what is most significant factor contributing to declines in physiological capacity at any age?

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Neurobiol Aging. Author manuscript; bachelor in PMC 2010 Apr 1.

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When does historic period-related cognitive reject brainstorm?

Timothy A. Salthouse

Department of Psychology, University of Virginia, Charlottesville, VA 22904-4400, Telephone: (434) 982-6323, Electronic mail: ude.ainigriv@esuohtlaS

Abstract

Cross-exclusive comparisons have consistently revealed that increased historic period is associated with lower levels of cognitive performance, even in the range from 18 to 60 years of age. However, the validity of cross-sectional comparisons of cognitive functioning in young and middle-anile adults has been questioned because of the discrepant age trends found in longitudinal and cantankerous-sectional analyses. The results of the current project suggest that a major gene contributing to the discrepancy is the masking of historic period-related declines in longitudinal comparisons by large positive furnishings associated with prior test experience. Results from three methods of estimating retest furnishings in this projection, together with results from studies comparing non-human animals raised in constant environments and from studies examining neurobiological variables non susceptible to retest effects, converge on a conclusion that some aspects of age-related cognitive decline begin in healthy educated adults when they are in their 20s and 30s.

Keywords: cerebral aging, early machismo, normal aging

Although in that location have been many reports over the last 100 years of age-related differences in cerebral functioning, there is still considerable controversy well-nigh the historic period at which cognitive decline begins. This lack of consensus is unfortunate considering the question is of import for both practical and theoretical reasons. For example, the age at which cognitive decline begins is relevant to the optimum time to implement interventions designed to forbid or reverse historic period-related declines. Many interventions currently target adults lx years of age and older. However, if people start to refuse when they are in their 20s and 30s, a large amount of change volition likely have already occurred by the time they are in their 60s and 70s. This may touch the likelihood that interventions at that age will be successful considering the changes might take accumulated to such an extent that they may be difficult to overcome.

The question of when decline begins is also relevant to the theoretical investigation of potential causes of declines in cognitive functioning considering declines that brainstorm early are unlikely to be owing to conditions specific to later on life, such as menopause, retirement from paid employment, or sure historic period-related diseases. The answer to the question of when pass up begins may also indicate which flow in machismo is probable to be most informative for learning about causes of historic period-related cognitive decline because, for example, studies restricted to samples of older adults might have limited value for discovering the causes of a phenomenon that originated decades before.

Ane type of show suggesting that age-related cognitive declines begin relatively early in adulthood are the historic period trends in a variety of neurobiological variables that can be assumed to exist related to cognitive functioning. Among the variables that accept been establish to exhibit most continuous age-related declines in cross-sectional comparisons showtime when adults are in their 20s are measures of regional brain book (Allen, et al., 2005; Fotenos, et al., 2005; Kruggel, 2006; Pieperhoff, et al., 2008; Sowell, et al., 2003), myelin integrity (Hsu, Leemans, et al., 2008; Sullivan & Pfefferbaum, 2006), cortical thickness (Magnotta, et al., 1999; Salat, et al., 2004), serotonin receptor binding (Sheline, et al., 2002), striatal dopamine binding (Erixon-Lindroth, et al., 2005; Volkow, et al., 2000), accumulation of neurofibrillary tangles (Del Tredici & Braak, 2008), and concentrations of diverse brain metabolites (Kadota, et al., 2001).

Furthermore, cross-sectional declines in comparisons of cerebral functioning based on samples of 250 or more adults beyond a wide age range have been reported since the 1930s (Jones & Conrad, 1933), and have been described in numerous recent publications (Salthouse, 1998; Salthouse, 2005; Salthouse, et al., 2003; Schroeder & Salthouse, 2004; Schaie, 2005). In almost every case, the historic period trends in these studies have revealed nearly monotonic declines in average level of cognitive performance starting in early on adulthood.

It might appear on footing of these well-replicated results with neurobiological and cognitive variables that there is a simple answer to the question of when cerebral reject begins. That is, because cantankerous-exclusive age comparisons accept consistently revealed about continuous age-related decreases in presumably relevant neurobiological variables and in various measures of cognitive performance that appear to begin when adults are in their 20s or early 30s, one might conclude that cognitive pass up begins shortly after individuals reach maturity. All the same, in striking contrast to these empirical results are numerous assertions that cognitive pass up begins belatedly in life:

• "Cognitive decline may begin after midlife, but near often occurs at higher ages (70 or higher)." (Aartsen, et al., 2002)

• "…relatively little decline in performance occurs until people are about 50 years former." (Albert & Heaton, 1988).

