You can't think and hit at the same time: neural correlates of baseball pitch classification
published: 19 December 2012

Jason Sherwin
Jordan Muraskin
Paul Sajda
Marieke K Van Vugt
Luke Hallum
Paul Sajda

Department of Biomedical Engineering
Columbia University
New YorkNYUSA

University of Groningen
Netherlands

Department of Biomedical Engineering
New York University
USA

Columbia University
10027New YorkNYUSA

You can't think and hit at the same time: neural correlates of baseball pitch classification
published: 19 December 201210.3389/fnins.2012.00177ORIGINAL RESEARCH ARTICLE Edited by: Tobias H. Donner, University of Amsterdam, Netherlands Reviewed by: *Correspondence:single-trial analysesEEGperceptual decision-makingbaseballsource localization
Hitting a baseball is often described as the most difficult thing to do in sports. A key aptitude of a good hitter is the ability to determine which pitch is coming.This rapid decision requires the batter to make a judgment in a fraction of a second based largely on the trajectory and spin of the ball. When does this decision occur relative to the ball's trajectory and is it possible to identify neural correlates that represent how the decision evolves over a split second? Using single-trial analysis of electroencephalography (EEG) we address this question within the context of subjects discriminating three types of pitches (fastball, curveball, slider) based on pitch trajectories. We find clear neural signatures of pitch classification and, using signal detection theory, we identify the times of discrimination on a trial-to-trial basis. Based on these neural signatures we estimate neural discrimination distributions as a function of the distance the ball is from the plate. We find all three pitches yield unique distributions, namely the timing of the discriminating neural signatures relative to the position of the ball in its trajectory. For instance, fastballs are discriminated at the earliest points in their trajectory, relative to the two other pitches, which is consistent with the need for some constant time to generate and execute the motor plan for the swing (or inhibition of the swing). We also find incorrect discrimination of a pitch (errors) yields neural sources in Brodmann Area 10, which has been implicated in prospective memory, recall, and task difficulty. In summary, we show that single-trial analysis of EEG yields informative distributions of the relative point in a baseball's trajectory when the batter makes a decision on which pitch is coming.

INTRODUCTION

The baseball great, Ted Williams, said, "the hardest thing to do in a sport is to hit a baseball." The hitter has a fraction of a second to decide whether the pitch will be a ball or a strike and whether he will swing or not. Another great, Yogi Berra, summarized the split second timing necessary for this task by saying, "you can't think and hit at the same time." Rather, hitters must rely on a rapid decision-making process that tracks the trajectory and speed of the ball with sufficient accuracy to predict its location when it crosses the plate and decide on an appropriate motor response. Due to the different speeds and trajectories that pitches can follow, batters cannot blindly guess and maintain accuracy. A key element of this rapid decision-making process is determining what type of pitch is thrown, e.g., fastball, curveball, or slider, because the type of pitch constrains the possible trajectories of the ball.

Previous studies have examined the pitch classification process using behavioral/physiological markers. For instance, eye movements before and during pitches have been extensively studied (Shank and Haywood, 1987;Kato and Fukuda, 2002;Takeuchi and Inomata, 2009) and have been used to identify optimal visual search strategies employed by expert vs. novice players. These findings show that experts focused their visual (spatial) attention closer to the estimated release point of the pitch, when compared to novices, suggesting that early-trajectory tracking can be crucial for batting performance. The middle phase of the trajectory also has been found to have significant impact on pitch identification. In particular, it has been shown that the middle third of a pitch's trajectory was most predictive of whether subjects made contact with a pitched softball (De Lucia and Cochran, 1985). This implies that, in addition to early-trajectory tracking, continued tracking of the pitch can influence trajectory estimation.

To date, the only approach directly aimed at investigating neural signatures related to pitch identification has used electroencephalography (EEG) to examine the P300 event-related potential (ERP) in response to two categories of pitches (fastballs and curveballs) when either a correct or incorrect verbal cue preceded the pitch (Radlo et al., 2001). Results showed differences between intermediate and advanced batters in terms of their behavioral accuracy as well as P300 amplitude and latency, with the conclusion being intermediate batters are more easily "fooled" by a preceding cue which was not congruent with the pitch. Though this result suggests how use of prior knowledge and attentional allocation might differ between batters of different aptitudes, it does not directly address the question of what are the neural correlates of the decision event and when they occur relative to the type of pitch thrown and its trajectory in space-time.

In this paper we employ a forced-choice decision-making task, in which subjects must discriminate between three pitch types while under time pressure. We utilize a multivariate classifier to project the neural data (measured via EEG) into a space that optimally separates trials into their predicted pitch class. By doing this on a single-trial basis, we can examine at what point in a pitch's trajectory the subjects' underlying neural activity discriminates a given pitch. Furthermore, we utilize source reconstruction techniques (Pascual-Marqui et al., 1999) to identify the neural generators of the decision-making process when the "batter" makes a mistake.

MATERIALS AND METHODS

SUBJECTS

Six subjects participated in the study (one female, mean age-27.33 years). None of the subjects had professional or collegiate baseball experience. All subjects reported normal or corrected vision and no history of neurological problems. Informed consent was obtained from all participants in accordance with the guidelines and approval of the Columbia University Institutional Review Board.

STIMULI OVERVIEW AND BEHAVIORAL PARADIGM

Subjects viewed on a computer monitor 12 blocks of 50 simulated baseball pitches with a mean jittered inter-stimulus interval (ISI) of 2150 ms. The simulated view was that of where the catcher would sit on a standard baseball diamond, i.e., at the end point of the pitch trajectory. From a library of 50 pitches, each coming from one of three pitch types ("fastballs," "curveballs," and "sliders"), the subject was presented, on each trial, a pitch chosen at pseudorandom.

For those not familiar with baseball, the three pitches differ in their speed and path through space. A fastball has a trajectory that is straight with very little horizontal or vertical break compared to a simple parabolic trajectory. A curveball has a combination of side spin and top spin that creates both a rightward and downward break. A slider only has sidespin, which creates only a rightward break. Subjects were to discriminate pitches based on these trajectories and the difference in speeds. All pitches were simulated using the equations of motion (see below). Video clips showing one example for each of the three pitch types are provided in the online Supplemental Material.

The subjects were instructed to identify the type of pitch as quickly as possible via a keyboard button response, where each pitch choice was mapped to a unique button ("j," "k," and "l"). Subjects were told to respond while the ball was still on the screen. All button responses were executed with the right hand, regardless of handedness. In an initial training phase, subjects learned the general trajectory of each pitch by viewing examples, and for a short practice session they responded with the button response. The practice session contained 20 pitches selected at pseudorandom with no feedback and subjects were asked afterward if they felt comfortable doing the task in the amount of time needed. All subjects responded in the affirmative and the 12 blocks of 50 pitches began with EEG data being recorded. Participants did not receive feedback or collect a reward for their performance; however, they did receive compensation for their time.

