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Systematic examination of methodological inconsistency in operationalizing cognitive reserve and its impact on identifying predictors of late-life cognition

BMC Geriatrics volume 23, Article number: 547 (2023) Cite this article

Abstract

Background

Cognitive reserve (CR) is the ability to maintain cognitive performance despite brain pathology. CR is built through lifecourse experiences (e.g., education) and is a key construct in promoting healthy aging. However, the operationalization of CR and its estimated association with late-life cognition varies. The purpose of this study was to systematically examine the operationalization of CR and the relationship between its operationalization and late-life cognition.

Methods

We performed a comprehensive review of experiences (proxies) used to operationalize CR. The review informed quantitative analyses using data from 1366 participants of the Memory and Aging Project to examine 1) relationships between proxies and 2) the relationship between operationalization and late-life cognition. We also conducted a factor analysis with all identified CR experiences to create a composite lifecourse CR score. Generalized linear mixed models examined the relationship between operationalizations and global cognition, with secondary outcomes of five domains of cognition to examine consistency.

Results

Based on a review of 753 articles, we found the majority (92.3%) of the 28 commonly used proxies have weak to no correlation between one another. There was substantial variability in the association between operationalizations and late-life global cognition (median effect size: 0.99, IQR: 0.34 to 1.39). There was not strong consistency in the association between CR operationalizations and the five cognitive domains (mean consistency: 56.1%). The average estimate for the 28 operationalizations was 0.91 (SE = 0.48), compared to 2.48 (SE = 0.40) for the lifecourse score and it was associated with all five domains of cognition.

Conclusions

Inconsistent methodology is theorized as a major limitation of CR research and barrier to identification of impactful experiences for healthy cognitive aging. Based on the weak associations, it is not surprising that the relationship between CR and late-life cognition is dependent on the experience used to operationalize CR. Scores using multiple experiences across the lifecourse may help overcome such limitations. Adherence to a lifecourse approach and collaborative movement towards a consensus operationalization of CR are imperative shifts in the study of CR that can better inform research on risk factors related to cognitive decline and ultimately aid in the promotion of healthy aging.

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Introduction

Cognitive decline that occurs with aging includes changes in multiple domains, such as memory and executive function, and can contribute to functional deficits [1]. As the population lives longer and large generations enter their later adult years, cognitive deficits are expected to intensify their burden on families and the healthcare system, creating a major public health concern [2,3,4]. The rate of cognitive decline is highly variable from individual to individual [5, 6]. Although there are likely multiple factors that contribute to the variability of cognitive decline in older adults, cognitive reserve (CR) has been put forth as an important contributor to successful cognitive aging. The CR hypothesis theorizes that individuals encounter experiences throughout the lifecourse that may produce varying levels of protective benefits, increasing the brain’s adaptability to age-related brain changes and allowing for maintainance of higher cognitive function [7, 8]. Therefore, individuals with high CR have delayed onset of symptoms of cognitive decline, despite the presence of brain pathology. However, collectively, approaches to the operationalization of CR through proxies, such as education and occupational attainment, vary between studies, perhaps creating inconsistencies in the relationship between CR and late-life cognition [9, 10]. As the field of aging moves forward, a systematic examination of CR operationalization and a better understanding of the relationship between CR operationalization and late-life cognition is necessary to facilitate research to discern how to slow cognitive decline through CR promoting activities.

CR cannot be directly measured due to its theoretical nature and, therefore, the study of CR is open to threats to construct validity [10, 11]. As a result, studies examining CR must operationalize it, or define it in a way that can be measured. The most common operationalization of CR relies on proxy measures. Proxies are experiences that are hypothesized to contribute to the development of CR [12]. Common proxies include education, occupation, and cognitively-stimulating activities, among a large variety of considered experiences. However, the association between these proxies and their relative importance to late-life cognition remain unclear. For example, while eduction is frequently used as a measure of CR, a systematic review found a lack of consensus for its protective effect [13], and others have debated the utility of educational attainment relative to other experiences, such as occupational attainment [14, 15]. A portion of variance may stem from measurement differences. Education is most often measured as years of formal education, while some argue that quality may be more indicative of CR, and perhaps more important, than years [9, 16, 17]. Occupation, another widely-used CR proxy, has been measured based on level (e.g., professional worker, service worker) [18, 19], or based on the occupation’s cognitive demands through the Occupational Information Network (O*NET) [20, 21]. The variety of proxies may be problematic through a resulting failure to produce a consistent representation of important life experiences.

Furthermore, the timing of proxies within a person’s life produce additional measurement challenges and alternatives. Many studies investigating the relationship between CR and late-life cognition rely on experiences from single life timepoints (e.g., late-life physical activity); however, at its foundation, the CR hypothesis attributes the development of higher CR to experiences across the lifecourse [12, 22]. In a set of studies, Xu and colleagues developed lifespan CR scores, an accummulation of multiple variables: education; early-, mid-, and late-life cognitive activities; and late-life social activities [23, 24]. Their results showed that a higher lifespan CR score reduced risk of cognitive outcomes. Similarly, a number of questionnaires have been developed to produce cumulative CR scores, including the Cognitive Reserve Index questionnaire (CRIq) [25] and the Lifetime of Experience Questionnaire (LEQ) [26]. The CRIq focuses on total years of education, years of different types of labor, and years of activities [25]; and the LEQ asks participants to recall participation in activities at three different life timepoints, young adult (13–30), adult (30–65), and older adult (from 65 on) [26]. As such, the approaches used to examine the relationship between CR and late-life cognition show wide variability.

The purpose of the present study was to explore characteristics of CR proxies commonly used in the literature, investigate the relationship between different proxies, evaluate the impact of CR operationalization on its association with late-life cognition, and examine operationalizations of CR which incorporate multiple lifecourse experiences. We extracted proxies that have been used in the published literature to operationalize CR through a comprehensive review of the literature. Findings from the review were utilized to inform quantitative analysis using data from the Rush Memory and Aging Project (MAP) to investigate the operationalization of CR and how operationalization relates to understanding late-life cognition.

