Centre de formation et physiothérapie de Lutry

Centre de formation et physiothérapie de Lutry Centre de formation et physiothérapie

08/08/2025

"Femoroacetabular Impingement: Critical Analysis Review of Current Nonoperative Treatments" Dancy et al.

Read online: https://bit.ly/3Y3WqZq

04/08/2025

Just published 🔥

Digital pain diagrams to identify common lumbar spinal stenosis pain distribution patterns

✅ Lumbar spinal stenosis (LSS) is a prevalent degenerative condition affecting approximately one-third of patients in secondary spine care settings, contributing significantly to disability and reduced quality of life [1]. While classic clinical descriptions distinguish between central canal stenosis (typically bilateral posterior lower extremity pain) and lateral recess/foraminal stenosis (often unilateral radicular pain in a dermatomal distribution) [2,3], real-world presentations are often heterogeneous. Pain diagrams (PDs) have long been used to document pain distribution, offering insight into the subjective pain experience and aiding diagnosis [4]. Digital PDs now allow for large-scale, quantitative analysis of pain patterns, and latent class analysis (LCA) has emerged as a valuable data-driven approach for identifying distinct clinical phenotypes [5–7].

📘 Prior studies have demonstrated the validity of PDs for musculoskeletal conditions [4,6], but LSS-specific pain distribution phenotypes have not been systematically mapped. The aim of this brand-new study by Young and colleagues (https://pubmed.ncbi.nlm.nih.gov/40751839/) was to identify and characterize common pain distribution patterns in patients with LSS using digital PDs and LCA.

✅ Methods
This was a cross-sectional study using baseline data from the SpineData registry at the Spine Centre of Southern Denmark [10] between February 2019 and April 2021. Inclusion criteria were age ≥18 years, LSS diagnostic code post-consultation, and completion of a digital PD. Exclusion criteria included diagnostic codes for malignancy, fracture, neurological disorders, chronic widespread pain, or lower extremity musculoskeletal/vascular conditions.
👫 Patients completed an electronic questionnaire before consultation, including demographic data, psychosocial screening (anxiety, depression, pain catastrophizing, fear of movement, risk of pain persistence, social isolation) [12], numeric rating scales (NRS) for back and leg pain, and the Oswestry Disability Index (ODI).

📷 PD data were processed using MATLAB polyshape functions to map pain areas to predefined anatomical regions [13,14]. Unilateral right-sided pain was mirrored to the left for analysis. LCA tested models with 2–7 classes, selecting the optimal model based on Bayesian Information Criterion (BIC), posterior probabilities (>0.9), class size (>5%), and clinical interpretability [15–17].

📊 Results

👉 From 16,114 patients presenting with low back pain, 2,379 met inclusion criteria. The mean age was 66.6 years, 50.7% were female, and mean ODI was 39.0. Pain duration exceeded 12 months in 52% of patients.

A six-class LCA model provided the best fit (relative entropy 0.92), identifying distinct LSS pain distribution patterns:

1️⃣ Bilateral posterior leg pain – 11.4% (n=272)

2️⃣ Bilateral posterior & anterior leg pain – 8.7% (n=207)

3️⃣ Unilateral posterior leg pain – 26.1% (n=620)

4️⃣ Unilateral posterior leg pain with low back pain – 21.0% (n=499)

5️⃣ Unilateral anterior & posterior leg pain – 22.9% (n=545)

6️⃣ Multisite pain – 9.9% (n=236)

💡 Mean NRS scores for back and leg pain were consistent across classes, with slight variation in Class 4 (lower leg pain). Multisite pain was associated with higher social isolation and longer pain duration.

Discussion 💬

▶️ This study is the first to map LSS pain phenotypes using digital PDs and LCA, revealing substantial heterogeneity beyond “textbook” bilateral or unilateral posterior leg pain patterns [2,3]. Notably, unilateral anterior and posterior leg pain (22.9%) was more prevalent than any bilateral pattern. This underscores the diagnostic complexity of LSS and the potential value of PDs in differentiating it from conditions like hip osteoarthritis.

▶️ The findings support the concept of LSS clinical phenotypes, which could improve diagnostic accuracy, guide treatment selection, and facilitate patient–clinician communication. However, limitations include reliance on diagnostic codes (risk of misclassification), absence of imaging correlation, and lack of pain quality descriptors in PDs. Future research should validate these phenotypes in diverse settings and assess prognostic and therapeutic implications.

