Abstract
Background:
A number of large-scale population studies have provided valuable information about physical performance in aged individuals; however, there is little information about trajectories of function and associations with age across the adult life span. We developed a mobility-focused physical performance screener designed to be appropriate for the adult life span.
Methods:
The physical performance battery includes measures of mobility, strength, endurance, and balance. Physical activity (PA) was assessed with accelerometry. We examined age-related trends in physical performance and PA, and the relationship between physical performance and PA across the age range (30–90+), by decade, in 775 participants enrolled in the study 2012–2014.
Results:
Physical performance was worse with increasing age decade. Although men performed better than women across all ages, the decrement by age group was similar between genders. Worsening physical performance was observed as early as the fifth decade for chair stands and balance and in the sixth decade for gait speed and aerobic endurance. The number and strength of significant associations between physical performance and PA increased with greater age: the greatest number of significant associations was seen in the 60–79 age groups, with fewer reported in the 30–59 and 80–90+ age groups. More PA was associated with better physical function.
Conclusion:
These results emphasize the importance of a life span approach to studies of function and aging. This work points to the need for a physical performance screener that spans across adulthood as a clinical tool for identifying functional decline.
Keywords: Function, Mobility, Prevention, Epidemiology, Birth cohort
Physical performance (eg, strength, endurance, balance) is a key contributor to late-life mobility and independence (1–3). There is much research on physical performance in large, prospective studies of older populations, however, there is growing evidence that the onset of chronic disease burden—and the negative lifestyle behaviors that contribute to these diseases—occur earlier in the life span (4). The American College of Sports Medicine (5,6) suggests that early detection and treatment of functional decline plays a critical role in preventing or delaying the onset of functional impairment and physical disability. Studies that examine elements of life-span health in both younger and older adults are needed to establish trajectories of physical performance across the adult life span.
The patterns of age-related decline, and the age at which decreases in physical performance can first be detected, have not been widely investigated. Two notable exceptions are the InCHIANTI study and the Baltimore Longitudinal Study of Aging (BLSA), which included performance-based tests of mobility and muscle strength among adults 20+ years of age (7,8). The primary obstacle to studying physical performance in young and older adults concurrently is the significant heterogeneity in fitness capacity among these groups. To examine functional pathways across the adult life span requires a battery of physical performance tests that are broadly age- and ability-appropriate, sensitive to change, and without floor or ceiling effects. These tests then can be used to compare group estimates and changes across a wide range of ages and ability levels (9). After a review of the literature and a number of consultations with internal experts and external discussions, we selected five physical performance tests that assess domains of mobility including gait, balance, aerobic endurance, and lower body strength. Each of these tests has previously demonstrated reliability and sufficient discrimination in a variety of populations. Finally, each of these tests is scored along a continuous scale, minimizing floor and ceiling effects.
To address the deficiencies in published work, we obtained objective measures of physical performance and physical activity (PA) in an adult cohort ages 30 years and above (90+), in a study designed to examine patterns of age-related differences in physical performance and the associations between physical performance, PA, and age across the adult life span.
Design and Methods
This study, the Physical Performance Across the Life-span Study, is a nested study derived from The Measurement to Understand the Reclassification of Disease Of Cabarrus/Kannapolis (MURDOCK) Study. The MURDOCK Study is a population-based longitudinal health study derived from an evolving registry of participants that will use banked specimens and curated clinical data to redefine how diseases are classified, diagnosed, and treated (10–12). A collaborative sub-study focused on aging and physical performance, the Physical Performance Across the Life-span Study (M.C.M., PI), with an enriched biennial functional assessment was proposed and approved. Enrollment in this study is ongoing and will be followed yearly indefinitely.
This manuscript describes the protocol, study design, and baseline characteristics and associations of the initial 775 participants enrolled (2012–2014) in the Physical Performance Study launched in 2012.
Participant Eligibility Criteria
Study participants were eligible for the parent MURDOCK Study if they were (a) at least 18 years of age, (b) residents of Cabarrus County, the city of Kannapolis or certain portions of Rowan, Mecklenburg, or Stanly counties, as well as other surrounding areas in North Carolina for at least 6 months, and (c) provided written informed consent (10). Participants were designated as ineligible for the Physical Performance Across the Life-span Study if they were under the age of 30, self-reported pregnancy; unstable angina or myocardial infarction within the previous 6 months; active congestive heart failure; inability to walk 30 feet without human assistance (nonmotorized assistive mobility devices were allowed); or required consent by a legally authorized representative.
