Edited by: Lutz Jäncke, Universität Zürich, Switzerland
Reviewed by: Csaba Jozsef Nyakas, Semmelweis University, Hungary; Vijay Varma, National Institute on Aging (NIA), United States
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Over the last few decades, considerable evidence shows that greater levels of aerobic exercise and cardiovascular fitness benefit cognitive performance. However, the degree to which free-living activity in community settings is related to cognitive performance remains unclear, particularly in older adults vulnerable to disability. Also, it is unknown whether the manner in which daily physical activity (PA) and sedentary time are accumulated throughout the day is associated with cognition. Cross-sectional associations between accelerometer-characterized PA and sedentary patterns and cognitive performance were examined in 1,274 mobility-limited older adults. Percent time spent in various bout lengths of PA (≥1, ≥2, and ≥5 min) and sedentary (≥1, ≥30, and ≥60 min) was defined as the number of minutes registered divided by total wear time × 100. Percent time was then tertiled for each bout length. Multiple linear regression models were used to estimate the associations between accelerometer bout variables and separate cognitive domains that included processing speed (Digit Symbol Coding; DSC), immediate and delayed recall (Hopkins Verbal Learning Test; HVLT), information processing and selective attention (Flanker), working memory (
Due to the growing aging population within the United States, there is considerable interest in identifying lifestyle interventions that can enhance brain health and potentially reverse and/or prevent cognitive decline. Over the last few decades, both cross-sectional and longitudinal evidence suggest that higher physical activity (PA) is associated with higher cognitive performance (
Chronic and excessive time spent being sedentary are associated with deleterious effects on cardiovascular and metabolic outcomes, regardless of PA engagement (
Portable activity monitors such as accelerometers objectively capture both time spent in PA and sedentary behaviors continuously throughout the day. Not only can accelerometer data be utilized to quantify daily volumes (e.g., total accumulated amount of sedentary time), they can be also used to characterize accrual patterns into bouts (
The purpose of this study was to evaluate the cross-sectional association between accrual patterns of PA and sedentary time with cognitive performance in mobility-limited older adults. The first aim was to assess whether total PA and accrual patterns in short and long bout lengths of PA were associated with multiple domains of cognitive performance. We hypothesized that total PA, particularly PA time spent in longer bout durations, were associated with higher cognitive performance in mobility-limited older adults. The second aim examined the association of total sedentary time and accrual of sedentary time in various bout lengths with multiple domains of cognitive performance. We hypothesized that higher amounts of sedentary time, particularly in prolonged bouts, were associated with lower cognitive performance in mobility-limited older adults.
Baseline accelerometer and cognitive data were utilized from the Lifestyle Interventions and Independence for Elders (LIFE) Study. Details about specific study inclusion and exclusion criteria of the LIFE study have been reported previously (
