Measuring the impact of exercise on cognitive aging: methodological issues

Neurobiology of Aging 33 (2012) 622.e29 – 622.e43 www.elsevier.com/locate/neuaging

Review

Measuring the impact of exercise on cognitive aging: methodological issues Delyana I. Millera, Vanessa Talera,c, Patrick S. R. Davidsona,b,c, Claude Messiera,* a School of Psychology, University of Ottawa, Ottawa, Ontario, Canada Heart and Stroke Foundation of Ontario Centre for Stroke Recovery, Ontario, Canada c Élisabeth Bruyère Research Institute, Bruyère Continuing Care, Ottawa, Ontario, Canada b

Received 4 September 2010; received in revised form 21 February 2011; accepted 22 February 2011

Abstract Physical exercise and fitness have been proposed as potential factors that promote healthy cognitive aging. Support for this hypothesis has come from cross sectional, longitudinal, and intervention studies. In the present review, we discuss several methodological problems that limit the conclusions of many studies. The lack of consensus on how to retrospectively measure exercise intensity is a major difficulty for all studies that attempt to estimate lifelong impact of exercise on cognitive performance in older adults. Intervention studies have a much better capacity to establish causality, but still suffer from difficulties arising from inadequate control groups and the choice and modality of administration of cognitive measures. We argue that, while the association between exercise and preserved cognition during aging is clearly demonstrated, the specific hypothesis that physical exercise is a cause of healthy cognitive aging has yet to be validated. A number of factors could mediate the exercise-cognition association, including depression, and social or cognitive stimulation. The complex interactions among these 3 factors and the potential impact of exercise on cognition remain to be systematically studied. At this time, the best prescription for lifestyle interventions for healthy cognitive aging would be sustained physical, social, and mental activities. What remains unknown is which type of activity might be most useful, and whether everyone benefits similarly from the same interventions. © 2012 Elsevier Inc. All rights reserved. Keywords: Alzheimer’s disease; Cognition; Mild cognitive impairment; Physical exercise; Methods; Fitness; Metabolism; Social stimulation; Cognitive stimulation

1. Introduction Of the known possible ways to preserve physical and mental abilities, four stand out and have received some scientific support. The first is food restriction, which consists of eating a balanced but restricted diet that contains fewer calories than normally needed. This diet extends the life of organisms, from fruit flies to monkeys, and is associated with preserved physical health and cognition (Piper and Bartke, 2008). The second is cog-

* Corresponding author at: School of Psychology, University of Ottawa, 200 Lees Avenue Room 260J, Ottawa, Ontario, K1N 6N5, Canada. Tel.: ⫹1 613 562 5800 ⫻ 4562; fax: ⫹1 613 562 5147. E-mail address: [email protected] (C. Messier). 0197-4580/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.neurobiolaging.2011.02.020

nitive exercise, in the form of sustained mental challenges and lifelong learning, which are associated primarily with preserved cognition but also with improved health (Daffner, 2010). The third is often intertwined with cognitive exercise: sustained social engagement and interactions (Depp et al., 2010). The fourth is physical exercise, the focus of this review, which has also been claimed to help preserve physical and mental abilities throughout aging. These four putative avenues to a long and healthy life are not equally appealing to people because following them involves some effort (physical and/or mental) and appears to go against our natural inclination to eat and to rest physically and mentally. In the case of physical exercise, it is ironic that, until a century ago, most people engaged in strenuous daily physical activity

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but lived a much shorter life. In Westernized societies today, machines do much of the work, while the average lifespan is longer than ever, but as a society we are returning to physical activity to improve and lengthen our lives. A number of questions are raised by studies that have asked whether these lifestyle activities, and exercise in particular, have a positive impact on longevity and preserved physical and mental abilities. The first is whether physical exercise can really bring about cognitive benefits in humans. This is not a trivial question because, with the exception of a few long lasting longitudinal studies that have included objective measures of physical exercise or fitness, we still have to rely on unreliable retrospective data about lifetime exercise to answer this question. Moreover, if we do find an association between physical exercise and preserved cognitive abilities in aging, we must ask whether we are seeing a true causal relationship, or whether it is merely that those who naturally and effortlessly engage in exercise are the same people destined to lead long, healthy lives with preserved cognition in old age. The third set of questions relates to the exercise itself: how much and which type of exercise is optimal? Is this the same for everyone? Finally (and most importantly), can we mend our ways with the assurance that making lifestyle changes late in life will bring at least some of the same benefits as lifelong involvement in exercise? Interestingly, the same type of questions can be raised when examining the impact of “cognitive training” on cognitive aging (Thom and Clare, 2011). In the present review, we will examine whether available research answers these questions, and if so, what answers it provides. It is important to acknowledge at the outset that the four lifestyle factors (dietary restriction, cognitive exercise, social engagement and physical exercise) cannot be completely isolated from each other. For example, people who exercise in a social context (for example with friends) may also benefit from the social and cognitive stimulation that comes from these interactions. Moreover, people who eat less and maintain a lower weight may be more likely to engage in more vigorous exercise (Hagströmer et al., 2010). These interactions are difficult to disentangle even with sophisticated statistical models in human studies, and may be more amenable to animal research. Finally, a number of common aging-related pathological processes, such as hypertension and diabetes, also have an impact on cognition, likely due to the increase in cerebrovascular pathology they appear to promote (Messier and Gagnon, 2009). The present review does not list all previous studies: readers are directed instead to published reviews and meta-analyses (Angevaren et al., 2008; Colcombe and Kramer, 2003; Erickson and Kramer, 2009; Etnier et al., 2006; Heyn et al., 2004; Hillman et al., 2008; Kramer and Erickson, 2007; Kramer et al., 2006; Rockwood and Middleton, 2007) as well as a recent comprehensive review of the effect of enrichment on cognition (Hertzog et al., 2009). These reviews generally conclude that exercise is associated with improved cognition

during aging. Animal and molecular studies also support this contention (Cotman et al., 2007). In the present review, we examine human studies, and comment on specific ones to illustrate methodological and conceptual limitations in the field. We suggest methodological strategies to improve the ability of future studies to determine the impact of exercise on cognition during aging. We now turn to the major theme of this review and examine the main challenges for studies that aim to determine if physical exercise has an impact on cognition.

