Subjective aging and Instrumental activities of daily living: Moderation by healthcare access and mediation by healthcare resources.

doi:10.21203/rs.3.rs-4677655/v1

Download PDF

Research Article

Subjective aging and Instrumental activities of daily living: Moderation by healthcare access and mediation by healthcare resources.

https://doi.org/10.21203/rs.3.rs-4677655/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Older adults experience subjective aging associated with a decline in instrumental activities of daily living (IADL), for which healthcare resources and access would be beneficial. How they manage their IADL across age cohorts with healthcare support is less well known. We aimed to address this gap in knowledge by analyzing the National Health and Aging Trends round 11, 2021 data set on 2325 older adults (females = 54.1%, age range 1 to 90 and above) by age cohort (middle-old − 71–79, n = 1074; older-old − 80–89, n = 965, oldest- old- 90 plus, n = 286). Results indicate that regardless of healthcare access and resources, subjective aging is associated with a decline in IADL among all old age groups. However, this decline in IADL increases is higher at older-old (80–89 years) compared to the middle-old (71–79 years) and oldest-old (above 90 years old). These findings indicate a “doughnut effect” of higher subjective aging in IADL at older-old (80–89 years) may reported with IADL distress their subjective aging compared to the middle-old (71–79 years) and oldest-old (above 90 years old). The “doughnut effect” suggests that older-old (80–89 years) may be with higher subjective aging in their IADL transitioning from middle-old (71–79 years), whereas the oldest-old (above 90 years) may be more adjusted to their IADL limitations.

subjective age

subjective cognitive decline

healthcare access

healthcare resources

instrumental activities of daily living

As individuals advance in age, they may experience a reduction and changes in cognitive and neuromuscular functions, that may adversely affect their lives. They may report a sense of subjective cognitive decline (SCD), such as problems with memory, attention, and executive functions noticeable by a decline in their instrumental activities of daily living (IADL) [1]. Instrumental activities of daily living involve everyday activities such as laundering one’s own clothes, managing finances or transactions, and transportation from one place to another. Subjective cognitive decline (SCD) is when self-perceiving with poorer mental functioning while objective assessments may not reveal significant cognitive impairment [2]. Instrumental activities of daily living difficulties are associated with participation restrictions and poorer quality of life [3, 4], of which healthcare resources and access may contribute to differences in IADL function among older adults. Conceivably, older adults may differ by age cohort in how they perceive their IADL difficulties [5, 6], of which the explanatory variables are less well explored. We aimed to examine older adults’ subjective aging and IADL relationships by age cohort regarding their healthcare re-sources and access.

Health resources refer to the means for health well-being, including health insurance, food security, and health information, while healthcare access refers to the ease in the use of appropriate health services for health wellbeing [7]. Theoretically, access to healthcare and health resources utilization would enable IADL for health wellbeing [8]. Yet, we could not identify studies that have examined daily functional challenges associated with SCD at differing older adult age cohorts, controlling for their self-reported healthcare resources and access. This study examined the interplay between older adults' subjective aging in their IADL by their healthcare resources and access.

Subjective Aging and Instrumental activities of daily living in older adults

At older adult age, people engage in processes of self-discovery, reflecting, reliving, acting, and integrating new meaning [9], and often from their everyday activities. While self-perceptions of subjective aging contributed to how older adults understand their aging-related wellbeing, [10], it remains unclear the self-perceptions of aging relationships to IADL of middle-old (70–79 years), older- old (80–89), and oldest-old ages (90 years and above). Moreover, while IADL impairments would be of more significant concern at a show the older age cohorts [11], this may vary across age older adult age cohorts in ways yet to be explored. We assume no monotonic decline in sense of aging in IADL down the aging cascade. without taking into account the ways people adjust to life’s demands on them. Conceivably, the necessary coping adaptations may reach a low at older-old age (80–89 years) from increasing older age demands on that cohort, than at the middle old (70–70 years) being younger and less well exposed to older age from being younger, or the oldest-old (90 years and above) who may be better adapted to older age demands from resilient survivorship.

Also, be sociodemographic variables, older male adults with SCD experienced more difficulties performing IADL than women [12], while older adults who are not married or widowed were more likely to experience higher levels of subjective aging and their IADL than their married counterpart [13]. We aimed to control gender and marital status effects on the relationship between subjective aging as SCD and IADL across older adult age cohorts.

Healthcare Access moderation and Health Resources mediation

Healthcare accessibility would moderate older adults’ sense of coping with their IADL, reducing age cohort effects for a sense of equity in their subjective aging wellbeing [14]. As previously noted, older adults with higher subjective aging would, in theory, cope better with their IADL with healthcare access. Limitations in healthcare access would hamper older adults’ IADL function, which in turn contributes to their subjective aging experiences [15]. Healthcare resources as capabilities for maintaining and enhancing health and wellbeing [7, 16] would alleviate SCD in IADL for higher functionality of older adults their everyday living. In theory, this would be a healthcare resources mediation effect associated with a lower sense of subjective aging in IADL.