The hip-worn, Actigraph tri-axial accelerometer (Model GT3X; ActiGraphTM) was used to objectively measure time spent in PA and sedentary levels. Participants were instructed to wear the accelerometer for a minimum of 7 consecutive days immediately following their baseline clinic visit. Additionally, participants were instructed to wear the device when they were awake and to remove the device during sleep and water-based activities such as showering. Movements were recorded as activity counts (unit-less quantities of movement) collected in 1-s epochs and then later binned into counts per minute. Non-wear time was defined as a 90-min window of zero counts in the vertical axis, allowing for up to 2-min of non-zero counts <100 counts/min and removed for the analysis (
Each minute in the accelerometer data was labeled as either a sedentary (<100 counts/min) or an activity (≥100 counts/min) minute. Bouts were defined as consecutive minutes registering in a specific activity level (i.e., sedentary or active). Three bout lengths were used for activity (≥1, ≥2, and ≥5 min bout lengths) and for sedentary (≥1, ≥30, and ≥60 min bout lengths) time. Since more time registered as sedentary than active, longer sedentary bout lengths were used as previously reported (
The cognitive test battery consisted of a test of psychomotor speed, attention, and working memory [Wechsler Adult Intelligence Scale-III Digit Symbol Coding (DSC) (
Participants self-reported age, sex, race/ethnicity, education, income, and marital status. Body mass index (kg/m2) was calculated using height (m) on a stadiometer and weight (kg) on a balance-beam scale. Other covariates include self-described smoking status, sleep quality using the Pittsburgh Sleep Quality Index (
Differences in participant characteristics, cognitive scores, and accelerometer wear metrics were tested by either
Among 1,341 participants who had valid accelerometer data, 39 (3%) did not have any cognitive data and were excluded (final sample
Participant characteristics stratified by physical activity (PA)-based tertiles,
Low ( |
Medium ( |
High ( |
||
---|---|---|---|---|
Age (years), mean ( |
79.9 (5.2) | 78.9 (5.4) | 77.5 (5.0) | <0.001 |
Female, |
225 (52.9) | 294 (69.2) | 332 (78.1) | <0.001 |
Non-Hispanic white, |
340 (80.0) | 329 (77.4) | 303 (71.3) | 0.225 |
Beyond HS education, |
297 (69.9) | 265 (62.4) | 248 (58.4) | 0.009 |
$35 k or less annual income, |
139 (32.7) | 168 (39.5) | 187 (44.0) | 0.003 |
Married, |
181 (42.6) | 150 (35.3) | 145 (34.1) | 0.071 |
Body mass index (kg/m2), mean ( |
30.1 (6.3) | 30.7 (6.0) | 30.0 (5.8) | 0.823 |
Current smoker, |
15 (3.5) | 15 (3.5) | 10 (2.4) | 0.611 |
Pittsburgh Sleep Quality Index, mean ( |
5.7 (3.8) | 6.0 (3.8) | 6.0 (3.9) | 0.158 |
Perceived Stress Scale, mean ( |
10.6 (6.1) | 11.2 (6.3) | 11.2 (5.7) | 0.108 |
Two or more comorbidities, |
127 (29.9) | 130 (30.6) | 75 (17.7) | <0.001 |
One-back, % correct, mean ( |
0.81 (0.18) | 0.81 (0.18) | 0.83 (0.16) | 0.125 |
Two-back, % correct, mean ( |
0.50 (0.21) | 0.52 (0.20) | 0.51 (0.21) | 0.755 |
Digit Symbol Coding, # correct, mean ( |
43.9 (12.3) | 46.9 (12.5) | 47.7 (13.3) | <0.001 |
Task switching–No switch reaction time (ms), mean ( |
1,519.1 (1,075.1) | 1,450.0 (1,060.6) | 1,447.3 (803.4) | 0.321 |
Task switching–Switch reaction time (ms), mean ( |
2,473.2 (1,323.2) | 2,426.8 (1,287.8) | 2,385.2 (1,124.2) | 0.337 |
Flanker–Congruent (ms), mean ( |
662.5 (244.8) | 656.9 (208.6) | 649.9 (209.5) | 0.417 |
Flanker–Incongruent (ms), mean ( |
736.9 (348.5) | 737.7 (299.1) | 733.7 (303.3) | 0.886 |
HVLT-Immediate recall, # correct, mean ( |
22.7 (5.3) | 23.7 (5.3) | 23.8 (5.3) | 0.002 |
HVLT-Delayed recall, # correct, mean ( |
7.5 (2.7) | 7.8 (2.9) | 8.1 (2.8) | 0.002 |
-0.10 (0.70) | -0.01 (0.70) | 0.01 (0.70) | 0.016 | |
Wear days, mean ( |
8.1 (3.3) | 8.3 (3.3) | 7.8 (3.0) | 0.295 |
Wear minutes/day, mean ( |
845.5 (125.9) | 841.7 (112.6) | 827.1 (94.1) | 0.016 |
Average time spent in activity and sedentary bout lengths described as either a daily percentage or absolute time (minutes/day) are found in Table
Descriptive means and ranges of daily accelerometer metrics by bout-specific tertiles of either total or sedentary activity.
