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Studies suggest that physically active people have reduced risk of incident cognitive impairment in late life. However, these studies are limited by reliance on subjective self-reports of physical activity, which only moderately correlate to objective measures and often exclude activity not readily quantifiable by frequency and duration. The objective of this study was to investigate the relationship between activity energy expenditure (AEE), an objective measure of total activity, and incidence of cognitive impairment.
We calculated AEE as 90% of total energy expenditure (assessed over two weeks using doubly-labeled water) minus resting metabolic rate (measured using indirect calorimetry) in 197 men and women (mean 74.8 years) who were free of mobility and cognitive impairments at study baseline (1998–2000). Cognitive function was assessed at baseline and 2 or 5 years later using the Modified Mini-Mental State Examination (3MS). Cognitive impairment was defined as a decline of >1.0 SD (9 points) between baseline and follow-up.
After adjustment for baseline 3MS, demographics, fat free mass, sleep duration, self-reported health, and diabetes, older adults in the highest sex-specific tertile of AEE had lower odds of incident cognitive impairment than those in the lowest tertile (OR, 95% CI 0.09, 0.01–0.79). There was also a significant dose response between AEE and incidence of cognitive impairment (p-for-trend over tertiles=0.05).
These findings indicate that greater activity energy expenditure may be protective against cognitive impairment in a dose-response manner. The significance of overall activity in contrast to vigorous or light activity should be determined.
Physical activity appears to be one of the more promising preventive strategies against cognitive impairment in old age.(1) In most studies, people who are more physically active in mid- and late-life have lower rates of dementia and cognitive impairment in late life.(2–5) In addition, people who participate in higher levels of physical activity have slower rates of cognitive decline compared to those who are less active.(6–8)
A potential limitation of these studies is their reliance on subjective self-reports of physical activity, which may include inaccurate reporting or misclassification, particularly in people with cognitive dysfunction. Although many physical activity questionnaires are considered valid and reliable measures of intentional physical activity, they have low to moderate correlation with objectively measured total daily physical activity.(9) Physical activity questionnaires usually focus on moderate or vigorous, exercise-related physical activity, which is more readily quantifiable by frequency and duration, but do not adequately capture non-exercise, low intensity physical activity such as movement around the house, postural allocation, and fidgeting, which accounts for the great majority of AEE in people who do not regularly exercise.(10) Such activity may be important to health outcomes such as cognitive impairment. Indeed, a recent cross-sectional study of older women identified a positive association between cognitive performance and total daytime movement,(11) as measured using actigraphy, which suggests that total activity may be important for cognitive outcomes.
Activity energy expenditure (AEE) as measured by doubly-labeled water (DLW) is considered to be the gold standard measure of total physical activity.(9) AEE captures energy expended on all physical activity including moderate and vigorous physical activity (for example, jogging, walking and biking) and low intensity physical activity such as housework, daily chores, and postural allocation. People with higher levels of AEE have reduced rates of mortality and incident mobility impairment;(12–14) however, there are no published prospective studies that have examined the relationship between AEE and the incidence of cognitive impairment. Thus, this study aimed to examine the relationship between objectively assessed total physical activity as measured by AEE and incident cognitive impairment in late-life.
Participants were enrolled in the Health, Aging, and Body Composition (Health ABC) study, an ongoing prospective cohort study.(15) In brief, 3,075 participants aged 70–79 years were recruited from a random sample of white Medicare beneficiaries and all age-eligible, self-identified black community residents at two centers (University of Pittsburgh and University of Tennessee, Memphis) between 1997 and 1998. The participants were 51% women and 42% black. Participants were required to have no difficulty walking one-quarter of a mile (0.4km), climbing 10 stairs, or performing basic activities of daily living at enrollment. In addition, they could have no plans to leave the area for the next 3 years and no evidence of life-threatening illnesses (for example, acute leukemia, melanoma). Written informed consent, as approved by the institutional review boards at each center, was obtained from each participant. The study was also approved by the study coordinating center (University of California, San Francisco) institutional review board.
