Daily Fluctuations in Everyday Cognition

Abstract

Objective: This study examined whether there are daily fluctuations in everyday cognition that are consistent with daily fluctuations often observed in traditional measures of basic cognitive abilities. Method: Two hundred six independently living older adults (age range = 60-91 years) were asked to complete a computerized cognitive battery over eight occasions within a 2- to 3-week period. Results: Using multilevel model, significant within-person variability was observed across the Daily Everyday Cognition Assessment (DECA; 46%), with 54% between-person variability. At each occasion, better performance on the DECA was significantly associated with better performance on simple reaction time ( p < .01) and memory (Auditory Verbal Learning Task, p < .01) even after accounting for time, age, education, and performance on other cognitive measures. Conclusion: These findings demonstrate that within-person performance fluctuations can be observed for everyday cognition tasks, and these fluctuations are consistent with daily changes in basic cognitive abilities.

Keywords

everyday cognition cognition intraindividual variability

Everyday cognition involves the application of basic cognitive abilities (e.g., memory, processing speed, inductive reasoning) and domain-specific knowledge to solving problems that are integrated within instrumental domains of functioning (Allaire, 2012; Allaire & Marsiske, 1999; Willis, 1996). The concept of everyday cognition developed from an interest in assessing elders’ cognition in a more ecologically valid manner (Berg & Klaczynski, 1996; Blanchard-Fields, Chen, & Norris, 1997; Denney, 1989, 1990; Sternberg & Wagner, 1986; Willis, 1991). However, the existing methodology of assessing everyday cognition at a single occasion or on a single day runs counter to the original concept of everyday cognition. Thus, the overarching goal of the current study is to examine short-term fluctuations in older adults’ everyday cognitive functioning. In addition, the current study explores whether there are significant dynamic within-person relationships between fluctuations in everyday cognition and intraindividual variability in basic cognitive abilities.

A good deal of the previous research on everyday cognition has focused on understanding the underlying factors associated with individual differences. Results from cross-sectional studies suggest that individual differences in everyday cognition are strongly associated with performance on measures of basic cognitive abilities (Allaire & Marsiske, 1999; Bertrand & Willis, 1999; Burton, Strauss, Hultsch, & Hunter, 2009; Diehl et al., 2005; Diehl, Willis, & Schaie, 1995; Rebok et al., 2014). For instance, Allaire and Marsiske (1999) reported that as much as 80% of the variance in their Everyday Cognition Battery (ECB) was accounted for by basic cognitive abilities, particularly memory and inductive reasoning ability. A few longitudinal studies have also observed an association between long-term changes in everyday cognition and basic cognitive abilities (Gross, Rebok, Unverzagt, Willis, & Brandt, 2011; Tucker-Drob, 2011; Yam, Gross, Prindle, & Marsiske, 2014). In addition, a number of studies have reported that everyday cognition measures are associated with neurological compromise—that is, mild cognitive impairment and dementia (Allaire, Gamaldo, Ayotte, Sims, & Whitfield, 2009; Allaire & Willis, 2006; Goldberg et al., 2010), reduced brain volume (Farias et al., 2013), medication adherence (Neupert, Patterson, Davis, & Allaire, 2011), ability to independently perform instrumental activities of daily living (Allaire & Marsiske, 2002), and risk for earlier mortality (Weatherbee & Allaire, 2008).

Although the previous work on everyday cognition has laid an important groundwork, it is ultimately limited because studies often assess performance at a single occasion or at multiple occasions separated by longtime intervals (1, 2, and 5 years) between measurements. Everyday cognition developed from an interest in assessing elders’ cognition in a more ecologically valid manner (Berg & Klaczynski, 1996; Blanchard-Fields et al., 1997; Denney, 1990, 1989; Sternberg & Wagner, 1986; Willis, 1991), but assessing everyday cognition at a single point in time or multiple points in time with long periods of time between the occasions runs counter to this idea. Thus, the assessment of an individual’s everyday cognition using these existing research designs may not accurately reflect an individual’s ability. Furthermore, these existing research designs limit researchers from capturing short-term change or within-person fluctuations, often referred to as intraindividual variability, which can be a meaningful precursor to long-term change (Bielak, Hultsch, Strauss, MacDonald, & Hunter, 2010b; Gamaldo, An, Allaire, Kitner-Triolo, & Zonderman, 2012; MacDonald, Hultsch, & Dixon, 2003).

