Theories of Cognitive Aging and Work

Chapter 2

Theories of Cognitive Aging and Work Gwenith G. Fisher, Marisol Chacon and Dorey S. Chaffee Colorado State University, Fort Collins, CO, United States

The link between cognitive aging and work is more important than ever before for many reasons. First, the proportion of older workers in the labor force continues to grow in many countries as recent trends point to an increase in retirement age and individuals remaining the in the workforce until later ages (Fisher, Chaffee, & Sonnega, 2016). Second, cognitive functioning is essential for performing work and successful aging at work (Kooij, 2015; Zacher, 2015). Cognitive ability is related to job performance in part because of its influence on workers’ capacity to learn the knowledge and skills necessary to carry out work-related functions (Salthouse, 2012). Cognitive functioning also has clear implications for work motivation and other individual and organizational outcomes (Kanfer & Ackerman, 2004). Maintaining high levels of cognitive functioning is essential for preserving work ability, which refers to workers’ job-related functional capacity, or a worker’s ability to continue working in his or her current job, given the challenges or demands of the job and his or her resources (Ilmarinen, Gould, Ja¨rvikoski, & Ja¨rvisalo, 2008). Cognitive functioning is also important for continuing to work longer, such that individuals can continue working as long as they are able and desire to do so (McGonagle, Fisher, Barnes-Farrell, & Grosch, 2015). A growing body of research highlights the need to understand cognitive functioning across the lifespan, how it impacts work, and how it is impacted by work. For example, prior research has highlighted important intersections between cognitive abilities and work on many topics. Such issues include personnel selection and human resource management (Hough, Oswald, & Ployhart, 2001), age discrimination (Klein, Dilchert, Ones, & Dages, 2015; Fisher, Truxillo, Finkelstein, & Wallace, 2017), occupational health and well-being (Fisher et al., 2014; Fisher, Chaffee, Tetrick, Davalos, & Potter, 2017), and work ability and retirement (Fisher et al., 2016; Work Across the Lifespan. DOI: https://doi.org/10.1016/B978-0-12-812756-8.00002-5 © 2019 Elsevier Inc. All rights reserved.

17

18

PART | I Lifespan Perspectives

Rohwedder & Willis, 2010). Additionally, there is strong empirical evidence to indicate that there are important age-related changes in cognitive functioning over the lifespan (Salthouse, 2012). Therefore, it is important to understand how cognitive functioning changes over age and what implications that has for work and workers. The purpose of this chapter is to discuss cognitive functioning and work from a lifespan perspective. First, we define cognitive functioning. Second, we summarize multiple theories of cognitive functioning and describe how cognitive abilities change over the lifespan. Third, we relate and integrate theories of cognitive aging to work. Next, we summarize empirical research from recent studies of cognitive aging and work in two ways: how work impacts cognitive functioning, and how cognitive functioning impacts work. Finally, we suggest practical implications and offer some recommendations to guide future research.

DEFINING COGNITIVE FUNCTIONING Psychometric Approach Cognitive functioning refers to multiple mental abilities, including learning, thinking, reasoning, remembering, problem solving, decision making, and attention. The dominant approach to the measurement and conceptualization of cognitive functioning in lifespan developmental psychology is the psychometric approach, which arose from efforts to define, measure, and quantify cognitive abilities using the most basic underlying constructs of abilities such as general intelligence (g), fluid intelligence (Gf), and crystallized intelligence (Gc; Carroll, 1993; Cattell, 1963, 1987; Horn & Cattell, 1967). General intelligence (g) derived from a single common factor underlying all cognitive abilities. Fluid cognitive abilities (Gf) refers to reasoning or thinking, processing speeds, and one’s ability to solve problems in novel situations, independent of acquired knowledge. Crystallized cognitive abilities (Gc) refer to “acquired knowledge,” which includes the accumulation of lifetime intellectual knowledge and achievements. Gc is often measured by abilities like knowledge and vocabulary. Lifespan psychologists (e.g., Baltes, Staudinger, & Lindenberger, 1999) have referred to these dimensions as “cognitive mechanics and pragmatics” (p. 486). The psychometric method relies upon the administration and scoring of multiple cognitive performance tests. This approach has had a strong influence on applied psychological research (e.g., Ackerman & Beier, 2012; Fisher et al., 2014; Klein et al., 2015; Salthouse, 2012). Prior research has identified distinct intra-individual trajectories over the lifespan for different cognitive abilities, including important differences across one’s working life (Klein et al., 2015; McArdle, Hamagami, Meredith, & Bradway, 2000; Schaie, 1994). In particular, Gf peaks in early

Theories of Cognitive Aging and Work Chapter | 2

19

adulthood (around age 20) and then declines throughout the remainder of the lifespan (which includes the time during which people work; Salthouse, 2012). Alternatively, Gc typically increases over the lifespan due to the acquisition of new knowledge and experience. Gc is less likely to decline until much later ages and typically after people retire. Increases over age in Gc are believed to compensate for the losses in Gf and may account for the general stability (or even slight increase) in work performance as people age (Ng & Feldman, 2008). Fig. 2.1 illustrates distinct trajectories of Gf and Gc within individuals over time, with advancing age, and how the trajectory of g alone occludes the distinct patterns of more specific ability measures. When investigating cognitive abilities, it is important to be specific about which cognitive abilities are being investigated, given that there is not one single pattern of intellectual functioning over age across all abilities (Schaie, 1994). Furthermore, the patterns depicted in Fig. 2.1 show that age-related changes in cognitive functioning are more likely to be masked when using more general measures (such as g) compared to the use of more specific abilities. It is important to consider age differences in cognitive abilities when investigating cognitive functioning and work-related issues. For example, Ackerman and Beier (2012) indicated that “over a 20 or 30 1 year span of one’s lifetime of work, both rank order and raw scores [of cognitive abilities] change in marked ways” (p. 151). Klein et al. (2015) investigated cognitive predictors (e.g., general and specific mental abilities) related to age among business executives. Their results indicated that the largest mean differences in cognitive ability scores were among individuals age 65 or older, suggesting evidence of cognitive decline among older adults even in the working population. However, it is very important to note that there are vast Crystallized intelligence (Gc)

Cognitive test performance

Traditional intelligence (g)

Fluid intelligence (Gf)

Age FIGURE 2.1 Trajectories of cognitive abilities within individuals across time with advancing age.

20

PART | I Lifespan Perspectives

individual differences in the aging process such that the extent and rate at which people decline can vary considerably (Salthouse, 2012).

Cohort differences Previous research has identified important patterns in cognitive performance across birth cohorts (Baltes, 1968; Gerstorf, Ram, Hoppmann, Willis, & Schaie, 2011; Salthouse, 2013). A birth cohort refers to individuals born during the same year (Glenn, 2005). It is important to distinguish birth cohorts from other ways that cohorts have been defined (Rudolph & Zacher, 2016), such as age (i.e., one’s biological age in years) and period (i.e., refers to the same point in calendar time, such as an event that affects all age groups on one date/point in time). The distinction between birth cohort, age, and period is important for clarifying distinctions in time-varying phenomena. Specifically, prior research has found large birth cohort differences in interpersonal levels of cognitive abilities such that more recent birth cohorts perform better on tests of general intelligence and fluid cognitive abilities than previous birth cohorts, which is referred to as the Flynn effect (Flynn, 1984). Similarly, Gerstorf et al. (2011) obtained results consistent with Flynn’s, such that more recent cohorts have higher levels of cognitive functioning. Gerstorf et al. (2011) also found a slower rate of decline among more recent cohorts, with the only exception being steeper cognitive decline as individuals approached mortality. Dodge, Zhu, Lee, Chang, and Ganguli (2014) examined differences in executive function, psychomotor speed, and language across four sequential cohorts born between 1902 and 1943. Dodge et al. (2014) identified cohort differences on all three cognitive measures (even after adjusting for educational attainment), with more recent cohorts experiencing less cognitive decline. Measures of executive functioning resulted in the largest differences between cohorts. Skirbekk, Stonawski, Bonsang, and Staudinger (2013) examined immediate recall, delayed recall, and verbal fluency in a nationally representative sample of older adults in the English Longitudinal Study of Ageing (ELSA) and found further evidence to support the Flynn effect. Furthermore, their results suggested that if cognitive functioning continues to improve with succeeding birth cohorts, improvements in cognitive functioning will offset population-level cognitive decline associated with population aging (Skirbekk et al., 2013).