People were randomly selected from the Health ABC cohort within sex and race strata and asked to participate in the energy expenditure substudy at year 2 or 3. The substudy enrolled 323 participants between 1998 and 1999, as previously described.(12, 13) Of the 323 participants, 21 were excluded from the analyses because of failure to complete the protocol, lack of appropriate urine volume specimens, or failure of isotope or resting metabolic rate data to meet a priori quality control criteria. We also excluded those with no Modified Mini-Mental Examination (3MS) score at Health ABC year 3, which corresponds approximately to the energy expenditure substudy and served as cognitive baseline (n=14). In addition, we excluded those with low cognitive performance at year 3 that may represent prevalent cognitive impairment (3MS <80) (n=50) and those and those with mobility limitation (inability to walk a quarter-mile) at the energy expenditure visit (an additional n=33), which may have impeded participation in physical activity and so would confound the analyses. Finally, we excluded those with no follow up 3MS score (8 people), to yield a final analytical sample of 197. Compared with the full Health ABC cohort, our study sample included a slightly greater proportion of blacks, but was similar in age, sex, and self-reported physical activity (walking, stair climbing, working, volunteering, and caregiving).
Total energy expenditure between two clinic visits was measured using DLW, as detailed elsewhere.(16) At the first visit, the participant ingested an estimated 2g dose of DLW per kilogram of total body water and provided urine samples at approximately 2, 3, and 4 hours after the ingested the dose. At the second visit, approximately 15 days later, the participant provided two consecutive urine voids and a 5mL blood sample was collected. The plasma was only analyzed if participants showed evidence of delayed isotopic equilibration (n = 28).(16) Total energy expenditure was derived using Weir’s equation,(17) as previously described.(12) Based on blinded, repeat, urine isotopic analysis, the intra-tester repeatability of total energy expenditure was excellent (mean difference = 1.2% (standard deviation, 5.4%), n = 16) and compared well with that in a recent review article.(18)
Resting metabolic rate (RMR) was measured via indirect calorimetry using a Deltatrac II respiratory gas analyzer (Datex Ohmeda Inc., Helsinki, Finland), as detailed previously.(19) Testing occurred in the morning with the participant in a fasting state of at least 6 hours and after a 30 minute rest period. A gas exchange hood was placed over the participant’s head and metabolic rate was measured minute by minute for 40 minutes, with only the last 30 minutes used for calculation of RMR. Time periods that included participant movement or sleeping were also excluded from the RMR calculation.
AEE was calculated as 90% of total energy expenditure minus RMR. The thermic effect of meals was estimated to account for the remaining 10% of total energy expenditure and was not included in the calculation of AEE.(20, 21) Participants were classified into sex-specific tertiles of AEE for the purpose of these analyses.
At the first energy expenditure visit, an interviewer administered a questionnaire to ascertain physical activity over the previous 7 days. The questionnaire was a modified version of the Leisure Time Physical Activity Questionnaire,(22–24) which was adapted to include tasks applicable to older adults. The questionnaire captured duration and intensity of walking for exercise, other walking, climbing stairs, working for pay, volunteering, and caregiving. This information was then converted to kcal/day based on intensity of activity.(25) Participants were divided into tertiles of self-reported physical activity based on reported kcal/wk. Although participants were also asked whether they performed vigorous exercise such as bicycling, swimming, jogging, racquet sports, stair stepping, rowing, or cross country skiing, information on duration and intensity was not collected. Since only a small minority of our sample reported participation in vigorous exercise, we report percent participation in vigorous exercise.
