Skip to main content

MINI REVIEW article

Front. Aging Neurosci., 24 August 2022
Sec. Neurocognitive Aging and Behavior
Volume 14 - 2022 | https://doi.org/10.3389/fnagi.2022.980599

Busyness, mental engagement, and stress: Relationships to neurocognitive aging and behavior

  • Department of Psychology, University of Tampa, Tampa, FL, United States

Considerable research identifies benefits of sustaining mental engagement in older adulthood. Frequent social, mental, and physical activities (e.g., exercise) and lifestyle factors that bolster cognitive reserve (i.e., education, occupation complexity) have been associated with cognitive benefits and delayed onset of dementia. Nevertheless, the relationship between general daily levels of busyness and cognition has been relatively understudied. Open questions remain about whether a causal link exists between a busy lifestyle and mental prowess, the relationship between busyness and stress, and methodological approaches to measure and track busyness levels. Here, the existing evidence is considered, along with future directions for research aimed at characterizing the effects of a busy lifestyle on neurocognitive aging and behavior.

Introduction

Extensive evidence indicates that cognition shows deficits with increasing age (see Salthouse, 2009; Park and Festini, 2017, 2018). In particular, fluid cognitive abilities like processing speed and episodic memory show the largest age-related decrements, whereas crystallized abilities like semantic memory (e.g., vocabulary) remain stable or increase with age (Park et al., 2002). Age-related diseases, like Alzheimer’s and cerebrovascular disease, also contribute to age-related cognitive deficits, and it can be difficult to differentiate non-pathological aging from underlying pre-clinical disease processes (Sperling et al., 2011). To counteract cognitive deficits associated with advanced age, some research has aimed to identify sources of cognitive preservation in older adulthood. Multiple studies have documented benefits of social, mental, and physical activities (i.e., exercise; new learning) on cognition and brain health in older adults (e.g., Colcombe et al., 2006; Carlson et al., 2009; Park et al., 2014). Further, lifestyle factors that promote cognitive reserve (Stern, 2002), such as high education and occupation complexity (see Hussenoeder et al., 2019) have been found to be related with better cognitive functioning (e.g., Correa Ribeiro et al., 2013) and lower risk of Alzheimer’s disease (e.g., Andel et al., 2005).

However, relatively little research has been conducted to examine the effects of general daily levels of busyness on cognition. Busyness can be defined as having one’s time occupied by frequent obligations (Gershuny, 2005). The Martin and Park Environmental Demands Questionnaire (MPED; Martin and Park, 2003) busyness subscale has been used as an assessment of busyness (e.g., Festini et al., 2016, 2019; Kaya et al., 2019). This self-report busyness subscale includes questions like, “How often do you have too many things to do each day to actually get them all done?” or “How often do you rush out of the house in the mornings to get where you need to be?” Thus, it is distinct from other measures of mental engagement and stress because it specifically assesses busyness and task load. Festini et al. (2016) reported that busier middle-aged and older adults tended to have better cognition, with the strongest relationship observed for episodic memory. This provided an initial demonstration of the potential benefits of living a busy, engaged lifestyle, but much additional research needs to be conducted. It is still unknown whether this relationship is causal–that is, whether or not being busy causes preservation of cognitive abilities, or if the relationship was observed simply because people with better mental function are capable of living busier lives. Moreover, it will be imperative to examine the interaction between busyness, cognition, and stress within the same individuals because it is possible that busyness that becomes stressful may impair cognitive performance, as literature also frequently observes negative consequences of stress on cognitive and brain health (Lupien et al., 2009). Here, I address several important areas for future research, while situating these future studies in the current literature. The focus is primarily on psychological research that addresses “busyness” and “busy lifestyles” directly. Important relevant literature on related concepts regarding mental engagement is briefly considered (for comprehensive reviews see Butler et al., 2018; Bielak and Gow, 2022; Roheger et al., 2022).

Brief review of critical literature

Activity levels

Substantial correlational evidence documents a relationship between heightened activity levels and better cognitive and neural health. Activity level research often implements self-report assessments of a broad range of daily activities, including how often individuals partake in social, physical, and cognitive activities. For example, Seeman et al. (2011) observed that greater social engagement was associated with better episodic memory and executive functioning in middle-aged and older adults. Similarly, Valenzuela and Sachdev (2007) found that individuals with more lifetime experiences, an assessment of intellectual activity across one’s lifetime, had less cognitive decline over 18 months. Activity level research has repeatedly documented favorable associations between frequent activities and neurocognitive aging (see Anaturk et al., 2018; Gheysen et al., 2018).

Cognitive reserve, brain maintenance, and STAC-r

The related concepts of cognitive reserve and brain maintenance propose that characteristics like education, occupation complexity, and intellectual challenge can promote maintenance of cognitive function despite brain pathology (e.g., Stern, 2002; Barulli and Stern, 2013; Stern et al., 2020). That is, certain lifestyle factors are proposed to be protective that allow older adults to maintain better overall cognition and delay symptoms of cognitive decline (Soldan et al., 2017; for a review see Song et al., 2022). For example, those individuals with higher education showed more brain atrophy, despite similar cognitive performance (Coffey et al., 1999), suggesting that higher education enables preservation of cognitive faculties despite more pronounced brain pathology. When examining a composite measure of cognitive reserve that included education, occupation, IQ, and intellectual/social activities, Sole-Padulles et al. (2009) observed that cognitive reserve was associated with both larger brain size and increased neural efficiency (cf. Foubert-Samier et al., 2012).

