abstract: We have developed software for fully automated tracking of vibrissae (whiskers) in high-speed videos (\>500 Hz) of head-fixed, behaving rodents trimmed to a single row of whiskers. Performance was assessed against a manually curated dataset consisting of 1.32 million video frames comprising 4.5 million whisker traces. The current implementation detects whiskers with a recall of 99.998% and identifies individual whiskers with 99.997% accuracy. The average processing rate for these images was 8 Mpx/s/cpu (2.6 GHz Intel Core2, 2 GB RAM). This translates to 35 processed frames per second for a 640 px×352 px video of 4 whiskers. The speed and accuracy achieved enables quantitative behavioral studies where the analysis of millions of video frames is required. We used the software to analyze the evolving whisking strategies as mice learned a whisker-based detection task over the course of 6 days (8148 trials, 25 million frames) and measure the forces at the sensory follicle that most underlie haptic perception.
author: Nathan G. Clack; Daniel H. O'Connor; Daniel Huber; Leopoldo Petreanu; Andrew Hires; Simon Peron; Karel Svoboda; Eugene W. Myers\* E-mail: <firstname.lastname@example.com>[^1]
date: 2012-07
institute: Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America; University of California, San Diego, United States of America
references:
title: Automated Tracking of Whiskers in Videos of Head Fixed Rodents

> "This is a *PLoS Computational Biology* Software article."

# Introduction

Rats and mice move their large whiskers (vibrissae), typically in a rhythmic pattern, to locate object features \[1\], \[2\], or to identify textures and objects \[3\], \[4\]. Whisker movements in turn are influenced by touch \[1\], \[5\]. The whisker system is a powerful model for studying the principles underlying sensorimotor integration and active somatosensation \[6\]. Critical to any mechanistic study at the level of neurons is the quantitative analysis of behavior. Whisker movements have been measured in a variety of ways. Electromyograms, recorded from the facial muscles, correlate with whisker movements \[7\]. This invasive method does not report whisker position and shape per se and is complimentary to whisker tracking. Imaging individual labeled whiskers, for example by gluing a high-contrast particle on the whisker \[8\], provides the position of the marked whisker, but does not reveal whisker shape. In addition, the particle will change the whisker mass and stiffness and thereby perturb whisker dynamics. Monitoring a single point along the whisker using linescan imaging \[9\], or a linear light sheet \[10\], can provide high-speed information about whisker position, but only at a single line of intersection.

High-speed (\>500 Hz) videography is a non-invasive method for measuring whisker movements and forces acting on whiskers and yields nearly complete information about whiskers during behavior \[1\], \[11\]–\[15\]. The position of the whisker base with respect to the face reveals the motor programs underlying behavior. The deformation of whisker shape by touch can be used to extract the forces felt by the mouse sensory follicles \[16\], \[17\]. However, high-speed videography brings its own technical challenges. The large number of images required makes manual analysis impossible for more than a few seconds of video. Comprehensive studies require fully automated analysis. In addition, extraction of motions and touch forces demands accurate measurement of whisker shape, often with sub-pixel precision, and identification of rapidly moving whiskers across time. Finally, the large volume of video data potentially places severe demands even on advanced computational infrastructures, making efficient algorithms necessary.

Tracking whiskers is challenging. Whiskers can move at high speeds \[12\] and in complex patterns \[1\], \[18\]. Adjacent whiskers can have distinct trajectories. Moreover, whiskers are thin hairs (e.g. mouse whiskers taper to a thickness of a few micrometers) and thus provide only limited contrast in imaging experiments.

To address these challenges, we have developed software for tracking a single row of whiskers in a fully automated fashion. Over a manually curated database of 400 video sequences of head fixed mice (1.32×10^6^ cumulative images, 4.5×10^6^ traced whiskers, 8 mice), whiskers were correctly detected and identified with an accuracy of 99.997% (1 error per 3×10^4^ traced whiskers). In other whisker tracking systems, models constraining possible motions and whisker shapes have been used to aid tracking. In contrast, our approach uses statistics gleaned from the video itself to estimate the most likely identity of each traced object that maintains the expected order of whiskers along the face. As a result, the shapes of highly strained whiskers (curvature\>0.25/mm) can be traced with sub-pixel accuracy, and tracking is faithful despite occasional fast motion (deflections \>10,000 degrees/s).

Our method consists of two steps performed in succession: tracing and linking. Tracing produces a set of piecewise linear curves that represent whisker-like objects for each individual image in a video. Each image is analyzed independently. The linking algorithm is then applied to determine the identity of each traced curve. The trajectory of a whisker is described by collecting curves with the same identity throughout the video. Importantly, once a faithful tracing is completed, the original voluminous image data set is no longer required for downstream processing.

Once tracing is complete, a small set of features is tabulated for each curve. Based on these features, a heuristic is used to make an initial guess as to the identity of curves in a set of images where it works well. These initial identifications are then used to get a statistical description of the shapes and motions of whiskers. These statistics are then used to compute a final optimal labeling for each traced curve subject to the constraint that whiskers are ordered along the face.

We validated this approach using manually curated video acquired from a head-fixed, whisker-dependant object localization task \[1\]. In these experiments the field of view contains a single row of partially and wholly imaged whiskers as well as the stimulus object (a thin pole, normal to the imaging plane) (Figure 1A). The camera is positioned below the whiskers and illuminated from above (Figure 1B) producing silhouetted views of the whiskers (Figure 1C–D). Automated tracing is robust to occlusions introduced by the stimulus and to whisker crossings. Tracing yields curves for both whiskers and facial hairs (Figure 1C–D). Linking is used to uniquely identify each whisker so features such as angle of deflection (Figure 1F) and curvature (Figure 1G) can be extracted as time-series.

# Design and Implementation

## Tracing

The process of tracing the backbone of a whisker consists of three phases: initiation, extension, and termination. Tracing starts by using the angle and position estimated at the highest scoring candidate initiation site. As a curve is extended, initiation sites that fall under the traced curve are removed from consideration. When none remain, potentially duplicate curves are resolved.

Before any analysis, images were preprocessed to remove imaging artifacts specific to particular cameras (Text S1, Figure S1). Tracing begins by searching the image for candidate initiation sites. These are found by performing a pixel-level segmentation isolating locally line-like features in the image. With each segmented pixel, a score and an angle are computed. This is an optimization step; it is possible to initiate tracing at any pixel in an image. However, tracing is relatively expensive, and, by filtering out unproductive initiation sites, computation can be focused on the small number of relevant pixels. On the other hand, the filtration threshold should be set conservatively so that every whisker is sure to have at least one initiation site. Parameters were set by examining results over a small set of 10 images but worked well over the entire data set of over 1 million images. Typically, 50–100 candidate sites were found per whisker with 10–20 false positives per image.

A variety of methods have been developed to detect interest points in natural images \[19\], and the Hough transform is conventionally used to find linear features. However, given the high contrast and controlled imaging conditions used to acquire data (Text S1), we developed a linear time algorithm, summarized in Figure 2, that requires only local image information and yields an estimate of salience and line orientation in a single pass.

First, a 7×7 square box is centered on a pixel of interest (Figure 2A). The box is divided into two partitions and the position of the intensity minima found in each subset of the two partitions is found. These are designed to ensure that when a whisker passes through the box, one partition will preferentially collect minima along the whisker backbone, and, as a result, the position of those minima will be linearly correlated. For each partition, principle components are computed over the covariance of the collected positions. The principle components describe the major and minor axis of an ellipse. The partition with the highest eccentricity is chosen, and the eccentricity is used to score how line-like the image is at the queried point (Figure 2B). The orientation of the major axis is used to estimate the direction of the line (Figure 2C, Video S1).