A Dell Precision 530 Workstation was used to present the visual stimuli with E-Prime 2.0 (Sharpsburg, PA, USA). The subjects sat in an RF-shielded room 100 cm from the center of the computer screen, where the stimulus display area covered a horizontal angle of ±6.5˚and a vertical angle of ±5.0˚.

The start of each pitch video clip was the stimulus event by which EEG time-locking occurred. Stimulus events were passed to the EEG recording system through a TTL pulse in the event channel. In post hoc analysis, response events were synchronized to the EEG via their latencies from the stimulus event.

PITCH SIMULATIONS

Each pitch video clip was created using a differential equation solver in Matlab 2010a (Mathworks, Natick, MA, USA; see Pitch Simulations below) and exported to an audio-video Interleaved (.avi) movie file (see Supplementary Material for examples) sampled at 60 Hz (refresh rate of display monitor). Most baseball pitches can be simulated using six-coupled differential equations (Armenti, 1992;Adair, 1995).
dx dt = v x (1) dy dt = v y (2) dy dt = v z (3) dv x dt = −F (v) vv x + Bω v z sin φ − v y cos φ (4) dv y dt = −F (v) vv y + Bωv x cos φ (5) dv z dt = −g − F (v) vv z − Bωv x sin φ (6) F (v) = 0.0039 + 0.0058 1 + e (v−v d )/∆(7)
The first three equations specify the change in spatial location in each direction, which equals the velocity of the baseball. The last three equations specify the accelerations due to the drag [F (v)], the Magnus force (B), and gravity (g ) acting on the baseball. Equation 7 is used to calculate the drag force at different velocities with v d = 35 m/s and ∆ = 5 m/s. The Magnus force (B), which occurs due to differential drag on a spinning object, is approximated here to be 4.1 × 10 −4 (dimensionless). After specifying the initial conditions [x 0 , y 0 , z 0 , v x 0, v y 0, v z 0, ω (rotational frequency)], the six ordinary differential equations were solved in MATLAB.

The three pitches -fastball, curveball, and slider -have welldefined individual initial conditions. To create each pitch, we only need to vary the initial velocity and the rotation angle. For each pitch class, 50 pitches were created by randomly sampling distributions of initial conditions for velocity, rotation angle, launch angle, and horizontal launch angle. The values and distributions used for each pitch class are specified in Table 1.

For each simulated pitch, a blue circle was plotted on a gray grid for every frame of the trajectory. The size of the blue circle increased as it approached the viewer, so as to give the illusion of depth. When the ball crossed "home plate," the blue circle disappeared. The frames were compressed into .avi movie format (see examples of each pitch simulation in Supplementary Material). The trajectories, for each simulation, were saved in a separate file for later use.

DATA ACQUISITION

Electroencephalography data was acquired in an electrostatically shielded room (ETS-Lindgren, Glendale Heights, IL, USA) using a BioSemi Active Two AD Box ADC-12 (BioSemi, The Netherlands) amplifier from 64 scalp electrodes. Data were sampled at 2048 Hz. A software-based 0.5 Hz high pass filter was used to remove DC drifts and 60 and 120 Hz (harmonic) notch filters were applied to minimize line noise artifacts. These filters were designed to be linear-phase to minimize delay distortions. Stimulus eventsi.e., pitch-movie start times and pitch types -were recorded on separate channels. Independent components analysis (ICA) was run using EEGLAB (Delorme and Makeig, 2004) to remove eye-blink artifacts. In stimulus-locked epoching (−1000 to 1500 ms), the average baseline was removed using data from −1000 to 0 ms. After epoching to stimulus events, an automatic artifact epoch rejection algorithm from EEGLAB (Delorme and Makeig, 2004) was run to remove all epochs that exceeded a probability threshold of 5 SDs from the average. Similarly, in response-locked epoching (−1500 to 1000 ms), the average baseline was removed from −1500 to −500 ms and the same automatic artifact epoch rejection algorithm was run.

DATA ANALYSIS

We performed a single-trial analysis of the filtered, epoched, and artifact-removed EEG to discriminate between a set of stimulus or response conditions. First, we considered only behaviorally correct pitches, where the user's response was within 100 ms of the end of the pitches' trajectory, and trained the classifier to classify a given pitch (e.g., a fastball) vs. pitches of the other classes (e.g., curveball and slider). Second, we classified behaviorally correct vs. incorrect pitches within each pitch class (e.g., correctly identified fastballs vs. incorrectly identified fastballs). A table summarizing the classification analysis is shown in Table 2.

Logistic regression was used as a classifier to find an optimal projection for discriminating between the chosen two conditions over a specific temporal window (Parra et al., 2002Conroy and Sajda, 2012). This approach has been previously applied to identify neural components underlying rapid perceptual decisionmaking . Specifically, we defined a training window starting at either a pre-stimulus or post-stimulus onset time τ, with a duration of δ, and used logistic regression to estimate a spatial weighting vector w T τ,δ which maximally discriminates between EEG sensor array signals X for each class (e.g., fastballs vs. not-fastballs): In Eq. 8, X is an N × T matrix (N sensors and T time samples). The result is a "discriminating component" y that is specific to activity correlated with each condition, while minimizing activity correlated with both task conditions. The term "component" is used instead of "source" to make it clear that this is a projection of all activity correlated with the underlying source. For our experiments, the duration of the training window (δ) was 50 ms and the center the window (τ) was varied across time τ = 0, 25, 50, . . . , 975, 1000 ms in 25 ms steps for stimulus-locked, and was varied across time τ = −575, −550, . . . , 550, 575 ms in 25 ms steps for response-locked. We used the re-weighted least squares algorithm to learn the optimal discriminating spatial weighting vector w T τ,δ (Jordan and Jacobs, 1994).
y = w T τ,δ X(8)
In order to provide a functional neuroanatomical interpretation of the resultant discriminating activity, and due to the linearity of the model, we computed the electrical coupling coefficients (Eq. 9). a = X y y• y

This equation describes the electrical coupling a of the discriminating component y that explains most of the activity X.

We quantified the performance of the linear discriminator by the area under the receiver operator characteristic (ROC) curve, referred to here as A z , using a leave-one-out procedure (Duda, 2001). We used the ROC A z metric to characterize the discrimination performance as a function of sliding our training window from 0 ms pre-stimulus to 1000 ms post-stimulus (i.e., varying τ) for stimulus-locked and −575 ms pre-response to 575 ms postresponse for response-locked. These time periods provided substantial time both after the stimulus and behavioral response (button press) to observe any electrophysiological response to the pitch.

We quantified the statistical significance of A z in each window (τ) using a relabeling procedure. With 41 windows for stimulus-locked and 47 for response-locked, we had to correct for this number of comparisons within stimulus-and responselocked leave-one-out respectively. To have a Bonferroni corrected www.frontiersin.org p < 0.05 threshold in both locking conditions, we ran enough permutations to have a suitable number of samples within the p < 0.001 threshold (i.e., p < 0.05/41 and p < 0.05/47). To this end, we randomized the truth labels (i.e., pitch was a fastball, curveball, or slider) for each trial and retrained the classifier. This was done 3750 times for each subject (1250 permutations for each pitch comparison combination), giving a total of 22,500 permutations for a group level analysis. The A z values from these permutations were used to establish a p < 0.001 threshold, i.e., a p < 0.05 Bonferroni corrected significance level. All significant results are thus reported at p < 0.05 corrected for multiple comparisons.