Methods

Comprehensive literature search

We assessed articles published between January 1990 and December 2021 [27]. All searches were done in January of 2022 through three search engines, PsycInfo, Web of Science, and PubMed, and excluded special populations and study designs other than cohort and cross-sectional [27, 28]. The search was undertaken in two phases: an initial search for possible proxies and a follow-up search of each proxy to determine prevalence of that proxy among the CR literature and qualities of its use. Additional details of the literature search, including inclusion criteria and PRISMA flowchart [29], are provided in Appendix 1. After assessment of 5326 screened articles, 1412 of which required retrieval, 753 articles met inclusion criteria. Experiences used as proxies (e.g., reading, going to the library) were grouped into 20 proxy categories based on their use in the literature. For example, researchers may examine playing games, reading, and going to the library all under the category of cognitive activities. These categories were agreed upon by two researchers. Total number of articles retrieved and final inclusion number by proxy categories is shown in Table 1.

Table 1 Literature search frequency results by each proxy category
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CR proxy use in the literature varied widely. Education was the most common proxy category by a large margin (N = 280), with over three times that of the second-most common, occupation (N = 91). Education and occupation were used together most frequently of any pairing (N = 58). Leisure activities (N = 29), cognitive activities (N = 26), and premorbid intelligence quotient (IQ) (N = 24) were also frequently used with education. Additionally, several proxies were measured in different ways. Occupation was split almost equally in being categorized based on one’s level (54.76%) or based on qualities of the work (45.24%). On the other hand, IQ was divided among a larger variety of measures, with the National Adult Reading Test (NART) at 46.15%; verbal or vocabulary ability, including the Wide Range Achievement Test (WRAT), at 28.21%; literacy level at 20.51%; and formal IQ at 5.13%. Leisure activities and social activities often showed overlapping components or experiences.

Information from the comprehensive literature review about prevalence and qualities of proxies were used to inform quantitative analysis exploring (1) the relationships between proxies through a correlation and multiphase confirmatory factor analysis and (2) the relationship between differing operationalization and late-life cognition.

Data source and participants

Data was obtained from the Rush Memory and Aging Project (MAP). MAP is an ongoing, hybrid cohort study that recruits participants ≥ 65 years of age, without known dementia, from retirement communities in Illinois, United States [30]. At baseline, participants were asked about history of activities throughout their lives. Participants underwent yearly follow-up, including tests for cognitive functioning. The MAP dataset included 2131 individuals. Individuals presenting with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) at baseline were removed from the analysis, resulting in a final sample of 1459 healthy participants. MAP has been approved by the Institutional Review Board of Rush University Medical Center.

CR operationalization techniques

Proxies

The MAP dataset contained variables related to 18 of the 20 proxy categories from the literature search, with no reasonable equivalents for nutrition and novelty. Observed variables in MAP (life experiences) were used to create composite scores relating to the proxy category (see Table 2). Composite scores were based on frequency of activities for leisure activities, cognitive activities, and social activities. Late-life leisure activities and all cognitive and social activities were rated on a frequency scale from 1 (once a year or less) to 5 (every day or about every day). The three variables which retrospectively comprised leisure activities at age 18 (attending concerts, plays, or musicals; taking music lessons; and taking art, dance, or theatre lessons) were coded as 0 (have not attended any or taken any lessons) and 1 (have attended or taken lessons). Social network and physical function were defined by multiple variables, while different ages provided multiple measurements for leisure activities, cognitive activities, and socioeconomic status. Primary life occupation was classified based on U.S. census categories and based on cognitive demands through O*NET, a database of job characteristics sponsored by the United States Department of Labor. As described elsewhere [18, 19], census classification used a rank from 1–6: 1 = no occupation; 2 = farm workers; 3 = unskilled laborers; 4 = operative and service workers; 5 = craftsmen, managers, administrators, and sales; and 6 = professional and technical workers. Using established scores from O*NET, we also created an occupational cognitive requirements score (OCRS) for each participant [21]. Details for the calculation of the OCRS are described elsewhere [21] and in Appendix 2. The 18 available proxy categories, in addition to multiple measurements for social network, physical function, leisure activities, cognitive activities, socioeconomic status, and occupation resulted in 28 different forms of operationalization using proxy categories, including two levels for job attainment (professional and managerial compared to operative and service workers). All variables were coded such that higher values indicated a better score or better health (e.g., more mobility, less symptoms of depression).

Table 2 Cognitive reserve operationalization and available MAP variables used in analyses
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Questionnaires

We created composite scores based on the CRIq and the LEQ. While the two questionnaires could not be mimicked exactly due to incompatibility of some MAP variables with the questionnaires’ items (e.g., years of education rather than different training), we were able to base the composite score on the types of experiences that are included in each questionnaire. The CRIq-based score was a combination of education, job attainment level, children, frequency of participation in leisure activities at age 18, reading magazines and newspapers at the age with the highest frequency, and the following in late life: seeing relatives, participation in groups, going to church, going to restaurants and sporting events, traveling, and volunteering. The LEQ-based score was divided into three ages: ages 13–30 employed the variables of education, taking music lessons, and reading; ages 30–65 employed the variables of job attainment level and reading; and age 65 and older employed the variables of retirement, participation in groups and clubs, volunteering, going to restaurants and sporting events, visiting relatives, traveling, physical activities, and reading. Reading included books, newspapers, and magazines. Weights were applied to the variables based on the scoring guide for the CRI-q [25] and a validation of the LEQ in an American population [34].

Development of Lifecourse CR score

A lifecourse CR score was assigned to each participant using variables from MAP. The score was developed through a multiphase confirmatory factor analysis (CFA). Observed variables were grouped and evaluated under six overarching dimensions based on prior uses of MAP variables: Leisure Activities, Social Characteristics/Activities, Physical Characteristics/Activities, Cognitive Activities, Socioeconomic Status (SES), and Demographics/Personality [23, 30, 35, 36]. Four variables were examined under more than one dimension to assess best fit: volunteering in late life, going to restaurants and sporting events in late life, foreign language training, and team sports participation at age 12. Additional detail is provided in the Statistical Analyses Subsection.

A list of dimensions, factors within each dimension, and observed variables loaded to those factors is provided in Table 3. Physical Characteristics/Activities (late-life physical activities, mobility, and BMI) and Demographics/Personality (marriage status, retirement status, number of depression symptoms, and smoking status in late life) were underspecified and thus excluded from the factor analysis.

