Abstract
-
Objective
To evaluate early postoperative mobility after lumbar decompression using real-time location system (RTLS)-derived objective metrics and to explore differences in mobility patterns between biportal endoscopic decompression and open decompression.
-
Methods
This retrospective cohort study included 323 patients who underwent lumbar decompression for degenerative lumbar spinal stenosis between March 2020 and May 2024. RTLS sensors embedded in wristbands continuously recorded patient mobility during postoperative days (PODs) 1–4. Primary RTLS-derived outcomes included total walking distance, mean walking speed, and active movement ratios (top 20% and top 50%). Between-group comparisons were performed using nonparametric tests. Propensity score matching and multivariable median quantile regression adjusting for age, American Society of Anesthesiologists physical status, and preoperative mobility were conducted.
-
Results
RTLS identified differences in early postoperative activity patterns between surgical approaches. In adjusted analyses, activity-intensity–based metrics, particularly the top 20% activity ratio, remained significantly higher in the biportal endoscopic decompression group across multiple PODs. Subgroup analyses demonstrated minimal differences after single-level decompression, whereas activity-based differences were more frequently observed in multilevel procedures.
-
Conclusion
RTLS-based continuous monitoring detected differences in early postoperative activity patterns following lumbar decompression. These findings support the role of RTLS as an objective tool for assessing early functional recovery in spine surgery.
-
Keywords: Lumbar spinal stenosis, Minimally invasive spine surgery, Biportal endoscopic spine surgery, Real-time location system, Postoperative recovery, Objective mobility assessment
INTRODUCTION
Degenerative lumbar spinal stenosis is a common and debilitating condition associated with aging, leading to chronic pain and impaired mobility [
1]. Decompression surgery remains the treatment of choice for patients with persistent symptoms and progressive neurologic deficits [
2-
4]. Minimally invasive techniques, including biportal endoscopic spine surgery, have demonstrated advantages in reducing postoperative pain and preserving paraspinal structures, potentially accelerating recovery compared with traditional open decompression [
5-
13].
However, existing outcome measures for evaluating recovery after spine surgery rely heavily on patient-reported scales and physician-rated scores, which are inherently subjective and vulnerable to recall bias. These assessments are also limited by their intermittent nature and dependence on active patient cooperation. Therefore, there is a need for objective, continuous, and scalable methods to monitor early postoperative functional recovery, particularly in the context of value-based healthcare and enhanced recovery after surgery protocols [
14,
15].
Real-time location systems (RTLSs) represent a promising solution to address these challenges. RTLS uses wireless sensors, typically embedded in lightweight wristbands, to transmit patient location data in real-time through a hospital-based receiver network. These systems leverage radiofrequency or Bluetooth signals to continuously record patients’ positions and movement trajectories without requiring any active patient input, allowing for uninterrupted, high-resolution monitoring of mobility patterns. This automated approach overcomes the compliance problems of wearable accelerometers and provides a granular picture of functional status during the critical postoperative recovery period. Although RTLS systems have been successfully applied in hospital operations and infection control [
16-
18], they have rarely been applied in postoperative outcome assessment tools.
In this retrospective cohort study, we aimed to characterize early postoperative mobility patterns using RTLS-derived parameters and to explore differences according to surgical approach. We hypothesized that RTLS would detect meaningful differences in early mobility between techniques, particularly in multilevel procedures, and that RTLS could provide an objective framework for postoperative functional assessment.
MATERIALS AND METHODS
1. Patient Selection and Data Collection
We conducted a retrospective cohort study of patients who underwent lumbar decompression surgery for degenerative lumbar spinal stenosis between March 1, 2020, and May 30, 2024. This study was approved by the institutional review board (IRB No. 9-2024-0100), and the requirement for written informed consent was waived because of the retrospective study design. Also, our RTLS-based data was conducted using YIRS-DB (Yongin Severance Hospital Integration & Response Space Database), an anonymized institutional clinical database. Surgical treatment was indicated for patients with at least moderate-to-severe lumbar spinal canal stenosis on magnetic resonance imaging, with disabling symptoms despite at least 3 months of conservative care or those with progressive neurologic deficits. Among 597 patients screened, 323 were included after applying exclusion criteria to avoid confounding from postoperative complications that may independently restrict ambulation such as patients with intraoperative dural tear, postoperative infection, revision surgery within the same admission or within 1 month, or conditions requiring prolonged bed rest (concomitant lower extremity procedures, prolonged intensive care unit stay), missing RTLS data and fusion surgery were excluded from the analysis. Only patients who were able to participate in early postoperative ambulation under the standardized ward protocol were included in the final analysis. Patients were classified according to the surgical approach as either open decompression or biportal endoscopic decompression (
Fig. 1). All surgeries were performed by experienced spine surgeons (≥5 years of practice in their respective technique). All patients had provided broad consent for RTLS tracking on hospital admission. In our institution, all postoperative lumbar decompression patients routinely undergo laboratory testing through postoperative day (POD) 3, and discharge is determined only after confirming the absence of abnormal laboratory findings or wound-related issues. Therefore, even patients undergoing single-level decompression typically remain hospitalized until at least POD 4, ensuring complete and unbiased RTLS acquisition during the 4-day observation window.
All patients wore wristbands equipped with an RTLS sensor that continuously recorded movement data throughout hospitalization. The RTLS captured location coordinates and timestamps whenever a change of location was detected. RTLS data engineering was performed according to the methods described by Kim et al. [
17], who used similar protocols to extract and integrate movement data with clinical variables for predictive modeling. Data were automatically anonymized and integrated into the hospital information system.