The 100-point 3MS was administered to participants at most Health ABC clinic visits. The 3MS is a brief test of global cognitive function with components for orientation, concentration, language, praxis, and immediate and delayed memory.(26) The 3MS score from Year 3, which corresponds approximately to the energy expenditure substudy, was considered the baseline score for these analyses. Incident cognitive impairment was defined as a decline of at least one standard deviation (9 points) from baseline to the latest follow-up visit of Health ABC year 5 or 8.(27)
At baseline, participants self-reported age, race, and years of education. Additionally, at each visit, participants reported smoking history, hours per day sleeping or lying down, and level of difficulty walking a quarter mile. Participants’ blood pressure, height, and weight were measured. Depressive symptoms were assessed using the 20-item Center for Epidemiologic Studies–Depression Scale (CES-D) with a score >15 indicative of having high depressive symptoms.(28) Presence of medical conditions was determined based on a combination of selfreported physician-diagnosed disease information, clinic data (for example, blood pressure), and medication use. Hypertension was diagnosed by self-report of a clinical diagnosis, use of an antihypertensive medication, systolic blood pressure >140 mm Hg, or diastolic blood pressure >90 mm Hg. Diabetes was defined by self-report of a clinical diagnosis, use of diabetes drug, fasting plasma glucose >126 mg/dL, or 2-hour post-challenge glucose >200 mg/dL. Apolipoprotein E (APOE) genotype was determined at Bioserve.com (Laurel, MD) and coded as APOE e4 or no e4. At the energy expenditure visit, fat free mass was assessed using dual energy x-ray absorptiometry (Hologic QDR 4500, software version 8.21; Hologic, Inc., Bedford, Massachusetts). Participants also reported their usual eating habits over the previous year. Estimated percent caloric intake from fat was estimated derived from reports.
The participants’ baseline characteristics were compared by sex-specific tertile of AEE and tertile of self-reported physical activity using analysis of variance (ANOVA) for normally distributed continuous data, Kruskal-Wallis for skewed continuous data, or chi-squared (χ2) for categorical data. Follow up t-tests, Wilcoxon rank sum tests, or chi-square test were conducted as necessary to differentiate between pairs of tertiles.
The correlation between AEE and self-reported physical activity (kcal/d) was examined using Spearman’s correlation. Median and interquartile range of self-reported physical activity (overall and by type) was also described by sex-specific tertiles of AEE. Difference by sex-specific tertile was determined by Kruskal-Wallis equality-of-population rank test and follow up Wilcoxon rank sum tests, as appropriate.
We conducted a series of logistic regressions to evaluate the odds of cognitive impairment over the follow-up period and trend for dose response by tertile of AEE and tertile of reported physical activity (Models 1, 2, 3). For all regression analyses, Model 1 adjusted for baseline 3MS score only. Model 2 adjusted for baseline 3MS score and demographics (age, education, sex, race, and site). Model 3 was adjusted for baseline 3MS score, demographics, and variables that were associated with tertile of AEE or physical activity status (p<0.2), as appropriate, in bivariable analyses. Trend across tertiles was determined by entering tertiles of AEE or self-reported physical activity into the logistic model as a continuous variable. Fit of models were determined using Akaic information criterion (AIC). All statistical analyses were conducted using SAS software version 9.1.3 (SAS Institute, Inc. Cary, NC).
There were some differences in participant characteristics across tertiles of AEE (Table 1). Participants who were in the middle and highest sex-specific tertile of AEE were younger (p<.001 and p=0.02 respectively) than those in the lowest tertile. People in the highest tertile of AEE were also more frequently from the Pittsburgh site (p=0.01). Those in the middle tertile of AEE slept less than people in the lowest tertiles (p=0.006). People did not vary significantly (p<0.05) across tertile of AEE in race, education, smoking, fat free mass, percent dietary intake from fat, self-rated health, APOE e4 allele, and depression. Sex (lowest to highest tertile, female: 64.6%, 48.5%, 37.9%; p=0.009), race (black: 24.6%, 34.8%, 47.0%; p=0.03), fat free mass (43.2kg, 46.2kg, 50.7kg; p<0.001), sleep duration (8.4hrs, 8.8hrs, 7.8hrs; p=0.02), self-rated health (fair/poor: 13.8%, 18.2%, 4.5%; p=0.05), and diabetes (18.5%, 34.8%, 39.4%; p=0.02) were significantly different by tertile of self-reported physical activity. People did not vary significantly across tertile of self-reported physical activity in age, site, education, smoking, percent dietary intake of fat, sleep, APOE e4 allele, or depression.
