2013. Vol.4, No.1, 33-37
Published Online January 2013 in SciRes (
Copyright © 2013 SciRes. 33
Body Mass Index Does Not Affect Grooved Pegboard
Performance in Healthy South African Adults
Charles H. van Wijk
Private Practice, Simon’s Town, South Africa
Received October 26th, 2012; revised November 25th, 2012; accepted December 22nd, 2012
Obesity has been associated with poorer performance on the Grooved Pegboard (GP) among healthy older
adults. The GP is widely used in South Africa, among others for the assessment of HIV Associated Neu-
rocognitive Disorders. Obesity is growing among the younger adult population in South Africa, which is
the group also most at risk for HIV. It is not clear what the interaction between body mass and GP per-
formance would be among a group of healthy younger adults. This study investigated whether body mass
might affect fine psychomotor skills. A sample of 850 healthy adults (20 - 49 years) completed the GP
and had their Body Mass Index (BMI) calculated. The relationship between GP and BMI was examined
using ANOVA and correlation coefficients. The expected gender differences in GP performance found
elsewhere were demonstrated in this sample. No significant interactions between BMI categories and GP
times were found, and no significant correlations between BMI continuous scores and GP times were
found either. In spite of the presence of a wide weight spectrum among the participants and the absence of
any history of known medical disease, the lack of significant BMI-GP interactions suggest that the effect
of BMI may generally be discounted when interpreting GP results.
Keywords: Body Mass Index; BMI; Gender Differences; Grooved Pegboard; Psychomotor Performance;
The Grooved Pegboard (GP) is a manipulative dexterity task
that can be used for neuropsychological assessment (Lezak,
Howieson, & Loring, 2004). The HIV epidemic has stimulated
renewed interest in the GP because of its potential to identify
HIV associated neurocognitive disorders (HAND). It is a com-
mon neuropsychological instrument used in HAND (Grant,
2008), which can differentiate between the HIV statuses of
asymptomatic patients in Sub-Saharan Africa (Moshani, 2009;
Sacktor et al., 2005). In particular the GP non-dominant hand
score is sensitive for HIV-associated neuropsychological im-
pairment (Carey et al., 2004; Davis, Skolasky, Selnes, Burgess,
& McArthur, 2002), and the GP non-dominant hand test has
been established to detect signs of HIV dementia (cf. Sacktor et
al., 2005). Completion time has been related to stage of HIV
disease (Heaton, Grant, Butters, White, Kirson, & Atkinson,
1995), while declining times have been linked to future pro-
gression to HIV dementia (Selnes, Galai, McArthur, & Cohen,
1997). The GP is also sensitive to improvement in neuropsy-
chological performance in HIV+ individuals receiving HAART
(Joska, Gouse, Paul, Stein, & Flisher, 2010).
A large percentage of asymptomatic HIV infected persons
show mild neurocognitive difficulties (Heaton et al., 1995), and
recent South African (SA) figures indicated that 17% - 23.5%
of HIV patients display cognitive impairment (Ganasen, Fin-
cham, Smit, Seedat, & Stein, 2008; Joska et al., 2010). HIV
prevalence for SA adults (15 - 49) was estimated at 16.9% in
2008. SA is home to the world’s largest population of people
living with HIV (5.7 million) (UNAIDS, 2009).
To meaningfully use the GP non-dominant hand test to
screen for HAND, performance of HIV individuals need to be
compared to age and education adjusted peer means (Sactor et
al., 2005). There are many factors that could influence GP per-
formance in healthy adults. Obesity is one example of this, and
is a growing concern for SA healthcare. SA studies found that
between 24% and 27% of women are overweight, with a further
30% to 54% obese, while figures for men indicate that between
15% and 22% are overweight, with a further 7% to 19% obese
(Malhotra et al., 2008; Puoane et al., 2002). Recent studies
indicate that obesity seems to start at an increasingly young age,
with about 10% of women obese by the age of 24 years
(Puoane et al., 2002, Reddy, Panday, Swart, Jinabhai, Amosun,
& James, 2003).