✅ Conclusion

Six clinically recognizable pain distribution patterns were identified in LSS patients, reflecting significant heterogeneity in presentation. These patterns may represent distinct clinical phenotypes with potential diagnostic and therapeutic relevance. Further validation and longitudinal outcome studies are needed.

📚 References

1. Jensen RK, Jensen TS, Koes B, Hartvigsen J (2020) Prevalence of lumbar spinal stenosis in general and clinical populations: a systematic review and meta-analysis. Eur Spine J 29(9):2143–2163.

2. Tomkins-Lane C, Melloh M, Lurie J, Smuck M, Battié MC, Freeman B et al (2016) Consensus on the clinical diagnosis of lumbar spinal stenosis: results of an international Delphi study. Spine 41(15):1239–1246.

3. Jensen RK, Harhangi BS, Huygen F, Koes B (2021) Lumbar spinal stenosis. BMJ 373:n1581.

4. Shaballout N, Aloumar A, Neubert TA, Dusch M, Beissner F (2019) Digital pain drawings can improve doctors’ understanding of acute pain patients: survey and pain drawing analysis. JMIR Mhealth Uhealth 7(1):e11412.

5. Chang NHS, Nim C, Harsted S, Young JJ, O’Neill S (2024) Data-driven identification of distinct pain drawing patterns and their association with clinical and psychological factors: a study of 21,123 patients with spinal pain. Pain 165(10):2291–2304.

6. Harsted S, Chang NHS, Nim C, Young JJ, McNaughton DT, O’Neill S (2024) Exploring the association between patient-drawn pain diagrams and psychological and physical health variables: a large‐scale study of patients with low back pain. Eur J Pain. eJP.4711.

7. Shaballout N, Neubert TA, Boudreau S, Beissner F (2019) From paper to digital applications of the pain drawing: systematic review of methodological milestones. JMIR Mhealth Uhealth 7(9):e14569.

8. Southerst D, Côté P, Stupar M, Stern P, Mior S (2013) The reliability of body pain diagrams in the quantitative measurement of pain distribution and location in patients with musculoskeletal pain: a systematic review. J Manipulative Physiol Ther 36(7):450–459.

9. Schott GD (2010) The cartography of pain: the evolving contribution of pain maps. Eur J Pain 14(8):784–791.

10. Kent P, Kongsted A, Secher Jensen T, Albert H, Manniche C, Schiøttz-Christensen B (2015) SpineData: a Danish clinical registry of people with chronic back pain. Clin Epidemiol 13(7):369–380.

11. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP (2008) The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol 61(4):344–349.

12. Kent P, Mirkhil S, Keating J, Buchbinder R, Manniche C, Albert HB (2014) The concurrent validity of brief screening questions for anxiety, depression, social isolation, catastrophization, and fear of movement in people with low back pain. Clin J Pain 30(6):479–489.

13. The MathWorks Inc. intersect - intersection of polyshape objects. Natick, Massachusetts, United States (2020).

14. Margolis RB, Tait RC, Krause SJ (1986) A rating system for use with patient pain drawings. Pain 24(1):57–65.

15. Lezhnina O, Kismihók G (2022) Latent class cluster analysis: selecting the number of clusters. MethodsX 9:101747.

16. Kongsted A, Nielsen AM (2017) Latent class analysis in health research. J Physiotherapy 63(1):55–58.

17. Linzer DA, Lewis JB (2011) PoLCA: an R package for polytomous variable latent class analysis. J Stat Softw 42:1–29.

18. Young JJ, Hartvigsen J, Roos EM, Ammendolia C, Kongsted A, Skou ST et al (2021) Symptoms of lumbar spinal stenosis in people with knee or hip osteoarthritis or low back pain: a cross-sectional study of 10,234 participants in primary care. Osteoarthritis Cartilage 29(11):1515–1520.

19. Young JJ, Kongsted A, Jensen RK, Roos EM, Ammendolia C, Skou ST et al (2023) Characteristics associated with comorbid lumbar spinal stenosis symptoms in people with knee or hip osteoarthritis: an analysis of 9,136 good life with osteoarthritis in Denmark (GLA:D®) participants. BMC Musculoskelet Disord 24(1):250.