2. Physical exercise and cognition Normal aging is associated with a decrease in brain size and plasticity (Peters, 2006) that results in cognitive changes, although the cognitive and brain changes are not always proportional to each other. Moreover, some cognitive domains are more affected than others (Davidson and Winocur, 2010). Normal aging is also associated with a decrease in the intensity and frequency of physical activity (Lindwall et al., 2008). A number of possible reasons for this decrease have been proposed (and most are obvious). First, the physical component of daily activities usually decreases with age; this is likely related to decreased social engagement. Reduced interactions with family and friends may lead to reduced opportunities for daily exercise. Second, some older people believe that physical exercise as such (i.e., “sports” activities) may not be appropriate for their age, and that exercise could be harmful to them (Irwin et al., 2004). Others are simply not convinced that exercise could be beneficial to them in any way (Hassmén et al., 1992). Physical handicaps associated with aging are also a potential obstacle; however, many older adults have little knowledge that exercise programs can be adapted to their physical capacities and limitations (Hassmén et al., 1992) and that benefits appear to occur with even small increases in exercise that would be regarded as negligible for another age group (Buchner, 2009; Kruger et al., 2009). This apparent lack of enthusiasm for exercise among many older adults is problematic because exercise is one of the few simple lifestyle changes that has been proposed to be protective against cognitive decline, possibly even reversing some of the age-associated changes in the brain. In healthy older adults, exercise is associated with improved cardiovascular function (Colcombe et al., 2004a; Dustman et al., 1984; Kramer et al., 1999; Madden et al., 1989), reduced age-associated brain volume tissue loss (Colcombe et al., 2003; Erickson et al., 2009; Haskell et al., 1992), and increased brain volume (Colcombe et al., 2006). Moderately vigorous physical activity has also been associated with decreased mortality in middle aged men followed for over 20 years (Paffenbarger et al., 1993). Increased functional status, as measured by the ability to perform activities of daily living (ADL) and instrumental

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activities of daily living (IADL), is also associated with moderately vigorous physical activity (Gu et al., 2009). In general, the published longitudinal and cross sectional studies have consistently shown a small but positive relationship between greater physical activity and lower risk of cognitive decline in older people, whereas the results of the intervention studies (where cognition is measured first, exercise is then introduced and cognition is again measured after a period of time) are mixed. Each type of study has its strengths and weaknesses. Cross sectional studies are the most feasible (and consequently, the most numerous because of their relatively low cost). However, they cannot establish causality and they usually rely on self-report for lifelong involvement in physical exercise, which is bound to be inaccurate over the lifespan (Haskell et al., 1992). Longitudinal studies cannot establish causation either, but they include, in some instances, longitudinal objective measurements of fitness, which is a much better proxy for exercise participation than self-report. However, fitness is a relative measure, because some fit people may exercise moderately while others may exercise much more often but still remain less fit for other reasons (e.g., incipient heart disease). Longitudinal studies can also be designed to study representative samples of a population, increasing generalizeability. Intervention studies are the most useful to establish causality because the intervention is controlled. However they tend to be of short duration because of their high cost. They are also highly susceptible to selection and dropout biases. They typically use only older participants and thus cannot answer the question of whether lifelong exercise preserves cognitive abilities. With these caveats in mind, we now turn to major limitations of the conclusions of studies that have aimed to establish that physical exercise leads to better and preserved cognition during aging.

3. Measurement of physical activity and fitness Measurement of physical activity is difficult. Existing methods generally fall into two categories: self-report (e.g., questionnaires, diaries, logs) and objective measures (e.g., motion sensors, accelerometers, pedometers, heart rate monitoring, direct observation) (Warren et al., 2010). The accuracy of each method is directly related to its costs, with self-reports being the least expensive but also the least accurate (Shephard, 2003). The limitation of using selfreported exercise history rather than more objective measures of physical fitness such as maximal heart rate (VO2 max), applies to cross sectional and longitudinal studies. Most studies use some type of interview or questionnaire to assess the physical fitness level of participants (Bixby et al., 2007; Clarkson-Smith and Hartley, 1989; Hatta et al., 2005; Hillman et al., 2004, 2006; Iwadate et al., 2005; Lindwall et al., 2008; Roth et al., 2003; Vance et al., 2005), but some studies have also used VO2 max obtained during a maximal

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exercise test on a treadmill (Colcombe et al., 2003; Dustman et al., 1990). Self-reported measures of physical fitness vary greatly from study to study in their definition of what constitutes physical activity. Some researchers have asked participants to report only athletic activities. For example, a study of the relationship between self-reported exercise and change in exercise and cognition in 813 men and women enrolled in the Swedish National Study of Aging and Care used two survey questions to measure older adults’ participation in exercise. The first question was “How often did you exercise with light intensity (e.g., walking on roads, in parks or in woods, short bicycle tours, light gymnastics, golf or similar) in the last 12 months?” and the second was “How often did you exercise strenuously (e.g., jogging, long and high-intensity walking, heavy garden work, long bicycletour, intense gymnastics, skating on lakes, skiing, ballsports or similar activities)?” (Lindwall et al., 2008). Similarly, a study of the association between physical activity and visual attention in 140 older adults used a questionnaire that was specifically designed to measure participation in exercise and physical training (Roth et al., 2003). Yet, the questionnaire did not include other activities such as gardening and house chores. Another large study initially gathered information on participants’ engagement in household chores (Vance et al., 2005) but later dropped them from the calculation of physical activity, although a “physical fitness index” in the same study included yard work and gardening. In contrast, other researchers have been very liberal in their definitions of physical activity, including such activities as housecleaning, gardening, and even walking up stairs (Bixby et al., 2007; Clarkson-Smith and Hartley, 1989; Hillman et al., 2004). This varying scope of what constitutes “exercise” makes comparison across studies difficult. One eye-catching study used a “sweat index” to calculate the physical fitness level of participants (Hillman et al., 2006) by asking people if they participated at least once a week in a physical activity that was sufficiently intense for them to start sweating. Participants who answered “yes” were then asked to indicate the approximate number of times per week this occurred. Although such an index is novel and creative, it is unfortunately confounded with age, because exercise that does not raise a sweat at age 60 may do so at age 70. Conversely, a high sweat index may indicate a poor physical status at 60 but a rather good one at 80 years of age. The usefulness of the measure is also limited by the physiological decrease in sweating with age (Foster et al., 1976). Clear evidence for the limited usefulness of self-reported measures of exercise comes from a study that found a correlation between peak oxygen consumption (VO2 max) at baseline and memory performance (Barnes et al., 2003). However, there was no association between the participants’ self-reported participation in physical activity and their performance on memory tests (Barnes, 2001). In a recent study,

