The correlation between spatiotemporal gait and pain in patients with musculoskeletal pain using smartphone-based gait technology. A retrospective cross-sectional study

The correlation between spatiotemporal gait and pain in patients with musculoskeletal pain using smartphone-based gait technology. 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A retrospective cross-sectional study Stephen Stache, Gabriel Furey, Christopher J. Mehallo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7301065/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background This study aimed to investigate the relationship between spatiotemporal gait parameters and pain severity in patients with musculoskeletal (MSK) conditions, specifically knee and back pain. Additionally, it sought to compare how gait compensation strategies differ based on pain location and their association with pain intensity. Methods A retrospective analysis was conducted on 3,595 patients attending clinics in the US between December 2023 and April 2025. Participants performed barefoot gait assessments using an AI-driven smartphone-based app, which collected data to extract gait metrics such as velocity, step length, cadence, and limb support times. Pain severity was assessed via the Numeric Pain Rating Scale (NPRS). Gait parameters were stratified into quintiles, and logistic regression analyses examined associations between gait deviations and severe pain (NPRS > 7), adjusting for age and gender. Results Patients with gait parameters indicating greater impairment exhibited higher odds ratio of reporting severe pain. Gait velocity emerged as the most influential predictor, with walking speeds below 80 cm/s associated with over a 2.7-fold increased likelihood of severe pain. Subgroup analyses revealed that knee pain was more strongly linked to reduced cadence, while back pain correlated primarily with shorter step length. Conclusion Spatiotemporal gait parameters are significantly associated with pain severity in MSK conditions and can be effectively measured using accessible mobile technology. Recognizing distinct gait patterns based on pain location supports the development of tailored clinical interventions and targeted objectives for monitoring treatment outcomes. Smartphone gait technology Pain Musculoskeletal Knee Back Figures Figure 1 Figure 2 Figure 3 Background Gait, a fundamental aspect of human movement, is increasingly recognized as a vital sign of an individual’s functional status ( 1 ). It serves as a comprehensive indicator of a person's mobility, balance, and physical health ( 1 , 2 ). Observing how an individual walk can provide valuable insights into their overall functional condition and the compensation pattern they adopt to cope with pain ( 3 ). As such, gait analysis has become an essential tool in clinical settings, helping to assess and monitor the physical capabilities and rehabilitation progress of patients across various medical conditions ( 3 ). Musculoskeletal (MSK) conditions, particularly those affecting the lower back and lower extremities, are prevalent and often debilitating. MSK diseases affect more than one out of every two persons in the United States age 18 and over. Nearly three out of four people aged 65 and over are affected, with an estimated $ 980 billion cost of care. This cost is more than the cost of any other chronic condition ( 4 – 6 ). Chronic MSK disorders, such as knee osteoarthritis (OA) or chronic lower back pain (LBP) are most common and can significantly impair mobility and function ( 7 ). These conditions can lead to a gradual deterioration in physical capabilities and are closely associated with a reduced quality of life ( 8 , 9 ). Some studies suggested that impaired gait is associated with the likelihood of having major surgery (i.e., total knee replacement) ( 10 , 11 ). Understanding the relationship between these conditions and functional impairment is critical. It can help to develop effective treatment strategies that may improve patient outcomes. It can provide an objective measure of functional capabilities. These functional capabilities can be monitored over time to assess response to interventions. Patients with knee and back pain demonstrate altered gait. For example, patients with knee OA may exhibit reduced walking speed, decreased stride length, and increased stance time on the unaffected limb to minimize pain ( 12 – 14 ). Moreover, in more severe OA or unilateral OA patients, there is a reduction in single leg stance, a proxy of the patient's ability to bear single loads while the contralateral limb swings forward ( 14 ). Individuals suffering from chronic LBP might demonstrate altered trunk motion, reduced lumbar spine rotation, and asymmetrical step lengths, which serve as compensatory strategies to alleviate discomfort ( 15 , 16 ). Patients with severe back pain will adopt a gait pattern where step length is reduced and cadence is increased in order to maintain gait velocity, a strategy that helps reduce loads from the lower back during initial contact ( 15 , 16 ). Historically, the assessment of gait was conducted in sophisticated 3-Dimention (3D) motion capture laboratories, which required extensive equipment and expertise ( 17 , 18 ). These gait tests, while highly detailed, are cumbersome and expensive, and require trained technicians to oversee the process. The turnaround time for obtaining results is lengthy, and the significant volume of data generated poses challenges in interpretation and in drawing meaningful, actionable conclusions ( 18 – 20 ). As technology has advanced, more commercial gait assessment tools have emerged. These include spatio-temporal gait mats, pressure distribution mats, and inertial measurement unit (IMU) sensors, which offer more practical and accessible means for gait analysis ( 18 – 20 ). A notable development in this space is the advent of artificial intelligence (AI)-driven mobile-based gait technology ( 21 ), which offers a portable, cost-effective solution for routine gait assessment. It can be done in clinical or home settings and allows objective, real-time monitoring of functional status. Recent studies have shown its validity in assessing gait in patients with knee and back pain, providing a promising alternative to traditional methods ( 22 , 23 ). However, data on the correlation between AI-driven mobile-based gait technology and pain in patients with MSK conditions is still missing. Therefore, the purpose of the current study is to examine the relationship between spatiotemporal gait metrics and pain severity in patients suffering from knee and back pain utilizing AI-driven mobile-based gait technology. Moreover, the study will compare patients with primary knee pain and patients with primary back pain to see if pain location might affect gait compensation strategies and their correlation with pain. Methods This was a retrospective cross-sectional analysis based on the records of patients who attended the AposCare clinic in the United States between December 2023 and April 2025. Patients with primary knee or back pain were included in the study. Patients who used a walking aid or patients who did not complete a walking trial independently were excluded. The study was approved by Pearl IRB. The committee approved the protocol as an exempt research determination in accordance with the applicable federal regulations and has determined the study noted above to be Exempt according to 45 CFR 46.104(d)( 4 ) Secondary Research Uses of Data or Specimens 45 CFR 46.104(d)( 4 )(ii). Pearl IRB is in full compliance with good clinical practice as defined under the U.S. food and drug administration (FDA) regulations, U.S. department of health and human services (HHD) regulations, and the international conference on harmonization (ICH) guidelines. All patients signed consent acknowledging that their data might be used anonymously for research purposes. All patients completed a computerized gait assessment as part of their routine visit to the clinic, using an AI-driven smartphone-based gait analysis application (OneStep, Celloscope Ltd., Tel Aviv, Israel). Patients were asked to walk barefoot, at a self-selected pace along a 10-meter walkway in two consecutive trials (back and forth). When a new test is performed, the application starts with