Low ( |
Medium ( |
High ( |
||||
---|---|---|---|---|---|---|
Mean | Min–Max | Mean | Min–Max | Mean | Min–Max | |
%/day | 14.3 | 3.9–18.8 | 22.3 | 18.8–25.8 | 31.9 | 25.8–60.6 |
Minutes/day | 120.7 | 29.3–249.1 | 188.0 | 123.0–306.2 | 263.2 | 167.8–511.8 |
%/day | 9.2 | 1.4–12.7 | 15.4 | 12.8–17.9 | 22.7 | 17.9–43.9 |
Minutes/day | 77.5 | 10.3–167.3 | 129.3 | 85.7–217.6 | 187.4 | 125.5–384.3 |
%/day | 2.2 | 0.2–3.6 | 4.8 | 3.7–6.1 | 8.9 | 6.1–23.0 |
Minutes/day | 19.0 | 1.3–42.6 | 40.8 | 25.3–77.6 | 73.2 | 42.2–206.0 |
%/day | 68.1 | 39.3–74.2 | 77.7 | 74.2–81.2 | 85.7 | 81.2–96.1 |
Minutes/day | 563.8 | 313.0–938.0 | 653.6 | 496.2–1,127.4 | 724.7 | 532.0–1,303.9 |
%/day | 19.8 | 2.9–27.9 | 34.1 | 27.9–40.6 | 51.0 | 40.7–82.6 |
Minutes/day | 162.7 | 21.0–313.1 | 286.4 | 196.0–536.7 | 436.5 | 272.4–922.5 |
%/day | 6.0 | 0–10.3 | 15.0 | 10.3–20.2 | 29.8 | 20.2–68.4 |
Minutes/day | 49.5 | 0–100.2 | 126.0 | 69.6–260.2 | 257.6 | 144.3–682.6 |
In fully adjusted models, participants who spent a medium percentage of their time in activity (Table
Adjusted associations of tertiles of percent activity time by bout length with cognition outcomes, beta-coefficient (SE).
DSC | Task switching | Flanker | HVLT | Global composite | ||||||
---|---|---|---|---|---|---|---|---|---|---|
% activity time | One-back ( |
Two-back ( |
( |
No ( |
Yes ( |
Congruent ( |
Incongruent ( |
Immediate ( |
Delayed ( |
( |
A. Low | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
B. Medium | <0.001 (0.011) | 0.012 (0.015) | 2.104 (0.827)∗ | -63.201 (72.822) | -62.972 (91.951) | -16.206 (15.502) | -13.372 (22.447) | 0.621 (0.351) | 0.113 (0.191) | 0.083 (0.046) |
C. High | 0.011 (0.013) | -0.001 (0.016) | 2.030 (0.854)∗ | -86.220 (74.541) | -117.952 (94.122) | -24.541 (15.993) | -18.602 (23.158) | 0.385 (0.363) | 0.252 (0.197) | 0.085 (0.047) |
E. Low | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
F. Medium | 0.005 (0.013) | 0.018 (0.016) | 2.514 (0.826)∗∗ | -42.912 (72.537) | -49.930 (91.572) | -14.145 (15.482) | -11.805 (22.420) | 1.001 (0.350)∗∗ | 0.213 (0.191) | 0.105 (0.045)∗ |
G. High | 0.016 (0.013) | 0.005 (0.016) | 1.849 (0.849)∗ | -99.048 (74.116) | -141.906 (93.566) | -27.796 (15.895) | -23.111 (23.019) | 0.476 (0.360) | 0.236 (0.196) | 0.100 (0.047)∗ |
I. Low | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
J. Medium | -0.006 (0.013) | 0.003 (0.015) | 2.286 (0.825)∗∗ | -77.574 (72.30) | -50.018 (91.341) | -24.840 (15.399) | -41.153 (22.278) | 1.045 (0.350)∗∗ | 0.386 (0.190)∗ | 0.115 (0.045)∗ |
K. High | 0.019 (0.013) | 0.015 (0.016) | 2.008 (0.845)∗ | -107.309 (73.880) | -112.888 (93.326) | -23.873 (15.850) | -28.538 (22.931) | 0.696 (0.358) | 0.387 (0.194)∗ | 0.114 (0.046)∗ |
For HLVT-immediate, those who spent a medium percentage of their time in activity had an estimated 1 unit score higher in HLVT-immediate when compared to those who spent a low percentage of their activity for ≥2 (
For HLVT delayed, those who spent a medium percentage of their time in activity (Table
Those who spent a medium percentage of their time in activity and high percentage of their time in activity had approximately 0.1 higher standardized global cognitive scores when compared to those with a low percentage of activity, respectively, for both ≥2 (
Table
Adjusted associations of tertiles of percent sedentary time by bout length with cognition outcomes, beta-coefficient (SE).