COMPONENT-INFORMED SOURCE LOCALIZATION

We used source localization to investigate the differences between correctly vs. incorrectly identified pitches. First, we classified the stimulus-locked EEG data of incorrectly vs. correctly identified pitches, as summarized in the right half of Figure 1 and Table 2. This was done on a subject-specific basis, except for one subject for whom there were no errors in discriminating the slider; therefore that subject was removed so as not to bias the results. For each of the remaining five subjects, we then selected the window at which the LOO A z value was maximum, with the constraint that the subject-specific maximum was not outside the range of 3 SEs of the pitch-specific mean peak timing. This was done to ensure that the localization analysis was investigating a temporally common phenomenon across subjects.

Using these markers in time, we trial-averaged the EEG sensor data across all epochs that were either correctly identified or incorrectly identified, creating a grand average ERP for each of the five subjects, for each pitch, and for both accuracies. Given five subjects, three pitches, and two conditions, this results in a total of 15 ERPs for each condition (i.e., for correctly identified and incorrectly identified pitches).

Using these grand average ERP values, we then utilized a source localization algorithm (sLoreta; Pascual-Marqui et al., 1999) to estimate the most likely cortical source distributions. This algorithm solves for the most likely current source distribution in the cortex based on EEG sensor data and array topology. We used these distributions to compare the incorrect vs. correct classification conditions across subjects and pitches.

RESULTS

BEHAVIORAL PERFORMANCE

From the behavioral data summarized in Figure 1, we see mean accuracy was 72, 82, and 91% for fastballs, curveballs, and sliders, respectively. We also calculated the positive predictive value (PPV) -i.e., number of true positives divided by the sum of true positives and false positives-for each pitch class. The PPV of each pitch class showed that the subjects were confident when selecting sliders and fastballs, however, for curveballs the PPV is significantly less than the accuracy, indicating that the curveball could possibly be the default choice for the subjects -i.e., it was often selected as a false positive.

The behavioral results (Figure 2A) show response times as a probability density function with truncations on the right-side of the distributions, indicating the threshold enforced 100 ms after the pitch arrived at the plate. Mean response times for correctly identified pitches were 590, 618, and 594 ms for fastballs, curveballs, and sliders, respectively. The first peaks of each pitch's response distribution are at 494 ms (fastballs), 558 ms (sliders), and 590 ms (curveballs).

To test whether response times were significantly different from one other, we ran a three-way ANOVA with the three factors being subject, pitch type, and correct/incorrect classification. The subject factor was treated as a random effect while the other two factors were treated as fixed effects in the model. We found no significant differences in all of the comparisons tested (p > 0.05)difference between pitch types (p = 0.08, F = 3.38, df = 2), difference between correct/incorrect (p = 0.68, F = 10.86, df = 5), the interaction between pitch type and correct/incorrect (p = 0.24, F = 1.66, df = 10). The ANOVA indicates that the mean behavioral responses are not statistically different with regards to the pitch type or whether the subject classified the pitch correctly. Figure 2B shows the mean discrimination performance (A z values) across all subjects and for each pitch using stimulus-locked EEG discrimination. From this stimulus-locked analysis, we see a relationship between the speed of the pitch and the timing of peaks in both neural and behavioral data. In particular, in Figure 2B Frontiers in Neuroscience | Decision Neuroscience  and (B) stimulus-locked EEG discrimination results for each pitch averaged across all subjects. In (B) each A z curve shows the mean and standard error bands computed using leave-one-out discrimination for the indicated pitch vs. other pitches (e.g., for fastballs, the discrimination is between fastballs and not-fastballs; i.e., curveballs and sliders). The significance line (dotted) is corrected for multiple comparisons (line at p = 0.05 Bonferroni corrected for 41 time window comparisons). Note the two plots are time aligned and have the same time-scale so as to compare the timing of EEG discrimination and behavioral response.

NEURAL MARKERS OF CORRECTLY IDENTIFIED PITCHES: STIMULUS-LOCKED ANALYSIS

correctly identified fastballs exhibit the earliest significant EEG discrimination (300 ms), while sliders (425 ms), and curveballs (500 ms) follow. As expected, the sequence of these peaks follows the relative speeds of these pitches, i.e., fastballs, sliders, and then curveballs. Comparing these peaks to the behavioral results of Figure 2A, each of the response distribution peaks immediately follows the relative timing of each pitch's first significant neural discrimination.

As the response times show, the stimulus-locked discrimination overlaps the responses ∼420-720 ms, so it is difficult to isolate non-motor elements in the signal during these time periods. However, after the responses, the highest peaks of discrimination for each pitch are seen (750 ms for fastballs, 700 ms for sliders, and 850 ms for curveballs). To test whether these post-response peaks are due to the differences in the response time distributions, a separate analysis was run on a subset of the data where the RT distributions were matched between classes. These large peaks remain and are therefore possibly indicating a post-response evaluative process specific to identifying each pitch correctly.

Group mean stimulus-locked forward models, shown as scalp plots, are given in Figure 3. Plots are for selected time points across all three pitches, where the center of the discrimination window is indicated at the top of each subfigure. Dark red and blue colors indicate strong discriminatory power from electrodes in that region. While the areas of discrimination change over time for each pitch type, discrimination power is consistently located in the posterior and occipital portions of the scalp plots.

NEURAL MARKERS OF CORRECTLY IDENTIFIED PITCHES: RESPONSE-LOCKED ANALYSIS

Due to the possibility of a motor confound in the neural signal, we also classified EEG data locked to the response times (see Figure 4). Once again, using only correctly identified pitches, we calculated the A z values across all subjects and for each pitch.

Similar to the stimulus-locked results, we find significant preresponse peaks (Bonferroni corrected, p = 0.05) for each pitch that follow the relative speeds of each pitch. In particular, the mean pre-response peak for fastballs (−350 ms), precedes that of sliders (−125 ms), and then curveballs (−50 ms). As with the stimuluslocked discrimination, there is a post-response period in which the mean discrimination is as high or higher than it was pre-response for each pitch (fastballs at +50 ms, sliders at +125 ms, and curveballs at +50 and +125 ms), indicating a possible post-response evaluation of the evidence gathering and subsequent decision.

Similar to the stimulus-locked figure, mean response-locked forward model scalp plots are shown in Figure 5. Again, we can see that discrimination power is located in the posterior of the brain and the spatial distributions change over time. Only the slider post-response scalp map shows a pattern that might be indicative of a button response (125 ms window shows lateralized contralateral discriminatory activity -i.e., left side activity indicative of a right handed button press).