Table 3 Dimensions, factors, and observed variables of preliminary confirmatory factor analysis models
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Cognition

Cognitive scores for each participant were used as outcomes in analyses examining the association between operationalization of CR and late-life cognition. As previously described [37, 38], participants completed a battery of 19 cognitive tests annually which examined five cognitive domains: episodic memory, perceptual orientation, perceptual speed, semantic memory, and working memory. Details on tests and scoring are given in Appendix 3. In addition to the domains, all test scores are averaged to create a composite score for global cognition [35, 39, 40]. The primary outcome in the present analyses was global cognition across follow-up. We also used each of the five domains as secondary outcomes, in order to assess the consistency of the CR operationalization techniques. While NART has been combined into cognitive score composites and the semantic memory domain in other uses of MAP data with these outcomes, we excluded NART from the domain and global scores in order to use it as a predictor as the IQ CR proxy [35], given its use as an estimate for premorbid IQ [41].

Statistical analyses

Correlation

We examined the correlation 1) between CR proxy categories, 2) between observed variables or experiences in the same proxy category when different variables for measurement were used within the same category (leisure activities, cognitive activities, and social activities), and 3) between observed variables that overlapped categories in the literature (variables included in leisure activities and social activities). Due to the categorical natural of variables, Spearman’s rank order correlations were applied for these analyses. Data analysis was completed using SAS software version 9.4. Strength of the correlation was based on the value of Spearman’s rho (ρ) and boundaries defined by Cohen (1988) [42] with small, moderate, and large defined as ρ ≥ 0.1, ρ ≥ 0.3, and ρ ≥ 0.5, respectively.

Multiphase confirmatory factor analysis

Multiphase confirmatory factor analysis was used to compute the lifecourse CR score. Each dimension went through multiple iterations in which factor loadings (λ) for each variable were assessed for the strength of the relation between the variable and latent factor [43, 44]. A single variable was removed from the factors after each iteration until all variables had a factor loading (λ) ≥ 0.4. A value of 0.4 was chosen as < 0.4 is considered low item loading, indicative of poor fit [43, 45]. Additionally, a cut-off near 0.5 was preferred because 0.5 indicates that the variable is reflecting more variance than within-factor error and a value of 0.4 was ultimately chosen in favor of variable conservation. A robust maximum likelihood technique was utilized throughout because it is superior to simple maximum likelihood at producing reliable estimates despite potential data uncertainty, such as missing data and nonnormality due to data not measured on a continuous scale (e.g., frequency of activities) [46, 47]. Collectively, model fit was examined through a combination of model chi-square (χ2), the robust version of the Comparative Fit Indices (CFI), the robust version of the Root Mean Squared Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). Levels closer to one for CFI (i.e., ≥ 0.95); and levels closer to zero for RMSEA (i.e., ≤ 0.07) and SRMR (i.e., < 0.08) were indicative of better model fit [44, 45, 48,49,50,51]. Chi-square, CFI, RMSEA, and SRMR are the recommended indices to be reported [44]. In cases in which all variables showed a loading ≥ 0.4, but the model fit was weak (i.e., failing to reflect sufficient factor loading levels as theorized), we examined model indices to find errors using a combination of empirical and theoretical justification for covarying errors.

Once the above criteria were met, the dimensions were combined into a single overall measurement model, which similarly went through multiple iterations to retain loadings ≥ 0.4. Finally, a predicted value of the latent, overall factor of CR was obtained as a weighted composite of the factors retained in the final CFA model, such that a unique lifecourse CR score could be assigned to each participant [23]. All analyses were conducted with R software version 4.2.1, implementing the Lavaan package (version 0.6–12) [52].

Generalized linear mixed effects models

We used generalized linear mixed effects models to investigate the association between each of the CR operationalizations techniques and late-life cognition. In total, 28 CR operationalizations based on proxy categories, two operationalizations based on questionnaires, and one lifecourse CR score from the factor analysis resulted in 31 CR operationalization techniques for analysis. We examined the relationship between each CR operationalization technique and cognitive scores over the course of follow-up. Continuous predictor variables were standardized to a mean of 0 and a standard deviation of 1 for straightforward comparison of effect sizes. One CR proxy, job attainment level, was ordinal with three levels: operative and service workers (reference group), managerial jobs, and professional jobs. Each CR proxy, questionnaires score, and lifecourse CR score were modeled separately. All models included (linear) time and adjusted for age at study entry, sex, race, ApoE4 genotype (a genetic risk factor for AD), and baseline Mini-Mental State Exam score. Models incorporated a random slope for time in follow-up years and a random intercept, while accounting for repeated measures on the same participant. Analyses were performed using SAS software version 9.4. A Bonferroni correction for multiple comparisons was applied to evaluate the statistical significance, such that p < 0.0016 was considered statistically significant, thus reducing potential Type 1 error rates.

Results

Baseline characteristics of study population

Of 1459 MAP participants who presented as healthy at baseline (had not been diagnosed with MCI or AD), 93 participants were excluded because they did not have or provide any follow-up data. Therefore, the sample for the present study’s analyses consisted of 1366 participants. Characteristics of the sample are in Table 4. The average age at entry was 79.5 (SD = 6.51) years. Most identified as women (76.50%) and Non-Hispanic White (91.29%). On average, participants had 15 years of education. Most had done managerial-level work (53.19%) and a high percent (88.36%) had retired from their job by study entry. Few participants had copies of ApoE4 (20.68%). There was a small amount of missing data across all variables (7.10%). While there is not a consensus on an acceptable amount of missing data, < 10% is unlikely to bias the results if there is not a pattern to the missingness [53,54,55]. Based on examination of the relationship between observed experience variables in the dataset and dummy variables indicative of missing or nonmissing, there was not evidence of a pattern to the missingness [55].

Table 4 Characteristics of study population
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Associations between proxy categories

The first analysis examined the association across proxy categories, including composite scores for activity variables (Appendix 4). Few associations showed a substantial effect size (see Fig. 1). Large effect sizes were seen for four associations: education and job attainment level (ρ = 0.558), cognitive activities at age 12 and cognitive activities at age 18 (ρ = 0.642), cognitive activities at age 18 and cognitive activities at age 40 (ρ = 0.570), and cognitive activities at age 40 and cognitive activities in late life (ρ = 0.527). These findings show that greater frequency of cognitive activities at 12, 18, and 40 are associated with greater frequency of cognitive activities at 18, 40, and late life, respectively; and more years of education is associated with higher level of job attainment. Weak effect sizes (ρ ≥ 0.1) were seen for 165 associations and moderate effect sizes (ρ ≥ 0.3) were seen for 23 associations. Therefore, there were 27 associations with at least moderate effect sizes of 351 correlations (7.69%).