• "…cognitive abilities generally remain stable throughout developed life until around age sixty." (Plassman, et al., 1995)

• "…no or little drib in performance before historic period 55…" (Ronnlund, et al., 2005)

• "…most abilities tend to peak in early midlife, plateau until the late fifties or sixties, and and so show decline, initially at a slow pace, but accelerating as the late seventies are reached." (Schaie, 1989).

A dramatic discrepancy therefore exists between a substantial body of empirical results on i manus, and frequent claims about the time course of cerebral aging on the other hand. Because one cannot hope to explicate a phenomenon until its nature, including its trajectory, is accurately described, it is essential to understand the reasons for this discrepancy.

Some of the differences between the evidence merely mentioned and the cited assertions may be attributable to variations in how the same findings are interpreted, or to emphases on dissimilar types of cognitive variables. Withal, it is likely that a major reason for the discrepancy is that different patterns of age-knowledge relations take been found with longitudinal, or within-person, comparisons, and with cross-sectional, or betwixt-person, comparisons. One of the start reports of a longitudinal comparison with cognitive variables was described in a 1928 book (Thorndike, et al., 1928). Although other researchers at nigh the same time reported cross-sectional declines betwixt 18 and l years of age on the Army Alpha test, these authors described a study in which the scores for people between 16 and 45 years of age increased over a v-to-9 year interval. Rather than revealing decline, therefore, these results suggested that there were improvements in cognitive functioning with increased historic period when the comparisons were based on observations of the same people at different ages. Subsequent longitudinal studies have replicated the finding of relatively preserved, or even enhanced, levels of cognitive functioning with increased age in longitudinal comparisons involving adults up to nearly sixty years of historic period.

Effigy 1 illustrates these patterns with cantankerous-sectional and longitudinal age trends on two tests from the Seattle Longitudinal Study (Schaie, 2005). The top two panels illustrate that in that location are about monotonic historic period-related declines in the cross-sectional comparisons (dotted lines), but that longitudinal comparisons (solid lines) reveal either stable or increasing historic period trends. The lesser two panels portray the aforementioned data in a unlike format. In these figures the vertical centrality corresponds to standard deviation units rather than T-scores, and the confined represent the cross-sectional difference (black bars) or the longitudinal changes (gray confined). In guild to maximize comparability with the results of the current project in which the average retest interval was 2.five years, the vii-year differences and changes in these figures have been converted to a two.5 yr interval by algebraic exchange (i.e., X = 2.5 * [Score/7]). Despite the unlike formats, the upper and lower panels reveal the same design of moderately large negative historic period trends in the cross-sectional comparisons (dotted lines and blackness confined), but fiddling or no historic period decline in the longitudinal comparisons (solid lines and greyness bars).

Estimates of cross-exclusive differences and longitudinal changes over 7 years in ii variables from the Seattle Longitudinal Written report. Cross-sectional information from Table iv.2 and longitudinal data from Table 5.1 of Schaie (2005). The figures in the top two panels portray results of cantankerous-exclusive (dotted lines) and longitudinal (solid lines) comparisons in T-score units. The figures in the bottom two panels portray the same data as differences or changes over a 2.5 year interval in standard deviation units.

It is not surprising that divergent results such as those portrayed in Effigy 1 have led some researchers to the determination that picayune or no cerebral decline occurs before nigh age 60. Still, a critical assumption of this interpretation is that the results of longitudinal comparisons are more accurate or valid than cantankerous-sectional comparisons with respect to "true" age relations, and it is important to consider what might be responsible for the unlike patterns of results in the 2 types of comparisons earlier accepting this assumption. But after this issue is resolved can a definitive conclusion exist reached virtually when cognitive decline begins because reject may begin late if cross-sectional results are misleading, but turn down may brainstorm early if longitudinal results were plant to be influenced past a multifariousness of non-maturational determinants in addition to the maturation effects of principal interest.

