Abstract
-
Objective
To longitudinally analyze smartphone-based real-life activity data and compare it with established clinical outcome measures in patients undergoing lumbar spine surgery for sciatica, focusing on identifying divergence in recovery trajectories.
-
Methods
Fifty patients were assessed preoperatively and at 6 weeks (6W), 3 months (3M), and 6 months (6M). Outcomes included smartphone-derived daily Step Count, objective capacity (6-minute walking test [6WT]), and subjective disability (visual analogue scale [VAS] leg/back, Core Outcome Measures Index [COMI] back, and Oswestry Disability Index [ODI]). All metrics were standardized into z-scores relative to baseline. Piecewise linear mixed-effects (LME) models compared recovery slopes across 2 segments: phase I (early: 0–6 weeks) and phase II (late: 6 weeks–6 months).
-
Results
The cohort (mean age, 50.7 years; 24 females) included 33 patients with lumbar disc herniation and 17 with lateral recess stenosis. All measures improved significantly during phase I (all p<0.05). However, LME modeling revealed a significant interaction between time segment and measurement type in phase II. Daily Step Count was the only metric maintaining a significant, linear upward recovery slope during the late phase (β=0.31 Z/mo). Conversely, slopes for 6WT, ODI, and COMI were significantly flatter (p<0.001 vs. Step Count), indicating a statistical plateau or “ceiling effect.” Spearman correlations between Step Count and traditional metrics weakened from strong at baseline to weak at 6 months.
-
Conclusion
Smartphone-derived real-life activity data detect continuous functional improvement up 6 months postoperatively, whereas conventional objective and subjective measures plateau by 6 weeks. Real-world activity monitoring provides a more sensitive assessment of long-term surgical success.
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Keywords: Real-life activity data, Physical functional performance, Objective functional impairment, Lumbar spine, external validity, Sciatica, Mobile applications
INTRODUCTION
Sciatica is a highly prevalent disability worldwide, affecting more than 500 million individuals [
1]. These conditions are associated with pain, neurological deficits, impaired mobility, and reduced health-related quality of life [
2,
3]. Although surgery can provide significant relief, accurate monitoring of functional recovery remains a challenge.
Traditionally, patient-reported outcome measures (PROMs) such as the Oswestry Disability Index (ODI) and Core Outcome Measures Index (COMI back) have been used to evaluate subjective disability [
4-
6]. While PROMs are validated and widely applied, they are limited by subjectivity, recall bias, and ceiling effects [
7,
8]. Objective functional tests such as the 6-minute walking test (6WT) and the Timed-Up-and-Go test have been introduced to provide standardized measures of physical capacity in patients with degenerative lumbar disorders (DLDs) [
9-
11]. Yet, these tests require maximal effort in controlled settings and may not fully reflect patients’ average daily activity.
Physical performance, defined as the habitual execution of daily activities, is distinct from physical capacity and may better represent real-life disability [
12]. With the widespread use of smartphones equipped with accelerometers and Global Positioning System (GPS), continuous monitoring of physical activity metrics such as Step Count has become feasible [
13]. Prior studies have shown that wearable- or smartphone-based measures correlate with PROMs and functional tests and can be stratified against normative reference populations [
14-
16]. However, most work has focused on heterogeneous patient groups or preoperative baseline assessments, leaving limited knowledge about longitudinal recovery in certain patient groups such as patients with sciatica undergoing surgery.
The aim of this study was to longitudinally evaluate smartphone-based daily Step Count in patients with sciatica undergoing lumbar spine surgery and to compare them with established outcome measures including 6WT, ODI, COMI back, and visual analogue scale (VAS) pain scores. We hypothesized that smartphone-derived activity data would provide a more sensitive measure of recovery, capturing continued improvements beyond the plateau observed with PROMs.
MATERIALS AND METHODS
1. Patient Inclusion
Between July 2022 and July 2025, we prospectively evaluated adult patients suffering from sciatica at Departments of Neurosurgery in Cantonal Hospital Lucerne (Switzerland, Center A), St. Gallen (Switzerland, Center B) and University Hospital in Innsbruck (Austria, Center C). This included patients with lumbar disc herniation (LDH) or lateral recess stenosis (LRS) due to bulging of a disc confirmed with magnetic resonance imaging of lumbar spine, who received microsurgical treatment. Our analysis focused specifically on patients exhibiting sciatica symptoms; we excluded those primarily experiencing back pain and/ or spinal claudication. The patient selection process followed specific inclusion and exclusion criteria detailed in
Supplementary Content 1, which established clear parameters for determining participant eligibility. Potential adverse events included those commonly associated with lumbar spine surgery, such as infection, neurological deficit, or reoperation, as well as study-related risks such as discomfort or technical issues related to smartphone-based activity monitoring.
