Frequency of But Not Capacity for Participation in Everyday Activities Is Associated With Cognitive Impairment in Late Life

Abstract

We examined features of everyday activities (capacity and frequency) between older adults with and without cognitive impairment over 12 months. Participants aged ≥60 years and at risk for depression were included (n = 260); 26% (n = 69) had an acquired cognitive impairment at baseline. Cognitive impairment was defined as one standard deviation below norms on the Repeatable Battery for the Assessment of Neuropsychological Status. Features of everyday activities were measured by a computerized adaptive test version of Late-Life Function and Disability Instrument (LLFDI) at six time points (baseline, 6 weeks, 3, 6, 9, 12 months). There were significant between-group differences in activity frequency (p = .04), but not activity capacity (p = .05). The group difference in activity frequency exceeded minimal detectable changes (MDC₉₀ = 3.7) and reached moderate clinical meaningfulness (∆ at six time points = 3.7–4.7). Generalized linear mixed models revealed no Group × Time interactions on activity capacity and frequency (p = .65 and p = .98). Practitioners may assess changes in activity frequency to monitor cognitive status of clients even when there is no loss of activity capacity.

Keywords

activities of daily living (ADLs)cognitive function disability prevention

Introduction

Understanding cognitive impairment in late life is an aging research priority due to the elevated risk of dementia and disability (Davis et al., 2018). The estimated prevalence of cognitive impairment in older adults varies from 5.1% to 35.9% in population studies, depending on the measurement approach (Ward et al., 2012). Common methods include neurocognitive assessments of domains of attention, memory, executive function, and processing speed (Eshkoor et al., 2015). At the outset, subtle changes in cognitive function may not be easily detected, particularly among individuals with higher education or greater cognitive reserve (Kirova et al., 2015). Therefore, declines in attention, memory, executive function, and processing speed may be difficult to detect (Kirova et al., 2015). In these cases, considering additional clinical manifestations to complement neurocognitive assessments may optimize the understanding of cognitive impairment in older adults (U.S. Food & Drug Administration, 2018).

Cognitive decline and its clinical manifestations evolve in parallel trajectories, as cognitive decline is often associated with declining abilities in performance of everyday activities (Lindbergh et al., 2016). Features of performance of everyday activities can be characterized in terms of “activity capacity” and “activity frequency” (Freedman et al., 2014). Activity capacity describes the “ability” to complete a daily task (can do), whereas activity frequency suggests the “periodicity and willingness” of doing everyday activities (does do; Carlson et al., 2012). Changes in activity capacity are well documented in people with dementia and mild cognitive impairment (MCI) and serve as diagnostic criteria for neurocognitive disorders in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013). Systematic reviews reveal that older adults with MCI experience deficits with complex everyday tasks, especially financial management (Jekel et al., 2015; Lindbergh et al., 2016). These deficits may not be characterized as “incapacity,” as older adults with MCI may be able to complete daily tasks, but they may require supervision or verbal reminders from others (Rodakowski et al., 2014).

More challenging to observe, measure, and describe is activity frequency (Eckel et al., 2014). However, changes in activity frequency—a proxy of change in daily routines—may emerge prior to problems with activity capacity, overt disability, and functional decline in late life (Freedman et al., 2014; Fried et al., 1991; Gignac et al., 2002). That said, the relationship between cognitive impairment and activity frequency has not been clarified. Knowledge pertaining to the trajectories of activity capacity and frequency may improve understanding of the clinical manifestations of cognitive decline and potentially inform the timing of interventions to slow down cognitive impairment in late life.

The aim of the study was to investigate activity capacity and activity frequency in older adults with and without cognitive impairment over time. We hypothesized that there would be group differences (between people with and without cognitive impairment) in activity capacity and activity frequency and that activity capacity and activity frequency would decline more rapidly in the cognitive impairment group over 12 months.