Also considering protective neural enrichment factors, the Scaffolding Theory of Aging and Cognition-revised (STAC-r; Reuter-Lorenz and Park, 2014) proposes that lifestyle factors like education, physical fitness, and multilingualism can promote compensatory neural scaffolding that assists performance. Older adults who have better brain health and who more efficiently use alternate neural resources are proposed to exhibit better cognition and less cognitive decline (see Festini et al., 2018).

Stress and cognition

Much prior research has documented the detrimental effects of stress on neurocognitive function. Non-human animal studies display that unpredictable chronic stress can impair memory, increase anxiety and depressive symptoms, as well as reduce the growth of new neurons in the hippocampus (see Parihar et al., 2011). For instance, Li et al. (2008) exposed mice to chronic mild stress, such as periods of restricted access to food or continuous light, for 5 weeks, and observed memory disruption. Chen et al. (2010) found that even a 5-h period of acute stress impaired memory, reduced hippocampal dendritic spine density, and disrupted long-term potentiation in mice. Similarly, in humans, stress impairs mental functioning under certain contexts. Oei et al. (2006) observed that stress impaired human working memory performance at high memory loads only (for a review see Martin et al., 2019). The stress hormone cortisol has also been shown to impair memory retrieval of well-learned memories in humans (Wolf et al., 2004). And, literature on burnout finds that uncontrollable stress and feeling over-worked can disrupt not only cognitive performance but also interpersonal interactions and wellbeing (Arnsten and Shanafelt, 2021; Romito et al., 2021).

Nevertheless, some studies report benefits of mild stress. Kofman et al. (2006) observed that undergraduates exhibited superior task-switching and attentional control when anxiety levels were higher at the end-of-the-semester. Some studies also observed that prolonged mild stress can increase neurogenesis in the hippocampus, improve memory, and reduce symptoms of depression and anxiety in rats (Parihar et al., 2011).

Thus, it has been proposed that the relationship between stress and cognition follows an inverted-U pattern (Lupien et al., 2009), such that optimal performance occurs with moderate stress, but that too little or too much stress impairs performance (cf. Yerkes and Dodson, 1908). Perhaps the relationship between busyness and mental functioning follows a similar pattern.

Current research on busyness

Festini et al. (2016) examined the relationship between busyness and cognition in middle-aged and older adults. Those participants who were busier tended to have better processing speed, working memory, reasoning, and crystallized knowledge, with the strongest correlation between busyness and episodic memory. Moreover, busyness accounted for additional variance in all cognitive constructs, even after controlling for age and education.

Notably, these effect sizes between busyness and cognition were small to moderate (magnitudes of 0.16 to 0.32). These effects were observed with a relatively large sample size (330 participants). Thus, in future research, although cumbersome, relatively large samples will be needed to have sufficient power to detect such effect sizes.

Additional busyness research has observed that many, but not all, individuals perceive themselves as busy. Kaya et al. (2019) reported that over three-quarters of their sample of 22- to 54-year-olds characterized themselves as a busy person. Moreover, being busy has been proposed to be a badge of honor, demonstrating high social status and frequent contributions toward society (Gershuny, 2005; Bellezza et al., 2016). Relatedly, research on time shortage perceptions indicates that people often report feeling that they do not have sufficient time to complete what they want to do and feel rushed (Rudd, 2019). A model that considered demographic, personality, health, and activity measures found that busyness was best predicted by younger age, female gender, agreeable and neurotic personality, high levels of need for cognition (i.e., enjoyment of effortful thinking; Cacioppo et al., 1984), and frequent participation in novel activities (Festini et al., 2019).

Related research on retirement has found that partial retirement in the same job negatively impacted cognition, whereas partial retirement with a new employer benefited cognition in those with complex occupations (Mizuochi and Raymo, 2022; cf. Kajitani et al., 2016). This is consistent with the notion that busyness levels drop during retirement, and that new learning at a novel workplace is beneficial to cognition. Interestingly, Atalay et al. (2019) observed similar cognitive decline following retirement, regardless of whether it was forced or voluntary, suggesting that, indeed, the reduction in mental engagement contributes to cognitive decline.

Areas for future research on busyness

Research targeting a causal question

Although methodologically difficult, experimentally manipulating busyness levels is needed to address causality. Currently, only correlational assessments between busyness and cognitive abilities have been performed due to the difficulty of randomly assigning busyness.1 Nevertheless, lifestyle interventions have been conducted previously with success. See Table 1 for example intervention studies and their observed benefits (see also Butler et al., 2018; Gomes-Osman et al., 2018). This experimental evidence provides support for use-dependent neural plasticity (e.g., May, 2011; McDonough et al., 2015) and offers a proposed mechanism for why cognition can improve with sustained mental challenge.