20. Young JJ, Jensen RK, Hartvigsen J, Roos EM, Ammendolia C, Juhl CB (2022) Prevalence of multimorbid degenerative lumbar spinal stenosis with knee or hip osteoarthritis: a systematic review and meta-analysis. BMC Musculoskelet Disord 23(1):177.

31/07/2025

Isometric training and long‐term adaptations: Effects of muscle length, intensity, and intent

📌 Key Takeaways

-Isometric training at longer muscle lengths produced greater muscular hypertrophy than shorter muscle length training.

-Ballistic intent resulted in greater neuromuscular activation and rapid force production than ramped contractions.

-Substantial improvements in muscular hypertrophy and maximal force production were reported regardless of training intensity.

-High‐intensity (≥70%) contractions are required for improving tendon structure and function.

-Long muscle length training results in greater transference to dynamic performance.

-Sustained contractions increase metabolite concentrations in the muscle, stimulating hypertrophy.

-Higher volumes are better for inducing muscular hypertrophy, regardless of contraction intensity.

-Isometric training is a highly reliable means of assessing and tracking changes in force production.

-Link to article in the comments 🔗

26/07/2025

Just published 🔥

Hip muscle strength in patients with chronic ankle instability: A systematic review and meta-analysis

🦶 Chronic ankle instability (CAI) is a common sequela of lateral ankle sprains (LAS), which recur in up to 73% of cases and result in persistent instability symptoms in approximately 40% of patients (Roos et al., 2017; Gribble et al., 2013). While the neuromuscular deficits around the ankle have been widely studied, growing evidence points to the potential involvement of proximal muscle groups, particularly the hip (Hall et al., 2015; Khalaj et al., 2020).

🦵 The hip plays a central role in lower limb stability and dynamic control, and deficits in hip muscle strength may contribute to poor postural control and injury recurrence in individuals with CAI (McCann et al., 2017; De Ridder et al., 2017). Prospective studies further reveal that insufficient hip abduction and extension strength may predispose individuals to LAS, with stronger athletes showing significantly lower LAS risk (De Ridder et al., 2017; Powers et al., 2017).

📘 Previous systematic reviews have provided limited and inconclusive evidence due to small sample sizes and methodological heterogeneity (Dejong et al., 2020; Khalaj et al., 2020). The current systematic review and meta-analysis by Zheng et al. (2025) seeks to address these limitations by comprehensively assessing both isometric and isokinetic hip muscle strength in individuals with CAI compared to healthy controls.

✅ Summary of Findings

This systematic review and meta-analysis included 11 studies involving 548 participants (271 with CAI and 277 healthy controls). The primary outcomes analyzed were isometric and isokinetic strengths of various hip muscle groups.

▶️ Isometric strength: Individuals with CAI demonstrated significant deficits in hip abduction (SMD = 0.56, p < 0.001), extension (SMD = 0.62, p = 0.003), and external rotation (SMD = 0.59, p = 0.005). Adduction strength differences were not significant, with high heterogeneity. (s. Infographic)

▶️ Isokinetic strength: At 60°/s angular velocity, no significant differences were observed between CAI and control groups across all muscle groups. However, limited data and heterogeneity temper these findings.

▶️ Sensitivity analysis showed instability in the findings for external rotation strength but confirmed robustness for abduction and extension.

▶️ Quality assessment: Most included studies were of moderate quality, with methodological concerns such as lack of control for confounders.

💡 Clinical implication: The results suggest that proximal muscle deficits—particularly isometric strength reductions—are integral to CAI pathomechanics and should be addressed in rehabilitation strategies.

🧠 Conclusion

This meta-analysis provides robust evidence that individuals with CAI exhibit notable deficits in isometric hip abduction, extension, and external rotation strength. The decreased hip muscle strength in individuals with CAI may be related to central nervous system reorganization and altered movement strategies. Neuroplasticity theory suggests CAI represents a comprehensive neurophysiological dysfunction affecting the entire sensorimotor system rather than merely a localized peripheral injury (Hertel & Corbett, 2019; Maricot et al., 2023;)

💪 These findings support the inclusion of proximal strength assessment and training in clinical evaluation and rehabilitation protocols for CAI. Further high-quality studies are needed to clarify isokinetic strength characteristics and refine therapeutic interventions.