DISTRIBUTION ACROSS PITCH TRAJECTORY OF MAXIMALLY DISCRIMINATING NEURAL COMPONENTS

Using our stimulus-locked single-trial analysis, we constructed spatial distributions of the neural markers across trials and compared these distributions across pitch types. Specifically, we computed the spatial position of the maximum y value for τ ∈ |0 < x < τ plate ), where τ plate is the time at which the pitch reaches the end of its trajectory, i.e., at home plate. Doing this analysis across all subjects, and for each pitch, we created a "heat map" representation of the probability density function of the spatial position in the pitch trajectory having the most discriminating neural component. Figure 6 shows these distributions for both a side view of the trajectory (i.e., the "dugout view") and a heads-on view (i.e., the "catcher's view"). Features of the baseball diamond, such as the pitcher's rubber, home plate, and the batter's boxes, have been added (not to scale) for a frame of reference.

Both views provide insight into the amount of evidence (e.g., time integration and spatial information) required to classify each pitch. From Figure 6A, we see that common to all pitches is peak www.frontiersin.org FIGURE 3 | Group averaged stimulus-locked forward models, shown as scalp maps, for each of the three pitch types. Only behaviorally correct trials are used to estimate these forward models. For each pitch, three time windows are shown for discriminating components estimated for those windows. The center time of each window is given above each plot together with that window's discrimination (A z ) value.

FIGURE 4 | Group mean and standard error bands for response-locked

leave-one-out EEG discrimination performance across all subjects. The significance line (dotted) is corrected for multiple comparisons (line at p = 0.05, Bonferroni corrected for 47 time window comparisons). discrimination happening when the ball arrives at the plate, likely a result of the tight coupling of the decision with the motor response. However there are significant differences between these distributions if we consider the probabilities prior to when the ball reaches the plate. For example, the fastball has discrimination peaks from mid-trajectory to home plate. The slider also shows this trend, though the probability mass is spread more throughout the trajectory and is thus less localized in terms of pitch position. Finally, the curveball (i.e., the slowest pitch) shows local peaks in the spatial distribution in the later half of the trajectory, presumably due to the slower speed of this pitch relative to the fastball and slider.

Similarly, the catcher's view ( Figure 6B) shows peak discrimination early in the fastball's trajectory, whereas both the slider and the curveball exhibit distributions spread across the entire trajectory of the pitch. Together, these plots indicate that, due to the higher relative speed and distinct trajectory of the fastball compared to the curveball and slider, the decision process resulting in a correct identification of the fastball occurs earlier in the spatial trajectory than it does for the sliders and curveballs, both of which take incrementally longer periods of time to arrive at the plate.

Frontiers in Neuroscience | Decision Neuroscience

FIGURE 5 | Group averaged response-locked forward models, shown as scalp maps, for each of the three pitch types. Only behaviorally correct trials are used to estimate these forward models. For each pitch, three time windows are shown for discriminating components estimated for those windows. The center time of each window is given above each plot together with that window's discrimination (A z ) value.

SOURCE LOCALIZATION OF INCORRECTLY IDENTIFIED PITCHES

Thus far, our results have focused on an analysis of those pitches (trials) that are correctly classified by the subject(s). However as Figure 1 shows, not all pitches were classified correctly. We therefore analyzed correct vs. incorrect pitch classifications in terms of the EEG discriminating components. Using the temporal windows having maximum A z within each subject and pitch combination (see Materials and Methods), we found pitch-specific common windows covering 570 ± 89, 744 ± 69, and 522 ± 96 ms for fastballs, curveballs, and sliders, respectively. For only 2 of the 15 combinations of three pitches and five subjects, we found maximum A z values that exceeded 3 SEs from these mean timings. For these two cases, we chose the second maximum peak in A z from the next concave down region in the epoch. These points turned out to be within 3 SEs of mean peak Az timings. Thus all analysis was in a temporal period that could be considered as "common decision processing" at a group level.

We extracted EEG data from these windows and solved for the source distributions using sLoreta (see Materials and Methods). We did a paired t -test for correct vs. incorrect identification distributions, with the resulting t -distribution of the log of the F -ratio [F (1, 13)] shown in Figure 7. Five thousand permutations were used to establish significance levels (p < 0.01) for the null hypothesis of no difference in activity between incorrectly identified and correctly identified pitches. Though the hypothesis of correctly identified pitches shows no significant similarities (red), that of incorrectly identified pitches (blue) shows a common neuronal current source located in the left frontal cortex, showing peaks in Brodmann Area 10 [BA 10, MNI (−35, 55, 20)]. This result indicates left-lateralized common neuronal activity when subjects incorrectly identify the pitch, i.e., when they "miss." This result is invariant to the type of pitch, given that all pitches were considered in this analysis.

DISCUSSION

In this paper, we have shown that we are able to identify neural correlates, in scalp EEG, of baseball pitch classification. Furthermore, using single-trial analysis, we identified at what points in the pitches' trajectory the EEG correlate is most discriminating, enabling us to infer how decision points may vary across trials and pitch types. Finally, we also showed that there appears to be a common neural generator when subjects make decision errors in this task. We now consider these results in the context of previous literature.

www.frontiersin.org FIGURE 6 | "Dugout" (A) and "catcher's" (B) views showing spatial distribution of single-trial peak discrimination across all subjects.

For each subject and for each trial, the timing of peak significant discrimination (i.e., maximum value of A z above the p = 0.05 Bonferroni corrected threshold) from the leave-one-out analysis was related to the trajectory of the pitch on a given trial through the differential equation used to calculate its motion from the simulated pitcher's release [0 ft on the x -axis of (A)] to construct a marker in space. Repeating this process for all subjects and all trials results in a distribution of points in space for each pitch at which peak discrimination occurred in a given trial and for a given pitch. The "dugout" view (A) shows a two-dimensional projection along the initial trajectory of the pitch, while the "catcher's" view (B) shows the two-dimensional projection in the plane perpendicular to the pitch's initial trajectory. Color heat maps indicate the height of the distribution.

RELATED BEHAVIORAL STUDIES

Though the paradigms and data collection methods are slightly different, we can view our results in the context of other baseball experiments in which behavioral markers were used as performance indicators. In particular, Takeuchi and Inomata (2009) and Kato and Fukuda (2002) used behavioral responses and eyetracking to monitor subject performance in judging balls and strikes between experts and novices. While the task is slightly different than ours (i.e., balls and strikes vs. fastballs, curveballs, and sliders), and our paradigm did not test experts vs. novices, the fundamental concept of a forced-choice decision based on trajectory remains the same.