All patients followed a standardized institutional postoperative care pathway, including early mobilization and routine follow-up. Decisions regarding postoperative physical therapy, including timing and duration, were individualized based on clinical assessment by the treating surgeon. No postoperative bracing was prescribed.
Missing data were handled using a complete-case analysis. No imputation methods were applied. Participants with incomplete follow-up data were excluded from analyses requiring those time points.
2. Demographic, Clinical, and Smartphone-Based Real-Life Activity Data Collection
Demographic and clinical data were collected at study inclusion. Assessment of lower extremity motor deficit was done according to the British Medical Research Council paresis grading (M0–M5, M0–plegia, M5–full strength). After inclusion all patients were subject to a comprehensive subjective (PROM-based) and objective (6WT) assessment preoperatively, 6 weeks (6W), 3 months (3M), and 6 months (6M) postoperatively. At last follow-up (FU), patients were contacted and asked to provide their smartphone activity data as additional objective performance assessment. To ensure comparability, only data retrieved from Apple iOS devices were included in the study. Patients without iPhone or patients not agreeing to transfer their data therefore had to be excluded from further analysis.
3. Health Export CSV Application
Health Export CSV (comma-separated values) is an application that extracts activity data—such as Step Count, walking distance, and other Apple Health metrics—from the iPhone’s Health database and converts them into a structured CSV file (
Supplementary Fig. 1). The user selects which data categories to export, and the app compiles the chosen information into a file that can be saved, shared, or imported into analysis software. This allows objective daily activity data to be easily reviewed and used for clinical or research purposes. Accordingly, Step Count was exported from a period of 30 days before surgery and the average daily Step Count was calculated.
The 6WT was carried out using a mobile application, which is freely available for iOS and Android devices and is capable of recording GPS coordinates to calculate total distance walked in 6 minutes (
Supplementary Fig. 2). The app displayed both elapsed time and distance in real-time during the test and has been shown to be highly reliable [
10]. Participants were encouraged to walk as quickly as possible on a flat, straight route in their usual environment. Before testing, detailed verbal and written instructions were provided, including a demonstration of how to install and use the application. The 6-minute walking distance (6WD) served as the primary outcome, representing an estimate of maximal ambulatory capacity under naturalistic conditions. The patients were encouraged to perform the 6WT at least once before the surgery. In case of several attempts, average 6WT values (6WD, z-scores) of all attempts were calculated.
Physical activity reference data were derived from the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) study [
17]. Deidentified summary data on MIPACT participants are available for researchers through an online research toolkit (https://researchtools.mipactstudy.org). Please find a description of the MIPACT study and reference population’s daily mean± standard deviation (SD) values stratified by gender and age in
Supplementary Content 1.
The study was approved by the local ethic committees of all 3 study centers (Cantonal Hospital Lucerne – EKNZ:2025-00572, Cantonal Hospital St. Gallen - EKOS:2019-01209, University Hospital Innsbruck – EK-Nr: 1395/2022). All patients provided written informed consent prior to study inclusion.
8. Statistical Considerations
Patient characteristics are expressed as mean±SD for continuous variables and count (percentage) for categorical variables. Raw smartphone Step Count of all patients were extracted and used to computationally derive average daily Step Count in the last month before the surgery.
MIPACT participants’ activity results stratified by age and sex were used as normative population values to create standard scores (z-scores) within our patients with sciatica undergoing lumbar spine surgery as detailed in
Supplementary Content 1 [
17,
18].
The results of the 6WT are presented as raw 6WD (m)±SD and as standardized z-scores, adjusted for age and sex using reference values from the normal population [
10].
Longitudinal analyses of outcome measures at every time point from baseline to last follow-up was conducted using paired-sample t-tests.
To enable direct comparison between subjective PROMs and objective measures, all scores were converted to standardized z-scores relative to the baseline cohort mean and SD. For PROMs, signs were inverted so that positive z-score changes consistently represented improvement. Recovery trajectories were analyzed using piecewise linear mixed-effects (LME) models, splitting the follow-up into an ‘early recovery’ phase (phase I: baseline to 6 weeks) and a ‘late recovery’ phase (phase II: 6 weeks to 6 months). This allowed for the formal statistical comparison of recovery slopes (interactions between time segment and measurement type) to identify potential ceiling effects.
Spearman correlation coefficients (r) were used to define the relationship between Step Count, 6WT results and PROMs. R values between 0–0.2 were interpreted as negligible, 0.3–0.4 as a weak, 0.4–0.7 as a moderate and 0.7–1 as a strong relationship. The negative direction represented a negative correlation between measured outcomes.
