Understanding the Link Between Retirement Timing and Cognition: A Scoping Review

Abstract

Organization for Economic Co-operation and Development (OECD) countries have increased the age for full retirement benefits to alleviate financial pressures. Older age is linked to higher rates of cognitive impairment. Therefore, it is crucial for public policymakers to understand the relationship between retirement timing and cognition. The purpose of this scoping review was to review the retirement timing and cognition literature and to assess possible modifying factors. A search across three databases yielded a total of 10 studies. Five studies revealed mixed findings regarding the relationship between retirement timing and cognitive decline, with reported positive, negative, and null associations. In contrast, five studies found that later retirement age reduced the risk of dementia. More cross-sectional and longitudinal studies are needed to investigate modifiable factors such as job characteristics and leisure activities to clarify the mechanisms underlying the relationship between retirement timing and cognition.

Keywords

retirement cognition Alzheimer’s disease dementia scoping review

What this paper adds

• This is the first paper to examine the existing literature on the link between retirement timing and cognition.

• This review identifies gaps in the current retirement timing and cognition literature.

• The review highlights the importance of cognitively stimulating activities in the relationship between retirement timing and cognition.

Application of study findings

• Future research should prioritize investigating the impact of modifiable factors, including job characteristics, leisure activities, and part-time work post-retirement, particularly for individuals facing constraints on delaying retirement. Researchers can identify effective strategies to mitigate cognitive decline.

• This review underlines the need for further research of both cross-sectional and longitudinal designs to investigate how retirement timing affects cognitive decline.

• Longitudinal research will help disentangle the causal direction between retirement timing and cognitive decline.

Introduction

The aging population has undergone substantial growth in Organization for Economic Co-operation and Development (OECD) countries. In 2015, individuals over 65 accounted for 17% of the population in OECD countries, and this percentage is projected to reach 28% by 2050 (OECD, 2017). These age changes mean that those who are 65 and older now spend approximately 18.4 years in retirement (OECD Data, 2021). To alleviate financial pressures resulting from the novel demographic shift, OECD countries have made changes to retirement-age pensions. For instance, the United States has gradually increased the full retirement age for Social Security from 65 to 67 (Auerbach et al., 2017; Social Security Administration, 2022). Similarly, Sweden has reformed its public pension system, including restricted access to unemployment and disability benefits before the legal retirement age of 65 (König et al., 2016), while France has raised its legal retirement age from 62 to 64 (Parks, 2023).

The literature to date has not thoroughly addressed the potential cognitive consequences related to retirement timing. With the population now spending almost two decades in retirement, additional examination is needed to avoid potential adverse health outcomes and avoid the costs associated with caring for those with cognitive impairment (Schaller et al., 2015; Zissimopoulos et al., 2015).

Retirement as a Concept

Retirement gained traction in the 20th century to make room for younger workers (Warner et al., 2010). However, men’s life expectancy in the early 20th century was around 58 years of age (Social Security Administration, n.d.). The retirement age of 65, which seemed like the ideal standard, has now come under scrutiny, with people now living well into their 70s (CDC, 2022). Retirement has been defined in several ways, including self-reporting of leaving the workforce, a reduction in yearly income, and receiving a pension (Denton & Spencer, 2009). Most commonly, self-reports of working for pay are used to describe if one has yet to retire from the workforce (Bonsang et al., 2012; Rohwedder & Willis, 2010). Work constitutes a significant part of an individual’s life, with most people spending over 2,000 hours a year at work (U.S. Department of Labor, 2023). Work stoppage may, therefore, have a significant effect on an individual’s lifestyle, which may affect cognitive health.

The Relationship Between Retirement and Cognitive Health

The relationship between retirement and cognition is still not well understood. For example, Bonsang et al. (2012) found that retirement was associated with an increased risk of cognitive decline, while Nishirmura and colleagues (2018) argued that different methodological approaches may account for differences in retirement and cognition research. Researchers controlled for the heterogeneity of the older adult sample using fixed analyses and found that retirement was positively related to health outcomes. In comparison, Celidoni et al. (2017) and Denier et al. (2017) studies had mixed results.

One of the reasons for the mixed findings in retirement and cognition research is that it is difficult to determine if it is the act of retirement itself that causes cognitive decline or if individuals retire due to poor health or financial incentives (Celidoni et al., 2017; Xue et al., 2018). This selection bias is an important issue, and researchers have used several methodologies to account for it. For instance, instrumental variables such as early retirement offers have been used to assess causality, as well as various methodologies such as regression analysis and propensity score matching (PSM) (Baumann et al., 2022; Coe et al., 2012; Li et al., 2021). Among studies that focus on dementia, researchers often restrict the sample to those who have been retired for more than five or ten years to hopefully remove individuals who may have retired due to poor health (Dufouil et al., 2014; Grotz et al., 2015). However, longitudinal research runs the risk of potential retest bias that also must be considered (Bartels et al., 2010). Researchers have also begun to emphasize the importance of potentially modifiable factors in retirement (Hamm et al., 2020), as some people may be unable to continue to work into older age or may wish to retire due to personal reasons.