One advantage of this approach is that the entire image need not be queried for line-like objects. For example, in the data analyzed here, whiskers span hundreds of pixels. A grid of lines spaced appropriately (50 px; this is an adjustable parameter) will cross each whisker at least once. Restricting the search for line-like features to this grid greatly reduces the amount of time required for this step. Additionally, since the algorithm relies on local minima, demands on background subtraction and other preprocessing is minimal. The structure of the partitions ensures that lines in any orientation are detected. Greater than 80% of the pixels that could be used to start tracing a whisker (typically 1000 per whisker over the full image) are detected using an eccentricity threshold of 0.95; only 1 is required to fully trace a whisker. This detector systematically avoids potential starting locations near the face, near occlusions or where whiskers cross or nearly touch.

Curves are bidirectionally extended from candidate initiation sites by performing a small step (1 px) along the measured direction, and repeating until one of several criteria is met. The sub-pixel position and tangent angle are estimated by optimizing the correlation between an oriented line detector that is parameterized as a function of sub-pixel position, angle and width (Figure 3).

The line detector is designed based on modeling the intensity profile of a whisker as a rectangular valley in the image with variable position, width and angle. The center of the whisker is estimated with sub-pixel precision by finding a position that minimizes the Laplacian of the correlation between the model and the image \[20\]. The line detector approximates the Laplacian of the model. It consists of two rectangular parallel step-edge detectors (20×1 px) spaced by the detector width (Figure 3A). The length was chosen to match the distance over which highly curved whiskers remain approximately linear. The correlation at a given point is evaluated using a pixel representation of the detector that is computed by evaluating the area integral of the detector over the square domain of each pixel (Figure 3B). The value of the correlation at that position is then the dot product between pixels in the image and pixels in the evaluated detector. For efficiency, pixel representations of the detector are pre-tabulated for discrete values of the sub-pixel position (0.1 px), width (0.2 px), and angle (2.5 degree precision). Optimizing the width of the detector was important for reliable tracing of whiskers of different width and for reliable tracing of the very tips of whiskers.

Tracing is stopped if any one of several criteria indicates that the optimization procedure cannot be trusted to yield accurate results. This is necessary to handle cases where whiskers were partially occluded, such as when whiskers crossed or contacted an obstructing object, such as the stimulus pole in an object localization experiment (Figure 4). The tests were for low correlation with the line detector, large left-right asymmetry in the image intensity about the detector, low mean intensity about the detector, and large angular change between steps. A corresponding user-adjustable threshold parameterizes each test. The tests are applied at each step as the curve is traced from the initiation point (Figure 4A,B). If one of these fails (Figure 4C,D), a number of single pixel steps are taken along the last trusted direction up to a user-settable maximum distance in an attempt to jump past the distrusted region (Figure 4E,F). Linear extrapolation is usually a good guess. If all tests are satisfied at one of the points, normal tracing is resumed (Figure 4G,H). Otherwise, the trace is terminated at the last trusted point (Figure 4G).

Occasionally, overlapping curves are generated during the tracing step. These need to be resolved before tracking so that individual whiskers have exactly one associated curve per frame. Searching for curves that cross the same pixel identifies pairs of potentially redundant curves. If all the points within an interval of one of the curves is within a small distance (typically 2 px) of the other curve and that interval consists of over 50% of the total length, then it is considered redundant. The shorter of the two curves is discarded; the shapes of shorter curves were not reliable enough to average. This procedure is repeated until there are no redundant curves left.

## Linking

Identifying individual whiskers is challenging. First, the tracing algorithm generates curves (false positives) for facial hairs, trimmed whiskers, or other artifacts in the scene (Figure 1C). The linking algorithm must correctly identify true whiskers against this noise. Second, the motion of whiskers can be difficult to reliably model \[8\], \[12\]. A typical trial may consist of slow, predictable motions interrupted by rapid transient motions that are occasionally too fast even for high speed (1000 fps) cameras to adequately sample \[1\]. Although difficult to track, these unpredictable motions may correspond to some of the most interesting data.

Our approach is based on the observation that whiskers are relatively long and maintain a consistent ordering along the face. We use a statistical model to describe the shapes and motions that are characteristic of whiskers. The model is then used to assign each traced whisker the most probable identity subject to the constraint that whiskers are ordered along the face.

For the majority of images, a simple length threshold can be used to remove false positives and leave exactly one traced curve per whisker. The linking algorithm is either supplied the number, *N*, of whiskers on the subject mouse, or estimates it as follows: For a given length threshold , let be the set of frames that have *n* curves longer than . If *N* is not supplied, then we find and such that is maximal over all possible choices. In most of the data sets we have tested, , i.e. this heuristic is spot on. But when one or more whiskers frequently leave the field of view it may fail. If *N* is supplied, then we let be the value for which is maximal because the set of long curves in each frame in are always true whiskers. For the data sets tested here, represented 99.8% of a video on average (min: 90.1%, max: 100%).

The curves in the frames and their heuristic classification into whisker and non-whisker based on supplies a training data set for the specific video. From these, we can learn various characteristics to estimate the probability of a curve being one or the other. Specifically, we build normalized histograms, *n~f~*,*~W~* and *n~f~*,*~FP~* (Figure 5C), one for whiskers (*W*) and one for false positives (*FP*), for each of six features, *f*, using the training set. The features used are the angle near the face, average curvature, average tracing score, length, and endpoints of a curve. The angle and curvature are computed from a parametric third degree polynomial fit to each curve. These histograms are then used to estimate, , the probability that a curve *c* is of kind *k*, either *W* or *FP*, as the product,

![](pcbi.1002591.e015.jpg)

To ensure no feature has zero likelihood, a count of one is added to each bin in a histogram before normalizing.

In (1) above, the features are assumed to be conditionally independent in order to simplify estimating feature distributions, even though this not strictly true in practice. Moreover, errors in the heuristic may introduce a systematic sampling bias. For example, at high deflection angles near the edge of the field of view, only a small segment of a whisker is imaged (Figure 1E, purple whisker). The traced curves from such segments might systematically fall below and be excluded from whisker feature distributions. As a result, estimated probabilities will be biased towards longer whiskers at milder deflection angles. Despite these caveats, the use of these feature distributions leads to a highly accurate result.

Appearance alone is not sufficient to uniquely identify individual whiskers in some cases. To address this, we designed a naive Bayes' classifier to determine the most probable identity of each traced curve subject to ordering constraints. The traced curves are ordered into a sequence, *C*, according to their time and relative anterior-posterior position. Identifying a curve, *c*, involves assigning a particular label, *l*, from the following set of *2N+1* labels. There is a label for each whisker, *W~1~* to *W~N~*, and labels, *F~0~* to *F~N~*, where *F~i~* identifies all false positives between whisker *W~i~* and *W~i+1~* (Figure 5A). The kind of a label, *K(l)*, is *W* if *l* is a whisker label, and *FP* if *l* is a false positive label. A curve labeled *W~i~* or *F~i~* must be posterior to curves labeled *W~j~* or *F~j~* for all *i\<j*. For a given *i*, only one curve per frame may be labeled *W~i~*, but several curves may be labeled *F~i~*.