The key point from both of these studies is that the expert group's better performance depends on early-trajectory tracking of the ball from the release point, when compared to that of novices. We see some concordance between this result and our neural discrimination results. Recalling that these studies only focused on fastballs, we can compare this early-trajectory preference for experts to the neural discrimination for correct pitches shown in Figure 2. Being the fastest of the three pitches in our paradigm, subjects had a higher probability of maximum discrimination early in the fastball trajectory when compared to the curveball, with differences with the slider not being nearly as significant. This is indicated by the brighter intensity of the top row's heat map in Figure 6 on early parts of the fastball pitch's trajectory, when compared to that of the other pitches. Though while we find that the early parts of the trajectory are less likely, on a single-trial basis, for discriminating neural activity, we do find that most trials are discriminable from the EEG in the middle and latter periods of the pitch trajectory.

De Lucia and Cochran (1985) conducted a behavioral study with findings supporting the importance of the latter parts of the pitch trajectory (middle and late phases). In this study, each third of a fast-pitch softball pitch was visually masked and the subjects' ability to make contact was used as a behavioral metric on performance. While the masking of each third of the trajectory dropped performance from the non-masking condition, the masking of the middle third proved to have the most significant impact on performance. Once again, using the fast-pitch softball equivalent of an overhand fastball, we can compare this result to the top row of Figure 6 and see that their result is in general concordance with our neural discrimination results. In particular, we see a peak in discrimination in the middle third between 32 and 40 ft from the pitcher's release. But as with the eye-tracking result, this behavioral-only result does not tell the complete story. Rather, for the latter part of the trajectory the EEG at those time/positions contains substantial discriminatory information for correct fastballs, as indicated by the brightest peaks close to the plate in the top row of Figure 6. Our results suggest that single-trial discrimination of neural markers of pitch classification provides additional insight into the timing of decision-making processes of the"hitter," particularly with respect to how these may vary across pitch and Frontiers in Neuroscience | Decision Neuroscience FIGURE 7 | Six-views of neuronal current independent groups t -tests (in corrects in purple/blue, corrects in orange/yellow) for all pitches combined. In both plots, the log of the F -ratio for each voxel is shown [F (1, 13)] with brighter colors indicating higher values of the t -statistic according to the color scale. Significance was established with a permutation test (5000 permutations). The EEG data used for these neuronal source calculations were the result of averaging scalp potentials at each channel at the subject-specific peak discrimination during the group average discrimination peaks. This analysis is done for each subject (one subject removed due to no errors in sliders), for both correctly and incorrectly identified pitches, and for each pitch type. trial and both confirms and complements previous results using only eye-tracking and behavioral responses.

COMPARISON TO OTHER RESULTS RELATING NEURAL MEASURES TO BASEBALL PITCH CLASSIFICATION

As mentioned in the Introduction, we are aware of only one other baseball experiment with neural data. In that experiment, Radlo et al. (2001) measured the P300 of subjects in a cued vs. non-cued condition for fastballs and curveballs. To directly compare this study to ours, we consider only the non-cued condition since we provide no evidence to the subject before the ball appears onscreen from the right handed pitcher's release point. The response distributions for both pitches were faster for the Radlo study than they were for ours. However, we believe that this is due to their experiment being only two-choice, whereas ours is three-choice. Furthermore, the difference between a fastball and a curveball are, trajectory-wise, quite large, whereas the difference between the two breaking balls used in our task (curveball and slider) is smaller. Considering both of these differences in paradigm structure, we expect that the response time distributions of Radlo et al. would be shifted to earlier times relative to ours, even though the pitch speeds are approximately the same.

Using the P300 as an indicator of pitch-specific neural response in the subjects, we find concordance between the sequencing of our pre-response peaks of leave-one-out discrimination across subjects with their results. Radlo et al. (2001) found that fastball P300 peaks preceded those of curveballs and we found this relative sequencing with our discriminator for both stimuluslocked ( Figure 2B) and response-locked (Figure 4) analysis. Due to the earlier response times of their subject population though, it is difficult to compare the exact timings of their results and ours.

Lastly, Radlo et al. (2001) used an experimental paradigm which was designed to evoke a P300 (i.e., they used an oddball task, where the oddball was defined by the incorrect cue being given only 25% of the time) whereas our task is a three choice task in which all three pitch types are equally likely. Thus we would not expect our results to generate a typical P300 neural response. However, recalling that their P300 peak latencies followed their response time distributions, whereas our discriminating component preceded responses in both stimulus-and response-locked analyses, we can reason that our discriminator is utilizing a neural signal that precedes the response, and therefore is not the same signal that Radlo et al. find on a group level.

www.frontiersin.org

INCORRECT DECISIONS AND PREFRONTAL CORTICES

The other major finding of this paper is that for incorrectly identified pitches there is a common neuronal current source active across our population. The timing for this activity is determined from the peak EEG-based discrimination within each pitch and within each subject; therefore it is controlled for the variability between both subjects and pitches. The common neuronal activity is found from the stimulus-locked discrimination between correct and incorrect identifications. The timings of these peaks are after nearly all of the response time distribution. So while likely not an indicator of upcoming performance, this common neuronal current source, which is peaked at MNI (−35, 55, 20) in BA 10, could be a largely post hoc evaluation of the executed decision process.

While direct connection of this area in a baseball task has not been reported in the literature, other studies have found its role in prospective and working memory. For instance in PET studies, Burgess et al. (2001Burgess et al. ( , 2003 and Okuda et al. (2007) found activation of this area in a prospective memory task, i.e., a task to be executed after a period of delay. fMRI studies have also shown this area to be active for working memory and other recall-based tasks (Schacter et al., 1996;Gilbert et al., 2006). In light of these findings, it is possible that the activation we find is a post hoc evaluation of the errant decision process, since such a process must engage the prospective and working memory regions of the prefrontal cortex.

Furthermore, this area has also been implicated in task difficulty. For example, Mangina et al. (2009) find that this location in BA 10, among others, plays a role in monitoring task difficulty. Considering our result from incorrect trials in this context, it is possible that on these trials, the subjects simply could not integrate the spatio-temporal information fast enough to make a correct decision, thereby causing an incorrect response, which is only realized in the post-response self-evaluation period. Even though this aspect of the task was not the primary focus of our study, since no explicit feedback was given to the subject on whether their response was correct, it is nonetheless a possible reason for the neuronal current source localizing in BA 10.

CONCLUSION

In summary we have identified neural markers that can be used to determine when/where on the pitch trajectory the "hitter" integrates significant evidence for classifying the pitch. We also identified BA10 as an activated region during incorrect trials. Future work will focus on using combined EEG and fMRI (Goldman et al., 2009) to localize, with better spatial resolution, the brain areas involved in classifying pitches. Another possible research question that results from this work is "how do expert batters compare to novices in terms of their corresponding neural markers of pitch classification?" Studying professional or semi-professional hitters may give us insight into performance monitoring and more efficient strategies in identifying pitches. Finally, and more generally, our approach can be extended to other physics-based models of visual stimuli. Interesting to consider would be whether EEG components would be useful for identifying specific physical parameters to which subjects are particularly sensitive.

FIGURE 1 |
1Mean behavioral responses across subjects for (A) accuracy and positive predictive value (PPV) and (B) mean response times for correctly and incorrectly identified pitches. All bars are plotted with standard errors (N = 6).