The Relationship Between Retirement Timing and Cognitive Health

There is a growing body of literature focusing on retirement timing and its effect on cognition in later life. Results to date have also been inconclusive, yielding a range of negative, positive, and null findings. Some longitudinal studies suggest a positive association between working longer and cognitive functioning (Belbase et al., 2015; Hale et al., 2021; Li et al., 2021), but these effects have been shown to diminish over time (Bonsang et al., 2012), indicating potential stabilization of cognitive decline in older age. Conversely, Celidoni et al. (2016) found that later retirement was related to worse cognitive performance. Lastly, Baumann and authors (2022) found no differences when comparing early retirees and late retirees. The variability in findings may be partially attributed to the use of different cognitive domains across studies. The most common classification of cognition is crystallized and fluid intelligence, and past studies found that fluid intelligence declines at a faster rate than crystallized intelligence (Singer et al., 2003). The investigation of retirement timing as an event and this event’s relationship to cognitive decline remains understudied. To the authors’ knowledge, no scoping reviews have examined the relationship between retirement timing (e.g., early, on-time, and late retirement) and cognitive decline or the risk of dementia.

Potential Modifying Factors in the Link Between Retirement Timing and Cognition

Variations in occupational factors may provide additional understanding of mixed results observed in past research. Previous occupations are key factors when examining the relationship between retirement timing and cognitive function or dementia risk. For example, blue-collar workers often perform physically demanding or unskilled labor, and retirement may therefore provide a positive effect on cognitive functioning. For instance, Mazzona and Perachi (2017) observed an immediate improvement in cognition when individuals retired from physically strenuous jobs. Given that blue-collar occupations are typically classified as less cognitively demanding and are associated with higher rates of cognitive decline (Denier et al., 2017), retirement may either alleviate the physically strenuous pressures or may provide more opportunities to engage in mentally stimulating activities (Then et al., 2014).

Adding the reason for retirement as an additional variable may help provide insights into the mixed literature of retirement timing and cognition. Retirement can be broadly defined as voluntary or involuntary, with the latter potentially being related to adverse health outcomes (Rhee et al., 2016). For example, health or disability is commonly cited as a reason for involuntarily exiting the labor force (Employee Benefit Research Institute & Greenwald Research, 2021). Poor health may limit opportunities for engagement in mentally stimulating activities which in turn increases the risk of cognitive decline and dementia. The “healthy worker effect” or reverse causality is often argued as a reason for why cognitive decline is found to be associated with early retirement (Chatterji et al., 2015). Some researchers propose that reverse causality, where individuals retire early due to existing dementia or health issues, may explain findings that later retirement is associated with better cognition (Rhee et al., 2016).

Theoretical Background

Reviews focusing on retirement and cognition have found mixed results (Meng et al., 2017; Zulka et al., 2019). However, these reviews did not consider retirement timing as a possible indicator of late-life cognitive decline. Furthermore, no previous review has investigated the effect of retirement timing on the risk of dementia. Researchers have argued that the time of retirement may play an important role in cognitive functioning due to work tasks keeping individuals cognitively engaged (Carr et al., 2020). Prior research suggests a link between increased participation in cognitive activities and reduced risk of dementia (Hall et al., 2009; Wilson et al., 2002). Along these same lines, the “use it or lose it” hypothesis and the cognitive reserve theory suggest that engagement in a cognitively demanding work environment can delay cognitive aging or enhance cognitive reserve (Fisher et al., 2016; Hultsch et al., 1999; Salthouse, 2006; Stern, 2002). Specifically, the “use it or lose it” hypothesis suggests that retirement may lead to a sudden reduction in daily mental demands, accelerating the risk of cognitive decline. Individuals who retire earlier may be more prone to cognitive decline due to the decrease in cognitive demands from employment (Fisher et al., 2016).

Similarly, cognitive reserve theory proposes that repeated exposure to intellectually stimulating environments activates and stimulates neural connections, thereby increasing cognitive reserve (Stern, 2012). Consequently, individuals who continue to work in cognitively stimulating jobs into older age may build higher cognitive reserve. Previously the “use it or lose it” hypothesis and cognitive reserve theory have been applied to study the overall relationship between retirement and cognitive decline. Applying these theoretical concepts to retirement timing and cognitive decline may provide a deeper understanding of the importance of timing in this multifaceted relationship.