To prevent artificial inflation of mobility metrics due to assisted mobilization, movements occurring in the designated rehabilitation area were excluded, as patients are transported to this area by wheelchair or cart and do not ambulate independently during therapy sessions. Also, all included patients were admitted to the same surgical ward and managed under identical postoperative mobilization instructions, ensuring consistency in activity opportunities and ward-level movement patterns. Additionally, movements with a velocity exceeding 3 m/sec were considered to be associated with elevator use or other mechanical transportation, and were therefore excluded from the analysis. Only data collected for at least 4 hours per POD were included in the analysis. Preoperative RTLS data were collected within 24 hours prior to surgery. Because monitoring initiation depended on individual admission timing and surgical scheduling, the duration of preoperative observation varied among patients. Only cases with at least approximately 5 hours of recorded preoperative data were included for baseline mobility comparison.
The following RTLS-derived parameters were calculated for PODs 1–4:
• Mean total distance (m): daily average cumulative distance moved.
• Mean walking speed (m/sec): average velocity, computed as total distance divided by total movement time.
• Active moving ratio top 20% (%): proportion of time spent moving at speeds in the top 20% of observed values (above 0.98 m/sec).
• Active moving ratio top 50% (%): proportion of time spent moving at speeds in the top 50% of observed values (above 0.301 m/sec).
These thresholds were derived from the distribution of walking speeds within the present study cohort and were used to enable relative comparisons of early postoperative activity patterns within the same inpatient environment. They were not intended to represent externally validated or normative functional cut-offs.
3. Clinical Outcomes Assessment
Postoperative clinical improvement was evaluated using patient-reported pain scores. Back pain and leg pain were assessed preoperatively and again on POD 3 using a standard 0–10 visual analogue scale (VAS). These time points were selected because VAS assessment on POD 3 is routinely performed at our institution as part of discharge readiness evaluation, and more than 95% of patients remain hospitalized at that time, allowing for complete data capture.
Because pain may influence early ambulation and activity levels, intravenous patient-controlled analgesia (IV PCA) was administered immediately after surgery according to a standardized ward-based institutional protocol for postoperative pain control. IV PCA-related parameters, including removal time and remnant volume, were collected as secondary variables to indirectly reflect early postoperative analgesic requirements and are presented in the results tables. The use of pro re nata oral analgesics was determined based on individual patient condition.
4. Statistical Analysis
Continuous variables were summarized as medians with interquartile ranges (IQRs) and compared using the Mann-Whitney U-test due to their nonnormal distribution. Categorical variables were analyzed using the chi-square or Fisher exact test, as appropriate.
Given the baseline imbalance in age and American Society of Anesthesiologists physical status (ASA PS) classification between groups, adjusted comparisons for RTLS-derived mobility outcomes were performed using median quantile regression (τ=0.5). The primary multivariable model included age, ASA PS classification, and preoperative RTLS mobility parameters as covariates. Sensitivity analyses additionally incorporating operative time and estimated blood loss were conducted to evaluate the robustness of the findings.
Propensity score matching (PSM) was additionally performed to further address baseline imbalance. Propensity scores were estimated using logistic regression including age and ASA PS classification as covariates, followed by one-to-one nearest-neighbor matching without replacement. Covariate balance was evaluated using standardized mean differences (SMDs), with SMD <0.1 indicating adequate balance. Multivariable regression and PSM were applied as complementary approaches to account for baseline differences.
For patient-reported pain outcomes, between-group differences in improvement from baseline to POD 3 (Δ) were assessed using the Mann-Whitney U-test. Within-group changes were summarized descriptively, and the primary comparison focused on between-group differences in Δ.
Because RTLS metrics (e.g., total walking distance) may be influenced by variability in observation duration, RTLS outcomes were summarized using medians and analyzed as group-level distributions rather than paired longitudinal trajectories. For between-group comparisons of multiple mobility metrics across PODs, p-values were adjusted using the Holm method to control the family-wise error rate.
Subgroup analyses were performed according to the number of decompressed levels (1, 2, or ≥3). All analyses were conducted using R ver. 4.1.2 (R Foundation for Statistical Computing, Austria). A 2-sided adjusted p-value <0.05 was considered statistically significant.
RESULTS
1. Baseline Characteristics
Of the 323 patients included, 201 underwent open decompression and 122 underwent biportal endoscopic decompression. The baseline characteristics were similar between groups except for age, with the open decompression group being significantly older (median age, 74.0 [IQR, 67.0–79.0] years vs. 69.0 [IQR, 61.0–77.0] years; p<0.001). The distribution of ASA PS classification differed marginally between groups (p=0.054). Sex distribution (male: 40.8% vs. 44.3%, p=0.62), body mass index (26.0 kg/m² vs. 26.1 kg/m², p=0.376), and number of decompressed levels (p=0.08) showed no significant differences between groups. Operative time was longer in the biportal endoscopic decompression group (93.8±30.6 minutes vs. 80.8± 26.0 minutes, p<0.001), whereas estimated blood loss was lower (60.2±22.7 mL vs. 78.3±23.0 mL, p<0.001) (
Table 1). After PSM based on age and ASA PS classification, baseline covariates were well balanced between groups (
Table 2).
Preoperative RTLS data were available for 317 patients, and no significant between-group differences were observed across baseline RTLS mobility metrics.
In unadjusted analyses, the biportal endoscopic decompression group demonstrated significantly greater total walking distance than the open decompression group from POD 1 through POD 4 (POD 1: 3,333 m vs. 2,540 m, p=0.025; POD 2: 4,558 m vs. 3,372 m, p=0.003; POD 3: 4,697 m vs. 3,723 m, p=0.043; POD 4: 5,121 m vs. 3,814 m, p=0.018). Walking speed and activity-based metrics also showed more favorable trends in the biportal endoscopic decompression group during the early postoperative period.
Following PSM based on age and ASA PS classification, 81 matched pairs were generated and baseline covariates were adequately balanced (
Table 2). In the matched cohort, total walking distance remained significantly higher in the biportal endoscopic decompression group only on POD 2 (Holm-adjusted p=0.048). The top 20% active movement ratio was significantly higher in the biportal endoscopic decompression group on POD 1 and POD 2 (Holm-adjusted p=0.001 and p=0.009, respectively). The top 50% active movement ratio was significantly higher on POD 2 (Holm-adjusted p=0.043). No significant differences were observed in walking speed after matching.