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the correlations between the results of exercise questionnaires administered on 2 occasions ranged from 0.5 for moderate exercise and 0.47 for light exercise to 0.33 for vigorous exercise (Geda et al., 2010). Taken together, these results indicate the need to perform objective assessments of fitness to complement exercise questionnaires (Moy et al., 2008). Recently, two studies have attempted to verify if cardiorespiratory fitness could be predicted using demographic data and one simple self-reported physical activity scale (Jurca et al., 2005; Mailey et al., 2010). Both studies showed that sex, age, body mass index, and a self-reported scale could together provide a good estimate of cardiorespiratory fitness. However, the self-reported scale was found to contribute the least to the estimate of cardiorespiratory fitness (Mailey et al., 2010). As we can see, the heterogeneous definition of physical activity in studies that examine its interaction with cognition during aging is difficult to untangle from the aging process itself, which leads to progressively less physical activity in many older people (Center for Disease Control, 2008; Dong et al., 2004; Hagströmer et al., 2010). Following the “use it or lose it” tendency, less physically fit people will tend to exercise less and thus become even less fit. Conversely, healthier or more physically fit older adults may continue exercising at a constant rate for a longer period. This relationship makes it more difficult to determine a causal link between exercise and cognition. The foundations for a consensus position to define what constitutes exercise and how best to measure exercise objectively has recently been addressed in the context of epidemiological studies (Warren et al., 2010); the reader is directed to that review for a more extensive discussion of exercise and fitness measurements.

4. Type and intensity of physical activity The type and intensity of physical activity appears to have an important influence on the relationship between exercise and cognition. Within the intervention studies, there seems to be a general consensus among researchers that aerobic exercise (i.e., geared toward enhancement of cardiovascular function), such as running, biking, or fast walking, is associated with better performance on cognitive measures, whereas anaerobic exercise such as stretching, toning, or yoga does not have the same effect. In some intervention studies, participants in the stretching, toning, or yoga groups were used as controls for an aerobic exercise group (Colcombe et al., 2004a, 2006; Dustman et al., 1984). Participants in the anaerobic group in these studies did show a small improvement in aerobic capacity. However, their improvements on neuropsychological tests (Dustman et al., 1984), increases in brain activation (Colcombe et al., 2004a), or increases in brain tissue density (Colcombe et al., 2006) were smaller than the changes observed in the aerobic group. In 1 study, the combined changes on cognitive tests after the toning/stretching intervention were significant but

absent in the “no intervention” group (Dustman et al., 1984) suggesting a possibility of an effect of nonaerobic exercise that is smaller than that observed with aerobic exercise. Intervention studies vary widely in the duration of exercise interventions used by researchers; the length of exercise interventions ranges from 8 weeks (Bakken et al., 2001) to 3 years (Rikli and Edwards, 1991). However, most interventions have lasted between 4 and 6 months (Colcombe et al., 2004a, 2006; Dustman et al., 1984; Kramer et al., 1999; Lautenschlager et al., 2008; Madden et al., 1989; O’Dwyer et al., 2007). Perhaps not surprisingly, the duration of the exercise intervention seems to exert a strong influence on the observed effect size of the association with cognition. Two meta-analyses examining the relationship between exercise and cognition found that studies that used long lasting training programs (6 or more months) have a greater effect on cognitive performance than do short term programs (1–3 months) (Colcombe and Kramer, 2003). One alternative hypothesis is that people who are less fit and less healthy may stop participating earlier in the study (particularly in a long intervention or in the highest intensity group), resulting in a different group composition at the end of the intervention (Ghisletta et al., 2006; Lindenberger et al., 2002). The exercise protocols employed by most researchers in intervention studies consist of sessions that last between 30 minutes and 1 hour and occur 3 days per week (Bakken et al., 2001; Blumenthal et al., 1991; Colcombe et al., 2004b; Dustman et al., 1984; Madden et al., 1989; O’Dwyer et al., 2007). However, some researchers have used a gradual increase in exercise intensity and duration (Colcombe et al., 2004a) or 2 exercise sessions per week (Fabre et al., 1999). The duration of each exercise session appears to be important for improving cognitive function. Colcombe and Kramer’s meta-analysis found that studies using sessions of moderate duration (31– 45 minutes) had the largest effect sizes and studies using exercise sessions lasting less than 30 minutes did not show an association with cognitive function measures (Colcombe and Kramer, 2003). Some more recent cross sectional studies also support this conclusion. A study of 813 participants aged 60 years and older found that moderate exercise rather than strenuous physical activity had a stronger association with better cognition (Lindwall et al., 2008). Another cross sectional study by the same research group, examining the relationship between exercise and depression, found similar results: moderate exercise was associated with fewer depressive symptoms in adults over the age of 60, while the participants in the light and strenuous exercise groups had more symptoms of depression (Lindwall et al., 2007). In these last two studies, light intensity was defined as walking on roads, in parks, or in the woods, short bicycle tours, light gymnastics, golf, or similar exercise, while strenuous activity was defined as jogging, long and high-intensity walking, heavy

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garden work, long bicycle tours, intense gymnastics, skating on lakes, skiing, ball sports, or similar activities. Epidemiological studies also support an intensity effect. One such study followed 295 healthy men from the surviving representative cohort of 2285 men born between 1900 and 1920 and recruited from the Finland, Italy and Netherlands Elderly Study (van Gelder et al., 2004). Follow-up was carried out in 1990, 1995, and 2000. Exercise was assessed with a self-administered questionnaire; time spent exercising and average intensity were computed based on responses to the questionnaire. Only men who were healthy and cognitively intact in the 1990 follow-up were included (average age of 74 years in 1990). Complete information was gathered from 1149 men in 1990. The researchers found no significant difference in cognition, as measured by the Mini-Mental State Examination (MMSE), between the exercisers and nonexercisers in 1990. A decade later, however, the older adults who had maintained or increased their physical activity during the 10-year follow-up period showed lower levels of cognitive decline compared with the older adults who either did not participate in physical activity or who exercised at lower levels of intensity. In this study, intensity was estimated by summing products of the number of minutes per week of a given activity multiplied by an estimated energy expanded for that activity (in kcal/ kg-hour). The estimated intensity of physical exercise was found to moderate the relationship between exercise and cognitive decline: the older adults who exercised at the lowest intensity were more likely to develop dementia 10 years later compared with those who exercised at higher intensity. Another epidemiological study examined the relationship between walking and the risk of dementia later in life in a sample of 2257 men from the Honolulu-Asia Aging Study (Abbott et al., 2004). This project followed a large sample of Hawaiian men of Japanese descent, starting in 1965 (Yano et al., 1984); the study of the impact of exercise on cognition and dementia was initiated in 1991. In this study, exercise in the form of walking was assessed at baseline via a combination of self-report and a test that evaluated the ability to do a number of simple movements associated with balance and walking (e.g., length of time to walk 10 feet). Men who walked faster and more regularly (as evaluated 6 years previously) were less likely to develop dementia. A Canadian epidemiological study also reported that physical activity reduced the risk of cognitive decline in a representative sample of elderly people (Laurin et al., 2001). This study followed 4615 men and women aged 65 and older from the Canadian Study of Health and Aging over a period of 5 years. The results showed a significant positive effect of exercise (as self-reported at baseline) in reducing the risk of developing Alzheimer’s dementia; notably, however, this association was much stronger for women than for men. This study revealed a dose-response effect, indicating