Analyses were performed with IBM SPSS Statistics ver. 31.0 (IBM Co., USA). A p-value <0.05 was considered significant.
RESULTS
1. Patients’ Demographics
This study enrolled 50 (Center A: 15, Center B: 25, Center C: 10) individuals with sciatica due to LDH (33 patients, 66%) or LRS (17 patients, 34%) who underwent microsurgical decompression without fusion. The cohort had a mean age of 50.7±14.3 years.
Table 1 provides a comprehensive presentation of demographic characteristics and clinical parameters for the study population.
Table 2 shows significant improvement in all clinical outcome measures from baseline to the 6-month follow-up (detailed standardized individual z-score trajectories as
Supplementary Fig. 3).
1) Standardized recovery trajectories
To enable direct comparison across metrics, all outcomes were standardized to z-scores. Standardized trajectories revealed a distinct temporal dissociation between real-life physical performance and traditional metrics (
Fig. 1).
2) Phase I: early recovery (baseline to 6 weeks)
During the first 6 weeks postoperatively, all objective and subjective measures improved significantly (all p<0.05). Mean daily Step Count improved from 4,602±1,074 at baseline to 5,818±803 at 6 weeks. Similarly, objective capacity (6WT) and subjective disability (COMI back, ODI, VAS back, and VAS leg) demonstrated their most rapid rates of recovery during this period.
3) Phase II: late recovery (6 weeks to 6 months)
Piecewise LME modeling revealed a significant interaction between time segment and measurement type during the late recovery phase. Daily Step Count was the only metric that maintained a significant, linear upward recovery slope throughout phase II (β=0.31 Z/mo, p<0.05). Mean steps increased from 5,818 at 6 weeks to 7,808 at 3 months and 9,858 at 6 months. In contrast, traditional measures exhibited a marked plateau. The recovery slopes for the 6WT, ODI, and COMI in phase II were significantly flatter compared to Step Count (all p<0.001), indicating a statistical “ceiling effect” (
Table 3). While Step Count continued to improve, conventional objective capacity (6WT) and subjective PROMs showed no significant further gains beyond the 6-week mark.
Correlation coefficients between preoperative, 6W, 3M and 6M postoperative Step Count, 6WT values and PROMs are outlined in
Table 4. The Step Count showed a significant correlation with all outcome measures at each time-point indicating that patients with improved physical performance experienced better physical capacity as measured by 6WT and better subjective perception of disability as measured by PROMs. Correlations between Step Count and 6WT and all PROMs decreased from strong at baseline, to moderate at 6W and weak at 3M as well as 6M FU.
No adverse events attributable to the smartphone-based monitoring were reported.
DISCUSSION
This prospective, multicenter study demonstrates that smartphone-derived Step Count is a valid and responsive measure of physical performance in patients with sciatica undergoing lumbar spine surgery. Daily Step Count increased progressively up to 6 months, whereas PROMs and 6WT showed substantial improvement mainly within the first 6 weeks, with little further change thereafter. These findings highlight the added value of smartphone-based monitoring in capturing long-term recovery trajectories.
1. Physical Performance Versus Conventional Outcomes
A key observation of this study is the strong relationship between physical performance (daily Step Count) and physical capacity (6WT), particularly at baseline (r=0.70). This correlation confirms that real-life activity largely but not entirely mirrors the ability to perform standardized physical tasks. When standardized using age- and sex-adjusted z-scores, individual discrepancies became apparent—some patients demonstrated moderate impairment in Step Count but only mild limitation in the 6WT, and vice versa. These variations underscore that the 2 constructs capture different dimensions of recovery: physical capacity reflects maximal effort under controlled conditions, while physical performance represents the habitual execution of daily activities in real-world environments [
14]. Combining both measures may thus provide a more comprehensive and ecologically valid assessment of function and recovery. The use of standardized z-scores allowed for a direct comparison of the magnitude of recovery across domains (
Fig. 1). The divergence observed at 6 weeks indicates that patients reach their maximum physical capacity and perceived relief much faster than they return to their full physical performance in daily life. This highlights the unique ‘behavioral’ component of recovery captured by continuous monitoring that episodic clinical tests fail to reflect.
The longitudinal correlation patterns further strengthen the construct and convergent validity of smartphone-based Step Count. As demonstrated in
Table 3, daily Step Count correlated strongly with 6WT (r=0.70) and moderately with PROMs (ODI r=-0.63, COMI r=-0.65) at baseline, consistent with prior cross-sectional observations [
12]. However, these associations weakened progressively over time, decreasing to moderate at 6W and weak by 3M and 6M postoperatively. This trajectory mirrors the temporal dissociation seen in
Tables 2,
3, and
Fig. 1, where PROMs and 6WT plateau after early improvement, while Step Count continues to increase. Such evolution suggests that once patients reach perceived symptom stability, physical performance continues to improve through gradual re-engagement in everyday mobility. Consequently, Step Count maintains convergent validity early after surgery but increasingly diverge later, capturing a unique behavioral component of recovery that traditional metrics may overlook. These findings reinforce that objective, ecologically valid activity monitoring provides complementary—not redundant—information alongside established capacity-based and subjective outcome measures [
19,
20].
