Decompression surgery improves gait quality in patients with symptomatic lumbar spinal stenosis

Loske, Stefan; Nüesch, Corina; Byrnes, Kimberly Sara; Fiebig, Oliver; Schären, Stefan; Netzer, Cordula

doi:10.1016/j.spinee.2018.04.016

Cited by 48 publications

(65 citation statements)

References 31 publications

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“…The nine constants together with the subject's attractor approximations are characteristic for a person's movement and the influence of the recording sensors. As known from previous studies [21,22] attractor is reached or a strange one is to be seen. Here, data of the cyclic motion never reaching compete regularity, neither is the behavior completely chaotic.…”

Section: Discussionmentioning

confidence: 57%

“…A study on cycling at two different power outputs (150 W and 300 W) at a cadence of 90 rpm [20] found differences in the muscle activities detected via EMG, while kinematic data stayed almost unchanged. This result together with the stability of the individual's attractor over time and after rehabilitation [21,22] is motivation to examine the possibility to quantitatively describe movement without the knowledge of muscle activity. The purpose of this paper is to precisely outline the kinematics of cyclic motion by establishing the necessary mathematical equations, which allow simulation.…”

Section: Introductionmentioning

confidence: 99%

“…Such an approximated attractor is characteristic for each individual [21,22]. The actual calculation starts with dividing each data set into one-minute sections and calculation of the attractors [23].…”

mentioning

confidence: 99%

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The kinematics of cyclic human movement

Vieten

Weich

2019

Preprint

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Models describing cyclic movement can roughly be divided into the categories theory or data driven. Theory driven models include anatomical and physiological aspects. They are principally suitable for answering questions about the reasons for movement characteristics. But, they are complicated and substantial simplifications do not allow generally valid results. Data driven models allow answering specific questions but lack the understanding of the general movement characteristic. With this paper we try a compromise not having to rely on anatomy, neurology and muscle function. We hypothesize a general kinematic description of cyclic human motion is possible without having to specify the movement generating processes, and still getting the kinematic right. The model proposed consisting of a superposition of six contributions -subject's attractor, morphing, short time fluctuation, transient effect, control mechanism and sensor noise -, with characterizing numbers and random contributions. We test the model with data form treadmill running and stationary biking. Applying the model in form of a simulation results in good agreement between measured data and simulation values.

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Section: Discussionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

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The kinematics of cyclic human movement

Vieten

Weich

2019

Preprint

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“…We found the daily step count to be a very responsive marker of overall physical activity, reflecting the reduced activity levels in the first 8-12 weeks after surgery well. Changes in activity were subtle after this time period, which likely mirrors the more gradual further improvement and rehabilitation often described for PROMs in a similar manner 29,35 . Our findings are in line with a prior study that found improved PROMs but no higher physical activity than baseline, six months after successful surgery for LSS 36 .…”

Section: Discussionmentioning

confidence: 59%

“…Increased physical activity can lead to better physical and mental outcomes [23][24][25][26] , and level of physical activity can serve as a useful measure of overall health 27 . Postoperative mobility has long been touted as a reliable indicator of surgical recovery and good post-operative prognosis 2,3,6,7,14,[28][29][30][31][32] . Most mobility data, especially in the perioperative setting, take the form of subjective self-reported questionnaires that suffer from recall bias, conformation to socially desirable responses, and are heavily influenced by mood, depression, anxiety, cognition, and disability 3,33,34 .…”

Section: Discussionmentioning

confidence: 99%

Objective activity tracking in spine surgery: a prospective feasibility study with a low-cost consumer grade wearable accelerometer

Stienen

Rezaii

et al. 2020

Sci Rep

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patient-reported outcome measures (pRoMs) are commonly used to estimate disability of patients with spinal degenerative disease. emerging technological advances present an opportunity to provide objective measurements of activity. In a prospective, observational study we utilized a lowcost consumer grade wearable accelerometer (LCA) to determine patient activity (steps per day) preoperatively (baseline) and up to one year (Y1) after cervical and lumbar spine surgery. We studied 30 patients (46.7% male; mean age 57 years; 70% Caucasian) with a baseline activity level of 5624 steps per day. The activity level decreased by 71% in the 1 st postoperative week (p < 0.001) and remained 37% lower in the 2 nd (p < 0.001) and 23% lower in the 4 th week (p = 0.015). At no time point until Y1 did patients increase their activity level, compared to baseline. Activity was greater in patients with cervical, as compared to patients with lumbar spine disease. Age, sex, ethnic group, anesthesia risk score and fusion were variables associated with activity. there was no correlation between activity and PROMs, but a strong correlation with depression. Determining activity using LCAs provides real-time and longitudinal information about patient mobility and return of function. Recovery took place over the first eight postoperative weeks, with subtle improvement afterwards.

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Classification of inertial sensor‐based gait patterns of orthopaedic conditions using machine learning: A pilot study

Dammeyer,

Nüesch,

Visscher

et al. 2024

Journal Orthopaedic Research

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Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making. Therefore, we investigated whether gait patterns could be used to identify degenerative diseases using machine learning. Data were extracted from a clinical database that included sagittal joint angles and spatiotemporal parameters measured using seven inertial sensors, and anthropometric data of patients with unilateral knee or hip osteoarthritis, lumbar or cervical spinal stenosis, and healthy controls. Various classification models were explored using the MATLAB Classification Learner app, and the optimizable Support Vector Machine was chosen as the best performing model. The accuracy of discrimination between healthy and pathologic gait was 82.3%, indicating that it is possible to distinguish pathological from healthy gait. The accuracy of discrimination between the different degenerative diseases was 51.4%, indicating the similarities in gait patterns between diseases need to be further explored. Overall, the differences between pathologic and healthy gait are distinct enough to classify using a classical machine learning model; however, routinely recorded gait characteristics and anthropometric data are not sufficient for successful discrimination of the degenerative diseases.

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Decompression surgery improves gait quality in patients with symptomatic lumbar spinal stenosis

Cited by 48 publications

References 31 publications

The kinematics of cyclic human movement

The kinematics of cyclic human movement

Objective activity tracking in spine surgery: a prospective feasibility study with a low-cost consumer grade wearable accelerometer

Classification of inertial sensor‐based gait patterns of orthopaedic conditions using machine learning: A pilot study

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