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that higher reported levels of physical activity were associated with lower risk of cognitive impairment and lower mortality after 5 years, with a 40% death occurrence in the group that did not exercise compared with a 13.5% rate for people that engaged in the most exercise activities. However, another large epidemiological study found that both moderate exercise (such as playing 18 holes of golf once a week, playing tennis twice a week, or walking 1.6 km/day) and strenuous exercise (such as jogging or skiing) were associated with better cognition (Yaffe et al., 2001). Although a number of studies suggest that “moderate” exercise (albeit often based on self-report) is most strongly correlated with preserved cognition, the lack of consensus on how to measure intensity leads to a lack of clear guidelines for the optimal duration/intensity, the bare minimum to yield cognitive improvement (or, at least, prevent cognitive decline over time), and how many people (and who) would benefit from which exercise protocol.

5. At what age is the strongest association between exercise and cognition found? One factor usually put forward to explain the larger facilitating effect of exercise on cognition in older adults is that they, as a group, have more limited cognitive resources and are unable to perform more complex tasks as effectively or as quickly as younger adults, leaving ample room for improvement. Of course, ceiling effects can easily occur in people younger than 70 years (Wang et al., 2009) because decreased cognition is more often found in people over 70 (Ryan and Geckle, 2000; Salthouse, 2010). Putting this caveat aside, support for a greater effect of exercise on cognition in older adults comes from a meta-analysis showing that the effect size of exercise on cognitive function varied as a function of participants’ age (Colcombe and Kramer, 2003). For the group aged 55– 65 years, the effect size was 0.29, for the group of 66 –70 years old the effect size was 0.69, and for the oldest group (aged 71– 80 years), the effect size was 0.55. Not all evidence, however, is in line with this idea. Another meta-analysis indicated that the largest association between exercise and cognition was found in adults aged 40 to 60 years; moreover, the youngest group (18 –30 years old) showed a larger association than the oldest group (60 –90 years old) (Etnier et al., 1997). The conflicting findings of the two meta-analyses could be due to different selection criteria, and/or the way in which the age groups were coded. In the Colcombe and Kramer (2003) metaanalysis, 3 age groups were included: 55– 65, 66 –70, and 71⫹ years old. They did not include studies in which participants were younger than 55 years of age. Etnier et al. (1997), in contrast, assigned participants from ages 60 to 90 into one broad group which spanned all three senior groups in Colcombe and Kramer (2003). In addition to the conflicting findings of the 2 meta-analyses discussed above, some

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studies have not found any changes in cognition in older adults (aged between 60 and 83) following either 16 weeks or 40 weeks of aerobic exercise intervention (Blumenthal et al., 1991; Madden et al., 1989). Clearly, the effect of age in moderating the relationship between exercise and cognitive function has yet to be clearly established, and future research is needed to examine the magnitude and the direction of the effect, as well as the impact of factors such as intensity of exercise (as a function of age), and the interaction between exercise, health status (as a function of age), and cognition. Such knowledge is central if we are to be able to formulate exercise recommendations. In the next section, we discuss whether health status (including depression) can be a mediating factor for the effect of exercise on cognition.

6. Do health status and depression play a role? Two additional variables may be important: general physical health, and mental health (especially depression). For the former variable, people in good health at the start of a study may tend to exercise more and exhibit better cognitive health. For the latter variable, physical activity has been linked to improved mood in depressed patients, improved overall psychological well-being, and decreased stress and anxiety levels (Hill et al., 1993; Lindwall et al., 2006; Taylor et al., 1985). Exercise can improve depressed patients’ ability to deal with stress and their self-concept, confidence, self-image, and social skills (Taylor et al., 1985). Improved mood in depressed patients is, in turn, also associated with improved cognition (Thomas and O’Brien, 2008). Whether this association holds true for nondepressed older adults and reflects a causative link between the improved cognition and the effect of exercise on depression remains to be determined (Brown et al., 2009; Williams and Lord, 1997). Because these two factors are often intertwined, we review them together in the relatively few studies that have examined them. In the Canadian Study of Health and Aging discussed above, two additional analyses were used to address the possibility that the association between exercise and cognition (or cognitive disorders) may result from the fact that people in good health at the start of the study tended to exercise more and exhibit better cognitive health. The researchers in this study added variables related to health status into their logistic models, and observed that risk estimates remained very similar to those initially reported (when controlling for age and education only). The data were also reanalyzed excluding subjects who reported early cognitive decline symptoms in the first 2 years of follow-up; conclusions remained unchanged in this analysis (Laurin et al., 2001). When the participants were followed up 5 years later, the protective effect of exercise (at least 3 times per week, at least as intense as walking) was still present (Middleton et al., 2008) although no analysis of the contri-

bution of health status was performed. This large epidemiological study suggests that the association between exercise and preserved cognition cannot be solely explained by an association between good health at the outset of the study and sustained levels of exercise. Similar results were obtained in a prospective epidemiological study of a representative sample of 9704 women aged 65 and over from the Study of Osteoporotic Fracture (Yaffe et al., 2001). In this study, lower self-reported weekly exercise in general, and self-reported weekly amount of walking in particular, were associated with cognitive decline as measured by the Mini-Mental State Examination (MMSE) (Gagnon et al., 1990) 6 to 8 years later. This association remained unchanged after statistical adjustment for baseline health and functional status including depression. The Adult Changes in Thought longitudinal study of aging and dementia followed 1740 men and women aged 65 years and older for 6 years (Larson et al., 2006). Exercise was estimated from self-reports and a brief physical functioning test (timed short walk, time to stand up from a chair 5 times, balance test and grip strength). Those who exercised more at baseline had higher education and lower depression symptoms and there was a 32% risk reduction for developing dementia among those participants who had reported 6 years earlier that they exercised 3 or more times per week. The risk reduction was greater for participants who had low physical functioning at baseline. Survival analysis suggested that exercise reduces the dementia risk by delaying its onset in people who exercised regularly. The incidence of dementia was 13.0 per 1000 person-years for people who exercised 3 or more times per week, compared with 19.7 per 1000 person-years for people who exercised fewer than 3 times per week. When several confounding factors were simultaneously adjusted for statistically (including education and depression), exercise was still protective (odds ratio of 0.68). Because depression has been associated with both exercise and cognitive deficits (Yaffe et al., 1999), many experiments have tried to establish the benefits of exercise for depressive symptoms (e.g., Hamer et al., 2009). One important caveat is that to date most studies have not included a large number of clinically depressed participants, most likely because they excluded themselves by refusing to participate or did not complete the physical activity intervention. This general bias may reduce the ability of secondary statistical analyses to shed light on the issue, because the studies include few depressed participants with a smaller range of depressive symptoms. With this caveat in mind, we now turn to a review of some of the findings. Few cross sectional and intervention studies have included a measure of depression, either to exclude depressed participants from the study (Bixby et al., 2007) or as a covariate in the statistical analyses (Dustman et al., 1984). We are aware of only one study that included both a mea-