Neurocognitive Method The second approach for conceptualizing and measuring cognitive functioning is the neurocognitive method, which is grounded in clinical neuropsychology, cognitive psychology, and neuroscience. Although the neurocognitive approach has a psychometric basis as well, it focuses more on cognition as a function through brain-behavioral relationships

Theories of Cognitive Aging and Work Chapter | 2

21

(Lezak, Howieson, Bigler, & Tranel, 2012). The primary dimensions of interest in neurocognitive measurement (along with corresponding brain regions) include: (1) attention, executive functions, and higher order problemsolving (prefrontal cortex and frontostriatal networks); (2) memory functions (hippocampus and fronto-temporal systems); (3) language (temporal and prefrontal); (4) visual perception and processing (parietal, occipital), and (5) social and emotional processing (limbic and inferior prefrontal). Executive functions are a broad construct that include such functions as updating (of working memory), task switching, inhibitory control of attention, and response inhibition (Diamond, 2013; Stuss & Knight, 2013). Researchers have also argued that information processing speed underlies age-related changes in many neurocognitive abilities, including executive functions (Diamond, 2013; Rozas, Juncos-Rabada´n, & Gonza´lez, 2008). Because Gf includes short-term memory, processing speed, and quantitative, verbal, inductive, and deductive reasoning, it is similar to the neurocognitive conceptualization of executive functions. This is one area where the psychometric approach may inform the neurocognitive approach. Neuroscience research has shown that age differences in processing speed are influenced by underlying age-related differences in the integrity of white-matter pathways that facilitate communication among discrete brain regions (Kerchner et al., 2012). Although the rate of cognitive decline for each individual is influenced by a variety of factors, many researchers have observed patterns of prefrontal cortex white matter degradation among older adults associated with decline in Gf tasks as compared to Gc tasks (Bugg, Zook, DeLosh, Davalos, & Davis, 2006; Horn & Cattell, 1967). White matter pathways affect communication and processing of information throughout the brain (Fisher et al., 2017). Older adults are thought to be more susceptible to Gf deficits due to associated white matter abnormalities in their prefrontal cortices as they age. Skills associated with Gc, however, have been proposed to be fairly stable, with select measures (such as knowledge and vocabulary) even improving with age and life experience. The link between aging and decline on Gf measures has led researchers to question whether older adults appear to get a greater benefit in Gf from work-related activities that may not be addressed adequately in retirement (Fisher et al., 2014; Peterson, Kramer, & Colcombe, 2002).

Changes in Cognitive Abilities over the Lifespan Lifespan researchers have long been interested in cognitive functioning and within-person cognitive changes across the lifespan. For example, Baltes, Mayer, and colleagues conducted the Berlin Aging Study to examine cognitive, physical, and psychological health, and social and economic status among a group of 516 individuals between the ages of 70 100 who live(d)

22

PART | I Lifespan Perspectives

in former West Berlin (Baltes & Mayer, 2001). In the United States, Schaie began the Seattle Longitudinal Study in 1956, which studied a sample of individuals from the Seattle, Washington, area who ranged in age from their early 20 s to late 60 s in 1956 and from age 22 through 101 in later waves. Data on a wide variety of cognitive functioning tests were collected in seven-year intervals from 1956 to 2005 (Schaie, 1994, 2005). Taken together, these studies have played a prominent role in contributing to our knowledge about cognitive functioning and perspectives about cognitive mechanisms and changes across the lifespan. Park (2000) described four mechanisms that explain intra-individual differences in cognitive functioning over the lifespan. These mechanisms include (1) the rate of perceptual speed, which affects the speed of cognitive processing; (2) working memory functions; (3) inhibitory function; and (4) sensory function. Perceptual speed rate is essential for all cognitive tasks (not just speeded tasks) because perception affects how we acquire and process information. Salthouse (1996) developed a processing speed theory to explain age differences in cognitive abilities among adults. According to Salthouse’s processing speed theory, how long it takes a person to perform complex cognitive operations is limited by how long it takes to process the each individual cognitive function (e.g., visual working memory, attention, etc.) of the cognitive operations. Slower processing at lower-level stages may lead to lost information when needed at higher-level stages (Salthouse, 1996). Cognitive processing speed is more important for cognitively difficult or complex tasks because it allows us to filter important and relevant information from what is less important (Park, 2000; Salthouse & Madden, 2015; Salthouse, 2012). Previous empirical research found that there are age-related differences on multiple measures of processing speed, and age differences in cognition are typically attenuated after controlling for processing speed (Salthouse & Madden, 2015). According to Park (2000), working memory is defined as “the amount of online cognitive resources available at any given moment to process information” (p. 10). Craik and Byrd (1982) suggested that one way to assist older adults is by providing environmental supports to decrease processing requirements. For example, presenting information visually, (e.g., in written form) and not just verbally (e.g., spoken instructions) may be helpful and more effective for older employees, as a mixed communication method would allow for retaining the most amount of details. The cognitive aging mechanism called inhibition refers to age-related differences in how well individuals filter out (i.e., inhibit) irrelevant information that can distract from focused attention to relevant information. One explanation regarding why older adults may be more apt to be distracted when faced with multiple competing sources of information is due to inhibition. Although inhibition is not as well understood as working memory and processing speed functions, it is relevant to work because inhibition relates to one’s ability to focus on a task and avoid or manage distractions at work. Prior research by

Theories of Cognitive Aging and Work Chapter | 2

23

von Hippel and Dunlop (2005) found empirical evidence to suggest that older adults experience declines in inhibitory ability. The last of these four mechanisms is sensory function, which includes visual and auditory acuity. According to Lindenberger and Baltes (1994), sensory function is a basic measure of brain “integrity” and may serve to affect cognitive performance.

Mechanisms of Cognitive Aging The two methods to the assessment of cognitive functioning we have reviewed (i.e., psychometric and neurocognitive approaches) overlap with the four mechanisms of cognitive aging that Park (2000) described (i.e., processing speed, working memory, inhibitory function, and sensory function). The neurocognitive method includes these four mechanisms, but also extends Park’s framework by adding executive functions, attention, language, and social and emotional processing. The neurocognitive method to conceptualizing cognitive functioning also complements the psychometric approach. For example, lower levels of Gf performance among healthy older adults have been associated with smaller prefrontal and orbitofrontal white matter (Raz et al., 2008). In addition, functional neuroimaging studies have suggested that older adults who perform better on executive or Gf-type tasks appear to compensate for age-related changes in brain structure and connectivity via increased engagement of the prefrontal cortex (Hakun, Zhu, Brown, Johnson, & Gold, 2015; Reuter-Lorenz & Cappell, 2008). Although a thorough explanation of neuroanatomy and brain functions is well-beyond the scope of this chapter, we describe the neurocognitive method for three reasons. First, neuroscience and clinical neuropsychology are important and relevant to enhance our understanding of cognitive functioning and work in a multidisciplinary way. Second, the neurocognitive approach expands the conceptualization of cognitive mechanisms or functions beyond the four described by Park (2000). Third, there is an opportunity to integrate findings from neuropsychology and cognitive neuroscience as applied to work settings. Cognitive dysfunction is important for understanding the aging workforce because older workers are at higher risk for age-related cognitive decline, dementia, and difficulties with cognitive functioning that have implications for successful aging at work (i.e., maintenance of health, motivation, and work ability), as well as work disability, which refers to having health-related limitations that result in unemployment or result in decreased work productivity (Pransky, Loisel, & Anema, 2011).

COGNITIVE FUNCTIONING AND SUCCESSFUL AGING Society and organizations have a need to support active and successful aging among older adults at work, and during and after retirement as well

24

PART | I Lifespan Perspectives

(Kooij, 2015; Zacher, 2015). The aging and work literature describes two recent frameworks for understanding successful aging at work. First, Kooij (2015) defined successful aging as “the maintenance of workers’ health, motivation, and working capacity or work ability now and in the future” (p. 309). Second, Zacher (2015) offered a theoretical framework of successful aging that describes a process that includes intra-individual change over time, person and contextual mediators and moderators, and a variety of work outcomes (e.g., work motivation, job performance, turnover, and occupational health and well-being). The maintenance of cognitive functioning among older adults fits in Zacher’s model of successful aging as a person-level mediator between chronological age and work outcomes, and functions as a personal resource for successful aging. We conceptualize cognitive functioning as a mediator between intra-individual temporal changes and personal and contextual factors (which are also mediators and moderators). Recently some of our team’s research has identified cognitive functioning as an important antecedent of work ability (Fisher, McGonagle, Chaffee, & McCall, 2016). It is necessary to understand the intersection of aging, cognitive functioning, and work in order to advance our understanding of, and mechanisms to achieve, successful aging at work.

THE EFFECTS OF WORK ON COGNITIVE FUNCTIONING Next we conceptualize and summarize previous research in two ways: first we focus on ways in which work affects individuals’ cognitive functioning. Second, we discuss how cognitive functioning affects work.

Theoretical Framework The use-it-or-lose-it hypothesis is the primary theory that has guided the investigation of the effects of work on cognition (Hultsch, Hertzog, Small, & Dixon, 1999; Salthouse, 1991, 2006). According to the use-it or-lose-it hypothesis, an individual’s level of cognitive functioning is determined by two mechanisms: differential preservation and preserved differentiation. Differential preservation indicates that a person’s level of cognitive functioning depends on his or her current mental activity. In other words, individuals who are consistently mentally active are preserving their function more than those who are not consistently mentally active. On the other hand, preserved differentiation suggests that individuals with higher levels of cognitive functioning in earlier life are the ones who are more apt to maintain higher levels of mental activity as they age. Many empirical studies (e.g., (Bielak, Anstey, Christensen, & Windsor, 2012; Potter, Helms, & Plassman, 2008; Schooler, 2007) have found evidence to support differential preservation even though Salthouse’s more recent research (e.g., Salthouse, 2006; Salthouse, Berish, & Miles, 2002) has cast doubt on differential preservation. Many studies also

Theories of Cognitive Aging and Work Chapter | 2

25

support the concept of preserved differentiation (e.g., Bielak, Cherbuin, Bunce, & Anstey, 2014; Finkel, Reynolds, McArdle, & Pedersen, 2007). Two other theories relevant for understanding the impact of work on cognitive functioning include the brain or cognitive reserve hypothesis (Fratiglioni & Wang, 2007), and Schooler’s (1984, 1990) theory of environmental influences on cognitive functioning. According to the cognitive reserve hypothesis, engagement in mentally stimulating activities and environments is linked to increased neuronal development, which leads to the development of a cognitive reserve. Mental stimulation helps build additional cognitive strategies or neuronal resources, which, in turn, may increase an individual’s resilience to neuronal insults. These resources may serve to buffer against measurable cognitive decline. Schooler’s theory posits environmental influences on intellectual functioning, in which a person’s environment includes both stimulus and demand characteristics that contribute to the cognitive complexity of the environment (Schooler, Mulatu, & Oates, 1999). The presence of greater and varied environmental stimuli requires individuals to make more decisions, which, in turn, creates a more complex cognitive environment. In complex environments that reward cognitive effort, people should be motivated not only to develop their intellect, but to apply cognitive processes across different situations, thereby improving intellectual functioning. However, according to Schooler and colleagues, if high levels of cognitive functioning are unnecessary, intellectual abilities will not be maintained and consequently lead to decreases in cognitive functioning. In the previous section, we described mechanisms by which work may influence cognitive functioning. Zacher’s (2015) theory of successful aging at work suggests such that work experiences provide a context by which cognitive functioning develops over time for each individual. Altogether, there are multiple theories suggesting that work provides a context that may influence cognitive functioning.