After multivariable adjustment using median quantile regression including age, ASA PS classification, operative time, estimated blood loss, and preoperative RTLS mobility parameters, no significant between-group differences were observed in total walking distance across PODs (
Table 3;
Fig. 2). Walking speed was significantly higher in the biportal endoscopic decompression group on POD 3 (Holm-adjusted p=0.016), whereas other PODs were not significant. The top 20% active movement ratio remained significantly higher in the biportal endoscopic decompression group on POD 1–4 (Holm-adjusted p=0.006, p<0.001, p=0.030, and p=0.030, respectively). No significant between-group differences were observed for the top 50% active movement ratio after adjustment. Sensitivity analyses incorporating operative time and estimated blood loss demonstrated consistent findings (
Supplementary Table 1).
Preoperative back and leg VAS scores were comparable between groups (
Table 4). At POD 3, both groups demonstrated reductions in back and leg VAS scores compared with baseline. Between-group comparisons showed no significant difference in Δ back VAS (p=0.441). However, Δ leg VAS was significantly greater in the biportal endoscopic decompression group compared with the open decompression group (p=0.048).
Regarding analgesic-related variables, the biportal endoscopic decompression group demonstrated earlier IV PCA removal compared with the open decompression group (median POD 1 vs. POD 2, p<0.001). In addition, the remnant PCA volume was greater in the biportal endoscopic decompression group (median 45.0 mg vs. 25.0 mg, p<0.001) (
Table 4).
In single-level decompression, no significant between-group differences were observed in RTLS-derived mobility metrics (
Table 5;
Fig. 3). Regarding clinical outcomes, Δ back VAS was significantly greater in the open decompression group (p<0.001), whereas Δ leg VAS did not differ between groups (p=1.000). For analgesic-related variables, IV PCA removal occurred earlier in the biportal endoscopic decompression group (p<0.001), and remnant PCA volume was higher (p<0.001) (
Table 6).
In 2-level decompression, the top 20% active movement ratio was significantly higher in the biportal endoscopic decompression group on POD 1 and POD 2 (p=0.005 and p=0.005, respectively) (
Table 5). Clinical outcomes showed greater reductions in both Δ back VAS and Δ leg VAS in the biportal endoscopic decompression group (both p<0.001) (
Table 6). IV PCA removal occurred earlier (p<0.001), and remnant PCA volume was higher in the biportal endoscopic decompression group (p<0.001).
In 3-level decompression, walking speed was significantly higher in the biportal endoscopic decompression group on POD 3 and POD 4 (p=0.028 and p<0.001, respectively). The top 20% active movement ratio was higher on POD 2–4, and the top 50% ratio was higher on POD 3–4 (
Table 5). In contrast, no significant between-group differences were observed in Δ back VAS (p=0.053) or Δ leg VAS (p=1.000) (
Table 6). IV PCA removal occurred earlier in the biportal endoscopic decompression group (p=0.023), whereas remnant PCA volume did not differ significantly (p=0.21).
DISCUSSION
This retrospective cohort study demonstrates that RTLS metrics provide objective, continuous, and unobtrusive monitoring of early functional recovery following lumbar decompression surgery. Compared with conventional open decompression, biportal endoscopic decompression was associated with differences in postoperative mobility that were most consistently observed in activity-intensity–based metrics rather than total walking distance. While quantitative differences in total ambulation were attenuated after PSM and multivariable adjustment, higher-intensity activity measures—particularly the top 20% active movement ratio—remained significantly different across PODs. In subgroup analyses, these differences were more evident in multilevel decompressions, although the magnitude and consistency of between-group differences varied according to surgical complexity. The observed age imbalance between groups reflected surgeon-specific preferences for surgical techniques during the study period rather than differences in surgical indication. To minimize potential confounding, both PSM based on age and ASA PS classification and multivariable regression adjustment were performed.
1. Advantages of RTLS Technology
Traditional endpoints such as pain scores and health-related quality-of-life questionnaires capture only discrete snapshots of recovery and are vulnerable to recall bias and subjective interpretation. RTLS continuously and automatically records patient movement within the hospital ward, enabling passive collection of objective real-world clinical data without requiring additional effort from patients. This capability enables consistent and uninterrupted mobility monitoring, which is particularly valuable for assessing recovery in vulnerable populations such as elderly patients or those with cognitive impairment.
In this study, incorporation of preoperative RTLS data enabled confirmation that baseline mobility metrics were comparable between groups prior to surgery. This baseline similarity supports the interpretation that subsequent between-group differences observed in postoperative activity patterns—particularly in activity-intensity–based metrics—were not attributable to preexisting mobility disparities.
The analysis of high-activity segments using RTLS-derived gait speed and active movement ratios provides complementary information beyond total walking distance. Patients may accumulate substantial distance at low speeds, which may not fully reflect differences in activity intensity. Gait speed has been widely studied as a health indicator in older adults, with prior reports suggesting associations between speeds of approximately 0.8 m/s and functional vulnerability, and ≥1.0 m/sec and favorable long-term outcomes [
19,
20]. In the present study, the cohort-derived threshold used to define the top 20% activity ratio corresponded to approximately 0.98 m/sec. This value was not intended to represent a validated clinical cut-off, but rather to serve as an internally defined analytic reference point for relative comparisons within the same postoperative inpatient environment.
In subgroup analyses stratified by the number of decompressed levels, no consistent between-group differences in RTLS-derived mobility metrics were observed in single-level decompression. In contrast, in 2- and 3-level procedures, between-group differences were more frequently observed in activity-intensity–based parameters, particularly the top 20% active movement ratio, as well as in walking speed at selected postoperative time points. However, these differences were not uniform across all mobility measures or across all PODs.