Purpose of the Study

To the best of the authors’ knowledge, no reviews have focused on retirement timing as a possible factor influencing cognition or dementia risk. Because retirement timing has only recently begun to be studied (OECD data, 2021), it is unclear what literature is currently available and what gaps might yet exist. Therefore, the authors have determined that a scoping review of the literature is appropriate to understand how retirement timing defined as early, on-time, or late may affect cognition or risk of dementia. A scoping review is used to examine the emerging evidence in a field to promote understanding and possibly provide research for a systematic review to be completed in the future (Munn et al., 2018). In this case, we aim to gain knowledge of the association between retirement timing and a person’s cognitive functioning after retirement by identifying available studies on the topic, possible modifiable factors, and knowledge gaps by searching existing literature. Identifying modifiable factors that reduce the risk of cognitive decline or dementia will aid in the creation of policies to support individuals’ cognitive functioning after retirement. Furthermore, it will promote health and well-being after retirement, as cognitive decline is of great concern to aging populations around the globe (WHO, 2023).

Methods

This review adheres to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping review (PRISMA-ScR) (Tricco et al., 2018) (see Supplemental Materials PRISMA-ScR Checklist Table 1). This scoping review was further guided by the framework provided by Arksey & O’Malley (2005) and Joanna Briggs Institute (JBI) for scoping reviews to ensure methodological rigor (Peters et al., 2015). The protocol for the scoping review was registered at Open Science Framework (OSF) on September 17, 2023 (osf.io/hyw3g). This scoping review aimed to answer the following questions: (1) What is known from the existing literature on the relationship between retirement timing and cognition? and (2) What modifiable factors related to retirement timing and cognition were addressed?

Table 1.

Inclusion and Exclusion Criteria.

	Inclusion criteria	Exclusion criteria
Population	Retired adults age 40+	Medical conditions such as traumatic brain injury. Individuals under 40 years of age in study population
Years of publication	1980–present (when dementia and Alzheimer’s was added to APA terms)	Anything written before 1980
Design	Longitudinal studies, observational cohort studies, case-control, randomized controlled trials, and cross- sectional studies. Written in English	Qualitative studies, case studies, discussion papers, theses, dissertations, preprint, grey papers, systematic reviews, and meta-analyses
Exposures	Timing of retirement (e.g., early, on-time, or late)	Working, not retired
Outcomes	Age-related cognitive decline, mild cognitive impairment, and/or dementia	Outcomes that do not include a defined measurement of cognition either from a survey or test (e.g., mental and physical health, stress, and depressive symptoms will not be included)

Search Strategy and Study Selection

Literature was included from three databases containing articles published in English between 1980 and 27 February 2022. The Medline database was accessed through PubMed (n = 1,784), PsycINFO was available through the University of South Florida Library (n = 331), and SCOPUS was available through the University of South Florida Library (n = 1,276). A social science librarian was consulted to help with the development of the search strategy. The search strings were modified for each database, and a title/abstract search was conducted in each database. The search term “retirement timing” was paired with “dementia” AND “Alzheimer’s disease” OR “cognitive decline” AND “cognitive change.” Dementia and cognitive change/decline were separated, as many studies on assessing dementia used a dementia-only sample. Since we are conducting a scoping review, we utilized a broad set of search terms that included all types of cognitive decline and dementia to ensure that no studies were missed. (For the complete search string used in each database, see Supplemental Materials Table 2.) The search results were further restricted to persons aged 40 and above, and case studies, discussion papers, and grey literature were excluded. Furthermore, studies related to cognitive decline or dementia due to underlying pathology (e.g., traumatic brain injury) were excluded. (See Table 1 for a full list of inclusion and exclusion criteria.)

Table 2.

Overview of Ten Included Studies.

