Success in skill sports such as basketball requires optimal concentration and attentional skills to facilitate adaptation to a dynamic performance environment. When basketball players are in their optimal performance state, they are focusing their attention solely on the cues relevant to the task being performed. For example, a successful point guard has clear vision of the court and of the movements of his or her teammates (and defenders) so that he or she can react quickly and pass the ball to a teammate who is in scoring position (2). Conversely, players may perform poorly if they lose concentration (e.g., player missed the coaches' instructions or play call) or become distracted (e.g., player missed a free throw because of distraction by crowd noise). These examples illustrate the importance of optimal, selective, sustained attention to basketball performance (1,14).
Attentional skills influence the efficiency and effectiveness of an athlete's information processing because they affect the types of information selected for processing, the amount of information being processed, and the athlete's readiness to respond to environmental cues (18). Whereas information-processing capacity is a fixed resource, the processes that athletes use to select cues for attention (i.e., selectivity) and their readiness to respond to different cues (i.e., alertness) are more dynamic and are subject to change in different situations or in response to different stressors. The present study focuses on vigilance, a particular aspect of alertness that represents an individual's ability to sustain a high level of alertness for an extended period of time. Vigilance is a particularly important skill for athletes such as basketball players, whose competitive environments impose strong attentional demands because of their complexity and dynamic nature, last extended periods of time, and provide minimal or infrequent rest.
Continuous performance tests are commonly used to assess vigilance-related attentional performance (13). These tests require participants to respond selectively to stimuli presented over an extended time period. To ensure that participants are processing stimulus qualities, a number of irrelevant stimuli are mixed into the array of relevant stimuli. The ratio of relevant-to-irrelevant stimuli can also be varied to study the effects of stimulus frequency/rareness on attentional performance. For example, the Test of Variables of Attention (TOVA; 12) is a 21.8-min continuous performance test that uses a 3.5:1 ratio (target-frequent) of relevant-to-irrelevant stimuli for the first half of the test, and a 1:3.5 ratio (target-infrequent) of relevant-to-irrelevant stimuli for the second half of the test.
The results of a TOVA assessment are derived from signal-detection theory (11). Briefly, signal-detection theory posits that vigilance represents an individual's ability to consistently and accurately distinguish attentional signals from attentional noise. Every environment creates a certain amount of random neural activity (noise) for information processors that is generated without a relevant cue (signal) being present. When a relevant signal is added to the environment, neural activation increases. Repeated sampling thus yields separate distributions for noise and signal + noise. The displacement between these distributions represents an individual's sensitivity to the signal. When neural activation exceeds an established cutoff point, individuals conclude that a signal is likely to be present.
Signal-detection theory offers several parameters for assessing attentional performance using simple decision tasks. These measures include the mean response time for correctly identified stimuli, the number of omission errors (false-negatives), the number of commission errors (false-positives), and sensitivity (i.e., ratio of correct responses to false-positives). Longer response times for correctly identifying signals in the environment represent greater information-processing demands as individuals attempt to determine whether the neural activation exceeds the cutoff threshold. When a signal is present in the environment, individuals may either detect (true positive) or fail to detect (i.e., omission error, false-negative) the signal. Likewise, when a signal is absent from the environment, individuals may identify it as absent (true negative), or they may mistakenly respond as if the signal were present (i.e., commission error, false-positive). True positives and true negatives represent accurate decisions. Omission errors imply the adoption of an overly conservative cutoff threshold, whereas commission errors imply the adoption of overly liberal cutoff thresholds. Finally, the ratio of correct responses to commission errors provides an index of an individual's sensitivity to signal in the environment. The most sensitive individuals are able to maximize true positives while minimizing false-positives.
Two salient stresses in sport environments that may affect these indices of attentional vigilance are fatigue and dehydration (DEH). Subjective symptoms of fatigue are associated with even modest levels of DEH. For example, in a study by Cian et al. (3), subjective ratings of fatigue indicated that subjects felt less fatigued in a control session (where subjects were allowed to drink) than under DEH conditions (induced by passive heat and exercise stress). In addition, Shirreffs et al. (21) found that self-ratings of alertness and ability to concentrate decline and ratings of tiredness increase when fluid intake is restricted during a 37-h period to induce 1-2% DEH. Research also suggests that as little as 2-3% DEH impairs various aspects of cognitive performance, such as arithmetic ability, short-term memory, visuomotor tracking, response time, and coordination (3,4,10,20).
