The DigiGait Imaging System was recently used to assess pain in the mouse model of carrageenan-induced arthritis (1). The authors sought to compare the DigiGait patented ventral plane videography technology with a competitor’s copy of the instrumentation. DigiGait detected important gait alterations after carrageenan injection that the competitor’s device was not able to detect.  The DigiGait system captured digital images with ~50% greater temporal sensitivity, and was reported to have double the range of speeds to study [0 to 99 cm/s] than the competitor’s machine.

The importance of the appropriate uniform lighting afforded by DigiGait was highlighted.  Whereas the DigiGait system includes controlled uniform LED lighting, the researchers found it necessary to add lighting to make up for inadequate overhead lighting with the competitor’s offering (1). The competitor’s device requires that background, calibration, and foot model data files be loaded or generated before commencing analysis. The background file is a reference picture of the running chamber that must be captured with the same lighting conditions as the recorded trials, as subtle changes in lighting can contribute to errors in paw recognition.  Yet, the researchers stated that providing a picture of the empty running chamber was less time-consuming.  Presumably, however, this would include only a static image of the overhead fluorescent lighting, the brightness and colors of which would differ from the “live” fluorescent bulbs overhead during an in vivo study, dynamically changing in brightness and color as the lamps oscillate with the line current at 60 Hz.  Analysis, therefore, was highly contingent on [in]consistent and [sub]optimal ambient and equipment lighting, neither of which was provided with the competitor’s device.

The authors elegantly demonstrated via DigiGait how exquisitely sensitive stride length, a gold standard gait metric, is to walking speed, even at the relatively slow speeds tested [14 – 20 cm/s; see Figure 1].  This is important for researchers who might be considering an overground voluntary walking paradigm for their research; should their animals choose to walk different speeds because of their lesion, the gait metrics will not be comparable.



The authors indicated that even novice operators of gait analysis instrumentation experienced a nearly 25% faster speed of analysis with DigiGait compared to the competitor’s software;  this is notable given that DigiGait computed nearly 50% more digital images and reported more gait metrics.   The DigiGait software was user-friendly during setup and analysis because it was more automated. The DigiGait software allowed rapid optimization of paw detection. The organizational system for video storage and the user interface were simple and intuitive (1). The automated analysis phase for numerous mice did not require operator presence, freeing the operator for other tasks.  Aspects of the competitor’s software, in contrast, were described as “cumbersome”.

Although the authors did not report any information about coordination, previous published reports of this model have indicated that the animals maintain a coordinated gait despite carrageenan-induced pain (2).  Therefore, one would not expect to see differences between the arthritic hind limb and the non-arthritic contralateral limb in certain of the gait metrics such as stride length and stride frequency, as Table 1 shows.









The relatively slow speeds used by the researchers, moreover, introduce a fair amount of variability into the gait.  Lab mice can exceed treadmill speeds of 75 cm/s (3) and routinely exceed speeds of 40 cm/s in their cages (4); the speed selected by the researchers [<20 cm/s] is more of a foraging speed than a walking speed and would contribute greatly to variability in numerous gait metrics.  Even in humans, slow self-selected walking speeds can contribute to increased gait variability and the propensity for falls among the elderly (5).

DigiGait reports several postural and kinematic metrics of gait not offered by other vendors that are more sensitive to unilateral pain (6).  DigiGait, for example, reports paw placement angle, braking and propulsion durations, Tau-during-push-off, stance factor, and several metrics of gait variability.  Though the authors here did not describe how these metrics were affected, others who have used DigiGait to quantify gait in a rat model of carrageenan-induced arthritis (6) and a mouse model of collagen-induced arthritis (7) have reported several metrics [apparently not provided by the imitator’s device] to be sensitive indicators of arthritic pain.  DigiGait reported significant differences in gait parameters soon after carrageenan injection (4 hours post injection) and long before knee swelling or physical deformities occur. Animals showed the most dramatic changes in temporal and spatial gait parameters in the carrageenan injected right hind limb in response to pain. This study suggests that gait parameters are a suitable and objective method for assessment of early behavioral changes associated with inflammatory pain and may reduce the need for classic indices of hyperalgesia and allodinya.

Researchers are encouraged to challenge animals to faster walking speeds, and incline or decline walking, to further highlight subtle but relevant pre-clinical evidence of pain and discomfort during walking.  We humans may not experience a gait abnormality at a “foraging” speed [a stroll to the local coffee shop, for example!], but may experience a limp as we ascend the stairs rapidly at 7:59AM for that 8AM grand rounds in the 2nd floor lecture hall!   Importantly, there is a  variation in the magnitude of acute and chronic inflammatory responses in different mouse strains (8).  DigiGait provides the most versatile instrumentation to study a range of species [mouse, rat, guinea pig, and hamster] at mice walking voluntarily or on a treadmill, at a range of walking speeds [0 to 99.9 cm/s] up an incline, or down a decline.

            IF a gait disturbance exists, DigiGait will report it!


    1. Dorman et al.   A comparison of DigiGait™ …: assessment of pain using gait analysis in murine monoarthritis.  Journal of Pain Research 2014; 7:25–35.


    1. Min SS et al. A novel method for convenient assessment of arthritic pain in voluntarily walking rats. Neurosci Lett.  2001; 308(2):95-8.


    1. Girard et al. Selection for high voluntary wheel-running increases speed and intermittency in house mice (Mus domesticus),  The Journal of Experimental Biology 2001; 204:4311–4320.


    1. Cepeda et al.  Rescuing the Corticostriatal Synaptic Disconnection in the R6/2 Mouse Model of Huntington’s Disease: Exercise, Adenosine Receptors and Ampakines PLoS Curr. 2010; September 20; 2: RRN1182.
    2. Perracini MR et al. Fall-related factors among less and more active older outpatients. Rev Bras Fisioter. 2012; 16(2):166-72.


    1. E.R. Berryman Strain dependence in murine models of al.: DigiGait is a sensitive method for assessing pain.  J Musculoskelet Neuronal Interact 2009; 9(2):89-98.


    1. Vincelette J, Xu Y, Zhang LN, Schaefer CJ, Vergona R, Sullivan ME, Hampton TG, Wang YXJ. DigiGait analysis in a murine model of collagen-induced arthritis. Arthritis Research and Therapy 2007; 9:R123.


    1. Kelso EB et al.   Strain dependence in murine models of monoarthritis. Inflamm Res. 2007; 56(12):511-4.