A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease

Abstract

Background:Deficits in motor movement automaticity in Parkinson's disease (PD), especially during multitasking, are early and consistent hallmarks of cognitive function decline, which increases fall risk and reduces quality of life. This study aimed to test the feasibility and potential efficacy of a wearable sensor-enabled technological platform designed for an in-home music-contingent stepping-in-place (SIP) training program to improve step automaticity during dual-tasking (DT).Methods:This was a 4-week prospective intervention pilot study. The intervention uses a sensor system and algorithm that runs off the iPod Touch which calculates step height (SH) in real-time. These measurements were then used to trigger auditory (treatment group, music; control group, radio podcast) playback in real-time through wireless headphones upon maintenance of repeated large amplitude stepping. With small steps or shuffling, auditory playback stops, thus allowing participants to use anticipatory motor control to regain positive feedback. Eleven participants were recruited from an ongoing trial (Trial Number: ISRCTN06023392). Fear of falling (FES-I), general cognitive functioning (MoCA), self-reported freezing of gait (FOG-Q), and DT step automaticity were evaluated.Results:While we found no significant effect of training on FES-I, MoCA, or FOG-Q, we did observe a significant group (music vs podcast) by training interaction in DT step automaticity (P<0.01).Conclusion:Wearable device technology can be used to enable musically-contingent SIP training to increase motor automaticity for people living with PD. The training approach described here can be implemented at home to meet the growing demand for self-management of symptoms by patients.

Nonpharmacological, Nonsurgical Treatments for Freezing of Gait in Parkinson's Disease: A Systematic Review

“…In order to avoid studies with lower statistical power, 15 studies reporting samples with <10 patients were excluded. The therapies evaluated in those studies included visual cueing (laser light, staircase climbing) auditory cueing, tactile cueing, cueing exercise program, cognitive training, physical training, rTMS, and tDCS . All these approaches have been covered as well in the articles included in our review.…”

Development and Validation of Ambulosono: A Wearable Sensor for Bio-Feedback Rehabilitation Training

“…Advancements in technology and manufacturing have expanded the application of new wearable technology-based measurement systems for the detection and monitoring of a range of functional aspects of disease under a variety of conditions [1,2,3,4,5,6]. The comprehensive characterization and long-term monitoring of patients by wearable sensor systems offers a practical tool to reduce the standard deviation of clinical endpoints and minimize inter-rater variability of clinical assessments [2].…”

“…The comprehensive characterization and long-term monitoring of patients by wearable sensor systems offers a practical tool to reduce the standard deviation of clinical endpoints and minimize inter-rater variability of clinical assessments [2]. These wearable technologies have the potential to improve diagnostic precision and therapeutic management of patients, as well as the quality of life for patients and their families [1,2,3,4,5,6]. However, in order for wearable sensor systems to be of any practical use, objective measurements of function need to represent variables that are relevant to the condition, be amenable to interventions by clinicians and researchers, be accurately measured, and should exhibit consistency among repeated measurements [2,5].…”

“…We recently developed the Ambulosono sensor system. This system utilizes high-precision movement sensors to digitally link self-generated movements with physiologically defined sound and music cues to enable contingency-based perceptual-motor learning and anticipatory gait control [4,7]. The Ambulosono system was initially developed using the iPod touch (4th generation), which has built-in motion sensors consisting of a 3-axis Micro-Electro-Mechanical Systems (MEMS)-based gyroscope and a 3-axis accelerometer (six dimensions).…”

“…MEMS technologies, in which miniaturized mechanical and electro-mechanical elements of the device are made using the techniques of microfabrication, can often far exceed their macroscale counterparts with respect to device performance. The iPod touch firmware uses proprietary fusion codes for automatic gravity and sensitivity calibration and real-time attitude angles output (pitch, roll and yaw), while our GaitReminder™ App utilizes algorithms to sample sensor output for real-time gait calculations after corrections for limb length, angular excursion, signal filtering and drift, which has shown promise for use in clinical assessments and therapeutic applications [4,7].…”