Job Characteristics and Work One mechanism that explains how work may influence cognitive functioning is exposure to specific job characteristics. The job demands-resources (JD-R) model is a very useful framework for conceptualizing job characteristics and how they relate to worker health and well-being (Bakker & Demerouti, 2017; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001). According to the JD-R model, jobs characteristics are comprised of demands and resources. Job demands refer to “physical, social, or organizational aspects of a job that require sustained physical or mental effort and are therefore associated with certain physiological or psychological costs” (Demerouti et al., 2001, p. 501). Thus far, we have discussed cognitive and physical job demands, as well as mental and social demands. In the context of the JD-R model, resources facilitate the completion of work goals, or may serve to buffer the

26

PART | I Lifespan Perspectives

relation between job demands and other worker or organizational outcomes. Next, we summarize prior research to describe what we know about the relation between each of these job characteristics and workers’ levels of cognitive functioning.

Cognitive Job Complexity and Cognition Many studies during the last decade have examined the link between cognitive complexity of work (i.e., mental job demands) and workers’ level of cognitive functioning. These studies have relied upon the aforementioned theories (i.e., the “use-it-or-lose-it” hypothesis, cognitive reserve hypothesis, and environmental influences on intellectual functioning) and have provided empirical support for the notion that more cognitively complex work is related to better cognitive functioning in later life (Andel, Finkel, & Pedersen, 2015; Bosma, van Boxtel, Ponds, Houx, & Jolles, 2003; Finkel, Andel, Gatz, & Pedersen, 2009; Fisher et al., 2014; Potter et al., 2008; Schooler et al., 1999). A subset of these studies identified cognitively complex work as being associated with a lower prevalence of cognitive impairment and dementia (Andel et al., 2015; Potter et al., 2008). Andel et al. (2015) examined leisure pursuits and found that engagement in cognitively stimulating leisure activities was associated with higher levels of cognitive functioning; moreover, participation in such non-work activities appeared to be particularly helpful for individuals who do not work in cognitively complex jobs. This result is important, especially in light of prior research by Potter et al. (2008), who found an interaction between earlier life cognitive ability, cognitive job complexity, and cognitive functioning such that workers with lower levels of cognitive abilities in earlier life seemed to benefit more from cognitively complex work. Grzywacz, Segel-Karpas, and Lachman (2016) examined cross-sectional data from the National Survey of Midlife Development in the United States (MIDUS) and found that job complexity positively related to cognitive functioning assessed by episodic memory, executive functioning, and self-rated memory. Additionally, Grzywacz and colleagues reported that greater exposure to physical workplace hazards was associated with lower cognitive functioning. Staudinger, Finkelstein, Calvo, and Sivaramakrishnan (2016) suggested three different dimensions of work that may impact cognitive functioning among older adults: the degree of routinization, level of difficulty, and novelty of exposure. Recently, Oltmanns et al. (2017) reported results of a workplace intervention that introduced work task changes (i.e., novelty) among automotive manufacturing workers in Germany. Oltmanns et al. (2017) found that workers who experienced more work task changes over a seventeen-year period demonstrated better cognitive processing speed, working memory, and more gray matter volume in brain regions associated with learning and age-related cognitive decline.

Theories of Cognitive Aging and Work Chapter | 2

27

To summarize, working in more cognitively complex jobs is associated with better cognitive outcomes primarily for individuals in their 60 and 70 s, including g, assorted Gf measures such as memory and processing speed, as well as research diagnoses pertaining to dementia. Altogether, research results to date support the phenomenon of differential preservation (Salthouse, 2006).

Physical Job Demands and Cognition In addition to mental job demands (i.e., cognitive job complexity), physical job demands may affect cognitive functioning. There is a large literature suggesting that greater physical activity and cardiovascular fitness in aging is associated with decreased risk of cognitive impairment (Schlosser Covell et al., 2015). In addition, robust physical functioning can protect against both physical and cognitive disability (Avila-Funes et al., 2009). Despite positive physical and cognitive benefits associated with physical activity during leisure time, there is an ostensibly paradoxical relationship between cognitive performance and physical activity during work. Several studies have found that jobs characterized by higher physical activity demands are associated with greater cognitive decline and higher risk of dementia (Marengoni, Fratiglioni, Bandinelli, & Ferrucci, 2011; Potter, Plassman, Helms, Foster, & Edwards, 2006; Smyth et al., 2004). One interpretation for this pattern is that physically demanding activities, often characterized as manual labor or blue-collar work, are proxies for the cognitive correlates of lower socioeconomic status, educational attainment, and income that are associated with many of these occupations (Russo et al., 2006; Walker-Bone et al., 2016). A second interpretation is that many physically demanding jobs in occupations like construction, agricultural processing, and manufacturing, are cognitively overlearned or repetitive in nature, and thus do not often have a corresponding level of complex and varied intellectual demands. By this interpretation, the cognitive benefits of intellectually stimulating work may not be available to individuals in many physically demanding occupations. A third interpretation is that physical hazards related to an occupation or its work environment play a key role in physical decline, which may put individuals at higher risk for cognitive decline over time. When considering the appearance of diverging outcomes associated with leisure-time physical activity and work-related physical activity, it is important to note that the intensity, type, and duration of physical activity in leisure time is controlled by the individual, whereas the intensity, type, and duration of job-related physical activity is often dictated by the demands of the job. Many manual jobs involve physically repetitive movements that are strenuous in nature and occur over durations that are often not under the worker’s control. This lack of control over the parameters of physical activity could lead to disability through repetitive motion injuries, reduced range

28

PART | I Lifespan Perspectives

of joint movement, and wear and tear on muscles and joints. This “wear and tear” interpretation suggests age-related increases in work-related injuries and a gradual accumulation of injuries can cause changes in lower extremity mobility and functions like gait, as well as upper extremity functions such as grip strength (Cooper, 2016; Walker-Bone et al., 2016). Deficits in physical functions such as gait and grip strength have shown to be predictors of cognitive decline (Clouston et al., 2013).

Safety and Injury It is also important to consider how cognitive decline among older workers may pose a safety risk to affected individuals, given evidence that older workers are more susceptible than younger workers to injuries due to slips, trips, and falls (Schwatka, Butler, & Rosecrance, 2012; Yeoh, Lockhart, & Wu, 2013). Although age-related physical declines increase the risk of falls (Zhang, Lockhart, & Soangra, 2015), cognition also plays a role in injury risk. For example, motor tasks such as walking and balance require cognitive resources (e.g., attention and executive function) that tend to decline with age (Hsu, Nagamatsu, Davis, & Liu-Ambrose, 2012). Prior research has shown age-related differences in motor-cognitive dual-task performance such that older adults perform significantly worse when completing a cognitive task (e.g., memorizing word lists) and a secondary physical/motor task (e.g., walking; Lindenberger, Marsiske, & Baltes, 2000). Li, Lindenberger, Freund, and Baltes (2001) investigated this relationship within the selection, optimization, and compensation framework, with a specific focus on how older adults prioritize performance goals in response to age-related losses (e.g., declines in cognitive resources). Because falling has a higher cost of physical injury for older adults, these researchers hypothesized that older adults would be more likely to “protect” their walking performance at the expense of the cognitive task. Consistent with their hypothesis, results showed older adults prioritized motor performance (walking and balance) over cognitive task performance (memorizing a word list) as indicated by greater age differences in memory performance than walking performance. Further, results identified age-related differences in the use of compensatory external aids, such as using a device to delay the presentation of words in a word recall task and use of a handrail during a walking task. Younger adults optimized use of the memory aid whereas older adults optimized use of the handrail. Taken together, the findings show people address age-related declines in abilities by “prioritizing what should be preserved, and then maintaining prior performance levels by using compensatory means” (Li et al., 2001, p. 236). These findings were based on laboratory studies, and their ecological validity in the workplace remains to be established; however, it represents an important area of future research.

Theories of Cognitive Aging and Work Chapter | 2

29

To summarize, regular physical activity and cardiovascular fitness is associated with less cognitive impairment. However, prior research has found counter-intuitive results regarding participation in physically demanding jobs and cognitive functioning such that workers in more physically demanding jobs are more likely to experience cognitive decline. We provided multiple possible explanations for this paradoxical relationship. Declines in cognitive functioning may increase the risk for work-related accidents and injuries. Future research is needed to investigate the association between intraindividual changes in cognitive functioning and workplace accidents and injuries.

