Inter-bout and intra-bout gait variability—proposed objective measures of gait deterioration during prolonged walking in spine care

Walking analysis using wearable devices

Walking is a fundamental part of independent living and relies on a complex interplay between a person’s neurological and musculoskeletal systems. Alterations to walking patterns can occur in spinal pathologies such as lumbar disc herniation (1-3), lumbar spinal stenosis (LSS) (4-6), mechanical low back pain (MLBP) (1), and cervical myelopathy (7). In these spinal diseases, walking has been shown to be a relevant biomarker of both decline and recovery (8).

Wearable devices (such as smartphones, smartwatches, and activity trackers) can contain microelectromechanical sensors such as accelerometers, gyroscopes, and magnetometers. These sensors can objectively quantify walking patterns using metrics such as step count, walking speed, cadence, and step length (9). Wearable devices can be worn at a single point on the body (such as the wrist, sternum, lower back, or ankle) or at multiple points (though single-point wearable sensors may have greater clinical utility due to being less obtrusive) (10). Being small, cheap, and convenient, wearable devices can be taken home by people into their everyday environment, allowing for continuous remote monitoring without the Hawthorne effect (where an individual’s walking patterns may altered due to the awareness of being observed by a clinician or laboratory personnel) (11-14).

Objective measurement of gait deterioration

There is little agreement in the literature regarding an objective metric for the quantification of gait deterioration (or improvement) over time, and, to our knowledge, almost no objective exploration of this concept in the field of spine health.

Within a walking bout

Gait deterioration may occur over a single walking bout, as is the case in patients with LSS. LSS is classically associated with neurogenic claudication, a clinical syndrome of back or leg pain, weakness, and paraesthesia that worsens with prolonged walking—causing patients to walk slower and “hunch” their backs over the course of a walking bout (15). Nagai et al. attempted to quantify gait deterioration in LSS patients by comparing their gait using wearable sensors at the beginning of their walking bout and at the end (when patients expressed walking difficulty) (16). Patients had slower walking speeds by the end of the bout (1.01 vs. 0.96 m/s, though this did not reach statistical significance with the study’s small sample size of 12 patients), and significantly worse postural sway (P<0.05). However, when measuring walking deterioration between the start and end of each patient’s walking bout, comparisons between patients are complicated as each patient walked for a unique distance. Interestingly, there have been attempts to objectively quantify walking deterioration in patients with multiple sclerosis (17-19). Engelhard et al. introduced the Warp Score and found increases (signifying deterioration) in the Warp Score by a mean of about three points over six minutes of walking (17). While acting as a continuous and objective measure of walking deterioration, this score is not easily replicable (involving dynamic time warping, an algorithm for curve registration) for the purpose of routine clinical use.

Across different walking bouts

On the other hand, gait deterioration can occur across walking bouts over the course of a day. Patients with spinal disorders frequently report variation in symptom intensity throughout the day. Patients with MLBP due to osteoarthritic changes (1,20) typically report worsening symptoms over the day with increased joint use, whilst patients with low back pain due to inflammatory processes (21,22) report morning stiff that alleviates with activity during the day. To our knowledge, no studies in the field of spine care have investigated how gait deterioration with low back pain (23) varies over the course of a day. Again, some studies have investigated this concept using patients with multiple sclerosis (24,25). First investigated by Morris et al., no clinically significant differences were found between five-hour interval gait trials in terms of gait speed, cadence, stride length and double limb support duration (24). Later investigated by Jacob et al., significant time of day dependent gait changes were once again not detected in terms of both 6MWT performance (299.98 vs. 293.39 m, P=0.237) and gait speed (0.71 vs. 0.69 m/s, P=0.385) (25). However, these studies measured gait deterioration at distinct snapshots. Continuous tracking of ‘free-living’ gait metrics may be more suitable in holistically capturing holistic fluctuations in gait over the course of a day.

Intra- and inter-bout gait variability

To objectively quantify these facets of gait deterioration, we propose a framework leveraging the capabilities of wearable devices to perform continuous capture of gait metrics:

• Intra-bout gait variability: continuous tracking of gait deterioration within a single walking bout;
• Inter-bout gait variability: continuous tracking of gait deterioration when comparing walking bouts over the course of a day.

One way of objectively and continuously measuring intra-bout gait deterioration is by graphical representation (as a gait variability curve) of fluctuations in specific components of gait such as gait velocity and cadence, as we have done in Figures 1,2. However, besides obvious differences, this graphical approach is likely difficult to compare between patients, and future studies may look to instead propose simple summary scores (for example, out of 100) to capture the extent of inter- or intra-bout gait deterioration (or regularity in the normative population).

Figure 1 Intra-bout gait variability of gait velocity. Continuous data was collected for example patient Mr. R using a chest-based wearable sensor, over a single walking bout of 12 minutes duration. Gait velocity for each gait cycle was analyzed to construct an Intra-Bout Gait Variability Curve of moving average trendline per 100 gait cycles.

Figure 2 Intra-bout gait variability of cadence. Continuous data was collected for example patient Mr. R using a chest-based wearable sensor, over a single walking bout of 12 minutes duration. Cadence for each gait cycle was analyzed to construct an Intra-Bout Gait Variability Curve of moving average trendline per 100 gait cycles.

Future directions

Healthy patients would also be expected to experience at least some gait deterioration during prolonged walking. Pathological gait deterioration during prolonged walking can only be appreciated after normal gait deterioration is investigated. To our knowledge, no study has investigated normal gait deterioration in prolonged walking, marking a large space of research potential in this area.

Once paired with wearable sensors which can collect continuous gait and walking data from patients in their everyday environment, we envision that both intra- and inter-bout gait variability will be useful in the identification and monitoring of disease. These diseases may include but are not limited to hip and knee osteoarthritis, spinal pathologies, Parkinson’s disease, motor neuron disease, myopathies, and cardiorespiratory disorders. Future research can be focused towards analysing intra- and inter-bout variability trends across different diseases, such that a patient’s unique intra- and inter-bout variability can be matched with disease-specific patterns to aid in clinical decision-making, the assessment of disability, and potentially pathology recognition with artificial intelligence assistance. In addition, once disease is established, changes in a patient’s intra- and inter-bout variability could indicate an improvement or deterioration in symptoms and quantify the benefit of any intervention.

Acknowledgments

The NeuroSpine Clinic aided with recruitment of our example patient, Mr. R. The Wearables and Gait Analysis Research Group provided the wearable sensor used to evaluate our example patient.

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Spine Surgery for the series “Objective Monitoring and Wearable Technologies including Sensor-Based Accelerometers and Mobile Health Applications for the Spine Patient”. The article did not undergo external peer review.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jss.amegroups.com/article/view/10.21037/jss-21-88/coif). The series “Objective Monitoring and Wearable Technologies including Sensor-Based Accelerometers and Mobile Health Applications for the Spine Patient” was commissioned by the editorial office without any funding or sponsorship. RJM served as the unpaid Guest Editor of the series and serves as the Editor-in-Chief of Journal of Spine Surgery. RDF and PN served as the unpaid Guest Editors of the series and serve as unpaid Assistant Managing Editors of Journal of Spine Surgery. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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