Health care access and resourcing would vary by age cohort so that older-olds (80 years and above may be disadvantaged by diminishing health supports relative to the increase in their aging process related health conditions [13]. For instance, in the United States, some of the oldest-old may not have adequate Medicare and Medicaid coverage for new age-related conditions they may experience over time [16] and would not afford pricey or expensive innovative treatments that may be reaching the market.

This Present study

We examined the association between SCD and IADL, with moderation of healthcare access and the mediating role of healthcare resources in that relationship (see Fig. 1) for the conceptual model). Following our conceptual model, we tested the following hypothesis:

Hypothesis 1

Subjective cognitive decline in instrumental activities of daily living is higher among the older- old cohorts (80–89 years) than the middle -old (70–79 years) and the oldest- old (90 years and over) cohorts.

Hypothesis 2

Health resources mediate the relationship between subjective cognitive decline and IADL in older adults, for higher IADL functioning.

Hypothesis 3

Healthcare access moderates the relationship between subjective cognitive decline and IADL in older adults so that their IADL functioning is higher.

Sources of data and procedure:

This cross-sectional analysis of a nationally representative cohort study used data from the National Health and Aging Trends Study (NHATS), an annual study of U.S adults 71 years and older. The study utilized the 2021 National Health and Aging Trends (NHATS) round 11 data on 2,325 older adults who had complete data on their IADL, SCD, healthcare access, healthcare resources and by personal factors of race, gender, and marital status. Table 1 presents the participants’ characteristics.

Table 1

Participant characteristics
Variable	Frequency (n)	%
Gender
Male	1067	45.9
Female	1258	54.1
Age cohorts
71–79 (Middle-old)	1074	46.2
80–89 (Older-old)	965	41.5
90 and above (Oldest-old)	286	12.3
Marital status
Married	1053	45.3
Divorced/living without partner	1272	54.7
Race/Ethnicity
White	1673	72
African Americans/Black	462	19.9
American Indian or Asian, native Hawaiian and pacific islanders, Hispanic	53	2.3
Hispanic	111	4.8
DKRF/Others	26	1.1

INSERT Table 1 ABOUT HERE

The NHATS is a publicly available prospective cohort study of Medicare enrollees aged 65 years and older (Johns Hopkins Bloomberg School of Public Health Grant number NIA U01AG032947). Participants were 70 years and older for the round 11 cohort. The Johns Hopkins University Institutional Review Board approved the NHATS protocol. Participants granted study consent, and these publicly available data are de-identified. Approval for use of these publicly available data is not required in accordance with local / national legislation” (www.nhats.org)".

Variables and Measurement

The NHATS SCD and IADL scales were adapted from Lawton-Brody rating scales (Weihe et al., 2022) [17]. We describe the NHATS measures for this study below.

Criterion variable – Instrumental Activities of Daily Living (IADL). Participants self-reported their IADL on four items assessing difficulties carrying out instrumental living activities: laundry, shopping, banking, and taking medication. Questions included “In the last month, how much difficulty did you have doing laundry by yourself? Would you say none, a little, some (coded 0), or a lot (coded 1). Higher scores represent a higher difficulty level in instrumental activities of daily living. In this study, Cronbach’s alpha for IADL scores was 0.957.

Predictor variable- Subjective Cognitive Decline (SCD): Participants self-reported their SCD on three questions 1) “How would you rate memory at the present time? Would you say it is excellent, very good, good (coded as 0), fair, or poor?” (Coded 1), 2) “Com-pared to 1 year ago, would you say your memory is much better now, better now, about the same (coded 0), worse now, or much worse now than it was then?” (coded 1). 3). “In the last month, how often did memory problems interfere with your daily activities? Would you say every day, most days, some days (coded 1), rarely, or never (coded 0)?”. A lower score indicates a higher level of SCD. In this study, Cronbach’s alpha for the SCD was 0.563.

Mediator variable

Healthcare resources. Healthcare resources assessment was based on one question. “Some people have additional coverage besides Medicare to pay for doctors and other medical care. This is sometimes referred to as Medigap or a Medicare Supplement. Do you have this type of health insurance coverage?” Yes/No (Yes = 1, No = 0).

Moderator variable

Healthcare access. Healthcare accessibility was assessed by the question “Have you seen your regular doctor/ a doctor within the last year? This includes in-person visits or online telehealth visits” Yes/No (Yes = 1, No = 0).