The formal comparison of late-phase slopes using LME models confirms that traditional clinical tools may prematurely signal a ‘full’ recovery. While patients reach a perceived state of symptom stability (PROMs) and maximal walking capacity (6WT) as early as 6 weeks, their actual real-world physical performance continues to trend upward for at least 6 months. This divergence suggests that Step Count captures a unique ‘behavioral’ component of recovery—the gradual reintegration of movement into daily life—which occurs long after the initial surgical relief of pain. These findings reinforce the value of continuous monitoring to provide a more comprehensive picture of the long-term surgical success that episodic clinic visits may miss.
4. Clinical Implications
The integration of smartphone-based activity data into spine surgery follow-up has important clinical and translational potential. First, continuous Step Count monitoring provides an objective, unobtrusive, and patient-friendly assessment of daily mobility, overcoming the limitations of episodic clinic-based evaluations. Second, standardizing of patients’ impairment into z-scores enables clinicians to identify those at risk of delayed recovery or persistent functional limitation despite satisfactory subjective improvement [
16]. Third, the observed temporal dissociation between Step Count and traditional outcome assessment tools suggests that maintaining or improving real-life activity may serve as a more sensitive indicator of functional restoration beyond early postoperative stages. This may be particularly useful in guiding personalized rehabilitation programs, setting realistic patient expectations, and optimizing return-to-activity counseling. In addition, habitual physical activity has broader implications for long-term health—reduced mobility is associated with elevated cardiovascular and metabolic risk—underscoring the potential value of Step Count monitoring in preventive and holistic care [
21-
23]. Finally, as smartphone-based monitoring requires no additional hardware, its routine integration into clinical practice or telemedicine platforms could enable scalable, cost-effective, and patient-centered outcome tracking.
Our results align with and extend previous studies examining activity-based measures in degenerative spine disorders [
16,
24,
25]. Maldaner et al. [
16] demonstrated that standardized Step Count obtained from wearable devices correlate with PROMs preoperatively and allow for activity-based stratification of impairment in heterogeneous DLD cohorts. Importantly, while Maldaner et al. [
16] relied on smartwatch-based data collected in a heterogeneous cohort including predominantly patients undergoing fusion procedures, our study focused exclusively on patients with sciatica undergoing lumbar spine surgery. This more homogeneous cohort increases the clinical specificity of our findings and avoids confounding from broader degenerative pathologies.
Similarly, McIlroy et al. [
24] reported preoperative Step Count averaging 4,879±2,488 steps with shorter 6WT distances (239 m) in patients with lumbar spinal stenosis, which is comparable to our baseline findings in sciatica. Mean Step Count of 5,461±3,025 and 299 m in 6WD at 3M FU were significantly lower comparing to our cohort, which was younger and presented with different pathologies and symptoms (sciatica vs. neurogenic claudication).
Stienen et al. [
25] further validated the feasibility of preoperative Step Count analysis as a measure of objective functional impairment in mixed degenerative pathologies, though their smaller sample (18 DLD patients) yielded weaker correlations with ODI (r=-0.16). By contrast, our study—limited to patients with sciatica undergoing lumbar spine surgery—demonstrates stronger associations between Step Count and disability (ODI r=-0.63), likely due to the more homogeneous cohort and larger sample size. Furthermore, Step Counts detected continued recovery when PROMs and 6WT plateaued, underscoring their complementary role.
Voglis et al. [
12] were the first to correlate 2 different objective outcome measures. The group presented longitudinal analyses of miles walked within 1 year postoperatively in 8 patients with DLD that showed solid correlation with 6WT at baseline and at 6-week FU as well as with PROMs. Interestingly, there was no improvement in physical performance after the 6 months FU.
Ziga et al. [
8] showed in a cohort of patients with DLD undergoing surgical treatment that PROMs reach a plateau at approximately 6W postoperatively, whereas physical capacity, assessed by 6WT, plateaus between 6W and 3M. Our findings are consistent with these observations and have important clinical implications: patients initially experience subjective improvement during the early postoperative period (as reflected by PROMs), followed by measurable enhancement in functional capacity (as indicated by the 6WT), and ultimately a return to routine daily activities and routines (as evidenced by Step Count).
Collectively, these findings position smartphone-derived activity monitoring as an accessible, reliable, and pathology-specific method for quantifying real-life functional recovery.