Intraindividual variability has been observed across several physical and mental health parameters. Specifically, intraindividual variability has shown to be a reliable and quantifiable index of cognitive performance (Allaire & Marsiske, 2005; Hultsch, MacDonald, & Dixon, 2002), affect (Kuppens, Van Mechelen, Smits, De Boeck, & Ceulemans, 2007), stress (Neupert, Almeida, Mroczek, & Spiro, 2006), control beliefs (Neupert & Allaire, 2012), sensorimotor functioning (Li, Aggen, Nesselroade, & Baltes, 2001), blood pressure (Gamaldo, Weatherbee, & Allaire, 2008), sleep (Gamaldo, Allaire, & Whitfield, 2010), vision (Weatherbee, Gamaldo, & Allaire, 2009), and daily activities (Ghisletta, Nesselroade, Featherman, & Rowe, 2002).

Variability, particularly on cognitive measures, has been associated with cognitive decline (Bielak, Hultsch, Strauss, Macdonald, & Hunter, 2010a; Bielak et al., 2010b) and neurological compromise, such as head injury (Strauss et al., 2002), mild cognitive impairment (Gamaldo, Allaire, & Whitfield, 2012), and dementia (Burton, Strauss, Hultsch, Moll, & Hunter, 2006; Gamaldo, An, et al., 2012; Hultsch, MacDonald, Hunter, Levy-Bencheton, & Strauss, 2000).

Much of the previous literature examining cognitive variability has included measures of basic cognitive abilities, such as measures of processing speed, reaction time, and memory (Bielak et al., 2007; Burton, Strauss, Hultsch, & Hunter, 2006; Hultsch et al., 2000). In addition, cognitive variability has been measured across different time scales (e.g., across minutes or across days), which has assisted in identifying the underlying mechanisms associated with cognitive functioning. For example, variability, particularly on tasks of processing speed or reaction time, assessed over short intervals (e.g., trail-to-trial assessments) appears to capture signs of neuronal compromise or cognitive impairment (Hultsch, Strauss, Hunter, & MacDonald, 2008). In contrast, variability, particularly on tasks of memory or global cognition, assessed over longer intervals (e.g., days or weeks) appears to capture the influence of environmental factors on behaviors, such as stress (Neupert et al., 2006) and sleep (Gamaldo et al., 2010). Few studies have explored variability, particularly over daily time intervals, using measures of everyday cognition. Unlike the traditional psychometric and/or neurological tests used in the previous literature, measures of everyday cognition have shown to be better clinical measurements of real-world competency than the basic cognitive ability measures (Allaire & Marsiske, 2002; Allaire & Willis, 2006). Furthermore, the exploration of variability of everyday cognition across daily intervals may offer an additional useful technique for identifying potentially modifiable environmental factors, which are influencing an individual’s ability to effectively perform his or her daily home, work, and social tasks.

Thus, the current study plans to investigate two aims. The first aim is to explore whether there is quantifiable variability of everyday cognition performance across eight occasions within a 2- to 3-week period. The second aim is to examine whether within-person fluctuations in everyday cognition performance couples with within-person fluctuations of standard tests of basic cognitive abilities.

Method

Participants

The current study used data from the MEDLI-NC (Mental Exercise for Daily Living With Independence in North Carolina) project, which was designed to examine intraindividual variability in cognition and health indices in older adults from Raleigh and Cary, NC. The MEDLI-NC project included a sample of 236 community-dwelling elders, aged 60 to 94 (M = 73, SD = 7.00) who were recruited from senior centers, church groups, and community organizations.