Body Mass Index (BMI) is calculated from a person’s weight
and height, and is an effective method for population assess-
ment of overweight and obesity (Centers for Disease Control
and Prevention, 2010). Its main importance lies in the relation-
ship between body weight and disease and death (World Health
Organisation, 1995), with overweight and obese individuals at
increased risk for many diseases and health conditions (Na-
tional Institutes of Health, 1998). The negative health cones-
quences associated with increased BMI in SA have been well
described (Joubert, Norman, Bradshaw, Goedecke, Steyn, &
Puoane, 2007). BMI serves two functions, firstly, to indicate
risk for various diseases, and secondly as a general indication
of body fatness.
Obesity has been associated with poorer cognitive function in
several studies (Elias, Elias, Sullivan, Wolf, & D’Agostino,
2003, 2005; Kilander, Nyman, Boberg, & Lithell, 1997; Wald-
stein & Katzel, 2006). Obesity is associated with lower memory
and executive function in late middle-aged and elderly men (but
not women), independent of other common cardiovascular dis-
ease (CVD) risk factors (Elias et al., 2003, 2005), and further
related to diminished performance on tests of motor speed and
manual dexterity, independent of other components of the
metabolic syndrome and several other potential confounding
factors, among healthy older adults (Waldstein & Katzel, 2006).
Interactions between BMI and Grooved Pegboard (GP) scores
in older adults were significant, with individuals with greater
BMI performing most poorly on the GP (Waldstein & Katzel,
The mechanism for this is not well understood. Waldstein &
Katzel (2006) hypothesised that central obesity has been asso-
ciated with various neuroendocrine disturbances (Bjorntorp &
Rosmond, 2000) that have also been associated with enhanced
sympathetic nervous system activity (Bjorntorp & Rosmond,
2000; Ren, 2004) and that may promote structural brain abnor-
malities including silent cerebrovascular disease and stroke
(Everson, Lynch, Kaplan, Lakka, Sivenius, & Salonen, 2001;
Waldstein, Siegel, Lefkowitz, Maier, Pelletier-Brown, & Obu-
chowski, 2004). Obesity has also been associated with en-
hanced pro-inflammatory factors (Toni, Malaguti, Castorina,
Roti, & Lechan, 2004), which have been shown to exert nega-
tive effects on cognitive function (Yaffe, Lindquist, Penninx,
Simonsick, Pahor, & Kritchevsky, 2003).
With the interaction of BMI and neurocognitive performance
in healthy older adults established, it is not clear whether the
same patterns will hold for healthy younger adults. It could be
hypothesised that shorter exposure to the neurotoxic effects of
CVD factors, hormonal abnormalities, or inflammatory factors
may leave younger brains less affected by the disease related
aspects of obesity. However, in an environment of reduced
neurotoxic risk (i.e. youth), BMI might still influence GP per-
formance through, for example, clumsy fingers, and thus con-
found the results of this psychomotor task.
In SA the effect of BMI on GP performance is of interest
because of its potential to identify HAND. The demographic
curve of HIV is skewed towards younger people, creating a
younger risk group for HAND, which is the same demographic
group increasingly at risk for obesity. While obesity is not usu-
ally associated with HIV positive status, it is widespread within
the general population. Thus, in order to use the GP non-
dominant hand test with HIV positive persons, the effect of
BMI on GP performance among healthy individuals needs to be
clarified first, in order to determine the usefulness of peer
norms. If BMI does affect GP performance, accurate peer
norms for HIV positive persons may need to control for the
confounding effects of BMI.
This study thus set out to explore whether excessive body
mass might affect fine motor skills, in particular GP perform-
ance, among healthy younger adults. This could potentially
have implications for the interpretation of neuropsychological
scores, in particular for the screening of HIV associated neuro-
cognitive decline.
This study used a convenience sample, recruiting participants
through an occupational health surveillance program. This al-
lowed for the measurement of BMI without inconveniencing
participants. Individuals were excluded from the study if they
had a history of neurological, psychiatric or cardio-vascular
disorders, were HIV positive, had any physical impediments
that could affect motor performance, or were not adequately
proficient in English to understand the GP instructions or an-
swer the health questionnaire. Due to the recruitment channel,
all participants were employed at the time.