Reference		Population/data source		Exposure and covariates	Outcome- cognition		Statistical model	Results
Baumann et al. (2022)		3 databases: Swedish Level of Living Survey (LNU) and SEOLD, and the Longitudinal Integration Database of Health Insurance and Labour Market Studies (LISA) annual income register		Cognitive functioning, retirement timing (before or after age 65), sex, age, education, physical inactivity, smoking status, physician visit, fist occupation, physical working conditions, job control, job demands, complexity, mobility limitations, musculoskeletal pain, psychological health, chronic conditions, and socializing with friends and Family	Cognition (Mini-Mental State Examination (MMSE). Used shorter 11-item version). Measured cognition at one point (between ages of 70–85)		Propensity score matching (PSM). Used post-hoc power analysis using G*Power	Individuals who retired after 65 did not statistically differ in cognition from those who retired early. No differences between unskilled and non-manual workers. Engagement in leisure activities did not change the results
Coe et al. (2012)		The U.S. Health and Retirement Study (HRS). Men only. n = 22,114		Age (younger than 80), males, race/ethnicity, education, white-collar, blue-collar workers, early retirement window offer, and retirement duration (time from self-reported retirement and interview date)	Subjective memory (self-rated memory)		Standard ordinary least squares (OLS) estimates	Negative association between retirement duration and late-life cognition. Stronger association found for white-collar workers. However, no relationship of retirement duration and late-life cognition for white-collar works once retirement window was controlled for. A positive relationship was found for blue-collar workers
Coe et al. (2012)					Episodic memory (immediate, delayed, and total word recall) working memory (numeracy)		Standard ordinary least squares (OLS) estimates
Dufouil et al. (2014)		French study. Régime Social des Indépendants. n = 429,803		Gender, area of residence, marital status, number of semesters of work, pension amount, previous occupation, type of retirement, medication claims, stroke, diabetes, coronary disease, chronic arteriopathy, severe cardiac failure, and psychiatric disorders	Dementia (purchase of anti-dementia medication or diagnosis requested by a physician)		Cox proportional hazard models	Older age at retirement was significantly linked to a lower risk of dementia (HR = .968; 95% CI = .962–.973) but this was stronger for men (HR = .956; 95% CI = .947–.964) than women (HR = .976; 95% CI = .970–.983)
Grotz et al. (2015)	French study. Cholinergic Treatment Use/Data Sharing Alzheimer cohort. n = 1380		Gender, education, occupational status, country, clinical center, household income, history of diabetes, hypercholesterolemia, hypertension, ischemic heart disease, depression, and stroke		Dementia (NINCDS-ADRDA and MMSE score between 10 and 26)	Linear mixed regression		Later retirement was significantly associated with delayed age of dementia diagnosis (β = .31, p < .0001) and a delayed age onset of symptoms (β = .30, p < .0001)
Grotz et al. (2016)	Three city French population–based study of community-dwelling individuals aged 65 to 95. n = 1658. 12-year follow-up		Gender, education, occupation, socioeconomic status, depression, cardiovascular risk factors, and APOE genotype		Dementia (NINCDS-ADRDA and NINDS-AIREN score between 10 and 26)	Cox proportional hazard models		Age at retirement was significantly related to risk of dementia (p = .022). Results varied for age groups although only trending in significance for <55 years old (HR = 1.19, p = .383), 55–60 years old (HR = .65, p = .066), 60–65 years old (HR = .72, p = .81), and greater than 65 years old (HR = .57, p = .100)
Hale et al. (2021)	The U.S. Health and Retirement Study (1996–2014). n = 20,469. Did not have to participate in all waves, ages (55–75 years)		Age, gender, race/ethnicity, early-SES, education, longest job, wealth, depression, comorbidities, self-responses of cognition (proxy not used), worked, retired prior to study entry, employment type		Overall cognition (TICS-m Measure). Episodic memory (immediate and delayed word recall, serial 7s, and counting backward from 20 (0–27))	Parametric g-formula. Multivariable models. Multinomial logistic regression		Postponing retirement to age 67 decreased cognitive decline. 30%–34% reduction for both men and women when compared to retiring younger. Those with the highest education had the most benefit
Ihle et al. (2016)	Vivre-Leben-Vivere (VLV) survey. A survey in Switzerland. Cross-sectional sample design from 2011 and 2012. n = 2263 older adults performed the Mill Hill vocab test. N = 1686 completed the TMT A and n = 1338 completed the TMT B		Age, retirement age (early, at legal age, 64 for women and 65 for men), late retirement, education, occupation, number of leisure activities, last job before retirement (unskilled, skilled, skilled non-manual or technical work, highly intellectual work). Physically demanding job		Psychometric tests- Trail Making Test Part A (TMT A), Trail Making Test Part B (TMT B), and Mill Hill	Hierarchical regression analysis. Moderating effect of cognitive level of job, physical demands of job, educational attainment, and leisure activities		Earlier retirement compared to retirement at 65 was associated with better TMT A, TMT B, and Mill Hill scores. A moderate number of leisure activities moderated the relationship between TMT A (β = .35, p < .001) and TMT B (β = 16, p < .001, and retirement timing). Compared with retirement at legal age, those who retired later and had lower cognitive level jobs did better on the Mill Hill test.
Li et al. (2021)		The Health and Retirement Study-China. n = 9126 sample 1 and n = 8024 sample 2	Age, gender, education, marital status, area of residence, health insurance, and social behavior		Overall cognition (Informant Questionnaire on Cognitive Decline in the Elderly)		Heckman two-stage regression	Individuals that retired later had had better cognition (coefficient = −.069; 95% CI = −.110, −.018; p < .001)
Lupton et al. (2010)		United Kingdom study. Medical Research Council- genetic resource for late onset AD. Data from n = 1320 probable cases of AD. Final sample n = 382 men	Age, Caucasian, males, education, occupational complexity (# of people at work, foreman, supervisor, manager, self-employed), # of employees responsible for retirement age, and age of onset of AD		Dementia (AD diagnoses probable as diagnosed by a doctor)		Principle component analysis. Multiple regression analysis	For every additional year of employment, the onset of AD was .13 years later. Those who retired earlier had AD onset at earlier ages
Sundström et al. (2020)		Swedish study. Linnaeus database n = 63,505	Age, gender, education, marital status, previous occupation, cardiovascular disease		Dementia (National Patient Register and Cause of Death Register)		Competing risk regression	Age at retirement was associated with risk of dementia (HR = .98; 95% CI = .97–1.00; p = .045). Retiring at 64 or younger was not significantly related to risk of dementia (HR = 1.00; 95% CI = .95–1.06; p = .890), but retiring at 66 or older was significantly related to a reduced risk of dementia (HR = .34; 95% CI = .24–.49; p < .001)