6. Strengths and Limitations
The strengths of our study include its prospective design, multicenter recruitment, the use of personal smartphone-derived activity data in a well-defined surgical cohort, and the application of normative population reference values to calculate z-scores, which allowed standardized interpretation. In addition, patients were assessed with objective measure of physical capacity – 6WT and subjective PROMs, giving a robust information on characteristics of the study group. Importantly, this is the first study to investigate real-life activity exclusively in patients with sciatica undergoing lumbar spine surgery, whereas previous studies examined mixed cohorts. By restricting inclusion to patients with sciatica, our findings are more directly applicable to this highly prevalent and clinically distinct subgroup, avoiding confounding from heterogeneous pathologies and treatment strategies.
Nevertheless, certain limitations warrant consideration. First, the restriction to iPhone users introduces potential socioeconomic bias and limits generalizability. Patient’s preference may change with the applied objective and subjective outcome measures as well as with the education and socioeconomic background of the patient. Our study is therefore limited to the applied measures and cannot simply be generalized to patients who do not, or cannot use the certain smartphone technology. Future studies might address this “digital divide” by giving patients the opportunity to assess the physical performance using the same e.g., hospitals technological resources. Second, we could not account for geographic, climatic, or seasonal variations that may affect Step Count. Third, our sample size limited further subgroup analyses. Furthermore, while Step Count quantify activity volume, they do not capture qualitative aspects such as gait quality or endurance.
CONCLUSION
In summary, smartphone-based step counts provide a sensitive measure, representative of physical performance in sciatica patients undergoing lumbar spine surgery. Unlike PROMs and functional tests, Step Count captured ongoing improvements beyond the early postoperative recovery. These findings support the integration of smartphone-based monitoring as a complementary outcome measure to enhance individualized patient assessment and align surgical outcomes with real-life functional recovery.
Supplementary Materials
Supplementary Fig. 1.
Health Export CSV (comma-separated values) application. Screenshot of the Health Export CSV app showing the data categories to be extracted from the iHealth (preinstalled iPhone app). In this case, all the Distance and Steps data that had been recorded from the moment iPhone had been switched on for the first time ever, will be saved as dataset in .csv format. This dataset can be easily converted to Microsoft Office Excel format (.xcl).
Supplementary Fig. 2.
Six-minute walking test. Screenshot of the 6-minute walking test smartphone application (“6WT” app) depicting the beginning of the test which currently is at 0:21 minutes and 16 m. The main outcome of the app is the 6-minute walking distance which can be converted into a standardized age- and sex-adjusted z-scores. The patients z-score in this case would be -7.1, which represents severe objective functional impairment (OFI) according to our previous research (Tosic L, Goldberger E, Maldaner N, et al. Normative data of a smartphone app-based 6-minute walking test, test-retest reliability, and content validity with patient-reported outcome measures. J Neurosurg Spine 2020;33:480-9).
Supplementary Fig. 3.
Individual and mean recovery trajectories for clinical outcome measures. Standardized standard scores (z-scores) relative to baseline are shown for Step Count (A), 6-minute walking test (6WT; B), Oswestry Disability Index (ODI; C), Core Outcome Measures Index (COMI; D), visual analogue scale (VAS) back pain (E), and VAS leg pain (F). The light gray lines represent individual patient trajectories (n=50), while the bold colored lines represent the cohort mean. The horizontal dashed line at 0 indicates the baseline cohort mean. To maintain consistency, patient-reported outcome measure scores (C–F) were inverted so that an upward trajectory represents clinical improvement (reduction in pain or disability).
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: MZ, MNS, DN, UCS, NM, OP; Data curation: MZ, LB, RG, EY; Formal analysis: MZ; Methodology: MZ, MNS, DN, UCS, NM, OP; Project administration: MZ; Visualization: MZ; Writing – original draft: MZ; Writing – review & editing: LB, RG, EY, MNS, DN, UCS, NM, OP.
Fig. 1.Standardized mean recovery trajectories of subjective and objective outcomes over 6 months. Z-score changes from baseline illustrate a distinct divergence at 6 weeks: while smartphone-derived daily Step Count continues a significant linear improvement through 6 months, traditional subjective (ODI, COMI, VAS) and objective (6WT) metrics reach a statistical plateau. The vertical dashed line separates early recovery (phase I) from the late recovery phase (phase II), where the ceiling effect of conventional measures becomes apparent. 6WT, 6-minute walking test; ODI, Oswestry Disability Index; COMI, Core Outcome Measures Index; VAS, visual analogue scale.