As the general HIV prevalence figures are available for the
ages up to 49, the same maximum age limit was used for this
study. In SA, both BMI profiles (Malhotra et al., 2008) and GP
performance (Van Wijk, 2012) differs across gender, and
women completed the GP faster than men. Further, the associa-
tion of BMI and GP performance also differed along gender
lines among older people, and it was thus decided to treat the
data for women and men separately.
Eight hundred and fifty volunteers between the ages of 20
and 49 years (mean age = 32 ± 8) completed the GP on an indi-
vidual basis. All participants had at least 8 years of formal edu-
cation (mean years = 12 ± 1). The sample comprised 307
women (36.1%) and 543 men (63.9%), and was drawn from all
SA language groups and provinces of origin.
The female group had a mean age of 28 years (±7), and a
mean of 12 years (±1) of education. Of the women, 67% were
between 19 and 29 years, 27% were between 30 and 39 years,
and 6% were between 40 and 49 years, while 61% were Black,
20% were Coloured, 3% were Indian, and 16% were White.
The male group had a mean age of 35 years (±8), and a mean of
12 (±1) years of education. Of the men, 33% were between 19
and 29 years, 36% were between 30 and 39 years, and 31%
were between 40 and 49 years, while 41% were Black, 33%
were Coloured, 5% were Indian, and 21% were White. The
women were significantly younger than the men (p < 0.01).
1) Grooved Pegboard. The GP is a manipulative dexterity
test (Lafayette Instrument Company, 1989), which measures
psychomotor speed, fine motor control, and rapid visual-motor
coordination (Mitrushina, Boone, Razan, & D’Elia, 2005).
Performance is highly dependent on psychomotor speed (Lezak
et al., 2004). Scores represe nt time in seconds required to com-
plete the matrix with each hand, with higher scores reflecting a
lower level of performance. The non-dominant hand is often
considered more sensitive for psychomotor slowing. The task
was administered according to the standard instruction set, as
described in the manual (Lafayette Instrument Company, 1989).
One trial each was allowed with first the dominant hand (GPd),
and then the non-dominant hand (GPn).
2) Anthropometric measurement. Participants were measured
while wearing light clothes without shoes or jackets. Measure-
ments were done on a Secca scale, and took place under the
supervision of a dietician. The scale’s automatic BMI calcula-
tion feature was used; height had to be entered manually, and
was rounded to the nearest centimetre for this purpose. BMI
was computed as weight (in kilograms) divided by the square of
the height (in meters). The following WHO (1995) categories
were used: underweight (BMI < 18.5), healthy weight (BMI
18.5 to 24.9), overweight (BMI 25.0 to 29.9), and obese (BMI
> 30).
3) Health questionnaire. Participants completed a self-report
health questionnaire, developed for this study, inquiring about
neurological or psychiatric history, and history of cardiovascu-
lar disease.
Copyright © 2013 SciRes.
All participants completed an informed consent form. They
did their BMI measurement as part of their health surveillance
program, and did the GP individually, usually at the end of their
health screening. The study was approved by the Surgeon Gen-
eral’s Health Research Ethics Committee.
Data Analysis
Some previous studies used dichotomous variables to index
the presence of obesity (Elias et al., 2003, 2005), while others
used continuous measures of obesity relating to cognition
(Waldstein & Katzel, 2006). This present study will employ
both. Firstly, the relationship between the WHO’s BMI catego-
ries and GP timed scores will be calculated with ANOVA. Post
hoc analysis will use Tukey HSD tests. The calculations will be
done separately for each gender group. Secondly, to explore
further the ability of BMI scores to predict GP performance,
correlation coefficients will be calculated for BMI raw scores
and GP timed scores, again separately for each gender group.
As there was no significant time-score difference between race
groups, the results of the total group will be used for analysis
here. All analyses were done using STATISTICA 7.
BMI profile for the sample: None of the participants fell into
the underweight category. In the female sample, 37% were of
healthy weight, 34% were overweight, and 29% were obese. In
the male sample, 30% were of healthy weight, 38% were over-
weight, and 32% were obese. The gender difference may be the
result of the gendered age profiles, as there were more older
men and more younger women in the group.
GP performance of the sample: Women completed the GPd
in a mean time of 60.9 seconds (±8.0), while the men did it in a
mean time of 65.8 seconds (±9.2). The difference (using t-tests
for independent samples) was significant (p < 0.001). Women
completed the GPn in a mean time of 66.3 seconds (±9.4),
while the men did it in a mean time of 70.8 seconds (±11.2).