These findings are consistent with established biomechanical and histologic evidence [
21-
30]. Multilevel open decompression may require more extensive muscle dissection and posterior ligament disruption, potentially resulting in greater soft-tissue injury. In contrast, biportal endoscopic decompression is designed to preserve posterior supporting structures and minimize muscular trauma. Differences in the extent of tissue disruption may partially contribute to variations in early postoperative activity patterns, particularly in higher-intensity ambulation.
With respect to clinical outcomes, both surgical approaches demonstrated improvement in back and leg pain by POD 3. Improvement in back pain was comparable between groups, whereas reduction in leg pain was greater in the biportal endoscopic decompression group in the overall cohort. These findings suggest that differences in RTLS-derived activity patterns cannot be explained solely by differences in back pain improvement. Previous studies have reported reduced pain, lower infection rates, and shorter hospital stays with biportal endoscopic decompression; however, these investigations relied predominantly on patient-reported outcomes [
8-
13]. By incorporating continuous RTLS-based mobility monitoring, the present study provides a more detailed characterization of early postoperative ambulation patterns that may not be fully captured by pain scores alone.
Several recent studies have explored postoperative outcome assessment using wearable devices, such as accelerometers, to quantify physical activity following spine surgery [
31,
32]. While these wearable approaches provide valuable real-world performance data, they face significant limitations related to patient compliance, device-wearing errors, and data collection gaps. The scalability of RTLS technology represents a substantial advantage over wearable devices. RTLS systems integrate seamlessly into existing hospital infrastructure, enabling passive monitoring that ensures comprehensive data collection without compliance concerns.
An additional advantage of RTLS is the ability to segment mobility data according to activity intensity, enabling differentiation between low-speed ambulation and higher-intensity movement. This feature is particularly relevant in the early postoperative period, when total distance alone may not fully reflect differences in functional activity patterns.
To avoid overestimation of voluntary ambulation, RTLS signals detected within designated rehabilitation therapy areas were excluded from analysis. Patients are transported to and from these areas via wheelchair or cart, and activities performed under supervised therapy may not represent spontaneous, patient-initiated mobility. Excluding these zones allowed for more accurate characterization of genuine inpatient ambulation patterns.
4. Limitations
This study has several limitations. First, its retrospective design precludes definitive causal inference. Although PSM and multivariable adjustment based on age and ASA PS classification were performed, residual confounding cannot be entirely excluded. In addition, as a single-center study, the generalizability of these findings may be limited. Second, RTLS-based mobility monitoring was confined to the inpatient period and does not capture postoperative recovery after discharge. Variability in preoperative monitoring duration limited detailed patient-level longitudinal trajectory analysis. Postoperative analgesic regimens were not fully standardized, and the potential influence of additional oral analgesic use or individual pain tolerance on early ambulation cannot be completely excluded. Finally, pain assessment was limited to short-term VAS scores obtained at POD 3, and no long-term patient-reported outcome measures (e.g., Oswestry Disability Index or EuroQoL-5 dimensions) were collected. Therefore, the findings should be interpreted as reflecting early postoperative recovery, and the relationship between RTLS-derived mobility metrics and long-term functional outcomes remains to be established. Moreover, the activity-intensity thresholds used in this study were derived from the walking speed distribution of the study cohort and do not represent externally validated functional cut-off values.
CONCLUSION
RTLS identified differences in activity patterns during the early recovery phase following lumbar decompression surgery. Differences between surgical approaches were more consistently observed in activity-intensity–based mobility metrics, with this tendency being more pronounced in multilevel procedures. These findings support the utility of RTLS as an objective tool for assessing early postoperative mobility and suggest its potential value in recovery monitoring and quality improvement strategies in spine surgery.
Supplementary Material
NOTES
-
Conflict of Interest
The authors have nothing to disclose.
-
Funding/Support
This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
-
Author Contribution
Conceptualization: SRP, JL; Data curation: SRP, NK, SK, DY, DWK; Formal analysis: BHL, NK, SK, KMK, DY, DWK; Methodology: SRP, BHL, JL, DY, DWK; Writing – original draft: SRP, JL; Writing – review & editing: SRP, BHL, NK, JL, KMK.
Fig. 1.Patient selection flowchart. RTLS, real-time location system; ICU, intensive care unit; BESS, biportal endoscopic spine surgery.
Fig. 2.Adjusted comparison of RTLS-derived mobility parameters between open decompression and biportal endoscopic decompression. Between-group comparisons were performed using median quantile regression adjusted for age, ASA PS classification, and preoperative RTLS mobility parameters. p-values were corrected for multiple comparisons using the Holm method. *p<0.05, statistically significant between-group differences. The open decompression group is shown in light gray and the biportal endoscopic decompression group is shown in dark gray. RTLS, real-time location system; ASA PS, American Society of Anesthesiologists physical status; Preop, preoperative; POD, postoperative day; BESS, biportal endoscopic spine surgery.
Fig. 3.Level-stratified comparison of RTLS-derived mobility parameters between open decompression and biportal endoscopic decompression. Between-group comparisons within each surgical level were performed using the Mann-Whitney U-test, with p-values adjusted for multiple comparisons using the Holm method. *p<0.05, statistically significant between-group differences. The open decompression group is shown in light gray and the biportal endoscopic decompression group is shown in dark gray. RTLS, real-time location system; L1, 1-level decompression; L2, 2-level decompression; L3, 3-level decompression; Preop, preoperative; POD, postoperative day.
Table 1.