Stress and Strain Prior research by cognitive psychologists has examined the role of stress in relation to cognitive functioning and cognitive aging. For example, Stawski, Sliwinski, and Smyth (2006) examined stress-related cognitive interference and distress among older adults (ranging in age from 66 to 95, with an average age of 80 years) using multiple measures of cognitive functioning. These researchers found that cognitive interference was negatively related to processing speed, episodic and working memory. Sliwinski, Smyth, Hofer, and Stawski (2006) examined between- and within-person change in daily stress experiences and cognition over time and found that reaction time was slower on high-stress days compared to low stress days. These researchers concluded that cognitive interference resulting from the stress process competes for cognitive attentional resources. More recently, Andel et al. (2015) reported that working in jobs characterized by higher levels of strain and low levels of control were not associated with lower levels of cognition when individuals were working, but were associated with poorer memory at the time of retirement and a faster rate of cognitive decline following retirement. In addition to direct links between job strain and cognitive functioning, an indirect relationship between job strain and cognitive functioning as mediated by physical health. In other words, job strain may lead to declines in physical health, which, in turn, affects cognitive functioning. For example, chronic diseases that increase in incidence and prevalence with age, such as heart disease, stroke, and diabetes, are major sources of disability and are each associated with a greater risk of cognitive decline and dementia (Gottesman et al., 2014; Norton, Matthews, Barnes, Yaffe, & Brayne, 2014; Yaffe et al., 2014). There is substantial evidence that job strain compounds the association between these chronic diseases and unhealthy cognitive aging. Job strain is of particular concern among older workers, who tend to take longer to recover from work stress and show greater physiological stress in response to high job demands (Kiss, De Meester, & Braeckman, 2008; Ritvanen, Louhevaara, Helin, Va¨isa¨nen, & Ha¨nninen, 2006).

30

PART | I Lifespan Perspectives

A meta-analysis of six prospective cohort studies found that high-strain jobs were associated with a greater relative risk of incident stroke compared to low-strain jobs, particularly ischemic stroke (Huang et al., 2015). This study also found that active and passive job types as defined by the job demands-control model were not associated with increased stroke risk. One review of heart disease and stroke argued that the combination of high strain and social isolation is most harmful with respect to the development of these conditions (Kobayashi, 2004). This review also noted associated risk factors including long working hours, lack of sleep, shift work, number of business trips, exposure to cold temperatures, and noise exposure. Along with isostrain, these working conditions may represent a form of “accumulated fatigue” that predisposes individuals to increased risk of cardiovascular disease and stroke. To summarize, past research has identified both direct as well as indirect associations among the work-related stress process, strains, and cognitive functioning. The mechanisms for understanding these relations are less wellunderstood and future research is needed to advance our understanding regarding the work stress process, cognitive functioning, and outcomes such as worker health and well-being.

THE EFFECTS OF COGNITIVE FUNCTIONING ON WORK In the prior section, we discussed multiple ways in which work may affect cognitive functioning, particularly among older adults. Next, we discuss how cognitive functioning may affect various aspects of work, including work motivation, training, learning, development, and retirement.

Work Motivation Kanfer, Beier, and Ackerman (2013) proposed a theory of work motivation relevant to aging and older workers in which they differentiated among three specific types of work-related motivation: motivation to work, motivation to retire, and motivation at work. Based on this perspective, there are multiple ways that declines in cognitive functioning may influence work-related motivation. First, workers who experience declines in cognitive function may be less motivated to work and more motivated to retire. This is consistent with the push/pull theory of retirement (Barnes-Farrell, 2003), which indicates that workers may feel “pushed” out of the workforce if they are not able to meet the demands of their job. Decline in cognitive function may also affect one’s motivation at work; for example, motivation at work may decline if people no longer feel they can accomplish their assigned tasks. There are two predominant theories from the lifespan development literature relevant to work motivation (Rudolph, 2016). These theories include selection, optimization, and compensation (SOC; Baltes & Baltes, 1990) and

Theories of Cognitive Aging and Work Chapter | 2

31

socioemotional selectivity theory (Carstensen, 1991, 1995; Carstensen, Isaacowitz, & Charles, 1999). (See Chapters 4 and 6 of this volume for more information about these theories, although we briefly describe them for the reader here.) SOC describes three processes relating to the setting of goals, selecting strategies for goal attainment, and compensating for strategies that are not working by either substituting other strategies or even changing the goals. According to SOC, people want to maximize gains and minimize losses; therefore, as people age they tend to select goals that they can meet and not select goals that are likely to result in losses. Similarly, certain strategies for goal attainment may be more effective in some goal domains than others; so, as individuals age, they may switch strategies for goal attainment. Lastly, as individuals age they may lose the ability to use a certain strategy and need to acquire a new means or strategy for attaining the goal. Carstensen (1991) developed socioemotional selectivity theory, which has two primary propositions. First, as people age, their future time perspective shifts from being open-ended to more constrained. In the work environment, this may be triggered by age, but also by organizational policies such as mandatory retirement ages or organizational practices such as norms regarding when one retires. Secondly, goals may change over time such that knowledge acquisition becomes more salient when future time perspective is open-ended, whereas positive emotional experiences become more salient as future time perspective becomes more constrained. Many studies have incorporated SOC and socioemotional selectivity theory to explain the relation between aging and work motivation. Recently attention has focused on work design for understanding the relation between work characteristics and aging. For example, Truxillo, Cadiz, Rineer, Zaniboni, and Fraccaroli (2012) used Hackman and Oldham’s (1976) job characteristics model as a framework and identified four motivational characteristics related to work: (1) task characteristics (autonomy, variety, significance, and feedback), (2) knowledge characteristics (job complexity, information processing, problem solving, skill variety, and specialization), (3) social job characteristics (social support, interdependence, interactions outside the organization, and feedback from others), and (4) work context (ergonomics, physical demands, equipment use, and working conditions). Truxillo and colleagues suggested that these motivational characteristics result in psychological states, person-environment fit, and motivation subsequently affecting job satisfaction, work engagement, and performance. Although the study by Truxillo and colleagues is a straightforward interpretation of the job characteristics model, these researchers extended the job characteristics model by suggesting that aging results in a number of other changes, such as cognitive changes, personality changes, changes in physical health and abilities, changes in knowing and emotional regulation goals, future time perspective shifts, changes in generational status, changes in work experience, and changes in the subjective perception of age, loss,

32

PART | I Lifespan Perspectives

growth, reorganization, and exchange. These shifts affect an individual’s SOC strategies (Baltes & Baltes, 1990), socioemotional selectivity processes (Carstensen et al., 1999; Carstensen, 1995), and work motivation (Kanfer & Ackerman, 2004), which moderate the relation between motivational characteristics and the mediating psychological states. Additionally, Truxillo et al. (2012) suggested that there were other moderators of this process such as individual differences, contextual factors, and combinations of various job characteristics. In another study, Zaniboni, Truxillo, and Fraccaroli (2013) examined work motivation among older adults in relation to skill and task variety. Theoretically, task variety was posited to be more motivating for younger workers than older workers because younger workers were more interested in growth and development opportunities, and doing more tasks would give them more opportunities to expand upon their work experience. On the other hand, skill variety was posited to be more motivating for older workers than younger workers, because using varied skills would allow them to draw on their knowledge and experience. Based on these motivational effects, Zaniboni et al. (2013) found that task variety was more strongly related to job satisfaction and work engagement among younger workers than older workers. These researchers also found that increased task variety was associated with lower burnout and turnover intentions among younger workers, whereas for older workers, greater skill variety was associated with lower turnover intentions. Additionally, Bertolino, Truxillo, and Fraccaroli (2011) found more positive associations among proactive personality and training motivation, perceptions of career development from training, and intention to engage in training among younger workers than older workers. Please see Chapter 20 of this volume more detail on lifespan perspectives on work motivation. Consistent with Truxillo and colleagues’ (2012) suggestion that different job characteristics might interact with each other, Stamov-Roßnagel and Biemann (2012) suggested that a more detailed investigation job characteristics was warranted because the effects of age are at the task-specific level. Their study found that age was positively related to generativity-related tasks but not with growth-related tasks. As a result, they concluded that as workers get older, they are motivated to match their resources to the demands of the environment in order to maximize social and emotional gains consistent with SOC. See Chapters 11 and 21 of this volume for more information about job design and job attitudes, respectively.

Learning and Development As we mentioned earlier, one issue that is central to the discussion of cognitive aging and work is how age-related changes in cognitive functioning relate to individuals’ performance at work. Cognitive functioning is critical

Theories of Cognitive Aging and Work Chapter | 2

33

to job performance partly due to its influence on workers’ capacity to learn the knowledge and skills necessary to carry out work-related functions (Salthouse, 2012). Research has established that specific cognitive processes underlying learning capacity are adversely affected by the aging process (Salthouse, 2012). These age-related cognitive changes have important implications for older workers’ learning performance in job training, and, in turn, the ability to remain productive and marketable in work environments that require continuous skill learning (Beier, Teachout, & Cox, 2012; Karpinska, Henkens, Schippers, & Wang, 2015; Raemdonck, Beausaert, Fro¨hlich, Kochoian, & Meurant, 2014). Because fluid cognitive abilities decline with age, older workers’ capacity to acquire the knowledge and skills necessary to maintain high levels of work performance and remain competitive in the labor market may diminish over time.