DSC | Task switching | Flanker | HVLT | Global composite | ||||||
---|---|---|---|---|---|---|---|---|---|---|
% Sedentary time | One-back ( |
Two-back ( |
( |
No ( |
Yes ( |
Congruent ( |
Incongruent ( |
Immediate ( |
Delayed ( |
( |
A. Low | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
B. Medium | -0.011 (0.013) | 0.013 (0.015) | 0.073 (0.826) | 23.018 (72.112) | 54.980 (91.055) | 8.336 (15.509) | 5.230 (22.457) | 0.236 (0.351) | -0.139 (0.190) | -0.002 (0.046) |
C. High | -0.012 (0.013) | 0.001 (0.016) | -2.030 (0.854)∗ | 86.220 (74.541) | 117.953 (94.122) | 24.541 (15.994) | 18.602 (23.158) | -0.385 (0.363) | -0.252 (0.197) | -0.085 (0.047) |
E. Low | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
F. Medium | 0.006 (0.013) | 0.007 (0.016) | -0.170 (0.829) | 9.018 (72.465) | -8.939 (91.520) | 10.580 (15.497) | 3.495 (22.429) | 0.355 (0.351) | 0.153 (0.190) | 0.038 (0.046) |
G. High | -0.014 (0.013) | -0.003 (0.016) | -0.519 (0.879) | 50.636 (76.635) | 27.604 (96.787) | 12.798 (16.416) | 3.798 (23.759) | 0.284 (0.372) | 0.199 (0.202) | -0.012 (0.048) |
I. Low | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. |
J. Medium | 0.001 (0.013) | 0.002 (0.015) | -0.539 (0.829) | 74.263 (72.442) | 13.485 (91.518) | 21.624 (15.501) | 26.424 (22.435) | 0.083 (0.351) | 0.150 (0.190) | -0.017 (0.457) |
K. High | -0.011 (0.013) | -0.002 (0.016) | -0.330 (0.862) | 44.905 (75.231) | 29.123 (95.040) | 4.761 (16.089) | 6.430 (23.285) | 0.418 (0.365) | 0.113 (0.198) | 0.0003 (0.0475) |
This study demonstrated that higher time spent in PA is associated with higher cognitive performance related to psychomotor speed, attention, and working memory among mobility-limited older adults. Further, greater time spent in longer bout lengths of PA was positively correlated with cognitive domains of working memory and learning as well as overall cognitive performance; a relationship not observed with overall total PA. Interestingly, total sedentary time, but not prolonged sedentary bouts, was found to be negatively associated with cognitive performance related to executive function; a relationship driven by participants with the highest sedentary time. Our findings suggest that, beyond a total summary measure, the manner in which daily PA and sedentary behaviors are accrued may be an important indicator of certain cognitive domains in sedentary older adults.