Table 1.
|
Variable |
Total (n = 323) |
Open decompression (n = 201) |
BESS decompression (n = 122) |
p-value |
|
Sex |
|
|
|
0.620 |
|
Male |
136 (42.1) |
82 (40.8) |
54 (44.3) |
|
|
Female |
187 (57.9) |
119 (59.2) |
68 (55.7) |
|
|
Age (yr) |
70.8 ± 10.5 |
72.6 ± 8.9 |
67.9 ± 12.1 |
< 0.001*
|
|
BMI (kg/m2) |
26.1 ± 3.8 |
25.9 ± 3.6 |
26.3 ± 4.0 |
0.376 |
|
Surgical level |
|
|
|
0.080 |
|
1 |
128 (39.6) |
85 (42.3) |
43 (35.2) |
|
|
2 |
156 (48.3) |
87 (43.3) |
69 (56.6) |
|
|
3 |
36 (11.2) |
26 (12.9) |
10 (8.2) |
|
|
4 |
3 (0.9) |
3 (1.5) |
0 (0) |
|
|
ASA PS classification grade |
|
|
|
0.054 |
|
I |
24 (7.4) |
9 (4.5) |
15 (12.3) |
|
|
II |
171 (52.9) |
114 (56.7) |
57 (46.7) |
|
|
III |
122 (37.8) |
75 (37.3) |
47 (38.5) |
|
|
IV |
6 (1.9) |
3 (1.5) |
3 (2.5) |
|
|
Operation time (min) |
85.8 ± 28.7 |
80.8 ± 26.0 |
93.8 ± 30.6 |
< 0.001*
|
|
Estimated blood loss (mL) |
71.6 ± 24.3 |
78.3 ± 23.0 |
60.2 ± 22.7 |
< 0.001*
|
Table 2.Baseline characteristics before and after age and ASA PS classification-based propensity score matching
Table 2.
|
Variable |
Open decompression (n = 81) |
BESS decompression (n = 81) |
SMD |
p-value |
|
Age (yr) |
73.5 (65.0–78.0) |
73.5 (65.0–78.0) |
0.001 |
|
|
ASA PS classification grade |
|
|
0.001 |
|
|
I |
3 (3.7) |
3 (3.7) |
|
|
|
II |
47 (58.0) |
47 (58.0) |
|
|
|
III |
30 (37.0) |
30 (37.0) |
|
|
|
IV |
1 (1.2) |
1 (1.2) |
|
|
|
Total distance (m) |
|
|
- |
|
|
Preoperative |
3,647.4 (2,282.5–4,880.3) |
3,579.1 (2,624.6–4,805.2) |
|
0.870 |
|
POD #1 |
2,460.5 (1,426.4–5,194.7) |
3,222.9 (1,931.0–5,212.5) |
|
0.132 |
|
POD #2 |
3,396.6 (2,143.6–5,334.7) |
4,343.2 (2,698.6–6,194.0) |
|
0.048 |
|
POD #3 |
3,948.0 (2,360.2–6,222.8) |
4,696.8 (2,774.6–6,408.3) |
|
0.273 |
|
POD #4 |
3,885.4 (2,274.6–6,082.3) |
5,378.9 (2,532.6–7,554.9) |
|
0.176 |
|
Walking speed (m/sec) |
|
|
- |
|
|
Preoperative |
0.06 (0.05–0.08) |
0.05 (0.04–0.07) |
|
0.145 |
|
POD #1 |
0.03 (0.02–0.06) |
0.04 (0.02–0.06) |
|
0.101 |
|
POD #2 |
0.04 (0.03–0.06) |
0.05 (0.03–0.07) |
|
0.051 |
|
POD #3 |
0.05 (0.03–0.07) |
0.06 (0.04–0.08) |
|
0.119 |
|
POD #4 |
0.05 (0.03–0.09) |
0.07 (0.03–0.10) |
|
0.382 |
|
Active moving ratio (top 20%) (%) |
|
|
- |
|
|
Preoperative |
1.0 (0.6–1.5) |
0.7 (0.5–1.1) |
|
0.011 |
|
POD #1 |
0.3 (0.1–0.9) |
0.6 (0.3–1.2) |
|
0.001 |
|
POD #2 |
0.5 (0.3–1.1) |
0.9 (0.6–1.5) |
|
0.009 |
|
POD #3 |
0.8 (0.4–1.4) |
1.0 (0.5–1.8) |
|
0.217 |
|
POD #4 |
0.7 (0.5–1.6) |
1.4 (0.5–2.5) |
|
0.248 |
|
Active moving ratio (top 50%) (%) |
|
|
- |
|
|
Preoperative |
4.7 (3.2–6.2) |
3.8 (3.0–5.3) |
|
0.061 |
|
POD #1 |
1.9 (1.0–4.6) |
2.7 (1.6–4.9) |
|
0.053 |
|
POD #2 |
2.9 (1.9–4.7) |
3.8 (2.4–5.6) |
|
0.043 |
|
POD #3 |
3.5 (2.3–6.0) |
4.4 (2.8–5.8) |
|
0.163 |
|
POD #4 |
3.4 (2.2–6.3) |
5.3 (2.2–6.9) |
|
0.337 |
Table 3.Adjusted between-group comparisons of RTLS-derived mobility outcomes using median quantile regression
Table 3.