Moran (14) suggests that fatigue depletes our attentional resources, leading to a reduced capacity for controlled information processing. Moran further suggests that fatigue may impair attentional performance by enhancing distractibility. According to this theory, increases in distractibility would be expected to decrease an individual's ability to detect relevant stimuli (i.e., increase in omission errors and decrease in sensitivity). Likewise, decreases in information-processing resources for signal detection would be expected to result in longer response time for correctly identifying relevant stimuli.
Several studies have evaluated the effects of DEH on perceived fatigue and/or cognitive performance (3,4,10,20,21,23); however, no study has tested the effects of DEH on attentional vigilance in basketball players. Furthermore, it is not yet known whether fatigue associated with DEH makes basketball players more liberal (i.e., prone to commission errors) or conservative (i.e., prone to omission errors) information processors. Therefore, the purpose of the present study was to determine the effects of DEH on attentional vigilance in 17- to 28-yr-old male basketball players. We hypothesized that basketball players would become more conservative information processors as a result of DEH. According to this hypothesis, DEH would lead to slower response time, which would, in turn, mitigate increases in commission errors; however, the time demands of the task would cause increased omission errors and decreased sensitivity.
Eleven skilled male basketball players (17-28 yr) volunteered to participate in this study. Participants were informed of the experimental procedures and associated risks before providing written informed consent. This study was approved by the institutional review board for the protection of human subjects of the Pennsylvania State University. Preliminary screening included a resting 12-lead electrocardiogram, skinfold measurements to determine adiposity, blood analysis (CHEM-24), a graded exercise test on a treadmill (modified Balke protocol) to determine maximal oxygen uptake (V˙O2max), and a physical exam. Criteria for exclusion included abnormal resting or exercise electrocardiogram, smoking, and use of medications or supplements that could influence physiological or attention variables of interest. Subject characteristics are presented in Table 1.
The visual form of the TOVA (Universal Attention Disorders) was used to measure the subjects' visual information processing and attention. The TOVA program was run on a PC in MS DOS mode. During the test, the subject sat in a chair directly in front of the computer in a small, quiet room. The researcher read the directions for the test as outlined in the TOVA manual (8) before the subjects' first test (i.e., baseline TOVA of the first trial). During the test, two different visual stimuli were presented (one at a time) on the screen for 100 ms at 2-s intervals in a fixed random order. The stimuli were white squares and differed only in that the target had a black hole near the top and the nontarget had a black hole near the bottom. The subject was asked to press a handheld button when the target square appeared on the screen, and to refrain from pressing the button when the nontarget appeared on the screen. Both speed and accuracy of responses were emphasized in the instructions. After the instructions were given, the subject completed a 2.5-min practice test. Next, the TOVA (21.8 min in duration) was taken by the subject. The test consisted of four quarters (5.45 min each) or two halves (10.9 min each). The first half consisted of quarters 1 and 2, and the second half consisted of quarters 3 and 4. In the first half, the target-to-nontarget ratio was 1:3.5 (target-infrequent condition); in the second half, the ratio was 3.5:1 (target-frequent condition). The total number of stimuli presented for the test was 648 (324 of each). The TOVA program measured four different dependent variables of interest: errors of omission (inattention), errors of commission (impulsivity), mean correct response time, and sensitivity (ratio of correct responses to errors of commission). TOVA scores were presented by quarters (Q) and halves (H) for each variable.
All subjects completed six experimental trials under the following hydration states: 1) euhydration (EUH) with a commercially available lemon/lime-flavored carbohydrate-electrolyte solution (CES; 6% carbohydrate and 18.0 mM NaCl); 2) EUH with a placebo (lemon/lime-flavored water and 18.0 mM NaCl); 3) 1% DEH; 4) 2% DEH; 5) 3% DEH; and 6) 4% DEH. Experimental trials were assigned in random order and were scheduled at least 1 wk apart. Additionally, both the subject and investigator were blinded to the fluid type during the EUH trials.