Stereotypes Known age-related declines in cognitive functioning have unfortunately contributed to the development of negative stereotypes regarding the “trainability” of older workers. For example, older workers are often perceived as having a lower ability to learn training content, as more difficult to train, and as less motivated or willing to participate in training and development activities (Bal, Reiss, Rudolph, & Baltes, 2011; Ng & Feldman, 2012b; Posthuma & Campion, 2009; Raemdonck et al., 2014). Employers may also be more reluctant to devote financial resources to train their older workforce based on beliefs that older workers will provide employers fewer working years to benefit from their training investment (Lazazzara, Karpinska, & Henkens, 2013; Posthuma & Campion, 2009). As a result, older workers often receive fewer career development and training opportunities compared to younger workers (Lazazzara et al., 2013; Raemdonck et al., 2014). Having fewer training opportunities may affect one’s subsequent ability to learn. According to the European Agency for Safety and Health at Work (2007), “Losing the ability to learn is not exclusively related to age, but is normally the result of a working biography with a lack of continuous learning demands, and, in particular, opportunities to learn” (p. 70). As a result, negative stereotypes pertaining to older workers’ ability to learn may, in fact, deny them important opportunities to maintain cognitive fitness. In addition to fewer opportunities for learning and development, negative stereotypes about older workers’ capability and interest in training and development may indirectly undermine training performance by lowering older workers’ self-efficacy regarding their ability to learn and develop their work competencies (Beier et al., 2012; Maurer, 2001; Raemdonck et al., 2014; Wang, Olson, & Shultz, 2013). Self-efficacy is important to achieve optimal training outcomes by having effects on participation in training and development, motivation to learn, and willingness to transfer what was learned back

34

PART | I Lifespan Perspectives

on the job (Beier et al., 2012; Colquitt, LePine, & Noe, 2000; Salas, Tannenbaum, Kraiger, & Smith-Jentsch, 2012). For example, self-efficacy predicts more favorable attitudes toward development, and, in turn, better participation in training and development activities (Maurer, Weiss, & Barbeite, 2003; Salas et al., 2012). Furthermore, self-efficacy is related to the effort and duration for which a person will work to learn the training content and how he or she will react when faced with challenges and difficulties encountered during training (Salas et al., 2012). Older employees tend to rate themselves as possessing fewer learning qualities and are as less confident in their ability to learn new material, recall training information, retain new skills, and master training content (Beier et al., 2012; Maurer et al., 2003; Raemdonck et al., 2014). Employees’ beliefs about their ability to perform well and succeed at work begins to decline as they enter their mid- to late-careers (Maurer et al., 2003; Ng & Feldman, 2012a). Agerelated declines in self-efficacy may relate to corresponding declines in cognitive functioning with age (Maurer, 2001; Ng & Feldman, 2012a; Raemdonck et al., 2014). To the extent that older workers believe cognitive decline reduces their capacity to learn, their self-efficacy for training may decrease, which may subsequently lead to poorer training performance that is independent of actual learning capacity. Additionally, past research has found that negative age stereotypes can influence cognition in long-lasting ways (Nelson, 2016). For example, individuals who endorsed more negative age stereotypes showed a 30.2% greater decline in memory performance over 38 years compared to individuals who held less negative age stereotypes (Levy, Zonderman, Slade, & Ferrucci, 2012). In sum, the role of negative age stereotypes in influencing behavior and shaping work environments may create a type of self-fulfilling prophecy regarding older workers’ capacity for and interest in learning and development. As a result, the ongoing presence of such stereotypes may have adverse and widespread effects on learning performance and success in training and development activities among older workers. Although most stereotypes about older workers have been empirically refuted (e.g., job performance; Ng & Feldman, 2012b), evidence of age differences in job training and development is equivocal. For example, empirical evidence exists to indicate that training performance declines as workers age (Kubeck, Delp, Haslett, & McDaniel, 1996). Specifically, older adults have poorer training performance relative to younger adults based on metaanalytic evidence showing that older adults mastered less training content, completed training tasks more slowly, and took longer to finish the training program. However, a subsequent meta-analysis examining age in relation to various work performance criteria found age was largely unrelated to training performance (Ng & Feldman, 2008). Training performance was slightly lower for older workers relative to younger workers, and this relationship may be in part due to an overrepresentation of studies using technology

Theories of Cognitive Aging and Work Chapter | 2

35

training programs (a result of the inclusion criteria) that likely favor younger workers. Ng and Feldman (2008) suggested the age-training performance relationship may be confounded by age differences in attitudes toward technology. Inconsistent findings in the literature about the consequences of cognitive aging on job training performance may be partially understood by considering the contextual factors that constrain and facilitate learning performance. For example, most prior research that has found a negative relationship between age and learning performance was based on laboratory research from cognitive aging literature (Salthouse, 2012). The relationship between age-related changes in cognition and learning performance is often tested using controlled experiments designed to isolate the specific cognitive processes by limiting confounding variables related to individuals’ experience, motivation, and learning strategies (Jeske & Stamov Roßnagel, 2015). Although the aim of cognitive aging research is to understand the mechanics of cognition, the cognitive methodological approach often assesses maximal cognitive functioning that relies heavily on fluid abilities. Therefore, performance in the lab is not necessarily indicative of the typical level of functioning required in everyday life. For example, learning in the workplace typically allows workers to draw upon prior job knowledge and experience (i.e., one’s Gc) and apply selection, optimization, and compensation strategies to compensate for any changes in capabilities (e.g., arrange work schedule to allow for more time to complete training [optimization], record training to compensate for slower note taking [compensation]; Raemdonck et al., 2014). In reality, age-related cognitive differences may be less evident when using criteria of typical performance and may only be detected when cognitive decline reaches pathological limits (Jeske & Stamov Roßnagel, 2015; Salthouse, 2012). Kubeck et al. (1996) suggested that some of the variance in their findings regarding the age-training performance relationship may be attributed to differences in the magnitude of the effects between laboratory and field studies. Although laboratory research may overestimate the consequences of agerelated changes in cognitive functioning in everyday life, these studies have been extremely useful in explicating the underlying cognitive processes that change with age and underlie learning performance. Extant research has identified three related cognitive processes that are both critical for learning performance and negatively influenced by the aging process: (1) decrease in working memory capacity, (2) slowing of cognitive processing speed, and (3) reduced ability to inhibit the processing of irrelevant information (Beier et al., 2012; Hsu, 2013; Schulz & Stamov Roßnagel, 2010; Wolfson, Cavanagh, & Kraiger, 2014). The negative effects of reduced working memory capacity on learning performance relates closely to the slowing of cognitive processing speed. As explained earlier, reduced processing speed makes it more difficult to recall

36

PART | I Lifespan Perspectives

and use previously learned information with recently acquired information during task performance (Salthouse & Babcock, 1991). To the extent that these cognitive processes decline with age, tasks that require higher levels of information-processing, such as novel or difficult tasks, may be more difficult for older workers to perform compared to younger workers. Further, performing cognitive tasks under time pressure may amplify the negative effects of age-related changes in cognition. For example, research has shown that when time pressure is lifted, age-related differences in learning performance are reduced (Czaja, Sharit, Ownby, Roth, & Nair, 2001; Van Gerven, Paas, Van Merrie¨nboer, Hendriks, & Schmidt, 2003), which is consistent with meta-analytic evidence that showed self-paced training had the largest positive impact on outcomes of training performance among older learners (Callahan, Kiker, & Cross, 2003). Finally, extant research suggests that a reduced ability to filter irrelevant information impedes learning performance by further depleting already limited cognitive resources. Readers should note that these processes are consistent with the neurocognitive model of aging discussed previously in this review. In sum, the effects of cognitive aging on training performance may be most pronounced when learning involves novel or complex information delivered in a fast-paced learning environment or when performing under time pressure. Allowing older workers to self-select their learning strategies and draw upon prior knowledge may help optimize training performance.

Retirement Changes (declines) in cognitive functioning may affect workers’ perceptions of their ability to meet their job demands (Fisher et al., 2016) and the length of time they remain in the labor force. Belbase, Khan, Munnell, and Webb (2015) used data from the U.S Health and Retirement Study and the Occupational Information Network (O NET) to examine age-related cognitive decline in relation to three potential workplace outcomes in the United States: (1) coping with increased job difficulty; (2) shifting to a less cognitively demanding job; and (3) retiring early. Belbase et al. found that approximately 10% of workers between the ages of 55 and 69 experienced steep cognitive decline over a 10-year period. Workers experiencing cognitive decline were more likely to “downshift” to a less demanding job or retire significantly earlier than planned compared to workers who did not experience cognitive decline.

PRACTICAL IMPLICATIONS The research we discussed in this chapter suggests a number of practical implications to enhance work design, work motivation, work ability, and successful aging at work. Consistent with research by Truxillo et al. (2012),

Theories of Cognitive Aging and Work Chapter | 2

37

organizations as a whole, human resource managers, and supervisors should take care to enhance the (1) task characteristics (autonomy, variety, significance, and feedback), (2) knowledge characteristics (job complexity, information processing, problem solving, skill variety, and specialization), (3) social job characteristics (social support, interdependence, interactions outside the organization, and feedback from others), and (4) work context (ergonomics, physical demands, equipment use, and working conditions) of jobs. Improving job characteristics on these dimensions may increase worker motivation and person-environment fit, and, in turn, result in higher levels of job satisfaction, work engagement, performance, work ability, and employability. In addition, our review of cognitive aging and work in this chapter highlights the need for career counseling and resources for older adults to aid individuals in finding work that will fit with their abilities and interests (Lytle, Clancy, Foley, & Cotter, 2015). Resources should consider aging and development across the lifespan, including changes in cognitive and physical health as well as work motivation. One specific example of a resource to help middle age and older workers is encore.org, which aims to leverage and engage the interests and talents of adults in a variety of ways to benefit society. As individuals approach retirement or consider bridge employment opportunities, they should seek positions that will maximize personenvironment fit. For some workers, this may mean reducing job demands or work hours. For others, it may be best to leave a specific job or organization altogether and seek a new, perhaps less-demanding job. Fisher et al. (2017) suggested that for some workers, reducing cognitive job demands in their current role in a way that still allows them to rely upon acquired job-related knowledge may be much better than learning an entirely new job in a new work environment. However, more research is necessary to evaluate this recommendation. Consistent with this idea, research by Kooij and colleagues on aging and job crafting indicated that workers can craft or modify their job to adjust to age-related changes so that they can continue to perform their job (Kooij, Tims, & Akkermans, 2017; Kooij, Tims, & Kanfer, 2015). More specifically, workers can craft their jobs to increase resources, increase challenging job demands, and reduce hindering job demands (Kooij et al., 2017).

CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH Taken together, the research we summarized in this chapter suggests that workers’ cognitive functioning is directly related to whether workers can age successfully at work, and continue to remain employed in later life. Past research in cognitive and lifespan developmental psychology has demonstrated clear and distinct patterns of cognitive functioning across the lifespan, particularly with regard to fluid and crystallized cognitive abilities (Carroll, 1993; Horn & Cattell, 1967; Salthouse, 2012; Schaie, 1994). Although

38

PART | I Lifespan Perspectives

cognitive and lifespan developmental psychology has studied within and between person changes in cognitive functioning over the lifespan for decades, the linkage to and discussion of what this means for work is relatively new. Therefore, numerous questions at the individual and organizational level of analysis remain. For a detailed list of specific research questions, please see the table presented by Fisher et al. (2017). Some specific recommendations include developing and evaluating interventions to help workers maintain or improve their work ability, particularly to meet the cognitive job demands of their job. Consistent with the practical recommendations suggested earlier, more research is necessary to evaluate how changes in work design interact with cognitive functioning to achieve positive individual and organizational outcomes. Given the novelty of frameworks that outline successful aging at work, additional work should investigate how changes in cognitive functioning over the lifespan are related to these concepts and how organizations can facilitate successful aging and work ability for older workers.

REFERENCES Ackerman, P. L., & Beier, M. E. (2012). The problem is in the definition: g and intelligence in I–O psychology. Industrial and Organizational Psychology, 5, 149 153. Andel, R., Finkel, D., & Pedersen, N. L. (2015). Effects of preretirement work complexity and postretirement leisure activity on cognitive aging. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 71, 849 856. Andel, R., Infurna, F. J., Hahn Rickenbach, E. A., Crowe, M., Marchiondo, L., & Fisher, G. G. (2015). Job strain and trajectories of change in episodic memory before and after retirement: Results from the Health and Retirement Study. Journal of Epidemiology and Community Health, 69, 442 446. Avila-Funes, J. A., Amieva, H., Barberger-Gateau, P., Le Goff, M., Raoux, N., Ritchie, K., . . . Dartigues, J.-F. (2009). Cognitive impairment improves the predictive validity of the phenotype of frailty for adverse health outcomes: The three-city study. Journal of the American Geriatrics Society, 57, 453 461. Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273 285. Bal, A. C., Reiss, A. E. B., Rudolph, C. W., & Baltes, B. B. (2011). Examining positive and negative perceptions of older workers: A meta-analysis. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 66, 687 698. Baltes, P. B. (1968). Longitudinal and cross-sectional sequences in the study of age and generation effects. Human Development, 11, 145 171. Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful aging: The model of selective optimization with compensation. In P. B. Baltes, & M. M. Baltes (Eds.), Successful aging: Perspectives from the behavioral sciences (pp. 1 34). New York, NY: Cambridge University Press. Baltes, P. B., & Mayer, K. U. (Eds.), (2001). The Berlin aging study: Aging from 70 to 100. Cambridge, UK: Cambridge University Press.

Theories of Cognitive Aging and Work Chapter | 2

39

Baltes, P. B., Staudinger, U. M., & Lindenberger, U. (1999). Lifespan psychology: Theory and application to intellectual functioning. Annual Review of Psychology, 50(1), 471 507. Barnes-Farrell, J. L. (2003). Beyond health and wealth: Attitudinal and other influences on retirement decision-making. In G. A. Adams, & T. A. Beehr (Eds.), Retirement: Reasons, processes, and results (pp. 159 187). New York, NY: Springer Publishing Company. Beier, M. E., Teachout, M. S., & Cox, C. B. (2012). The training and development of an aging workforce. Oxford Handbooks Online. Belbase, A., Khan, M., Munnell, A. H., & Webb, A. (2015). Slowed or sidelined? The effect of normal cognitive decline on job performance among the elderly. Report CRR WP 2015-12. Boston, MA: Center for Retirement Research at Boston College. Bertolino, M., Truxillo, D. M., & Fraccaroli, F. (2011). Age as moderator of the relationship of proactive personality with training motivation, perceived career development from training, and training behavioral intentions. Journal of Organizational Behavior, 32, 248 263. Bielak, A. A., Cherbuin, N., Bunce, D., & Anstey, K. J. (2014). Preserved differentiation between physical activity and cognitive performance across young, middle, and older adulthood over 8 years. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 69, 523 532. Bielak, A. A. M., Anstey, K. J., Christensen, H., & Windsor, T. D. (2012). Activity engagement is related to level, but not change in cognitive ability across adulthood. Psychology and Aging, 27, 219 228. Bosma, H., van Boxtel, M. P. J., Ponds, R. W. H. M., Houx, P. J. H., & Jolles, J. (2003). Education and age-related cognitive decline: The contribution of mental workload. Educational Gerontology, 29, 165 173. Bugg, J. M., Zook, N. A., DeLosh, E. L., Davalos, D. B., & Davis, H. P. (2006). Age differences in fluid intelligence: Contributions of general slowing and frontal decline. Brain and Cognition, 62, 9 16. Callahan, J. S., Kiker, D. S., & Cross, T. (2003). Does method matter? A meta-analysis of the effects of training method on older learner training performance. Journal of Management, 29, 663 680. Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. New York, NY: Cambridge University Press. Carstensen, L. L. (1991). Selectivity theory: Social activity in life- span context. In K. W. Schaie, & M. P. Lawton (Eds.), Annual review of gerontology and geriatrics (Vol. 11, pp. 195 217). New York, NY: Springer. Carstensen, L. L. (1995). Evidence for a life-span theory of socioemotional selectivity. Current Directions in Psychological Science, 4, 151 156. Carstensen, L. L., Isaacowitz, D. M., & Charles, S. T. (1999). Taking time seriously: A theory of socioemotional selectivity. American Psychologist, 54, 165 181. Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54, 1 22. Cattell, R. B. (1987). Intelligence: Its structure, growth, and action. New York, NY: Elsevier Science. Clouston, S. A. P., Brewster, P., Kuh, D., Richards, M., Cooper, R., Hardy, R., . . . Hofer, S. M. (2013). The dynamic relationship between physical function and cognition in longitudinal aging cohorts. Epidemiologic Reviews, 35(1), 33 50. Colquitt, J. A., LePine, J. A., & Noe, R. N. (2000). Toward an integrative theory of training motivation: A meta-analytic path analysis of 20 years of research. Journal of Applied Psychology, 85, 678 707.

40

PART | I Lifespan Perspectives

Cooper, R. (2016). Occupational activity across adult life and its association with grip strength. Occupational and Environmental Medicine, 73, 425 426. Craik, F. I. M., & Byrd, M. (1982). Aging and cognitive deficits: The role of attentional resources. In F. I. M. Craik (Ed.), Aging and cognitive processes (pp. 191 211). New York, NY: Plenum Press. Czaja, S. J., Sharit, J., Ownby, R., Roth, D. L., & Nair, S. (2001). Examining age differences in performance of a complex information search and retrieval task. Psychology and Aging, 16, 564 579. Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demandsresources model of burnout. Journal of Applied Psychology, 86, 499 512. Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135 168. Dodge, H. H., Zhu, J., Lee, C. W., Chang, C. C. H., & Ganguli, M. (2014). Cohort effects in age-associated cognitive trajectories. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 69, 687 694. European Agency for Safety and Health at Work. (2007). Expert forecast on emerging psychosocial risks related to occupational safety and health. Luxembourg, Belgium: Office for Official Publications of the European Communities. Finkel, D., Andel, R., Gatz, M., & Pedersen, N. L. (2009). The role of occupational complexity in trajectories of cognitive aging before and after retirement. Psychology and Aging, 24, 563 573. Finkel, D., Reynolds, C. A., McArdle, J. J., & Pedersen, N. L. (2007). Age changes in processing speed as a leading indicator of cognitive aging. Psychology and Aging, 22, 558 568. Fisher, G. G., Chaffee, D. S., & Sonnega, A. (2016). Retirement timing: A review and recommendations for future research. Work, Aging and Retirement, 2, 230 261. Fisher, G. G., Chaffee, D. S., Tetrick, L. E., Davalos, D. B., & Potter, G. G. (2017). Cognitive functioning, aging, and work: A review and recommendations for research and practice. Journal of Occupational Health Psychology, 22(3), 314 336. Fisher, G.G., McGonagle, A.M., Chaffee, D.S., & McCall, B.P. (2016). Depressive symptoms, cognitive functioning, work ability, and retirement expectations. (manuscript in preparation). Fisher, G. G., Stachowski, A., Infurna, F. J., Faul, J. D., Grosch, J., & Tetrick, L. E. (2014). Mental work demands, retirement, and longitudinal trajectories of cognitive functioning. Journal of Occupational Health Psychology, 19, 231 242. Fisher, G. G., Truxillo, D., Finkelstein, L., & Wallace, L. E. (2017). Age discrimination: Potential for adverse impact and differential prediction related to age. Human Resources Management Review, 27(2), 316 327. Flynn, J. R. (1984). The mean IQ of Americans: Massive gains 1932 to 1978. Psychological Bulletin, 95, 29 51. Fratiglioni, L., & Wang, H. X. (2007). Brain reserve hypothesis in dementia. Journal of Alzheimer’s Disease, 12, 11 22. Gerstorf, D., Ram, N., Hoppmann, C., Willis, S. L., & Schaie, K. W. (2011). Cohort differences in cognitive aging and terminal decline in the Seattle Longitudinal Study. Developmental Psychology, 47, 1026 1041. Van Gerven, P. W. M., Paas, F., Van Merrie¨nboer, J. J. G., Hendriks, M., & Schmidt, H. G. (2003). The efficiency of multimedia learning into old age. British Journal of Educational Psychology, 73, 489 505. Glenn, N. D. (2005). Cohort analysis (Vol. 5). Thousand Oaks, CA: Sage. Gottesman, R. F., Schneider, A. L. C., Albert, M., Alonso, A., Bandeen-Roche, K., Coker, L., . . . Mosley, T. H. (2014). Midlife hypertension and 20-year cognitive change: The atherosclerosis risk in communities neurocognitive study. JAMA Neurology, 71, 1218 1227.