|
Variable |
Open decompression group (n = 201) |
BESS decompression group (n = 122) |
Adjusted p-value |
|
Total distance (m) |
|
|
|
|
Preoperative |
3,421 (2,239–5,197) |
4,021 (2,667–5,405) |
|
|
POD #1 |
2,540 (1,498–4,923) |
3,333 (1,991–5,793) |
0.914 |
|
POD #2 |
3,372 (2,111–5,383) |
4,558 (2,905–6,211) |
0.872 |
|
POD #3 |
3,723 (2,313–6,137) |
4,697 (2,755–6,408) |
0.872 |
|
POD #4 |
3,814 (2,249–5,999) |
5,121 (2,764–7,434) |
0.252 |
|
Walking speed (m/sec) |
|
|
|
|
Preoperative |
0.06 (0.04–0.08) |
0.06 (0.04–0.08) |
|
|
POD #1 |
0.03 (0.02–0.06) |
0.04 (0.02–0.07) |
0.428 |
|
POD #2 |
0.04 (0.03–0.06) |
0.05 (0.03–0.07) |
0.428 |
|
POD #3 |
0.04 (0.03–0.07) |
0.06 (0.04–0.08) |
0.016*
|
|
POD #4 |
0.05 (0.03–0.08) |
0.07 (0.04–0.09) |
0.396 |
|
Active moving ratio top 20% (%) |
|
|
|
|
Preoperative |
1.0 (0.5–1.5) |
0.8 (0.5–1.3) |
|
|
POD #1 |
0.4 (0.1–0.7) |
0.6 (0.3–1.2) |
0.006*
|
|
POD #2 |
0.5 (0.2–1.2) |
0.9 (0.6–1.5) |
< 0.001*
|
|
POD #3 |
0.8 (0.4–1.4) |
1.1 (0.6–1.9) |
0.030*
|
|
POD #4 |
0.8 (0.4–1.6) |
1.4 (0.5–1.9) |
0.030*
|
|
Active moving ratio top 50% (%) |
|
|
|
|
Preoperative |
4.3 (3.0–6.3) |
4.4 (3.2–5.8) |
|
|
POD #1 |
2.2 (1.1–4.5) |
2.9 (1.6–5.0) |
0.112 |
|
POD #2 |
2.9 (1.7–4.8) |
3.8 (2.5–5.6) |
0.255 |
|
POD #3 |
3.4 (2.2–5.9) |
4.5 (2.9–6.0) |
0.112 |
|
POD #4 |
3.5 (2.2–6.3) |
5.2 (2.4–6.9) |
0.112 |
Table 4.Between-group comparisons of early postoperative clinical outcomes and analgesic exposure
Table 4.
|
Variable |
Open decompression (n = 201) |
BESS decompression (n = 122) |
p-value |
|
Back VAS |
|
|
|
|
Preoperative |
5.0 (5.0–6.0) |
6.0 (4.0–7.0) |
0.882 |
|
POD #3 |
3.0 (3.0–4.0) |
3.0 (2.0–3.0) |
0.090 |
|
Δ Back VAS |
2.0 (2.0–3.0) |
3.0 (2.0–4.0) |
0.441 |
|
Leg VAS |
|
|
|
|
Preoperative |
7.0 (6.0–8.0) |
7.0 (6.0–7.0) |
0.771 |
|
POD #3 |
4.0 (3.0–5.0) |
3.0 (2.0–3.0) |
< 0.001* |
|
Δ Leg VAS |
3.0 (3.0–4.0) |
4.0 (3.0–5.0) |
0.048* |
|
Postoperative analgesia |
|
|
|
|
IV PCA removal day |
2.0 (2.0–2.0) |
1.0 (1.0–2.0) |
< 0.001* |
|
Remnant IV PCA (mg) |
25.0 (10.0–45.0) |
45.0 (30.0–60.0) |
< 0.001* |
Table 5.Surgical level-stratified comparisons of RTLS-derived mobility outcomes
Table 5.
|
Variable |
1-Level decompression
|
2-Level decompression
|
3-Level decompression
|
|
Open (n = 85) |
BESS (n = 43) |
p-value |
Open (n = 87) |
BESS (n = 68) |
p-value |
Open (n = 29) |
BESS (n = 10) |
p-value |
|
Total distance (m) |
|
Preoperative |
3,041 (2,125–5,269) |
4,146 (2,829–5,400) |
0.432 |
3,563 (2,368–5,297) |
3,579 (2,650–5,423) |
0.580 |
3,761 (2,415–5,033) |
4,527 (2,489–5,078) |
0.660 |
|
POD #1 |
2,755 (1,194–5,364) |
3,293 (1,902–5,880) |
0.750 |
2,674 (1,623–4,865) |
3,419 (2,036–5,204) |
0.504 |
2,085 (1,724–3,738) |
4,363 (2,058–6,403) |
0.240 |
|
POD #2 |
3,707 (2,144–5,648) |
4,579 (3,396–6,126) |
0.360 |
3,296 (2,253–5,276) |
4,537 (2,485–6,203) |
0.420 |
2,690 (1,729–5,106) |
4,760 (3,200–7,273) |
0.160 |
|
POD #3 |
3,948 (2,205–6,670) |
4,603 (2,775–5,796) |
0.750 |
3,814 (2,621–6,125) |
4,827 (2,735–6,413) |
0.504 |
3,068 (2,118–4,156) |
4,735 (3,525–8,738) |
0.186 |
|
POD #4 |
3,242 (2,241–6,627) |
5,753 (3,032–7,434) |
0.750 |
3,924 (2,615–5,560) |
4,611 (2,597–6,957) |
0.580 |
3,842 (1,781–5,111) |
8,427 (4,961–10,811) |
0.050 |
|
Walking speed (m/sec) |
|
Preoperative |
0.06 (0.04–0.08) |
0.06 (0.05–0.08) |
0.822 |
0.06 (0.04–0.08) |
0.05 (0.04–0.07) |
1.000 |
0.06 (0.04–0.08) |
0.08 (0.04–0.09) |
0.404 |
|
POD #1 |
0.03 (0.01–0.06) |
0.04 (0.02–0.07) |
0.822 |
0.03 (0.02–0.06) |
0.04 (0.03–0.06) |
0.500 |
0.03 (0.02–0.04) |
0.05 (0.03–0.08) |
0.240 |
|
POD #2 |
0.04 (0.03–0.07) |
0.05 (0.04–0.07) |
0.305 |