Table 1.Patients’ demographics and clinical characteristics of the study group (n=50)
Table 1.
|
Variable |
Value |
|
Age (yr) |
50.7 ± 14.3 |
|
Sex |
|
|
Male |
26 (52) |
|
Female |
24 (48) |
|
Body dimensions |
|
|
Height (cm) |
171.8 ± 9.9 |
|
Weight (kg) |
80.1 ± 14.3 |
|
BMI (kg/m²) |
27.1 ± 3.8 |
|
Working status |
|
|
Full-time |
32 (64) |
|
Part-time |
2 (4) |
|
Retired |
15 (30) |
|
Disabled |
1 (2) |
|
Smoking status |
|
|
Smoker |
16 (32) |
|
Non-smoker |
34 (68) |
|
ASA PS classification grade |
|
|
I |
19 (38) |
|
II |
22 (44) |
|
III |
7 (14) |
|
IV |
2 (4) |
|
Previous spine surgery |
|
|
Yes |
7 (14) |
|
No |
43 (86) |
|
Indication |
|
|
Lumbar disc herniation |
33 (66) |
|
Lateral recess stenosis |
17 (34) |
|
Lower extremity motor deficit†
|
|
|
M2 |
1 (2) |
|
M3 |
4 (8) |
|
M4 |
12 (24) |
|
M5 |
33 (67) |
|
Type of surgery |
|
|
Discectomy |
34 (68) |
|
Decompression |
16 (32) |
|
Affected level |
|
|
L2–3 |
2 (4) |
|
L3–4 |
7 (14) |
|
L4–5 |
22 (44) |
|
L5–S1 |
19 (38) |
Table 2.Subjective (VAS, COMI, ODI) and objective (6WD, daily Step Count, z-scores) outcome measures prior to surgery and at every follow-up
Table 2.
|
Variable |
Baseline |
6 Weeks |
3 Months |
6 Months |
Δ6 Weeks–baseline |
Δ3 Months–baseline |
Δ6 Months–baseline |
|
VAS back |
3.4 ± 2.2 |
2.6 ± 0.7 |
1.7 ± 0.5 |
1.5 ± 0.5 |
-0.8 ± 2.1 |
-1.7 ± 2.0 |
-1.9 ± 1.9 |
|
VAS leg |
6.6 ± 1.1 |
2.8 ± 0.7 |
1.7 ± 0.5 |
1.3 ± 0.5 |
-3.8 ± 1.2*
|
-4.9 ± 1.2 |
-5.3 ± 1.2 |
|
ODI |
45 ± 14 |
23 ± 9 |
17 ± 6 |
15 ± 5 |
-22 ± 11*
|
-29 ± 12 |
-30 ± 12 |
|
COMI back |
6.5 ± 1.8 |
3.0 ± 0.7 |
2.6 ± 0.7 |
2.4 ± 0.6 |
-3.5 ± 1.4*
|
-3.9 ± 1.4 |
-4.1 ± 1.4 |
|
6WD (m) |
398 ± 88 |
531 ± 84 |
549 ± 85 |
554 ± 79 |
133 ± 90*
|
151 ± 88 |
156 ± 86 |
|
6WT z-score |
-1.4 ± 1.0 |
-0.2 ± 1.0 |
-0.1 ± 1.0 |
0.1 ± 0.9 |
1.2 ± 0.8*
|
1.3 ± 0.2 |
1.5 ± 0.2 |
|
Step Count |
4,602 ± 1,074 |
5,818 ± 803 |
7,808 ± 908 |
9,858 ± 886 |
1,216 ± 1,122*
|
3,206 ± 1,162*
|
5,256 ± 1,261*
|
|
Step Count z-score |
-1.1 ± 0.4 |
-0.6 ± 0.3 |
0.1 ± 0.4 |
0.8 ± 0.4 |
0.5 ± 0.4*
|
1.2 ± 0.4*
|
1.9 ± 0.5*
|
Table 3.Comparison of recovery slopes between Step Count and other clinical measures during the late recovery phase (6 weeks to 6 months) based on piecewise linear mixed-effects modeling
Table 3.
|
Measure |
Late phase slope (ΔZ/mo) |
p-value (vs. Step Count) |
|
Step Count |
0.307 |
Reference |
|
6WT |
0.036 |
< 0.001 |
|
COMI back |
0.061 |
< 0.001 |
|
ODI |
0.107 |
< 0.001 |
|
VAS back |
0.105 |
< 0.001 |
|
VAS leg |
0.276 |
0.396 |
Table 4.Spearman correlation of the daily Step Count with other objective (6WD and z-score) and subjective (VAS, COMI, ODI) outcome measures prior to surgery and at every follow-up
Table 4.