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sure of depressive symptoms and a measure of social stimulation (Vance et al., 2005). Results were mixed: while Dustman et al. (1984) observed an improvement in cognition but not in depression scores after a 4-month exercise intervention, Vance et al. (2005) found that sedentary lifestyle was associated with better cognition and worse depression. However, several epidemiological studies have included measures of depression, most often the Geriatric Depression Scale (Herrman et al., 1996). The results of the Swedish National Study on Aging and Care provide indirect support for the depression-reduction hypothesis of exercise effects on cognition. Older individuals who had previously been physically active and had decreased the frequency and intensity of exercise or stopped exercising in the previous 12 months showed the same cognitive decline as individuals who had never exercised (Lindwall et al., 2008). Moreover, individuals who had stopped or decreased their physical activity in the preceding 12 months had similar depression scores as the inactive group; the depression scores were higher than in the continuously exercising group (Lindwall et al., 2006). Finally, a recent cross sectional study found that, in patients with coronary heart disease, VO2 max was associated with executive functions (as measured by the Trail-Making Test Part B, the Stroop test, and the Digit Symbol Coding task) even after adjustment for health status and depression (Swardfager et al., 2010). However the association between exercise and verbal memory was not significant after adjustment for health status, including diabetes and depression. However, a recent study with identical twin pairs found that the twin who exercised the most (in each pair) displayed as many symptoms of depression as the twin who exercised less; moreover, longitudinal analysis of exercise participation showed no relation between increased exercise and decreased depressive symptoms, even though there was a significant association between exercise and depression across all participants similar to the one observed in nontwin studies (De Moor et al., 2008). These results suggest that the longitudinal association between exercise and depressive symptoms in this twin study was due not to a causal relationship between exercise and depression, but rather to underlying overlapping common genetic factors. That is, there might be a common genetic susceptibility for both depressive symptoms and lack of habitual physical exercise. In summary, some studies suggest that participation in exercise is associated with a decrease in depressive symptoms, indicating that depression could mediate the effect of exercise on cognition. However, the few studies that have controlled for depressive symptoms have not found this (note, that this conclusion should be tempered by the low number of depressed participants and the restricted range of depressive symptoms in most studies, both of which would reduce their ability to reject the null hypothesis). A twin study indicated that the association between exercise and

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depression might not be causal, but rather might reveal a common underlying genetic factor. Of course, such an underlying genetic factor might only be valid for spontaneous activities of daily life and not extend to voluntary “sport” physical activities or a prescribed exercise regimen (Wolff and Strohle, 2010). The existing literature has tried to disentangle the possible negative impact of depression and pre-existing health problems as mediator variables. In the same fashion, the contribution of the positive effects of other factors, such as social and cognitive stimulation, is difficult to untangle from the effects of exercise. The next section briefly reviews some studies that illustrate the potential impact of these factors, and some of the difficulties in assessing them.

7. The potential contributions of social and cognitive stimulation Some studies have attempted to control for other factors that may moderate the relationship between exercise and cognition. However, despite many researchers’ intuitions that cognitive and social stimulation might be important, most studies have not attempted to measure these factors. The only variable sometimes included that might give investigators a sense of participants’ engagement in cognitively stimulating activities is years of education, which is at best a limited proxy for involvement in cognitively stimulating activities (Baldivia et al., 2008; Hultsch et al., 1999). The Chicago Health and Aging Project examined whether the impact of physical exercise on cognitive decline was independent of cognitive stimulation in a sample of 4055 men and women aged 65 and older; participants comprised 52% of the residents in 3 Chicago neighborhoods aged 65 years and older that were able to at least walk a short distance (Sturman et al., 2005). The researchers gathered information from participants concerning participation in physical activity and seven cognitively stimulating activities including the following: watching TV (although the classification of TV as cognitively stimulating is debatable), reading newspapers, reading books, going to museums, listening to the radio, and playing games such as puzzles or crosswords. The results indicated that regular exercise is associated with a small reduction in the risk of cognitive decline; however, this effect was no longer significant after controlling for cognitive stimulation. This finding suggests that cognitive activity may mediate the effect of exercise, or that exercise and cognitive activities are covariates. To our knowledge, only one small study has attempted to examine the separate, and possibly combined, effects of exercise and cognitive stimulation on cognition. It involved 3 memory-training programs in 32 older adults aged between 60 and 76 years (Fabre et al., 1999). Participants were randomly assigned to 1 of 4 groups: (1) aerobic training, (2) aerobic plus memory training, (3) memory training only, and (4) a control group. Participants in the exercise groups

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performed aerobic exercise (walking and running outdoors) for 1 hour, twice a week for a month. The aerobic intensity was individualized according to the baseline physical fitness level of each participant. The memory training sessions lasted 90 minutes, and occurred once a week for 8 weeks. In these sessions, participants learned mnemonic strategies. Overall, the improvement observed in the combined training group (physical and memory) was greater than either in the physical or the memory training groups on the two test batteries assessing learning, recall, orientation, semantic fluency, visual reproduction, and memory. This study suggests that exercise training may have additive effects with other training programs that could increase cognition in the elderly. From a practical point of view, it may prove very useful to learn more about the types of combination programs that include exercise and can help older adults improve their quality of life. More research is needed to identify the optimal combinations of exercise interventions. Only one epidemiological study, the MRC National Survey of Health and Development, has attempted to control for socially stimulating activities in examining the relationship between exercise and cognitive status. This study found no association between exercise participation and cognitive status later in life (Richards et al., 2003). It included a representative sample of 3035 men and women from the British 1946 birth cohort, and examined the relationship between two types of activities at the age of 36 (leisure activities and physical exercise) with verbal memory at the age of 43 and 53. Greater physical activity was associated with lower risk of memory decline between the ages of 43 and 53. Both leisure activities (all involving social contacts) and physical activities were associated with verbal memory performance at the age of 43: after controlling for leisure activities, physical activity no longer had an independent effect on verbal memory at the age of 43. However, the association between physical activities and cognition was no longer explained by leisure activities at 53. No further analyses were performed because, in this study, the measure of leisure activities changed between the two follow-ups. This study also included relatively young participants; the absence of effects of other variables may not be surprising in this younger cohort because of possible ceiling effects in the cognitive measures. An experiment by Hassmén et al. (1992) is the only one, to our knowledge, that attempted to control for social stimulation. In this study, the control group met as frequently as the experimental group, but performed mental arithmetic, problem solving, and logical thinking tasks rather than physical exercise. The use of the social control group was well-justified, but in our opinion the choice of activity that would represent “social stimulation” was questionable, because the problem solving, math problems, and logical thinking training may actually have prepared the “control” subjects for the cognitive tests that were administered at the end of the intervention period (face recognition and three