Method

We conducted a secondary analysis of data combined from three parallel clinical trials examining learning-based interventions to prevent depression in older adults at risk of major depression and anxiety disorders. These studies were described in detail elsewhere (Albert et al., 2016; Gildengers et al., 2016; Karp et al., 2016). Briefly, these studies developed and tested selective and indicated prevention interventions for older adults at risk of depression due to cognitive impairment, knee pain, or multiple chronic conditions. Selective and indicated prevention interventions target on those who are at risk or showing early signs of depression. These parallel intervention studies utilized problem-solving therapy for older adults to identify problems, develop personalized strategies, break them down to manageable components, and generate strategies to regulate situations that trigger negative mood. All three intervention studies assessed cognitive function at baseline and activity capacity and frequency at six assessment time points, such as baseline, postintervention (6 weeks), 3-, 6-, 9-, and 12-month follow-up. Data were collected at either a clinic or the participants’ home. With a computerized adaptive test version of Late-Life Function and Disability Instrument (LLFDI), participants were instructed by the assessor to answer questions via a software installed on a portable study computer. The University of Pittsburgh Institutional Review Board approved procedures (PRO10110050; PRO10110251; PRO10100591) and written informed consent was obtained in the parent studies.

Participants

Participants aged 60 years and older, who had depressive symptoms (defined as Patient Health Questionnaire-9 [PHQ-9] score between 1 and 9 and at least a score of 1 for one of the two cardinal symptoms of depression) were included in the three parent studies (Table 1). Those with dementia, major depressive disorder, or anxiety disorder at baseline were excluded. In addition, participants met the specific criteria for one of the three studies: MCI, knee osteoarthritis, or disability conditions with low socioeconomic status. MCI diagnosis was conferred through diagnostic adjudication sessions via neuropsychiatric testing scores, medical history, and report of changes by an informant. Knee arthritis was diagnosed by clinicians based on the American College of Rheumatology clinical criteria for knee osteoarthritis. Disability condition was defined by reporting difficulties in at least one instrumental or personal self-maintenance activity. Low socioeconomic status was defined as residing in subsidized senior housing or being eligible for long-term care services in the homes (Albert et al., 2016).

Table 1.

Description of Measurements.

Concept	Measurement	Items	Item scale	Total score
Comorbidity	Cumulative Illness Rating Scale-Geriatric (CIRS-G) (Miller et al., 1992)	14	5-point scale (0 = no problem; 4 = extremely severe/immediate treatment required)	0 (no comorbidity) to 56 (severe comorbidity)
Depressive symptoms	Patient Health Questionnaire-9 (PHQ-9) (Kroenke et al., 2001; Kroenke & Spitzer, 2002)	9	4-point scale (0 = not at all; 3 = nearly every day)	0 (no depressive symptoms) to 27 (severe depressive symptoms)
Anxiety	Generalized Anxiety Disorder-7 (GAD-7) (Spitzer et al., 2006)	7	4-point scale (0 = not at all; 3 = nearly every day)	0 (no anxiety) to 21 (severe anxiety symptoms)
Mobility disability	Short Physical Performance Battery (SPPB) (Guralnik et al., 1994)	3 components (balance, gait speed, transfer)	4-point scale (0 = not at all; 3 = nearly every day)	0 (worse lower extremity functioning) to 12 (no lower extremity functioning impairment)

Measurement

Characteristics of Participants

Participants’ age, gender, race, years of education, retirement status, comorbidity, depressive symptoms, anxiety symptoms, and mobility disability were collected via interviews and standardized testing from the parent studies (Table 1).

Features of Everyday Activities (Activity Capacity & Activity Frequency)

Activity capacity and activity frequency were assessed with the computerized adaptive test version of LLFDI-Disability component (Jette et al., 2002). The LLFDI is a valid and reliable measure (Beauchamp et al., 2014) that is based on Nagi’s model of disability (Jette et al., 2002) and the International Classification of Function, Disability, and Health (ICF; World Health Organization, 2002). The disability component assesses socially defined activities of daily living (i.e., visit friends and family in their homes) and basic and instrumental activities of daily living (i.e., take care of your own personal care needs such as bathing, dressing, and toileting; take care of house hold business and finances). The disability component has two scales (activity capacity and activity frequency), providing an estimate of each respondent’s activity capacity and activity frequency, in relation to others who have completed the measure. Each scale is unidimensional and has been validated using item response theory. In the LLFDI, each respondent is provided with five to seven questions, selected based on responses to the first two questions. The activity capacity scale questions are rated on a 5-point scale ranging from 1 (not at all) to 5 (completely limited). The activity frequency scale questions are rated on a 5-point scale ranging from 1 (never) to 5 (very often).