Recruitment and Enrollment Procedures
We had two sources of subject recruitment: participants enrolled in the parent MURDOCK Study who volunteered for this ancillary study and self-referrals. All study materials were translated into Spanish to facilitate enrollment of persons whose primary language was Spanish.
Enrollment was stratified by sex (50/50) and age. The older age cohorts were purposefully oversampled given the heterogeneity in physical performance expected in these age groups. All potential participants were scheduled for an in-person baseline study visit, during which time informed consent, HIPAA authorization, and study measures were administered. The MURDOCK Study and the Physical Performance Study were approved by the Institutional Review Boards of Duke University Medical Center and the Carolinas Healthcare System. Study data were collected and managed using REDCap electronic data capture tools hosted at Duke University (13).
Study Population and Study-Specific Assessments
Sample characteristics
Demographic variables (education, race, ethnicity) and chronic conditions were collected with self-report.
Physical performance measures
Both rapid and usual gait speed (meters/second) were assessed using a 4-meter walk, adapted from the Short Physical Performance Battery (14,15). Two timed trials of each condition were completed, with the best (fastest) time for each condition retained for analyses. Balance was assessed using the timed single leg stance test, which measures the time participants are able to stand unassisted on one leg up to 60 seconds with eyes open, as described by Yoshimura (16). The average of the best trial for both legs was used for analyses. Lower body strength was measured as the number of completed chair stands in 30 seconds, as described by Rikli and Jones (9). Aerobic endurance was assessed using the 6-minute walk test as described by Troosters and colleagues (17). Each of these physical performance measures has demonstrated excellent validity and reliability in healthy and diseased populations covering the adult age span (9,18–21).
Physical activity
A waist-mounted triaxial Actigraph accelerometer (models GT3X and GT3X+; Pensacola, FL) was used to measure PA over a period of 7 consecutive days. The monitor and instructions were given to the participant during their baseline appointment and returned to the study office by mail.
Data from the Actigraph activity monitors were processed (22) using Actilife 6.11 software. PA was examined in three ways: (a) average number of daily steps taken, (b) time spent in moderate-vigorous PA (MVPA; minutes), and (c) time spent in sedentary-light intensity activities (%; (22)). Only those with ≥10 hours of valid wear time on 4 or more days were retained for analyses (23). The Actigraph triaxial accelerometer has been widely validated and is a reliable instrument (24,25).
Statistical Analyses
The primary focus of the analytic plan was to explore age-related trends across performance variables. Analysis of variance was used to test for significant main effects and for potential age-by-sex interaction effects on each of the performance variables. Tukey post hoc analysis was used to test for significant differences among the six age groups. Pairwise comparisons were used to test for differences between adjacent age groups on each of the performance outcomes. Differences were considered significant if they reach a Bonferroni-adjusted alpha level of 0.008, derived from dividing the common 0.05 alpha level by 6, the number of age-group comparisons in the study.
The associations between physical performance and PA variables across the age groups were examined through bivariate correlation analyses. These data presented deviations from normality, and analysis of variance has demonstrated robustness to deviations from normality. We compared the results from parametric and nonparametric tests and found no difference; for ease of interpretation we present only the parametric tests. Given the exploratory nature of the research question, we did not correct for multiple tests and are willing to tolerate some inflation of Type I error resulting from our extensive analysis. All analyses were conducted using IBM SPSS Statistics version 21.0.
Results
Demographics and Clinical Characteristics
Between 2012 and 2014, 775 individuals completed baseline measures of the Physical Performance Across the Life-span Study (Table 1). Wide variations were observed within age groups on most demographic and biometric variables. Composite statistics suggest that the participant population had a fair representation of minority groups (85.3% white vs 14.7% non-white), and was fairly well educated (44.4% reported at least some college education). By design this sample was evenly split between sexes (46.8% male vs 53.2% female). Participants reported on average three chronic conditions.
Table 1.