The difference was again significant (p < 0.001).
When using ANOVA, women did not display any significant
differences between the BMI categories and GPd time scores
(F2,304 = 1.13; p = 0.3) or the GPn time scores (F2,304 = 0.95; p =
0.4). Men did not display any significant differences between
the BMI categories and GPd time scores (F2,540 = 1.3; p = 0.3)
and GPn time scores (F2,540 = 2.1; p = 0.1) either.
A small stepwise progression of obesity and performance
was observed in both the women and men’s groups, but never
achieved significance. In general, participants who were obese
posted slightly longer times than those who were overweight,
who in turn posted slightly longer times than those in the
healthy weight category (see Table 1).
When correlation coefficients were calculated for BMI raw
scores and GP time scores, no significant correlations were
found for both GPd and GPn for either gender or the total group
(see Table 2).
This study demonstrated the expected gender difference in
GP performance reported elsewhere (Bryden & Roy, 2005;
Schmidt, Oliveira, Rocha, & Abreu-Villaca, 2000). The results
Table 1.
Means and standard deviations o f GP times across weight categories.
mean SD mean SD
weight 113 60.0 7.5 65.3 9.0
Over weight 106 61.1 8.5 66.8 9.0
Obese 88 61.8 8.0 66.9 10.8
mean SD mean SD
weight 162 65.0 8.8 69.4 10.3
Over weight 209 65.8 9.4 71.3 10.9
Obese 172 66.6 9.4 71.7 12.4
Table 2.
Correlation coefficients between BMI and GP scores.
Total group 850 0.04 0.05
Women 307 0.00 0.04
Men 543 0.04 0.04
further found no significant interaction between BMI categories
and GP times, nor significant interactions between BMI scores
and GP times.
This stands in apparent contrast to earlier studies that found
obesity associated with poorer performance on the GP (Wald-
stein & Katzel, 2006), among older adults.
The findings differed from previous studies for a number of
reasons: Firstly, the age distribution in the female group was
skewed towards younger women. As age is associated with
weigh (Puoane et al., 2002), the weight distribution was thus
also skewed towards lower body mass. Age is also associated
with GP performance (Heaton, Ryan, Grant, & Matthews,
1996), and the total skewed distribution could have influenced
possible effects of BMI on GP performance. Secondly, as the
sample was comprised of younger adults, the results may sug-
gest that lesser exposure to the neurotoxic effects of CVD,
neuroendocrine disorders, and so forth might indeed lessen the
effect of such factors on psychomotor performance. This would
give support to current hypotheses regarding the mechanism of
the obesity/cognitive performance interaction (Elias et al., 2003;
Waldstein & Katzel, 2006).
This study has a number of limitations. Most notably, the
women sample did not reflect the normal population distribu-
tion in either age or BMI categories. Further to this, there was
no objective control (e.g. an examination by a physician) of
possible CVD history or risk. As unknown or unreported CVD
risk factors could have been present, the results need to be in-
terpreted with caution. Lastly, the difference in composition
(age and BMI categories) of the gender sub-samples precludes
Copyright © 2013 SciRes. 35
direct comparisons of women and men’s results.
Future studies need to include participants that are more re-
flective of the general population distribution (with regard to
age, gender, employment status, education, and so forth), as
well as external (vs self-reported) controls for medical and
lifestyle risk factors (e.g. hypertension, diabetes, exercise and
To conclude: in the presence of a wide weight spectrum
among the participants, and the absence of history of known
medical risk factors, the lack of significant BMI-GP interaction
suggests that there is no real evidence of body mass signifi-
cantly affecting GP performance among younger adults.
Slowed performance on the GP (in the presence of elevated
body mass) would thus probably be due to neurological disease
processes, rather than body fat percentage.
Thus, the effect of BMI may generally be discounted when
interpreting GP results until further corroboration of the inter-
action has been reported.
I wish to acknowledge Chesray Hans-Arendse, Sadia Edross,
Marilize Willers and Ruby Muller who assisted in the admini-
stration of the GP, and Aileen van der Spuy who did the BMI
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