What is probable the dominant estimation of the different age trends establish in cantankerous-sectional and longitudinal comparisons of cognitive functioning attributes the discrepancy to characteristics other than age confounding cross-sectional comparisons. Kuhlen (1940) may have been the offset to describe what are now usually referred to equally cohort effects, which include a multifariousness of influences on cognitive performance associated with changes in the social and cultural surroundings, such as quantity and quality of education, nature of wellness care, etc.

Although the cohort estimation is widely accepted, it is currently somewhat underspecified. For instance, a disquisitional expectation of the accomplice interpretation is that statistical command of cohort-defining variables should eliminate the cross-exclusive age trends. Some variables, such as years of instruction, are easily assessed, just the prediction that cross-sectional historic period trends would be eliminated later on adjusting for these other variables cannot be adequately tested until all of the cohort-relevant variables are identified and measured. Another limitation of the accomplice interpretation is that little is currently known about the fourth dimension course of cohort influences relative to the historic period range at which cross-exclusive age differences are credible. That is, accomplice influences are sometimes referred to every bit generational furnishings, as though they occur over intervals of 25 years or more, but they would have to operate over periods as short equally v or 10 years to account for the cross-exclusive historic period differences found in some cognitive variables.

Another factor that has been mentioned as a possible contributor to the dissimilar cross-sectional and longitudinal age trends is retest effects associated with prior testing. Retest effects refer to influences on the difference in performance between the commencement and a subsequent measurement occasion that are owing to the previous assessment. That is, the mere fact that an individual has already been evaluated could modify his or her performance on a successive measurement occasion, in which case the age trends inferred from longitudinal comparisons may exist misleading with respect to "true" age effects. Cantankerous-sectional comparisons do not involve testing the same individuals again, and therefore retest furnishings could be contributing to the discrepancy between cross-sectional and longitudinal results by distorting the age trends in longitudinal comparisons. Although seldom mentioned in discussions of the discrepancy between cross-sectional and longitudinal age trends, several findings announced more consistent with the retest estimation than with the cohort interpretation.

First, considering non-human laboratory animals are typically raised in nearly abiding environments, age comparisons in non-human animals tin can exist assumed to exist gratis of cohort contaminations attributable to changing environments. To the extent that accomplice differences distort cross-exclusive comparisons, therefore, little or no age differences in cognitive functioning might exist expected in comparisons of not-human animals. Nevertheless, there are numerous reports of cross-sectional historic period-related declines in measures of retentiveness and cognition in species ranging from not-human primates (Herndon, et al., 1997) to fruit flies (LeBourg, 2004). Second, although relatively few longitudinal studies have been conducted with not-human animals, it is noteworthy that several studies with rats have reported smaller longitudinal age changes than cross-exclusive age differences in measures of maze learning (Caprioli, et al., 1991; Dellu, et al., 1997; Markowska & Savonenko, 2002). Because the dissimilar cross-exclusive and longitudinal age trends cannot be attributed to cohort differences distorting the cross-sectional results in organisms raised in constant environments, the discrepancies in these studies are likely attributable to retest furnishings distorting the longitudinal comparisons. And third, several studies examining regional brain volume, which is a variable presumably related to cognitive performance but non susceptible to practice effects, have reported longitudinal age declines that are at least as large as the cross-sectional differences (Fotenos, et al., 2005; Raz, et al., 2005; Scahill, et al., 2003).

Although the results just described are consequent with the estimation that longitudinal historic period trends in cognitive functioning are distorted by the presence of retest effects, they are all indirect. The chief prediction from the retest interpretation is that estimates of retest effects should be moderately large and positive, such that they offset any negative effects of aging or maturation. Several methods have been proposed to estimate the magnitude of retest effects, just only a few take been applied to adults under the age of 60, which is the menstruum most relevant to the question of when age-related decline begins. Ane method of estimating retest effects is based on a comparison of the cerebral performance in samples of people tested once with those tested twice (Ronnlund, et al., 2005; Schaie, 2005). This difference, subsequently adjusting for any differences in initial level of performance, has been used equally an approximate of the benefit of prior examination feel. Most of the estimates derived from this method have been positive, and considerably larger than the annual cross-sectional age differences. A second method of assessing retest furnishings has relied on variability across inquiry participants in the retest intervals to decompose the observed change into maturational effects and retest effects. Considering this latter method requires a special type of longitudinal design in which people vary in the interval between successive assessments, such that there is non a perfect confounding of the increase in age and the increase in test experience, information technology has only rarely been used. Nevertheless, both McArdle, et al., (2002) and Salthouse, et al., (2004) found that in adults nether the age of 60, the retest estimates derived from this method were positive and moderately large.

