| Literature DB >> 31835870 |
Niveditha Muthukrishnan1, James J Abbas1, Holly A Shill2, Narayanan Krishnamurthi1,3.
Abstract
Progressive gait dysfunction is one of the primary motor symptoms in people with Parkinson's disease (PD). It is generally expressed as reduced step length and gait speed and as increased variability in step time and step length. People with PD also exhibit stooped posture which disrupts gait and impedes social interaction. The gait and posture impairments are usually resistant to the pharmacological treatment, worsen as the disease progresses, increase the likelihood of falls, and result in higher rates of hospitalization and mortality. These impairments may be caused by perceptual deficiencies (poor spatial awareness and loss of temporal rhythmicity) due to the disruptions in processing intrinsic information related to movement initiation and execution which can result in misperceptions of the actual effort required to perform a desired movement and maintain a stable posture. Consequently, people with PD often depend on external cues during execution of motor tasks. Numerous studies involving open-loop cues have shown improvements in gait and freezing of gait (FoG) in people with PD. However, the benefits of cueing may be limited, since cues are provided in a consistent/rhythmic manner irrespective of how well a person follows them. This limitation can be addressed by providing feedback in real-time to the user about performance (closed-loop cueing) which may help to improve movement patterns. Some studies that used closed-loop cueing observed improvements in gait and posture in PD, but the treadmill-based setup in a laboratory would not be accessible outside of a research setting, and the skills learned may not readily and completely transfer to overground locomotion in the community. Technologies suitable for cueing outside of laboratory environments could facilitate movement practice during daily activities at home or in the community and could strongly reinforce movement patterns and improve clinical outcomes. This narrative review presents an overview of cueing paradigms that have been utilized to improve gait and posture in people with PD and recommends development of closed-loop wearable systems that can be used at home or in the community to improve gait and posture in PD.Entities:
Keywords: Parkinson’s disease; cueing; gait; posture; rehabilitation; wearable sensors
Mesh:
Year: 2019 PMID: 31835870 PMCID: PMC6960538 DOI: 10.3390/s19245468
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.576
Figure 1(A) Sensory-motor areas for movement execution in the basal ganglia and the impaired motor pathways in Parkinson’s disease (PD) with the prevalence of the indirect pathway over the direct pathway and the affected SN’s input to the circuit. SN—Substantia nigra, GPi—globus pallidus internus, GPe—globus pallidus externus, Put—putamen, Th—thalamus, CN—caudate nucleus, STN—sub-thalamic nucleus. This results in increased neuronal firing activity in the output nuclei of the basal ganglia that leads to excessive inhibition of thalamo-cortical and brainstem motor systems which, in turn, interferes with movement onset and execution [28,29]. (B) Representation of brain areas activated during external cueing reported from findings of image analysis studies conducted on people with PD during cueing experiments [17,30,31,32].
Summary of key findings of closed-loop cueing strategies that were included in this review.
| Study | Intervention Type | Sensors; Feedback Mode | Outcome Measures | Study Protocol | Results | Limitations |
|---|---|---|---|---|---|---|
| Badarny et al. 2014 [ | Visual | Wearable motion sensors; virtual reality based eye-glasses | Walking speed, stride length | Single-session study with cue and a follow-up evaluation (1 week later) | Increases in both walking speed and stride length, immediate effects and at follow-up | No control group; only assessed short-term effects |
| Jellish et al. 2015 [ | Visual | Treadmill-based, video-based motion capture system and a feedback monitor | Step length, postural (back) angle measured during treadmill walking | Single-session study using multiple trials with and without cues | Increases in uprightness and step length | Utilized technology that is only available in research labs |
| Chomiak et al. 2019 [ | Auditory | IMU sensors with a smartphone application-(Ambulosono sensor system) | Step length, walking distance, velocity, and cadence | Single-session study multiple trials | Evaluation of the sensor’s performance on healthy controls | Use of iPod Touch for feedback is not cost-effective and the system has not been evaluated on PD population |