Fig. 1
figure 1

Illustration of effect sizes for correlation between operationalizations based on proxy categories

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Associations between observed variables

The observed variables that are classified within proxy categories also demonstrated weak associations. Proxy categories of leisure activities, social activities, and cognitive activities were composites of three to six observed variables/activities. We examined the correlation between the observed variables classified within leisure activities, within social activities, and across leisure activities and social activities, due to overlap between the activities in these categories in the literature search. All correlations among observed variables/activities for leisure and social activities had relatively minor effect sizes (Appendix 4). Of the 36 associations, 20 had a small effect size and none had moderate or large effect sizes. The strongest association among all variables was between an observed variable for leisure activities in late life and an observed variable for social activities in late life: traveling and going to restaurants/sporting events (ρ = 0.255).

Observed variables within the proxy categories of cognitive activities at different ages were also examined. Of the 69 associations, 10 had a moderate effect size and 1 had a large effect size (Appendix 4). The strongest association was between reading and hearing stories at age 6 (ρ = 0.626). Reading with going to the library and reading newspapers with reading magazines were consistently the strongest associations within age groups after age 6.

Factor analysis

The multiphase confirmatory factor analysis systematically narrowed 54 observed variables from MAP to 22 variables which showed good fit. Details on removed observed variables are provided in Appendix 5. Due to the large number of variables and factors, each dimension was examined separately before they were combined. All dimensions showed a higher CFI, lower χ2, and lower SRMR in the final models, indicating the final models were improvements over the preliminary models. The final combined model showed adequate model fit: χ2 (158) = 434.190, CFI = 0.956, RMSEA = 0.035 (90% confidence interval: 0.031 to 0.038), SRMR = 0.030 [44, 51]. Fit indices from preliminary models to final models for each dimension and the combined model are in Appendix 5.

All variables that were retained in the final model, standardized loadings, and latent factors are shown in Fig. 2. Latent factors of the observed variables in the final model were leisure activities at age 18; social activities in late life; social features; cognitive activities at ages 6, 12, 18, 40, and in late life; youth SES; and adult SES. The standardized loadings of these factors for the overall latent CR factor ranged from 0.36 to 0.87. The largest loadings were for leisure activities at age 18 (0.84); cognitive activities at ages 12 (0.82), 18 (0.87), and 40 (0.78); and youth socioeconomic status (0.75).

Fig. 2
figure 2

Diagram of final multiphase confirmatory factor analysis model. e1-e22 indicate the measurement error for observed variables. Values indicate the standardized loadings of variables and factors of the latent cognitive reserve factor

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Using the final model, we computed predicted values for the CR factor, creating a unique score for each participant. The score ranged from -3.20 to 2.02 (Mdn = 0.13, IQR [-0.44, 0.65]), based on their values on observed variables and standardized loadings of the factors of the CR construct.

Association between operationalization and late-life global cognition

The point estimates and 95% CIs for the (standardized) CR predictors are illustrated in Fig. 3. CR predictors that showed an estimate significantly different from 0 with the Bonferroni correction are indicated by an asterisk. One standard deviation increase in these predictors was associated with higher global cognition scores over the course of follow-up, with the exception of retirement, which was associated with lower global cognition scores. There was substantial variability in the strength of association between CR operationalization and global cognition (median effect size: 0.99, IQR: 0.34 to 1.39). Detailed results are provided in Table 5. The highest estimates were as follows: IQ (2.75, SE = 0.18, p < 0.001), lifecourse CR score (2.44, SE = 0.18, p < 0.001), education (2.29, SE = 0.19, p < 0.001), LEQ-based score (2.22, SE = 0.20, p < 0.001), and CRIq-based score (2.00, SE = 0.20, p < 0.001).

Fig. 3
figure 3

Point estimates and 95% confidence intervals for generalized linear mixed effects models for different cognitive reserve operationalization techniques and global cognition scores over the course of follow-up. Dotted lines separate proxy categories from measurement through questionnaires and the lifecourse CR score created in this study. * indicates estimates significantly different from zero. OCRS: occupational cognitive requirements score; BMI: body mass index; CRIq: Cognitive Reserve Index questionnaire; LEQ: Lifetime of Experience Questionnaire; CR: cognitive reserve

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Table 5 Results of generalized linear mixed effects models for cognitive reserve predictors and global cognition scores over the course of follow-up
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Association between operationalization and cognitive domains

In addition to global cognition, we also evaluated five cognitive domains as outcomes: episodic memory, perceptual orientation, perceptual speed, semantic memory, and working memory. The point estimates and 95% confidence intervals for the cognitive domains are provided in Appendix 6. There was not strong consistency in the association between CR operationalizations and the five cognitive domains (mean consistency: 56.1%) and variation in the effect sizes across domains (median effect size: 0.82, IQR: 0.40 to 1.37). The consistency of operationalization techniques for higher late-life cognition scores across the domains is provided in Fig. 4A, with effect sizes across the domains provided in Fig. 4B. As illustrated in Fig. 4B, IQ, lifecourse CR score, education, LEQ-based score, and CRIq-based score were consistently among the highest estimates. The average estimate for the 28 proxy category operationalizations was 0.91 (SE = 0.48), with the average estimate for IQ and education at 2.88 (SE = 0.49) and 2.39 (SE = 0.49), respectively. The average estimate for the lifecourse CR score was 2.48 (SE = 0.40) and the average estimate for the questionnaires was 2.21 (SE = 0.43). The lifecourse CR score was associated with all five domains (100% consistency) and had little variation in effect sizes (median effect size: 2.36, IQR: 2.21 to 2.82).