an internal automatic calibration process that accounts for variations in phone placement and decreases the need for a specific location. The application uses data from the smartphone’s IMU, including 3D acceleration, angular velocity, and magnetic intensity data at a sampling rate of 100Hz. Using machine learning algorithms, the application first segments the raw data into gait cycles and filters out curves and turns. Then, using machine learning, the algorithms extract spatiotemporal parameters from the gait cycles taken during the straight-line walking segments. For this study, several spatio-temporal gait metrics were calculated including gait velocity (cm/s), step length (cm), cadence (steps/min), double support time (% gait cycle), single support time (% gait cycle), step width (cm) and step-to-step variability in velocity and cadence. Step length and single limb support were calculated separately for the left and right limbs. For each patient, the shorter step length and lower single support time were identified and classified as the more affected limb while the contralateral limb was classified as less affected. Following the gait test patients were asked to rate their pain levels. Pain intensity was assessed using the Numeric Pain Rating Scale (NPRS), a widely accepted tool in clinical research for evaluating pain severity ( 24 , 25 ) including patients with knee and back pain ( 26 , 27 ). The NPRS is a straightforward, self-reported measure in which participants rate their pain on an 11-point scale ranging from 0 to 10, where 0 indicates "no pain" and 10 represents "the worst possible pain." Patients were asked to rate their current level of pain. Statistical analysis Data were analyzed with IBM SPSS software version 29.0 and were presented as frequencies and percentages for baseline characteristics (categorical variables) and as mean and standard deviation for all gait spatio-temporal parameters. The distributions of the variables in the study were examined using the Kolmogorov-Smirnov non-parametric test. We employed descriptive statistics, including frequencies, means, and standard deviations, to present patient characteristics. Logistic regression analyses were performed to examine the relationship between gait patterns and pain severity. First, Spearman rank-order correlation coefficients were calculated between pain scores (as a continuous variable) and gait metrics including gait velocity, cadence, step length of the involved limb, and single support time of the involved limb. Due to high intercorrelations among gait variables, each gait metric was entered into a separate logistic regression model to reduce multicollinearity. Each gait parameter was stratified into quintiles to establish gait severity groups, whereby quintile 1 (Q1) indicates highly impaired gait patterns, and quintile 5 (Q5) represents near-normal gait values (Table 1 ). The dependent variable was severe pain, defined by a score above 7 on a 0 to 10 numeric scale (where 0 = no pain, 10 = severe pain), based on previous studies that have set pain severity thresholds for patients with chronic MSK pain ( 25 ). For each model, odds ratio (OR) and 95% confidence intervals (CI) were calculated to express the likelihood of experiencing severe pain in each gait quintile relative to the reference group (Q5). An OR > 1 indicates increased odds of severe pain compared to the reference, while an OR < 1 suggests lower odds. Furthermore, we applied the same models separately for patients with knee pain and back pain, adjusted for age and gender. Significance levels were set at 0.05. Table 1 Gait quintiles All Q1 Q2 Q3 Q4 Q5 Velocity (cm/s) 114.0 Cadence (steps/min) 112.0 Step Length (cm) 61.8 Single limb support (% gait cycle) 34.6 Q = quintiles Results Three thousand five hundred and ninety-five (n = 3,595) patients were included in the study. Of these, 1,945 had primary back pain and 1,650 had primary knee pain. Patient characteristics, pain levels, and spatiotemporal gait metrics are summarized in Table 2 . Table 2 Patient characteristics. All Back Knee N 3,595 1,945 1,650 Age, (years) 51.9 (11.5) 49.2 (10.8) 55.1 (11.5) Gender, F/M (%) 70/30 66/34 74/26 Pain (0–10) 6.2 (1.7) 6.1 (1.7) 6.3 (1.8) Velocity (cm/s) 97.0 (21.3) 102.2 (18.7) 91.0 (22.5) Cadence (steps/min) 102.7 (11.4) 104.6 (10.3) 100.4 (12.2) Step Length, affected limb (cm) 54.5 (9.0) 56.7 (7.8) 52.0 (9.7) Single limb support, affected limb (% gait cycle) 32.6 (2.5) 33.2 (2.0) 31.9 (2.9) Double limb support (% gait cycle) 32.6 (5.0) 31.7 (4.1) 33.6 (5.6) Velocity variability 10.0 (4.1) 9.5 (3.8) 10.7 (4.4) Cadence variability 2.1 (1.6) 1.9 (1.2) 2.3 (1.8) Results are presented as mean (sd) except for gender the is presented as % of total cohort. Figure 1 presents a multivariate regression results visualized as a chart, displaying the beta coefficients and 95% confidence intervals for the association between pain severity and four core gait parameters (velocity, step length, cadence, and single limb support), adjusted for age and sex. These results are shown for the full dataset, and separately for knee and back pain patients as seen in Figs. 2 and 3 . Across all groups, pain was significantly associated with worse gait parameters, particularly velocity, which had the strongest negative relationship. Looking at the likelihood of severe pain across quintiles of gait parameters. The results suggest that patients in Q1 (severe gait deviations) are more likely to report severe pain (> 7) compared to those in Q5. For example, patients with gait velocity 7 than those with gait velocity > 114 cm/s (p < 0.001). Similar significant correlations were calculated for cadence, step length, and single limb support, indicating a higher likelihood of severe pain among individuals with more impaired gait patterns. Consistent trends were observed in both back and knee groups; however, patients with knee pain exhibited the highest ORs in gait velocity (OR = 3.172) and cadence (OR = 2.436), whereas patients with back pain showed the highest ORs in gait velocity (OR = 2.345) and step length (OR = 2.763). In essence, patients in Q1 who represent a cohort with the most compromised gait pattern including slower walking speed, lower cadence, shorter step length and lower single limb support) had higher odds of having severe pain. These results for all patients, as well as subgroup analyses for knee and back pain, are summarized in Table 3 . Table 3 Adjusted logistics regression for Q1 versus Q5 gait quintiles and severe pain across all patients and patients with primary knee pain or primary back pain. All Exp(B) Exp(B), 95% C.I. P Velocity (cm/s) 2.74 2.173–3.455 < 0.001 Cadence (steps/min) 1.788 1.433–2.232 < 0.001 Step Length (cm) 2.535 2.009–3.198 < 0.001 Single limb support (% gait cycle) 2.270 1.806–2.853 < 0.001 Back Exp(B) Exp(B), 95% C.I. P Velocity (cm/s) 2.346 1.680–3.276 < 0.001 Cadence (steps/min) 1.325 0.966–1.817 0.081 Step Length (cm) 2.793 1.988–3.925 < 0.001 Single limb support (% gait cycle) 2.144 1.542–2.981 < 0.001 Knee Exp(B) Exp(B), 95% C.I. P Velocity (cm/s) 3.172 2.237–4.497 < 0.001 Cadence (steps/min) 2.436 1.757–3.379 < 0.001 Step Length (cm) 2.371 1.659–2.390 < 0.001 Single limb support (% gait cycle) 2.357 1.666–3.334 < 0.001 Discussion The findings of this study suggest a significant association between spatiotemporal gait parameters and pain severity in individuals with musculoskeletal (MSK) conditions, specifically knee and back pain. Our analysis suggests that gait velocity had the strongest negative relationship with pain. As pain increases, velocity, step length, cadence, and single limb support all consistently decline, supporting the regression findings and emphasizing the clinical potential of gait as a marker of pain burden. However, further analysis suggests that the correlation is not linear and that gait compensations are not the same across pain locations. Therefore, we offer a different approach when conducting a gait analysis in clinical practice. Moreover, we found that specific gait metrics were more strongly linked to severe pain, with velocity being the most prominent one. In both groups (knee and back pain) gait velocity emerged as the most influential predictor of pain severity. The data indicates that individuals walking at slower speeds are