significantly predicted mentally unhealthy days (Table 4, model 1). Figure 2 shows a simple slope analysis indicating the statistically significant interaction at all levels of the age cohorts (65–69, 70–74, 75–79, and ≥80 years old), with the 70–74 age cohort having the most pronounced effect (b_simple = 5.25, t = −5.34, p < .001).

Table 4.

Testing the Moderated Mediation Effect of Age Cohort on SCD-IADLs among Older Adults with SCD in the U.S., 2019.

Predictors	Model 1 (MUDs)				Model 2 (SCD-IADLs)
Predictors	β	t	95% CI		β	t	95% CI
			LL	UL			LL	UL
Healthcare access	2.53	4.84*	1.50	3.55	−.039	−.654*	−.155	.078
Mentally unhealthy days					.025	9.62*	.020	.030
Age cohort 2 versus 1 (W1)	−.609	−1.97**	−1.22	−.001	.013	.378***	−.056	.082
Age cohort 3 versus 1 (W2)	<.001	−.001***	−.645	.644	.040	1.08***	−.033	.114
Age cohort 4 versus 1 (W3)	−3.43	−11.79*	−4.00	−2.86	.189	5.36*	.123	.254
Healthcare access x W1	2.72	3.55*	1.22	4.23	.362	4.10*	.189	.534
Healthcare access x W2	−4.86	−5.34*	−6.65	−3.08	.176	1.70***	−.027	.379
Healthcare access x W3	−1.23	−1.64***	−2.71	.239	.157	1.85***	−.010	.325
Mentally unhealthy days x W1					.004	1.21***	−.003	.011
Mentally unhealthy days x W2					−.010	−2.77*	−.017	−.003
Mentally unhealthy days x W3					.001	.228***	−.006	.008
Gender	2.92	13.55*	2.49	3.34	.088	3.58*	.040	.137
Education	−.83	−7.25*	−1.06	−.61	−.142	−10.89*	−.168	−.116
R ²	0.72				.082
F	63.87*				51.13*

Age cohort 1 (65–69), Age cohort 2 (70–74), Age cohort 3 (75–79), Age cohort 4 (≥80).

W1 (age cohort 2 vs. 1), W2 (age cohort 3 vs. 1), W3 (age cohort 4 vs. 1).

SCD-IADLs: subjective cognitive decline in instrumental activities of daily living; MUDs: mentally unhealthy days.

*p < .001; ** p < .05; *** p > .05.

LL, lower-level confidence interval; UL, upper-level confidence interval.

Table 5.

Mediating Effect Values at Different Levels of Age Cohort among Older Adults with SCD, U.S., 2019.

Age cohort	Effect	BootSE	BootLLCI	BootULCI
65–69 years old	.062	.017	.031	.098
70–74 year	.152	.026	.104	.204
75–79 year	−.034	.013	−.061	−.010
≥ 80 year	.033	.013	.008	.061

BootSE: bootstrap standard error; BootLLCI: bootstrap lower-level confidence interval; BootULCI: bootstrap upper-level confidence interval.

Figure 2.

Simple slope analysis shows the predictive effect of healthcare access on mentally unhealthy days. The predictive effect of healthcare access was significant in all age cohorts. The 70–74 age cohort had the most substantial effect.

In the path from mentally unhealthy days and SCD-IALDs, the interaction term between mentally unhealthy days and W2 (age cohort 3 vs. 1) significantly predicted SCD-IADLs (β = −.10, t = −2.77, p < .01) (Table 4, model 2). Figure 3 shows that the predictive effect of mentally unhealthy days on SCD-IADLs was statistically significant at each level of the age cohorts. The predictive effect of mentally unhealthy days on SCD-IADLs was equal in age cohorts 65–69 and ≥80 years old, with the 70–74 age cohort having the most substantial effect (b_simple = .029, t = 11.77, p < .001). In the direct path from healthcare access and SCD-IADLs, the interaction term between healthcare access and W1 (age cohort 2 vs. 1) significantly predicted SCD-IADLs (β = .3.2, t = 4.11, p < .01) (Table 4, model 2). Figure 4 shows the predictive effect of healthcare access on SCD-IADLs was statistically significant in the 70–74 age cohort (b_simple = .323, t = 4.96, p < .01). In addition, the mediating effect value of the mentally unhealthy days on the relationship between healthcare access and SCD-ADL was substantial at all levels of the age cohort (moderated mediation effect), with the strongest effect in the 70–74 age cohort (b = .152, 95% CI: .104–.204) (Table 5). These findings were consistent with our second hypothesis. The indirect mediation effects of mentally unhealthy days between healthcare access and the SCD-IADLs were stronger for individuals in the older-old age cohort, with the most pronounced effects in the 70–74 age cohort.