Three dissimilar methods of estimating retest furnishings in longitudinal studies were examined in the electric current project. As just mentioned, two of the methods, comparison operation of people of the same historic period tested twice with those tested one time, and capitalizing on variability in the retest intervals to distinguish maturation and retest components of longitudinal modify, take been used in earlier research. A new method relied on a comparing of the magnitude of change in a longitudinal study with the change in a short-term retest report in which the test-retest interval ranged from i to fourteen days as the primary basis for distinguishing maturation and retest effects. The rationale is that it is very unlikely that maturation influences are operating over such a short interval, and thus the results of short-term retest studies provide a relatively pure gauge of the potential impact of retest influences that can be compared with estimates of longitudinal alter. Furthermore, because the retest interval in the longitudinal report varied from 1 to 7 years, the effect of the retest interval on the magnitude of longitudinal change tin can also be examined to determine the time course of these retest influences.

Methods

Sample

Characteristics of the samples of participants are summarized in Table 1. The research participants were recruited from newspaper advertisements, flyers, and referrals from other participants, and all were tested individually. The cantankerous-sectional sample included the first assessment from the participants in the samples with longitudinal and curt-term retest data, plus additional participants from other studies (Salthouse, 2004; 2005). All enquiry participants were between 18 and threescore years of historic period, and most participants rated their health as "very expert" or "first-class".

Table 1

Characteristics of samples

	All	Longitudinal	Curt-term Retest
N	2350	729	139
Age	twoscore.7 (13.3)	42.i (12.0)	43.3 (thirteen.2)
% Females	68.0	69.0	70.0
Education	15.5 (2.5)	15.four (2.five)	15.5 (2.3)
Health	2.i (0.nine)	2.1 (0.9)	two.0 (0.ix)
Scaled Scores
Vocabulary	12.5 (3.0)	12.half-dozen (iii.0)	eleven.8 (two.7)
Digit Symbol	11.iii (2.9)	11.4 (2.9)	11.1 (two.9)
Logical Memory	xi.seven (2.7)	11.7 (2.7)	11.9 (2.half dozen)
Give-and-take Remember	12.iii (3.2)	12.5 (three.two)	xi.seven (3.0)
Retest Interval	North.A.	two.5 (1.1)	vii.1 (viii.five)

Age-adjusted scaled scores on iv standardized tests (i.e., WAIS 3 Vocabulary and Digit Symbol and WMS III Word Retrieve and Logical Memory) can exist used to assess the representativeness of samples. Scaled scores accept ways of 10 and standard deviations of 3 in the nationally representative normative samples (cf., Wechsler, 1997). The values in Table 1 indicate that the scaled score means in the current samples were about ½ to ane SD above the means in the normative sample, merely considering the standard deviations were close to 3, it can be inferred that the samples had nearly the same magnitude of between-person variability as the nationally representative normative sample.

The retest intervals in the longitudinal written report varied across participants, and ranged from ane to 7 years, with an boilerplate of 2.5 years. The number of participants at each retest interval was 122 at 1 year, 295 at two years, 186 at 3 years, 88 at 4 years and 36 at 5 or more years. There was no correlation between age and retest interval (i.e., r = .02). The retest intervals in the short-term retest report ranged from about 1 to fourteen days.

Variables

Most all of the participants performed the complete battery of 12 tests, with identical versions of the tests administered on the 2d examination session in the longitudinal and short-term retest studies. Iii dissimilar types of tests were used to assess the cognitive abilities of inductive reasoning, spatial visualization, episodic memory, and perceptual speed. The cognitive tests, and their reliabilities, are listed in the supplementary materials and have been described in previous reports (Salthouse, 2004; Salthouse, et al., 2003), as have results of confirmatory factor analyses establishing that the variables can be causeless to represent four distinct cognitive abilities.

Results

All of the cerebral variables were converted to z-scores by subtracting the score from the full sample mean at the first assessment, and dividing the difference by the standard deviation (SD). Figure 2 portrays the cantankerous-sectional age trends for the 12 variables, where information technology can exist seen that every variable had a linear relation with historic period. Three variables, Matrix Reasoning, Form Boards, and Pattern Comparison, also had significant quadratic trends, only in each case the quadratic trend was associated with less than 0.two% of the variance. The operation difference from age eighteen to age sixty was virtually i SD for the speed and spatial visualization variables, and was between .6 and .seven SD for the reasoning and memory variables.