Applying Bayes' theorem, the most probable sequence of labels, , is given by

![](pcbi.1002591.e018.jpg)

where is the *a priori* probability of a labeling sequence, and is the likelihood that the curves in *C* are generated from the sequence of items labeled by *L*. By design, certain labeling sequences are ruled out *a priori*. Conceptually drawing a directed edge between one identity and the possible identities of the next curve yields a directed graph (Figure 5B). For a video with *T* frames,

![](pcbi.1002591.e021.jpg)

where *N^t^* is the number of curves traced in frame *t*, and is the label of the *i*'th curve found in frame *t*. Values for and are estimated by computing the frequency of observed label pairs that occur in the a training set, . This describes a hidden Markov model for labeling curves in a single video frame. The optimal labeling can be computed efficiently with well-known dynamic programming techniques \[21\].

The likelihood, , is computed under the simplifying assumption that the likelihood of a single curve depends only on the curves in the previous or following frame. Using this and applying Bayes' theorem,

![](pcbi.1002591.e027.jpg)

where is the label *L* assigns to the curve *c*, *C^t^* is the (possibly empty) set of curves found in frame *t*, and are the labels assigned to the curves in *C^t^*. The first component, , is the likelihood that *c* is an object of the kind denoted by label *l* which we estimate with formula (1).

The second component of (4), , is interpreted as the likelihood that a curve is part of the same trajectory as corresponding curves in the previous (or following) frame. Similar to the approach used in equation (1) for estimating , we need normalized histograms and of the changes of whiskers and false positives between successive frames for each feature *f* over a "training" data set in which the corresponding curves in successive frames is known. While we could use the implied assignment over , we first estimate a hopefully better assignment by restricting the model in (4) to use shape features alone. That is, is treated as a constant and thus the assignment to labels in a frame can be computed independently of other frames. We then use this preliminary labeling over the frames of as the training set over which to build and .

Given these change histograms, one can estimate the correspondence likelihood according to the formula,

![](pcbi.1002591.e040.jpg)

Note that when evaluating this likelihood, a unique corresponding curve is not always present in the previous (or following) frame. There may be zero or many false positive curves in the previous frame with the same label. Similarly a whisker may be missing because it exited the field of view.

Directly solving for the most probable labeling (2) is complicated by the fact that the likelihood of a labeling in one frame depends on the labeling in neighboring frames. Our approach is to initially score each frame by computing , where is obtained using shape features alone. In decreasing order of this score, we 'visit' the next best frame, say *t*, and update the label assignment for each of the two adjacent frames that maximizes the full version of (4), provided the adjacent frame has not already been visited. The new assignment replaces the current assignment and the frame's visitation order is updated according to the score of this new assignment (under the full model of (4)). In this way, we let the most confident frames influence their neighbors in a transitive fashion until every frame has been visited. This was critical for achieving high accuracy. Previous whisker tracking algorithms have relied on propagating the identity of a whisker frame-wise from the beginning of the video to the end, and as a result, an error in one frame is propagated throughout the rest of the video \[11\]–\[13\].

# Results/Discussion

Average processing time was 8 Mpx/s/cpu (35 fps for 640×352 pixel video) measured on a 2.6 GHz Intel Core2 Duo Macbook Pro with 2 GB of RAM, and was dominated by the CPU time required to trace curves; linking time was 0.5 ms per frame. This is faster than published speeds of similar whisker analysis packages (typically, 1–5 fps.) \[11\], \[13\], \[15\]. However, performance can be difficult to compare as implementations may improve over time. For example, our software is equally fast as the current version of the only other publically available whisker tracker\[13\]. More importantly, our software can readily be run on inexpensive cluster nodes to process videos in parallel. This is not the case for whisker trackers that require supervision \[13\] or that depend on software with expensive licensing requirements (such as Matlab) \[11\]–\[15\].

Tracing was accurate to within 0.2 px as estimated by analyzing 39 hand-traced whiskers. Individual hand-tracings had an accuracy of 0.23±0.2 pixels when compared against consensus curves. Mouse whiskers (row C) were automatically traced in images with resolutions from 5 µm/px to 320 µm/px using default settings and in noisy, low-contrast images (Text S1). For the best results, it is important to minimize motion blur and uniformly illuminate the scene, although illumination inhomogeneities can be partially corrected with background subtraction. In contrast with methods that use Kalman filtering \[13\], traced curves are not biased by whisker dynamics. Additionally, tracing is faithful even when curves are not well approximated by low order polynomials, in contrast to published tracing methods \[13\]–\[15\]. Whiskers could be detected and traced in videos of freely behaving rats (Video S2) and mice (Video S3) with all whiskers intact.

Linking accuracy was measured against a hand-annotated set of videos selected by choosing 100 random trials from behavioral sessions of 4 mice (1.32 million frames) \[1\], \[17\]. The curated videos captured a range of behavior including protracted bouts (\>1 s) of whisking (Video S4), multiple whiskers simultaneously contacting an object (a thin metal pole) (Video S4), and extremely fast motion (\>10,000°/second) (Video S5). Of the 4.5 million traced whiskers, 130 were incorrectly identified or not detected, less than one mistake per behavioral trial on average. Linking is robust to whiskers that occasionally leave the field of view (Video S6), and works well even at relatively low frame rates (100 fps; see Text S1). Lesser image quality will ultimately degrade results.

There are two principle sources of error in the linking. First, covariance between different features is ignored. For example, when the field of view is small, whiskers at high deflection angles might not be fully contained within the image. Any curve tracing one of these whiskers would appear shorter than the full whisker length. Under these conditions there is a strong correlation between angle and whisker length. The current model penalizes the shorter length because it is rarely observed; accounting for the covariance should eliminate this problem. Second, the estimated feature distributions are affected by systematic biases introduced from the heuristic used to initially guess whisker identity. A heuristic relying on a length threshold, like the one used here, may systematically reject these short curves at high deflection angle. This will result in a bias against strongly deflected whiskers.

Fundamentally, our linking method is applicable only to single rows of whiskers. The whiskers in videos imaging multiple rows of whiskers will be traced, but, because whisker images no longer maintain a strict anterior-posterior ordering, the linking will likely fail. Although this software was validated with images of one side of the mouse's whiskers, we have successfully tracked whiskers in images containing both whisker fields (Video S7). Also, the whiskers of freely moving rats and mice can be traced (Videos S2,S3). This suggests that it will be possible to use this software for two-dimensional tracking of single rows of whiskers in freely moving animals by incorporating head tracking \[2\], \[11\], \[15\], though we have not explicitly tested this.

We analyzed data acquired during learning of an object detection task (see Text S1) \[17\]. We tracked whiskers from four mice during the first 6 days of training (8148 trials, 3122 frames each). This allowed us to observe the changes in whisking behavior that emerge during learning of the task. We looked specifically at correct rejection trials (2485 trials), which were not perturbed by contact between object and whisker, to analyze the distribution of whisking patterns during learning. We characterized whisking by computing normalized histograms of whisker angle (1° resolution) over time (100 ms bins) for a single representative whisker (C1). The angle and position of other whiskers were strongly correlated throughout (r^2^\>0.95) in these trials. Two patterns emerged. After learning, two mice (JF25607 and JF25609) showed changes in whisking amplitude and mean angular position when the stimulus was present (Figure 6A). The other two mice (JF27332 and JF26706) did not show nearly as much stereotypy and appeared to rely on lower amplitude whisking (Figure 6B). All mice held their whiskers in a non-resting anterior position during most of the trial \[1\]. For JF25607 and JF25609, reduction in task error rate was correlated with an increase in mean deflection angle during stimulus presentation (r^2^ = 0.97,0.84 respectively) (Figure 6C). This correlation was smaller for the other two mice, which also learned the task but appeared to rely on a different whisking strategy.