Figure 3.

Simple slope analysis showed the predictive effect of mentally unhealthy days on the SCD-IADLs was statistically significant at each level of the age cohorts. The predictive effect of mentally unhealthy days was equal in age cohort 65–69 and ≥80 years old. The 70–74 age cohort had the most substantial effect. SCD-IADLs (subjective cognitive decline in instrumental activities of daily living).

Figure 4.

Simple slope analysis shows the predictive effect of healthcare access on SCD-IADLs was statistically significant only at 70–74 age cohort. SCD-IADLs (subjective cognitive decline in instrumental activities of daily living).

In addition, through t-test and analysis of variance (ANOVA) analysis, we found that more SCD-IADLs difficulties were related to female gender (b = .088, t = 3.58, p < .001) and lower education level (b = −.142, t = −10.89, p < .001). Previous studies have also found that SCD was associated with age and education (Lee et al., 2020; Tsang et al., 2019). We controlled for age and education in this study.

Discussion

We found mentally unhealthy days to be associated with higher SCD-IADLs in older population with SCD. Older adults with more SCD-related IADL deficits might experience chronic stress due to negative affect in response to constantly challenging daily situations. The chronic stress theory of aging suggests that an individual’s affective reactivity may determine how people respond to aversive daily events in their lives (Almeida, 2005), as measured in mentally unhealthy days. Extant evidence suggests that affective responses to seemingly minor daily events are associated with general affective distress (Charles et al., 2013; Hahn et al., 2014; Sin et al., 2020) measured on daily negative days. As the authors suggested, this effect may be associated with disruptive emotional states in older age across eight consecutive evenings (Charles et al., 2013). As reported in this study, the emotional wear and tear (Charles et al., 2013) may show in SCD-IADL deficits.

Mentally Unhealthy Days Mediation of Healthcare Access and SCD-IADLs

The current study found mentally unhealthy days to mediate the association between healthcare access and SCD-IADLs partially so that as healthcare access increased, mentally unhealthy days decreased. Health factors such as healthcare access may affect how individuals cope with daily experiences, contributing to daily wellbeing (Sin et al., 2020). The partial mediation may be explained by the fact that a wide range of healthcare access-related factors not directly measured by the BFRSS may have had a substantial impact on mentally unhealthy days (e.g., usual source of care, means of transportation to care, difficulty in receiving care, amount of time taken to get to care, amount of time since last doctor’s visit, delay receiving care because of cost, and inability to get a necessary prescription (Dobson-Brown, 2018). Our study extends those of previous studies determining the role of mentally unhealthy days in the relationship between healthcare access and SCD-IALDs in older adults with SCD. Chronic stress theory (Almeida, 2005; Almeida et al., 2009; Sin et al., 2020) suggests that older adults experience a negative affect in responding to increased disruptions in day-to-day activities. We extend the predictions of chronic stress theory that affective reactivity to a daily stressor, that is, lower healthcare access, may magnify IADL limitations in older adults with SCD.

This finding may be explained by the fact that older adults have an increased predisposition to inflammation response to stress, a biological response to physiological dysregulation by repeatedly adjusting to stressors (Almeida, 2005; Sin et al., 2015). People who experience more frequent daily stressors, like older adults with SCD-related IADL restrictions, tend to have higher levels of circulating and stimulated inflammatory markers, including interleukin (IL)-6 and C-reactive protein (Davis et al., 2008). Higher log C-reactive protein, in particular, was associated with heightened negative affect reactivity to minor stressors in older adults with cognitive and functional impairment (Sin et al., 2015), which may be better managed with improved healthcare access.

