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. 2024 Mar 1:12:1355254.
doi: 10.3389/fbioe.2024.1355254. eCollection 2024.

Long-term continuous instrumented insole-based gait analyses in daily life have advantages over longitudinal gait analyses in the lab to monitor healing of tibial fractures

Elke Warmerdam  1 , Christian Wolff  2 , Marcel Orth  3 , Tim Pohlemann  3 , Bergita Ganse  1   3
Affiliations

Long-term continuous instrumented insole-based gait analyses in daily life have advantages over longitudinal gait analyses in the lab to monitor healing of tibial fractures

Elke Warmerdam et al. Front Bioeng Biotechnol. .

Abstract

Introduction: Monitoring changes in gait during rehabilitation allows early detection of complications. Laboratory-based gait analyses proved valuable for longitudinal monitoring of lower leg fracture healing. However, continuous gait data recorded in the daily life may be superior due to a higher temporal resolution and differences in behavior. In this study, ground reaction force-based gait data of instrumented insoles from longitudinal intermittent laboratory assessments were compared to monitoring in daily life. Methods: Straight walking data of patients were collected during clinical visits and in between those visits the instrumented insoles recorded all stepping activities of the patients during daily life. Results: Out of 16 patients, due to technical and compliance issues, only six delivered sufficient datasets of about 12 weeks. Stance duration was longer (p = 0.004) and gait was more asymmetric during daily life (asymmetry of maximal force p < 0.001, loading slope p = 0.001, unloading slope p < 0.001, stance duration p < 0.001). Discussion: The differences between the laboratory assessments and the daily-life monitoring could be caused by a different and more diverse behavior during daily life. The daily life gait parameters significantly improved over time with union. One of the patients developed an infected non-union and showed worsening of force-related gait parameters, which was earlier detectable in the continuous daily life gait data compared to the lab data. Therefore, continuous gait monitoring in the daily life has potential to detect healing problems early on. Continuous monitoring with instrumented insoles has advantages once technical and compliance problems are solved.

Keywords: digital medicine; fracture; ground reaction force; injury; pedography; postoperative treatment; rehabilitation; wearable sensors.

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Conflict of interest statement

TP is president and board member of the AO Foundation, Switzerland, and extended board member of the German Society of Orthopedic Trauma Surgery (DGU), the German Society of Orthopedic Surgery and Traumatology (DGOU), and the German Society of Surgery (DGCH). TP is also the speaker of the medical advisory board of the German Ministry of Defence. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Vertical ground reaction force curve of a stance phase during walking.
FIGURE 2
FIGURE 2
One Flow chart of patients enrolled in the study with information about the exclusion for the continuous data analysis.
FIGURE 3
FIGURE 3
Continuous daily life (solid lines) and lab data (dots connected by dashed lines) with each patient in a different color. The continuous daily-life data are presented as a 3-day moving average. The patient represented with the blue color developed an infected non-union. BW = body weight; T = time. (A) Maximal force, (B) Loading slope, (C) Unloading slope, (D) Stance duration, (E) Asymmetry of the maximal force, (F) Asymmetry of the loading slope, (G) Asymmetry of the unloading slope, (H) Asymmetry of the stance duration.
FIGURE 4
FIGURE 4
The distribution and boxplot of the lab data (blue) and continuous daily-life data (coral). p-values are written in each subfigure in case of a significant difference. The data in c and d were not normally distributed. (A) Maximal force, (B) Loading slope, (C) Unloading slope, (D) Stance duration, (E) Asymmetry of the maximal force, (F) Asymmetry of the loading slope, (G) Asymmetry of the unloading slope, (H) Asymmetry of the stance duration.
FIGURE 5
FIGURE 5
The total number of steps and the average walking bout length per day. Data are presented as a 3-day moving average. The patient represented with the blue color developed an infected non-union. (A) Total number of steps, (B) Average walking bout length.
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