Evidence that a positive relationship between daily PA and cognition is emerging, particularly for higher intensity activity (
Existing research suggests that self-reported engagement in sedentary behaviors is associated with poorer cognitive performance (
Intervention results for the LIFE study demonstrated that a 24-month moderate intensity PA program, when compared to a health education program, did not result in improvements in global or domain-specific cognitive function, nor did it alter the incidence of mild cognitive impairment or dementia (
Strengths of the present study include a clinically relevant and large sample of older adults at risk for mobility disability and cognitive decline. Additional strengths include a battery of cognitive assessments, objective movement-based activity data through hip-worn accelerometry, and a multitude of demographics, behavioral, anthropometric, and medical history data. However, a limitation to the current study was the inability to determine a longitudinal relationship between activity/sedentary patterns and cognitive outcomes using a cross-sectional design. Additional research is needed to attempt to elucidate a causal relationship and potential mechanisms that explain how activity/sedentary lifestyle patterns contribute to cognitive impairment and possibly dementia with aging. Another limitation is the generalizability of the results to an older adult population is restricted because the LIFE study excluded those who were either physically well-functioning or severely cognitively impaired. Older adults with low cognition were screened out of the LIFE study to ensure adherence to the study sessions and compliance with the study protocols. Further, LIFE participants were already sedentary and excluded if they reported >20 min per week in moderate-to-vigorous PA. As such, stronger associations between activity and sedentary patterns with cognition may be observed in a population-based sample rather than a sample of older adults with high of disability.
Overall, this research found that higher amounts of PA was positively associated with psychomotor speed, attention, and working memory performance. Individuals who accumulated PA in longer bouts showed better performance on measures of verbal learning and memory. Thus, the impact of PA on memory function is potentially influenced by way its accumulated. Alternatively, it could also be interpreted that those with more preserved cognitive function have the capability to be active for longer continuous periods of time as in accomplishing a task-oriented goal. Another finding from this study was that time spent being sedentary was largely not associated with multiple measures of cognitive function. Future research is needed to better characterize longitudinal changes in the accumulation of daily activity and sedentary behaviors as potential biophysical markers or intervention targets of cognitive status in older adults.
The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.
AW, TM, ME, and JN contributed to the conceptual design, acquisition, analysis, and interpretation of the data and also the writing of the manuscript. DC, JK, RF, AFK, JV, SR, KS, ACK, TB, SA, NN, JJ, KR, ME, TG, and MP contributed to the interpretation and writing of the manuscript. AW, TM, and JN contributed to revising the work critically and the final approval of the work.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the United States Department of Agriculture.
Administrative Coordinating Center, University of Florida, Gainesville, FL, United States: Marco Pahor, MD (Principal Investigator of the LIFE Study); Jack M. Guralnik, MD, PhD (Co-Investigator of the LIFE Study) (University of Maryland School of Medicine, Baltimore, MD, United States); Stephen D. Anton, PhD; Thomas W. Buford, PhD; Christiaan Leeuwenburgh, PhD; Susan G. Nayfield, MD, MSc; Todd M. Manini, PhD; Connie Caudle; Lauren Crump, MPH; Latonia Holmes; Jocelyn Lee, PhD; Ching-ju Lu, MPH.
Data Management, Analysis and Quality Control Center, Wake Forest University, Winston Salem, NC, United States: Michael E. Miller, PhD (DMAQC Principal Investigator); Mark A. Espeland, PhD (DMAQC Co-Investigator); Walter T. Ambrosius, PhD; William Applegate, MD; Daniel P. Beavers, PhD, MS; Robert P. Byington, PhD, MPH, FAHA; Delilah Cook, CCRP; Curt D. Furberg, MD, PhD; Lea N. Harvin, BS; Leora Henkin, MPH, Med; John Hepler, MA; Fang-Chi Hsu, PhD; Kathy Joyce; Laura Lovato, MS; Juan Pierce, AB; Wesley Roberson, BSBA; Julia Robertson, BS; Julia Rushing, BSPH, MStat; Scott Rushing, BS; Cynthia L. Stowe, MPM; Michael P. Walkup, MS; Don Hire, BS; W. Jack Rejeski, PhD; Jeffrey A. Katula, PhD, MA; Peter H. Brubaker, PhD; Shannon L. Mihalko, PhD; Janine M. Jennings, PhD.