📒 References

De Ridder, R. et al. (2017). Am J Sports Med, 45(2), 410–416. https://doi.org/10.1177/0363546516672650

Dejong, A. F. et al. (2020). Med Sci Sports Exerc, 52(7), 1563–1575. https://doi.org/10.1249/MSS.0000000000002282

Gribble, P. A. et al. (2013). J Orthop Sports Phys Ther, 43(8), 585–591. https://doi.org/10.2519/jospt.2013.0303

Hall, E. A. et al. (2015). J Athl Train, 50(1), 36–44. https://doi.org/10.4085/1062-6050-49.3.71

Khalaj, N. et al. (2020). Br J Sports Med, 54(14), 839–847. https://doi.org/10.1136/bjsports-2018-100070

McCann, R. S. et al. (2017). J Sci Med Sport, 20(11), 992–996. https://doi.org/10.1016/j.jsams.2017.05.005

Roos, K. G. et al. (2017). Am J Sports Med, 45(1), 201–209. https://doi.org/10.1177/0363546516660980

Zheng, J. et al. (2025). Phys Ther Sport, 75, 48–57. https://doi.org/10.1016/j.ptsp.2025.07.007

Powers, C.M. et al. (2017). Journal of Athl Train, 52(11), 1048–1055. https://doi.org/10.4085/1062-6050-52.11.18

Hertel, J., & Corbett, R. O. (2019). Journal of Athl Train, 54(6), 572–588. https://doi.org/10.4085/1062-6050-344-18

Maricot, A. et al. (2023). Sports Med Auckl NZ, 53(7), 1423–1443. https://doi.org/10.1007/s40279-023-01834-z

23/07/2025

Just published 🔥

Diagnostic accuracy of the compression overload test versus straight leg Raise test in detecting lumbar disc herniation: an MRI-Validated Cross-Sectional study

👉 Low back pain (LBP) is the leading global cause of years lived with disability, affecting over 540 million people worldwide [1]. In India, its prevalence is especially high, with 66% of the population reportedly affected at some point in life [1]. Lumbar intervertebral disc herniation (IVDH)—especially at L4–L5 and L5–S1 levels—is among the most common structural causes of LBP [2,4]. Biomechanical studies indicate that IVDH frequently originates from vertebral endplate (VEP) failure due to axial compression and flexion forces [3,5].

👉 Magnetic resonance imaging (MRI) remains the diagnostic gold standard for IVDH, but clinical tests such as the Straight Leg Raise Test (SLRT) are commonly used in early screening. However, SLRT shows reduced sensitivity in non-radicular presentations and older adults, where mechanical rather than neurogenic symptoms dominate [8–10,12]. The Compression Overload Test (C**T), rooted in spinal biomechanics [13], has emerged as a novel clinical test hypothesized to detect mechanical disc pathology by mimicking compressive spinal loading. Despite growing clinical use, its diagnostic accuracy has not been validated.

📘 A brand-new study by Madhesh et al., published in European Spine Journal, aimed to compare the diagnostic accuracy of C**T versus SLRT in detecting MRI-confirmed IVDH. (https://link.springer.com/article/10.1007/s00586-025-09164-6)

Methods 🧪

A cross-sectional diagnostic accuracy study was conducted on 53 participants (mean age: 37.26 ± 12.16 years) with acute LBP and recent MRI-confirmed lumbar IVDH. The C**T and SLRT were administered by a blinded examiner. Diagnostic metrics including sensitivity, specificity, predictive values, and receiver operating characteristic (ROC) curve analysis were computed against MRI as the reference standard.

Compression Overload Test (C**T, figure below upper part):

To perform C**T, the participant was positioned supine with hips and knees flexed at 90°. The examiner supported the knees with one arm while stabilizing the pelvis at the anterior superior iliac spine (ASIS) with the other. Axial compression was applied vertically through the femur by shifting the examiner’s body weight in a cephalad direction, thereby replicating spinal loading. If initial compression did not provoke symptoms, a hypothenar tap over the ischium was added to increase mechanical stress. A test was considered positive if it reproduced the patient's characteristic pain, indicating possible vertebral endplate stress or disc pathology [13,14].