Each respondent’s raw LLFDI score is converted into a 0 to 100 scaled scale. Higher scores indicate higher capacity and greater frequency of everyday activities. The standard error of measurement (SEM) is 1.6, and the minimal detectable change with 90% confidence (MDC₉₀) is 3.7 for small changes in activities. A minimal clinically important difference (MCID) ranges from 2 to 4.5, with 2 and 4.5 points indicating small and substantial changes in mobility and functioning (Beauchamp et al., 2019).

Cognitive Impairment

The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) yields a standardized total scores–based performance in five cognitive domains (immediate memory, visuospatial/constructional, language, attention, delayed memory; Randolph et al., 1998). The standardized score compares with a population mean of 100 and a standard deviation of 15. A higher score indicates better cognitive function. In the current study, cognitive impairment was operationalized by a standardized total score one standard deviation lower than the population mean (total scaled score <85) at baseline.

Data Analyses

All analyses were conducted using Statistical Packages for the Social Sciences (SPSS, v. 25, Armonk, NY, USA) and Statistical Analysis Software (SAS, v. 9.4, Cary, NC, USA) with a significance level of alpha of .05. Prior to analyses, data were screened to assess assumptions. The main effects of groups on activity capacity and frequency were examined. Changes in activity capacity and activity frequency were examined separately using longitudinal generalized linear mixed models with SAS PROC GLIMMIX. In each model, group (with or without cognitive impairment) and time (baseline, postintervention, 3, 6, 9, and 12 months), along with the Group × Time interaction, were included as fixed effects, whereas subjects were considered random effects. To control for potential covariates, demographic variables that showed significant group differences were included in the model. Variables that showed moderate correlation to the dependent variable (Spearman’s r = .5) were included as covariates (Assmann et al., 2000). We did not conduct the baseline adjustment of dependent variables to avoid potential biased causal effect estimates (Glymour et al., 2005).

Results

Participant characteristics were presented in Table 2. Participants were on average 73.92 (SD = 8.75) years old, mostly White, female, and had an average of 14.38 (SD = 2.67) years of education. A total of 260 participants were included in the analyses and 69 had cognitive impairment (RBANS mean score: intact cognition group = 100.20 [SD = 9.58]; cognitive impairment group = 79.22 [SD = 5.68]; Figure 1). Baseline group differences were detected in race, years of education, severity of mobility disability, LLFDI activity capacity, and activity frequency. Activity capacity was moderately correlated activity frequency (r = .68) to be treated as a covariate controlled in the model.

Table 2.

Baseline Participant Characteristics.

Characteristics, n (%)	All, 260 (100)	With cognitive impairment, 69 (26.5)	Without cognitive impairment, 191 (73.5)	t-statistics/χ²-statistics	p
Age, M (SD)	73.92 (8.75)	73.70 (8.99)	73.99 (8.68)	t(258) = 0.24	.81
Female, n (%)	179 (68.8)	48 (69.6)	131 (68.6)	χ²(1) = 0.02	.88
White, n (%)	200 (76.9)	43 (62.3)	157 (82.2)	χ²(1) = 11.29	<.01*
Years of education, M (SD)	14.38 (2.67)	13.15 (2.73)	14.83 (2.51)	t(256) = −4.63	<.01*
Retired, n (%)	191 (73.5)	50 (72.5)	141 (73.8)	χ²(1) = 0.05	.83
Comorbidity, M (SD)^a	9.73 (3.83)	10.00 (3.94)	9.63 (3.80)	t(256) = 0.69	.49
Depressive symptoms, M (SD)^a	5.94 (2.48)	6.07 (2.61)	5.89 (2.44)	t(258) = 0.52	.60
Anxiety, M (SD)^a	3.23 (2.73)	3.10 (2.67)	3.28 (2.76)	t(258) = −0.47	.64
Mobility disability, M (SD)	7.57 (3.80)	5.94 (4.30)	8.16 (3.43)	t(99.5) = −3.83	<.01*
RBANS, M (SD)
Immediate memory	98.52 (14.56)	85.09 (10.61)	103.27 (12.93)	t(258) = −10.74	<.01*
Visuospatial/constructional	90.51 (16.28)	77.36 (12.94)	95.59 (14.44)	t(258) = −9.23	<.01*
Language	98.54 (10.63)	92.32 (8.42)	100.83 (10.49)	t(258) = −6.72	<.01*
Attention	97.93 (16.29)	84.46 (12.46)	103.05 (14.29)	t(258) = −9.57	<.01*
Delayed memory	95.01 (14.39)	81.39 (13.64)	100.12 (11.07)	t(258) = −10.25	<.01*
Total	94.63 (12.72)	79.22 (5.68)	100.20 (9.58)	t(258) = 21.56	<.01*
LLFDI, M (SD)
Activity capacity	52.04 (6.83)	50.92 (5.57)	52.68 (7.07)	t(250) = −2.05	.04*
Activity frequency	47.69 (8.36)	45.32 (9.07)	49.09 (7.74)	t(246) = −3.22	<.01*