substantially more likely to report high pain levels (> 7). Specifically, gait velocity less than 80 cm/s was associated with a 3.17-fold for knee 2.35-fold for back increase in the odds of experiencing severe pain compared to those walking faster than 114 cm/s. Interestingly, the association of other gait parameters with pain severity differed depending on the location of pain, i.e., primary knee pain or primary back pain. For patients with primary knee pain, it is more likely that lower cadence will be linked to severe pain whereas for patients with primary back pain it is more likely that shorter step length will be linked to severe pain indicating that gait compensations might be specific to pain locations, however this should be verified in future studies. This correlation aligns with previous literature suggesting that reduced walking speed, shorter stride length, and lower single limb support in knee OA serve as a protective or compensatory mechanism to minimize joint loading and impact forces during ambulation ( 11 – 14 ). It is plausible that patients voluntarily slow their gait to decrease pressure on the affected knee, thereby alleviating discomfort while walking. This behavioral adaptation might also delay disease progression and the risk for a total knee replacement surgery ( 11 ); therefore, monitoring changes in gait characteristics can potentially serve as an objective marker for assessing the severity of knee pathology. Conversely, patients with back pain demonstrated a different gait compensation pattern. Step length was most likely to be affected by pain severity, followed by gait velocity. Specifically, a reduced step length was strongly linked to higher pain scores, with gait velocity and step length exhibiting the highest odds ratios (OR = 2.35 and OR = 2.80, respectively). This finding resonates with prior biomechanical research indicating that patients with chronic low back pain often adapt their gait by shortening stride length ( 15 ). Such a strategy likely functions to reduce angular moments and strain on the lumbar spine by decreasing the moment arm during gait ( 15 , 16 ). Shorter step length diminishes the lever arm during heel strike and toe-off phases, potentially mitigating load and subsequent pain. This differential pattern underscores the importance of tailored gait assessment in clinical practice. For patients with knee pathology, monitoring gait velocity may provide a straightforward, objective measure of pain severity and functional impairment. In contrast, for patients with back pain, step length might serve as a more sensitive indicator of symptom severity and compensatory gait strategies. This distinction has practical implications; routine gait analysis using accessible mobile-based technologies can facilitate early detection of worsening symptoms and monitor responses to interventions. Furthermore, our results contribute to the growing body of evidence emphasizing the role of gait analysis as a non-invasive, objective biomarker for pain and functional status. The consistency of these patterns across different MSK conditions highlights the potential for gait metrics to inform personalized treatment plans and track disease progression. Future research could explore how rehabilitative interventions targeting these specific gait parameters influence pain outcomes and functional recovery. This study has several limitations that should be considered when interpreting the findings. First, its retrospective design inherently limits the ability to establish causality between gait parameters and pain severity, as temporal or directional relationships cannot be definitively determined. Second, reliance on a mobile-based AI gait analysis platform, while practical and accessible, may introduce measurement variability compared to gold-standard laboratory-based motion analysis systems; despite validation efforts, device accuracy can be affected by factors such as phone placement and environmental conditions. Therefore, more research using this emerging technology would help strengthen, support, and provide validation to the results of this study. In addition, it should be acknowledged that the gait test was performed when walking barefoot. Patients wearing shoes may have had different results. That being said, if bias exists, it was applied to all patients, as they all walked barefoot. Third, the cross-sectional nature of the data restricts insights into how gait parameters and pain levels evolve over time or in response to treatment interventions. We recommend that future studies examine the correlation between gait and pain over time and examine which gait metrics are most responsive to treatment and are associated with changes in pain. Lastly, we used primary pain location for sub-classification and were able to demonstrate different gait compensation mechanisms. However, additional patient characteristics such as knee alignment, radiographic severity of knee osteoarthritis, back pain diagnosis, and physical examination can potentially provide valuable information as to the correlation between pain and gait. Moreover, controlling for height, weight, BMI, duration of symptoms, activity level and medication use might help clarify and explain some of the results, yet those were not available. Therefore, we recommend that future studies expand patient characteristics and assess their contribution to the correlation between pain and gait patterns. Conclusion In conclusion, this study illustrates the correlation between spatiotemporal gait parameters captured with AI-driven smartphone-based gait technology, which can be utilized in clinical practice and even in the patient’s home. While both gait velocity and step length are associated with pain severity, their relative importance varies between different pain locations. Understanding these distinct patterns can enhance clinical assessment and enable more targeted gait-based interventions, which may improve patient management, reduce symptoms, and improve quality of life. Abbreviations MSK Musculoskeletal NPRS Numeric Pain Rating Scale OA Osteoarthritis LBP Lower back pain IMU Inertial measurement unit AI Artificial intelligence 3D − 3 Dimention OR odds ratio CI Confidence intervals Declarations Conflict of interests: None to declare Funding This study was not funded in any way. Availability of data and materials The datasets used and/or analyzed during the current study were received by AposHealth and were further processed. Rothman and AposHealth have an ongoing clinical relationship and AposHealth has given the authors permission to use the datasets for research purposes. Ethics approval and consent to participate The study was approved by Pearl IRB. The committee approved the protocol as an exempt research determination in accordance with the applicable federal regulations and has determined the study noted above to be Exempt according to 45 CFR 46.104(d)(4) Secondary Research Uses of Data or Specimens 45 CFR 46.104(d)(4)(ii). Pearl IRB is in full compliance with good clinical practice as defined under the U.S. food and drug administration (FDA) regulations, U.S. department of health and human services (HHD) regulations, and the international conference on harmonization (ICH) guidelines. All patients signed consent acknowledging that their data might be used anonymously for research purposes. Clinical trial number: not applicable Consent for publication Not applicable Competing interests None to declare Authors' contributions SS - Conceptual and design, data analysis, major contributor in writing the manuscript GF – Data analysis, drafting the manuscript CJM - Conceptual and design, data analysis, major contributor in writing the manuscript All authors read and approved the final manuscript. Acknowledgements Not applicable References Middleton A, Fritz SL, Lusardi M. Walking speed: the functional vital sign. J Aging Phys Act. 2015;23(2):314–22. Middleton A, Fritz SL. Assessment of Gait, Balance, and Mobility in Older Adults: Considerations for Clinicians. Curr Transl Geriatr Exp Gerontol Rep. 2013;2:9. Lord SE, Halligan PW, Wade DT. Visual gait analysis: the development of a clinical assessment and scale. Clin Rehabil. 1998;12(2):107–19. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7301065","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522206500,"identity":"ffc023fa-d7c1-4b23-b5a3-9a1d88a368ab","order_by":0,"name":"Stephen Stache","email":"data:image/png;base64,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","orcid":"","institution":"The Rothman Orthopaedic Institute","correspondingAuthor":true,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Stache","suffix":""},{"id":522206501,"identity":"ae2a3a8a-599e-497d-931a-1a0429587b2b","order_by":1,"name":"Gabriel Furey","email":"","orcid":"","institution":"The Rothman Orthopaedic Institute","correspondingAuthor":false,"prefix":"","firstName":"Gabriel","middleName":"","lastName":"Furey","suffix":""},{"id":522206502,"identity":"54d0aa00-5ee2-46ed-b514-7318f3c635fa","order_by":2,"name":"Christopher J. 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16:29:01","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82713,"visible":true,"origin":"","legend":"","description":"","filename":"9b7c85c31d194d13827bab8c0b26285f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7301065/v1/786da65a65c55e81a9fe7fad.xml"},{"id":92529165,"identity":"ca051880-1eab-4dcb-b8fb-a4bd93e530e4","added_by":"auto","created_at":"2025-09-30 16:29:01","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89101,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7301065/v1/be5c7d13facff7f2ea739bf7.html"},{"id":92530198,"identity":"eded3627-0818-4f85-801d-529d157f6279","added_by":"auto","created_at":"2025-09-30 16:37:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":106854,"visible":true,"origin":"","legend":"\u003cp\u003eThe Effect of Pain on Velocity, Cadence, Step Length, and Single Limb Support\u003c/p\u003e\n\u003cp\u003eThis chart shows beta estimates (with 95% confidence intervals) for the relationship between pain severity and gait parameters (velocity, step length, cadence, and single limb support), adjusted for age and sex, across the full cohort, knee pain group, and back pain group.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7301065/v1/8751295799c04228fb5cf018.jpeg"},{"id":92529151,"identity":"463849f7-e253-4393-a519-20d61ef24fb4","added_by":"auto","created_at":"2025-09-30 16:29:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88417,"visible":true,"origin":"","legend":"\u003cp\u003eThe Effect of Knee Pain on Velocity, Cadence, Step Length, and Single Limb Support.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7301065/v1/84d2bb5ce73a7779c9799104.jpeg"},{"id":92531955,"identity":"6c36249c-3d49-46d8-9d80-bd607e80fc37","added_by":"auto","created_at":"2025-09-30 16:45:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101839,"visible":true,"origin":"","legend":"\u003cp\u003eThe Effect of Back Pain on Velocity, Cadence, Step Length, and Single Limb Support\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7301065/v1/38adb21a9f34a9a6493092fa.jpeg"},{"id":92532443,"identity":"75fd9cec-5905-4c95-a3b0-6bdc8e3658ae","added_by":"auto","created_at":"2025-09-30 16:53:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":866227,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7301065/v1/8b613029-4928-4d0e-810a-2e7e3a29ded4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The correlation between spatiotemporal gait and pain in patients with musculoskeletal pain using smartphone-based gait technology. A retrospective cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eGait, a fundamental aspect of human movement, is increasingly recognized as a vital sign of an individual\u0026rsquo;s functional status (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It serves as a comprehensive indicator of a person's mobility, balance, and physical health (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Observing how an individual walk can provide valuable insights into their overall functional condition and the compensation pattern they adopt to cope with pain (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). As such, gait analysis has become an essential tool in clinical settings, helping to assess and monitor the physical capabilities and rehabilitation progress of patients across various medical conditions (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMusculoskeletal (MSK) conditions, particularly those affecting the lower back and lower extremities, are prevalent and often debilitating. MSK diseases affect more than one out of every two persons in the United States age 18 and over. Nearly three out of four people aged 65 and over are affected, with an estimated \u003cspan\u003e$\u003c/span\u003e980\u0026nbsp;billion cost of care. This cost is more than the cost of any other chronic condition (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Chronic MSK disorders, such as knee osteoarthritis (OA) or chronic lower back pain (LBP) are most common and can significantly impair mobility and function (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These conditions can lead to a gradual deterioration in physical capabilities and are closely associated with a reduced quality of life (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Some studies suggested that impaired gait is associated with the likelihood of having major surgery (i.e., total knee replacement) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Understanding the relationship between these conditions and functional impairment is critical. It can help to develop effective treatment strategies that may improve patient outcomes. It can provide an objective measure of functional capabilities. These functional capabilities can be monitored over time to assess response to interventions.\u003c/p\u003e\u003cp\u003ePatients with knee and back pain demonstrate altered gait. For example, patients with knee OA may exhibit reduced walking speed, decreased stride length, and increased stance time on the unaffected limb to minimize pain (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Moreover, in more severe OA or unilateral OA patients, there is a reduction in single leg stance, a proxy of the patient's ability to bear single loads while the contralateral limb swings forward (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Individuals suffering from chronic LBP might demonstrate altered trunk motion, reduced lumbar spine rotation, and asymmetrical step lengths, which serve as compensatory strategies to alleviate discomfort (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Patients with severe back pain will adopt a gait pattern where step length is reduced and cadence is increased in order to maintain gait velocity, a strategy that helps reduce loads from the lower back during initial contact (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHistorically, the assessment of gait was conducted in sophisticated 3-Dimention (3D) motion capture laboratories, which required extensive equipment and expertise (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These gait tests, while highly detailed, are cumbersome and expensive, and require trained technicians to oversee the process. The turnaround time for obtaining results is lengthy, and the significant volume of data generated poses challenges in interpretation and in drawing meaningful, actionable conclusions (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). As technology has advanced, more commercial gait assessment tools have emerged. These include spatio-temporal gait mats, pressure distribution mats, and inertial measurement unit (IMU) sensors, which offer more practical and accessible means for gait analysis (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). A notable development in this space is the advent of artificial intelligence (AI)-driven mobile-based gait technology (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), which offers a portable, cost-effective solution for routine gait assessment. It can be done in clinical or home settings and allows objective, real-time monitoring of functional status. Recent studies have shown its validity in assessing gait in patients with knee and back pain, providing a promising alternative to traditional methods (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, data on the correlation between AI-driven mobile-based gait technology and pain in patients with MSK conditions is still missing. Therefore, the purpose of the current study is to examine the relationship between spatiotemporal gait metrics and pain severity in patients suffering from knee and back pain utilizing AI-driven mobile-based gait technology. Moreover, the study will compare patients with primary knee pain and patients with primary back pain to see if pain location might affect gait compensation strategies and their correlation with pain.