Subjects reported to the laboratory on the morning of test days, having swallowed a disposable temperature sensor (CorTemp) the night before and having fasted overnight. Immediately on arrival, they voided, and then they were weighed wearing shorts only. Next, each subject ate a low-carbohydrate standardized breakfast (36% carbohydrate, 25% fat, 39% protein) and drank 5 mL of distilled water per kilogram of body mass. After breakfast, the researcher administered the baseline TOVA (test 1) to the subject. Next, a nurse placed an 18-gauge Teflon catheter in an antecubital vein in the subject's arm. After emptying his bladder, the subject entered an environmental chamber set at 40°C and 20% relative humidity. Next, the subject (wearing shorts only) was weighed (initial body mass) and was then asked to stand on a treadmill while the baseline blood sample and core temperature (Tc) were obtained. Next, the subject walked at an intensity of 50% of V˙O2max for 15 min, followed by 5 min of rest. This interval-walking protocol continued until the subject completed nine 15-min bouts of walking, separated by 5-min rest periods.
Subjects were weighed (wearing shorts only) during each rest period (i.e., after each walking bout) to determine periodic sweat loss. During the EUH trials, subjects drank enough fluid (either CES or placebo) during rest periods to fully replace sweat and urine losses and to maintain the initial body mass. During the DEH trials, fluid was restricted until the subjects reached their target body mass (i.e., incurred the desired fluid deficit). Target body mass was determined by calculating the body mass that corresponds with the desired % DEH (e.g., 2% DEH body mass = initial body mass × 0.98). If a subject's body mass fell below his target body mass, he ingested distilled water to maintain the desired % DEH body mass. Ten minutes into each walking bout, a blood sample and Tc were obtained.
At the end of the 3-h interval-walking protocol, each subject exited the chamber and emptied his bladder. Next, the subject completed a fatigue survey, a visual-analog rating scale with questions pertaining to physical well-being, and then he completed the TOVA (test 2). After completion of test 2, the subject had the catheter removed from his arm. Next, the subject sat in a thermoneutral room for a 50-min recovery period to rest his legs before doing the basketball drills. During this recovery period, the subject was weighed at 15-min intervals and drank water, CES, or placebo as needed to maintain the desired hydration state. Tc was also recorded at 15-min intervals during the recovery period. The subject emptied his bladder at the end of recovery.
After the 50-min recovery period, the subject was transported to a gymnasium where he completed a sequence of drills designed to simulate a fast-paced basketball game. The drill session commenced 20 min after the 50-min recovery period. Basketball drills were 80 min in duration and consisted of four 15-min quarters with 5-min breaks between quarters and a 10-min break at halftime. The desired hydration state was maintained throughout the basketball drill session by weighing the athletes at the end of each quarter and having them drink the appropriate volume of fluid during the rest periods. The fatigue survey was administered at halftime and at the end of the basketball drills. Lastly, the subjects were administered their final TOVA (test 3) at the laboratory 20 min after the end of the basketball drills. A schematic of this experimental protocol is provided in Figure 1.
Body mass was measured to the nearest 0.05 kg using a Seca 770 scale. A CorTemp disposable temperature sensor (COR-100) and CorTemp recorder (CT-2000) were used to measure Tc.
Venous blood samples (10 mL each) were drawn without stasis. A 2-mL aliquot was transferred into an EDTA-treated test tube and was immediately analyzed for hematocrit (microhematocrit centrifugation) and hemoglobin (Hemacue Hb 201+) in triplicate. The percent change in plasma volume from baseline (ΔPV) was calculated from hematocrit and hemoglobin (7). The remaining aliquot was transferred into a serum separator tube, allowed 30-60 min to clot, and then centrifuged at 4°C for 15 min. Serum was analyzed for glucose concentration (Sgluc; hexokinase UV method, Olympus Model AU5200).