Covariates. Participants self-reported their age as a numeric variable. We coded the age variables into three age cohorts: middle-old (71–79 years old: Age cohort 1) older-old: 80–89 years old: Age cohort 2) and oldest-old (90 years and above) (see Table 1). We also reported their gender (coded males = 1; females = 0), and marital levels (married was coded = 1, not married OR without a partner or living alone = 0).

Data Analysis

All analyses were performed using IBM SPSS V.29. 0 (IBM, Armonk, New York, USA). We utilized multiple regression and the bias-corrected percentile bootstrap method for the data analysis by age cohort separately: middle-old (71–79 years), older-old (80–89), and oldest-old (90 and above). Missing data was computed using expectation maximization. The theoretical model was tested by estimating the 95% CI for mediation and moderating effects with 5000 bootstrap samples. A chain mediation and moderation analysis were carried out utilizing the PROCESS V.4.0 macro for SPSS (Model 59) [18] to test the mediation effects of healthcare resources and Model 4 for the mediating effect of healthcare re-sources on the association between instrumental activities of daily living and subjective cognitive decline in subjective aging, controlling for gender and marital status.

Spearman Correlation among variables

Middle-old cohort (71–79) ;(Age cohort1):SCD (Subjective Cognitive Decline) has a significant negative correlation with IADL (r=- 0.182, p = 0.01). IADL has a significant positive correlation with female gender (r = 0.092, p = 0.01) and a negative correlation with marital status (r= -0.093, p = 0.01). HCA has positive correlation with HCR (r = 0.133, p = 0.01) and a positive correlation with female gender (r = 0.067, p = 0.005). Gender is negatively correlated with marital status (r= -0.318, p = 0.01).

Older-old cohort (80–89 years, Age cohort 2):SCD (Subjective Cognitive Decline) has a negative correlation with IADL (r= -0.116, p = 0.01). IADL has a significant negative correlation with gender (r = 0.085, p = 0.01) and marital status (r= -0.068, p = 0.05). HCA has a positive correlation with HCR (r = 0.130, p = 0.01). Gender is negatively correlated with marital status (r= -0.442, p = 0.01).

Oldest-old cohort (Above 90 years; Age cohort 3): SCD (Subjective Cognitive Decline) has a negative correlation with IADL (r = − 0.141, p = 0.05). Gender is negatively correlated with marital status (r= -0.501, p = 0.01). HCA is positively correlated with marital status (r = 0.139, p = 0.05).

INSERT Table 2 ABOUT HERE

Table 2

Spearman correlation of variables
Middle-old (71–79 years)	SCD	IADL	HCA	HCR	Gender	Marital status
SCD	1
IADL	-0.182**	1
HCA	-0.004	-0.011	1
HCR	-0.004	0.034	0.133**	1
Gender	-0.039	.092**	0.067*	-0.004	1
Marital status	-0.052	-0.093**	0.016	-0.023	− .318**	1
Older-old (80–89 years)	SCD	IADL	HCA	HCR	Gender	Marital status
SCD	1
IADL	-0.116**	1
HCA	-0.051	0.018	1
HCR	-0.016	0.018	0.130**	1
Gender	-0.003	0.085**	-0.01	-0.022	1
Marital status	0.024	-0.068*	0.05	0.016	-0.442**	1
Oldest-old (Above 90 years)	SCD	IADL	HCA	HCR	Gender	Marital status
SCD	1
IADL	-0.141*	1
HCA	-0.095	-0.02	1
HCR	-0.074	-0.015	-0.005	1
Gender	-0.005	0.11	-0.02	-0.055	1
Marital status	0.003	-0.1	0.139*	0.087	-0.501**	1
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
SCD: Subjective cognitive decline; IADL: Instrumental activities of daily living.
HCR: Healthcare resources; HCA: Healthcare access

Predicting IADL by Age Cohort and from Subjective Cognitive Decline.

Middle-old cohort (71–79 years; age cohort 3): As in Table 3, The Correlation Coefficient (R) of 0.26 suggests a moderate positive relationship between the predictors and the outcome variable, IADL. The R-squared value (R-sq) of 0.07 indicates that approximately 7% of the variance in IADL scores is explained by the predictors in the model. The Mean Squared Error (MSE) of 0.12 represents the average squared difference between observed and predicted values of IADL. The F-statistic = 10.68, (p < 0.05), suggesting that at least one predictor variable in the model has a significant relationship with IADL scores. The intercept of the regression equation is -0.24; the predicted IADL score is approximately − 0.24. SCD has a coefficient of 0.20, indicating a significant positive relationship with IADL scores (p < 0.05). The coefficient for SCD and HCA is -0.05, marginally significant (p = 0.09), suggesting a possible negative relationship with IADL.