Measures

The current analyses included the demographic variables age, education, and gender. Psychometric measures assessing three cognitive abilities were also included in the analyses. Declarative memory was assessed using the Rey Auditory Verbal Learning Task (AVLT; Rey, 1941), this measure tests a participant’s ability to remember lists of semantically related or semantically unrelated words. Inductive reasoning was assessed using the Letter Series test (Thurstone, 1962), a test of a participant’s ability to identify the most logical item. Perceptual speed was assessed using Digit Symbol Substitution Test (Wechsler, 1981) as well as the Simple Reaction Time (SRT; Hultsch et al., 2002) and Complex Reaction Time (CRT; Hultsch et al., 2002) tasks, which tests participants’ reaction time to stimuli on a computer screen. High scores on SRT and CRT were indicative of participants performing slower or worse on these tasks. To reduce the impact of practice effects, eight alternate versions of each basic test were used, many of which been used in previous studies (Allaire & Marsiske, 2005; Gamaldo et al., 2010; Gamaldo, Allaire, & Whitfield, 2012; Gamaldo et al., 2008; Weatherbee et al., 2009).

The Daily Everyday Cognition Assessment (DECA) is a computer administered measure of everyday cognitive functioning similar to and derived from the ECB (Allaire & Marsiske 1999) and Everyday Problems Test (Willis, Jay, Diehl, & Marsiske, 1992). The DECA consists of eight alternate versions each consisting of 14 multiple-choice items drawn directly from the EPT (Willis et al., 1992) as well as the ECB Inductive Reasoning and ECB Memory tests.

The eight versions of the DECA tests each consist of 14 items drawn from the everyday domains of medication use, financial management, and nutrition/food preparation (4 items per domain). The remaining 2 items were specifically drawn from the EPT and assessed everyday problems (e.g., ordering from a catalog and driving directions). For the inductive reasoning items drawn from the EPT or ECB, participants were presented with real-world stimuli such as a medication label and had to answer multiple-choice questions based on the stimuli. For memory items drawn from the ECB, participants were shown real-world stimuli for 1 min after which it was removed and they were asked to answer multiple-choice questions about information found in the stimuli. The number of correct items was summed to provide a total score ranging from 0 to 14.

Design

Participants completed eight testing sessions over a 3-week period and participants could not complete more than one 45-min testing session per day. Testing sessions were conducted at the location for which participants were recruited and at each session participants completed a battery of measures administered via computer. At each testing session participants completed the DECA and the four basic ability measures. As previously mentioned, to reduce practice effects alternate versions of each cognitive ability test and the DECA were used. In addition, the order the alternate versions of the DECA and basic ability were administered over the eight occasions was counterbalanced.

Statistical Analyses

An interclass correlation (ICC), an intercept only model, was estimated to determine the amount of within-person and between-person variability in the everyday cognition (DECA). Two multilevel models (MLMs) were conducted to test our second aim, our main aim of interest. The first model tested whether the measures of the basic cognitive abilities were significantly associated with the DECA after accounting for linear time, age, sex, and education. In the second model, interactions were added. Specifically, the interaction term explored whether the relationship between the DECA and within-person changes in a particular cognitive ability was dependent upon an individual’s mean level of performance on the same cognitive ability over time. We controlled for potential differences among the eight versions of the DECA by including dummy codes in the MLMs.

Results

Participants (n = 30) were excluded from the analyses due to having less than three follow-up visits and/or incomplete evaluations (i.e., missing cognitive data). Participants excluded (M_age = 77, SD = 8.50) from the analyses were significantly older than the participants included in the analyses, M_age = 73, SD = 6.82, t(218) = 2.35, p < .05, but there were no significant differences with respect to education or distribution of gender. The final analyses included 206 participants (females = 124; males = 82). The sample was predominantly White (n = 136) and highly educated (M = 15.26 years of education, SD = 3.09; range = 5-24).