TABLE 1
www.frontiersin.org

Table 1. Example lifestyle interventions and their impacts on cognitive health.

To experimentally manipulate busyness, one group of participants could be required to engage in a certain number of tasks for a specified duration/frequency. Pilot studies could determine the optimal number of activities to require, by video-tracking or detailed logging of pilot participants’ busyness/activity levels.

The key would be to allow participants to choose which activities they perform to keep themselves busy, rather than prescribing activities. Thus, the intervention would manipulate the busyness of the individuals rather than the exact type of tasks. Many prior intervention studies understandably focus on specific tasks, such as exercise (see Gomes-Osman et al., 2018), volunteering (Musick et al., 1999), or student mentoring (Carlson et al., 2008). Leaving the choice of the activities up to the participants may reduce stress, as enjoyment would likely be higher for self-chosen activities.

The control group may best be designed as a wait-list control group, where participants eventually receive the option to bolster their busyness levels once the control period is complete. A wait-list design would additionally allow researchers to perform analyses within-participants, when the same individual leads a less versus more busy lifestyle. Cognitive abilities would be assessed pre- and post-intervention and compared between the experimental busy group to the non-busy control group/condition.

Interaction between busyness, cognition, and stress

It will also be informative to simultaneously track busyness and stress levels within the same individuals. One individual may find their busy schedule stressful, whereas another may find it enjoyable and fulfilling. Thus, assessing both stress and busyness levels would help determine if busyness is only beneficial if it does not result in a stress response. Indeed, the relationship between busyness and cognition may follow an inverted-U pattern (Yerkes and Dodson, 1908), where moderate levels of busyness are best. See Figure 1. One study observed that, in undergraduates, busier participants also reported more stress (Ramsdell and Festini, 2021). Additional work is needed to systematically evaluate the relationship between busyness and stress as it relates to cognition throughout the adult lifespan.

FIGURE 1
www.frontiersin.org

Figure 1. Hypothesized inverted-U relationship between busyness and cognition. Additional research is needed to test this proposed relationship. Optimal cognitive performance is predicted with moderate-to-high busyness, before extreme stress/burnout is reached.

Another aspect worthy of investigation is whether people enjoy the activities that are keeping them busy. One could imagine different types of busy lifestyles–one with activities of their own choosing, and another with obligatory rather than self-selected activities. The type of activities that keep one busy may predict stress. Therefore, future research would benefit from assessing factors such as the enjoyment of, and the type of, activities contributing to busyness.

Busyness, cognitive reserve, and brain reserve

Research devoted to cognitive and brain reserve often uses education, occupation complexity, and IQ as proxies of reserve (e.g., Speer and Soldan, 2015; Franzmeier et al., 2017), the idea being that those with higher levels of education, more cognitively demanding occupations, and higher mental capacity are better able to cope with age-related brain pathology (e.g., Stern, 2002; Richards and Deary, 2005). It may be worthwhile to include assessments of busyness in evaluations of cognitive reserve, as busyness may promote cognitive resources similarly to the existing proxies. For instance, occupation complexity is often coded based on the degree to which one’s job requires complex interactions with data (analyzing), people (mentoring), or things (precision working) (Smart et al., 2014). In a similar vein, greater busyness is likely to provide more frequent opportunities for complex daily interactions and new learning, which have been shown to be beneficial (Park et al., 2014; Shors, 2014). Future research could include busyness as a proxy of cognitive reserve, either in isolation, or in conjunction with other measures, as it can provide another window into the complexity of one’s life.

Longitudinal assessments of busyness and cognition

Just as influential research has evaluated longitudinal changes in both activity levels and cognition, it may similarly be useful to assess longitudinal changes in busyness and cognition. New or existing longitudinal studies that track cognitive ability or conversion to dementia could add a busyness assessment to determine if there are changes in busyness and cognition across the lifespan within the same individuals. Such longitudinal research is also informative for determining how much variability there is in busyness within an individual over the course of their life. The busiest younger adults may similarly be the busiest older adults. Further, it may be that busyness in young- or middle-adulthood is more beneficial at promoting cognitive reserve and resilience in older age. Such questions remain to be evaluated.

Methodological considerations for the assessment of busyness

Ecological momentary assessments

Ecological momentary assessments (EMAs) offer another promising direction for future research on busyness. Instead of asking people to reflect back, EMAs ask participants to answer questions at the present moment, while they are living their normal daily lives (Shiffman et al., 2008). For example, EMAs ask research participants periodically throughout the day to record what they were doing at that moment. This would provide more quantifiable data regarding the number of tasks that people engage in, as well as the relative proportion of time that was spent during different types of activities. One benefit of EMAs is that they are less prone to recall errors (Shiffman et al., 2008), and would provide more ecologically valid measures of busyness. Kamarck et al. (2007) demonstrated that real-life EMAs of job strain collected at 45-min intervals for 6 days predicted future carotid artery blockage, whereas a global questionnaire did not, providing evidence for the superiority of real-time measurements. Further, EMAs of cognitive abilities, like working memory, have been found to be reliable (Sliwinski et al., 2018), demonstrating the option to assess both busyness and cognition using EMAs in real-life settings.