| Bartels et al. 2018 [ | Auditory | IMU sensors with a smartphone application | Stride length | Single-session study with multiple trials | Evaluation of the sensor’s performance on healthy controls | The system has not been evaluated on PD population |
| Young et al. 2014 [ | Auditory-sonification of gait–swing phase | Video-based motion capture system with a smart phone application | Step length CoV | Single-session study in the lab with multiple trials | Reduction in step length variability | Utilized technology that is only available in research labs |
| Thompson et.al. 2017 [ | Somatosensory | IMU sensors with a software application on the laptop and a vibratory device | Step length, lateral trunk sway, cadence, gait velocity, arm swing | Single-session study in the lab with multiple trials. | Increases in step length, arm swing magnitude, reduced cadence | Though somatosensory cues have been successful in helping with the rhythm of the movement, they are less effective in increasing the amplitude of the desired movement |
| Schlick et al. 2016 [ | Visual | Treadmill-based pressure platform and video feedback monitor | Gait speed, stride length and cadence | Long-term training (5 weeks) at lab, RCT | Both the training and control group showed increases in gait speed and stride length post training, but sustained effects after 2 months were observed only in the case of feedback-based training | Small sample at follow-up because of attrition |
| Mirelman et al. 2016 [ | Visual | Video-based motion capture system with virtual reality feedback | Fall incident rates | Long-term training (3 times/week for 6 weeks) at lab | Reduction in the rate of falls during the 6 month follow-up evaluation | No control group |
| Baskaran. 2017 [ | Visual | Treadmill-based video-based motion capture system and a feedback monitor | Step length, postural (back) angle measured during treadmill walking | Long-term training (3 times/week for 6 weeks) at lab | Increases in uprightness and step length | No control group and a small sample size |
| Yang et al. 2016 [ | Visual | Video-based motion capture system with virtual reality feedback | BBS, DGI, TUG test | Long-term training (2 times/week for 6 weeks) at lab, RCT | Increase in clinical score, BBS performance which was retained at 2 week follow-up | Small sample size |
| Van den Heuvel et al. 2014 [ | Visual | Video-based augmented feedback system with treadmill and IMU sensors | FRT, BBS, UPDRS | Long-term training (2 times/week for 5 weeks) at lab, RCT | Improvements in balance scores in favor of the feedback system | Changes in scores were not statistically significant |
| Ginis et al. 2015 [ | Auditory | IMU sensors with a smartphone application (CuPiD system) | Gait speed, cadence, stride length and stride length asymmetry | Long-term training (3 times/week for 6 weeks) at home, RCT | Increase in gait speed at post-training | Assessors were not blinded |
| Carpinella et al. 2016 [ | Auditory and visual | IMU sensors and monitor for exercise therapy with a Gamepad (Gaming Experience in Parkinson’s Disease) | BBS and gait speed | Long-term training (3 times/week for 6 weeks) at lab, RCT | Increase in clinical score, BBS performance and retained effects at 1 month follow-up | Lack of online computation of gait measures and the use of technology that is only available in research labs |
| Frazzitta et al. 2009 [ | Auditory and visual | Treadmill-based strain gauge and a visual feedback monitor | Stride length, gait speed | Long-term training (4 weeks) at lab | Greater increase in gait speed and stride length following treadmill-based cue training than with overground-based cue training | No control group and the study did not evaluate residual effect at follow-up |
| Rochester et al. 2010 [ | Auditory, visual, and somatosensory | IMU-based rhythmical feedback system | Walking speed, step length, step frequency | Long-term training study for 6 weeks at lab | Increase in walking speed and step length with all cue types in both single and dual-tasking after training. | No control group |
| Espay et al. 2010 [ | Auditory and visual | IMU sensors and a head-mounted display and headphones | Gait velocity, stride length and cadence | Home-based training for 2 weeks | Increase in gait velocity and stride length after training | No control group |
| Pompeu et al. 2012 [ | Auditory and visual | Wii Fit games | UPDRS | Long-term training (2 times/week for 7 weeks) with exercise therapy at lab | Decrease (improvement) in UPDRS post-training and at 2 month follow-up evaluation | No control group |
Inertial measurement unit (IMU), Coefficient of variation (CoV)), Unified Parkinson’s Disease Rating Scale (UPDRS), Berg balance score (BBS), Dynamic gait index (DGI), Functional reach test (FRT), Randomized controlled trial (RCT). The table includes only experimental studies and does not list the review articles mentioned in the text.