Fig. 4
figure 4

Frequency of significant associations between each CR operationalization technique and five cognitive domain outcomes (4A) and boxplots of the strength of the associations (effect sizes) between CR operationalization techniques and late-life cognition for the five cognitive domains (4B). Dotted lines separate proxy categories from measurement through questionnaires and the lifecourse CR score created in this study. OCRS: occupational cognitive requirements score; BMI: body mass index; CRIq: Cognitive Reserve Index questionnaire; LEQ: Lifetime of Experience Questionnaire; CR: cognitive reserve

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Discussion

The study explored the operationalization of CR in the published literature and examined the effect of this variability on the association between CR and late-life cognition. Our search of the literature resulted in 20 categories of proxies that have been used in studies related to CR and which show wide variability in their degree of use and, in some cases, the experiences that are classified into these categories. We employed quantitative analyses based on the comprehensive literature review. The results revealed three key considerations for research on CR. First, among the variety of experiences that have been used as proxies of CR, there was a lack of strong association between them. Second, the association between CR and late-life cognition was highly sensitive to the technique (proxy categories, questionnaires, or a lifecourse indicator) used to operationalize CR. Third, operationalizations that include multiple experiences, including questionnaires and the lifecourse CR score developed in this study, are strongly associated with late-life cognition, demonstrating the utility of CR measures that incorporate experiences throughout the lifecourse. An awareness of qualities of operationalization and the impact of operationalization is imperative as the field of healthy aging continues to investigate the practical advantage of CR.

The results of this study revealed problems that have been suspected within the practical use of CR in research [9, 17, 56, 57]. The lack of strong associations between CR proxy categories, particularly for the chosen experiences within proxy categories, asserts that variability in the operationalization of CR stems from the experiences of choice and is exacerbated by different ways to measure those experiences. In fact, the negligible associations among proxies suggests that a change in operationalization could be measuring something different from the intended life experience. For example, within cognitive activities in late life, experiences that were not strongly associated were playing games and reading. While both were classified within the same proxy category of cognitive activities, they could more so represent personality traits or social preferences between spending time playing games with others or quiet time reading. Alternatively, it may be that the term “playing games,” is imprecise, with the potential for different meanings for different individuals. Due to the lack of strong associations, it is expected that different CR operationalization techniques were differentially associated with late-life cognition and the strength of the associations were to varying degrees. Similar differences in results was found in a study that compared different indices of CR based on education, occupation, and leisure activites [58]; and one study that compared five different operationlizations of education [17]. The results in our study extend these findings to an extensive list of CR operationalization techniques that have been utilized in the published literature. Collectively, our findings provide comprehensive support for the idea that methodological inconsistency is an important problem in the CR literature and with consequences for the field’s understanding of CR, its relationship to late-life cognition, and noteworthy CR promoting experiences.

In an effort to overcome variability in CR operationalization and weakly associated proxies, the present study utilized the CFA to develop a composite lifecourse CR score as alternative operationalization. The CFA highlighted the weak associations between different life experiences, such that over half of the observed variables in the original factors were excluded from the final model for poor fit with the other variables. Based on the observed variables that were retained through multiple iterations, several experiences, through the lifecourse, may need to be considered for CR operationalization. Similar methods have been used to create composite scores for CR in past research [23, 24]. Our study expands on these through the inclusion of more observed variables or experiences (rather than pre-created composites) than any known study. Such approaches are particularly important given that optimal life exposures for building CR or the complexity of CR regarding multiple experiences remain unclear. In fact, theorists have considered that multiple experiences could contribute uniquely to building CR and the quantity of experiences may be more important than any specific type of experience [9, 12]. These ideas are supported by the fact that the lifecourse CR score was one of the strongest and most consistent operationalization techniques associated with higher cognitive scores over the course of follow-up. Other techniques that utilize multiple experiences, the CRIq and the LEQ questionnaires, were also among the strongest and most consistent. A lifecourse approach, utilizing multiple proxies and considering the multitude of experiences that can occur across the lifespan, may be the most faithful and comprehensive representation of CR [59,60,61]. We urge researchers to use this approach as a basis for examining the complexity that may appropriately characterize CR in future studies.

The findings have key implications for researchers. While the CR hypothesis is a commonly used explanation for the delay in symptoms of cognitive decline, the present results reinforce concerns by theorists that the research is in a state of disorder, failing to unanimously identify the most important life experiences largely due to methodological concerns [58]. Therefore, continued research into both already-identified or new, modifiable life experiences that are theorized to enhance CR may add noise. Rather, a more effective use of resources may be a shift in focus to identifying or creating a consensus operationalization [51, 58]. Researchers must be aware of the sensitivity of results to measurement of CR, such that future studies can avoid measurement concerns [10, 58]. The current exploration of proxies offers a foundation for this future research, highlighting variables that may be particularly relevant, as well as a focus on a lifecourse approach such as a lifecourse CR score. These practical implications can aid the field in continued progress towards prevention of symptoms of cognitive decline.

The present study has several limitations. Our search of the literature, while comprehensive, does not meet criteria for a systematic review. As the goal was to examine the ways in which CR proxies have been utilized in the literature in order to inform quantitative analysis, the search can best be described as a scoping review [62]. Therefore, there is an inherent risk of bias when synthesizing research that would need to be addressed in a systematic review. The results are not intended to be used as evidence to support or refute the CR hypothesis, but for comparison based on CR operationalization and a foundation for future research. The results from our study provide a point of departure for narrowing the experiences related to CR operationalization within a lifecourse approach to CR. Furthermore, an intact model of the CR hypothesis would require an in-vivo measure of neuropathology [9, 10, 23, 63]. We did not have access to data to explore changes to results based on the inclusion of neuropathological indications. Furthermore, our approximation of CR variables utilized in the published literature is limited to variables in MAP, which does not always reflect the intended nature of a proxy. For example, bilingualism is frequently utilized as a proxy and our closest approximation was years spent learning a language. It is possible that this is not a suitable approximation of bilingualism and, thus, studies with different operationalizations may yield different results. Similarly, NART was utilized as a proxy for IQ, thereby failing to consider fluid IQ within the use of IQ in this study. While this is consistent with operationalizations of IQ in the literature, we recommend that future studies utilize crystallized and fluid IQ in order to get a more complete understanding of the potential role of IQ. Additionally, we were unable to explore novelty and nutrition, which have been identified as CR proxy categories in the literature.