FIGURE 2 |
2(A) Behavioral

Table 1 |
1Parameters for generating pitch trajectories.Initial velocity

(MPH)

Rotation

angle (˚)

Vertical launch

angle (˚)

Horizontal launch

angle (˚)

Rotational

frequency (rpm)

Duration (s)

Fastball
83 ± 3
270 ± 3
0.5 ± 0.3
0.7 ± 0.3
1800
0.53 ± 0.01

Curveball
70 ± 3
50 ± 10
1.7 ± 3
0.7 ± 0.3
1800
0.64 ± 0.02

Slider
75 ± 3
0 ± 5
1.7 ± 3
0.7 ± 0.3
1800
0.59 ± 0.02

Table 2 |
2Definition of classes used in discrimination analysis.Correct pitches
Correct-incorrect

Class 1
Class 2
Class 1
Class 2

Fastball
Not-fastball
Correct fastball
Incorrect fastball

Curveball
Not-curveball
Correct curveball
Incorrect curveball

Slider
Not-slider
Correct slider
Incorrect slider

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ACKNOWLEDGMENTSThis work was supported by grants from the Army Research Office (W911NF-11-1-0219), the National Institutes of Health (R01-MH085092), and in part by an appointment to the U.S. Army Research Laboratory Postdoctoral Fellowship Program administered by the Oak Ridge Associated Universities through a contract with the U.S. Army Research Laboratory.SUPPLEMENTARY MATERIALThe Supplementary Material for this article can be found online at http://www.frontiersin.org/Decision_Neuroscience/10. 3389/fnins.2012.00177/abstractConflict of Interest Statement:The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Received
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BMC Family Practice The association of patient trust and self-care among patients with diabetes mellitus

Denise E Bonds firstname.lastname@example.com
Department of Public Health Sciences
Wake Forest University School of Medicine
Winston-SalemNCUSA

Department of Internal Medicine
Wake Forest University School of Medicine
Winston-SalemNCUSA

Fabian Camacho firstname.lastname@example.com
Department of Public Health Sciences
Wake Forest University School of Medicine
Winston-SalemNCUSA

Ronny A Bell email@example.com
Department of Public Health Sciences
Wake Forest University School of Medicine
Winston-SalemNCUSA

Vanessa T Duren-Winfield firstname.lastname@example.com
Maya Angelou Center for Minority Health
Wake Forest University School of Medicine
Winston-SalemNCUSA

Roger T Anderson firstname.lastname@example.com
Department of Public Health Sciences
Wake Forest University School of Medicine
Winston-SalemNCUSA

David C Goff email@example.com
Department of Public Health Sciences
Wake Forest University School of Medicine
Winston-SalemNCUSA

Department of Internal Medicine
Wake Forest University School of Medicine
Winston-SalemNCUSA

BMC Family Practice The association of patient trust and self-care among patients with diabetes mellitus
10.1186/1471-2296-5-26BioMed Central Page 1 of 7 (page number not for citation purposes) Research article * Corresponding author
Background: Diabetes requires significant alterations to lifestyle and completion of self management tasks to obtain good control of disease. The objective of this study was to determine if patient trust is associated with reduced difficulty and hassles in altering lifestyle and completing self care tasks.Methods:A cross-sectional telephone survey and medical record review was performed to measure patient trust and difficulty in completing diabetes tasks among 320 medically underserved patients attending diabetes programs in rural North Carolina, USA. Diabetes tasks were measured three ways: perceived hassles of diabetic care activities, difficulty in completing diabetes-related care activities, and a global assessment of overall ability to complete diabetes care activities. The association of patient trust with self-management was examined after controlling for patient demographics, physical functioning, mental health and co-morbidities.Results: Level of patient trust was high (median 22, possible max 25). Higher trust levels were associated with lower levels of hassles (p = 0.006) and lower difficulty in completing care activities (p = 0.001). Patients with higher trust had better global assessments of overall ability to complete diabetes care activities (p < 0.0001).Conclusion:Higher patient trust in physicians is associated with reduced difficulty in completing disease specific tasks by patients. Further studies are needed to determine the causal relationship of this association, the effect of trust on other outcomes, and the potential modifiability of trust

with diabetes and other diseases often have difficulty in adopting lifestyle changes and completing self-care tasks [6]. The cause of this poor adherence is multi-factorial and includes patient characteristics such as educational level, [7-9] treatment regimen characteristics, [10] and characteristics of the clinical setting [11,12]. Patient's perception on the intrusiveness of treatment regimens and their perceived self-efficacy in completing the task have also been demonstrated to affect adherence to diabetes treatment [13,14].

Aspects of the patient-physician relationship such as communication and empathy have been shown to be important to patient's adherence [15][16][17][18] and ability to complete self-care tasks [7,19,20]. Patient trust is another component essential to the doctor-patient relationship [21]. Defined variously as a set of beliefs or expectations that the doctor will act in the patient's best interest, or as a reassuring feeling of confidence in the doctor, [22,23] trust provides the foundation for many of the other aspects of the relationship such as communication and empathy [23]. Despite the significance of trust, there has been relatively little research on this aspect of the doctor-patient relationship. Most studies examining trust have focused on characteristics that predict trust levels [24][25][26][27][28] or the influence insurance characteristics have on trust [29,30]. Limited studies have been done examining the relationship between patient trust and medical outcomes. Safran et al found that adherence to physician recommended lifestyle changes was significantly associated with patient trust levels [31]. Higher levels of patient trust have also been associated with lower hemoglobin A1c levels in patients with type 2 diabetes [32]. None have measured trust and its effect on patient's ability in completing selfcare tasks.

Given the underlying importance of trust in the doctorpatient relationship and the effect of the doctor-patient relationship on adherence and self-care, we hypothesized that higher levels of patient trust in physicians may be associated with reduced difficulty of self-care tasks. To explore this hypothesis, we conducted a survey of patients with diabetes to determine if level of trust is associated with the outcomes of difficulty and hassles in completing self-care tasks.

Methods

Data Source

Project IDEAL (Improving Diabetes Education, Access to Care and Living) was an initiative to increase the quality of care and quality of life for underserved patients with diabetes in North Carolina. Full details of the study have been published elsewhere [33]. Briefly, fourteen clinical sites across North Carolina were funded by the Kate B. Reynolds Charitable Trust to establish programs to improve the quality of care provided to underserved patients with diabetes. Each developed its own unique diabetes intervention. Evaluation of the initiative was conducted by the Wake Forest University School of Medicine Department of Public Health Sciences using both quality of care and quality of life indicators. To assess the effectiveness of the initiative, a random sample of patients from each program was selected for inclusion at each point in time (1998 pre-initiative and 2001 post-initiative). Quality of care was assessed through a chart review of randomly selected patient. Diabetes Quality of Care (DQIP) [34] and Health Plan Employer Data and Information Set (HEDIS) [35] measures were used as standards for quality of care. Quality of life was assessed through a telephone survey of the same patients, allowing both quality of care and quality of life indicators to be linked. The telephone survey was composed of portions of the Medical Outcomes Study Short Form 36 (SF-36) [36], the DQIP Patient Measures [34] and Diabetes-39 Health Related Quality of Life instruments [37]. To determine the relationship between self-management and level of patient trust, an established trust instrument was added to the post intervention survey.