|
Variable |
Step Count |
Step Count z-score |
6WD |
6WT z-score |
VAS back |
VAS leg |
COMI back |
ODI |
|
Baseline |
|
|
|
|
|
|
|
|
|
Step Count |
1 |
|
|
|
|
|
|
|
|
Step Count z-score |
0.93*
|
1 |
|
|
|
|
|
|
|
6WD |
0.70*
|
0.63*
|
1 |
|
|
|
|
|
|
6WT z-score |
0.45*
|
0.45*
|
0.72*
|
1 |
|
|
|
|
|
VAS Back |
-0.50*
|
-0.40*
|
-0.50*
|
-0.49*
|
1 |
|
|
|
|
VAS Leg |
-0.30*
|
-0.30*
|
-0.33*
|
-0.32*
|
0.23 |
1 |
|
|
|
COMI Back |
-0.65*
|
-0.62*
|
-0.55*
|
-0.40*
|
0.51*
|
0.40*
|
1 |
|
|
ODI |
-0.63*
|
-0.63*
|
-0.44*
|
-0.39*
|
0.46*
|
0.26 |
0.81*
|
1 |
|
6 Weeks |
|
|
|
|
|
|
|
|
|
Step Count |
1 |
|
|
|
|
|
|
|
|
Step Count z-score |
0.91*
|
1 |
|
|
|
|
|
|
|
6WD |
0.78*
|
0.69*
|
1 |
|
|
|
|
|
|
6WT z-score |
0.30*
|
0.30*
|
0.38*
|
1 |
|
|
|
|
|
VAS Back |
-0.47*
|
-0.56*
|
-0.43*
|
-0.21 |
1 |
|
|
|
|
VAS Leg |
-0.40*
|
-0.42*
|
-0.48*
|
-0.37*
|
0.42*
|
1 |
|
|
|
COMI Back |
-0.45*
|
-0.46*
|
-0.42*
|
-0.26 |
0.22 |
0.20 |
1 |
|
|
ODI |
-0.43*
|
-0.30*
|
-0.43*
|
-0.28 |
0.10 |
0.10 |
0.35*
|
1 |
|
3 Months |
|
|
|
|
|
|
|
|
|
Step Count |
1 |
|
|
|
|
|
|
|
|
Step Count z-score |
0.91*
|
1 |
|
|
|
|
|
|
|
6WD |
0.53*
|
0.49*
|
1 |
|
|
|
|
|
|
6WT z-score |
0.42*
|
0.43*
|
0.41*
|
1 |
|
|
|
|
|
VAS Back |
-0.20*
|
-0.21*
|
-0.32*
|
-0.10 |
1 |
|
|
|
|
VAS Leg |
-0.38*
|
-0.35*
|
-0.44*
|
-0.46*
|
0.23 |
1 |
|
|
|
COMI Back |
-0.43*
|
-0.43*
|
-0.43*
|
-0.29*
|
0.40*
|
0.32*
|
1 |
|
|
ODI |
-0.33*
|
-0.29*
|
-0.43*
|
-0.27*
|
0.10 |
0.34*
|
0.30*
|
1 |
|
6 Months |
|
|
|
|
|
|
|
|
|
Step Count |
1 |
|
|
|
|
|
|
|
|
Step Count z-score |
0.88*
|
1 |
|
|
|
|
|
|
|
6WD |
0.39*
|
0.38*
|
1 |
|
|
|
|
|
|
6WT z-score |
0.20*
|
0.22*
|
0.35*
|
1 |
|
|
|
|
|
VAS Back |
-0.22 |
-0.22 |
-0.16 |
-0.17 |
1 |
|
|
|
|
VAS Leg |
-0.11 |
-0.21 |
-0.24 |
-0.27 |
0.27 |
1 |
|
|
|
COMI Back |
-0.33*
|
-0.33*
|
-0.35*
|
-0.10 |
0.33*
|
0.38*
|
1 |
|
|
ODI |
-0.29*
|
-0.26 |
-0.41*
|
-0.37*
|
0.13 |
0.15 |
0.39*
|
1 |
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. Gautschi OP, Corniola MV, Smoll NR, et al. Sex differences in subjective and objective measures of pain, functional impairment, and health-related quality of life in patients with lumbar degenerative disc disease. Pain 2016;157:1065-71.
- 3. Otani K, Kikuchi S, Yabuki S, et al. Lumbar spinal stenosis has a negative impact on quality of life compared with other comorbidities: an epidemiological cross-sectional study of 1862 community-dwelling individuals. ScientificWorldJournal 2013;2013:590652.
- 4. Stienen MN, Ho AL, Staartjes VE, et al. Objective measures of functional impairment for degenerative diseases of the lumbar spine: a systematic review of the literature. Spine J 2019;19:1276-93.