The fatigue survey was completed at minutes 180, 310, and 350 to determine the effect of DEH versus EUH on lightheadedness (not lightheaded to very lightheaded), hotness (not feeling hot/overheated to feeling very hot/overheated), and total body fatigue (no total body fatigue to severe total body fatigue). The subjects answered these questions by placing a mark on a 100-point scale between the extreme answers at opposite ends of the line.
Significant differences between hydration states in the TOVA scores, subjective ratings, and physiological variables were determined using a two-way analysis of variance (ANOVA) (hydration state vs time) with repeated measures. A three-way repeated-measures ANOVA (hydration state vs time vs test) was used to compare TOVA performance among tests within hydration states. The Bonferroni post hoc test was used to correct for multiple comparisons in the analyses of the subjective ratings and physiological variables. PROC MIXED in SAS 9.1 was used to perform all statistical analyses. The significance level for all statistical tests was set at alpha = 0.05. All data are presented as means ± SD.
No statistically significant differences in TOVA performance were observed among the four DEH levels or between CES and placebo EUH. To simplify presentation, every subject's (N = 11) score for 1, 2, 3, and 4% DEH were averaged to one score (DEH), and every subject's (N = 11) score for placebo EUH and CES EUH were averaged to one score (EUH) for comparison. All TOVA results are presented as change from baseline (test 1). There were no significant differences in baseline TOVA performance between trials.
Results for Tc, Sgluc, and %ΔPV are presented in Table 2. There were no differences in Tc or Sgluc between trials at baseline. Sgluc was significantly higher in CES compared with the placebo and DEH trials after the exercise/heat exposure, that is, at the start of test 2. Also, Sgluc was significantly higher in the EUH versus the DEH trials at the start of test 2. PV was significantly lower and Tc was significantly higher during the DEH compared with the EUH trials at the start of test 2. There were no significant differences between trials in Tc at the start of test 3.
Figure 2 shows the subjective ratings for lightheadedness, hotness, and total body fatigue at the end of the exercise/heat phase, halftime, and at the end of the basketball drills. The subjects felt significantly more lightheaded and hot/overheated during DEH compared with EUH at all three time points. Ratings of total body fatigue were significantly higher during DEH compared with EUH at the end of the exercise/heat phase and at halftime, but not at the end of the basketball drills.
Attentional performance with target-infrequent stimuli.
The left side of Figure 3 presents four indicators of attentional performance relative to baseline for target-infrequent stimuli. During DEH trials, sensitivity decreased significantly relative to baseline after both the exercise/heat phase (−1.1 ± 1.3) and the drills (−0.9 ± 1.3). Neither response time, omission errors, nor commission errors differed from baseline levels after either the exercise/heat phase or the drills. Additionally, after the drills, significantly higher sensitivity (+0.4 ± 1.2 vs −0.9 ± 1.3), faster response time (−8 ± 20 vs +16 ± 28), and fewer omission errors (−0.4 ± 0.7 vs +1.3 ± 2.4) characterized EUH trials compared with DEH trials. There were no differences in commission errors between hydration states.
Attentional performance with target-frequent stimuli.
The right side of Figure 3 presents four indicators of attentional performance relative to baseline for target-frequent stimuli. Neither sensitivity, response time, nor omission errors differed from baseline after either the exercise/heat phase or the drills. During the EUH trials, commission errors decreased significantly from baseline after the exercise/heat phase (−1.9 ± 3.2), but not after the drills. After the exercise/heat phase, EUH trials were characterized by significantly fewer omission errors (−4 ± 15 vs +5 ± 7) and commission errors (−1.9 ± 3.2 vs 0.6 ± 1.4) than DEH trials. After the drills, EUH trials were characterized by significantly greater sensitivity (+0.7 ± 2.6 vs −0.7 ± 1.1) and faster response time (−21 ± 28 vs +5 ± 31) than DEH trials.