computerized tests: simple reaction time, complex reaction time, and digit span). The study found no cognitive differences between subjects in the exercise and control groups, and attributed the lack of effect to the low intensity of the exercise intervention. It is also possible that the choice of “socially stimulating” activities in the control group may have obscured the impact of the exercise intervention. One longitudinal study that attempted to separate the effects of social stimulation from those of physical exercise on verbal memory performance found that, after including participants’ leisure and social activities (going out, religious affiliation) in the analysis, the effect of physical activity was no longer significant (Richards et al., 2003). Indirect support for the social stimulation hypothesis also comes from a meta-analysis in which the researchers reported that the size of the group in which participants performed exercise influenced the effect size on cognition measures (Etnier et al., 1997). The effect sizes in studies in which exercise groups consisted of more than 20 people were significantly larger than those studies in which participants were exercising alone. That is, the positive effects of exercise on cognition increased as the size (alone, 1–10, 11–20, and more than 20 participants) of the exercise group increased. It is possible that the observed improvements in cognition were largely (or partially) due to the social stimulation that the participants received in addition to the exercise intervention. The exact mechanism by which social stimulation improves cognition is unknown. One proposed explanation focuses on the motivational factors that may come into play. Most people see exercise as a desirable behavior; thus, many people who engage in regular physical activity are praised by others for their exercise routine (Hughes, 1984). The social reinforcement that exercisers receive may in turn impact their performance on cognitive measures by increasing their motivation, self-efficacy, and/or self-confidence, or by reducing anxiety (Bennett et al., 2006). One of the main methodological limitations in the existing interventional research is the lack of adequate controls to take into account the social and cognitive stimulation that participants in the intervention groups receive in addition to physical exercise. Some of the studies reviewed here used control groups that did not receive any intervention, but were contacted at the beginning and at the end of the investigation (Bakken et al., 2001; Dustman et al., 1984; Hawkins et al., 1992; Hill et al., 1993). Some studies used a control group that received an alternative to aerobic exercise, which consisted of stretching and toning exercises (Colcombe et al., 2004a, 2006; Kramer et al., 1999). Other studies used two control groups, one that performed nonaerobic exercise (yoga) and another wait list control (Blumenthal et al., 1991; Madden et al., 1989). Only one study used a cognitive stimulation group, a group that received a combination of cognitive and physical stimulation, and a control group that underwent no intervention (Fabre et

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al., 1999). It is interesting that in this study, both the cognitive training and the aerobic training improved cognition to the same extent, but the combination of cognitive and aerobic training did not lead to additional gains. Finally, one study used a control group that was contacted as frequently as the experimental group but engaged in cognitive tasks rather than exercise (Hassmén et al., 1992). In that latter study, the control group improved in some of the cognitive measures but the low number of participants (n ⫽ 8) in each group is problematic. The interaction of cognitive activities and cognitive performance independently of physical fitness is suggested by a recent study that examined the predictors of cognitive performance in older women (Eskes et al., 2010). This study showed that, age and education explained 22% of the variance of cognitive test results, fitness (VO2 max) explained 12% of the variance, while self-reported cognitive activities explained an additional 22% of the variance. However in this study, self-reported cognitive activities did not correlate with physical fitness or cardiovascular measures, indicating independent factors. One serious but rarely-acknowledged problem in these studies is that just by participating, at least some members of the control group may be led to increase their physical activity (imitation of treatment). For example, Ruscheweyh et al. (2009) examined the impact of a 6-month intervention involving low- and medium-intensity aerobic exercise on cognition and brain volume. Yet, in postintervention interviews, all participants, including controls, indicated that they had increased their daily physical activities at least somewhat. Another common problem is especially evident in the studies that have included some kind of an alternative to a “no contact” control group as an attempt to rule out the social stimulation component of the physical intervention. Some used a yoga control group to reduce the social stimulation confound by providing social contact but no aerobic training (Blumenthal et al., 1991; Madden et al., 1989). However, the 3 studies that have included only a stretching and toning control group and no other “no contact” control group all reported an increase of aerobic capacity (VO2 max) in these stretching and toning participants. In 1 study, there was a 9% improvement in the VO2 max levels of “control” participants as well as improvement on neuropsychological measures (Dustman et al., 1984). Two other studies reported a 2.9% (Colcombe et al., 2004a) and a 5.3% improvement in VO2 max (Colcombe et al., 2006) in the toning and stretching exercise control group. Although these changes did not reach statistical significance, they make it seem unlikely that any “control” group that performs anaerobic exercise is controlling only for social stimulation; any additional exercise likely leads to some improvement in fitness. A more appropriate social stimulation control might be a group that met to perform some kind of social activity (such as involvement in an interesting discussion) but did not perform any type of exercise. To our

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knowledge, however, this has rarely been done (but see, for example, Stine-Morrow et al., 2008). Interestingly, a recent study reporting a 4.5% improvement in VO2 max in an aerobic walking group (and no change in VO2 max in a stretching and toning group) did not find any significant cognitive function improvement in the exercise group although, a modest trend toward cognitive improvement was observed in the aerobic walking group (Voss et al., 2010). Together, these studies indicate that the amplitude of VO2 max may be important to observe cognitive changes following exercise training. It is clear that any positive effect of physical activity on cognitive aging is still confounded with a number of other factors, including level of cognitive activities and social interactions. This is a serious limitation because prescriptions of more exercise may not benefit people who engage in fewer social and cognitive activities, and exercise may simply be a marker variable for people with high involvement in cognitive and social activities. On the other hand, exercise is more beneficial to people with lower education levels (Yaffe et al., 2001), which is a proxy measure of cognitive engagement. This finding suggests that the effects of cognitive stimulation and exercise on cognitive aging are not necessarily additive (Fabre et al., 1999; O’Dwyer et al., 2007). This confound has not been sufficiently examined in longitudinal studies (using cognitive tests that avoid ceiling effects; for more on this see below) to conclude that cognitive and social activities underlie the positive effects of exercise on cognition. In the next, and final, section, we address some issues regarding the type of outcome measures used, which include cognitive measures and, more recently, brain volume measures.