Results 📈

▶️ Out of 53 participants, MRI confirmed IVDH in 50 cases. C**T showed higher sensitivity (92%) and diagnostic accuracy (90.6%) compared to SLRT (sensitivity: 28%, accuracy: 30.2%, s. table in comments).

🩻 C**T had a moderate agreement with MRI findings (κ = 0.399, Φ = 0.428, p = 0.002), while SLRT showed no significant correlation (κ = –0.008, Φ = –0.027, p = 0.842). C**T yielded an AUC of 0.793 in ROC analysis, suggesting fair discriminative ability, compared to SLRT's poor AUC of 0.473. The majority of MRI findings involved L4–L5 disc bulges, with VEP abnormalities (e.g., Schmorl’s nodes, Modic changes) present in 37.7% of cases.

Conclusion 🧠

▶️ C**T outperformed SLRT in detecting lumbar disc pathology, particularly in non-radicular presentations. Its diagnostic utility stems from its biomechanical relevance, simulating the compressive forces implicated in disc and endplate injury. These findings suggest that C**T may serve as a valuable clinical screening tool, especially in settings where access to MRI is limited.

❎However, this study has several limitations.

▪️The small sample size, especially the low number of MRI-negative cases, may have skewed sensitivity estimates and limited specificity assessment.

▪️Participants were recruited from a hospital setting, potentially introducing selection bias.

▪️Moreover, the study did not stratify subjects into distinct clinical subgroups (e.g., radicular vs. non-radicular pain), which limits generalizability and impact in clinical decision making.

▪️Lastly, the absence of axial-loaded MRI data restricts the biomechanical validation of C**T under functional loading conditions.

Future studies should address these limitations through multicenter designs, larger and more diverse samples, and comparison with dynamic imaging modalities.

References 📚

1. Shetty GM et al. (2022). Work, 73(2), 429–452.

2. Sasi Kuppuswamy D et al. (2017). Int J Orthop Sci, 3, 357–360.

3. Gracovetsky SA, Iacono S. (1987). J Biomed Eng, 9(2), 99–114.

4. Newell N et al. (2017). J Mech Behav Biomed Mater, 69, 420–434.

5. Pouriesa M et al. (2013). Spine J, 13(4), 402–407.

6. Van Dieën JH et al. (1999). Med Hypotheses, 53(3), 246–252.

7. Huang Z et al. (2023). Front Surg, 9, 1020766.

8. Omar S et al. (2016). Pak Armed Forces Med J, 66(1), 53–56.

9. Cichosz M et al. (2020). Pedagogy Psychol Sport, 6(1), 73–84.

10. Capra F et al. (2011). J Manip Physiol Ther, 34(4), 231–238.

11. Majlesi J et al. (2008). J Clin Rheumatol, 14(2), 87–91.

12. Al-Sharaa M et al. (2021). Al-Kindy Coll Med J, 17(1), 41–44.

13. Farfan HF. (1973). Mechanical Disorders of the Low Back.

14. ASPIREOMT (2022). YouTube. https://www.youtube.com/watch?v=ckWSY4Yrti0

15. Magee DJ, Manske RC. (2020). Orthopedic Physical Assessment.

16. Fang X et al. (2022). Quant Imaging Med Surg, 13(1), 58.

17. Hebelka H et al. (2022). J Clin Med, 11(8), 2122.

18. Sahoo MM et al. (2017). Global Spine J, 7(3), 230–238.

19. Negida A et al. (2019). Adv J Emerg Med, 3(3), e33.

21/07/2025

Considering and respecting AMI may be more relevant than trying to classify them !

15/07/2025

Hippocampus and Insula Team Up to Encode Emotional Memories

Researchers investigated how the hippocampus and insula collaborate to encode emotional memories in humans.

Sixteen participants with implanted electrodes viewed emotionally charged words and attempted to recall them later.

One subset of insular neurons showed activity changes that reliably predicted memory success, aligning temporally between hippocampal theta waves and ripple bursts.

A different subset of insular neurons instead tracked the emotional valence of words, regardless of recall.

When memory-related insular sites were stimulated, they evoked quick, specific responses in the hippocampus, unlike the more diffuse effects of hippocampal stimulation on the insula.

These findings reveal a nuanced, asymmetric interplay between memory and emotion processing in the brain.

https://neurosciencenews.com/hippocampus-insula-emotional-memory-29462/

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