0.04 (0.03–0.06) |
0.05 (0.03–0.07) |
0.500 |
0.03 (0.02–0.06) |
0.06 (0.04–0.08) |
0.141 |
|
POD #3 |
0.05 (0.03–0.08) |
0.06 (0.05–0.08) |
0.520 |
0.04 (0.03–0.07) |
0.06 (0.03–0.08) |
0.500 |
0.04 (0.03–0.06) |
0.08 (0.05–0.10) |
0.028 |
|
POD #4 |
0.05 (0.03–0.10) |
0.07 (0.04–0.09) |
0.822 |
0.05 (0.03–0.08) |
0.06 (0.03–0.08) |
1.000 |
0.05 (0.02–0.06) |
0.12 (0.08–0.13) |
< 0.001*
|
|
Active moving ratio (top 20%) (%) |
|
Preoperative |
1.0 (0.5–1.5) |
0.8 (0.6–1.6) |
1.000 |
1.0 (0.6–1.6) |
0.8 (0.5–1.2) |
0.302 |
0.9 (0.5–1.5) |
0.6 (0.5–0.8) |
0.256 |
|
POD #1 |
0.4 (0.1–0.9) |
0.8 (0.3–1.2) |
0.055 |
0.3 (0.1–0.7) |
0.6 (0.3–1.3) |
0.005*
|
0.3 (0.1–0.6) |
0.5 (0.3–0.6) |
0.256 |
|
POD #2 |
0.8 (0.3–1.3) |
0.9 (0.7–1.4) |
0.600 |
0.5 (0.3–1.0) |
0.9 (0.6–1.6) |
0.005*
|
0.3 (0.2–0.6) |
1.0 (0.9–1.1) |
0.012*
|
|
POD #3 |
0.9 (0.4–1.7) |
1.1 (0.5–1.8) |
1.000 |
0.7 (0.3–1.2) |
1.1 (0.6–1.9) |
0.051 |
0.6 (0.3–0.8) |
1.1 (0.8–1.7) |
0.036*
|
|
POD #4 |
1.0 (0.5–2.0) |
1.6 (0.5–1.6) |
1.000 |
0.8 (0.4–1.3) |
1.1 (0.4–2.0) |
0.302 |
0.6 (0.3–1.0) |
1.9 (1.6–2.5) |
< 0.001*
|
|
Active moving ratio (top 50%) (%) |
|
Preoperative |
4.3 (2.9–6.4) |
4.7 (3.5–6.3) |
1.000 |
4.5 (3.1–6.3) |
4.2 (3.1–5.5) |
0.888 |
4.4 (2.9–5.7) |
5.2 (2.9–5.9) |
0.732 |
|
POD #1 |
2.3 (1.0–5.0) |
2.7 (1.4–5.4) |
0.836 |
2.2 (1.2–4.5) |
3.0 (1.7–4.9) |
0.336 |
1.7 (1.0–3.3) |
3.6 (1.2–6.7) |
0.310 |
|
POD #2 |
3.2 (1.9–5.2) |
3.8 (3.0–5.5) |
0.530 |
3.1 (1.8–4.5) |
3.8 (2.3–5.7) |
0.215 |
2.1 (1.4–4.6) |
4.4 (2.4–6.5) |
0.069 |
|
POD #3 |
3.9 (2.2–6.2) |
4.8 (3.2–5.9) |
0.836 |
3.4 (2.2–5.4) |
4.5 (2.7–5.6) |
0.351 |
2.9 (1.8–3.8) |
5.6 (3.7–7.8) |
0.024*
|
|
POD #4 |
4.2 (2.1–7.5) |
5.6 (2.2–6.9) |
1.000 |
3.5 (2.2–6.2) |
4.2 (2.4–6.1) |
0.888 |
3.2 (1.8–4.7) |
10.3 (6.2–10.9) |
< 0.001*
|
Table 6.Surgical level-stratified comparisons of clinical outcomes and analgesic exposure
Table 6.
|
Variable |
Open decompression |
BESS decompression |
p-value |
|
Level 1 |
|
|
|
|
Δ Back VAS |
3.0 (2.0–4.0) |
2.0 (1.0–2.0) |
< 0.001*
|
|
Δ Leg VAS |
4.0 (3.0–5.0) |
4.0 (3.0–5.0) |
1.000 |
|
IV PCA removal day |
2.0 (1.0–2.0) |
1.0 (1.0–1.5) |
< 0.001*
|
|
Remnant IV PCA (mg) |
40.0 (20.0–50.0) |
55.0 (42.5–67.5) |
< 0.001*
|
|
Level 2 |
|
|
|
|
Δ Back VAS |
2.0 (2.0–3.0) |
3.0 (2.0–4.0) |
< 0.001*
|
|
Δ Leg VAS |
3.0 (3.0–4.0) |
4.0 (3.0–5.0) |
< 0.001*
|
|
IV PCA removal day |
2.0 (2.0–2.0) |
1.0 (1.0–2.0) |
< 0.001*
|
|
Remnant IV PCA (mg) |
20.0 (10.0–35.0) |
42.5 (25.0–60.0) |
< 0.001*
|
|
Level 3 |
|
|
|
|
Δ Back VAS |
2.0 (2.0–3.0) |
3.5 (3.0–4.0) |
0.053 |
|
Δ Leg VAS |
3.0 (3.0–4.0) |
4.0 (2.2–4.8) |
1.000 |
|
IV PCA removal day |
2.0 (2.0–2.0) |
1.0 (1.0–2.0) |
0.023*
|
|
Remnant IV PCA (mg) |
10.0 (0.0–20.0) |
27.5 (10.0–43.8) |
0.210 |
REFERENCES
- 1. Ravindra VM, Senglaub SS, Rattani A, et al. Degenerative lumbar spine disease: estimating global incidence and worldwide volume. Global Spine J 2018;8:784-94.
- 2. Airaksinen O, Herno A, Turunen V, et al. Surgical outcome of 438 patients treated surgically for lumbar spinal stenosis. Spine (Phila Pa 1976) 1997;22:2278-82.
- 3. Atlas SJ, Keller RB, Wu YA, et al. Long-term outcomes of surgical and nonsurgical management of lumbar spinal stenosis: 8 to 10 year results from the maine lumbar spine study. Spine (Phila Pa 1976) 2005;30:936-43.
- 4. Gibson JN, Waddell G. Surgery for degenerative lumbar spondylosis: updated Cochrane Review. Spine (Phila Pa 1976) 2005;30:2312-20.
- 5. Eum JH, Heo DH, Son SK, et al. Percutaneous biportal endoscopic decompression for lumbar spinal stenosis: a technical note and preliminary clinical results. J Neurosurg Spine 2016;24:602-7.