The study selection had three stages that were conducted between February 2022 and April 2022. In the first phase, three authors collaboratively developed a search string. In the second phase, two authors independently screened articles based on title and abstract, and weekly meetings were held to discuss progress. When a consensus could not be reached between the two authors, the third author was consulted. In the final phase, a full-text review of all potential articles was completed utilizing the agreed-upon criteria, and references of each article were examined to identify additional studies.

Data Extraction

Data from the studies that met the inclusion criteria were extracted and exported to EndNote for review. Details regarding the data source, population, covariates controlled for, outcome assessment (cognitive domains or dementia), statistical model used, and results were recorded in an Excel spreadsheet (see Table 2).

Data Synthesis

We analyzed data based on study location, design, participant number, and demographic information, including sex, gender, age, and race, to provide an overall picture of the current evidence in the field and help us identify knowledge gaps. Finally, we examined possible modifying factors. Examining these identified modifiable factors will help in our understanding of the “use it or lose it” and cognitive reserve hypotheses by exploring how cognitively stimulating activities after retirement or retiring from jobs that provide little cognitive stimulation are related to the risk of cognitive decline.

Results

Of the 3,391 identified studies, 2,587 were removed during the title/abstract analysis because they did not meet the inclusion criteria or were duplicates. Sixteen studies underwent full-text review. Six more studies were removed at this time, and three of these were discussed with the third reviewer to gain consensus. See Figure 1 for a flow chart of included and excluded articles.

Figure 1.

PRISMA flow chart.

Study Characteristics

The included studies were from a wide range of areas, with the highest number of papers from France (n = 3) (Dufouil et al., 2014; Grotz et al., 2016; Grotz et al., 2015), followed by the United States (n = 2) (Coe et al., 2012; Hale et al., 2021), Sweden (n = 2) (Baumann et al., 2022; Sundström et al., 2020), China (n = 1) (Li et al., 2021), and the United Kingdom (n = 1) (Lupton et al., 2010). Five papers focused on cognitive decline (Baumann et al., 2022; Coe et al., 2012; Hale et al., 2021; Ihle et al., 2016; Li et al., 2021) and five focused on dementia (Dufouil et al., 2014; Grotz et al., 2016; Grotz et al., 2015; Lupton et al., 2010; Sundström et al., 2020).

Recruitment

Most recruitment for reviewed studies were from community-dwelling samples and utilized secondary data sets (Bauman et al., 2022; Grotz et al., 2016; Hale et al., 2022; Ihle et al., 2016; Li et al., 2016; Sundström et al., 2020). Coe et al. (2012) used men only from a nationally representative data set, The Health and Retirement Study. Dufouil et al. (2014) focused on a sample of self-employed craftsmen and shopkeepers, and Lupton et al. (2010) utilized databases of patients diagnosed with dementia. The final sample included only White individuals. Grotz et al. (2015) recruited participants from European clinics and hospitals between 2003 and 2005. Sample sizes ranged from 815 (Grotz et al., 2015) to 489,803 (Dufouil et al., 2014).

Study Design

All included articles (n = 10) used quantitative methods to assess retirement timing and cognition (Bauman et al., 2022; Coe et al., 2012; Dufouil et al., 2014; Grotz et al., 2016; Grotz et al., 2015; Hale et al., 2022; Ihle et al., 2016; Lupton et al., 2010; Li et al., 2016; Sundström et al., 2020). Four of the studies used longitudinal data (Coe et al., 2012; Grotz et al., 2016; Hale et al., 2021; Sundström et al., 2022), and six studies utilized cross-sectional data (Baumann et al., 2022; Dufouil et al., 2014; Grotz et al., 2015; Ihle et al., 2016; Lupton et al., 2010; Li et al., 2021). The time of retirement was categorized as early, on-time, or late retirement. The ages for study inclusion ranged from 55 (Li et al., 2016) to 101 (Ihle et al., 2016).