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis was a retrospective cross-sectional analysis based on the records of patients who attended the AposCare clinic in the United States between December 2023 and April 2025. Patients with primary knee or back pain were included in the study. Patients who used a walking aid or patients who did not complete a walking trial independently were excluded.\u003c/p\u003e\u003cp\u003eThe study was approved by Pearl IRB. The committee approved the protocol as an exempt research determination in accordance with the applicable federal regulations and has determined the study noted above to be Exempt according to 45 CFR 46.104(d)(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Secondary Research Uses of Data or Specimens 45 CFR 46.104(d)(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)(ii). Pearl IRB is in full compliance with good clinical practice as defined under the U.S. food and drug administration (FDA) regulations, U.S. department of health and human services (HHD) regulations, and the international conference on harmonization (ICH) guidelines. All patients signed consent acknowledging that their data might be used anonymously for research purposes.\u003c/p\u003e\u003cp\u003eAll patients completed a computerized gait assessment as part of their routine visit to the clinic, using an AI-driven smartphone-based gait analysis application (OneStep, Celloscope Ltd., Tel Aviv, Israel). Patients were asked to walk barefoot, at a self-selected pace along a 10-meter walkway in two consecutive trials (back and forth). When a new test is performed, the application starts with an internal automatic calibration process that accounts for variations in phone placement and decreases the need for a specific location. The application uses data from the smartphone\u0026rsquo;s IMU, including 3D acceleration, angular velocity, and magnetic intensity data at a sampling rate of 100Hz. Using machine learning algorithms, the application first segments the raw data into gait cycles and filters out curves and turns. Then, using machine learning, the algorithms extract spatiotemporal parameters from the gait cycles taken during the straight-line walking segments. For this study, several spatio-temporal gait metrics were calculated including gait velocity (cm/s), step length (cm), cadence (steps/min), double support time (% gait cycle), single support time (% gait cycle), step width (cm) and step-to-step variability in velocity and cadence. Step length and single limb support were calculated separately for the left and right limbs. For each patient, the shorter step length and lower single support time were identified and classified as the more affected limb while the contralateral limb was classified as less affected.\u003c/p\u003e\u003cp\u003eFollowing the gait test patients were asked to rate their pain levels. Pain intensity was assessed using the Numeric Pain Rating Scale (NPRS), a widely accepted tool in clinical research for evaluating pain severity (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) including patients with knee and back pain (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The NPRS is a straightforward, self-reported measure in which participants rate their pain on an 11-point scale ranging from 0 to 10, where 0 indicates \"no pain\" and 10 represents \"the worst possible pain.\" Patients were asked to rate their current level of pain.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData were analyzed with IBM SPSS software version 29.0 and were presented as frequencies and percentages for baseline characteristics (categorical variables) and as mean and standard deviation for all gait spatio-temporal parameters. The distributions of the variables in the study were examined using the Kolmogorov-Smirnov non-parametric test. We employed descriptive statistics, including frequencies, means, and standard deviations, to present patient characteristics.\u003c/p\u003e\u003cp\u003eLogistic regression analyses were performed to examine the relationship between gait patterns and pain severity. First, Spearman rank-order correlation coefficients were calculated between pain scores (as a continuous variable) and gait metrics including gait velocity, cadence, step length of the involved limb, and single support time of the involved limb. Due to high intercorrelations among gait variables, each gait metric was entered into a separate logistic regression model to reduce multicollinearity.\u003c/p\u003e\u003cp\u003eEach gait parameter was stratified into quintiles to establish gait severity groups, whereby quintile 1 (Q1) indicates highly impaired gait patterns, and quintile 5 (Q5) represents near-normal gait values (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The dependent variable was severe pain, defined by a score above 7 on a 0 to 10 numeric scale (where 0\u0026thinsp;=\u0026thinsp;no pain, 10\u0026thinsp;=\u0026thinsp;severe pain), based on previous studies that have set pain severity thresholds for patients with chronic MSK pain (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). For each model, odds ratio (OR) and 95% confidence intervals (CI) were calculated to express the likelihood of experiencing severe pain in each gait quintile relative to the reference group (Q5). An OR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates increased odds of severe pain compared to the reference, while an OR\u0026thinsp;\u0026lt;\u0026thinsp;1 suggests lower odds. Furthermore, we applied the same models separately for patients with knee pain and back pain, adjusted for age and gender. Significance levels were set at 0.05.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGait quintiles\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVelocity (cm/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80.1\u0026ndash;93.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.1\u0026ndash;103.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e103.1\u0026ndash;114.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;114.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCadence (steps/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.1-100.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.4-105.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e105.7\u0026ndash;112.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;112.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep Length (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;47.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.8\u0026ndash;53.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.8\u0026ndash;57.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e57.6\u0026ndash;61.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;61.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle limb support (% gait cycle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;31.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.2\u0026ndash;32.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.7\u0026ndash;33.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.6\u0026ndash;34.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;34.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eQ\u0026thinsp;=\u0026thinsp;quintiles\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThree thousand five hundred and ninety-five (n\u0026thinsp;=\u0026thinsp;3,595) patients were included in the study. Of these, 1,945 had primary back pain and 1,650 had primary knee pain. Patient characteristics, pain levels, and spatiotemporal gait metrics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient characteristics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBack\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKnee\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,650\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.9 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.2 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.1 (11.