Alterations in energy consumption and plantar pressure distribution during walking in young adults with patellofemoral pain syndrome

Dağ, Figen; Dal, Uğur; Altinkaya, Zeynep; Erdoğan, Ayhan Taner; Özdemir, Emre; Yıldırım, Didem Derici; Çolak, Mehmet

doi:10.1016/j.aott.2018.10.006

Cited by 10 publications

(4 citation statements)

References 33 publications

(52 reference statements)

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“…Besides, the frequency of the PFP syndrome among women and men was revealed as 9% and 1.5%, respectively (Figure 2). Similarly, to the results of Roush JR. and Curtis R 17 , in our study women were more likely to have PFP than men, which may be a result of the alterations of lower extremity biomechanics in women, such as, the wider pelvis and larger Q angles. In the literature, the reason for having PFP has not been adequately clarified yet.…”

Section: Discussionmentioning

confidence: 49%

Is There a Relation between The Lower Extremity Mechanics and Patellofemoral Pain Syndrome?

KARA

Yeral

Aslan

et al. 2022

International Journal of Traditional and Complementary Medicine Research

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Objective: It has been theorized that changes in the lower extremity mechanics may lead to develop Patellofemoral Pain (PFP) in the young population. The present study aims to investigate the effects of lower extremity mechanics on Patellofemoral Pain Syndrome in private university students. Material and Methods: Kujala Pain Score (KPS) of 400 Yeditepe University students ages of 18-30 years was performed. Students having a less or equal point of 85 in KPS (n=30) and healthy groups randomly selected in students with KPS=100 (n=30) were measured in terms of Feiss Line, navicular drop, subtalar angle, tibial torsion, knee valgus angle, Q angle in standing and supine position and hamstring tightness. Results: The prevalence of PFP among students was found to be 10.5%(n = 42). In case and control groups were found 2nd Pes Planus (PP) as 16 (%53,3) and 3 (%10) respectively. A statistically significant difference was found between groups in navicular drop, subtalar angle, tibial rotation, Q angle in supine and hamstring tightness (p-value

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Section: Discussionmentioning

confidence: 49%

Is There a Relation between The Lower Extremity Mechanics and Patellofemoral Pain Syndrome?

KARA

Yeral

Aslan

et al. 2022

International Journal of Traditional and Complementary Medicine Research

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“…Plantar surface was subdivided into 6 areas to assess the dynamic plantar pressure data based on the peak pressure footprint (Figure 1). [20][21][22] Contact area (%), peak pressure (N/cm 2 ), and impulse values (pressure time integral, Ns/cm 2 ) beneath these areas were recorded.…”

Section: Plantar Pressure Assessmentmentioning

confidence: 99%

The Evaluation of Walking Energy Consumption and Plantar Pressure Distribution in Patients with Lumbar Spinal Stenosis: A cross-sectional, case control study

Körlü¹,

Dal²,

Dağ³

et al. 2021

Turkiye Klinikleri J Med Sci

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To evaluate the alterations of walking energy expenditure and plantar pressure distribution of patients with lumbar spinal stenosis. Material and Methods: Twenty-six subjects of both genders were included in the study, 13 patients suffering from lumbar spinal stenosis served as patient group and 13 healthy participants served as a control group. Preferred walking speeds were determined on the over ground. Oxygen consumption was recorded via a metabolic analyzer during walking on a treadmill for 2 km at preferred walking speed that determined on the over ground. Net oxygen consumption and oxygen cost were calculated for obtaining walking energy consumption. Plantar area was subdivided into six zones to measure plantar pressure distribution with a pedobarography device. Results: Compared with control group, patient group had significantly lower preferred walking speed (62.56±13.90 m/minimum and 76.66±10.90 m/minimum, p=0.008) and maximum walking distance [674.6 (105.0-2000.0) m and 2000.0 (2000.0-2000.0) m, p=0.019]. However, there were no statistically significant differences between groups in terms of energy expenditure parameters during walking at preferred walking speed (p>0.05). Similar findings were recorded between right and left foot with regard to weight distribution (%) to forefoot/hindfoot in patient group in the static pedobarographic measurements (p>0.05). Contact area value was significantly different between the affected and unaffected side at lateral forefoot in patient group (22.73±2.97 and 24.90±2.9, p=0.001). Conclusion: Patients with lumbar spinal stenosis do not exhibit more pressure on unaffected side compared to healthy subjects in both static and dynamic condition except contact area of lateral forefoot. Patient group optimized energy expenditure and oxygen cost by reducing their preferred walking speed owing to the pain.

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“…In recent years, increasing studies have focused on the relationship between PFPS and biomechanical parameters (2,14,15 (17). Myer et al used a multiple linear logistic regression model to predict the knee-abduction moment when athletes land and explore the relationship between high knee-abduction moment and increased risk of PFPS (18).…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Diagnostic Method for Patellofemoral Pain Syndrome Based on One-Dimensional Convolutional Neural Network

Shi

Xu³

et al. 2021

Front. Public Health

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Early accurate diagnosis of patellofemoral pain syndrome (PFPS) is important to prevent the further development of the disease. However, traditional diagnostic methods for PFPS mostly rely on the subjective experience of doctors and subjective feelings of the patient, which do not have an accurate-unified standard, and the clinical accuracy is not high. With the development of artificial intelligence technology, artificial neural networks are increasingly applied in medical treatment to assist doctors in diagnosis, but selecting a suitable neural network model must be considered. In this paper, an intelligent diagnostic method for PFPS was proposed on the basis of a one-dimensional convolutional neural network (1D CNN), which used surface electromyography (sEMG) signals and lower limb joint angles as inputs, and discussed the model from three aspects, namely, accuracy, interpretability, and practicability. This article utilized the running and walking data of 41 subjects at their selected speed, including 26 PFPS patients (16 females and 10 males) and 16 painless controls (8 females and 7 males). In the proposed method, the knee flexion angle, hip flexion angle, ankle dorsiflexion angle, and sEMG signals of the seven muscles around the knee of three different data sets (walking data set, running data set, and walking and running mixed data set) were used as input of the 1D CNN. Focal loss function was introduced to the network to solve the problem of imbalance between positive and negative samples in the data set and make the network focus on learning the difficult-to-predict samples. Meanwhile, the attention mechanism was added to the network to observe the dimension feature that the network pays more attention to, thereby increasing the interpretability of the model. Finally, the depth features extracted by 1D CNN were combined with the traditional gender features to improve the accuracy of the model. After verification, the 1D CNN had the best performance on the running data set (accuracy = 92.4%, sensitivity = 97%, specificity = 84%). Compared with other methods, this method could provide new ideas for the development of models that assisted doctors in diagnosing PFPS without using complex biomechanical modeling and with high objective accuracy.

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Alterations in energy consumption and plantar pressure distribution during walking in young adults with patellofemoral pain syndrome

Cited by 10 publications

References 33 publications

Is There a Relation between The Lower Extremity Mechanics and Patellofemoral Pain Syndrome?

Is There a Relation between The Lower Extremity Mechanics and Patellofemoral Pain Syndrome?

The Evaluation of Walking Energy Consumption and Plantar Pressure Distribution in Patients with Lumbar Spinal Stenosis: A cross-sectional, case control study

Auxiliary Diagnostic Method for Patellofemoral Pain Syndrome Based on One-Dimensional Convolutional Neural Network

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