Note. RBANS = Repeatable Battery for the Assessment of Neuropsychological Status; LLFDI = Late-Life Function and Disability Instrument.

Higher scores indicate worse.

p < .05.

Figure 1.

RBANS total score between groups.

There was no statistically significant effect of group (F_1,1225 = 3.74, p = .05) or time (F_5,1225 = 1.06, p = .38) in activity capacity. The mean difference in activity capacity was 2.6 points across six time points (range of mean differences at six time points = 1.8–3.7 points), which is a small clinically meaningful difference based on the MCID of LLFDI (Figure 2). The Group × Time interaction effect was not significant for activity capacity (F_5,1225 = 0.67, p = .65), after controlling for covariates (treatment assignment, race, years of education, mobility disability, baseline activity frequency).

Figure 2.

Raw scores of activity capacity and cognitive groups.

There was a statistically significant effect of group in activity frequency (F_1,1232 = 4.13, p = .04), but no effect of time (F_5,1232 = 1.69, p = .13). The mean difference in activity frequency was 4.1 points across six time points (range of mean differences at six time points = 3.7–4.7 points; Figure 3), which is a moderately-to-substantially clinically meaningful difference based on the MCID of LLFDI. The Group × Time interaction effect was not significant for activity frequency (F_5,1232 = 0.15, p = .98), after controlling for covariates (treatment assignment, race, years of education, mobility disability, baseline activity capacity).

Figure 3.

Raw scores of activity frequency and cognitive groups.

Discussion

We examined the group differences in activity capacity (can do) and activity frequency (does do) between older adults with and without cognitive impairment over 12 months. We found group differences in the activity frequency were moderately-to-substantially clinically meaningful, regardless of the time points over 12 months. In contrast, only small clinical differences between groups in the activity capacity were detected over the 12 months. Our findings suggest that low activity frequency is one of the clinical manifestations of cognitive impairment in older adults, even with preserved capacity for performing activities of daily living. That is, older adults with cognitive impairment may reduce the frequency with which they perform activities of daily living, even before their ability to perform these activities is compromised (Cassel, 2002; Wang et al., 2013). The DSM-5 diagnostic criteria for neurocognitive disorders include changes in activity capacity without considering the potential of activity frequency as a signal for having a neurocognitive disorder. Future diagnostic criteria for neurocognitive disorders may consider frequency of performance of everyday activities as complementary clinical manifestations.

Doody et al. (2010) have shown that individuals who showed declined cognition at baseline had a faster 15-year progression rate of instrumental activities of daily living (IADL) than those who were cognitively stable. We did not observe Group × Time effects in activity capacity and frequency within a year. There may be several explanations for these findings. The selection of these samples may have contributed to our findings. It may be that the participants in the parent studies were still able to compensate their cognitive loss, rather than those who were declining in their cognition within the 12 months. Furthermore, 12 months may not have been long enough to observe differences. Previous studies suggest that cognitive decline progresses slowly; therefore, cognitive decline could be harder to detect until more accumulation of brain damage, which could lead to clinically overt changes in activity frequency after years (Salthouse, 2012). Future studies should recruit a broader sample and examine associations among cognitive impairment and performance of everyday activities.