ICCs

Results indicated that 46% of the total variance in DECA was within-person (σ² = 0.02, z = 7.81, p < .001) while 54% was between-person (τ₀₀ = 0.02, z = 22.75, p < .001). To assure that there was a sufficient amount of within- and between-person variance in the cognitive dependent variables to conduct our subsequent MLM analyses (Raudenbush & Bryk, 2002), ICCs were also calculated for the basic cognitive abilities measures. The results suggested that there was significant within-person variance in AVLT (43%; σ² = 2.48, z = 26.52, p < .001), Letter Series (26%; σ² = 4.97, z = 26.39, p < .001), Digit Symbol (32%; σ² = 7.54, z = 26.43, p < .001), SRT 78%; σ² = 82710.00, z = 26.30, p < .001), and CRT (33%; σ² = 26321.00, z = 26.20, p < .001). Likewise, there was significant between-person variance in AVLT (57%; τ₀₀ = 3.26, z = 9.20, p < .001), Letter Series (74%; τ₀₀ = 14.48, z = 9.79, p < .001), Digit Symbol (68%; τ₀₀ = 15.87, z = 9.56, p < .001), SRT (22%; τ₀₀ = 23731.00, z = 6.69, p < .001), and CRT (67%; τ₀₀ = 53007.00, z = 9.39, p < .001).

Associations Between DECA and Basic Cognitive Abilities Performance

In the first model testing only main effects, daily performance on SRT and AVLT was a significant predictor of performance on the DECA (see Table 1), even after accounting for linear time, age, sex, education, and within- and between-person levels of performance on the other cognitive ability measures (i.e., CRT, symbol, letter). On those occasions, participants performed faster on the SRT and performed better on AVLT than on average, DECA performance improved. A significant association was also observed for DECA and the between-person level of performance on the AVLT. Participants whose average AVLT performance was high tended to have better DECA performance. This model accounted for 26% of the within-person (σ² = 0.01, z = 20.32, p < .001) and 62% of the between-person (τ₀₀ = 0.01, z = 4.39, p < .001) variance in DECA.

Table 1.

Basic Cognitive Ability Measures Unstandardized Coefficients (and Standard Errors) of Daily Within-Person Effects and Between-Person Effects for Everyday Cognition Measure.

	Model 1	Model 2
Within-person
Daily AVLT	0.0016** (0.0006)	0.0016** (0.0006)
Daily Letter Series	0.0009 (0.0007)	0.0010 (0.0007)
Daily Digit Symbol	0.0006 (0.0007)	0.0006 (0.0007)
Daily SRT	−0.0019** (0.0006)	−0.0019** (0.0006)
Daily CRT	0.0009 (0.0007)	0.0013 (0.0008)
Time (linear)	0.0040* (0.0018)	0.0044* (0.0019)
Between-person
Mean AVLT	0.0069*** (0.0017)	0.0085*** (0.0018)
Mean Letter Series	−0.0012 (0.0019)	−0.0013 (0.0024)
Mean Digit Symbol	−0.0008 (0.0018)	0.0010 (0.0021)
Mean SRT	−0.0005 (0.0015)	−0.0007 (0.0015)
Mean CRT	0.0016 (0.0017)	0.0011 (0.0019)
Age	−0.0026* (0.0013)	−0.0023 (0.0013)
Gender	−0.0054 (0.0178)	−0.0018 (0.0176)
Education	0.0081** (0.0028)	0.0078** (0.0028)
Interactions
Daily AVLT × Mean AVLT		−0.0002** (0.0001)
Daily Letter Series × Mean Letter Series		0.0000 (0.0001)
Daily Digit Symbol × Mean Digit Symbol		0.0001 (0.0000)
Daily SRT × Mean SRT		−0.0000 (0.0001)
Daily CRT × Mean CRT		0.0000 (0.0000)
Pseudo R² between	62%	63%
Pseudo R² within	26%	26%

Note. AVLT = Auditory Verbal Learning Task; SRT = Simple Reaction Time; CRT = Complex Reaction Time; DECA = Daily Everyday Cognition Assessment. Model 1 examines the relationship between DECA performances as it relates to each within-person basic cognitive ability measure and each between-person basic ability measure after accounting for age, gender, and education. Model 2 additionally examines whether the within-person coupling relationship between DECA and a basic cognitive ability is modified by participant’s average level of performance on the basic cognitive ability across the testing sessions.

p < .05. **p < .01. ***p < .001.