Finally, there is a limit to the generalizability of these results, based on the data. First, the data is from one region in the United States, Chicago, Illinois. Therefore, the population for this study was from an urbanized, metropolitan area and the data was comprised of a high percentage of individuals identifying as Non-Hispanic White and women. Second, the population was also highly educated, with an average of 15 years of education. This further limits generalizability as this population may be more protected against symptoms of cognitive decline than the general population, depending on the degree of importance that one puts on education for building CR. Lastly, our data also had some missing observations. While we do not expect the small amount of missingness to have biased the results, it is a possibility.

Conclusion

Inconsistent methodology in the study of CR is a major limitation of current research in this area. Our study is the most comprehensive and conclusive study to date demonstrating the challenges associated with commonly used CR proxies. We found weak associations among the variables and proxies used to operationalize CR, as well as a relationship between CR and late-life cognition that is highly dependent on the CR operationalization technique. Lifecourse measures, including questionnaires and a score that was developed in this study from multiple experiences, were among the strongest and most consistent predictors of late-life cognition. Attention to qualities of predictors, reigning in of the use of a wide variety of CR operationalizations, adherence to a lifecourse approach, and collaborative movement towards a consensus operationalization and definition of CR are imperative shifts in the study of CR that are highlighted by this study. Through the recommendation of these methodological considerations, we hope to help improve the study of risk factors related to cognitive decline and aid in the prevention of this growing public health concern.

Availability of data and materials

The data that support the findings of this study are available from Rush University Medical Center and applications requesting data can be submitted to the Rush Alzheimer’s Disease Center. Data for the current study are available from the corresponding author on reasonable request and with permission of the Rush Alzheimer’s Disease Center.

References

  1. Van der Mussele S, Le Bastard N, Vermeiren Y, Saerens J, Somers N, Mariën P, et al. Behavioral symptoms in mild cognitive impairment as compared with Alzheimer’s disease and healthy older adults. Int J Geriatr Psychiatry. 2013;28(3):265–75. https://doi.org/10.1002/gps.3820.

    Article  PubMed  Google Scholar 

  2. Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM. Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement. 2007;3(3):186–91. https://doi.org/10.1016/j.jalz.2007.04.381.

    Article  PubMed  Google Scholar 

  3. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet. 2020;396(10248):413–46. https://doi.org/10.1016/S0140-6736(20)30367-6.

    Article  Google Scholar 

  4. Patterson C. Hope & frustration. In: The state of the art of dementia research: New frontiers. London: Alzheimer’s Disease International; 2018. p. 6–7.

  5. Stern Y. Cognitive reserve. Neuropsychologia. 2009;47(10):2015–28. https://doi.org/10.1016/j.neuropsychologia.2009.03.004.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Negash S, Bennett DA, Wilson RS, Schneider JA, Arnold SE. Cognition and neuropathology in aging: Multidimensional perspectives from the Rush Religious Orders Study and Rush Memory and Aging Project. Curr Alzheimer Res. 2011;8(4):336–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Stern Y, Barnes CA, Grady C, Jones RN, Raz N. Brain reserve, cognitive reserve, compensation, and maintenance: operationalization, validity, and mechanisms of cognitive resilience. Neurobiol Aging. 2019;83:124–9. https://doi.org/10.1016/j.neurobiolaging.2019.03.022.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Stern Y. Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurol. 2012;11(11):1006–12. https://doi.org/10.1016/S1474-4422(12)70191-6.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Pettigrew C, Soldan A. Defining cognitive reserve and implications for cognitive aging. Curr Neurol Neurosci Rep. 2019;19(1):1. https://doi.org/10.1007/s11910-019-0917-z.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Jones RN, Manly J, Glymour MM, Rentz DM, Jefferson AL, Stern Y. Conceptual and measurement challenges in research on cognitive reserve. J Int Neuropsychol Soc. 2011;17(4):593–601. https://doi.org/10.1017/S1355617710001748.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Kartschmit N, Mikolajczyk R, Schubert T, Lacruz ME. Measuring cognitive reserve (CR) – A systematic review of measurement properties of CR questionnaires for the adult population. PLoS One. 2019;14(8):e0219851. https://doi.org/10.1371/journal.pone.0219851.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D, Belleville S, Cantilon M, Chetelat G, et al. Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimers Dement. 2020;16(9):1305–11. https://doi.org/10.1016/j.jalz.2018.07.219.

    Article  PubMed  Google Scholar 

  13. Sharp ES, Gatz M. The relationship between education and dementia an updated systematic review. Alzheimer Dis Assoc Disord. 2011;25(4):289–304. https://doi.org/10.1097/WAD.0b013e318211c83c.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Bonaiuto S, Rocca WA, Lippi† A, Giannandrea E, Mele M, Cavarzeran F, et al. Education and occupation as risk factors for dementia: a population-based case-control study. Neuroepidemiology. 1995;14(3):101–9. https://doi.org/10.1159/000109785.

    Article  CAS  PubMed  Google Scholar 

  15. Fritsch T, McClendon MJ, Smyth KA, Ogrocki PK. Effects of educational attainment and occupational status on cognitive and functional decline in persons with Alzheimer-type dementia. Int Psychogeriatr. 2002;14(4):347–63. https://doi.org/10.1017/S1041610202008554.

    Article  PubMed  Google Scholar 

  16. Fyffe DC, Mukherjee S, Barnes LL, Manly JJ, Bennett DA, Crane PK. Explaining differences in episodic memory performance among older African Americans and Whites: The roles of factors related to cognitive reserve and test bias. J Int Neuropsychol Soc. 2011;17(4):625–38. https://doi.org/10.1017/S1355617711000476.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Then FS, Luck T, Angermeyer MC, Riedel-Heller SG. Education as protector against dementia, but what exactly do we mean by education?. Age Ageing. 2016;45(4):523–8. https://doi.org/10.1093/ageing/afw049.