Trust Instrument

To measure patient trust, we used the Wake Forest University Trust Scale. This scale has been tested in multiple populations and found to be reliable with good construct validity [38,39]. We used the 5 question institutional trust scale as many of the patients were cared for by multiple physicians. This trust measure is a summation of five items that are adapted from a longer trust instrument to ask the patient's view of doctors in general [39]. The items are: "The doctors at 'this clinic' will do whatever it takes to get patients all the care they need;" "The doctors at 'this clinic' are extremely thorough and careful;" "The medical skills of the doctors here are not as good as they should be;" "You have no worries about putting your life in the hands of the doctors at 'this clinic;" "All in all, you trust the doctors at 'this clinic' completely." A five point scale was used with answers ranging from strongly disagree to strongly agree (1)(2)(3)(4)(5), with a higher score indicating a higher level of trust.

Self-Management Measures

Patient self-care measures were taken from the telephone survey. The methods for this survey have been described elsewhere [40]. We examined 3 measures: patients' perceived hassle associated with self-care tasks (hassles), patients' difficulty in completing recommended care activities (difficulty), and a global assessment of their ability to care for their disease (care). Briefly, hassle was measured with a 7-item scale that asked patients to rate how much of a problem or hassle it has been to complete diabetes-related tasks over the past 4 weeks. Items in the scale included remembering to take medication and test blood sugar, making meal plans, avoiding particular foods, having to keep a care schedule in mind, organizing the daily routine around a medical care activity, and total time spent in managing their disease. This scale is part of the DQIP instrument [34]. All questions were measured using a 5-point scale from "no problem" (1) to "a major hassle" (5) with a higher value equalling more hassle. Respondents also had the choice of indicating the question "does not apply". This resulted in a variable number of questions being answered for each patient. To account for this variation, we calculated an average response for each patient by summing the responses and dividing by the number of questions answered by that patient.

Our second outcome, difficulty in completing care activities, was measured in a 5-item scale also taken from the DQIP measures [34]. This scale asked patients about the difficulty they had in completing 5 specific care activities exactly as their doctor suggested. Activities included 1) taking their medication as prescribed, 2) exercising regularly, 3) following their diet, 4) checking their blood sugar, and 5) checking their feet for wounds or sores. Answers were scored on a 5-point scale from "not at all difficult" (1) to "extremely difficult" (5) with an option for "does not apply". Again, to account for variation in the number of questions answered, we calculated an average score, excluding measures that did not apply.

The final adherence measure we examined was a global measure of care. This single item asked patients to rate how they "...are at taking care of their diabetes". Possible responses on a 5-point scale ranged from "I stay right on top of it at all times" (1) to "I let it slip way too much" (5).

Independent Variables

We considered several modifying factors that might influence the relationship between trust and self-care measures. These factors were selected either because they have been shown to influence patient self-care or because they have been used in previous studies on patient trust and outcomes. Modifying factors were obtained from either the survey or through the chart review. Patient's age, gender, race (white, non-white) and insurance (any, none) were obtained from chart review. A co-morbidity score (none, 1-2, greater than 2) was generated based on the number of additional diabetes-related diseases (hypertension, coronary artery disease, non-traumatic amputation, nephropathy, neuropathy, peripheral vascular disease and smoking) identified during chart review. The Medical Outcomes Study Short Form 36 (MOS SF-36) [36] subscales for physical and mental health were included as part of the patient interview and were used to assess health-related quality of life. The length of the relationship with the health care professional and the number of visits to a provider for diabetes care were obtained from the telephone survey. The use of insulin by the patient was collected from the chart review and was included in the analyses. Imputed values were created for missing measures and analyses were run with and without imputed variables to detect the effect of these variables.

Statistical Analysis

To determine the association of patient trust and our outcomes of patient hassles, difficulty in completing care activities and global assessment, we used Generalized Estimating Equation [41] methods to account for clustering of patients within IDEAL program sites. A regression analysis using the xtgee command in STATA/SE 7.0 (STATA Corporation, College Station, TX) was performed for each outcome of interest. Models adjusted for age, gender, race, insurance status, number of co-morbidities, insulin use, number of visits for diabetes in the last year, length of relationship with the provider, physical functioning, and mental health.

Results

Three hundred and twenty-six individuals with diabetes had information from both the telephone survey and chart review portions of the study. The response rate for the telephone survey was 67%.

The mean age of the respondents was 60, over two-thirds were female and forty percent were non-white (see table  1). Most respondents (75%) had some sort of insurance, primarily Medicare or a Medicare HMO. Most (79%) had least one co-morbid condition and the average number of co-morbid diseases was one. Average score on the MOS-SF 36 physical functioning and mental health scale was 57.0 (sd 29.8) and 73.5 (sd 21.2), respectively. Diabetes control was generally good with the average hemoglobin A1c 7.3%, although the range was wide (4.4%-14.6%). Twenty four percent of the sample had insulin listed as a treatment in their medical records. When asked about the length of time they had been seeing a provider or clinic, 15% responded less than 1 year, 21% 1-2 years, and 65% more than 2 years. The average number of visits to a provider for diabetes care in the last 12 months was 3. The level of patient trust in their physician was high, with an average and median level of trust of 22 (sd 3, range 11-25).

When asked about hassles associated with their diabetes, respondents reported the highest degree of hassle with avoiding foods they enjoyed (higher score equals more hassle: mean 2.4, SD 1.4, range 1-5) (table 2). Taking medication (mean 1.2, SD 0.7, range 1-5) and testing blood sugar (mean 1.4, SD 0.9, range 1-5) were the care activities associated with the lowest level of hassle. Similarly, patients reported low levels of difficulty when asked about taking their medication for their diabetes (higher score equals more difficulty: mean 1.2, SD 0.5, range 1-4), while they reported higher levels of difficulty in following a diet for their diabetes (mean 2.1, SD 1.0, range 1-5) and exercising regularly (mean 2.5, SD1.3, range 1-5). Overall, the patients surveyed reported high global assessments of their self care for their diabetes (higher score equals worse self care: mean 2.0, SD 0.9, range 1-5).

In bivariate analyses, a higher levels of hassles was associated with younger age (p < 0.001), non-white race (p = 0.03), and lower score for mental health. There was no association with gender, insurance status, co-morbidities, use of insulin, physical health, length of the relationship with the provider, or the number of visits for diabetes in the past year. A higher difficulty in completing care activities was associated with younger age (p = 0.002), female gender (0.003), non-white race (p = 0.03), worse physical health (p < 0.001) and worse mental health (p = 0.001). There was no association with insulin use, co-morbidities, length of relationship, or number of visits in the past year. Better self rated ability to care for diabetes was also  1.5 (0.9, 1-5) Checking feet for wounds or sores 1.2 (0.5, 1-5) Global assessment of ability to care for diabetes*** 2.0 (0.9, 1-5) * Higher equals more hassles associated with care activity ** Higher equals more difficulty in completing care activities *** Higher equals lower assessment of ability to care associated with older age (p = 0.004), better physical health (p = 0.02), and better mental health (p < 0.0001).