The novel findings from this study were that 1) DEH impairs vigilance-related attentional performance, 2) attentional impairment is linked to athletes becoming generally more conservative information processors, and 3) DEH-related decrements in performance are most pronounced in stimulus-frequent situations. The basketball-specific relevance of each of these conclusions is as follows: 1) slowed response time and inattention to relevant cues would likely lead to costly errors during a basketball game (e.g., turnovers, missed shot attempts, or being out of position on defense); 2) conservative decision making is the process by which DEH impairs attention in basketball players (i.e., it causes slowed response time and increased number of omission errors); and 3) the game of basketball is an example of a stimulus-frequent situation (i.e., a dynamic environment); thus, the results of this study are directly applicable to basketball performance.
The impaired attentional performance in the present study can be partially explained by increased feelings of fatigue associated with DEH compared with EUH. These results support previous studies characterizing the relation between DEH, fatigue, and cognitive function (3,4,10,14,20,21). Further, the deleterious effects of DEH on performance can now be extended to attentional aspects of cognitive function in basketball players.
The results of the current study are in agreement with Moran's (14) theory. Moran suggests that fatigue may impair attention by enhancing distractibility and depleting information-processing resources. Thus, we would not expect the fatigue associated with DEH to cause excessive neural activation (high arousal); instead, we would expect these two stressors to cause a decrease in neural activation (low arousal) in response to relevant stimuli. The signal-detection model of vigilance performance (1) predicts that under low-arousal conditions, the most common type of error is failure to respond to target stimuli (i.e., increased number of omission errors). Conversely, the model predicts that overaroused or anxious athletes are more likely to make responses when they are not required (increased number of commission errors). In the present study, there were more significant differences between hydration states in omission errors than commission errors throughout Q1-Q4 in tests 2 and 3. These results show that, during DEH trials, the subjects minimized false alarms at the cost of failing to detect some target stimuli. The greater increase in omission errors compared with commission errors suggests that subjects became more conservative decision makers during the DEH trials.
In general, attentional performance was impaired to a greater extent during the target-frequent compared with the target-infrequent presentation of the TOVA. For instance, there were more significant differences between hydration states in response time and omission errors throughout Q3-Q4 (target-frequent presentation) compared with Q1-Q2 (target-infrequent presentation). One interpretation of these results could be that DEH is more detrimental to attentional performance when tasks are performed in a highly dynamic environment. Alternatively, because the target-frequent condition was presented during the second half of the TOVA, fatigue associated with the time demand of the task may have led to the attention decrements. Because of the design of the current study (order of stimuli frequency presentations was the same for each trial), we cannot determine the relative contributions of task duration versus stimuli frequency on the attentional performance impairment. Nonetheless, both interpretations seem plausible and have practical significance; that is, DEH may have more deleterious effects on performance during the latter half of complex tasks (i.e., end of a basketball game).
For each DEH and EUH trial, TOVA was completed after two different types of stress. Subjects performed test 2 after exercise and heat stress (walking at 50% V˙O2max in 40°C), whereas test 3 was performed after exercise stress only (basketball drills were performed at room temperature). TOVA performance was significantly impaired with DEH compared with EUH during both tests 2 and 3. Thus, DEH impaired attention regardless of the environmental conditions in this experiment (i.e., whether the test was administered after exercise with or without environmental heat stress). Cian et al. (3,4) have determined that inducing DEH by passive heat stress or by treadmill exercise produced similar impairments in cognitive function (perceptive discrimination and short-term memory) compared with EUH. Thus, DEH per se, and not necessarily the heat exposure or exercise stress associated with establishing DEH, is responsible for impairing various aspects of cognitive function, including vigilance-related attention. Conversely, submaximal aerobic exercise has been shown to improve information processing above that of preexercise/baseline conditions, provided that EUH is maintained (25). The current study shows a trend (P < 0.1) towards improvement in attentional performance in Q1 and Q3 of test 2 (response time) and Q2 (sensitivity) and Q4 (response time) of test 3 compared with baseline (test 1) of EUH trials. In addition, commission errors decreased significantly below baseline during the second half of test 2 in the EUH trials.