Theories of Cognitive Aging and Work Chapter | 2

41

Grzywacz, J. G., Segel-Karpas, D., & Lachman, M. E. (2016). Workplace exposures and cognitive function during adulthood: Evidence from National Survey of Midlife Development and the O* NET. Journal of Occupational and Environmental Medicine, 58, 535 541. Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance, 16, 250 279. Hakun, J. G., Zhu, Z., Brown, C. A., Johnson, N. F., & Gold, B. T. (2015). Longitudinal alterations to brain function, structure, and cognitive performance in healthy older adults: A fMRI-DTI study. Neuropsychologia, 71, 225 235. Horn, J. L., & Cattell, R. B. (1967). Age differences in fluid and crystallized intelligence. Acta Psychologica, 26, 107 129. Hough, L. M., Oswald, F. L., & Ployhart, R. E. (2001). Determinants, detection and amelioration of adverse impact in personnel selection procedures: Issues, evidence and lessons learned. International Journal of Selection and Assessment, 9(1-2), 152 194. Hsu, C. L., Nagamatsu, L. S., Davis, J. C., & Liu-Ambrose, T. (2012). Examining the relationship between specific cognitive processes and falls risk in older adults: a systematic review. Osteoporosis International, 23, 2409 2424. Hsu, Y. S. (2013). Training older workers: A review. In J. Field, R. J. Burke, & C. L. Cooper (Eds.), The SAGE handbook of aging, work and society (pp. 283 299). London: Sage. Huang, Y., Xu, S., Hua, J., Zhu, D., Liu, C., Hu, Y., . . . Xu, D. (2015). Association between job strain and risk of incident stroke: A meta-analysis. Neurology, 85, 1648 1654. Hultsch, D. F., Hertzog, C., Small, B. J., & Dixon, R. A. (1999). Use it or lose it: Engaged lifestyle as a buffer of cognitive decline in aging? Psychology and Aging, 14, 245 263. Ilmarinen, J., Gould, R., Ja¨rvikoski, A., & Ja¨rvisalo, J. (2008). Diversity of work ability. In R. Gould, J. Ilmarinen, J. Ja¨rvisalo, & S. Koskinen (Eds.), Dimensions of work ability. Results of the health 2000 survey (pp. 13 24). Helsinki: Finnish Institute of Occupational Health. Jeske, D., & Stamov Roßnagel, C. (2015). Learning capability and performance in later working life: towards a contextual view. Education + Training, 57, 378 391. Kanfer, R., & Ackerman, P. L. (2004). Aging, adult development, and work motivation. Academy of Management Review, 29, 440 458. Kanfer, R., Beier, M. E., & Ackerman, P. L. (2013). Goals and motivation related to work in later adulthood: An organizing framework. European Journal of Work and Organizational Psychology, 22, 253 264. Karpinska, K., Henkens, K., Schippers, J., & Wang, M. (2015). Training opportunities for older workers in the Netherlands: A Vignette Study. Research in Social Stratification and Mobility, 41, 105 114. Kerchner, G. A., Racine, C. A., Hale, S., Wilheim, R., Laluz, V., Miller, B. L., & Kramer, J. H. (2012). Cognitive processing speed in older adults: Relationship with white matter integrity. PLoS ONE, 7, e50425. Kiss, P., De Meester, M., & Braeckman, L. (2008). Differences between younger and older workers in the need for recovery after work. International Archives of Occupational and Environmental Health, 81, 311 320. Klein, R. M., Dilchert, S., Ones, D. S., & Dages, K. D. (2015). Cognitive predictors and agebased adverse impact among business executives. Journal of Applied Psychology, Advanced online publication. Kobayashi, F. (2004). Job stress and stroke and coronary heart disease. Journal of the Japan Medical Association, 47, 222 226. Kooij, D. T. A. M. (2015). Successful aging at work: The active role of employees. Work, Aging and Retirement, 1, 309 319.

42

PART | I Lifespan Perspectives

Kooij, D. T. A. M., Tims, M., & Akkermans, J. (2017). The influence of future time perspective on work engagement and job performance: the role of job crafting. European Journal of Work and Organizational Psychology, 26(1), 4 15. Kooij, D. T. A. M., Tims, M., & Kanfer, R. (2015). Successful aging at work: The role of job crafting. In P. M. Bal, D. T. A. M. Kooij, & D. M. Rousseau (Eds.), Aging workers and the employee-employer relationship (pp. 145 161). Switzerland: Springer International Publishing. Kubeck, J. E., Delp, N. D., Haslett, T. K., & McDaniel, M. A. (1996). Does job-related training performance decline with age? Psychology and Aging, 11, 92 107. Lazazzara, A., Karpinska, K., & Henkens, K. (2013). What factors influence training opportunities for older workers? Three factorial surveys exploring the attitudes of HR professionals. The International Journal of Human Resource Management, 24, 2154 2172. Levy, B. R., Zonderman, A. B., Slade, M. D., & Ferrucci, L. (2012). Memory shaped by age stereotypes over time. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 67, 432 436. Lezak, M. D., Howieson, D. B., Bigler, E. D., & Tranel, D. (2012). Neuropsychological Assessment (5th ed.). New York, NY: Oxford University Press. Li, K. Z., Lindenberger, U., Freund, A. M., & Baltes, P. B. (2001). Walking while memorizing: Age-related differences in compensatory behavior. Psychological Science, 12, 230 237. Lindenberger, U., & Baltes, P. B. (1994). Sensory functioning and intelligence in old age: a strong connection. Psychology and Aging, 9, 339. Lindenberger, U., Marsiske, M., & Baltes, P. B. (2000). Memorizing while walking: increase in dual-task costs from young adulthood to old age. Psychology and Aging, 15, 417 436. Lytle, M. C., Clancy, M. E., Foley, P. F., & Cotter, E. W. (2015). Current trends in retirement implications for career counseling and vocational psychology. Journal of Career Development, 42, 170 184. Marengoni, A., Fratiglioni, L., Bandinelli, S., & Ferrucci, L. (2011). Socioeconomic status during lifetime and cognitive impairment no-dementia in late life: The population-based aging in the Chianti Area (InCHIANTI) Study. Journal of Alzheimer’s Disease: JAD, 24, 559 568. Maurer, T. J. (2001). Career-relevant learning and development, worker age, and beliefs about self-efficacy for development. Journal of Management, 27, 123 140. Maurer, T. J., Weiss, E. M., & Barbeite, F. G. (2003). A model of involvement in work- related learning and development activity: The effects of individual, situational, motivational, and age variables. Journal of Applied Psychology, 88, 707 724. McArdle, J. J., Hamagami, F., Meredith, W., & Bradway, K. P. (2000). Modeling the dynamic hypotheses of Gf–Gc theory using longitudinal life-span data. Learning and Individual Differences, 12, 53 79. McGonagle, A. K., Fisher, G. G., Barnes-Farrell, J. L., & Grosch, J. W. (2015). Individual and work factors related to perceived work ability and labor force outcomes. Journal of Applied Psychology, 100, 376 398. Nelson, T. D. (2016). Promoting healthy aging by confronting ageism. American Psychologist, 71, 276 282. Ng, T. W., & Feldman, D. C. (2008). The relationship of age to ten dimensions of job performance. Journal of Applied Psychology, 93, 392 423. Ng, T. W., & Feldman, D. C. (2012a). Aging and participation in career development activities. Oxford Handbooks Online.