Age Cohort Moderation between the Variables

Older age cohorts (70–74 years old) with SCD self-reported a stronger association between healthcare access and mentally unhealthy days, mentally unhealthy days and SCD-IADLs, although the effect was more pronounced among the 70–74 age cohort. This finding suggests an inverted U-curve effect on SCD-IADLs with mentally unhealthy days experience affecting middle-old (70–74) compared to younger-old (65–69) and older-old adults (75–≥80). It was previously assumed that older adults would experience more psychopathology with advancing age (Carstensen, 2021). However, recent findings showed that oldest-old adults reported less frequent negative emotions and more positive affects than their middle-aged and younger counterparts (Blazer & Hybels, 2014; Carstensen, 2021). A study examining age-related trajectories in depressive symptoms from young-old to oldest-old found that depressive symptoms in older adults increased from younger-old to middle-old but reversed in oldest-old age, with men showing more depressive symptoms than women (Chui et al., 2015). The number of MUDs has been associated with higher depressive symptoms (Skarupsi et al., 2011). Accumulated emotional advantages in the oldest-old age may explain why the oldest-old have better subjective control over emotions and a more positive emotional experience than their younger counterparts (Carstensen, 2021). For example, empathy (Sze et al., 2012) and gratitude (Chopik et al., 2019) are a few qualities that may facilitate the oldest-old having less negative affects related to daily life stressors (Carstensen, 2021), like lower healthcare access, thus limiting SCD-IADL difficulties.

Implications for Research and Practice

Our study findings extend the extant literature on the chronic stress theory of aging (Almeida, 2005; Almeida et al., 2009; Sin et al., 2020). Self-perceived stress largely depends on an individual’s vulnerability and affective reactivity to daily stressors (Almeida et al., 2009; Sin et al., 2020). Daily stressors, like difficulty accessing health care, may lead to an increase in negative responses. Older adults with limited coping resources are more vulnerable to psychological distress (Almeida et al., 2009; Sin et al., 2020), which may magnify SC-IADL restrictions. The findings of this study contribute to a better understanding of the influence of emotional reactivity to daily stressors on determining levels of daily activities in people with SCD.

Our results also provide theoretical and practical implications for researchers that demonstrate that improving personal resources may benefit older-old adults with SCD who are coping with increased daily activity disabilities. For instance, improved access to health care may help decrease one’s negative affect. Older adults and family members may incorrectly perceive SCD as an inevitable part of aging (Taylor et al., 2018), deterring efforts to seek medical assistance. Discussing limitations in performing IADLs with a doctor or other health care providers can clarify this misconception (Taylor et al., 2018), potentially counteracting the individuals’ negative affect. Efforts to improve healthcare access for older-old adults would help buffer the frequent psychological distress associated with deficits in daily functional performance amongst older adults with SCD. Psychosocial supports, including teaching skills to improve mood, changing negative thinking patterns, and increasing engagement in enjoyable activities, have decreased depressive symptoms in older adults in general (Cuijpers et al., 2011). Similarly, younger-old adults in employment or voluntary community work may have greater daily function competences and coping with SCD in IADLs (Li et al., 2021), requiring consideration in the design of SCD in IADLs interventions with them.

Clinicians and caregivers (e.g., spouses or family members) may benefit from observing changes in IADLs in older adults with SCD as they may progress to MCI or AD (Jessen et al., 2014; Stogmann et al., 2016; Taylor et al., 2018). Past research examining SCD in older adults with depressive symptoms found superiority of assessment of complex ADLs (IADLs), like food preparation and driving, over basic ADLs (e.g., eating or bathing) in detecting a cognitive decline (Reppermund et al., 2013; Stogmann et al., 2016). The different cognitive domains involved in neurodegenerative processing may explain how changes in cognitive memory and language manifest in IADLs (Hall et al., 2011). The neuropathological changes occur years before distinct clinical symptoms (Stogmann et al., 2016).