Additional survey development

While the MPED (Martin and Park, 2003) is a useful tool, it would be beneficial to develop alternate validated self-report assessments of busyness that measure enjoyment of activities, number of different simultaneous obligations, and organized as opposed to rushed busyness. One could imagine a busy life that is scheduled and organized, still with many tasks and obligations, being different than a frantic and hectic busy schedule. Busyness could also be evaluated at different time frames, such as currently, during the last year, etc.

Busyness and age

Busyness in younger adults

Given the paucity of research on busyness in general, it is not surprising that little research has assessed busyness in younger adults. One study observed that undergraduates who were more academically engaged also tended to be more socially engaged, but there was no significant relationship between episodic memory and academic engagement, social engagement, or busyness (Ramsdell and Festini, 2021). It is likely that the relationship between busyness and cognition is weaker in younger than older adults. Several factors contribute to this hypothesis. First, evidence indicates that, on average, younger adults (ages 20–39) live busier lifestyles than older adults (Festini et al., 2019). Further, on average, younger adults have superior cognitive abilities (Park et al., 2002). Thus, the more limited variability in busyness and cognition, and the lower likelihood of neurocognitive decline in younger adults, makes it less likely that strong relationships will exist between busyness and cognition in younger adults.

Busyness and parenthood

Future research should examine differences in busyness for parents versus non-parents. Childrearing adds many daily responsibilities that likely influence busyness levels. It would be worthwhile to evaluate busyness in working and stay-at-home parents and non-parents, including assessment of potential gender differences. Prior research indicates that women tend to be busier (Festini et al., 2019), and, although parent-status was not measured, interestingly, women reported high levels of busyness in the 20s and 30s, whereas men reported low busyness in the 20s but high busyness in the 30s, potentially influenced by parenthood (see Verbrugge et al., 1996).

Busyness in older adults

Indeed, the potential beneficial effects of a busy lifestyle are likely most noticeable in older adults, who report lower levels of busyness in general (Festini et al., 2019), and have higher likelihood of cognitive decline due to normal aging or underlying pathology (e.g., McDonough et al., 2020). The beneficial effects of a busy lifestyle may also have the largest impact on the wellbeing of older adults and their families, as finding ways to postpone cognitive decline has truly meaningful impacts.

Discussion and conclusion

Overall, future research on busyness can target many unanswered questions. One critical question will be to test a causal mechanism with an experimental busyness intervention. It will also be valuable to develop additional tools to assess busyness, including EMAs, to measure both busyness and stress within-individuals, and to track busyness longitudinally. Living a busy, yet low stress, lifestyle may be one strategy to boost brain health and is a worthy avenue for additional research.

Author contributions

SF contributed to the conception of the manuscript, performed the literature review, and wrote and edited all sections of the manuscript.

Funding

This study was supported by the University of Tampa Research Innovation and Scholarly Excellence Award/David Delo Research Grant.

Acknowledgments

The author thanks the editor and reviewers for their constructive feedback on this manuscript.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

  1. ^ Forced retirement has causality implications too.

References

Adcock, M., Fankhauser, M., Post, J., Lutz, K., Zizlsperger, L., Luft, A. R., et al. (2020). Effects of an in-home multicomponent exergame training on physical functions, cognition, and brain volume of older adults: A randomized controlled trial. Front. Med. (Lausanne) 6:321. doi: 10.3389/fmed.2019.00321

CrossRef Full Text | Google Scholar

Anaturk, M., Demnitz, N., Ebmeier, K. P., and Sexton, C. E. (2018). A systematic review and meta-analysis of structural magnetic resonance imaging studies investigating cognitive and social activity levels in older adults. Neurosci. Biobehav. Rev. 93, 71–84. doi: 10.1016/j.neubiorev.2018.06.012

CrossRef Full Text | Google Scholar

Andel, R., Crowe, M., Pedersen, N. L., Mortimer, J., Crimmins, E., Johansson, B., et al. (2005). Complexity of work and risk of Alzheimer’s Disease: A population-based study of Swedish twins. J. Gerontol. 60, 251–258.

Google Scholar

Arnsten, A. F. T., and Shanafelt, T. (2021). Physician distress and burnout: The neurobiological perspective. Mayo Clin. Proc. 96, 763–769. doi: 10.1016/j.mayocp.2020.12.027

CrossRef Full Text | Google Scholar

Atalay, K., Barrett, G. F., and Staneva, A. (2019). The effect of retirement on elderly cognitive functioning. J. Health Econ. 66, 37–53. doi: 10.1016/j.jhealeco.2019.04.006

CrossRef Full Text | Google Scholar

Barulli, D., and Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: Emerging concepts in cognitive reserve. Trends Cogn. Sci. 17, 502–509. doi: 10.1016/j.tics.2013.08.012

CrossRef Full Text | Google Scholar

Bellezza, S., Paharia, N., and Keinan, A. (2016). Conspicuous consumption of time: When busyness and lack of leisure time become a status symbol. J. Consum. Res. 44, 118–138. doi: 10.1093/jcr/ucw076