In the second model (Table 1), a significant cross-level interaction was observed for AVLT performance (σ² = 0.01, z = 20.26, p < .001; τ₀₀ = 0.01, z = 4.54, p < .001). Computational tools were used to probe the interaction (Preacher, Curran, & Bauer, 2006). This computational tool estimates intercepts and simple slopes based on three group estimates: (a) 1 standard deviation above the group mean on AVLT performance, (b) at the group mean on AVLT performance, and (c) 1 standard deviation below the group mean on AVLT performance. As result, these intercepts and simple slopes are illustrated in Figure 1 to depict the differences across the three groups in terms of the relationship between daily AVLT and daily DECA performance. For those participants performing below the sample on overall/average AVLT performance, DECA performance improved particularly on those occasions when their AVLT performance was better than their average level of performance (b = .003, p < .001; Figure 1).

Figure 1.

Within-person coupling relationship between DECA and AVLT modified by mean AVLT performance.

Discussion

A growing body of research focusing on short-term variability has found that performance on basic cognitive ability measures is characterized by significant within-person variability (Hultsch et al., 2000; Stawski, Smith, & MacDonald, 2014). However, few studies to date have examined such variability in everyday cognition instead focusing on cross-sectional, age group differences or longitudinal, long-term changes on everyday cognition measures. Similar to previous studies examining intraindividual variability in basic abilities, the current study found evidence of significant short-term, with-person fluctuation in everyday cognition. Moreover, occasion-to-occasion fluctuation in performance on tasks of basic cognitive abilities was significantly and uniquely related to variability in everyday cognition.

One of the hallmarks of everyday cognition research is the focus on assessing elders’ ability to solve real-world problems. However, capturing this ability using a single assessment on a single occasion and/or multiple assessments with long intervals between the assessments seems to run counter to the spirit of term everyday. Indeed, the current study found that roughly half of the total variability in a daily assessment of everyday cognition was due to within-person variability while the other half was attributed to between-person differences. This ratio is higher than previous studies examining basic cognitive abilities (within-person variance: 21%-44% vs. between-person variance: 56%-79%; Gamaldo et al., 2010; Neupert & Allaire, 2012; Weatherbee et al., 2009) or subjective cognitive complaints (within-person variance: 34% vs. between-person variance: 66%; Whitbourne, Neupert, & Lachman, 2008). Given our results suggest that an individual’s performance on everyday cognition measures fluctuates from occasion to occasion as much as it fluctuates between individuals, it is important for future research to capture an older adult’s everyday cognitive ability over multiple occasions. Limited research exploring the within-person fluctuations on everyday cognition measures may be missing invaluable information regarding an individual’s cognitive functioning in a real-world setting.

Given that short-term intraindividual variability in basic cognitive abilities is significantly associated with neurological compromise—for example, head injury (Strauss et al., 2002), mild cognitive impairment (Gamaldo, Allaire, & Whitfield, 2012), dementia (Burton, Strauss, Hultsch, Moll, et al., 2006; Gamaldo, An, et al., 2012; Hultsch et al., 2000), and impaired physical functioning (Ghisletta et al., 2002)—the substantial portion of within-person variability on the everyday cognition measure may suggest that this may be a sensitive measure for cognitive and physical functioning.