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, F/M (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70/30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66/34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74/26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePain (0\u0026ndash;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.2 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.1 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.3 (1.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVelocity (cm/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.0 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102.2 (18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.0 (22.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCadence (steps/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102.7 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104.6 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.4 (12.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep Length, affected limb (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.5 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.7 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.0 (9.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle limb support, affected limb (% gait cycle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.6 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.2 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.9 (2.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDouble limb support (% gait cycle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.6 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.7 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.6 (5.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVelocity variability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.0 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.5 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.7 (4.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCadence variability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.1 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.9 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.3 (1.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eResults are presented as mean (sd) except for gender the is presented as % of total cohort.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a multivariate regression results visualized as a chart, displaying the beta coefficients and 95% confidence intervals for the association between pain severity and four core gait parameters (velocity, step length, cadence, and single limb support), adjusted for age and sex. These results are shown for the full dataset, and separately for knee and back pain patients as seen in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Across all groups, pain was significantly associated with worse gait parameters, particularly velocity, which had the strongest negative relationship. Looking at the likelihood of severe pain across quintiles of gait parameters. The results suggest that patients in Q1 (severe gait deviations) are more likely to report severe pain (\u0026gt;\u0026thinsp;7) compared to those in Q5. For example, patients with gait velocity\u0026thinsp;\u0026lt;\u0026thinsp;80.0 cm/s are 2.74 times more likely to report pain\u0026thinsp;\u0026gt;\u0026thinsp;7 than those with gait velocity\u0026thinsp;\u0026gt;\u0026thinsp;114 cm/s (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar significant correlations were calculated for cadence, step length, and single limb support, indicating a higher likelihood of severe pain among individuals with more impaired gait patterns.\u003c/p\u003e\u003cp\u003eConsistent trends were observed in both back and knee groups; however, patients with knee pain exhibited the highest ORs in gait velocity (OR\u0026thinsp;=\u0026thinsp;3.172) and cadence (OR\u0026thinsp;=\u0026thinsp;2.436), whereas patients with back pain showed the highest ORs in gait velocity (OR\u0026thinsp;=\u0026thinsp;2.345) and step length (OR\u0026thinsp;=\u0026thinsp;2.763). In essence, patients in Q1 who represent a cohort with the most compromised gait pattern including slower walking speed, lower cadence, shorter step length and lower single limb support) had higher odds of having severe pain. These results for all patients, as well as subgroup analyses for knee and back pain, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAdjusted logistics regression for Q1 versus Q5 gait quintiles and severe pain across all patients and patients with primary knee pain or primary back pain.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExp(B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExp(B), 95% C.I.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVelocity (cm/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.173\u0026ndash;3.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCadence (steps/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.433\u0026ndash;2.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep Length (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.009\u0026ndash;3.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle limb support (% gait cycle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.806\u0026ndash;2.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBack\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExp(B)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExp(B), 95% C.I.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVelocity (cm/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.680\u0026ndash;3.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCadence (steps/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.966\u0026ndash;1.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep Length (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.988\u0026ndash;3.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle limb support (% gait cycle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.542\u0026ndash;2.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKnee\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExp(B)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExp(B), 95% C.I.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVelocity (cm/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.237\u0026ndash;4.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCadence (steps/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.757\u0026ndash;3.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep Length (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.659\u0026ndash;2.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle limb support (% gait cycle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.666\u0026ndash;3.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study suggest a significant association between spatiotemporal gait parameters and pain severity in individuals with musculoskeletal (MSK) conditions, specifically knee and back pain. Our analysis suggests that gait velocity had the strongest negative relationship with pain. As pain increases, velocity, step length, cadence, and single limb support all consistently decline, supporting the regression findings and emphasizing the clinical potential of gait as a marker of pain burden. However, further analysis suggests that the correlation is not linear and that gait compensations are not the same across pain locations. Therefore, we offer a different approach when conducting a gait analysis in clinical practice. Moreover, we found that specific gait metrics were more strongly linked to severe pain, with velocity being the most prominent one. In both groups (knee and back pain) gait velocity emerged as the most influential predictor of pain severity. The data indicates that individuals walking at slower speeds are substantially more likely to report high pain levels (\u0026gt;\u0026thinsp;7). Specifically, gait velocity less than 80 cm/s was associated with a 3.17-fold for knee 2.35-fold for back increase in the odds of experiencing severe pain compared to those walking faster than 114 cm/s. Interestingly, the association of other gait parameters with pain severity differed depending on the location of pain, i.e., primary knee pain or primary back pain. For patients with primary knee pain, it is more likely that lower cadence will be linked to severe pain whereas for patients with primary back pain it is more likely that shorter step length will be linked to severe pain indicating that gait compensations might be specific to pain locations, however this should be verified in future studies. This correlation aligns with previous literature suggesting that reduced walking speed, shorter stride length, and lower single limb support in knee OA serve as a protective or compensatory mechanism to minimize joint loading and impact forces during ambulation (\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). It is plausible that patients voluntarily slow their gait to decrease pressure on the affected knee, thereby alleviating discomfort while walking. This behavioral adaptation might also delay disease progression and the risk for a total knee replacement surgery (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e); therefore, monitoring changes in gait characteristics can potentially serve as an objective marker for assessing the severity of knee pathology.