Table 3

Subjective Cognitive Decline (SCD) in IADL (Instrumental Activities of Daily Living) and with Health Care Resources (HCR) and Health Care Access (HCA)
		HCR				IADL
Middle-old (71–79 years	coeff	se	t	ULCI		coeff	se	t	ULCI
constant	1.91	0.2	9.78	2.39	constant	-0.2	0.25	-0.96	0.25
SCD	0	0.11	-0.15	0.2	SCD	0.2	0.06	3.24	0.32
HCA	0	0.1	-0.12	0.18	HCR	0.01	0.12	0.13	0.25
SCD*HCA	0.02	0.06	0.36	0.14	HCA	0.06	0.13	0.5	0.32
Gender	0	0.04	0.02	0.08	SCD*HCA	-0.1	0.03	-1.68	0.01
Marital status	0	0.04	-0.65	0.05	SCD*HCR	0	0.06	0.07	0.12
					Gender	0.05	0.02	2.49	0.1
					Marital status	-0	0.02	-1.87	0
R²				0	R²				0.07
F				0.32	F				10.68
Older-old (80–89 years)	coeff	se	t	ULCI		coeff	se	t	ULCI
constant	1.94	0.25	7.81	2.43	constant	-27	0.28	-0.94	0.29
SCD	0	0.14	-0.25	0.25	SCD	0.06	0.08	0.71	0.23
HCA	0	0.12	-0.17	0.22	HCR	0.07	0.13	0.56	0.33
SCD*HCA	0.02	0.08	0.21	0.16	HCA	0.11	0.15	0.73	0.39
Gender	0	0.04	-0.56	0.06	SCD*HCA	0	0.04	-0.07	0.08
Marital status	0.01	0.04	0.32	0.1	SCD*HCR	-0	0.07	-0.47	0.1
					Gender	0.05	0.02	2.08	0.1
					Marital status	-0	0.03	-0.85	0.03
R²				0	R²				0.02
F				0.16	F				3.43
Oldest-old (above 90 years)	coeff	se	t	ULCI		coeff	se	t	ULCI
constant	1.32	0.4	3.3	2.1	constant	-0.8	0.35	-2.28	-0.11
SCD	0.14	0.22	0.65	0.58	SCD	0.16	0.12	1.37	0.38
HCA	0.24	0.2	1.17	0.64	HCR	0.34	0.17	2	0.67
SCD*HCA	-0.1	0.12	-0.54	0.17	HCA	0.4	0.19	2.15	0.76
Gender	0.01	0.09	0.06	0.17	SCD*HCA	-0.1	0.06	-0.84	0.07
Marital status	0.17	0.11	1.57	0.38	SCD*HCR	-0.2	0.09	-2.13	-0.01
					Gender	0.06	0.04	1.36	0.15
					Marital status	-0	0.06	-0.71	0.07
R²				0.02	R²				0.06
F				1.39	F				2.32

Older-old cohort (80–89 years): The correlation coefficient is 0.24, indicating a weak positive relationship between the predictors and the outcome variable, IADL. Approximately 6% of the variance in IADL scores is explained by the predictors in the model (R-squared = 0.06). The MSE is 0.10, F-statistic is 2.32, indicating that the overall model is statistically significant. The p-value associated with the F-statistic is 0.03, suggesting that at least one predictor variable in the model has a significant relationship with IADL scores. The intercept of the regression equation is -0.81. When all predictor variables are zero, the predicted IADL score is approximately − 0.81. The coefficient for SCD is 0.16, but it's not statistically significant (p > 0.05), indicating no significant linear relationship with IADL scores. The coefficient for HCR is 0.34, and is significant (p = 0.05), suggesting a potential positive relationship with IADL scores, necessitating further investigation. The coefficient for HCA is 0.40, and it's statistically significant (p < 0.05), indicating a significant positive relationship with IADL scores.

Oldest-old cohort (Above 90 years): The correlation coefficient is 0.16, indicating a weak positive relationship between the predictors and IADL. Approximately 2% of the variance in IADL scores is explained by the predictors in the model. The MSE is 0.12, representing the average squared difference between observed and predicted values of IADL. The F-statistic is 3.43, indicating that the overall model is statistically significant. The p-value associated with the F-statistic is 0.00, suggesting that at least one predictor variable in the model has a significant relationship with IADL scores. The intercept of the regression equation is -0.27, indicating the predicted IADL score when all predictor variables are zero. The coefficient for SCD is 0.06, but it's not statistically significant (p > 0.05), indicating no significant linear relationship with IADL scores. The co-efficient for HCR is 0.07, and it's not statistically significant (p > 0.05), indicating no significant linear relationship with IADL scores. The coefficient for HCA is 0.11, and it's not statistically significant (p > 0.05), indicating no significant linear relationship with IADL scores. (see Fig. 2 and Table 3)

INSERT Table 3 ABOUT HERE

INSERT FIGURE 2 ABOUT HERE

INSERT FIGURE 3 ABOUT HERE

From these analysis, Hypothesis 1 (Subjective cognitive decline in instrumental activities of daily living is higher among the older- old cohorts (80–89 years) than the middle -old (70–79 years) and the oldest- old (90 years and over) cohorts) is supported.