CrossRef Full Text | Google Scholar

Bielak, A. A. M., and Gow, A. J. (2022). A Decade Later on How to “Use It” So We Don’t “Lose It”: An update on the unanswered questions about the influence of activity participation on cognitive performance in older age. Gerontology 1–20. doi: 10.1159/000524666

CrossRef Full Text | Google Scholar

Bonnechere, B., Klass, M., Langley, C., and Sahakian, B. J. (2021). Brain training using cognitive apps can improve cognitive performance and processing speed in older adults. Sci. Rep. 11:12313. doi: 10.1038/s41598-021-91867-z

CrossRef Full Text | Google Scholar

Boyke, J., Driemeyer, J., Gaser, C., Buchel, C., and May, A. (2008). Training-induced brain structure changes in the elderly. J. Neurosci. 28, 7031–7035. doi: 10.1523/JNEUROSCI.0742-08.2008

CrossRef Full Text | Google Scholar

Butler, M., McCreedy, E., Nelson, V. A., Desai, P., Ratner, E., Fink, H. A., et al. (2018). Does cognitive training prevent cognitive decline?: A systematic review. Ann. Intern. Med. 168, 63–68. doi: 10.7326/M17-1531

CrossRef Full Text | Google Scholar

Cacioppo, J. T., Petty, R. E., and Kao, C. F. (1984). The efficient assessment of need for cognition. J. Pers. Assess. 48, 306–307. doi: 10.1207/s15327752jpa4803_13

CrossRef Full Text | Google Scholar

Carlson, M. C., Erickson, K. I., Kramer, A. F., Voss, M. W., Bolea, N., Mielke, M., et al. (2009). Evidence for neurocognitive plasticity in at-risk older adults: The experience corps program. J. Gerontol. A Biol. Sci. Med. Sci. 64, 1275–1282. doi: 10.1093/gerona/glp117

CrossRef Full Text | Google Scholar

Carlson, M. C., Saczynski, J. S., Rebok, G. W., Seeman, T., Glass, T. A., McGill, S., et al. (2008). Exploring the effects of an “everyday” activity program on executive function and memory in older adults: Experience corps. Gerontologist 48, 793–801. doi: 10.1093/geront/48.6.793

PubMed Abstract | CrossRef Full Text | Google Scholar

Chan, M. Y., Haber, S., Drew, L. M., and Park, D. C. (2014). Training older adults to use tablet computers: Does it enhance cognitive function? Gerontologist 56, 475–484. doi: 10.1093/geront/gnu057

CrossRef Full Text | Google Scholar

Chen, Y., Rex, C. S., Rice, C. J., Dube, C. M., Gall, C. M., Lynch, G., et al. (2010). Correlated memory defects and hippocampal dendritic spine loss after acute stress involve corticotropin-releasing hormone signaling. Proc. Natl. Acad. Sci. U.S.A. 107, 13123–13128. doi: 10.1073/pnas.1003825107

CrossRef Full Text | Google Scholar

Coffey, C. E., Saxton, J. A., Ratcliff, G., Bryan, R. N., and Lucke, J. F. (1999). Relation of education to brain size in normal aging: Implications for the reserve hypothesis. Neurology 53, 189–196. doi: 10.1212/wnl.53.1.189

PubMed Abstract | CrossRef Full Text | Google Scholar

Colcombe, S., Erickson, K. I., Scalf, P. E., Kim, J. S., Prakash, R., McAuley, E., et al. (2006). Aerobic exercise training increases brain volume in aging humans. J. Gerontol. 61, 1166–1170. doi: 10.1093/gerona/61.11.1166

CrossRef Full Text | Google Scholar

Correa Ribeiro, P. C., Lopes, C. S., and Lourenco, R. A. (2013). Complexity of lifetime occupation and cognitive performance in old age. Occup. Med. (Lond) 63, 556–562. doi: 10.1093/occmed/kqt115

CrossRef Full Text | Google Scholar

Engvig, A., Fjell, A. M., Westlye, L. T., Moberget, T., Sundseth, O., Larsen, V. A., et al. (2010). Effects of memory training on cortical thickness in the elderly. NeuroImage 52, 1667–1676. doi: 10.1016/j.neuroimage.2010.05.041

CrossRef Full Text | Google Scholar

Festini, S. B., Hertzog, C., McDonough, I. M., and Park, D. C. (2019). What makes us busy? Predictors of perceived busyness across the adult lifespan. J. Gen. Psychol. 146, 111–133. doi: 10.1080/00221309.2018.1540396

CrossRef Full Text | Google Scholar

Festini, S. B., McDonough, I. M., and Park, D. C. (2016). The busier the better: Greater busyness is associated with better cognition. Front. Aging Neurosci. 8:98. doi: 10.3389/fnagi.2016.00098