\u003c/p\u003e\u003cp\u003eConversely, patients with back pain demonstrated a different gait compensation pattern. Step length was most likely to be affected by pain severity, followed by gait velocity. Specifically, a reduced step length was strongly linked to higher pain scores, with gait velocity and step length exhibiting the highest odds ratios (OR\u0026thinsp;=\u0026thinsp;2.35 and OR\u0026thinsp;=\u0026thinsp;2.80, respectively). This finding resonates with prior biomechanical research indicating that patients with chronic low back pain often adapt their gait by shortening stride length (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Such a strategy likely functions to reduce angular moments and strain on the lumbar spine by decreasing the moment arm during gait (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Shorter step length diminishes the lever arm during heel strike and toe-off phases, potentially mitigating load and subsequent pain.\u003c/p\u003e\u003cp\u003eThis differential pattern underscores the importance of tailored gait assessment in clinical practice. For patients with knee pathology, monitoring gait velocity may provide a straightforward, objective measure of pain severity and functional impairment. In contrast, for patients with back pain, step length might serve as a more sensitive indicator of symptom severity and compensatory gait strategies. This distinction has practical implications; routine gait analysis using accessible mobile-based technologies can facilitate early detection of worsening symptoms and monitor responses to interventions.\u003c/p\u003e\u003cp\u003eFurthermore, our results contribute to the growing body of evidence emphasizing the role of gait analysis as a non-invasive, objective biomarker for pain and functional status. The consistency of these patterns across different MSK conditions highlights the potential for gait metrics to inform personalized treatment plans and track disease progression. Future research could explore how rehabilitative interventions targeting these specific gait parameters influence pain outcomes and functional recovery.\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. First, its retrospective design inherently limits the ability to establish causality between gait parameters and pain severity, as temporal or directional relationships cannot be definitively determined. Second, reliance on a mobile-based AI gait analysis platform, while practical and accessible, may introduce measurement variability compared to gold-standard laboratory-based motion analysis systems; despite validation efforts, device accuracy can be affected by factors such as phone placement and environmental conditions. Therefore, more research using this emerging technology would help strengthen, support, and provide validation to the results of this study. In addition, it should be acknowledged that the gait test was performed when walking barefoot. Patients wearing shoes may have had different results. That being said, if bias exists, it was applied to all patients, as they all walked barefoot. Third, the cross-sectional nature of the data restricts insights into how gait parameters and pain levels evolve over time or in response to treatment interventions. We recommend that future studies examine the correlation between gait and pain over time and examine which gait metrics are most responsive to treatment and are associated with changes in pain. Lastly, we used primary pain location for sub-classification and were able to demonstrate different gait compensation mechanisms. However, additional patient characteristics such as knee alignment, radiographic severity of knee osteoarthritis, back pain diagnosis, and physical examination can potentially provide valuable information as to the correlation between pain and gait. Moreover, controlling for height, weight, BMI, duration of symptoms, activity level and medication use might help clarify and explain some of the results, yet those were not available. Therefore, we recommend that future studies expand patient characteristics and assess their contribution to the correlation between pain and gait patterns.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study illustrates the correlation between spatiotemporal gait parameters captured with AI-driven smartphone-based gait technology, which can be utilized in clinical practice and even in the patient\u0026rsquo;s home. While both gait velocity and step length are associated with pain severity, their relative importance varies between different pain locations. Understanding these distinct patterns can enhance clinical assessment and enable more targeted gait-based interventions, which may improve patient management, reduce symptoms, and improve quality of life.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMSK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMusculoskeletal\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNPRS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNumeric Pain Rating Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOsteoarthritis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLower back pain\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIMU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInertial measurement unit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArtificial intelligence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e3D \u0026minus;\u0026thinsp;3\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDimention\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eodds ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not funded in any way.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study were received by AposHealth and were further processed. Rothman and AposHealth have an ongoing clinical relationship and AposHealth has given the authors permission to use the datasets for research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by Pearl IRB. The committee approved the protocol as an exempt research determination in accordance with the applicable federal regulations and has determined the study noted above to be Exempt according to 45 CFR 46.104(d)(4) Secondary Research Uses of Data or Specimens 45 CFR 46.104(d)(4)(ii).\u003c/p\u003e\n\u003cp\u003ePearl IRB is in full compliance with good clinical practice as defined under the U.S. food and drug administration (FDA) regulations, U.S. department of health and human services (HHD) regulations, and the international conference on harmonization (ICH) guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll patients signed consent acknowledging that their data might be used anonymously for research purposes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to declare\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSS - Conceptual and design, data analysis, major contributor in writing the manuscript\u003c/p\u003e\n\u003cp\u003eGF – Data analysis, drafting the manuscript\u003c/p\u003e\n\u003cp\u003eCJM - Conceptual and design, data analysis, major contributor in writing the manuscript\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMiddleton A, Fritz SL, Lusardi M. Walking speed: the functional vital sign. J Aging Phys Act. 2015;23(2):314\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiddleton A, Fritz SL. Assessment of Gait, Balance, and Mobility in Older Adults: Considerations for Clinicians. Curr Transl Geriatr Exp Gerontol Rep. 2013;2:9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLord SE, Halligan PW, Wade DT. Visual gait analysis: the development of a clinical assessment and scale. Clin Rehabil. 