Instruments Used to Measure Cognitive Decline

Baumann et al. (2022) used the 11-point Mini-Mental State Examination (MMSE) to assess cognition, while Hale et al. (2022) employed the Telephone Interview for Cognitive Status (TICS-m). Coe et al. (2012) used several measures for cognition, including total word recall, serial 7s subtraction, numeracy tasks, and subjective memory, which were rated on a Likert scale. Ihle et al. (2016) relied on three measures for cognition: the Trail Making Test Part A (TMT A), Trail Making Test Part B (TMT B), and the Mill Hill vocabulary test. Li et al. (2021) measured subjective cognition through a brief survey where participants self-reported their recognition of faces and names of family members and friends. Dufouil et al. (2014) assessed participants’ risk of dementia based on physician diagnosis or participants being prescribed anti-dementia medication. Sundström et al. (2020) included individuals diagnosed with three types of dementia, including Alzheimer’s disease (AD), vascular dementia, and unspecified types of dementia, utilizing the National Patient Register and the Cause of Death Register. Lupton et al. (2010) defined dementia based on the presence of the APOE-4 allele and a diagnosis of dementia based on the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria. Grotz et al. (2016) diagnosed AD based on a neuropsychiatrist evaluation and an independent panel of experts, and finally, Grotz et al. (2015) defined diagnosis of AD based on the NINCDS-ADRDA criteria and the MMSE.

Study Methods

Baumann et al. (2022) utilized propensity score matching (PSM) to assess cognitive functioning while controlling for participants’ age, and nearest-neighbor matching was utilized to create a treatment group (retired after 65) and a control group (retired at or before 65). Baumann et al. (2022) conducted post-hoc power analysis using G*Power. Hale and researchers (2021) used the g-formula to analyze the impact of postponing retirement until age 67 on cognition. Subgroup analysis was performed that examined gender, education, and occupation type (e.g., professional vs. non-professional occupations). One research team utilized ordinary least squares (OLS) regressions to examine the impact of retirement duration, considering employer-offered financial incentives to retire before age 60 to account for reverse causality (Coe et al., 2012).

Li et al. (2021) used Heckman’s two-stage procedure to account for self-selection bias by predicting the probability of continued work in 2018 based on health status in 2015. Covariates included demographics, health variables, and social activities. Ihle et al. (2016) used hierarchical regression analysis and controlled for job complexity, education, physical job demand, and engagement in leisure activities after retirement. Sundström and colleagues’ (2020) longitudinal study utilized competing risk regression (CRR) to analyze the data, focusing on the time from retirement to death or dementia diagnosis during a 24-year follow-up period. Covariates in the analysis included income, pension, education, employment type, and history of cardiovascular disease. To address reverse causality, individuals diagnosed with dementia or who died within three years after retirement were excluded from the sample.

Dufouil et al. (2014) employed proportional hazards (Cox) regression models to examine the relationship between retirement age and the age of dementia diagnosis or no presence of dementia. Covariates in the analysis were gender, marital status, chronic diseases, and pension amount. Grotz et al. (2016) utilized multivariate Cox proportional hazards models to analyze retirement age, working years, and dementia risk, stratified by age groups (55, 55–60, 60–65, and >65 years old). Covariates included education, income, occupation type, number of working years, APOE-4 allele, depressive symptoms, and cardiovascular disease.

Grotz et al. (2015) employed linear mixed regression to analyze the relationship between retirement timing and AD risk, with sensitivity analyses accounting for selection bias and reverse causality. Selection bias was assessed by excluding participants diagnosed after age 65 and restricting the sample to those aged 50–65. Reverse causality was addressed by including only participants who retired before 65 and whose AD onset occurred after 65. Models controlled for sociodemographic factors, occupation type, chronic conditions, and depressive symptoms.

Lupton et al. (2010) performed three separate regression analyses. The first included the entire sample of 1,320 individuals, and the second and third analyses included a subset of 382 male participants. Past employment type was assessed in the second two models. Finally, sensitivity analysis was run only on individuals who retired five or more years before dementia onset. The sample consisted of White individuals, and covariates included birth year (1900–1910 and 1936–1944), education, APOE genotype, marital, drinking, and smoking status.

Modifying Factors

Eight of the ten included studies did not account for possible modifying factors (Baumann et al., 2022; Coe et al., 2012; Dufouil et al., 2014; Grotz et al., 2016; Grotz et al., 2015; Lupton et al., 2010; Li et al., 2021; Sundström et al., 2020). Hale et al.’s (2021) results indicated that participants with the highest education had the most benefit from postponing retirement to age 67. Hale et al. (2021) also used mediation analysis to assess depressive symptoms and comorbidities; however, these results were not significant. Additionally, Ihle et al. (2016) examined the moderating effects of physical and mental work demands, education, and leisure activities. They found that a moderate number of leisure activities were associated with better scores on the TMT A and TMT B.