Healthcare resources mediation of Instrumental Activities of Daily Living

Middle-old cohort (71–79 years)

As in Table 3, the model has a moderate R-squared value (0.06), indicating that the predictors (SCD, HCR and covariates) explain a moderate amount of variance in the outcome variable IADL. The coefficient for SCD is statistically significant (p < 0.001), suggesting that higher levels of SCD are associated with higher levels of IADL. The coefficient for HCR is not statistically significant (p = 0.18), suggesting that HCR does not significantly predict IADL. The interaction between SCD and HCR does not significantly predict IADL (p = 0.57). The direct effect of SCD on IADL is statistically significant (p < 0.001), with an estimated effect size of 0.10. The indirect effect of SCD on IADL through HCR is not statistically significant (p = 0.00). This suggests that HCR does not mediate the relationship between SCD and IADL.

Older-old cohort (80–89 years): The model has an R-squared value (0.02), indicating that the predictors (SCD, HCR, and the covariates explain a very small amount of variance in the outcome variable IADL. The coefficient for SCD is statistically significant (p < 0.05), suggesting that SCD significantly predicts IADL. However, the coefficients for HCR and covariates are not statistically significant (all p-values > 0.05). All the covariances are close to zero, indicating no multicollinearity among the predictors and covariates. The interaction between SCD and HCR does not significantly predict IADL (p = 0.51). The direct effect of SCD on IADL is statistically significant (p < 0.05). The indirect effect of SCD on IADL through HCR is not statistically significant (p > 0.05).

Oldest-old cohort (above 90 years): Results show a low-to-moderate R-squared value (0.04), indicating that the predictors SCD, HCR and covariates explain a small amount of variance in the outcome variable IADL. The coefficient for SCD is statistically significant (p < 0.05), suggesting that SCD significantly predicts IADL. However, the coefficients for HCR, and the covariates are not statistically significant (all p-values > 0.05). The interaction between SCD and HCR does not significantly predict IADL (p = 0.69). The direct effect of SCD on IADL is statistically significant (p = 0.01), with an estimated coefficient of 0.07. This suggests that SCD has a direct effect on IADL, independent of its effect through HCR. The indirect effect of SCD on IADL through HCR is not statistically significant. (see Fig. 4). Hence, for Hypothesis 2, Healthcare access moderates the relationship between subjective cognitive decline and IADL in older adults so that their IADL functioning is higher) was not supported.

Our study found that subjective aging in instrumental activities of daily living is high across all age groups and increases with age, even in the presence of healthcare accessibility and healthcare resources. Furthermore, we found that females had a higher rate of IADL and cognitive impairment, which may be due to females having a longer life expectancy.

These trends in the evidence appear to be not influenced by the presence of both healthcare access and resources for these study participants who were on Medicare, a US federal and state health insurance program for people over 65 years of age and those with chronic illness and disability.

Our unique finding is an explanatory model indicating the “doughnut effect” of higher subjective aging in IADL at older-old (80–89 years) compared to the middle-old (71–79 years) and oldest-old (above 90 years old). (see Fig. 2). Previous studies predicted subjective aging on increasing age associated with comorbidities, health problems, living alone or being single, and SCD, which increase with age [19]. In theory, cognitive reserve would be higher among the middle -old and the older-old compared to the oldest-old, so their subjective aging statuses in activity limitations would be incremental with aging [20]. Contrary to what we found in this study; the expectation would be for subjective aging to be higher with increasing age (middle- old to oldest-old). This may be because some of the improvements in old-age functioning might reflect the operation of changes impacting the older-old more than the middle-old and oldest-old.

However, those in the older-old cohorts (81–89 years) may be adjusting to transitions in their instrumental activity limitations with more challenges than the middle-old (70–79 years) and the oldest -old (90 years and above) who may be fully adjusted to those limitations [21]. The older-old (80–89 years) may be experiencing new health conditions for which they received considerable marketing from service providers to prolong their health function, which would lower their overall sense of subjective aging. Hence, while the availability of healthcare resources can potentially contribute to how older adults successfully address subjective aging [22], our findings caution against the assertion that in-creasing age would linearly predict health outcomes for older adults according to age-cohort effects.