The forces at the base of the whisker constitute the information available to an animal for somatosensory perception. These forces are proportional to changes in whisker curvature \[16\]. The instantaneous moment acting at a point on the whisker is proportional to the curvature change at that point. In contrast to whisker tracing software that represents whiskers as line segments\[14\], \[15\] or non-parametric cubic polynomials\[13\], the curves produced by our tracing algorithm provide a high-resolution representation of whisker shape, allowing measurement of varying curvature along the length of the whisker.

To illustrate this feature we measured curvature in the following manner: For each video frame (Figure 7A), the traced whisker midline (Figure 7B) was fit via least-squares to a parametric 5^th^ degree polynomial of the form:

![](pcbi.1002591.e043.jpg)

where \[*x(t),y(t)*\] are points that span the curve over the interval , and *a~i~,b~i~* are the fit parameters (Figure 7C). Curvature was measured at a specified distance along the curve from the follicle chosen for best signal to noise. The follicle was not imaged, and so the intersection between the curve and an arc circumscribing the face was used as a reference point instead. The curvature was then measured as the mean curvature inside a 1–2.5 mm long region about the point of interest where it was approximately constant. To ensure the measurement was not biased by shape outside the interval, another parametric polynomial fit (degree 2) was performed, but over the region of interest rather than the entire curve (Figure 7D). The follicle position was set by linearly extrapolating a set distance into the face. The point of whisker-pole contact was determined as the closest point to the center of the pole on the curve or on a line extrapolated from the nearest end of the curve (Figure 7E).

Time series of curvature measurement (Figure 7F, measurements at 5 mm from follicle) can be used to identify most contact events. For whisker-pole contacts first made during a protraction, the first touch tends to be followed by a series of more forceful contacts. Contacts are correlated with relatively constant angular position of the whisker (Figure 7G). This indicates the mouse may press the whisker against the pole by translating the whisker pad rather than by rotating the whisker. The distribution of peak curvature-change during a whisker-pole contact indicates more the bulk of contacts were made during protraction and that these were more forceful (Figure 7H).

## Availability

The software and documentation are freely available online (<http://whiskertracking.janelia.org>) as cross-platform (Windows, OS X, Linux) source code written in C with Python and Matlab interfaces included. Pre-built binaries are also available. A graphical user interface is provided to aid semi-automated tracing as well as the viewing and editing of results. The software is capable of analyzing 8-bit grayscale StreamPix (Norpix Sequence Format: SEQ), TIFF, MPEG, MOV, and AVI formatted video.

# Supporting Information

Sample data and tutorial.

(ZIP)

Click here for additional data file.

Line bias correction. (A) Raw data exhibits a fixed-pattern artifact where odd-numbered lines are systematically darker than bright lines. (B) This artifact is corrected by multiplying odd-lines by a factor estimated from the raw data itself. This removes the stripes without blurring or reducing the contrast of whiskers. Scale bar, 0.5 mm.

(TIF)

Click here for additional data file.

Methodological details.

(DOC)

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Video overlaid with high scoring seeds (eccentricity\>0.99) for each frame. Pixels are colored according to the orientation indicated by the seed.

(MP4)

Click here for additional data file.

Results of automatic tracing applied to video 500 Hz video (1.7 s duration) of a freely behaving rat exploring a corner with its full whisker field. The video was obtained with permission from the BIOTACT Whisker Tracking Benchmark \[22\] (Clip ID:behavingFullContact, courtesy of Igor Perkon and Goren Gordon). It has been cropped, and the frame rate has been changed for presentation.

(MP4)

Click here for additional data file.

Results of automatic tracing applied to a video acquired at 100 Hz video (8 s duration) of a freely behaving mouse with its full whisker field. Background subtraction was performed before tracing to reduce the effects of non-uniform illumination. Because no image of the scene without the animal present was available, the background was estimated for each pixel as the maximum intensity observed at that point throughout the video. The video was obtained with permission from the BIOTACT Whisker Tracking Benchmark \[22\] (Clip ID: behavingMouse100, courtesy of Ehud Fonio and Ehud Ahissar). It has been cropped, and the frame rate has been changed for presentation.

(MP4)

Click here for additional data file.

Results from a full trial. The results of automated tracing and linking applied to a video (500 Hz, 9.2 s duration) of a head fixed mouse trimmed to a single row of 4 whiskers interacting with a pole. Curves that were classified as whiskers are colored according to their identity, and otherwise they are not shown. Multiple whiskers simultaneously interact with the pole at 1.2–1.4 sec into the trial. Protracted bouts of whisking can be observed throughout the video.

(MP4)

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Tracing and linking is robust to rapid changes in whisker angle. The results of automated tracing and linking applied to a video (500 Hz, 150 ms) of a head fixed mouse trimmed to a single row of 3 whiskers interacting with a pole. Curves that were classified as whiskers are colored according to their identity, and otherwise they are not shown. One whisker (red) has been trimmed so it cannot contact the pole. The green whisker presses against the pole, and quickly flicks past it as it is removed from the field. This is the fastest angular motion (16°/ms) observed in the data set used to measure tracking accuracy.

(MP4)

Click here for additional data file.

Linking is robust to whiskers that leave the field of view. The results of automated tracing and linking applied to a video (500 Hz, 190 ms) of a head fixed mouse trimmed to a single row of 3 whiskers interacting with a pole. Curves that were classified as whiskers are colored according to their identity, and otherwise they are not shown. Two whiskers (green and blue) are frequently occluded by the lick-port (black bar, lower right), but they are properly identified before and after such events.

(MP4)

Click here for additional data file.

Tracing and linking of whiskers bilaterally. The results of automated tracing and linking applied to a video (500 Hz, 1 s duration) of a bilateral view to a head fixed mouse trimmed to a single row of whiskers (2 on each side).

(MP4)

Click here for additional data file.

# References

[^1]: Conceived and designed the experiments: NGC DHO KS EWM. Performed the experiments: NGC DHO DH LP. Analyzed the data: NGC DHO DH LP AH SP KS. Contributed reagents/materials/analysis tools: NGC DHO DH LP. Wrote the paper: NGC KS EWM.


abstract: # Background
  .
  Medications necessary for disease management can simultaneously contribute to weight gain, especially in children. Patients with preexisting obesity are more susceptible to medication-related weight gain.
  .
  How equipped are primary care practitioners at identifying and potentially reducing medication-related weight gain? To inform this question germane to public health we sought to identify potential gaps in clinician knowledge related to metabolic adverse drug effects of weight gain.
  .
  # Methods
  .
  The study analyzed practitioner responses to the pre-activity questions of six continuing medical education (CME) activities from May 2009 through August 2010.
  .
  # Results
  .
  The 20,705 consecutive, self-selected respondents indicated varied levels of familiarity with adverse metabolic effects and psychiatric indications of atypical antipsychotics. Correct responses were lower than predicted for drug indications pertaining to autism (−17% predicted); drug effects on insulin resistance (−62% predicted); chronic disease risk in mental illness (−34% predicted); and drug safety research (−40% predicted). Pediatrician knowledge scores were similar to other primary care practitioners.
  .
  # Conclusions
  .
  Clinicians' knowledge of medication-related weight gain may lead them to overestimate the benefits of a drug in relation to its metabolic risks. The knowledge base of pediatricians appears comparable to their counterparts in adult medicine, even though metabolic drug effects in children have only become prevalent recently.
author: Ingrid Kohlstadt; Gerold Wharton
date: 2013
institute: 1Johns Hopkins Bloomberg School of Public Health, Center for Human Nutrition, 198 Prince George St, Annapolis MD 21401, Maryland, USA; 2The U.S. Food and Drug Administration, Office of Pediatric Therapeutics, Silver Spring, Maryland, USA
references:
title: Clinician uptake of obesity-related drug information: a qualitative assessment using continuing medical education activities

# Background

No study to date assesses the knowledge base around medication-related weight gain in pediatric or adult primary care medicine. We therefore sought to characterize what practitioners know about metabolic drug effects in the context of clinical decision-making.