Limitations of the Study and Suggestions for Further Research

There are several limitations to this study. First, we included only older adults with SCD, thus mediation and moderated mediation of the study variables with SCD-IADL difficulties might not generalize to those without SCD. Moreover, our analyses are only among those with SCD, and not for detecting SCD. Second, this study was based on a cross-sectional study, which could not provide strong evidence of causation. In addition, using cross-sectional data to examine longitudinal mediation effects can lead to biased estimates of mediation effects (de Labra et al., 2015). Third, the data in this study are participants’ self-reported information, which might result in recall bias. Fourth, although this study shows a partial mediating effect of the mentally unhealthy days on the relationship between healthcare access and SCD-IADLs in older adults with SCD, there would be other mediating variables in this relationship for future study. Finally, the significant relationships we observed were weak, except for the association of MUDs and SCD-IADLs, suggesting a need for further aging health and function studies utilizing similar data sets as the BRFSS.

Conclusion

Our study has examined the association between healthcare access and SCD-IADL levels among older adults with SCD aged 65 and over and the potential mechanism underlying this relationship in a national U.S. sample. Our first hypothesis was fully supported such that the MUDs mediated the relationship between healthcare access and SCD-IADLs. Consistent with our second hypothesis, the indirect correlation between healthcare access and the SCD-IADLs via MUDs was stronger for people with SCD in the middle-old cohort (70–74 years old) than in other age cohorts. These findings suggest the potential effects of healthcare access on SCD-IADLs and facilitate the development of targeted person-centric interventions to slow down SCD progression to a less severe level in older adults with SCD and psychological distress.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Renata Komalasari

Elias Mpofu

References

Ahn

Mathiason

M. A.

Salisbury

(2020). Factors predicting the onset of amnestic mild cognitive Alzheimer’s dementia in persons with subjective cognitive decline. Journal of Gerontological Nursing, 46(8), 28–36. https://doi.org/10.3928/00989134-20200619-01

Almeida

D. M.

(2005). Resilience and vulnerability to daily stressors assessed via diary methods. Current Directions in Psychological Science, 14(2), 64–68. https://doi.org/10.1111/j.0963-7214.2005.00336.x

Almeida

D. M.

McGonagle

King

(2009). Assessing daily stress processes in social surveys by combining stressor exposure and salivary cortisol. Biodemography and Social Biology, 55(2), 219–237. https://doi.org/10.1080/19485560903382338

Blazer

D. G.

Hybels

C. F.

(2014). Depression in later life: Epidemiology, assessment, impact, and treatment. In Gotlib

I. H.

Hammen

C. L.

(Eds.), Handbook of Depression (3rd ed., pp. 429-447). Guilford Press.

Carstensen

L. L.

(2021). Socioemotional selectivity theory: The role of perceived endings in human motivation. The Gerontologist, 61(8), 1188–1196. https://doi.org/10.1093/geront/gnab116

Centers for Disease Control (2019a). Prevention. https://www.cdc.gov/brfss/annual_data/2019/pdf/codebook19_llcp-v2-508.HTML.

Centers for Disease Control and Prevention (2019b). Overview Behavioral risk factor surveillance system: 2019. https://www.cdc.gov/brfss/annual_data/2019/pdf/overview-2019-508.pdf

Charles

S. T.

Piazza

J. R.

Mogle

Sliwinski

M. J.

Almeida

D. M.

(2013). The wear and tear of daily stressors on mental health. Psychological Science, 24(5), 733–741. https://doi.org/10.1177/0956797612462222

Chopik

W. J.

Newton

N. J.

Ryan

L. H.

Kashdan

T. B.

Jarden

A. J.

(2019). Gratitude across the life span: Age differences and links to subjective well-being. The Journal of Positive Psychology, 14(3), 292–302. https://doi.org/10.1080/17439760.2017.1414296

10.

Chui

Gerstorf

Hoppmann

C. A.

Luszcz

M. A.

(2015). Trajectories of depressive symptoms in old age: Integrating age-, pathology-, and mortality-related changes. Psychology and Aging, 30(4), 940–951. https://doi.org/10.1037/pag0000054

11.

Cohen

West

S. G.

(2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.

12.

Cuijpers

Andersson

Donker

van Straten

(2011). Psychological treatment of depression: Results of a series of meta-analyses. Nordic Journal of Psychiatry, 65(6), 354–364. https://doi.org/10.3109/08039488.2011.596570

13.