Another interesting finding is that standardized mean activity capacity scores were higher than activity frequency scores. This aligns with previous studies that suggest that lower levels of activity frequency may be due to loss in activity capacity (Brown et al., 2009). For example, the inability to manage medication leads to low frequency in managing medication. However, our results may reveal that low levels of activity frequency may appear even in the presence of relatively preserved activity capacity. There may be more than one potential explanation for this phenomenon. First, older adults with cognitive impairment may experience fatigue when initiating everyday activities, although they are capable of doing activities. Second, cognitive impairment may lead to inefficiency in navigating complex daily tasks and routines. Thus, those with cognitive impairment may choose to prioritize and optimize important activities and give up things they used to enjoy, such as socializing and traveling (Buchman et al., 2014; Gignac et al., 2002). Third, cognitive impairment may be associated with depressive symptoms and these somatic symptoms of depression may prevent older adults from engaging in everyday activities (Karp et al., 2009). Altogether, low frequency of activities may, in turn, worsen cognitive impairments, contributing to the loss of activity capacity in everyday routines, eventually contributing to a pernicious path toward dementia and mortality risks (Menec, 2003).

We found between-group differences in both activity frequency and capacity at baseline. Yet, after controlling for covariates (treatment assignment, race, years of education, mobility disability), only activity frequency remained significant. Plausibly, mobility disability explains more variances in activity capacity relative to the influences of cognition, whereas activity frequency is highly associated with cognitive status regardless of mobility status.

Previous studies have examined the types of activities that older adults “give up” early in the progression of cognitive decline. Petersen et al. (2015) found that older adults with MCI spent 1.6 hours more inside the home compared with their healthy peers. It is likely that older adults with cognitive impairment are more sedentary (Falck et al., 2017) and spend less time engaged in the community than their healthy peers due to the decline in executive function or processing speed (Suzuki & Murase, 2010). Unfortunately, the trend of decreased frequency in daily activities may take years to detect, assuming that these trends are associated with slow progression of cognitive decline (Crowe et al., 2008). There are also internal and external reasons besides cognition (personal interests or geographic locations) that may result in people ceasing certain activities before others. More research is needed to explore the longitudinal changes in personal interests and in-home routines in older adults with early cognitive impairment.

In this study, we used a computerized version of LLFDI to assess features of everyday activities. This tool is efficient and satisfactory and may be useful as a complementary, screening tool for cognitive impairment in older adults. A strength of this tool is that it utilizes item response theory and adaptive selection of questions to efficiently estimate a respondent’s “placement” in reference to others in the population. However, this strength limits the ability to systematically describe what domains of activities are most strongly associated with low levels in activity frequency. In addition, activity capacity and activity frequency are self-reported, which may differ from objective, performance-based measurement (Rogers et al., 2003). Future studies could include ecological momentary assessments or sensor-based assessments in evaluating the performance of everyday activities (Hayes et al., 2008). Another limitation is that changes in cognition may interfere with changes in activity frequency and capacity, which we did not control for over time.

That said, it appears that frequency of performance of everyday activities may be an important clinical manifestation of cognitive impairment when seeking to detect cognitive impairment among older adults. Practitioners and home care providers may utilize a brief interview to monitor the change of frequency in meaningful activities to better glimpse cognitive status of their clients. Future studies seeking to elucidate trajectories and mechanisms that lead to aging-related cognitive decline may benefit from including brief measures of activity capacity and frequency to lineate the complex interactions among cognitive processes and performance of everyday activities.

Footnotes

Acknowledgements

We thank all the participants in the DEP-ABC, RAPID, and RECALL studies.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Institutes of Health (NIH) Grant P30 MH090333 (C.F.R., M.A.D., M.B., E.R.S.) and Clinical and Translational Science Institute Grants UL1RR024153 (C.F.R.) and UL1TR000005 (C.F.R.).

IRB Numbers

The University of Pittsburgh Institutional Review Board approved procedures (PRO10110050; PRO10110251; PRO10100591). Written consent was obtained.

ORCID iDs

Chao-Yi Wu

Lauren Terhorst

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