There was no association with gender, race, co-morbidities, use of insulin, length of relationship, or number of visits in the past year.

In multivariate regression analysis, level of patient trust was significantly associated with each of the outcomes we examined. Patients with higher levels of trust in their physician reported lower levels of hassles with disease (GEE parameter -0.03, 95% CI -0.05 to -0.008, p = 0.006). Similar finding were seen when self-reported difficulty was examined. Higher level of patient trust was again associated with less reported difficulty in caring for diabetes (GEE parameter -0.02, 95% CI -0.03 to -0.01, p = 0.001).

Our final patient outcome, global assessment of ability to care for diabetes, also showed a significant association with higher levels of patient trust associated with higher self-reported ability to care for diabetes (GEE parameter -0.05, 95% CI -0.08 to -0.02, p < 0.0001). Thus, if a patient responded "strongly agree" to all the trust questions rather than "agree" (representing a 5-point increase in level of trust), self-reported hassles would decrease by approximately 0.

Discussion

Diabetes is a disease requiring many types of interventions to prevent the associated morbidity and mortality. These interventions include medications to control elevated glucose levels and finger sticks to check blood sugars. Additionally, significant alterations in lifestyle such as increasing exercise and changing the type of food one eats are an essential portion of the treatment regimen. In our sample of low-income patients with diabetes, we found low reported hassle of completion of diabetes tasks, low reported difficulty in completing diabetes related tasks and good self reported ability to stay on top of their disease. The patients in this study did report more difficulty in making lifestyle changes such as exercising regularly and following recommended diets than taking medications. Other studies examining adherence in diabetic patients have found similar findings. Ruggiero et al, in a nationwide survey of individuals with diabetes, found that over 90% reported always or usually taking their medication but only 64% always or usually followed dietary recommendations and less than half always or usually exercised [6].

Higher levels of trust were associated with lower reported levels of hassles, lower self-reported difficulty in completing care activities, and improved self-reported global ability to take care of diabetes. These results support our hypothesis of increased completion of self-management tasks with higher levels of patient trust in physicians. There are several possible causes for this association.

Patients who are actively engaged in their medical care and jointly make health care decisions with their provider may have less difficulty and hassles in performing selfcare activities that they had input on. These patients may also have higher trust levels because they have been engaged as an active participant in the health care decision by their provider. Additionally, medical regimens used to treat chronic disease are complicated. Patients may not fully understand the medical rationale behind particular recommendations such as exercise and diet. Additionally, exercise and diet may not result in immediate improvement in symptoms and often cause initial discomfort or feelings of deprivation, thereby providing little positive feedback and reinforcement. Patients who have high levels of trust may be willing to overcome the initial discomfort and maintain faith that these difficult changes will ultimately be beneficial to them. Our results indicate that an intervention to improve trust that resulted in an increase of 5-points (moving from agree to strongly agree on all questions) would result in an improvement of 0.1-0.3 in our outcome scores. This improvement is equivalent to the difference in self care scores seen between a 60 year old patient and a 30 year old patient. Additionally, trust was significantly associated with all self care measures while insulin use was not.

Few studies have examined the association of completion of self-care tasks with patient trust. Safran et al surveyed adults employed by the state of Massachusetts using the Hassles associated with diabetes care -0.2 (-0.3 to -0.05) Difficulty in performing self care activities -0.1 (-0.20 to -0.05) Global assessment of ability to care for diabetes -0.3 (-0.40 to -0.10) Primary Care Assessment Survey which includes a section on patient trust [31]. They found that patient trust was significantly associated with patient satisfaction and selfreported adherence to lifestyle changes. Thom et al conducted a longitudinal survey of patients cared for in primary care clinics and found that higher levels of patient trust were associated with greater self-reported adherence to prescribed medications [42]. Our study differs from these by its focus on low-income patients and on measurements of difficulty of self-care in a common, chronic disease.

These results add to growing evidence that patient trust is important to patient outcomes, but are limited by the cross-sectional design of the study. Although the results support the contention that higher levels of trust result in decreased hassles and difficulty in completing self-management task, it is possible that the reverse is true. Longitudinal studies of individuals with newly diagnosed chronic diseases such as diabetes are needed to define the temporal nature of this association. Additionally, our outcome measures did not assess whether patients were actually performing the activities, rather, the instruments were designed to determine the level of difficulty and hassles. It is possible that individuals viewed particular activities as very difficult and having a high degree of hassle and still completed the task. Future studies with measurements of both patient perception and actual completion rates are needed. The shortened trust instrument we used lacked the important dimension of patient-centered care. Patients who feel their concerns are listened to and work in partnership with their health care provider may be more likely to attempt new self care activities. It is likely that the inclusion of this dimension through questions such as the providers' ability to listen to or advocate for the patient would strengthen the association of trust with reduced difficulty in completing self care tasks. We also did not collect information on several important mediators of patient trust and self-care such as the empathy level of the health care provider, educational level of the patient, and patients' overall perceived self-efficacy. These additional factors may provide further elucidation into the relationship of patient trust and ability to complete self care task. Despite these limitations, this study adds to our understanding of the predictors of patient self-management of disease. Further studies comparing strategies for measuring trust, measurement of trust at multiple points in time and the linkage of patient trust with clinical outcomes are needed.

Conclusions

Trust is an integral component of the doctor-patient relationship. Increasingly, external forces such as changes in reimbursement for medical services and increases in the cost of malpractice insurance place pressure on this rela-tionship [43]. These pressures may erode trust [43]. If trust is associated with self-care tasks such as adherence to medication or lifestyle changes, the turbulent changes in the health care industry may result in worse health outcomes. Efforts to define the impact of trust on patient ability and comfort in performing self-care activities and to identify the modifiable determinants of trust may translate into improved health outcomes, especially for vulnerable populations.

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Table 1 :
1Description of the Sample (n = 326)Age, mean

Table 2 :
2Average score for global outcome measures and individual components of each measureOutcome Measure

Table 3 :
3General estimating equations regression parameters for difference in outcomes associated with a 5-point difference in trustOutcome

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AcknowledgementsCompeting interestsThe author(s) declare that they have no competing interests.Authors' contributionsDEB wrote the text and conducted with the statistical analysis.FB assisted with the statistical analysis and the writing of the paper.RAB was responsible for selection of the quality of care markers, oversaw data abstraction and assisted with writing the article.VTD was responsible for data collection and clinic interventions and assisted with editing the article.Pre-publication historyThe pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2296/5/26/prepub
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