Informed clinicians can often modify their patients' risk of adverse metabolic drug effects, even when medications are essential for disease management \[1\]. Practitioners can choose lowest effective dosing and therapies with fewer metabolic effects; treat underlying medical conditions which can contribute to weight gain, such as sleep apnea and hypothyroidism; correct nutritional deficiencies such as vitamins B~12~\[2\] and D \[3\] to facilitate lifestyle adherence; and counsel patients on drug-related increases in appetite, emphasizing adherence to medication and healthful lifestyle choices.

Among the patient groups most vulnerable to metabolic drug effects are children. Children are more susceptible to central nervous system effects of medications \[4\]. Some metabolic drug effects are unique to children at certain growth stages and demonstrate a prolonged effect \[5,6\]. Metabolic drug effects also tend to be delayed relative to the therapeutic benefit, especially in children. Concurrently, drug exposure is increasing in children, the age group with the fastest growing number of prescriptions \[7\], in part due to obesity-related chronic diseases. Preexisting overweight and obesity heighten vulnerability to metabolic drug effects.

Managing adverse metabolic drug affects is relatively new to the practice of pediatrics. Historically pediatricians focused on medication-related weight loss and stunting, recorded as step-offs on patient growth charts. Today's pediatric practice may require as diligent a diagnosis and management of medication-related weight gain, especially since preexisting overweight and obesity, defined as a body mass index at or above the 85^th^ percentile, has reached approximately 32% of the U.S. population ages 2-19 \[8,9\].

Disseminating drug safety updates to pediatricians holds other challenges as well. Safety information specific to children represents a recent advance. Practitioners may not realize they need to watch for such updates \[10\]. Metabolic drug effects specific to children and adolescents may be first identified years after a drug is on the market \[11\] because the metabolic effects in children tend to manifest beyond the timeframe of clinical trials. Disseminating drug safety information may be additionally complicated by practice patterns. For example, psychiatrists may diagnose and prescribe highly specialized treatment and look to primary care practitioners to monitor patients for adverse drug effects.

Clinicians draw on their knowledge base of adverse metabolic drug effects for clinical decision-making. Elevated and unique risks of metabolic drug effects and major shifts in disease prevalence and practice patterns in pediatrics together prompted our interest in confirming that primary care clinicians who care for children have a knowledge base comparable to their adult medicine counterparts.

# Methods

## CME partners

Continuing medical education (CME) activities were developed in partnership with CME providers. Inclusion criteria for partners were: experience implementing pre-activity questions, having primary care practitioners as a target audience, willingness to co-develop programs relevant to medication-associated weight gain, providing free public access to associated media and print materials, and collaborating within time and budget constraints. Partners were selected across different media - audio, lectures, or web-based activities - Audio-Digest Foundation, Medscape CME, The Maryland Academy of Family Physicians, and The FDA Scientific Rounds Program.

## Instrument development

The instrument in this study, pre-CME activity questions, measures practitioners' baseline knowledge relevant to the content of the CME activities. Pre-activity questions were 4-choice multiple choice questions or true-false questions. They were directed at clinical decision-making and were organized into four categories: 1) drug indications, 2) metabolic drug effects, 3) drug safety updates, and 4) patients most at risk. Each CME program partner selected among the pre-activity questions and adapted the wording to their standard format.

## CME content

The 6 CME activities pertained to either atypical antipsychotic use in children or obesogenic medications in general. They were provided through the CME partners at varied intervals between June 2009 and August 2010. Each activity was an audio program, web-based program, or conference lecture; and awarded a maximum of 0.5 to 2 Category 1 CME credits. The program content, participant characteristics, and pre-activity questions are presented in Table 1.

Summary of continuing medical education (CME) programs, June 2009 – August 2010

| **CME partner and activity** | **Date, audience and promotions** | **Pre-activity questions** |
|:---|:---|:---|
| Activity 1: Maryland Academy of Family Physicians, 2009 Annual CME Assembly; Drug –nutrient interactions | 6/09 Conference attendees, primarily family physicians in the mid-Atlantic | 5 interspersed questions using an audience response system |
| Activity 2: Audio-Digest Pediatrics on Atypical antipsychotics in children | 8/09-8/10 Subscribers, practitioners who care for children, approximately 75% physicians | 10 questions submitted by mail, fax, or website |
| Activity 3: Audio-Digest Special Topics on Atypical antipsychotics in children \* | 8/09-8/10 Responders to free CME post on the Audio-Digest homepage; American Academy of Pediatrics newsletter feature | 10 questions submitted via website |
| Activity 4: Medscape CME on Atypical antipsychotics in children \* | 11/09-1/10 Participants in Medscape's Psychiatry and Mental Health listserv with multiple cross-posts | 6 web-based interspersed questions |
| Activity 5: Medscape CME on Medication-related weight gain \* | 5/10-8/10 Participants in Medscape's Preventive Medicine and Public Health listserv with multiple cross-posts; American College of Preventive Medicine feature | 4 web-based interspersed questions |
| Activity 6: Food and Drug Administration Scientific Rounds on Autism | 5/10 FDA employees with regulatory and clinical interests | 3 questions using an audience response system, 1 which was developed to better understand responses from prior CME programs |

\*Three of the activities may be viewed online \[12-14\].

In order to compare the knowledge of practitioners specializing in pediatrics with adult medicine practitioners, we developed Activity 5 which is applicable to the care of children and adults.

Activity 6 was the only program where the target audience was not primary care practitioners. The biweekly activity is attended by a diverse group of health care practitioners and scientists, all of whom work in regulation. The activity was included to better characterize practitioner knowledge of the autism indication for atypical antipsychotics.

## Response analysis

The information used for the response analysis was obtained from the CME providers as anonymized source data with no way to match responses with individuals. No personal identifiers were used. The respondents were participating in CME activities, where responses to the related learning questions are routinely aggregated to inform future CME development and related research.

We analyzed the data with and without comparing it to predicted scores. Predicted scores facilitate comparison between multiple choice questions with four choices and binomial true-false questions, which differ in the likelihood of selecting a correct answer by chance alone. For this analysis, predicted scores were 70% for multiple choice and 85% for true-false questions. The basis for these numbers comes from Audio-Digest's overall average pretest scores, which are 70%, \[personal communication August 2010\] and the pedagogic intent of a CME to build on participants' existing practice-relevant knowledge.

Each response holds an inherent error, since a participant with a constant knowledge base could score better or worse on the pre-activity depending on the circumstances at that moment. We estimated the two-tailed two standard deviations of this variability to be ten percent. We also analyzed the participants' responses to the choice identified as close to correct, also called the second best answer.

STATA® statistical software was used to run discrete-response regression analyses on pre-activity question responses. Probit regressions were used for binomial dependent variables, analyzing whether the respondent answered the CME question correctly. The probit models give a standard normal z-score rather than a t-statistic, with the total variability explained as a pseudo-R^2^ rather than a normal R^2^. McFadden's pseudo-R^2^ is reported. The probit analysis reports the overall significance of the model using an LR chi-square. The effect of a control variable in predicting correct responses (a certain percentage above/below the average) is calculated as the difference in probability of getting a question correct versus a baseline probability. For this analysis, baseline probability is where all control variables are set to their population means.