8. Outcome measures: the type of cognitive task used In general, the use of standardized tests that can be adjusted using normative data should be promoted (Geda et al., 2010) because it controls for effects of age and education, two major contributors of variability on cognitive measures. Early on, it was suggested that the positive effect of exercise was observed on “complex” rather than on “simple” cognitive tasks (Weingarten, 1973). An often-cited meta-analysis of 18 intervention studies suggested that exercise was more beneficial on tasks that require executive function control than tasks that do not. Exercise appeared to have a generally positive effect on a number of cognitive functions such as processing speed, visuospatial function, and controlled processing, but the largest effect sizes were on tests thought to depend more on executive functions (Colcombe and Kramer, 2003). Other studies have also supported this finding by showing that physical exercise is more strongly related to performance during task conditions that require greater amounts of interference control (Hillman et al., 2006). However, another meta-analysis found no effect of exercise on executive function and working mem-

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ory in 11 intervention studies (Angevaren et al., 2008). In that meta-analysis, the tasks that were associated with larger effect sizes included cognitive speed, auditory and visual attention, and delayed memory. Only four studies were included in both meta-analyses, however (Emery and Gatz, 1990; Emery et al., 1998; Madden et al., 1989; Moul et al., 1995), which may explain the diverging conclusions. Another advantage of using standardized testing is that methods and scoring procedures are the same. However, different studies have used a variety of cognitive tasks to measure cognitive improvement following exercise intervention. Some researchers have included in their cognitive batteries simple and choice reaction time (RT) tasks in which the participants are instructed to respond to a stimulus as quickly as possible (Colcombe et al., 2004a; Dustman et al., 1984; Hassmén et al., 1992; O’Dwyer et al., 2007). However, even when the tasks appear to be quite similar across studies, we still find only equivocal support for the benefits of exercise: Colcombe et al. (2004), Dustman et al. (1984), and Hawkins et al. (1992) found improvement on their RT tasks, while Hassmén et al. (1992) did not. Kramer et al. (1999) also found an improvement in performance on RT tasks that they argued involved an executive function component; Madden et al. (1989), in contrast, found no effect of exercise on performance in an RT task of episodic and semantic memory. Other studies have included measures of higher order cognitive processes such as planning, response inhibition, and working memory, but here again the data are equivocal. Dustman et al. (1984) found an improvement on some tasks (Digit Symbol, Dots Estimation [a detection task], and Stroop Color and Word Test) following 4 months of aerobic exercise. However, following an exercise intervention of 9 –12 months and an improvement of VO2 max of 23%, Hill et al. (1993) found no improvement on the Logical Memory Immediate Recall and the Digit Symbol subtests of the Wechsler Memory Scale and Wechsler Adult Intelligence Test (Revised version [1981]). In addition, Blumenthal et al. (1991) found no difference between their experimental and control groups on Digit Span, Benton Revised Visual Attention, Selective Reminding, Digit Symbol, Trail Making, and “2 and 7” tests (Ruff et al., 1986) after an exercise intervention of 4 months. Blumenthal et al. attributed the small improvement on test performance to practice effects. Practice effects are rarely explicitly discussed in the exercise literature, but must be considered when choosing tasks and analyzing results given that some tests are more prone to practice effects (Awad et al., 2004; Wilson et al., 2006). For example, in two studies, a small improvement in cognitive measures performance was attributed to practice effects, even though the exercise intervention led to significant improvement in participants’ VO2 max: 11.02% in Madden et al. (1989) and 10% to 15% in Blumenthal et al. (1991). Although practice effects appear not to be a function of age, sex, or education (Wilson et al., 2006, 2009), it

would be important to determine if exercise or fitness status are associated with differential practice effects. An added complication is that some tests are more prone to practice effects than are others (Awad et al., 2004; Wilson et al., 2006). Moreover, in some tasks practice effects may be observed in healthy adults but not patients with cognitive impairment (e.g., Cooper et al., 2004). One way around this general problem is to have participants initially perform the tasks that they will be tested on so that the improvement associated with familiarity is partly taken into account, a strategy used in a recent study of the effect of aerobic exercise in mild cognitive impairment (MCI) (Baker et al., 2010). This presents its own problems, of course, if different groups do not start out at the same baseline level of performance or if cognitive impairment diminishes practice effects in some participants but not others.

8.1. Brain volume measures Ultimately, preserved cognition in aging depends on a healthy brain, and one indicator of a healthy brain is preserved brain volume. Conversely, loss of neurons and connectivity is associated with reduced gray and white matter volumes. Studies that include brain volume measures are becoming more common, but they are still subject to many of the same confounds and caveats as purely behavioral studies. Various studies have indicated a link between physical activity and brain volume. Using an in vivo voxel-based morphometric technique, researchers measured differences in brain tissue volume between active and inactive older adults aged 55 years and older, and correlated volumes with fitness as measured by estimated VO2 max (Colcombe et al., 2003). Older adults with better fitness had greater gray matter volumes in the prefrontal, superior parietal, and middle/inferior temporal cortices, and greater white matter volumes in the anterior tracts and in transverse tracts running between the frontal and the posterior parietal lobes. In the study sample, the areas of the brain that were the most highly correlated with age also showed the greatest correlation between volume and physical fitness. In a subsequent study, the same researchers found an increase in brain volume in several regions of the brain of older adults (aged 60 –79 years) who performed 6 months of aerobic exercise compared with age-matched control participants (Colcombe et al., 2006). The regions that showed the greatest increase in brain volume were also associated with age-related decline in brain structure and cognition, namely the dorsal anterior cingulate cortex, the dorsolateral region of the inferior frontal gyrus, and the dorsal aspect of left superior temporal lobe, as well as the anterior third of the corpus callosum. The study did not include any type of neuropsychological assessment; it would be of interest in future to correlate the observed changes in brain volume with functional activity during cognitive tests known to depend on the areas that showed the largest improvements.