Our study findings of lower subjective aging in instrumental activities of daily living by age cohort is at the explanatory model level comprising SCD (as a proxy measure of subjective aging), healthcare accessibility, health resources, and socio-demographics of age and gender. Our model can be replicable in a similar population demographic from its strength of associations of instrumental activities of daily living based on a cluster of predictors rather than one premised on a single predictor variable. This study examined data from a nationally representative cohort of older US adults to determine the mediation role of healthcare resources and the moderating role of healthcare access in the association between SCD and IADL among different old age cohorts. The results of this study suggest that when SCD increases, IADL difficulties also increase with higher impact at older-old age. Conceivably, the capacity to carry out laundry, shopping, manage their finances and take prescribed medication independently may be challenging to the older-old transitioning from the middle-old age cohort, of which the oldest-old would be more accustomed. This effect would apply even in the presence of healthcare access and healthcare resources.

For the middle-old cohort (70–79 years), SCD significantly predicts IADL directly and HCR does not mediate the relationship between SCD and IADL. For the older-old cohort (80–89 years), neither SCD nor HCR significantly predicts IADL directly or indirectly through HCA (See Fig. 3). This could be due to increased dependence in older adults which is associated with hospitalization and admission to long-term care facilities, an increase in morbidity and mortality from diseases and a decrease in quality of life. Additionally, there are no significant interaction effects between SCD and HCA or between HCR and HCA on IADL. Therefore, the moderation-mediation analysis does not provide evidence of a significant relationship between these variables in predicting IADL. This lack of HCR and HCA effect could be explained by the fact that NHATS participants are all Medicare enrollees and would be comparable in their health resources and access un-der that federal and state program for all people over 65 years old including those with limited resources and income. Nonetheless, the oldest-old cohort with more consumption in HCA and HCR and SCD in IADL (See Fig. 4), a population with fragile health function [23]. IADL impairment continually declined across age cohorts.

Implications for Theory, Research, and Practice

Our study findings represent a valuable extension of the existing body of literature on cognitive reserve diminishing at older ages and the related everyday challenges, such as difficulties with instrumental activities of daily living would compound and worsen with poorer access to timely healthcare and healthcare resources. By implication, IADL-targeted support interventions are needed for older age adults transitioning from the middle-old (71–79 years) to oldest-old (80–89 years) for improved subjective aging at older ages. Older adults lacking sufficient support transitioning between different old age cohorts may be more susceptible to physical and psychological distress [21], which can remarkably impact their ability to carry out instrumental activities of daily living.

The findings of our study suggest the practical benefit of aging well from access to health resources likely alleviating emotional burdens experienced by older adults, especially when they perceive SCD as a natural aspect of aging [24]. Discussing their concerns with healthcare providers could facilitate the necessary assistance and improve well-being [24]. For instance, efforts to enhance healthcare access for older-old adults should be initiated and continue into older-old ages as the benefit may be higher among oldest-old ages than the middle-old and older-old ages at which the returns may diminish with the onset of significant cognitive decline. These efforts need to include carers of the older adults for personalized support to mitigate the risk of poorer wellbeing from subjective cognitive de-cline in instrumental activities of daily living compared to the younger and older-old cohorts.

Strengths, Limitations of the Study and Future Directions

A strength of the study is that its results are nationally representative of the Medicare-eligible population in the US 70 years and older. Accounting for the role of behavioral and environmental adaptations offers a more nuanced examination of disability than many prior investigations. Additionally, findings from this study suggest the need for interventions to optimize functionality and cognitive performance and promote functioning across the activity spectrum to maximize independence and optimal aging for a growing population of older adults with subjective aging and difficulties in IADL.

There are limitations to this study. First, this study utilized self-report data from the secondary dataset (i.e., the 2021 round 11 NHATS dataset). A smaller sample size obtained among the oldest-old group compared to the middle-old and older-old could make for less confidence in the analysis of the findings on the oldest-old age cohort. Additionally, our study exclusively included older adults with Subjective Cognitive Decline (SCD), potentially limiting the generalizability of mediation and moderated mediation findings to older adults without SCD. For instance, partial mediation may be explained by healthcare resources-related factors not directly measured by the NHATS and have a substantial impact on subjective cognitive decline in instrumental activities of daily living [25].

Furthermore, the National Health and Aging Trends Study (NHATS) may display an unknown healthy participant bias, leading to an unavoidable potential for underestimating or overestimating the effects of subjective cognitive aging we report in this study. Future studies should utilize self-report and observational data to generate more valid findings, and practitioners who work with clinical aging populations with SCD often co-exist with various psychosocial symptoms [26], which may not be accounted for in this study. Future studies should consider other co-occurring morbidities and other potential psychological variables influencing SCD in the study model.