As previously noted, previous cross-sectional (Allaire & Marsiske, 1999; Bertrand & Willis, 1999; Burton et al., 2009; Diehl et al., 2005; Diehl et al., 1995; Rebok et al., 2014) and longitudinal studies (Gross et al., 2011; Tucker-Drob, 2011; Yam et al., 2014) report that basic abilities explain much of intraindividual differences in elders’ everyday cognitive functioning. The current findings further support the underlying role of basic abilities in everyday cognition. That is, variability in everyday cognition was strongly associated with variability on tasks of basic cognitive abilities, particularly of speed and memory. Furthermore, the current findings support a within-person association or coupling of everyday cognition and basic cognitive measures, particularly memory and speed. Specifically, on those measurement occasions that an individual performs faster on a speed task or better on a memory tests, they also appear to perform better on performance-based measures of everyday cognition. Both the speed and memory tests appeared to be unique predictors of performance on everyday cognition. However, only 26% of the within-person variance of everyday cognition could be accounted for by the variability on the basic cognitive ability tasks (memory, reasoning, and perceptual speed) and linear time/practice-related effects. Given that a substantial portion of the intra-variability in everyday cognition remains unaccounted future research should explore whether intra-variability of everyday cognition may be additionally accounted for by other cognitive abilities (e.g., executive function, language), physical/mental health (e.g., pain, depression, stress), and/or socio-demographic factors (e.g., financial strain).

The findings of the current should be considered with the following caveats. Our study’s sample included predominantly older White and highly educated adults. Thus, these results may not generalize to other racial/ethnic groups and/or lower socioeconomic populations. Although the computer administration of the DECA has assisted in shortening the research staff’s administration of the test and in reducing data entry errors, variations in performance among our older adult population may be due to computer administration of the test. Older adults, particularly with limited experience using a computer and/or anxiety with using a computer, are likely to have performed worse on the computer administered everyday cognition test than the traditional paper-and-pencil everyday cognition test (Fredrickson et al., 2010; Karavidas, Lim, & Katsikas, 2005). However, older adults with limited computer knowledge, can successfully complete computerized tests when given opportunities to practice the computerized tasks (Karavidas et al., 2005). The research staff for the current project attempted to address this concern by providing participants the opportunity to practice using the DECA before starting the actual test. Finally, the current study did not assess participants beyond a 3-week period. Thus, it is difficult to support whether intraindividual variability on everyday cognition tasks may initially represent practice-related gains, but continued within-person variability on these tasks over time may represent neurological compromise (Allaire & Marsiske, 1999).

Overall, the current study reveals a promising direction for future research to implement models that estimate both between-person and within-person changes in performance on everyday cognition measures. Furthermore, the current study supports exploring intraindividual variability using alternative time scales and across various measurement scales, such as the performance-based measures of everyday cognition, to understand age-related changes in physical and/or mental health (Stawski et al., 2014). Our findings further support that basic abilities underlie everyday cognition and that there remains a substantial amount of within-person variance that is not accounted for and maybe explained by non-cognitive factors. These findings also highlight the potential for further exploring the within-person coupling relationship between everyday cognition performance and acute physical/mental health conditions. Indeed, Neupert and colleagues (2011) have supported the utility of further exploring within-person coupling relationship between everyday cognition performance and activities of daily living. Specifically, the authors observed that older adults were less likely to forget to take their medications, particularly on those days when they performed better on tasks of everyday cognition and had less “busy” schedules. Future research should also explore whether daily changes on environmental and/or behavioral factors (e.g., environmental noise, socioeconomic status, stress, and sleep) are associated with daily changes in performance on everyday cognition measures, even after accounting for variability in basic abilities. This type of research would be useful in identifying potential modifiable factors, which could be implemented in intervention programs to sustain or improve daily functioning. Finally, research will be needed to support whether individual variability on performance-based measures of everyday cognition may be used as a sensitive measurement for detecting early cognitive decline and/or cognitive impairment above and beyond the traditional cognitive measures. However, it might be beneficial to assess variability on everyday cognition measures across several shorter time intervals than current study to accurately capture the neurological compromise observed in cognitive impairment.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Aging/National Institutes of Health (NIA/NIH) under Grant R21 AG27223-01A2.

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