Baseline Characteristics of Study Participants by Age Groups
| Total Sample | 30–39 y | 40–49 y | 50–59 y | 60–69 y | 70–79 y | 80–90+ y | |
|---|---|---|---|---|---|---|---|
| N = 775 | n = 85 | n = 100 | n = 104 | n = 196 | n = 198 | n = 92 | |
| Age, mean | 62.1 | 34.5 | 45.3 | 54.7 | 64.8 | 73.6 | 83.6 |
| Sex (male) % | 46.8 | 41.2 | 47.0 | 49.0 | 49.5 | 50.5 | 35.9 |
| Ethnicity (non-Hispanic) % | 93.9 | 81.2 | 89.0 | 94.2 | 96.9 | 97.5 | 96.7 |
| Race (%) | |||||||
| White | 85.3 | 67.1 | 72.0 | 79.8 | 90.8 | 93.9 | 92.4 |
| Black | 9.4 | 12.9 | 18.0 | 14.4 | 7.1 | 5.6 | 4.3 |
| Other | 4.8 | 18.8 | 10.0 | 5.8 | 2.0 | 0.5 | 3.3 |
| Education (≥some college) % | 44.4 | 47.0 | 52.0 | 47.1 | 50.5 | 32.7 | 42.4 |
| BMI, kg/m2 | 27.7 (5.1) | 28.4 (5.9) | 28.5 (6.3) | 28.5 (4.8) | 28.0 (4.8) | 27.7 (4.7) | 24.9 (3.7) |
| Range: 16–51 | |||||||
| Number of comorbidities | 3.1 (2.3) | 1.5 (1.6) | 1.9 (1.8) | 3.3 (2.5) | 3.1 (2.2) | 3.6 (2.1) | 4.5 (2.4) |
| Range: 0–15 | |||||||
Notes: Values represent Mean (SD) or %.
BMI = body mass index.
Physical Performance
Means and standard deviations for all physical performance outcomes are presented in Table 2. Analysis of variance results indicated a significant main-effect for age group on all measures (p < .01). Tukey analysis indicated that significant differences occurred across many of the adjacent 10-year age group comparisons on most variables (p < .008). We observed significant (p < .008) age-related disparities in the single leg stand and chair stands, beginning with the 50- to 59-year cohort. Differences in aerobic endurance and gait speed were observed beginning with the 70- to 79-year cohort. Age-group means that differed significantly from those of the preceding age group are indicated in Table 2.
Table 2.
Physical Performance by Age Group
| Age | Usual Gait Speed (m/s) | Rapid Gait Speed (m/s) | Chair Stands (# in 30s) | 6-Minute Walk (yards) | Single Leg Stance (s)‡ | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total | Men | Women | Total | Men | Women | Total | Men | Women | Total | Men | Women | Total | Men | Women | |
| 30–39 (n = 85) | 1.4 (0.3) | 1.4 (0.3) | 1.4 (0.3) | 2.1 (0.4) | 2.1 (0.4) | 2.0 (0.4) | 19.5 (5.5) | 19.1 (5.1) | 19.8 (5.8) | 679.7 (105.8) | 701.9 (70.3) | 653.6 (118.7) | 57.4 (9.0) | 58.8 (7.0) | 56.4 (10.1) |
| 40–49 (n = 100) | 1.4 (0.2) | 1.4 (0.2) | 1.3 (0.2) | 2.0 (0.4) | 2.1 (0.4) | 2.0 (0.3) | 19.4 (5.7) | 20.0 (6.1) | 18.8 (5.2) | 680.7 (109.7) | 707.0 (101.5) | 657.4 (112.4) | 55.1 (13.8) | 55.5 (14.0) | 54.7 (13.8) |
| 50–59 (n = 104) | 1.4 (0.3) | 1.4 (0.3) | 1.3 (0.3) | 2.1 (0.5) | 2.2 (0.5) | 1.9 (0.4) | 17.2* (4.6) | 17.4 (4.5) | 16.9 (4.7) | 654.7 (110.1) | 673.1 (113.9) | 636.7 (104.1) | 44.8* (19.4) | 42.2 (20.3) | 47.6 (18.3) |
| 60–69 (n = 194) | 1.3 (0.3) | 1.3 (0.2) | 1.3 (0.3) | 1.9 (0.4) | 2.0 (0.4) | 1.8 (0.4) | 15.8 (4.5) | 16.4 (4.4) | 15.2 (4.6) | 634.0 (116.8) | 667.3 (110.9) | 601.0 (113.5) | 39.2 (20.6) | 40.4 (20.2) | 37.9 (21.0) |
| 70–79 (n = 198) | 1.2* (0.2) | 1.2 (0.3) | 1.1 (0.2) | 1.7* (0.4) | 1.8 (0.4) | 1.6 (0.3) | 14.1* (4.9) | 14.9 (4.9) | 13.3 (4.7) | 554.9* (110.6) | 581.8 (106.5) | 527.4 (108.4) | 26.4* (19.8) | 27.2 (19.8) | 25.6 (19.9) |
| 80–90+ (n = 91) | 1.1* (0.2) | 1.1 (0.2) | 1.0 (0.2) | 1.5* (0.4) | 1.6 (0.5) | 1.4 (0.3) | 10.9* (4.8) | 11.8 (4.4) | 10.4 (4.9) | 470.9* (131.7) | 508.4 (139.8) | 451.8 (124.3) | 12.1* (13.2) | 13.6 (17.2) | 11.3 (10.7) |
| % Difference over 50 years† | |||||||||||||||
| 23.6% | 22.7% | 23.5% | 27.0% | 23.7% | 28.3% | 44.1% | 37.9% | 47.3% | 30.7% | 27.6% | 30.9% | 76.1% | 76.8% | 79.9% | |
Notes: Values represent Mean (SD).