- 6. Heo DH, Son SK, Eum JH, et al. Fully endoscopic lumbar interbody fusion using a percutaneous unilateral biportal endoscopic technique: technical note and preliminary clinical results. Neurosurg Focus 2017;43:E8.
- 7. Kim JE, Choi DJ. Biportal endoscopic transforaminal lumbar interbody fusion with arthroscopy. Clin Orthop Surg 2018;10:248-52.
- 8. Aygun H, Abdulshafi K. Unilateral biportal endoscopy versus tubular microendoscopy in management of single level degenerative lumbar canal stenosis: a prospective study. Clin Spine Surg 2021;34:E323-8.
- 9. Park MK, Son SK, Park WW, et al. Unilateral biportal endoscopy for decompression of extraforaminal stenosis at the lumbosacral junction: surgical techniques and clinical outcomes. Neurospine 2021;18:871-9.
- 10. Kang TW, Park SY, Oh H, et al. Risk of reoperation and infection after percutaneous endoscopic lumbar discectomy and open lumbar discectomy: a nationwide population-based study. Bone Joint J 2021;103-B:1392-9.
- 11. Masuda S, Fukasawa T, Takeuchi M, et al. Reoperation rates of microendoscopic discectomy compared with conventional open lumbar discectomy: a large-database study. Clin Orthop Relat Res 2023;481:145-54.
- 12. Li Z, Zhang C, Chen W, et al. Percutaneous endoscopic transforaminal discectomy versus conventional open lumbar discectomy for upper lumbar disc herniation: a comparative cohort study. Biomed Res Int 2020;2020:1852070.
- 13. Heo DH, Lee DC, Park CK. Comparative analysis of three types of minimally invasive decompressive surgery for lumbar central stenosis: biportal endoscopy, uniportal endoscopy, and microsurgery. Neurosurg Focus 2019;46:E9.
- 14. Knight SR, Ng N, Tsanas A, et al. Mobile devices and wearable technology for measuring patient outcomes after surgery: a systematic review. NPJ Digit Med 2021;4:157.
- 15. Altman AD, Helpman L, McGee J, et al. Enhanced recovery after surgery: implementing a new standard of surgical care. CMAJ 2019;191:E469-75.
- 16. Kim MH, Ryu UH, Heo SJ, et al. The potential role of an adjunctive real-time locating system in preventing secondary transmission of SARS-CoV-2 in a hospital environment: retrospective case-control study. J Med Internet Res 2022;24:e41395.
- 17. Kim DW, Seo J, Kwon S, et al. Predicting in-hospital fall risk using machine learning with real-time location system and electronic medical records. J Cachexia Sarcopenia Muscle 2025;16:e13713.
- 18. Kamel Boulos MN, Berry G. Real-time locating systems (RTLS) in healthcare: a condensed primer. Int J Health Geogr 2012;11:25.
- 19. Perera S, Patel KV, Rosano C, et al. Gait speed predicts incident disability: a pooled analysis. J Gerontol A Biol Sci Med Sci 2016;71:63-71.
- 20. Studenski S, Perera S, Patel K, et al. Gait speed and survival in older adults. JAMA 2011;305:50-8.
- 21. Kawaguchi Y, Matsui H, Tsuji H. Back muscle injury after posterior lumbar spine surgery. A histologic and enzymatic analysis. Spine (Phila Pa 1976) 1996;21:941-4.
- 22. Park SM, Kim HJ, Kim GU, et al. Learning curve for lumbar decompressive laminectomy in biportal endoscopic spinal surgery using the cumulative summation test for learning curve. World Neurosurg 2019;122:e1007-13.
- 23. Huang YH, Lien FC, Chao LY, et al. Full endoscopic uniportal unilateral laminotomy for bilateral decompression in degenerative lumbar spinal stenosis: highlight of ligamentum flavum detachment and survey of efficacy and safety in 2 years of follow-up. World Neurosurg 2020;134:e672-81.
- 24. Son HJ, Chang BS, Chang SY, et al. Midterm outcomes of muscle-preserving posterior lumbar decompression via sagittal splitting of the spinous process: minimum 5-year follow-up. Clin Orthop Surg 2023;15:800-8.
- 25. Park SM, Kim HJ, Yeom JS. Is minimally invasive surgery a game changer in spinal surgery? Asian Spine J 2024;18:743-52.
- 26. Park J, Ahn DK, Choi DJ. Treatment concept and technical considerations of biportal endoscopic spine surgery for lumbar spinal stenosis. Asian Spine J 2024;18:301-23.
- 27. Lee J, Ham DW, Song KS. A beginner's perspective on biportal endoscopic spine surgery in single-level lumbar decompression: a comparative study with a microscopic surgery. Clin Orthop Surg 2023;15:793-9.
- 28. Kim JE, Choi DJ, Park EJ. Clinical and radiological outcomes of foraminal decompression using unilateral biportal endoscopic spine surgery for lumbar foraminal stenosis. Clin Orthop Surg 2018;10:439-47.
- 29. Choi DJ, Kim JE, Jung JT, et al. Biportal endoscopic spine surgery for various foraminal lesions at the lumbosacral lesion. Asian Spine J 2018;12:569-73.
- 30. Eun DC, Lee YH, Park JO, et al. A comparative analysis of biportal endoscopic spine surgery and unilateral laminotomy for bilateral decompression in multilevel lumbar stenosis patients. J Clin Med 2023;12:1033.
- 31. Smuck M, Muaremi A, Zheng P, et al. Objective measurement of function following lumbar spinal stenosis decompression reveals improved functional capacity with stagnant real-life physical activity. Spine J 2018;18:15-21.
- 32. Stienen MN, Rezaii PG, Ho AL, et al. Objective activity tracking in spine surgery: a prospective feasibility study with a low-cost consumer grade wearable accelerometer. Sci Rep 2020;10:4939.
, Jaenam Lee2,*
