In a recent article published in npj Digital Medicine, researchers focused on utilizing wearable sensor-based quantitative gait analysis to assess PD patients with different motor subtypes. The aim of this study was to identify objective gait biomarkers for early diagnosis, subtype differentiation, and disease severity monitoring.
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The recommended objective gait features for early diagnosis, subtype differentiation, and disease monitoring are listed in this figure. TD tremor-dominant, PIGD postural instability and gait disorder, MAS more affected side, LAS less affected side, RoM range of motion, SiSt sit to stand, StSi stand to sit. 1 Gait features for early detection of both TD and PIGD (for subtype-specific early detection details, please see Fig. 3 and Supplementary Table 1); 2a Gait features for disease monitoring in TD subtype; 2b Gait features for disease monitoring in PIGD subtype; 3 Gait features for subtypes differentiation. Image Credit: https://www.nature.com/articles/s41746-024-01163-z
Background
Gait impairments are prevalent and debilitating symptoms in Parkinson's disease (PD) patients, worsening as the disease progresses. Previous research has highlighted the significance of specific gait metrics, such as arm swing velocity and turning parameters, in evaluating PD-related motor impairments.
The study emphasizes the need for objective assessments to monitor disease severity in PD subtypes, enhancing the implementation of tailored treatment plans. The limitations of the study include the necessity for longitudinal research to validate gait parameters as early biomarkers and the need for further investigation into PD symptoms like freezing of gait and mild cognitive impairment.
The Current Study
Participants in this study included 44 patients with Parkinson's disease (PD) and 39 healthy controls (HC). PD patients were recruited from the outpatient center of Ruijin Hospital and diagnosed by a movement disorders specialist according to the MDS clinical diagnostic criteria.
All PD patients were in the early stage (H-Y stage: 1–2.5) and did not exhibit camptocormia, marked festination, or freezing of gait. Healthy controls were recruited from the community and were free from PD clinical manifestations.
Gait assessments were conducted using body-fixed sensors during the Timed Up and Go (TUG) test. Participants were instructed to stand up from a chair without armrests, walk comfortably for 5 meters, turn back, and sit down.
Before the formal tests, participants were given a practice session to familiarize themselves with the testing procedure. Each participant performed two consecutive walking trials, with a rest period between trials to ensure adequate recovery.
Sixty-four gait and postural-related features were collected using wearable sensors, categorized into lower body, trunk and lumbar, upper body, and postural transitions.
These features included parameters related to arm swing velocity, turning efficiency, trunk rotation range, and backswing range in lower extremities. Statistical analyses were performed to compare gait parameters among the healthy controls, tremor-dominant (TD), and postural instability gait difficulty (PIGD) groups.
The discriminatory ability of each gait variable in distinguishing between PD subtypes and healthy controls was assessed using receiver operating characteristic (ROC) analysis. The area under the curve (AUC) values were calculated to determine the discriminative value of the objective gait features. Correlations between specific gait parameters and disease severity in TD and PIGD subtypes were also investigated.
Results and Discussion
The study revealed significant gait alterations in both TD and PIGD dominant PD patients compared to healthy controls. Both PD subtypes exhibited restricted backswing range in bilateral lower extremities and the more affected side (MAS) arm, as well as reduced trunk and lumbar rotation range in the coronal plane.
Additionally, both groups showed lower turning efficiency during the TUG test. These objective gait features demonstrated high discriminative value in distinguishing PD subtypes from healthy controls, as indicated by the AUC values of 0.7~0.9 (p < 0.01) in the receiver operating characteristic analysis.
Furthermore, subtle but measurable gait differences were observed between TD and PIGD patients even before the onset of clinically apparent gait impairment. Specific gait parameters were found to be significantly associated with disease severity in both PD subtypes, highlighting the potential of these parameters as subtype-specific gait markers for disease severity monitoring.
The arm swing velocity and parameters related to turning were particularly correlated with disease severity in the PIGD group, emphasizing the importance of these metrics in evaluating motor impairments in PD.
The study's findings underscore the value of wearable sensor-based gait analysis in providing objective assessments for monitoring disease severity in PD subtypes.
By identifying gait biomarkers related to upper limbs, the study suggests the feasibility of utilizing fewer sensors, such as those in smartphones or smartwatches, for gait analysis. This approach not only reduces assessment costs but also enhances convenience and suitability across various settings, promoting continuous evaluation over time.
Moreover, the study highlights the potential of AI-based gait evaluation systems in managing PD and improving patient outcomes. The advantages of AI-based systems, including higher diagnostic accuracy, continuous monitoring, and personalized therapeutic interventions, hold promise for enhancing PD diagnosis and treatment.
By providing valuable gait biomarkers for the development of AI-based gait evaluation systems, this study contributes to the advancement of personalized treatment strategies for PD patients.
Conclusion
The study contributes valuable insights into gait biomarkers for the development of AI-based gait evaluation systems, potentially improving PD diagnosis and treatment.
Future research directions include longitudinal studies to validate gait parameters as early biomarkers, focused studies on specific PD symptoms, and exploration of methods to integrate key variables for enhanced clinical applicability. The findings underscore the potential of wearable sensor-based gait analysis in advancing personalized treatment strategies for PD patients.
Journal Reference
Zhang W., Ling Y., et al. (2024) Wearable sensor-based quantitative gait analysis in Parkinson’s disease patients with different motor subtypes. npj Digital Medicine 7, 169. doi: 10.1038/s41746-024-01163-z. https://www.nature.com/articles/s41746-024-01163-z