    Article  PubMed  Google Scholar 

  18. Massimo L, Xie SX, Rennert L, Fick DM, Halpin A, Placek K, et al. Occupational attainment influences longitudinal decline in behavioral variant frontotemporal degeneration. Brain Imaging Behav. 2019;13(1):293–301. https://doi.org/10.1007/s11682-018-9852-x.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Massimo L, Zee J, Xie SX, McMillan CT, Rascovsky K, Irwin DJ, et al. Occupational attainment influences survival in autopsy-confirmed frontotemporal degeneration. Neurology. 2015;84(20):2070–5. https://doi.org/10.1212/WNL.0000000000001595.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kaup AR, Xia F, Launer LJ, Sidney S, Nasrallah I, Erus G, et al. Occupational cognitive complexity in earlier adulthood is associated with brain structure and cognitive health in midlife: The CARDIA study. Neuropsychol. 2018;32(8):895–905. https://doi.org/10.1037/neu0000474.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Pool LR, Weuve J, Wilson RS, Bültmann U, Evans DA, Mendes de Leon CF. Occupational cognitive requirements and late-life cognitive aging. Neurology. 2016;86(15):1386–92. https://doi.org/10.1212/WNL.0000000000002569.

  22. Stern Y. The concept of cognitive reserve: a catalyst for research. J Clin Exp Neuropsychol. 2003;25(5):589–93. https://doi.org/10.1076/jcen.25.5.589.14571.

    Article  PubMed  Google Scholar 

  23. Xu H, Yang R, Qi X, Dintica C, Song R, Bennett DA, et al. Association of lifespan cognitive reserve indicator with dementia risk in the presence of brain pathologies. JAMA Neurol. 2019;76(10):1184. https://doi.org/10.1001/jamaneurol.2019.2455.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Xu H, Yang R, Dintica C, Qi X, Song R, Bennett DA, et al. Association of lifespan cognitive reserve indicator with the risk of mild cognitive impairment and its progression to dementia. Alzheimers Dement. 2020;16(6):873–82. https://doi.org/10.1002/alz.12085.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Nucci M, Daniela Mapelli, Sara Mondini. Cognitive Reserve Index questionnaire (CRIq): a new instrument for measuring cognitive reserve. Aging Clin Exp Res. 2012;24(3). https://doi.org/10.3275/7800.

  26. Valenzuela MJ, Sachdev P. Assessment of complex mental activity across the lifespan: development of the Lifetime of Experiences Questionnaire (LEQ). Psychol Med. 2007;37:1015–25. https://doi.org/10.1017/S003329170600938X.

    Article  PubMed  Google Scholar 

  27. Beydoun MA, Beydoun HA, Gamaldo AA, Teel A, Zonderman AB, Wang Y. Epidemiologic studies of modifiable factors associated with cognition and dementia: systematic review and meta-analysis. BMC Public Health. 2014;14(1):643. https://doi.org/10.1186/1471-2458-14-643.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Valenzuela MJ, Sachdev P. Brain reserve and cognitive decline: a non-parametric systematic review. Psychol Med. 2006;36(8):1065–73. https://doi.org/10.1017/S0033291706007744.

    Article  PubMed  Google Scholar 

  29. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021. https://doi.org/10.1136/bmj.n71.

  30. Bennett DA, Schneider JA, Buchman AS, Barnes LL, Boyle PA, Wilson RS. Overview and findings from the rush memory and aging project. Curr Alzheimer Res. 2012;9(6):646–63. https://doi.org/10.2174/156720512801322663.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kohout FJ, Berkman LF, Evans DA, Cornoni-Huntley J. Two shorter forms of the CES-D Depression Symptoms Index. J Aging Health. 1993;5(2):179–93. https://doi.org/10.1177/089826439300500202.

    Article  CAS  PubMed  Google Scholar 

  32. Nelson HE, Willison I. National Adult Reading Test (NART). NFER Nelson: Windsor; 1991.

    Google Scholar 

  33. Rosow I, Breslau N. A guttman health scale for the aged. J Gerontol. 1966;21(4):556–9. https://doi.org/10.1093/geronj/21.4.556.

    Article  CAS  PubMed  Google Scholar 

  34. Gonzalez C. The psychometric properties of the Lifetime Experience Questionnaire (LEQ) in older American adults [Dissertation]. Greensboro, NC: The University of North Carolina at Greensboro; 2012.

    Google Scholar 

  35. Jefferson AL, Gibbons LE, Rentz DM, Carvalho JO, Manly J, Bennett DA, et al. A life course model of cognitive activities, socioeconomic status, education, reading ability, and cognition. J Am Geriatr Soc. 2011;59(8):1403–11. https://doi.org/10.1111/j.1532-5415.2011.03499.x.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Boyle PA, Buchman AS, Wilson RS, Bienias JL, Bennett DA. Physical activity is associated with incident disability in community-based older persons. J Am Geriatr Soc. 2007;55(2):195–201. https://doi.org/10.1111/j.1532-5415.2007.01038.x.

    Article  PubMed  Google Scholar 

  37. Wilson RS, Barnes LL, Krueger KR, Hoganson G, Bienias JL, Bennett DA. Early and late life cognitive activity and cognitive systems in old age. J Int Neuropsychol Soc JINS. 2005;11(4):400–7. https://doi.org/10.1017/S1355617705050459.

    Article  PubMed  Google Scholar 

  38. Wilson RS, Beckett LA, Barnes LL, Schneider JA, Bach J, Evans DA, et al. Individual differences in rates of change in cognitive abilities of older persons. Psychol Aging. 2002;17(2):179–93. https://doi.org/10.1037/0882-7974.17.2.179.

    Article  PubMed  Google Scholar 

  39. Wilson RS, Scherr PA, Hoganson G, Bienias JL, Evans DA, Bennett DA. Early life socioeconomic status and late life risk of Alzheimer’s disease. Neuroepidemiol. 2005;25(1):8–14. https://doi.org/10.1159/000085307.

    Article  PubMed  Google Scholar 

  40. Wilson RS. Participation in cognitively stimulating activities and risk of incident Alzheimer disease. JAMA. 2002;287(6):742. https://doi.org/10.1001/jama.287.6.742.

    Article  PubMed  Google Scholar 

  41. Spreen O, Strauss E. A compendium of neuropsychological tests: administration, norms, and commentary. New York: Oxford University Press; 1991.

    Google Scholar 

  42. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. L. Erlbaum Associates: Hillsdale; 1988.

    Google Scholar 

  43. Brown TA. Confirmatory factor analysis for applied research. In: Little TD, editor. Methodology in the social sciences. New York City. The Guilford Press. 2015.