Outcomes

Two studies on overall cognitive abilities found differing results. Baumann et al. (2022) found no significant effects when using the MMSE, even after considering physical and leisure activities. In contrast, Hale et al. (2021) discovered a positive effect of late retirement (age 67) on cognitive abilities compared to retiring between the ages of 55–65. Coe et al. (2012) found no significant effect of early retirement among white-collar workers once the retirement window was accounted for but did find a positive association between early retirement and later-life cognition among blue-collar workers. In contrast, Ihle et al. (2016) found that earlier retirement positively affected fluid abilities. One study (Li et al., 2021) found a negative relationship between retirement timing and cognitive decline.

Three studies included in this analysis assessed the risk of dementia, each of which revealed a lower risk of dementia with later age retirement. Dufouil et al. (2014) and Sundström et al. (2020) both observed a lower risk of dementia for individuals who retired later. Grotz et al. (2015) found a negative association between retirement age and dementia risk, although the results were not significant when considering different age categories. Finally, two studies focusing on AD found that later retirement delayed AD onset. The first study by Grotz et al. (2016) found that later retirement delayed the age of AD diagnosis, but the significance disappeared when considering reverse causality. The second study by Lupton et al. (2010) also observed a lower likelihood of AD diagnosis among those who retired later.

Discussion

To the best of the authors’ knowledge, this is one of the first studies that provides insight into the existing literature on retirement timing (early, one-time, or late) and its relationship to cognition or risk of dementia. A total of ten articles were included (all from peer-reviewed journals) that were published in four countries: England, the United States, Sweden, China, and the United Kingdom. Articles varied in the type of cognitive measures used, the type of dementia assessed, and the definitions utilized for retirement timing. Due to the heterogeneous nature of the studies, comparison across study types is difficult.

For instance, Baumann et al. (2022) and Hale et al. (2021) examined global cognitive functioning, Li et al. (2021) assessed crystallized intelligence, and both Coe et al. (2012) and Ile et al. (2016) looked at fluid intelligence. Two studies included individuals with AD (Grotz et al., 2015; Lupton et al., 2010), whereas Dufouil et al. (2014) included a sample of craftsmen and shopkeepers. Moreover, the definition of retirement timing varied between studies. For example, two studies used different retirement ages for men and women. The first defined early retirement as 55 for women and 60 for men (Li et al., 2021), while the second by Ihle et al. (2016) defined on-time retirement as 65 for men and 64 for women. Two studies used age ranges for early retirement (55–66 years old). Specifically, Hale et al. (2021) and Sundström et al. (2020) defined retirement timing as 61–64 (early), 65 (on-time), and 66 or older (late). Finally, Baumann et al. (2022) and Dufouil et al. (2014) defined early retirement as before 65 and late retirement as after 65 years of age. Future research utilizing both “healthy” and “unhealthy” samples of older adults and clearly defining retirement timing will add to our understanding of the relationship. Future studies may also want to include multiple ages and age groups to help better identify which age range has the most significant effect on cognition.

One common challenge in retirement research is the possibility of reverse causality and self-selection bias. Early retirement may be influenced by underlying health issues, including cognitive decline, while those who continue working may be overall healthier (Chowdhury et al., 2017). Studies in the current review employed various strategies to address reverse causality, such as PSM (Baumann et al., 2022), instrumental variables (Coe et al., 2012), g-formula (Hale et al., 2021), and two-stage regression analysis (Li et al., 2021). Four of the studies that focused on the risk of dementia addressed reverse causality (Dufouil et al., 2014; Grotz et al., 2016; Grotz et al., 2015; Sundström et al., 2020). Finally, Grotz et al. (2015) accounted for both selection bias and reverse causality. Both cross-sectional and longitudinal studies using diverse methodological approaches will be useful in reaching a definitive conclusion, as potential retest effects in the longitudinal studies can bias the results (Bartels et al., 2010).

Another way to analyze the relationship between retirement timing and cognition is to look for potential modifiable factors. The cognitive reserve and “use it or lose it” hypotheses argue the importance of making neural connections throughout life by participation in cognitively stimulating activities/jobs or continued participation in cognitively stimulating activities after retirement. According to these theories, job complexity, education, and leisure activities that individuals participate in after retirement are components of pre- and post-retirement life and help maintain cognition (Ekerdt, 2010; Grotz et al., 2017). However, not all studies included assessments of job type or activity participation. For example, Coe et al. (2012) found that blue-collar workers reported better cognition when retiring early; however, they did not account for activities before and after retirement and, therefore, could only speculate that retirement from a physically demanding job left more time for cognitively stimulating activities. In contrast to Coe et al.’s (2022) findings, Baumann et al. (2022) found that engagement in leisure activities did not impact cognitive outcomes for manual or skilled laborers. Only two studies looked at possible mediating and moderating factors of activities outside of work (Hale et al., 2021; Ihle et al., 2016). Of these two, Ihle et al. (2016) found a moderate amount of leisure activities changed the relationship between retirement timing and cognition. In addition, for late retirees, mental work demands impacted the relationship between cognition and retirement. Hale et al. (2021) found that those with the highest education had the most benefit from postponing retirement. These findings lend some support to both the cognitive reserve and “use it or lose it” hypotheses. Future research on pre- and post-retirement activities as possible modifiable factors will help provide insight into the mechanism that may underly the relationship.