CrossRef Full Text | Google Scholar

Festini, S. B., Zahodne, L., and Reuter-Lorenz, P. A. (2018). “Theoretical perspectives on age differences in brain activation: HAROLD, PASA, CRUNCH—How do they STAC up?,” in Oxford research encyclopedia of psychology, ed. B. G. Knight (New York, NY: Oxford University Press), doi: 10.1093/acrefore/9780190236557.013.400

CrossRef Full Text | Google Scholar

Foubert-Samier, A., Catheline, G., Amieva, H., Dilharreguy, B., Helmer, C., Allard, M., et al. (2012). Education, occupation, leisure activities, and brain reserve: A population-based study. Neurobiol. Aging 33, e415–e425. doi: 10.1016/j.neurobiolaging.2010.09.023

CrossRef Full Text | Google Scholar

Franzmeier, N., Buerger, K., Teipel, S., Stern, Y., Dichgans, M., Ewers, M., et al. (2017). Cognitive reserve moderates the association between functional network anti-correlations and memory in MCI. Neurobiol. Aging 50, 152–162. doi: 10.1016/j.neurobiolaging.2016.11.013

CrossRef Full Text | Google Scholar

Gershuny, J. (2005). Busyness as the badge of honor for the new superordinate working class. Soc. Res. 72, 287–314.

Google Scholar

Gheysen, F., Poppe, L., DeSmet, A., Swinnen, S., Cardon, G., De Bourdeaudhuij, I., et al. (2018). Physical activity to improve cognition in older adults: Can physical activity programs enriched with cognitive challenges enhance the effects? A systematic review and meta-analysis. Int. J. Behav. Nutr. Phys. Act. 15:63. doi: 10.1186/s12966-018-0697-x

CrossRef Full Text | Google Scholar

Gomes-Osman, J., Cabral, D. F., Morris, T. P., McInerney, K., Cahalin, L. P., Rundek, T., et al. (2018). Exercise for cognitive brain health in aging: A systematic review for an evaluation of dose. Neurol. Clin. Pract. 8, 257–265. doi: 10.1212/CPJ.0000000000000460

CrossRef Full Text | Google Scholar

Hussenoeder, F. S., Riedel-Heller, S. G., Conrad, I., and Rodriguez, F. S. (2019). Concepts of mental demands at work that protect against cognitive decline and dementia: A systematic review. Am. J. Health Promot. 33, 1200–1208. doi: 10.1177/0890117119861309

CrossRef Full Text | Google Scholar

Kajitani, S., Sakata, K. E. I., and McKenzie, C. (2016). Occupation, retirement and cognitive functioning. Ageing Soc. 37, 1568–1596. doi: 10.1017/s0144686x16000465

CrossRef Full Text | Google Scholar

Kamarck, T. W., Muldoon, M. F., Shiffman, S. S., and Sutton-Tyrrell, K. (2007). Experiences of demand and control during daily life are predictors of carotid atherosclerotic progression among healthy men. Health Psychol. 26, 324–332. doi: 10.1037/0278-6133.26.3.324

CrossRef Full Text | Google Scholar

Kaya, Ç, ÖTken, B., and BeşEr, S. G. (2019). Is busyness a new trend among white collars? Bus. Manag. Stud. 7, 527–541. doi: 10.15295/bmij.v7i1.1088

CrossRef Full Text | Google Scholar

Kofman, O., Meiran, N., Greenberg, E., Balas, M., and Cohen, H. (2006). Enhanced performance on executive functions associated with examination stress: Evidence from task-switching and Stroop paradigms. Cogn. Emot. 20, 577–595. doi: 10.1080/02699930500270913

CrossRef Full Text | Google Scholar

Li, S., Wang, C., Wang, W., Dong, H., Hou, P., and Tang, Y. (2008). Chronic mild stress impairs cognition in mice: From brain homeostais to behavior. Life Sci. 82, 934–942. doi: 10.1016/j.lfs.2008.02.010

CrossRef Full Text | Google Scholar

Lovden, M., Schaefer, S., Noack, H., Bodammer, N. C., Kuhn, S., Heinze, H. J., et al. (2012). Spatial navigation training protects the hippocampus against age-related changes during early and late adulthood. Neurobiol. Aging 33, 620.e9–620.e22. doi: 10.1016/j.neurobiolaging.2011.02.013

CrossRef Full Text | Google Scholar

Lupien, S. J., McEwen, B. S., Gunnar, M. R., and Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nat. Rev. Neurosci. 10, 434–445. doi: 10.1038/nrn2639

CrossRef Full Text | Google Scholar

Martin, K., McLeod, E., Periard, J., Rattray, B., Keegan, R., and Pyne, D. B. (2019). The impact of environmental stress on cognitive performance: A systematic review. Hum. Factors 61, 1205–1246. doi: 10.1177/0018720819839817

CrossRef Full Text | Google Scholar

Martin, M., and Park, D. C. (2003). The Martin and Park Environmental Demands (MPED) questionnaire: Psychometric properties of a brief instrument to measure self-reported environmental demands. Aging Clin. Exp. Re. 15, 77–82. doi: 10.1007/BF03324483

CrossRef Full Text | Google Scholar

May, A. (2011). Experience-dependent structural plasticity in the adult human brain. Trends Cogn. Sci. 15, 475–482. doi: 10.1016/j.tics.2011.08.002