- 5. Chiarotto A, Boers M, Deyo RA, et al. Core outcome measurement instruments for clinical trials in nonspecific low back pain. Pain 2018;159:481-95.
- 6. Mannion AF, Haschtmann D, Loibl M, et al. The Core Outcome Measures Index (COMI) comes of age: 18 years of efficient and effective patient-reported outcome measurement in the field of spinal disorders. Part 2. COMI item-content, scoring, language versions, and applications. Eur Spine J 2025;34:2629-37.
- 7. Mannion AF, Haschtmann D, Puhakka J, et al. The Core Outcome Measures Index (COMI) comes of age: 18 years of efficient and effective patient-reported outcome measurement in the field of spinal disorders. Part 1. Development of the COMI, interpretation of scores, and use in research. Eur Spine J 2025;34:2618-28.
- 8. Ziga M, Sosnova M, Zeitlberger AM, et al. Objective outcome measures may demonstrate continued change in functional recovery in patients with ceiling effects of subjective patient-reported outcome measures after surgery for lumbar degenerative disorders. Spine J 2023;23:1314-22.
- 9. Stienen MN, Maldaner N, Sosnova M, et al. External validation of the Timed Up and Go Test as measure of objective functional impairment in patients with lumbar degenerative disc disease. Neurosurgery 2021;88:E142-9.
- 10. Maldaner N, Sosnova M, Zeitlberger AM, et al. Evaluation of the 6-minute walking test as a smartphone app-based self-measurement of objective functional impairment in patients with lumbar degenerative disc disease. J Neurosurg Spine 2020;33:779-88.
- 11. Maldaner N, Sosnova M, Zeitlberger AM, et al. Responsiveness of the self-measured 6-minute walking test and the Timed Up and Go test in patients with degenerative lumbar disorders. J Neurosurg Spine 2021;35:52-9.
- 12. Voglis S, Ziga M, Zeitlberger AM, et al. Smartphone-based real-life activity data for physical performance outcome in comparison to conventional subjective and objective outcome measures after degenerative lumbar spine surgery. Brain Spine 2022;2:100881.
- 13. Tosic L, Goldberger E, Maldaner N, et al. Normative data of a smartphone app-based 6-minute walking test, test-retest reliability, and content validity with patient-reported outcome measures. J Neurosurg Spine 2020;33:480-9.
- 14. Lamb SE, Keene DJ. Measuring physical capacity and performance in older people. Best Pract Res Clin Rheumatol 2017;31:243-54.
- 15. World Health Organization. How to use the ICF: a practical manual for using the International Classification of Functioning, Disability and Health (ICF). Exposure draft for comment. October 2013. Geneva (Switzerland): World Health Organization; 2013. p. 37.
- 16. Maldaner N, Tang M, Fatemi P, et al. Standardizing physical activity monitoring in patients with degenerative lumbar disorders. Neurosurgery 2024;94:788-96.
- 17. Golbus JR, Pescatore NA, Nallamothu BK, et al. Wearable device signals and home blood pressure data across age, sex, race, ethnicity, and clinical phenotypes in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) study: a prospective, community-based observational study. Lancet Digit Health 2021;3:e707-15.
- 18. Klukowska AM, Schröder ML, Stienen MN, et al. Objective functional impairment in lumbar degenerative disease: concurrent validity of the baseline severity stratification for the five-repetition sit-to-stand test. J Neurosurg Spine 2020;33:4-11.
- 19. Maldaner N, Sosnova M, Ziga M, et al. External validation of the minimum clinically important difference in the timed-up-and-go test after surgery for lumbar degenerative disc disease. Spine (Phila Pa 1976) 2022;47:337-42.
- 20. Ziga M, Stienen MN, Zeitlberger AM, et al. External validation of the smartphone-based 6-minute walking test in patients with degenerative lumbar disorders undergoing epidural steroid injection. N Am Spine Soc J 2024;20:100561.
- 21. Paluch AE, Bajpai S, Ballin M, et al. Prospective association of daily steps with cardiovascular disease: a harmonized meta-analysis. Circulation 2023;147:122-31.
- 22. Lin H, Sardana M, Zhang Y, et al. Association of habitual physical activity with cardiovascular disease risk. Circ Res 2020;127:1253-60.
- 23. Lin Z, Shi G, Liao X, et al. Correlation between sedentary activity, physical activity and bone mineral density and fat in America: National Health and Nutrition Examination Survey, 2011-2018. Sci Rep 2023;13:10054.
- 24. McIlroy S, Mah Y, Tahtis V, et al. Minimal clinically important difference of the 6-Minute walk test and daily step count at 3 months following surgery for lumbar spinal stenosis. Eur Spine J 2025;34:5690-700.
- 25. 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.
, Linda Bättig3