Research examining the relationship between retirement timing and cognitive decline was mixed. Our findings are similar to two recent studies examining retirement and cognitive decline (Meng et al., 2017; Zulka et al., 2019). Our results indicate that retirement timing studies may not be that different from past studies comparing changes in cognition before and after retirement. However, the relationship between retirement and cognition is complex, and retirement timing is a young field, as shown by the findings of only five studies on retirement timing and cognitive decline. Future research is needed to draw a definitive conclusion.

Among the five studies focusing on retirement timing and dementia, the overall findings suggest that later retirement age is associated with a delayed onset of dementia or AD (Dufouil et al., 2014; Grotz et al., 2016; Grotz et al., 2015; Lupton et al., 2010; Sundström et al., 2020). However, Grotz et al. (2016) found that the association between dementia and retirement timing was no longer significant once reverse causality was taken into account. The results overall support the “use it or lose it” and cognitive reserve hypotheses that postponing retirement is associated with a reduced risk of developing dementia. Individuals who work longer create more neural connections and, therefore, have a higher threshold before experiencing cognitive decline. Alternatively, the longer a person stays cognitively engaged, the less likely they will experience dementia symptoms. As this is still a growing field, with the oldest study completed in 2010 (Lupton et al., 2010), future research will clarify the relationship between retirement timing and dementia risk.

Future research may consider adopting a life course approach by including early and mid-life influences on cognitive decline in older adults. For example, ageism experienced throughout one’s life can affect cognition up to 38 years after the experience (Chang et al., 2020). Promoting a healthy working environment by combating ageist ideas for both younger and older adults may improve memory performance for older adults. Levy (2022) provides recommendations for how to end ageism in the workplace. Future research should explore including adequate enforcement of anti-discrimination laws, forced retirements, and inclusion of older age in diversity, equity, and inclusion training programs. Future research should also examine racially and ethnically diverse samples, as only one study in this review accounted for early life influences and examined racial and ethnic differences (Hale et al., 2021). Hale et al. (2021) found that Black and Latinx participants were more likely to be unemployed or disabled and also more likely to suffer from early life disadvantages.

Limitations

While this review is the first to provide evidence on the relationship between retirement timing and cognition, there are limitations to be noted. First, the inclusion of studies from different countries may have contributed to the variation in findings due to cultural differences in retirement timing. Heterogeneous pension schemes and disparities in healthcare access across countries may further influence retirement timing. There is also variability in access to healthcare across the lifespan when comparing the United States and European countries. For example, for those of working age, European countries with universal healthcare have higher scores in healthcare access compared to the United States (Weaver et al., 2021). Future studies should consider potential system differences when considering the definition of early, on-time, and late retirement. Furthermore, our study was limited to articles published in English, potentially limiting our results.

Conclusion

Although research has found a link between retirement timing and cognition or risk of dementia, several research gaps remain related to retirement timing’s influence on cognition among retired workers. The included papers were heterogeneous and operationalized both retirement timing and cognition in many ways, making it difficult to compare the results across studies. While studies in the current analysis tried to account for the possibility of reverse causality and self-section bias, the studies used different methodologies, and some of the methods employed limited the sample size. Furthermore, research on modifiable factors in pre- and post-retirement was lacking. To fully understand how retirement timing may lead to changes in cognition, high-quality studies utilizing both longitudinal and cross-sectional designs that involve diverse samples of adults across the lifespan are warranted to help fill the gaps still present in the field.

Supplemental Material

Supplemental Material - Understanding the Link Between Retirement Timing and Cognition: A Scoping Review

Supplemental Material for Understanding the Link Between Retirement Timing and Cognition: A Scoping Review by Jessica Yauk, Britney Veal, and Debra Dobbs in Journal of Applied Gerontology

Footnotes

Acknowledgements

We would like to extend a special thanks to Dr. Ardis Hanson, Ph.D., for her invaluable contributions in guiding the authors in how to properly conduct a systematic literature review, including how to search databases and incorporate PRISMA guidelines.

Declaration of Conflicting Interests

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

Funding

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

ORCID iDs

Jessica Yauk

Britney Veal

Supplemental Material

Supplemental material for this article is available online.

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