CrossRef Full Text | Google Scholar

McDonough, I. M., Festini, S. B., and Wood, M. M. (2020). Risk for Alzheimer’s disease: A review of long-term episodic memory encoding and retrieval fMRI studies. Ageing Res. Rev. 62:101133. doi: 10.1016/j.arr.2020.101133

CrossRef Full Text | Google Scholar

McDonough, I. M., Haber, S., Bischof, G. N., and Park, D. C. (2015). The Synapse Project: Engagement in mentally challenging activities enhances neural efficiency. Restor. Neurol. Neurosci. 33, 865–882. doi: 10.3233/RNN-150533

CrossRef Full Text | Google Scholar

Mizuochi, M., and Raymo, J. M. (2022). Retirement type and cognitive functioning in Japan. J. Gerontol. B Psychol. Sci. Soc. Sci. 77, 759–768. doi: 10.1093/geronb/gbab187

CrossRef Full Text | Google Scholar

Musick, M. A., Herzog, A. R., and House, J. S. (1999). Volunteering and mortality among older adults: Findings from a national sample. J. Gerontol. 54, S173–S180.

Google Scholar

Oei, N. Y. L., Everaerd, W. T. A. M., Elzinga, B. M., Van Well, S., and Bermond, B. (2006). Psychosocial stress impairs working memory at high loads: An association with cortisol levels and memory retrieval. Stress 9, 133–141. doi: 10.1080/10253890600965773

CrossRef Full Text | Google Scholar

Parihar, V. K., Hattiangady, B., Kuruba, R., Shuai, B., and Shetty, A. K. (2011). Predictable chronic mild stress improves mood, hippocampal neurogenesis and memory. Mol. Psychiatry 16, 171–183. doi: 10.1038/mp.2009.130

CrossRef Full Text | Google Scholar

Park, D. C., and Festini, S. B. (2017). Theories of memory and aging: A look at the past and a glimpse of the future. J. Gerontol. B Psychol. Sci. Soc. Sci. 72, 82–90. doi: 10.1093/geronb/gbw066

CrossRef Full Text | Google Scholar

Park, D. C., and Festini, S. B. (2018). “Cognitive health,” in The SAGE Encyclopedia of Lifespan Human Development, ed. M. H. Bornstein (Los Angeles, CA: SAGE), doi: 10.4135/9781506307633.n146

CrossRef Full Text | Google Scholar

Park, D. C., Lautenschlager, G., Hedden, T., Davidson, N. S., Smith, A. D., and Smith, P. K. (2002). Models of visuospatial and verbal memory across the adult life span. Psychol. Aging 17, 299–320. doi: 10.1037//0882-7974.17.2.299

CrossRef Full Text | Google Scholar

Park, D. C., Lodi-Smith, J., Drew, L., Haber, S., Hebrank, A., Bischof, G. N., et al. (2014). The impact of sustained engagement on cognitive function in older adults: The Synapse Project. Psychol. Sci. 25, 103–112. doi: 10.1177/0956797613499592

CrossRef Full Text | Google Scholar

Ramsdell, K., and Festini, S. B. (2021). Effects of academic and social engagement on episodic memory in young adults. Acta Spartae 5, 20–27.

Google Scholar

Reuter-Lorenz, P. A., and Park, D. C. (2014). How does it STAC Up? Revisiting the scaffolding theory of aging and cognition. Neuropsychol. Rev. 24, 355–370. doi: 10.1007/s11065-014-9270-9

CrossRef Full Text | Google Scholar

Richards, M., and Deary, I. J. (2005). A life course approach to cognitive reserve: A model for cognitive aging and development? Ann. Neurol. 58, 617–622. doi: 10.1002/ana.20637

CrossRef Full Text | Google Scholar

Roheger, M., Hranovska, K., Martin, A. K., and Meinzer, M. (2022). A systematic review and meta-analysis of social cognition training success across the healthy lifespan. Sci. Rep. 12:3544. doi: 10.1038/s41598-022-07420-z

CrossRef Full Text | Google Scholar

Romito, B. T., Okoro, E. N., Ringqvist, J. R. B., and Goff, K. L. (2021). Burnout and wellness: The Anesthesiologist’s perspective. Am. J. Lifestyle Med. 15, 118–125. doi: 10.1177/1559827620911645

CrossRef Full Text | Google Scholar

Rudd, M. (2019). Feeling short on time: Trends, consequences, and possible remedies. Curr. Opin. Psychol. 26, 5–10. doi: 10.1016/j.copsyc.2018.04.007

CrossRef Full Text | Google Scholar

Salthouse, T. A. (2009). When does age-related cognitive decline begin? Neurobiol. Aging 30, 507–514. doi: 10.1016/j.neurobiolaging.2008.09.023

CrossRef Full Text | Google Scholar

Seeman, T. E., Miller-Martinez, D. M., Stein Merkin, S., Lachman, M. E., Tun, P. A., and Karlamangla, A. S. (2011). Histories of social engagement and adult cognition: Midlife in the U.S. study. J. Gerontol. B Psychol. Sci. Soc. Sci. 66 (Suppl 1), i141–i152. doi: 10.1093/geronb/gbq091