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Another provocative study found a positive relationship between VO2 max and hippocampal volume after adjusting for age, sex, and education (Erickson et al., 2009). Interestingly, a 3-month aerobic exercise regimen can lead to a volume increase of the dentate gyrus of the hippocampus in younger (21– 45 years) participants (Pereira et al., 2007). Participation in exercise (albeit estimated from a questionnaire; Bowles et al., 2004) was associated with a reduction of the rate of shrinkage of the medial temporal lobe in older adults (with a mean age of 68 years) (Bugg and Head, 2011). Another study found that walking 72 city blocks a week was associated with preserved cortical gray matter volume 9 years later (Erickson et al., 2010). Similar findings have been observed in older adults at risk for Alzheimer’s disease and early stage Alzheimer’s patients (Clinical Dementia Rating: 0.5–1.0) (Burns et al., 2008a, 2008b; Honea et al., 2009). In one of these studies, no effect of the apolipoprotein E (APOE)-epsilon 4 allele was found on the fitness-brain volume association (Honea et al., 2009): Alzheimer’s disease (AD) patients with higher cardiorespiratory fitness had greater parietal and medial temporal volume, regardless of APOE genotype. Two large epidemiological studies examined if the association between exercise and reduced risk of dementia was modulated by APOE genotype. One study found this to be the case (Podewils et al., 2005) while the other did not (Larson et al., 2006). The association between exercise and brain volume is potentially significant because greater hippocampal and brain volume is associated with preserved cognition despite high burdens of pathological lesions associated with Alzheimer’s disease (Erten-Lyons et al., 2009). One important caveat in interpreting the results of these studies, however, is that other factors such as depression, diabetes, reduced high density lipoprotein (HDL) levels, and hyposmia (reduced olfaction) are also associated with volume reduction of the gray and white matter (Bitter, 2010; Bruehl et al., 2009; Colla et al., 2007; Convit et al., 2003; Ward et al., 2010). Because these conditions are observed in older people, they may mediate some of the observed structural changes described above. Also, morphometric data do not specify which brain compartment is contributing to the volume changes (it could be anything from extracellular fluid content to glial cells and blood vessels); the contribution of neurons and their neuropil to the volume changes remain to be studied. Furthermore, although many more functional magnetic resonance imaging (fMRI) studies should be expected in the near future, the field has yet to reach a consensus on whether greater activation in relation to a given level of cognitive performance should be considered to be “better” (reflecting a large region robustly at work to support a task) or “worse” (reflecting neural inefficiency; for a similar debate in the cognitive aging literature; see: Buckner, 2004; Cabeza et al., 2002). In summary, the few studies suggesting that exercise modifies brain structure, task-induced metabolic activity,

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and brain connectivity have attracted a lot of attention. However, at present it is possible that mediating variables (e.g., depression, impaired glucose tolerance, hyposmia, HDL cholesterol) associated with exercise and fitness could explain some or all of these observations.

9. Summary and suggestions Taken together, the studies reviewed here show that there is a high likelihood that social, cognitive, and physical activities interact in their ability to sustain cognitive performance during aging. Because of the relatively high prevalence of depression in aging and its well known impact on some cognitive processes (Butters et al., 2008) and the reduction of depressive symptomology by exercise (Barbour and Blumenthal, 2005), depression should be assessed in all studies using scales such as the Geriatric Depression Scale (Yesavage et al., 1982; for a discussion of these issues, see Kørner et al., 2007). When exercise is evaluated, some age adjustment should also be included. This would require developing ageadapted standards that determine the average exercise level at each age and describe its characteristics. Similarly, the neuropsychological tests that are used should be adjusted using normative data (e.g., age, education), have validated alternate forms and allow for practice or familiarity effects to be controlled (Geda et al., 2010). This would reduce bias resulting from the comparison of groups that may have the same mean on several characteristics despite having different distributions. It would also be important to do basic research on the correlation between real-life exercising and the participants’ personal estimates to determine the amount of bias in selfreported exercise and the factors associated with inexact self-reported estimates (Bowles et al., 2004). At this time, it appears unlikely we will gain any useful additional information from cross sectional studies using self-reported questionnaires to elucidate the beneficial impact of exercise on cognition during aging. The reader is referred to a previous extensive discussion on this topic (Haskell et al., 1992). Perhaps the use of electronic pedometers might provide additional information by evaluating one type of physical activity more objectively (Lautenschlager et al., 2008). However, compliance for the wearing of the device should be evaluated. Finally, age-normed objective exercise tests should be devised because intensity of exercise will vary with age and people should be compared within their age group as they are, for example, in long-distance running. Many cross sectional studies on aging relying on volunteer participants include a selection bias with overrepresentation of people of higher socioeconomic status, higher education, and superior cognition. For that reason, emphasis should be placed on obtaining a representative sample on these variables so that the conclusions of the study can be generalized to the population. There is some indication that

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time of day (circadian rhythms) influences cognitive performance, particularly in older adults, suggesting that testing all participants either at their peak or lowest performance period would reduce variability in cognitive testing results (Hasher et al., 2000). For intervention studies, postintervention questionnaires should be used to detect participants in control conditions who have increased their physical activity levels, possibly as a result of volunteering in the study and being made aware of the potential benefits of exercise (Ruscheweyh et al., 2009). Finally, retrospective studies all suffer from a limitation in the attribution of causality: does exercise increase longevity and protect cognitive abilities or do longevity, good health, and intact cognitive processes foster more physical activity? Although lifelong intervention studies are impractical, several short term intervention studies in which participants are randomly assigned to exercise conditions suggest that exercise does indeed lead to improved cognition. On the one hand, it seems intuitively obvious that physical exercise can only be good for us as we grow older. Indeed, we have considerable sympathy with this point of view, and would be happy if this turned out to be true. On the other hand, we have endeavored to point out that the evidence for a causative effect of exercise on cognition is still debatable, despite the many studies that have examined this hypothesis. Considering the still-unresolved question of possible interactions among physical exercise and social and cognitive stimulation, perhaps the best advice is to go for regular brisk walks with good friends while doing crossword puzzles (and watching our step!). Disclosure statement The authors have no actual or potential conflicts of interest. Acknowledgements C.M. and P.D. are funded by grants from the Natural Sciences and Engineering Research Council of Canada. V.T. is supported by a Young Investigator Award from the Alzheimer Society of Canada and the Canadian Institutes for Health Research. Delyana Miller was supported by a fellowship from the Natural Sciences and Engineering Council of Canada. References Abbott, R.D., White, L.R., Ross, G.W., Masaki, K.H., Curb, J.D., Petrovitch, H., 2004. Walking and dementia in physically capable elderly men. JAMA 292, 1447–1453. Angevaren, M., Aufdemkampe, G., Verhaar, H.J., Aleman, A., Vanhees, L., 2008. Physical activity and enhanced fitness to improve cognitive function in older people without known cognitive impairment. Cochrane Database Syst. Rev. 3, CD005381.

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