Means and standard errors for 12 cognitive variables past 5-year age intervals. The variables are grouped according to the blazon of cognitive ability as adamant past confirmatory factor analyses.

The simple correlations of the variables with age ranged from -.07 to -.41, with a median of -.26. The correlations were slightly more than negative (i.e., median of -.36) after control of years of education, cocky-rated health, number of medications taken per calendar week, report of current or past neurological treatment, reported loss of consciousness for more than five minutes, a measure of most-vision visual vigil, and self-reported measures of low and anxiety. (The actual correlations are provided in the supplementary materials). These results therefore imply that the cantankerous-sectional age-cognition relations in this project are not induced past age-related variations in these detail characteristics. Analyses were also conducted to determine the age group with the highest mean score, and the adjacent older historic period group in which the mean was significantly (p<.01) lower. The tiptop historic period across the 12 variables ranged from 22 to 27, and the next older historic period at which the mean was significantly different from the tiptop historic period ranged from 27 to 42. (The details for each variable are in the supplementary materials.)

Cross-sectional and longitudinal results for each cognitive variable are displayed in Figure 3. The longitudinal results correspond the change over the boilerplate interval of 2.5 years. The cross-sectional differences correspond to the slope of the regression equation relating the cerebral variable to age, multiplied past 2.5 to have a comparable interval for the cross-sectional differences and the longitudinal changes. Inspection of the figure reveals that most of the variables have a pattern similar to that in earlier studies with negative cross-sectional differences, and either stable or positive longitudinal changes. If anything, the discrepancy betwixt the cross-sectional and longitudinal age trends in this study was larger than that in the Seattle Longitudinal Study (Schaie, 2005). That is, the boilerplate cross-exclusive and longitudinal differences in the bottom panels of Figure 1 were -.06 and .01 SD units, respectively, and the medians for the reasoning and spatial visualization variables in Effigy 3 were -.05 for the cross-sectional difference and .08 for the longitudinal change.

Estimates of cross-exclusive differences and longitudinal changes over 2.five years, of the short-term retest outcome, and of retest effects derived from ii belittling methods for 12 cognitive variables. The vertical centrality is in standard deviation units.

Figure 3 too portrays the changes from the curt-term retest study in which the average retest interval was about one week. Information technology tin can be seen that all of the short-term retest changes were positive, and considerably larger than the negative cross-sectional historic period differences. Finally, Figure iii portrays estimates of the retest effects from the deviation between scores of participants taking the examination for the second time compared to participants taking information technology for the first time, and from the mixed effects regression analyses based on the variable retest intervals. (The estimates were derived from procedures very similar to those used in earlier studies, and details are provided in the supplementary materials.) Although there is clearly some variation in the absolute magnitude of the estimates, it is important to note that nearly all of the estimated retest furnishings were positive, and substantially larger than the cross-sectional differences.

A series of independent groups t-tests was conducted comparing the short-term retest changes with the longitudinal changes for the 122 participants who had a 1-yr interval between assessments. Most of the variables had significantly more positive changes in the short-term retest group than in the i-yr longitudinal group. The only exceptions were one speed test (Letter of the alphabet Comparison), one reasoning test (Letter Sets), and the three spatial tests (Spatial Relations, Paper Folding, and Form Boards). These results betoken that for nigh of the variables the benefits of prior test experience diminished over the i calendar week to one year interval after the initial assessment.

A final set of analyses examined the relation betwixt the length of the interval betwixt the two assessments and the magnitude of longitudinal modify in the longitudinal sample. The only variables with significant interval effects were the Digit Symbol, Pattern Comparison, Word Remember, and Paper Folding variables. In each instance the change values were smaller as the interval increased, with slopes ranging from -.12 to -.sixteen z-score units per year. For these specific variables, therefore, the change can be predicted to achieve zip at retest intervals of iii.vi years (Digit Symbol), i.vi years (Pattern Comparing), 2.5 years (Word Recall), and ii.4 years (Newspaper Folding). The lack of meaning interval effects for the other variables precludes meaningful estimates of the decay of the retest effects for those variables.