Davis

M. C.

Zautra

A. J.

Younger

Motivala

S. J.

Attrep

Irwin

M. R.

(2008). Chronic stress and regulation of cellular markers of inflammation in rheumatoid arthritis: Implications for fatigue. Brain, Behavior, and Immunity, 22(1), 24–32. https://doi.org/10.1016/j.bbi.2007.06.013

14.

de Labra

Guimaraes-Pinheiro

Maseda

Lorenzo

Millán-Calenti

J. C.

(2015). Effects of physical exercise interventions in frail older adults: A systematic review of randomized controlled trials. BMC Geriatrics, 15, Article 154. https://doi.org/10.1186/s12877-015-0155-4

15.

Dobson-Brown

T. S.

(2018). The relationship between mentally unhealthy days and access to health care and health care services utilization among the long-term uninsured in South Carolina. https://tigerprints.clemson.edu/all_theses/2832

16.

Fiske

Wetherell

J. L.

Gatz

(2009). Depression in older adults. Annual Review of Clinical Psychology, 5, 363–389. https://doi.org/10.1146/annurev.clinpsy.032408.153621

17.

Frey

(2018). The SAGE encyclopedia of educational research, measurement, and evaluation. SAGE Publications, Inc.

18.

Fulmer

Reuben

D. B.

Auerbach

Fick

D. M.

Galambos

Johnson

K. S.

(2021). Actualizing better health and health care for older adults. Health Affairs (Project Hope), 40(2), 219–225. https://doi.org/10.1377/hlthaff.2020.01470

19.

Gupta

(2021). Racial and ethnic disparities in subjective cognitive decline: A closer look, United States, 2015-2018. BMC Public Health, 21(1), Article 1173. https://doi.org/10.1186/s12889-021-11068-1

20.

Hahn

E. A.

Cichy

K. E.

Small

B. J.

Almeida

D. M.

(2014). Daily emotional and physical reactivity to stressors among widowed and married older adults. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 69(1), 19–28. https://doi.org/10.1093/geronb/gbt035

21.

Hall

J. R.

H. T.

Johnson

L. A.

Barber

R. C.

O’Bryant

S. E.

(2011). The link between cognitive measures and ADLs and IADL functioning in mild Alzheimer’s: What has gender got to do with it? International Journal of Alzheimer’s Disease, 2011: 276734. https://doi.org/10.4061/2011/276734

22.

Hayes

B. J. H.

Andrew

(2014). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. J Educ Meas, 51, 335–337.

23.

Jekel

Damian

Wattmo

Hausner

Bullock

Connelly

P. J.

Dubois

Eriksdotter

Ewers

Graessel

Kramberger

M. G.

Law

Mecocci

Molinuevo

J. L.

Nygård

Olde-Rikkert

M. G.

Orgogozo

J. M.

Pasquier

Peres

Salmon

Frölich

(2015). Mild cognitive impairment and deficits in instrumental activities of daily living: A systematic review. Alzheimer’s Research & Therapy, 7(1), Article 17. https://doi.org/10.1186/s13195-015-0099-0

24.

Jessen

Amariglio

R. E.

van Boxtel

Breteler

Ceccaldi

Chételat

Dubois

Dufouil

Ellis

K. A.

van der Flier

W. M.

Glodzik

van Harten

A. C.

de Leon

M. J.

McHugh

Mielke

M. M.

Molinuevo

J. L.

Mosconi

Osorio

R. S.

Perrotin

Petersen

R. C.

Subjective Cognitive Decline Initiative (SCD-I) Working Group . (2014). A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimer’s & Dementia: The Journal of the Alzheimer's Association, 10(6), 844–852. https://doi.org/10.1016/j.jalz.2014.01.001

25.

Komalasari

Mpofu

Prybutok

Ingman

(2022). Daily living subjective cognitive decline indicators in older adults with depressive symptoms: A scoping review and categorization using classification of functioning, disability, and health (ICF). Healthcare 10, Article 1508. https://doi.org/10.3390/healthcare10081508

26.

Lee

Sung

Choi

(2020). The factors associated with subjective cognitive decline and cognitive function among older adults. Journal of Advanced Nursing, 76(2), 555–565. https://doi.org/10.1111/jan.14261

27.

Yuan

Lan

(2021). The influence of late retirement on health outcomes among older adults in the policy context of delayed retirement initiative: An empirical attempt of clarifying identification bias. Arch Public Health 79, Article 59. https://doi.org/10.1186/s13690-021-00582-8

28.

