The disruption of the dopaminergic system in Parkinson’s Disease (PD) has a profound impact on motor networks needed to control movements.[1] Notably in people with PD, the automatic movements that typify normal walking activity are lost[2],[3] and a deteriorating a gait pattern develops characterised by quick, short, shuffling steps, narrow base of support, stooped posture, rigid trunk, and reduced arm swing. The short stride length often causes the foot to scuff the ground, causing trips and falls.[4–6] Starting, stopping, and changing direction are more difficult, gait pattern is inconsistent,[7] and freezing is common.[8] As gait impairments progress, asymmetries develop and people have difficulty adapting their walking to new or complex environments or to increased task burden.[9, 10] Walking is perceived as harder and, eventually, walking for enjoyment and health promotion abates and then ceases.
One solution to improve gait is to emphasize a heel-to-toe gait pattern,[6] something typically done during physical therapy to change posture and stride length. This strategy provides the walker with feedback and encouragement for this, usually automatic, movement. Relearning the pattern requires repeated practice and, once the therapist ceases this verbal cueing, the walker returns to their typical gait pattern.
Gait training is predominantly carried out by physical therapists with one-on-one interactions, however, there are not enough therapists for the number of people with gait vulnerabilities. Technology is poised to bridge the gap between supply and demand facilitating self-management of gait vulnerabilities. Some technologies are more successful than others, but many gaps remain in technology readiness, usability, access, training needs, and efficacy potential.
Researchers at McGill University have developed and commercialized through PhysioBiometrics Inc. a device that automates this verbal cueing by providing real-time auditory feedback when the heel strikes first when stepping. The Heel2Toe™ sensor, shown in Fig. 1, consists of a sensor that runs a real-time algorithm that discriminates good from poor steps with 94% accuracy,[11, 12] and generates appropriate feedback. It is classified by Health Canada as a Class I medical device (#167654). The sensor has a gyroscope, an accelerometer, and a magnetometer providing 9-degrees of freedom.
The gait cycle has been studied and described since the advent of bipedal gait.[13–15] Fig. 2 presents a graphic of the normal gait cycle when tracked from the ankle joint using the gyroscope. Normal gait is characterized by two troughs and one peak. The first trough is when the ankle moves clockwise from initial contact to foot flat when there is no ankle movement allowing for weight transfer from the heel to the ball of the foot. The second trough is when the ankle again moves clockwise to push the foot off the ground to propel the body forward. Typically, the ratio of push off to heel strike is estimated at 2:1 [16, 17] The peak represents the swing phase of the gait cycle when the foot leaves the ground and swings forward to initiate another step.
The sensor detects the velocity at which the ankle moves clockwise during the initial contact of the foot during a step (angular velocity: AV). When the AV crosses a threshold for a “good step” a signal is sent via Bluetooth to a smart phone and a sound is emitted. This external positive feedback drives motor learning, retraining gait patterns to be more normal, fluid, safe, and sustainable. To normalize walking, people must relearn motor sequences and develop needed adjuncts to efficient walking: strength, power, core stability, balance, etc. Physical therapy targets adjuncts but motor learning requires instruction, repetition, and practice.[18] At least some of the neural mechanisms underlying this learning are likely aberrant in PD. Motor learning via feedback involves neuroplasticity in corticostriatal and striato-cerebellar circuits in a partially dopamine-dependant manner.[18–21]
In two proof-of-concept studies of 6 people with PD[22] and 6 pre-frail seniors[23] receiving 5 training sessions with Heel2Toe™ over 2 weeks, every person made at least one clinically meaningful change on one gait parameter after training. The potential mechanism of action is a dopamine-driven reward and feedback loop.[24] Here we set out to estimate the extent to which training with the Heel2Toe™ over a longer period of time (3 months) was feasible and acceptable to participants and to estimate changes in walking capacity and gait pattern among people training with feedback from the sensor and among those training without feedback.
The hypotheses for which the pilot trial will provide supporting data is that people in the group training with feedback from the Heel2Toe sensor will make greater gains in walking capacity and motivation and will show more optimal changes in parameters of gait quality than will be observed in the control group.