AEE had a low but significant correlation with self-reported physical activity (r=0.19, p=0.007). Although self-reported physical activity was also different across tertiles of AEE (p<0.001, Table 2), the difference between the middle and highest tertiles of AEE was not significant (median (interquartile range) 251.1 (66.0–578.5) kcal/day vs. 323.3 (112.4–596.5) kcal/day respectively, p=0.54). Climbing stairs (p=0.003) and caregiving (p=0.03) were the only individual self-reported physical activities that were significantly different across tertiles of AEE (Table 2). Tertile of AEE was strongly associated with the likelihood of incident cognitive impairment. The incidence of cognitive impairment over the follow up period was 1.5% in the highest tertile of AEE, followed by 4.5% in the middle tertile and 16.9% in the lowest tertile. In analyses adjusted for baseline 3MS score only (Model 1: AIC=125.0), participants in the middle and highest and tertile of AEE were less likely to have incident cognitive impairment over the 5-year follow up period than those in the lowest tertile of AEE (middle tertile: odds ratio, OR, 95% confidence interval, CI: 0.23, 0.06–0.88; highest tertile: OR=0.07, 95%CI=0.01–0.60) (Table 3). When demographics were included to the model (Model 2: AIC 125.6) (Table 1), those in the highest tertile had a lower rate of cognitive impairment than those in the lowest tertile (OR=0.09; 95% CI 0.01–0.74). Although the difference between those in the middle and lowest tertiles was no longer statistically significant, there was a dose response across tertiles such that those with higher AEE had lower rates of incident cognitive impairment (p-for-trend=0.03). Even after controlling for additional factors that were associated with tertile of AEE (fat free mass, sleep duration, self-rated health, and diabetes) (Model 3 AIC 107.8), the odds of cognitive impairment remained significantly lower among participants in the highest tertile of AEE than in the lowest (OR=0.09; 95%CI 0.01–0.79)). In contrast, there was no difference in the likelihood of incident cognitive impairment across tertile of self-reported physical activity in any of the models (Table 3) and the dose response was only significant in models 1 and 2 (p=0.05 for each) but not model 3 (p=0.13).
In this study, older adults with higher objectively measured total daily activity had a lower incidence of cognitive impairment. The association of cognitive impairment and AEE was stronger and more dose-dependent than for self-reported physical activity. However, the relative contribution of total physical activity, moderate or vigorous physical activity, and low intensity physical activity is unclear.
Despite the many prior studies that suggested that people who are more physically active have lower risk of cognitive impairment in old age,(2–7, 29) there remains concern that the results of these studies may be biased due to use of self-reported physical activity. The results of this study allay these concerns by using an objective and all-encompassing measure of activity (AEE) to confirm that activity is inversely associated with the likelihood of developing incident cognitive impairment. Indeed, in this study, AEE had a strong dose response relationship with the rates of incident cognitive impairment. The difference in the odds of incident cognitive impairment between highest and lowest tertiles of AEE here are more disparate than reported in most, but not all,(4, 30) previous studies of self-report physical activity.
From the current study, the effect of total physical activity versus moderate or vigorous physical activity or light activity is unclear. However, it is possible that the more significant, dose-dependant association between cognitive impairment and AEE versus self-reported physical activity was due to more accurate capture of low intensity physical activity such as movement around the house, postural allocation, and fidgeting. Questionnaires to quantify low intensity, non-exercise physical activity, which is not easily quantified in frequencies and durations, have not been developed.(31, 32) Future studies should examine the role of very low intensity physical activity in optimizing cognitive outcomes in late life.
Future studies should consider using AEE in combination with self-reports and accelerometry to decipher any differences in the association between moderate or vigorous physical activity, activity of daily living, and very low intensity physical activity and the maintenance of cognition in old age. It may be particularly important to capture low intensity physical activity in the elderly, who are less likely to perform vigorous physical activity.(33) In this sample, AEE was more strongly associated with care giving and stair climbing, two activities of daily living, than with participation in vigorous exercise or exercise-related walking.