CrossRef Full Text | Google Scholar

Shiffman, S., Stone, A. A., and Hufford, M. R. (2008). Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4, 1–32. doi: 10.1146/annurev.clinpsy.3.022806.091415

CrossRef Full Text | Google Scholar

Shors, T. J. (2014). The adult brain makes new neurons, and effortful learning keeps them alive. Curr. Direct. Psychol. Sci. 23, 311–318. doi: 10.1177/0963721414540167

CrossRef Full Text | Google Scholar

Sliwinski, M. J., Mogle, J. A., Hyun, J., Munoz, E., Smyth, J. M., and Lipton, R. B. (2018). Reliability and validity of ambulatory cognitive assessments. Assessment 25, 14–30.

Google Scholar

Smart, E. L., Gow, A. J., and Deary, I. J. (2014). Occupational complexity and lifetime cognitive abilities. Neurology 83, 2285–2291.

Google Scholar

Soldan, A., Pettigrew, C., Cai, Q., Wang, J., Wang, M. C., Moghekar, A., et al. (2017). Cognitive reserve and long-term change in cognition in aging and preclinical Alzheimer’s disease. Neurobiol. Aging 60, 164–172. doi: 10.1016/j.neurobiolaging.2017.09.002

CrossRef Full Text | Google Scholar

Sole-Padulles, C., Bartres-Faz, D., Junque, C., Vendrell, P., Rami, L., Clemente, I. C., et al. (2009). Brain structure and function related to cognitive reserve variables in normal aging, mild cognitive impairment and Alzheimer’s disease. Neurobiol. Aging 30, 1114–1124. doi: 10.1016/j.neurobiolaging.2007.10.008

CrossRef Full Text | Google Scholar

Song, S., Stern, Y., and Gu, Y. (2022). Modifiable lifestyle factors and cognitive reserve: A systematic review of current evidence. Ageing Res. Rev. 74:101551. doi: 10.1016/j.arr.2021.101551

CrossRef Full Text | Google Scholar

Speer, M. E., and Soldan, A. (2015). Cognitive reserve modulates ERPs associated with verbal working memory in healthy younger and older adults. Neurobiol. Aging 36, 1424–1434. doi: 10.1016/j.neurobiolaging.2014.12.025

CrossRef Full Text | Google Scholar

Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., et al. (2011). Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 280–292. doi: 10.1016/j.jalz.2011.03.003

CrossRef Full Text | Google Scholar

Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. J. Int. Neuropsychol. Soc. 8, 448–460.

Google Scholar

Stern, Y., Arenaza-Urquijo, E. M., Bartres-Faz, D., Belleville, S., Cantilon, M., Chetelat, G., et al. (2020). Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimers Dement. 16, 1305–1311. doi: 10.1016/j.jalz.2018.07.219

CrossRef Full Text | Google Scholar

Stine-Morrow, E. A. L., Parisi, J. M., Morrow, D. G., and Park, D. C. (2008). The effects of an engaged lifestyle on cognitive vitality: A field experiment. Psychol. Aging 23, 778–786. doi: 10.1037/a0014341

PubMed Abstract | CrossRef Full Text | Google Scholar

Valenzuela, M. J., and Sachdev, P. (2007). Assessment of complex mental activity across the lifespan: Development of the Lifetime of Experiences Questionnaire (LEQ). Psychol. Med. 37, 1015–1025. doi: 10.1017/S003329170600938X

CrossRef Full Text | Google Scholar

Verbrugge, L. M., Gruber-Baldini, A. L., and Fozard, J. L. (1996). Age differences and age changes in activities: Baltimore Longitudinal Study of Aging. J. Gerontol. 51B, S30–S41. doi: 10.1093/geronb/51b.1.s30

CrossRef Full Text | Google Scholar

Wolf, O. T., Kuhlmann, S., Buss, C., Hellhammer, D. H., and Kirschbaum, C. (2004). Cortisol and memory retrieval in humans: Influence of emotional valence. Ann. N. Y. Acad. Sci. 1032, 195–197. doi: 10.1196/annals.1314.019

CrossRef Full Text | Google Scholar

Yerkes, R. M., and Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit-formation. J. Comp. Neurol. Psychol. 18, 459–482.

Google Scholar

Keywords: busyness, cognitive reserve, aging, stress, cognition, daily activities, memory

Citation: Festini SB (2022) Busyness, mental engagement, and stress: Relationships to neurocognitive aging and behavior. Front. Aging Neurosci. 14:980599. doi: 10.3389/fnagi.2022.980599

Received: 28 June 2022; Accepted: 04 August 2022;
Published: 24 August 2022.

Edited by:

Anja Soldan, Johns Hopkins University, United States

Reviewed by:

David Eugene Vance, University of Alabama at Birmingham, United States
Helena Blumen, Albert Einstein College of Medicine, United States

Copyright © 2022 Festini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sara B. Festini, sfestini@ut.edu

Download