*Age-group mean significantly different from the preceding younger age group (p < .008). All other group means are not significantly different.
†All mean-level differences are significant (p < .008).
‡Average of the best trial for both legs.
A cross-sectional decrease in physical performance was observed with each age group across the 5 cohorts (%; see Table 2). Mean differences between the youngest and oldest cohorts (30 vs 80–90+ year olds) was ~25% for gait speed and aerobic endurance measures, with comparable rates between men and women. The age group differences for lower body strength (44.1%) and balance (76.1%) were much greater, with strength also displaying differences between men (37.9%) and women (47.3%).
Cross-sectional trends in physical performance measures with increasing age are shown in Supplementary Figure 1. Significant (p < .05) sex differences were observed for gait speed, chair stands, and 6-minute walk test, with men scoring higher on physical performance assessments than women. No differences were observed for balance. No significant age-by-sex interactions were observed for any of the physical performance tests (p > .05), indicating that the pattern of decline across age groups was similar for men and women.
Physical Activity
Of the 775 individuals enrolled, 233 did not wear the accelerometer, and 53 did not provide at least 4 compliant days, leaving 489 adults for the activity analyses. Means and standard deviations for all PA outcomes are presented in Table 3. The accelerometer was worn between 4 and 7 days, for an average of 14 hours per day.
Table 3.
Step Counts and Physical Activity by Age Group
| Age | Average Daily Steps | % Waking Hours in Sedentary/Light Activity† | Average MVPA per day (min)† | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Total | Men | Women | Total | Men | Women | Total | Men | Women | |
| 30–39 (n = 47) | 6774.0 (2832.1) | 6858.4 (3373.0) | 6721.6 (2502.3) | 94.8 (3.2) | 95.1 (3.3) | 94.5 (3.2) | 44.4 (27.6) | 42.8 (28.1) | 45.4 (27.6) |
| 40–49 (n = 63) | 7443.4 (2357.0) | 8074.9 (2258.8) | 7127.6 (2367.6) | 95.0 (2.7) | 94.2 (2.4) | 95.5 (2.7) | 43.9 (24.2) | 53.2 (24.3) | 39.1 (22.9) |
| 50–59 (n = 69) | 7008.8 (2841.1) | 7131.8 (2129.7) | 6929.8 (3239.5) | 95.3 (3.0) | 94.8 (2.5) | 95.7 (3.2) | 40.4 (27.1) | 44.9 (21.1) | 37.2 (30.5) |
| 60–69 (n = 80) | 6311.0 (2668.4) | 6549.2 (2706.9) | 6116.0 (2651.6) | 96.0 (2.9) | 95.2 (3.2) | 96.7 (2.4) | 33.7 (24.8) | 40.1 (28.0) | 28.9 (21.0) |
| 70–79 (n = 80) | 5275.5 (2717.0) | 5640.0 (2095.1) | 4928.8 (3187.1) | 97.1* (2.9) | 96.6 (2.7) | 97.6 (3.0) | 24.7* (25.8) | 28.8 (23.7) | 20.8 (27.2) |
| 80–90+ (n = 26) | 3591.1 (2133.8) | 4822.6 (2321.0) | 2688.1 (1489.1) | 98.6* (1.8) | 97.5 (2.1) | 99.4 (0.9) | 12.3 (15.4) | 20.9 (18.0) | 5.6 (8.5) |
| % Difference over 50 y‡ | |||||||||
| 47.0% | 29.7% | 60.0% | 3.9% | 2.5% | 4.9% | 72.2% | 51.2% | 87.7% | |
Notes: Values represent Mean (SD).