Despite growing evidence to support an association between greater physical activity and better cognition, it remains possible that low activity is a pre-morbid indicator of vulnerability to cognitive impairment rather than a determinant of cognitive health. Indeed, frailty, which is a risk factor for incident cognitive impairment,(34, 35) is defined by loss of strength and weight loss, which would contribute to low AEE. In order to minimize the likelihood that AEE is simply a symptom of poor cognition or health, we eliminated people with mobility impairment or very low cognitive performance (3MS<80) at our study baseline and controlled for numerous health indicators associated with AEE. However, measurement of total activity might be an effective means to detect identify people at risk for cognitive decline early in its course, regardless of whether low total activity is a determinant of cognitive health or merely an indicator of vulnerability.
The mechanisms by which physical activity is related to late life cognition are likely to be multi-factorial. Research suggests that physical activity may improve neuroplasticity by modifying levels of brain-derived neurotrophic factor (BDNF).(36–38) Physical activity is also associated with reduced accumulation of β-amyloid plaque—one of the hallmark features of Alzheimer’s disease—in animal models.(39) Finally, physical activity is associated with reduced rates and severity of vascular risk factors, including hypertension, obesity, and type II diabetes,(40) which are each associated with increased risk of cognitive impairment.(41–43) However, it is unclear how low intensity physical activity is related to each of these mechanisms, a topic that needs attention in future studies.
Our study has several strengths. Most importantly, we use an accurate, encompassing, objective measure of physical activity to examine the relationship between total physical activity and cognitive impairment. In addition, our participants are well-characterized so we were able to control for many possible confounders including age, education, baseline cognition, and comorbidities. However, the study also has some limitations. Due to the expense of doubly labeled water methods, our study sample was small, providing us with limited power to detect associations with self-reported physical activity. Furthermore, the energy expenditure substudy was not powered to detect differences in cognitive outcomes. In addition, though our sample is similar to the full Health ABC cohort in age, sex, mobility, walking ability, and physical activity, there may be unaccounted for differences. In this study, cognitive impairment was defined by a decline of a standard deviation (9 points) on the 3MS rather than by a comprehensive clinical diagnosis, which may cause misclassification. However, as little as a 5-point decline in the 3MS is clinical meaningful decline.(44) Self-reported physical activity only reflected the previous week versus two weeks for AEE and may, thus, be less likely to reflect normal physical activity levels and the relationship to cognitive impairment. Finally, we did not account for seasonal variation in AEE; however, this is more likely to weaken rather than strengthen the relationship between AEE and cognitive impairment.
Our study provides new evidence that objectively measured total daily activity, as measured by energy expenditure, is associated with reduced incidence of cognitive impairment in older adults. The contribution of moderate and vigorous physical activity versus low intensity physical activity to this relationship remains unclear and future longitudinal studies should examine the role of moderate and vigorous physical activity versus low intensity physical activity in maintaining cognitive independence for older adults. Optimistically, it is possible that even low intensity activity of daily living may be protective against incident cognitive impairment.
This work was supported by National Institute on Aging contracts N01-AG-6-2101, N01-AG-6-2103, and N01-AG-6-2106.
This research was also supported in part by the Intramural Research Program of the National Institutes of Health, National Institute on Aging and with additional support from the National Institute of Diabetes and Digestive and Kidney Diseases.
Dr. Middleton is supported by a Canadian Institute of Health Research fellowship.
Dr. Manini is supported by the National Institute on Aging Claude D. Pepper Center P30AG028740.
Dr. Simonsick has nothing to disclose.
Dr. Harris has nothing to disclose.
Dr. Barnes was supported in part by the National Institute on Aging (K01-AG-024069) and the Alzheimer’s Association (IIRG-06-27306).
Dr. Tylavsky has nothing to disclose.
Dr. Brach is supported in part by a National Institutes on Aging and American Federation of Aging Research Paul Beeson Career Development Award (K23 AG026766-01).
Dr. Everhart has nothing to disclose
Dr. Yaffe is supported in part by NIA grant AG 031155 and an Independent Investigator Award from the Alzheimer’s Association.
None of the authors report a conflict of interest relevant to the current study.
None of the funding agencies had any role in the design and conduct of the study, collection, management, analysis, and interpretation of the data, or preparation, review, or approval of the manuscript.
Laura Middleton had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.