Theories of Cognitive Aging and Work Chapter | 2

43

Ng, T. W., & Feldman, D. C. (2012b). Evaluating six common stereotypes about older workers with meta-analytical data. Personnel Psychology, 65, 821 858. Norton, S., Matthews, F. E., Barnes, D. E., Yaffe, K., & Brayne, C. (2014). Potential for primary prevention of Alzheimer’s disease: An analysis of population-based data. The Lancet Neurology, 13, 788 794. Oltmanns, J., Godde, B., Winneke, A. H., Richter, G., Niemann, C., Voelcker-Rehage, C., & Staudinger, U. M. (2017). Don’t lose your brain at work–The role of recurrent novelty at work in cognitive and brain aging. Frontiers in Psychology, 8, 117. Park, D. C. (2000). The basic mechanisms accounting for age-related decline in cognitive function. In D. C. Park, & N. Schwarz (Eds.), Cognitive aging: A primer. New York, NY: Routledge. Peterson, M., Kramer, A., & Colcombe, A. (2002). Contextual guidance of attention in younger and older adults. Paper presented at the Cognitive Aging Conference, Atlanta, GA. Posthuma, R. A., & Campion, M. A. (2009). Age stereotypes in the workplace: Common stereotypes, moderators, and future research directions. Journal of Management, 35, 158 188. Potter, G. G., Helms, M. J., & Plassman, B. L. (2008). Associations of job demands and intelligence with cognitive performance among men in late life. Neurology, 70, 1803 1808. Potter, G. G., Plassman, B. L., Helms, M. J., Foster, S. M., & Edwards, N. W. (2006). Occupational characteristics and cognitive performance among elderly male twins. Neurology, 67, 1377 1382. Pransky, G. S., Loisel, P., & Anema, J. R. (2011). Work disability prevention research: current and future prospects. Journal of Occupational Rehabilitation, 21(3), 287. Raemdonck, I., Beausaert, S., Fro¨hlich, D., Kochoian, N., & Meurant, C. (2014). Aging workers’ learning and employability. In P. M. Bal, D. T. A. M. Kooji, & D. M. Rousseau (Eds.), Aging workers and the employee-employer relationship (pp. 163 184). Dordrecht: Springer. Raz, N., Lindenberger, U., Ghisletta, P., Rodrigue, K. M., Kennedy, K. M., & Acker, J. D. (2008). Neuroanatomical correlates of fluid intelligence in healthy adults and persons with vascular risk factors. Cerebral Cortex, 18, 718 726. Reuter-Lorenz, P. A., & Cappell, K. A. (2008). Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Science, 17, 177 182. Ritvanen, T., Louhevaara, V., Helin, P., Va¨isa¨nen, S., & Ha¨nninen, O. (2006). Responses of the autonomic nervous system during periods of perceived high and low work stress in younger and older female teachers. Applied Ergonomics, 37, 311 318. Rohwedder, S., & Willis, R. J. (2010). Mental retirement. Journal of Economic Perspectives, 24, 119 138. Rozas, A. X., Juncos-Rabada´n, O., & Gonza´lez, M. S. (2008). Processing speed, inhibitory control, and working memory: three important factors to account for age-related cognitive decline. International Journal of Aging and Human Development, 66, 115 130. Rudolph, C. W. (2016). Lifespan developmental perspectives on working: A literature review of motivational theories. Work, Aging and Retirement, 2, 130 158. Rudolph, C. W., & Zacher, H. (2016). Considering generations from a lifespan developmental perspective. Work, Aging and Retirement, 3(2), 113 129. Russo, A., Onder, G., Cesari, M., Zamboni, V., Barillaro, C., Capoluongo, E., . . . Ferrucci, L. (2006). Lifetime occupation and physical function: a prospective cohort study on persons aged 80 years and older living in a community. Occupational and Environmental Medicine, 63, 438 442. Salas, E., Tannenbaum, S. I., Kraiger, K., & Smith-Jentsch, K. A. (2012). The science of training and development in organizations: What matters in practice. Psychological Science in the Public Interest, 13, 74 101.

44

PART | I Lifespan Perspectives

Salthouse, T. A. (1991). Theoretical perspectives on cognitive aging. Hillsdale, NJ: Lawrence Earlbaum & Associates. Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403 428. Salthouse, T. A. (2006). Mental exercise and mental aging: Evaluating the validity of the “use it or lose it” hypothesis. Perspectives on Psychological Science, 1, 67 87. Salthouse, T. A. (2012). Consequences of age-related cognitive declines. Annual Review of Psychology, 63, 201 226. Salthouse, T. A. (2013). Within-cohort age-related differences in cognitive functioning. Psychological Science, 24, 123 130. Salthouse, T. A., & Babcock, R. L. (1991). Decomposing adult age differences in working memory. Developmental Psychology, 27, 763 776. Salthouse, T. A., Berish, D. E., & Miles, J. D. (2002). The role of cognitive stimulation on the relations between age and cognitive functioning. Psychology and Aging, 17, 548. Salthouse, T. A., & Madden, D. J. (2015). Information processing speed and aging. In J. DeLuca, & J. H. Kalmar (Eds.), Information processing speed in clinical populations (pp. 221 242). New York, NY: Routledge. Schaie, K. W. (1994). The course of adult intellectual development. American psychologist, 49 (4), 304 313. Schaie, K. W. (2005). Developmental influences on adult intelligence: The Seattle longitudinal study. Oxford, UK: Oxford University Press. Schlosser Covell, G. E., Hoffman-Snyder, C. R., Wellik, K. E., Woodruff, B. K., Geda, Y. E., Caselli, R. J., . . . Wingerchuk, D. M. (2015). Physical activity level and future risk of mild cognitive impairment or dementia: A critically appraised topic. The Neurologist, 19(3), 89 91. Schooler, C. (1984). Psychological effects of complex environments during the life span: A review and theory. Intelligence, 8, 259 281. Schooler, C. (1990). Psychological factors and effective cognitive functioning through the life span. In J. E. Birren, & K. W. Schaie (Eds.), Handbook of the psychology of aging (pp. 347 358). Orlando, FL: Academic Press. Schooler, C. (2007). Use it – and keep it, longer, probably. A reply to Salthouse (2006). Perspectives on Psychological Science, 2, 24 29. Schooler, C., Mulatu, M. S., & Oates, G. (1999). The continuing effects of substantively complex work on the intellectual functioning of older workers. Psychology and Aging, 14, 483 506. Schulz, M., & Stamov Roßnagel, C. (2010). Informal workplace learning: An exploration of age differences in learning competence. Learning and Instruction, 20, 383 399. Schwatka, N. V., Butler, L. M., & Rosecrance, J. R. (2012). An aging workforce and injury in the construction industry. Epidemiologic Reviews, 34, 156 167. Skirbekk, V., Stonawski, M., Bonsang, E., & Staudinger, U. M. (2013). The Flynn effect and population aging. Intelligence, 41(3), 169 177. Sliwinski, M. J., Smyth, J. M., Hofer, S. M., & Stawski, R. S. (2006). Intraindividual coupling of daily stress and cognition. Psychology and Aging, 21, 545 557. Smyth, K. A., Fritsch, T., Cook, T. B., McClendon, M. J., Santillan, C. E., & Friedland, R. P. (2004). Worker functions and traits associated with occupations and the development of AD. Neurology, 63, 498 503. Stamov-Roßnagel, C., & Biemann, T. (2012). Ageing and work motivation: A task-level perspective. Journal of Managerial Psychology, 27, 459 478.

Theories of Cognitive Aging and Work Chapter | 2

45

Staudinger, U. M., Finkelstein, R., Calvo, E., & Sivaramakrishnan, K. (2016). A global view on the effects of work on health in later life. The Gerontologist, 56(Suppl 2), S281 S292. Stawski, R. S., Sliwinski, M. J., & Smyth, J. M. (2006). Stress-related cognitive interference predicts cognitive function in old age. Psychology and Aging, 21, 535 544. Stuss, D. T., & Knight, R. T. (2013). Principles of frontal lobe function (2nd ed.). New York, NY: Oxford University Press. Truxillo, D. M., Cadiz, D. M., Rineer, J. R., Zaniboni, S., & Fraccaroli, F. (2012). A lifespan perspective on job design: Fitting the job and the worker to promote job satisfaction, engagement, and performance. Organizational Psychology Review, 2, 340 360. von Hippel, W., & Dunlop, S. M. (2005). Aging, inhibition, and social inappropriateness. Psychology and Aging, 20(3), 519 523. Walker-Bone, K., D’Angelo, S., Syddall, H. E., Palmer, K. T., Cooper, C., Coggon, D., & Sayer, A. A. (2016). Heavy manual work throughout the working lifetime and muscle strength among men at retirement age. Occupational and Environmental Medicine, 73, 284, dave286. Wang, M., Olson, D. A., & Shultz, K. S. (2013). Mid and late career issues: An integrative perspective. New York, NY: Routledge Academic Press. Wolfson, N. E., Cavanagh, T. M., & Kraiger, K. (2014). Older adults and technology-based instruction: Optimizing learning outcomes and transfer. Academy of Management Learning & Education, 13, 26 44. Yaffe, K., Vittinghoff, E., Pletcher, M. J., Hoang, T. D., Launer, L. J., Whitmer, R., . . . Sidney, S. (2014). Early adult to midlife cardiovascular risk factors and cognitive function. Circulation, 129, 1560 1567. Yeoh, H. T., Lockhart, T. E., & Wu, X. (2013). Non-fatal occupational falls on the same level. Ergonomics, 56, 153 165. Zacher, H. (2015). The importance of a precise definition, comprehensive model, and critical discussion of successful aging at work. Work, Aging and Retirement, 1, 320 333. Zaniboni, S., Truxillo, D. M., & Fraccaroli, F. (2013). Differential effects of task variety and skill variety on burnout and turnover intentions for older and younger workers. European Journal of Work and Organizational Psychology, 22, 306 317. Zhang, J., Lockhart, T. E., & Soangra, R. (2015). Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors. Annals of Biomedical Engineering, 42, 600 612.