*Age-group mean sig. different from the preceding younger age group (p < .008). All other group means are not significantly different.
†Freedson VM3 Equation (22).
‡All mean-level differences are significant (p < .008).
MVPA = moderate-vigorous PA.
Small age-related differences in PA were observed over most 10-year age spans for both men and women (all age group main effects p < .01). The most notable age-related differences in activity-related outcomes occurred in the 60 and 70 year age groups (Tukey p < .008). Age-group means that differed significantly from those of the preceding age group are indicated in Table 3.
A cross-sectional decrease in PA was observed across the 5 age groups (%; see Table 3). Mean differences between the youngest and oldest cohorts were large (daily steps: −47.0%; minutes of daily MVPA: −72.2%). Age-related differences were much larger among women compared with men, and were nearly double in the areas of daily steps and minutes of daily MVPA. When comparing women and men, 50-year differences in average steps were 60.0% versus 29.7%, respectively; differences in minutes per day of MVPA were 87.7% for women compared with 51.2% for men. Sedentary/light activity was relatively stable across the range of cohorts, showing a 3.9% increase between the 30–39 and 80–90+ age groups.
Significant (p < .01) sex differences were observed for all PA outcomes, with men, in general, being more physically active than women within the same age group. Patterns of decline across age groups were similar for men and women (age-by-sex interactions p > .05).
Associations Between Physical Performance, PA, and Age
The associations between physical performance and PA measures for each age group are shown in Supplementary Table 1. The 30–39 age group had no significant (p < .05) associations. In general, the number and strength of associations increased steadily with advancing age up to the 70–79 age group, after which the number of significant associations declined within the 80–90+ age group. However, the correlation coefficients were strongest in the 80–90+ age group. Among the 40+ age groups, the 6-minute walk test and chair stands were the most common correlates of PA, demonstrating significant, positive associations.
Discussion
Physical performance in the context of older populations has been thoroughly studied (Health ABC, NHANES, BLSA); however, these trajectories and associations with other behavioral indicators over time, across the broader adult life span, are less well understood. Due to the limitations of existing studies and the paucity of available data on these variables across age strata, we developed the Physical Performance across the Life-span Study, a cohort study focusing specifically on mobility and function in a nonclinical sample of adults 30–90+ years old. This study contributes valuable data to better our understanding of the degree to which physical performance declines with age.
What is the Age of Greatest Decline?
Different patterns of physical declines with age were observed for different outcomes. In general, the onset of divergence by age group was observed as early as the fifth and sixth decades. For physical performance there was stability in the first two decades of adulthood, and decrements in the middle years (50+) and late adulthood. For PA, decrements between age groups were largest in later adulthood (60+) and smaller at younger ages. Thus, physical declines in function and PA were much greater during late adulthood than during the younger years. Our results lend further support for interventions in younger cohorts, targeting the risk factors for late-life disability earlier in the life span in an effort to prevent, attenuate, or delay functional decline (26).
How do These Results Compare With Other Studies?
Compared with published age-related norms (9,16,19), this study sample performed at, or above, average on all physical performance measures. Because so few data have been published on physical performance in early and middle adulthood, similar comparisons of performance and changes over the years cannot be made for these tests. Additionally, the inconsistent methodology in assessing physical performance across studies limits our ability to compare this sample with other cohort samples. However, our results and the observed age-related trends in the 60–80+ age groups are similar to previous reports of function in older adults (9,16,19,27,28).
Overall, adults in all age groups accumulated very high levels of sedentary-light behavior (10–15h/d), which is consistent with other reports in comparably aged adults (29–31). The proportion of wear time in sedentary-light behavior (96% average), and time spent in MVPA that we observed, was higher compared with other studies (23,29). Differences in mean age, health status, or devices and data management methodology may account for inconsistent results between studies. Regardless, the measured amount of time in sedentary-light behavior for all age groups was very high in the current study, with older adults being the least active. These high rates of inactivity, combined with modest rates of MVPA and walking, emphasize the importance of behavioral interventions targeting these outcomes across the adult life span (32).