Sweating caused by heat exposure and/or exercise results in fluid loss from the extracellular space (5). Body water distribution is a function of solute distribution, and given that sodium is the major cation in the extracellular space, sodium replacement is necessary to promote more complete rehydration (smaller percentage of decrease in PV) compared with water alone (6,16,24). Thus, in the current study, both fluid-replacement beverages (CES and placebo) used in the EUH trials included sodium (18 mM NaCl) to provide an osmotic impetus to retain the ingested fluid in the vascular space. In addition, inclusion of sodium in the rehydration solution enhances palatability (15,16,24), which becomes especially important when athletes need to drink large volumes of fluid to completely replace sweat loss (e.g., mean sweating rate during the basketball drills of EUH trials in the current study was 2.2 ± 0.7 L·h−1).
In the current study, there were no differences in attentional performance when EUH was maintained with CES versus placebo (data not shown). There have been mixed results in the literature regarding the effects of glucose administration on cognitive performance. Some investigations have suggested that consumption of glucose drinks before testing improves memory (22) and results in faster, more consistent information processing (17). In contrast, others have found no effect of glucose administration on memory and attention (19) or performance on the Stroop color-word test (26).
Before test 2 of the EUH-CES trials, the subjects' Sgluc was 90 mg·dL−1 (5 mM), which is lower than that of the studies showing that glucose administration improves cognitive performance (≥ 6 mM). However, Sgluc does not explain the discrepancy between studies; Flint and Turek (9) found no differences in most aspects of TOVA performance (including omission errors, response time, or sensitivity) between a saccharin control group and the groups that consumed a 10-, 100-, or 500-mg·kg−1 or 50-g dose of glucose before testing. In that study, Sgluc varied from approximately 80 mg·dL−1 (saccharin) to approximately 115 mg·dL−1 (50-g dose). Thus, alterations in serum glucose concentration do not seem to affect most variables of attention. Again, it seems that DEH impairs attentional performance independently of factors that may be associated with the process of inducing DEH, such as alterations in Sgluc attributable to exercise or fasting.
There were statistically significant differences between EUH (CES and placebo averaged) and each level of DEH (1, 2, 3, and 4%) at various time points for the TOVA scores. However, there were no statistically significant differences in attentional performance among 1, 2, 3, and 4% DEH (data not shown). Therefore, the current data suggest that dehydration at any level (up to 4%) equally impairs vigilance-related attention.
Tc was significantly higher with 3 and 4% than 1 and 2% DEH at the start of TOVA tests 2 and 3; however, a significantly higher Tc did not result in significantly poorer attentional performance in 3 and 4% versus 1 and 2% DEH. For example, Tc at the end of the drill session (start of TOVA test 3) for each of the trials was as follows: EUH (CES and placebo averaged), 38.10 ± 0.70°C; 1% DEH, 38.09 ± 0.62°C; 2% DEH, 38.06 ± 0.34°C; 3% DEH, 38.52 ± 0.41°C; and 4% DEH, 38.41 ± 0.55°C. Attentional performance during TOVA test 3 was impaired during each level of DEH compared with EUH; however, Tc was only increased above that of EUH during 3 and 4% DEH. Therefore, our current data suggest that the DEH-induced impairment of attentional performance is not related to increases in Tc.
In summary, vigilance-related attentional performance of 17- to 28-yr-old male basketball players is impaired by DEH. Specifically, DEH decreases sensitivity to relevant cues, increases the number of omission errors, and slows response time. Further, the significant increase in omission errors and minimal change in commission errors suggests that DEH makes basketball players more conservative information processors. The deleterious effects of DEH on attention are particularly evident during the target-frequent presentations (representing a highly dynamic environment, such as a basketball game). These results suggest that fluid replacement is critical in preventing the decline in attentional vigilance associated with DEH. Therefore, basketball players should be advised to maintain EUH for optimal concentration and attentional skills during competition.
We are grateful to the subjects who gave their time and effort to make this study possible. In addition, we thank Jane Pierzga, Michael Hyduk, Kelly Dougherty, Doug Johnson, and Randy McCullough for their technical assistance, the General Clinical Research Center nursing staff for their medical support, and Mosuk Chow, PhD for statistical consultation. Support for this study was provided by the National Basketball Association, Gatorade Sports Science Institute, and General Clinical Research Center Grant MO1 RR010732.
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