Mroczek

D. K.

Stawski

R. S.

Turiano

N. A.

Chan

Almeida

D. M.

Neupert

S. D.

Spiro

(2015). Emotional reactivity and mortality: Longitudinal findings from the VA normative aging study. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 70(3), 398–406. https://doi.org/10.1093/geronb/gbt107

29.

Office of Disease Prevention and Health Promotion (2022). Access to Health Services. Healthy People 2000. http://www.healthypeople.gov/2020/topics-objectives/topic/Access-to-Health-Services

30.

Preacher

K. J.

Rucker

D. D.

Hayes

A. F.

(2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42(1), 185–227. https://doi.org/10.1080/00273170701341316

31.

Reppermund

Brodaty

Crawford

J. D.

Kochan

N. A.

Draper

Slavin

M. J.

Trollor

J. N.

Sachdev

P. S.

(2013). Impairment in instrumental activities of daily living with high cognitive demand is an early marker of mild cognitive impairment: The sydney memory and ageing study. Psychological Medicine, 43(11), 2437–2445. https://doi.org/10.1017/S003329171200308X

32.

Rovner

B. W.

Casten

R. J.

Leiby

B. E.

(2016). Determinants of activity levels in African Americans with mild cognitive impairment. Alzheimer Disease and Associated Disorders, 30(1), 41–46. https://doi.org/10.1097/WAD.0000000000000096

33.

Sin

N. L.

Graham-Engeland

J. E.

Ong

A. D.

Almeida

D. M.

(2015). Affective reactivity to daily stressors is associated with elevated inflammation. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 34(12), 1154–1165. https://doi.org/10.1037/hea0000240

34.

Sin

N. L.

Wen

J. H.

Klaiber

Buxton

O. M.

Almeida

D. M.

(2020). Sleep duration and affective reactivity to stressors and positive events in daily life. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 39(12), 1078–1088. https://doi.org/10.1037/hea0001033

35.

Skarupski

K. A.

Zack

M. M.

Bienias

J. L.

Scherr

P. A.

Evans

D. A.

(2011). The relationship between mentally unhealthy days and depressive symptoms among older adults over time. Journal of Applied Gerontology: The Official Journal of the Southern Gerontological Society, 30(20), 241–253. https://doi.org/10.1177/0733464810361348

36.

Stogmann

Moser

Klug

Gleiss

Auff

Dal-Bianco

Pusswald

Lehrner

(2016). Activities of daily living and depressive symptoms in patients with subjective cognitive decline, mild cognitive impairment, and Alzheimer's disease. Journal of Alzheimer’s Disease: JAD, 49(4), 1043–1050. https://doi.org/10.3233/JAD-150785

37.

Sze

J. A.

Gyurak

Goodkind

M. S.

Levenson

R. W.

(2012). Greater emotional empathy and prosocial behavior in late life. Emotion (Washington, D.C.), 12(5), 1129–1140. https://doi.org/10.1037/a0025011

38.

Taylor

C. A.

Bouldin

E. D.

McGuire

L. C.

(2018). Subjective cognitive decline among adults ages 45 years-United States, 2015-2016. MMWR. Morbidity and Mortality Weekly Report, 67, 753–757. http://doi.org/10.15585/mmwr.mm6727a

39.

Tillmann

B. W.

Hill

A. D.

Scales

D. C.

Fowler

R. A.

Cuthbertson

B. H.

Wunsch

(2021). Acute health care resource utilization by age: A cohort study. Plos One, 16(5), Article e0251877. https://doi.org/10.1371/joucirnal.pone.0251877

40.

Tsang

Sperling

S. A.

Park

M. H.

Helenius

I. M.

Williams

I. C.

Manning

(2019). Health variables are informative in screening for mild cognitive impairment among elderly African Americans. Journal of Applied Gerontology: The Official Journal of the Southern Gerontological Society, 38(10), 1421–1444. https://doi.org/10.1177/0733464817711961

41.

Wisniewski

Zelinski

(2018). Health care access and subjective cognitive decline in older Americans. Innovation in Aging, 2(1), Article 247, https://doi.org/10.1093/geroni/igy023.921