Associations Between Physical Performance and PA
Similar to previous work (33,34), we observed a progressive trend in the associations between physical performance and PA: greater PA was associated with better physical function. In general, the number and strength of significant associations increased with greater age. Poor performance in chair stands and the 6-minute walk were significantly associated with PA as early as the 40–49 cohort. Associations of inactivity with physical performance were not consistently observed until the 60+ cohort, when more sedentary time was associated with worse physical performance. Collectively, these results suggest that efforts to increase PA and improve strength and endurance should begin in middle age.
Generalizability of the Sample
Race/ethnicity percentages of the participants in this study were 85.3% white, 6.1% Hispanic, 9.4% black, and 4.8% from other groups. These proportions closely match the statistics for Cabarrus County, NC—the recruitment catchment area for this study—and the U.S. adult population in general (35). Compared with other large-scale cohort or population studies such as NHANES (36) and the Framingham Heart Study (37), this study has equal, or greater, representation of ethnic minorities.
Limitations
Despite obtaining objective measures of physical performance and PA, there are limitations to using these assessments. First, although waist-mounted accelerometers are excellent tools for monitoring ambulatory activity, they do not account for differences in cardiorespiratory fitness levels, which are strong predictors of outcomes in almost all cohorts examined (38). Second, there have been few published studies exploring the impact of gait disability/slow gait speed on the association between energy expenditure and accelerometer output. One study reported that walking speed is strongly and significantly correlated with accelerometer output and oxygen cost of walking in older adults (39), which may contribute to the differences in PA intensity we observe between age groups in this study. Second, caution is warranted in interpreting these results relative to the general population, as we used unweighted analytic models and this is a self-referred group that seems to have high function, limiting the generalizability of this sample. Third, this is a cross-sectional analysis, and the age-related trends observed do not reflect longitudinal change over time. The authors recognize that these age-related trends might be, at least partially, explained by the presence of a cohort effect. Finally, this sample is drawn from a rural/semi-urban geographic region of the Southeastern United States, which bears consideration when interpreting the results.
Conclusions
To the best of our knowledge, only two prior studies have implemented a consistent physical performance battery across the adult life span (7,27). This work greatly advances the field by developing a performance-based physical function screener that is suitable for adults of all ages and provides objective functional data not traditionally found in epidemiologic studies. Our examination of these factors in a broad sample of community-dwelling adults as opposed to select sub-groups of individuals (eg, clinic patients, those with specific chronic diseases, adults in a narrow age range), makes these results generalizable to a large segment of the adult population. As such, these data may serve two purposes: cross-sectionally as a database for subsequent evaluation and comparison of individual and group performance; longitudinally (since current participants will be reevaluated regularly) as a resource of rates of change across age groups.
Funding
This study was funded by a philanthropic gift to Duke University from the David H. Murdock Institute for Business and Culture, and by the Claude D. Pepper Older Americans Independence Center the National Institutes of Health/the National Institute on Aging Grant (P30AG028716 to M.C.M. and H.J.C.). This publication was made possible by Grant Number UL1TR001117 from the National Center for Research Resources (NCRR), a component of the NIH, and NIH roadmap for Medical Research. K.S.H. is funded by a Career Development Award from the Rehabilitation Research and Development Service of the the Department of Veterans Affairs (1IK2RX001316). This manuscript is the result of work supported with resources and the use of facilities at the Durham Veterans Affairs Medical Center, Durham, NC.
Conflict of Interest
None of the authors report any conflict of interest.
Supplementary Material
Acknowledgments
The authors thank research team members Ashley Dunham, Leah B. Bouk, Carla Kingsbury, Christopher E. Lewis, Betty Hover, Alice E. Glines, Deborah Alicea, Micki Roseman, Abha Singh, Melissa Johnston, Asia Lattimore, Cammie Yarborough, Sam Densen, Douglas Wixted, Siobhan Brunson, Tonya Furr, Juan Gomez, David T. Steele, Enya Rentas-Sherman, Ellen Emerson; and the study participants for their gracious contribution to this research. The views expressed by the authors do not necessarily reflect the views of the U.S. Department of Veterans Affairs, the United States Government, NCRR, or NIH. Trial Registration: Clinicaltrials.gov (NCT01720472; https://www.clinicaltrials.gov/ct2/show/NCT01720472).
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