National Institutes of Health, Bethesda, MD, United States: Evan C. Hadley, MD (National Institute on Aging); Sergei Romashkan, MD, PhD (National Institute on Aging); Kushang V. Patel, PhD (National Institute on Aging); Denise Bonds, MD, MPH (National Heart, Lung, and Blood Institute).
Field Centers Northwestern University, Chicago, IL, United States: Mary M. McDermott, MD (Field Center Principal Investigator); Bonnie Spring, PhD (Field Center Co-Investigator); Joshua Hauser, MD (Field Center Co-Investigator); Diana Kerwin, MD (Field Center Co-Investigator); Kathryn Domanchuk, BS; Rex Graff, MS; Alvito Rego, MA.
Pennington Biomedical Research Center, Baton Rouge, LA, United States: Timothy S. Church, MD, PhD, MPH (Field Center Principal Investigator); Steven N. Blair, PED (University of South Carolina); Valerie H. Myers, PhD; Ron Monce, PA-C; Nathan E. Britt, NP; Melissa Nauta Harris, BS; Ami Parks McGucken, MPA, BS; Ruben Rodarte, MBA, MS, BS; Heidi K. Millet, MPA, BS; Catrine Tudor-Locke, PhD, FACSM; Ben P. Butitta, BS; Sheletta G. Donatto, MS, RD, LDN, CDE; Shannon H. Cocreham, BS.
Stanford University, Palo Alto, CA, United States: Abby C. King, PhD (Field Center Principal Investigator); Cynthia M. Castro, PhD; William L. Haskell, PhD; Randall S. Stafford, MD, PhD; Leslie A. Pruitt, PhD; Veronica Yank, MD; Kathy Berra, MSN, NP-C, FAAN; Carol Bell, NP; Rosita M. Thiessen; Kate P. Youngman, MA; Selene B. Virgen, BAS; Eric Maldonado, BA; Kristina N. Tarin, MS, CSCS; Heather Klaftenegger, BS; Carolyn A. Prosak, RD; Ines Campero, BA; Dulce M. Garcia, BS; Jos Soto, BA; Linda Chio, BA; David Hoskins, MS.
Tufts University, Boston, MA, United States: Roger A. Fielding, PhD (Field Center Principal Investigator); Miriam E. Nelson, PhD; Sara C. Folta, PhD; Edward M. Phillips, MD; Christine K. Liu, MD; Erica C. McDavitt, MS; Kieran F. Reid, PhD, MPH; Dylan R. Kirn, BS; Evan P. Pasha, BS; Won S. Kim, BS; Julie M. Krol, MS; Vince E. Beard, BS; Eleni X. Tsiroyannis, BS; Cynthia Hau, BS, MPH.
University of Florida, Gainesville, FL, United States: Todd M. Manini, PhD (Field Center Principal Investigator); Marco Pahor, MD (Field Center Co-Investigator); Stephen D. Anton, PhD; Thomas W. Buford, PhD; Michael Marsiske, PhD; Susan G. Nayfield, MD, MSc; Bhanuprasad D. Sandesara, MD; Mieniecia L. Black, MS; William L. Burk, MS; Brian M. Hoover, BS; Jeffrey D. Knaggs, BS; William C. Marena, MT, CCRC; Irina Korytov, MD; Stephanie D. Curtis, BS; Megan S. Lorow, BS; Chaitalee S. Goswami; Melissa A. Lewis; Michelle Kamen, BS; Jill N. Bitz; Brian K. Stanton, BS; Tamika T. Hicks, BS; Charles W. Gay, DC; Chonglun Xie, MD (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States); Holly L. Morris, MSN, RN, CCRC (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States); Floris F. Singletary, MS, CCC-SLP (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States); Jackie Causer, BSH, RN (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States); Susan Yonce, ARNP (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States); Katie A. Radcliff, M.A. (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States); Mallorey Picone Smith, BS (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States); Jennifer S. Scott, BS (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States); Melissa M. Rodriguez, BS (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States); Margo S. Fitch, PT (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States); Mendy C. Dunn, BSN (Assessment) (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States); Jessica Q. Schllesinger, BS (Brooks Rehabilitation Clinical Research Center, Jacksonville, FL, United States).