The control variables used in the probit models were educational degree, medical specialty, and CME participation date. Geographic region was only provided by some respondents and was therefore not included in the analysis. The type of medical practice such as hospital-based or solo practice was not among the data obtained by the CME providers.

Partial incomplete responses were included in the analysis. Having all pre-activity questions left blank was considered equivalent to nonparticipation, and these entries were excluded. Since the sample size of the distinct CME activities varied, both the unweighted and weighted averages of correct responses are reported.

## Instrument validation

To assess the extent to which the pre-activity responses could be generalized among primary care practitioners, the responses were compared across the diverse CME programs detailed in Table 1.

The scores on pre-activity responses were compared to self-reported learning in Activities 2–3, where participants were asked, "Please list one concept or strategy gained from this activity."

Participant evaluations of the CME programs were recorded, to confirm satisfactory evaluations. The rating of the CME activity on a 1–5 Likert scale is a composite score which reflects practice relevance and appropriate teaching level of target population.

Not all participants completed all pre-activity questions. The data was analyzed both including and excluding question-specific non-responders, to detect a potential bias introduced by partial completion.

Activities 2 and 3 were the longest-running programs, each offered for 13 months. They were analyzed for a temporal trend, since a news story or regulatory change during the interval could potentially change practitioner baseline knowledge or practice patterns.

# Results

There were 20,705 participants in the combined six CME activities which spanned 15 months. Each participant answered one or more of the following questions.

## Drug indications

See Table 2. For the first question, both the average correct response rate of 76% and the weighted correct response of 79% are within the predicted range. For the second question, the average correct response rate is 53% (17% below predicted) and the weighted average correct response rate is 52% (18% below predicted).

Responses to multiple choice pre-activity questions on use of antipsychotic medications

| **CME activity** | **Activity 2** | **Activity 3** | **Activity 4** |
|:---|:---|:---|:---|
| Sample size | *n*=1237 | *n*=611 | *n*=2400 |
| Responses to: Which of the following is a labeled indication for one or more atypical antipsychotic drugs? |  |  |  |
| A. Refractory epilepsy |  |  |  |
| B. Refractory major depression in adolescents |  |  |  |
| **C. Acute mania associated with bipolar-I disorder** |  |  |  |
| D. Attention deficit/hyperactivity disorder |  |  |  |
| Incorrect (A) | 82 (7%) | 40 (6%) | 72 (3%) |
| Incorrect (B) | 188 (15%) | 92 (15%) | 264 (11%) |
| **Correct (C)** | **912 (74%)** | **439 (72%)** | **1992 (83%)** |
| Incorrect (D) | 55 (4%) | 40 (6%) | 72 (3%) |
| Correct vs. Predicted | +4% | +2% | +13% |
| Responses to: Which of the following is a labeled indication for an atypical antipsychotic in a child, 7 years of age? |  |  |  |
| **A. Irritability associated with autism** |  |  |  |
| B. Acute bipolar mania |  |  |  |
| C. Schizophrenia |  |  |  |
| D. Generalized anxiety disorder |  |  |  |
| **Correct (A)** | **777 (63%)** | **288 (47%)** | **1152 (48%)** |
| Incorrect (B) | 197 (16%) | 114 (19%) | 456 (19%) |
| Incorrect (C) | 222 (18%) | 189 (31%) | 720 (30%) |
| Incorrect (D) | 38 (3%) | 19 (3%) | 72 (3%) |
| Correct vs. Predicted | −7% | −23% | −22% |

See Table 3. The average correct response rate is 65% (20% below predicted) and the weighted average is 67% (18% below predicted).

Responses to the true-false pre-activity question on use of antipsychotic medications

| **CME activity** | **Activity 2** | **Activity 3** |
|:---|:---|:---|
| Sample size | *n*=1237 | *n*=611 |
| Responses to: The intramuscular administration of at least one atypical antipsychotic agent has been approved by the FDA for use in children. True or false? |  |  |
| Incorrect (True) | 341 (28%) | 265 (43%) |
| **Correct (False)** | **896 (72%)** | **346 (57%)** |
| Correct vs. Predicted | −13% | −28% |

The participants in Activity 6 were asked: Recommended treatment of autism includes all EXCEPT:

A Correct nutritional deficiencies 6 (12%)

B Treatment of concurrent attention-deficit hyperactivity disorder 5 (10%)

C Prescribe atypical antipsychotics 29 (57%)

D Use behavioral therapies following early diagnosis 11 (21%)

The rate of correct response, response C, is 57% (13% below predicted).

## Adverse metabolic effects

Participants in Activity 5 were asked to respond to: After diagnosing Ed with metabolic syndrome, Ed's doctor advised him to reduce his weight by 10%, a total of 18 pounds, by diet and exercise. Which of the following medications potentially makes it more difficult for Ed to achieve his goal?

A Angiotensin-converting enzyme inhibitors 6667 (41%)

B Diuretic 1243 (8%)

C Vitamin D 1106 (7%)

D Biguanide 7021 (43%)

The rate of correct response, choice B, is 8% (62% below predicted). Specialty did not predict response to this question.

Participants in Activity 5 were also asked to respond to: Within months of being diagnosed with bipolar disorder at age 14, Sara gained 20 pounds. Which of the following is likely to contribute to her recent weight gain and body mass index of 28?

A Vitamin D deficiency 952 (6%)

B Atypical antipsychotic agent 10349 (63%)

C An eating disorder 1072 (7%)

D Psychostimulant agent 3948 (24%)

The average correct response rate of 63% falls within the predicted range. Predicted probability of answering the question correctly given the regression control variables is 65%. Analysis by specialty indicates that mental health specialists scored 28% better than average (z=27; p\<0.01), family practitioners scored 14% higher (z=12; p\<0.01), internal medicine specialists scored 9% higher (z=7; p\<0.01), endocrinologists scored 8% higher (z=3; p\<0.01). The regression explains 9% (pseudo-R^2^=0.09) of the total variability in responses and was very significant in predicting scores (LR chi-square=1833; p\<0.01).

Table 4 indicates the responses to a pre-activity question on adverse drug effects. See Table 5. For the first question, the average correct response rate was 61% with a weighted correct response average of 67%. For the second question, the average correct response rate was 75% with a weighted average also of 75%. These are within the predicted range.