Our study examined the mediating role of healthcare resources on the moderating effect of healthcare access on subjective cognitive decline in instrumental activities of daily living among older adults aged 71 and above and the mechanism underlying this relationship in a national U.S. sample. We found evidence for older-old age cohort “doughnut effect” in difficulties in activities of daily living associated with subjective aging to be higher in older-old rather than middle-old to oldest-old cohorts. While difficulties associated with healthcare resource utilization are compounded with subjective cognitive de-cline across age cohorts, a survival effect at the oldest-old age cohort may present with better coping or adapting to the ageing process over the years. This oldest-old would be mostly resilient survivors from a population selection factor by which the oldest-old who were in worse health may have died while more of those of previous age strata with declining health may still be living.

In conclusion, our study extends previous findings by demonstrating that greater SCD is associated with a “doughnut effect” of higher odds of increased difficulties in IADL transitioning from middle-old into older-old, while the oldest-old may be improved adaptation their instrumental activities being a survivor age cohort effect. These findings suggest a need for targeted SCD and IADL support interventions for the older-old age cohort, managing the transition from middle-old to older-old to optimize health benefits and prevent a declining quality of life among older adults.

Ethical Approval: This study was exempt from ethical approval as it was a secondary analysis of publicly available data.

Consent to Participate declaration in the manuscript is not needed. Data used was from www.nhats.com

Clinical Trial Number is NOT APPLICABLE

Human Ethics and Consent to Participate declarations: not applicable.

Acknowledgements: this article is work in partial fulfillment of PhD course work within the Department of Applied Gerontology at the University of North Texas