Discussion

Effigy 2 reveals that in that location were pregnant negative relations between age and several different types of cognitive measures for salubrious educated adults ranging from 18 to lx years of historic period. Furthermore, boosted analyses revealed that the age relations were not attributable to a variety of plausible confounding variables. These results, together with similar findings in many other studies, clearly found the existence of cross-sectional historic period-related declines for many cognitive variables prior to historic period sixty. Characteristics related to cohort influences may still exist contributing to some of the cross-sectional differences, but these characteristics need to be identified and measured in order for this interpretation to be straight investigated.

Information technology is apparent in Effigy 3 that many cognitive variables exhibit the typical design of negative cross-exclusive age trends and either stable or positive longitudinal age trends. The unique feature of the electric current project is that 3 estimates of retest furnishings were besides bachelor for every variable. Almost all of these estimates were positive, and by and large much larger in magnitude than the cantankerous-sectional age differences, which suggest that longitudinal comparisons probably underestimate age-related alter for many cognitive variables.

If one assumes that longitudinal changes reflect a mixture of maturation effects and retest effects, a crude method of adjusting the longitudinal changes for retest furnishings is to subtract the retest estimates from the observed longitudinal changes. Inspection of Figure 3 reveals that this type of adjustment would dramatically alter the magnitude, and management, of the longitudinal age trends, and make them much more than similar to the cross-sectional patterns. This subtraction method is undoubtedly too simplistic, but only if the retest effects were very small would they not be expected to influence the 2d assessment in a longitudinal study, and this was non the case for near of the variables in this project.

Inspection of Figure 3 reveals that there was variability in the magnitude of the retest estimates across variables, and across analytical methods for the same variables. The reasons for this variation are non even so clear, only the results signify the importance of being cautious in basing conclusions on a unmarried variable, or a single belittling method. Despite the differences in the magnitudes of the retest furnishings, it is important to emphasize that near of the estimates were positive, indicating that the retest phenomenon is robust beyond different analytical methods, and sets of assumptions.

To summarize, the results of the current project, together with results from research on non-human animals and on neurobiological variables, advise that age-related cognitive decline begins relatively early in adulthood, but that it may not be detected in longitudinal comparisons until furnishings of prior test experience are taken into consideration. Not all aspects of cognitive functioning showroom early historic period-related declines because measures based on accumulated knowledge, such equally performance on tests of vocabulary or general information, are consistently institute to increase until at least age 60. However, only those variables exhibiting negative age-related differences in cross-sectional comparisons are straight relevant to the question of when historic period-related cognitive refuse begins.

There is some testify that the magnitude of historic period-related decline accelerates at older ages. To illustrate, a sample of most 800 adults betwixt 61 and 96 years of historic period in my laboratory had cross-sectional slopes of about -.04 to -.05 SD units per year compared to the slopes of -.02 to -.03 SD units per year for adults under historic period threescore. In absolute units, the decline in speed variables was nigh twice as nifty in this age range compared to adults nether age threescore, and the decline in the memory variables was nearly 4 times greater. What is not yet known is whether these quantitatively different age trends reflect changes in the aforementioned gear up of influences, or the functioning of qualitatively different types of influences. However, what does announced clear is that several dissimilar types of results converge on the conclusion that age-related cognitive decline begins relatively early in adulthood, and certainly before age 60 in good for you educated adults.

Finally, although the results of this projection suggest that at least some of the differences in the age trends in cantankerous-sectional and longitudinal comparisons are attributable to retest effects distorting longitudinal comparisons, the results should non be interpreted as implying that longitudinal comparisons are non meaningful or valuable. In fact, quite the opposite is truthful because simply with longitudinal data tin can one examine within-individual changes singled-out from between-person differences. Instead, the major betoken is that merely because the changes are observed within the same individual does not hateful that they only reverberate aspects of maturation. Strengths and weaknesses of both cross-exclusive and longitudinal information therefore demand to exist considered when reaching conclusions about historic period trends in cognitive functioning.

Supplementary Textile

01

Acknowledgments

Supported past Grant R37AG024270 from the National Constitute on Aging.

Footnotes

Disclosure Argument: The author has no financial or other conflicts related to this research.

Institutional Review Board Approving: The research described in this study was conducted with approval of the Institutional Review Board at The Academy of Virginia.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2683339/

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