Responses to the true-false pre-activity question on adverse drug effects

| **CME activity** | **Activity 2** | **Activity 3** |
|:---|:---|:---|
| Sample size | *n*=1237 | *n*=611 |
| Responses to: Hyperglycemia, hyperlipidemia, and elevated liver enzymes are labeled side effects for one or more atypical antipsychotics. True or false? |  |  |
| **Correct (True)** | **1154 (93%)** | **582 (95%)** |
| Incorrect (False) | 83 (7%) | 29 (5%) |
| Correct vs. Predicted | +8% | +10% |

Responses to multiple choice pre-activity questions on adverse drug effects

| **CME activity** | **Activity 1** | **Activity 2** | **Activity 3** | **Activity 4** |
|:---|:---|:---|:---|:---|
| Sample size | *n*=39 | *n*=1237 | *n*=611 | *n*=2400 |
| Responses to: Jeff is an athletic 14-year-old who has been diagnosed with schizophrenia and prescribed an atypical antipsychotic. You counsel the family about potential cardio-metabolic side effects. Which risk would you emphasize? |  |  |  |  |
| A. Sudden death from myocardial infarction |  |  |  |  |
| B. Cardiac arrhythmias |  |  |  |  |
| **C. Increases in triglycerides, total cholesterol, and low-density lipoprotein** |  |  |  |  |
| D. Because patient has normal weight, he is unlikely to experience significant weight gain |  |  |  |  |
| Incorrect (A) | n/a | 88 (7%) | 34 (6%) | 120 (5%) |
| Incorrect (B) | n/a | 173 (14%) | 132 (22%) | 408 (17%) |
| **Correct (C)** | **n/a** | **726 (59%)** | **288 (47%)** | **1848 (77%)** |
| Incorrect (D) | n/a | 245 (20%) | 156 (25%) | 48 (2%) |
| Correct vs. Predicted | n/a | −11% | −23% | +7% |
| Responses to: Three months after initiating treatment with antipsychotic medication, Jeff has blood-work to monitor lipids, liver enzymes, and glucose. A work-up for what endocrine condition may additionally be indicated? |  |  |  |  |
| A. Hypothyroidism |  |  |  |  |
| B. **Hyperprolactinemia** |  |  |  |  |
| C. Hyperparathyroidism |  |  |  |  |
| D. Hyperthyroidism |  |  |  |  |
| Incorrect (A) | 4 (10%) | 144 (12%) | 101 (17%) | 432 (18%) |
| **Correct (B)** | **27 (75%)** | **974 (79%)** | **443 (73%)** | **1752 (73%)** |
| Incorrect (C) | 3 (8%) | 57 (5%) | 22 (3%) | 72 (3%) |
| Incorrect (D) | 5 (13%) | 55 (4%) | 44 (7%) | 144 (6%) |
| Correct vs. Predicted | +5% | +9% | +3% | +3% |

## Patients at increased risk

Responses to a question about vulnerable populations are presented in Table 6. The average correct response rate is 36% (34% below predicted) with a weighted average of 33% (37% below predicted).

Responses to the pre-activity question on vulnerable populations

| **CME activity** | **Activity 1** | **Activity 2** | **Activity 3** | **Activity 4** | **Activity 5** |
|:---|:---|:---|:---|:---|:---|
| Sample size | *n*=45 | *n*=1237 | *N*=611 | *n*=2400 | *n*=16361 |
| Responses to: Mental illness shortens lifespan as follows: |  |  |  |  |  |
| **A. Patients with mental illness die 25 years earlier than the general population, mostly due to earlier onset of chronic medical conditions.** |  |  |  |  |  |
| B. The average 15 years of potential life lost can be explained by premature death early in life. |  |  |  |  |  |
| C. The causes of mortality are similar to the general population, but occur 10 years earlier on average. |  |  |  |  |  |
| D. Suicide and accidents including motor vehicle accidents account for most of the 10 years of shorter life expectancy. |  |  |  |  |  |
| **Correct (A)** | **13 (29%)** | **629 (51%)** | **215 (35%)** | **840 (35%)** | **5166 (32%)** |
| Incorrect (B) | 9 (20%) | 285 (23%) | 184 (30%) | 144 (6%) | 1584 (10%) |
| Next best answer (C) | 20 (44%) | 265 (21%) | 169 (28%) | 528 (22%) | 4557 (28%) |
| Incorrect (D) | 3 (7%) | 55 (5%) | 42 (7%) | 888 (37%) | 4718 (29%) |
| Correct vs. Predicted | −41% | −19% | −35% | −35% | −38% |

Figure 1 illustrates the correct responses compared to the predicted responses for the pre-activity question on mental illness and chronic disease risk. The responses are presented across CME activities 1–5. Standard error bars are shown. Since one of the three incorrect responses (Choice C) in the question was close to the correct answer, it may reflect a stronger knowledge base than the other two incorrect choices. We therefore included this response in the figure.

Activity 5's large sample size allowed for further analysis. Participants had a predicted probability of 31% in answering correctly. Participants specializing in mental health scored 14% higher than average (z=11; p\<0.01) and family practitioners scored 4% higher (z=3; p\<0.05). The probit regression explained 1% (pseudo-R^2^=0.01) of the total variability in the responses and was significant (LR chi-square=202, p\<0.01).

## Drug safety updates

Participants in Activity 5 were asked to respond to: Which of the following statements is correct?

A. Comparative effectiveness trials are part of the drug approval process \[5622 (34%)\]

B. Phase 3 clinical trials are powered to identify appetite-stimulating effects of medication \[3346 (20%)\]

C. Incidence of weight gain can be calculated from a passive adverse events reporting system \[4424 (27%)\]

D. Current legislation requires clinical trials in pediatric populations \[2452 (15%)\]

On average, 15% of participants answered correctly (55% below predicted) selecting choice D. Predicted probability of answering correctly given the regression variables is 14%. Analysis by specialty indicates that pediatricians scored 7% higher than average (z=6; p\<0.01) and mental health specialists scored 2% higher (z=2; p\<0.02). General practitioners scored 3% below average (z=−2; p\<0.03) and emergency medicine specialists scored 4% lower (z=−2; p\<0.04). The regression explained 2% of the total variability in answers (pseudo-R^2^=0.02) and was very significant (LR chi-square=242; p\<0.01).

Note that this question had the highest non-response rate, with 517 (3%) of participants leaving the question blank. Regression analysis excluding non-responders had the same significant outcome variables as the analysis which included non-responders.

See Table 7. The average correct response was 47% (23% below predicted) and the weighted average was 51% (19% below predicted).

Responses to pre-activity questions on drug safety information

| **CME activity** | **Activity 2** | **Activity 3** |
|:---|:---|:---|
| Sample size | n=1237 | n=611 |
| Responses to: Practitioners are asked to report drug adverse events: |  |  |
| **A. To MedWatch** |  |  |
| B. Only when the adverse event is not specified on the drug label |  |  |
| C. Within 30 days of occurrence, as required by law. |  |  |
| D. Voluntarily, if observed within 30 days of the first dose |  |  |
| **Correct (A)** | **698 (56%)** | **237 (39%)** |
| Incorrect (B) | 85 (7%) | 53 (9%) |
| Incorrect (C) | 203 (10%) | 174 (29%) |
| Incorrect (D) | 243 (20%) | 146 (24%) |
| Correct vs. Predicted | −14% | −31% |

For Activity 5 (*n*=16,361), the top three professions of participants were nurse practitioners (52%, *n*=8407), physicians (38%, *n*=6212), and physician assistants (3%, *n*=476). The top specialties were psychiatry/mental health (12%, *n*=2022), family medicine (11%, *n*=1875), internal medicine (10%, *n*=1639), general practice (6%, *n*=946), and pediatrics (6%, *n*=906).

We controlled the regression analysis for predicting correct pre-activity responses for specialty, professional degree, and date of CME participation by quartile. The time of participation was included in the regression analysis because it explained a significant portion of the variability but yielded no clear pattern for interpretation. Results of the regression analysis follow each applicable question. The results of the analysis by specialty concur with the practice demands of each specialty. For example, family physicians, practitioners who follow patients across the lifespan, were more likely to correctly identify the profound extent to which mental illness shortens life expectancy due to chronic diseases.

## Instrument analysis

The strength of the instrument is its ease of use in the context of CME programming, and its associated ability to identify trends in practitioner knowledge and some broad comparisons among practitioners. However since the instrument is comprised of multiple choice questions, responses to any one question are more appropriately viewed in the context of the full instrument.