University of Pittsburgh, Pittsburgh, PA, United States: Anne B. Newman, MD, MPH (Field Center Principal Investigator); Stephanie A. Studenski, MD, MPH (Field Center Co-Investigator); Bret H. Goodpaster, PhD; Oscar Lopez, MD; Nancy W. Glynn, PhD; Neelesh K. Nadkarni, MD, PhD; Diane G. Ives, MPH; Mark A. Newman, PhD; George Grove, MS; Kathy Williams, RN, BSEd, MHSA; Janet T. Bonk, MPH, RN; Jennifer Rush, MPH; Piera Kost, BA (deceased); Pamela Vincent, CMA; Allison Gerger, BS; Jamie R. Romeo, BS; Lauren C. Monheim, BS.
Wake Forest University, Winston Salem, NC, United States: Stephen B. Kritchevsky, PhD (Field Center Principal Investigator); Anthony P. Marsh, PhD (Field Center Co-Principal Investigator); Tina E. Brinkley, PhD; Jamehl S. Demons, MD; Kaycee M. Sink, MD, MAS; Kimberly Kennedy, BA, CCRC; Rachel Shertzer-Skinner, MA, CCRC; Abbie Wrights, MS; Rose Fries, RN, CCRC; Deborah Barr, MA, RHEd, CHES.
Yale University, New Haven, CT, United States: Thomas M. Gill, M.D. (Field Center Principal Investigator); Robert S. Axtell, PhD, FACSM (Field Center Co-Principal Investigator) (Southern Connecticut State University, Exercise Science Department); Susan S. Kashaf, MD, MPH (VA Connecticut Healthcare System); Nathalie de Rekeneire, MD, MS; Joanne M. McGloin, MDiv, MS, MBA; Raeleen Mautner, PhD; Sharon M. Huie-White, MPH; Luann Bianco, BA; Janice Zocher; Karen C. Wu, RN; Denise M. Shepard, RN, MBA; Barbara Fennelly, MA, RN; Rina Castro, LPN; Sean Halpin, MA; Matthew Brennan, MA; Theresa Barnett, MS, APRN; Lynne P. Iannone, MS, CCRP; Maria A. Zenoni, MS; Julie A. Bugaj, MS; Christine Bailey, MA; Peter Charpentier, MPH; Geraldine Hawthorne-Jones; Bridget Mignosa; Lynn Lewis.
Cognition Coordinating Center, Wake Forest University, Winston Salem, NC, United States: Jeff Williamson, MD, MHS (Center Principal Investigator); Kaycee M. Sink, MD, MAS (Center Co-Principal Investigator); Hugh C. Hendrie, MB, ChB, DSc (Indiana University); Stephen R. Rapp, PhD; Joe Verghese, MB, BS (Albert Einstein College of Medicine of Yeshiva University); Nancy Woolard; Mark Espeland, PhD; Janine Jennings, PhD; Valerie K. Wilson, MD.
Electrocardiogram Reading Center, University of Florida, Gainesville, FL, United States: Carl J. Pepine MD, MACC; Mario Ariet, PhD; Eileen Handberg, PhD, ARNP; Daniel Deluca, BS; James Hill, MD, MS, FACC; Anita Szady, MD.
Spirometry Reading Center, Yale University, New Haven, CT, United States: Geoffrey L. Chupp, MD; Gail M. Flynn, RCP, CRFT; Thomas M. Gill, MD; John L. Hankinson, PhD (Hankinson Consulting, Inc.); Carlos A. Vaz Fragoso, MD.
Cost Effectiveness Analysis Center: Erik J. Groessl, PhD (University of California, San Diego and VA San Diego Healthcare System); Robert M. Kaplan, PhD (Office of Behavioral and Social Sciences Research, National Institutes of Health).