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

Cloutier S, Chertkow H, Kergoat M, Gélinas I, Gauthier S, Belleville S. Trajectories of decline on instrumental activities of daily living prior to dementia in persons with mild cognitive impairment. Int J Geriatr Psychiatry. 2021;36(2):314–23. https://doi.org/10.1002/gps.5426.
Fieo R, Stern Y. Increasing the sensitivity of functional status assessment in the preclinical range (normal to mild cognitive impairment): exploring the IADL-extended approach. Dement Geriatr Cogn Disord. 2018;45(5–6):282–9. https://doi.org/10.1159/000487632.
Gitlin LN, Winter L, Dennis MP, Corcoran M, Schinfeld S, Hauck WW. A randomized trial of a multicomponent home intervention to reduce functional difficulties in older adults. J Am Geriatr Soc. 2006;54(5):809–16. https://doi.org/10.1111/j.1532-5415.2006.00703.x.
Amieva H, Ouvrard C, Meillon C, Rullier L, Dartigues J. Death, depression, disability, and dementia associated with self-reported hearing problems: a 25-year study. Journals Gerontology: Ser A. 2018;73(10):1383–9. https://doi.org/10.1093/gerona/glx250.
Rovner BW, Casten RJ, Leiby BE. Determinants of activity levels in African Americans with mild cognitive impairment. Alzheimer Disease Assoc Disorders. 2016;30(1):41–6. 10.1097/WAD.0000000000000096.
Wu S, Shi H, Cheng R, Xiang Z, Huang S. Impairment in activities of daily living and cognitive decline mediate the association between depressive symptoms and incident hip fractures in Chinese older adults. Bone. 2022;159:116374. https://doi.org/10.1016/j.bone.2022.116374.
Levesque J, Harris MF, Russell G. Patient-centred access to health care: conceptualising access at the interface of health systems and populations. Int J Equity Health. 2013;12:1–9. 10.1186/1475-9276-12-18.
Beltz S, Gloystein S, Litschko T, Laag S, van den Berg N. Multivariate analysis of independent determinants of ADL/IADL and quality of life in the elderly. BMC Geriatr. 2022;22(1):894. https://doi.org/10.1186/s12877-022-03621-3.
Komalasari R, Mpofu E, Prybutok G, Ingman SR. Subjective Aging Difficulties among Older Adults with Subjective Cognitive Decline. J Appl Gerontol. 2023;42(2):259–69. https://doi.org/10.1177/07334648221129546.
Hausknecht S, Low L, O’loughlin K, McNab J, Clemson L. Older adults’ self-perceptions of aging and being older: A scoping review. Gerontologist. 2020;60(7):e524–34. https://doi.org/10.1093/geront/gnz153.
Diehl M, Wettstein M, Spuling SM, Wurm S. Age-related change in self-perceptions of aging: Longitudinal trajectories and predictors of change. Psychol Aging. 2021;36(3):344. https://doi.org/10.1037/pag0000585.
Jang S, Kawachi I. Why do older Korean adults respond differently to activities of daily living and instrumental activities of daily living? A differential item functioning analysis. Annals geriatric Med Res. 2019;23(4):197. https://doi.org/10.4235/agmr.19.0047.
Connolly D, Garvey J, McKee G. Factors associated with ADL/IADL disability in community dwelling older adults in the Irish longitudinal study on ageing (TILDA). Disabil Rehabil. 2017;39(8):809–16. https://doi.org/10.3109/09638288.2016.1161848.
Levesque J, Harris MF, Russell G. Patient-centred access to health care: conceptualising access at the interface of health systems and populations. Int J Equity Health. 2013;12:1–9. https://doi.org/10.1186/1475-9276-12-18.
Venturini C, Moreira BS, Ferriolli E, Liberalesso Neri A, Lourenço RA, Sampaio RF. Can social resources explain the limitations in the activities of daily living of older adults classified by the phenotype of physical frailty? J Appl Gerontol. 2022;41(5):1445–53. https://doi.org/10.1177/07334648211064267.
Talbot L, Verrinder G. Promoting health: the primary health care approach. Elsevier Health Sciences; 2017.
Weihe S, Mortensen CB, Haase N, Andersen LP, Mohr T, Siegel H, et al. Long-term cognitive and functional status in Danish ICU patients with COVID‐19. Acta Anaesthesiol Scand. 2022;66(8):978–86. https://doi.org/10.1111/aas.14108.
Hayes AF. Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monogr. 2018;85(1):4–40. https://doi.org/10.1016/j.resourpol.2018.06.015.
Roehr S, Luck T, Pabst A, Bickel H, König H, Lühmann D, et al. Subjective cognitive decline is longitudinally associated with lower health-related quality of life. Int Psychogeriatr. 2017;29(12):1939–50. https://doi.org/10.1017/S1041610217001399.
Rovner BW, Casten RJ, Leiby BE. Determinants of activity levels in African Americans with mild cognitive impairment. Alzheimer Disease Assoc Disorders. 2016;30(1):41–6. 10.1097/WAD.0000000000000096.
Lehning AJ, Smith RJ, Dunkle RE. Do age-friendly characteristics influence the expectation to age in place? A comparison of low-income and higher income Detroit elders. J Appl Gerontol. 2015;34(2):158–80. https://doi.org/10.1080/01634372.2015.1088111.
Sin NL, Klaiber P, Wen JH, DeLongis A. Helping amid the pandemic: Daily affective and social implications of COVID-19-related prosocial activities. Gerontologist. 2021;61(1):59–70. https://doi.org/10.1093/geront/gnaa140.
Deardorff WJ, Jeon SY, Barnes DE, Boscardin WJ, Langa KM, Covinsky KE, et al. Development and External Validation of Models to Predict Need for Nursing Home Level of Care in Community-Dwelling Older Adults With Dementia. JAMA Intern Med. 2024;184(1):81–91. 10.1001/jamainternmed.2023.6548.
Taylor C, Lynn P, Bartlett J. Fundamentals of nursing: The art and science of person-centered care. Lippincott Williams & Wilkins; 2018.
Sudore RL, Mehta KM, Simonsick EM, Harris TB, Newman AB, Satterfield S, et al. Limited literacy in older people and disparities in health and healthcare access. J Am Geriatr Soc. 2006;54(5):770–6. https://doi.org/10.1111/j.1532-5415.2006.00691.x.
Zullo L, Clark C, Gholam M, Castelao E, von Gunten A, Preisig M, et al. Factors associated with subjective cognitive decline in dementia-free older adults—A population‐based study. Int J Geriatr Psychiatry. 2021;36(8):1188–96. https://doi.org/10.1002/gps.5509.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
11 Sep, 2024
Reviews received at journal
01 Sep, 2024
Reviewers agreed at journal
22 Aug, 2024
Reviews received at journal
20 Aug, 2024
Reviewers agreed at journal
20 Aug, 2024
Reviewers invited by journal
17 Aug, 2024
Editor invited by journal
16 Jul, 2024
Editor assigned by journal
15 Jul, 2024
Submission checks completed at journal
15 Jul, 2024
First submitted to journal
03 Jul, 2024

You are reading this latest preprint version

Subjective aging and Instrumental activities of daily living: Moderation by healthcare access and mediation by healthcare resources.

Status:

Version 1

Abstract

Figures

Introduction

Subjective Aging and Instrumental activities of daily living in older adults

Healthcare Access moderation and Health Resources mediation

This Present study

Materials and Methods

Sources of data and procedure:

INSERT Table 1 ABOUT HERE

Variables and Measurement

Data Analysis

Results

Spearman Correlation among variables

INSERT Table 2 ABOUT HERE

INSERT Table 3 ABOUT HERE

INSERT FIGURE 2 ABOUT HERE

INSERT FIGURE 3 ABOUT HERE

Healthcare resources mediation of Instrumental Activities of Daily Living

Discussion

Implications for Theory, Research, and Practice

Strengths, Limitations of the Study and Future Directions

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1