In order to assess the variability of the instrument, responses were compared across CME programs which varied in content, timeframe, recruitment, and question administration. Figure 1 depicts the responses. Responses among CME programs varied within the pre-established +/−10% test error, except for one program with a small sample size. The unweighted, correct response averages across CME programs are reported.

To assess the extent to which recruitment methods may influence the pre-activity responses, the overall scores of the two Audio-Digest programs were compared. The two programs differ only in how the participants were recruited. They were recruited as either, subscribers or one-time participants. The 10% difference in responses falls within the pre-established test error.

Practice-relevance and the perception of the CME program's usefulness were considered in the instrument analysis. The participants in each of the 6 activities were asked to evaluate the program on a 1–5 Likert scale, 5 being the highest score. The ratings for each program ranged from 4.0 to 5.0, with an unweighted mean score of 4.5, suggesting that all were well-received and applicable to participants' clinical practice.

The pre-activity question responses were correlated with what participants said they learned from the activity. Participants in Activities 2 and 3 were asked to "Please list one concept or strategy gained from this activity." The written responses fell into categories consistent with pre-activity responses: Pediatric indications (20), patient adherence (2), adverse effects (71), MedWatch reporting (9), drug interactions (5), and patient risk factor (8).

A temporal trend in correct responses was not observed between the first three months and the total 13 months of the responses to Activities 2 and 3. Neither was any single news event or regulatory change identified which might be anticipated to influence practitioner knowledge on this topic during the study period.

# Discussion

The childhood obesity epidemic is recent; however, practitioners who care for children appear as familiar with adverse metabolic drug effects as practitioners who care for adults. Those specializing in pediatrics performed better on a question about drug research, perhaps reflecting recent educational activities directed towards pediatricians.

Across medical specialties practitioner knowledge of medication-related weight gain was low in four areas of our study. Each of the knowledge gaps if practice relevant, would overestimate the medication's benefits or underestimate adverse metabolic effects. The net effect of each knowledge gap would therefore affect clinical decision-making in the same direction, potentially contributing to excess metabolic dysfunction. The four areas of low practitioner knowledge are as follows:

Responses to questions about drug indications and the use of antipsychotics in autism suggest that some practitioners may mistake the management of aggressive symptoms for treatment of the underlying disease process. Additionally, new oral preparations are available for children who have difficulty swallowing pills. These preparations should be used before prescribing intramuscular preparations, which have greater metabolic effects and do not have pediatric use indications at the time this manuscript is written.

Among the questions pertaining to adverse metabolic effects, only 8% of practitioners selected the intended response that some diuretics have been associated with promoting insulin resistance. The 41% who incorrectly selected "angiotensin-converting enzyme inhibitors" are unlikely to have been aware that some diuretics promote insulin resistance and angiotensin converting enzymes, in contrast, may be insulin sensitizing \[15\], the distinction between these two antihypertensive therapies in a patient with metabolic syndrome would be practice relevant. Furthermore, these respondents may have erroneously equated the reduction in peripheral edema with meaningful, long-term weight loss among their patients. The 43% of practitioners who incorrectly selected "biguanide" may not have realized that metformin is in this medication class, so the response would have been more informative if the answer had read "biguanide (metformin)."

Responses reflected low baseline knowledge of drug safety research and MedWatch, a passive surveillance program. It is possible that practitioners lack a framework for managing the escalating drug-related information. Our findings parallel those of a recent study of physician knowledge and adverse events reporting of dietary supplements \[16\].

Mental illness is associated with increased vulnerability to adverse metabolic effects. The profound chronic disease mortality among patients with mental illness was under-recognized across specialties as measured by our instrument. Awareness of the high mortality from chronic diseases among patients with mental illness might be unlikely to cause practitioners to alter the patient's psychiatric medications; however, it would guide overall care such as screening, referring, concurrent medication prescribing, and managing co-morbidities.

The knowledge gaps parallel the few peer-reviewed publications on medication-related weight gain, other than the atypical antipsychotics. Reviewing the proceeding of a large international conference on obesity \[17\] revealed a similar paucity of research and translational initiatives surrounding medication-related weight gain. Additionally, current drug product information and labeling lacks a consistent format or location for communicating the potential effects of a drug on the patient's appetite and underlying metabolism.

Practitioners were familiar with the general indications for use of atypical antipsychotics in children and the adverse metabolic effects including prolactinemia, dyslipidemia, elevated liver enzymes, insulin resistance, and weight gain, findings which correlate with the lay and medical literature's recent attention to the topic \[4\]. Similarly, education initiatives about pediatric drug labeling have been directed to pediatricians and pediatricians were more knowledgeable than other practitioners about ongoing pharmacovigilance.

The instrument demonstrated internal consistency across diverse CME programs (Table 1), suggesting the findings may appropriately be generalized across U.S. primary care practitioners. The sampling frame captures participants across the United States, with diverse patient populations in diverse practice settings. The degrees of the participants, nurse practitioners, physician assistants, and medical doctors, correctly represent the educational diversity of primary care practitioners. Pediatricians scored as well as their adult medicine counterparts, suggesting that future initiatives could appropriately be directed to all primary care practitioners.

Additional merits of the instrument are that it can be implemented in a timely and cost-sensitive way. It can be applied to assess evidence-based practice knowledge \[18\]. Study findings can provide baseline data, by which to gauge the effectiveness of future interventions. The instrument also provides a continuing education curriculum developed free of industry interests. An internet curriculum on safe medication use measurably improved clinician practice choices \[19,20\].

Knowledge is one of many clinical practice barriers to modifying medication-related weight gain, and merits incorporation into future initiatives. The findings, taken with the population prevalence of obesity, the emerging treatment options, and the central role of the primary care practitioner, suggest a significant prevention opportunity.

# Conclusions

Pediatricians' knowledge base of adverse metabolic drug effects appears comparable to their counterparts in adult medicine. Regardless of medical specialty, practitioners participating in the CME programs reflected low knowledge on specific questions pertaining to drug indications, adverse metabolic effects, patient risk profiles and safety updates. Each of the four knowledge gaps would potentially influence clinical decision-making in the same manner, leading clinicians to overestimate the benefits of a drug in relation to its metabolic risks. Therefore future efforts to detail cross-specialty practitioner knowledge of metabolic drug effects and initiate education strategies to bolster knowledge could meaningfully contribute to obesity prevention.

## Availability of supporting data

The full instrument (CME questions from all activities) is available at the journal's request.

# Competing interests

Neither author has financial or non-financial competing interests.

# Authors' contributions

IK developed the CME modules in collaboration with colleagues acknowledged elsewhere in the manuscript and published CME materials. She designed the study in collaboration with the Office of Pediatric Therapeutics and drafted the manuscript. GW participated in the design of the study and performed the statistical analysis. Both authors read and approved the final manuscript.

# Author's information

IK is a physician nutrition specialist board-certified in preventive medicine and public health. She is the editor of Advancing Medicine with Food and Nutrients, Second Edition (CRC Press, December 2012) and serves on the faculty of Johns Hopkins Bloomberg School of Public Health. As an inaugural FDA Commissioner's Fellow she worked within the Office of Pediatric Therapeutics on nutrition-related issues, which gave rise to this research collaboration.

## Acknowledgements

We thank Anne Myers for her background work on pediatrician focus groups; Lon Osmond, Executive Director, Audio-Digest Foundation for his collaboration; Michelle Surrichio of the American College of Preventive Medicine for her technical assistance; and Rachel Barr of the Maryland Academy of Family Physicians for her conference preparations.

## Disclaimer

This manuscript is a professional contribution developed by its authors. No endorsement by the FDA is intended or should be inferred.