1998;12(2):107\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDieleman JL, Cao J, Chapin A, Chen C, Li Z, Liu A, et al. US Health Care Spending by Payer and Health Condition, 1996\u0026ndash;2016. JAMA. 2020;323(9):863\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen AT, Aris IM, Snyder BD, Harris MB, Kang JD, Murray M, et al. Musculoskeletal health: an ecological study assessing disease burden and research funding. Lancet Reg Health Am. 2024;29:100661.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYelin E, Weinstein S, King T. The burden of musculoskeletal diseases in the United States. Semin Arthritis Rheum. 2016;46(3):259\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSelected Health Conditions. and Likelihood of Improvement with Treatment. Washington (DC)2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlfalogy E, Mahfouz S, Elmedany S, Hariri N, Fallatah S. Chronic Low Back Pain: Prevalence, Impact on Quality of Life, and Predictors of Future Disability. Cureus. 2023;15(9):e45760.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKawano MM, Araujo IL, Castro MC, Matos MA. Assessment of quality of life in patients with knee osteoarthritis. Acta Ortop Bras. 2015;23(6):307\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerman A, Chechik O, Segal G, Kosashvili Y, Lador R, Salai M, et al. The correlation between radiographic knee OA and clinical symptoms\u0026ndash;do we know everything? Clin Rheumatol. 2015;34(11):1955\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerman A, Mor A, Segal G, Shazar N, Beer Y, Halperin N et al. Knee Osteoarthritis functional classification scheme \u0026ndash; validation of time-dependent treatment effect. One year follow-up of 518 patients. J Arthritis 2018;7(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDebi R, Mor A, Segal G, Segal O, Agar G, Debbi E, et al. Correlation between single limb support phase and self-evaluation questionnaires in knee osteoarthritis populations. Disabil Rehabil. 2011;33(13\u0026ndash;14):1103\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElbaz A, Mor A, Segal G, Debi R, Shazar N, Herman A. Novel classification of knee osteoarthritis severity based on spatiotemporal gait analysis. Osteoarthritis Cartilage. 2014;22(3):457\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElbaz A, Mor A, Segal O, Agar G, Halperin N, Haim A, et al. Can single limb support objectively assess the functional severity of knee osteoarthritis? Knee. 2012;19(1):32\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElbaz A, Mirovsky Y, Mor A, Enosh S, Debbi E, Segal G, et al. A novel biomechanical device improves gait pattern in patient with chronic nonspecific low back pain. Spine (Phila Pa 1976). 2009;34(15):E507\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith JA, Stabbert H, Bagwell JJ, Teng HL, Wade V, Lee SP. Do people with low back pain walk differently? A systematic review and meta-analysis. J Sport Health Sci. 2022;11(4):450\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanko RM, Laende EK, Davis EM, Selbie WS, Deluzio KJ. Concurrent assessment of gait kinematics using marker-based and markerless motion capture. J Biomech. 2021;127:110665.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMenolotto M, Komaris DS, Tedesco S, O'Flynn B, Walsh M. Motion Capture Technology in Industrial Applications: A Systematic Review. Sens (Basel). 2020;20:19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBonato P, Feipel V, Corniani G, Arin-Bal G, Leardini A. Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience. Gait Posture. 2024;113:191\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHulleck AA, Menoth Mohan D, Abdallah N, El Rich M, Khalaf K. Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies. Front Med Technol. 2022;4:901331.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShema-Shiratzky S, Beer Y, Mor A, Elbaz A. Smartphone-based inertial sensors technology - Validation of a new application to measure spatiotemporal gait metrics. Gait Posture. 2022;93:102\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoeing KL, Hsieh KL, Sosnoff JJ. A systematic review of balance and fall risk assessments with mobile phone technology. Arch Gerontol Geriatr. 2017;73:222\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eObuchi SP, Tsuchiya S, Kawai H. Test-retest reliability of daily life gait speed as measured by smartphone global positioning system. Gait Posture. 2018;61:282\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNugent SM, Lovejoy TI, Shull S, Dobscha SK, Morasco BJ. Associations of Pain Numeric Rating Scale Scores Collected during Usual Care with Research Administered Patient Reported Pain Outcomes. Pain Med. 2021;22(10):2235\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoonstra AM, Stewart RE, Koke AJ, Oosterwijk RF, Swaan JL, Schreurs KM, et al. Cut-Off Points for Mild, Moderate, and Severe Pain on the Numeric Rating Scale for Pain in Patients with Chronic Musculoskeletal Pain: Variability and Influence of Sex and Catastrophizing. Front Psychol. 2016;7:1466.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChilds JD, Piva SR, Fritz JM. Responsiveness of the numeric pain rating scale in patients with low back pain. Spine (Phila Pa 1976). 2005;30(11):1331\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavis AM, King LK, Stanaitis I, Hawker GA. Fundamentals of osteoarthritis: outcome evaluation with patient-reported measures and functional tests. Osteoarthritis Cartilage. 2022;30(6):775\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Smartphone gait technology, Pain, Musculoskeletal, Knee, Back","lastPublishedDoi":"10.21203/rs.3.rs-7301065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7301065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study aimed to investigate the relationship between spatiotemporal gait parameters and pain severity in patients with musculoskeletal (MSK) conditions, specifically knee and back pain. Additionally, it sought to compare how gait compensation strategies differ based on pain location and their association with pain intensity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA retrospective analysis was conducted on 3,595 patients attending clinics in the US between December 2023 and April 2025. Participants performed barefoot gait assessments using an AI-driven smartphone-based app, which collected data to extract gait metrics such as velocity, step length, cadence, and limb support times. Pain severity was assessed via the Numeric Pain Rating Scale (NPRS). Gait parameters were stratified into quintiles, and logistic regression analyses examined associations between gait deviations and severe pain (NPRS\u0026thinsp;\u0026gt;\u0026thinsp;7), adjusting for age and gender.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients with gait parameters indicating greater impairment exhibited higher odds ratio of reporting severe pain. Gait velocity emerged as the most influential predictor, with walking speeds below 80 cm/s associated with over a 2.7-fold increased likelihood of severe pain. Subgroup analyses revealed that knee pain was more strongly linked to reduced cadence, while back pain correlated primarily with shorter step length.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSpatiotemporal gait parameters are significantly associated with pain severity in MSK conditions and can be effectively measured using accessible mobile technology. Recognizing distinct gait patterns based on pain location supports the development of tailored clinical interventions and targeted objectives for monitoring treatment outcomes.\u003c/p\u003e","manuscriptTitle":"The correlation between spatiotemporal gait and pain in patients with musculoskeletal pain using smartphone-based gait technology. 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