    Google Scholar 

  44. Kline RB. Principles and practice of structural equation modeling. In: Little TD, editor. Methodology in the social sciences. New York City. The Guilford Press. 2015.

    Google Scholar 

  45. Shi D, Lee T, Maydeu-Olivares A. Understanding the model size effect on SEM fit indices. Educ Psychol Meas. 2019;79(2):310–34.

    Article  PubMed  Google Scholar 

  46. Bertsimas D, Nohadani O. Robust Maximum Likelihood Estimation. Inf J Comput. 2019;31(3):445–58.

    Article  Google Scholar 

  47. Bandalos DL. Relative performance of categorical diagonally weighted least squares and robust maximum likelihood estimation. Struct Equ Model Multidiscip J. 2014;21(1):102–16.

    Article  Google Scholar 

  48. Byrne BM. Structural equation modeling with EQS: basic concepts, applications, and programming. 2nd ed. Mahwah: Lawrence Erlbaum Associates; 2006.

    Google Scholar 

  49. Bandalos DL. Measurement theory and applications for the social sciences. In: Little TD, editor. Methodology in the social sciences. New York City. The Guilford Press. 2015.

    Google Scholar 

  50. Xia Y, Yang Y. The influence of number of categories and threshold values on fit indices in structural equation modeling with ordered categorical data. Multivar Behav Res. 2018;53(5):731–55. https://doi.org/10.1080/00273171.2018.1480346.

    Article  Google Scholar 

  51. Ximénez C, Maydeu-Olivares A, Shi D, Revuelta J. Assessing cutoff values of SEM fit indices: advantages of the unbiased SRMR Index and its cutoff criterion based on communality. Struct Equ Model Multidiscip J. 2022;29(3):368–80.

    Article  Google Scholar 

  52. Rosseel Y. lavaan: An R Package for Structural Equation Modeling. J Stat Softw. 2012;48(2). https://doi.org/10.18637/jss.v048.i02.

  53. Bennett DA. How can I deal with missing data in my study?. Aust N Z J Public Health. 2001;25(5):464–9.

    Article  CAS  PubMed  Google Scholar 

  54. Peng CYJ, Harwell M, Liou SM, Ehman LH. Advances in missing data methods and implications for educational research. In: Real Data Analysis. Greenwich: Information Age; 2006. p. 31–78.

  55. Schlomer GL, Bauman S, Card NA. Best practices for missing data management in counseling psychology. J Couns Psychol. 2010;57(1):1–10.

    Article  PubMed  Google Scholar 

  56. Chapko D, McCormack R, Black C, Staff R, Murray A. Life-course determinants of cognitive reserve (CR) in cognitive aging and dementia – a systematic literature review. Aging Ment Health. 2018;22(8):921–32. https://doi.org/10.1080/13607863.2017.1348471.

    Article  Google Scholar 

  57. Harrison SL, Sajjad A, Bramer WM, Ikram MA, Tiemeier H, Stephan BCM. Exploring strategies to operationalize cognitive reserve: a systematic review of reviews. J Clin Exp Neuropsychol. 2015;37(3):253–64. https://doi.org/10.1080/13803395.2014.1002759.

    Article  PubMed  Google Scholar 

  58. Grotz C, Seron X, Van Wisson M, Adam S. How should proxies of cognitive reserve be evaluated in a population of healthy older adults? Int Psychogeriatr. 2017;29(1):123–36. https://doi.org/10.1017/S1041610216001745.

    Article  PubMed  Google Scholar 

  59. Opdebeeck C, Martyr A, Clare L. Cognitive reserve and cognitive function in healthy older people: a meta-analysis. Aging Neuropsychol Cogn. 2016;23(1):40–60. https://doi.org/10.1080/13825585.2015.1041450.

    Article  Google Scholar 

  60. Richards M, Deary IJ. A life course approach to cognitive reserve: a model for cognitive aging and development? Ann Neurol. 2005;58(4):617–22. https://doi.org/10.1002/ana.20637.

    Article  PubMed  Google Scholar 

  61. Rodriguez FS. Life-course pathways to cognitive aging: The significance of intellectual stimulation in the form of education and occupation for public policy and prevention plans. Front Psychiatry. 2021;12:719609. https://doi.org/10.3389/fpsyt.2021.719609.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18(1):143.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Stern Y. Cognitive reserve and Alzheimer disease. Alzheimer Dis Assoc Disord. 2006;20(2):112–7. https://doi.org/10.1097/01.wad.0000213815.20177.19.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Public Health Sciences, Clemson University, Clemson, SC, 29634, USA

    Kerry A. Howard, Sarah F. Griffin, Lu Zhang & Lior Rennert

  2. Center for Public Health Modeling and Response, Clemson University, Clemson, SC, 29634, USA

    Kerry A. Howard & Lior Rennert

  3. Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, USA

    Lauren Massimo

  4. School of Nursing, University of Pennsylvania, Philadelphia, PA, USA

    Lauren Massimo

  5. Department of Parks, Recreation and Tourism Management, Clemson University, Clemson, SC, USA

    Ryan J. Gagnon

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Contributions

K.A.H,: Conceptualization, Methodology, Software, Validation, Formal Analysis, Data Curation, Writing – Original Draft, Visualization. L.M.: Methodology, Writing – Review and Editing. S.F.G.: Conceptualization, Methodology, Writing – Review and Editing. R.J.G.: Methodology, Software, Writing – Review and Editing. L.Z.: Methodology, Writing – Review and Editing. L.R.: Conceptualization, Methodology, Validation, Writing – Review and Editing,Visualization, Supervision. All authors reviewed the manuscript.

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Additional file 1:

Appendix 1. Literature search procedures. Appendix 2. Occupational cognitive requirements score calculation. Appendix 3. Names and scoring for cognitive test in global cognition score. Appendix 4. Correlations. Appendix 5. Multiphase confirmatory factor analysis model fit indices. Appendix 6. Cognitive domain outcomes.

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Howard, K.A., Massimo, L., Griffin, S.F. et al. Systematic examination of methodological inconsistency in operationalizing cognitive reserve and its impact on identifying predictors of late-life cognition. BMC Geriatr 23, 547 (2023). https://doi.org/10.1186/s12877-023-04263-9

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