Elsevier

Gait & Posture

Volume 61, March 2018, Pages 188-196
Gait & Posture

Review
Methods to assess patellofemoral joint stress: A systematic review

https://doi.org/10.1016/j.gaitpost.2017.12.018Get rights and content

Highlights

  • Different methods were used to calculate patellofemoral joint stress.

  • Inconsistencies were found in the patellofemoral joint stress results.

  • Models used patellofemoral contact area from cadavers, healthy and injured people.

  • Some studies performed an adjustment of sagittal plane forces.

  • Many typos in the used formulas were identified among the included studies.

Abstract

Changes in patellofemoral joint (PFJ) stress are related to the development and course of PFJ dysfunctions. Different methods for PFJ stress calculation have been used, making the comparison of PFJ stress values across different studies difficult. The purpose of this study was to systematically review the methods for PFJ stress calculation and highlight the differences among the methods. A systematic literature search was conducted in Medline, Embase, CINAHL, SPORTDiscus and Web of Science databases. Included studies examined PFJ stress in subjects with or without musculoskeletal conditions. Of 12,670 identified studies, 53 were included, with a total of 1134 subjects evaluated. The main differences among the methods to calculate PFJ stress were: i) method to calculate PFJ contact area; ii) method to calculate a constant (coefficient k) that defines the relation between quadriceps force and PFJ reaction force; iii) the inclusion of adjustments for sagittal plane forces. Considerable variability in PFJ stress results was observed. The greatest PFJ stress value was 55.03 MPa during a dance jump and the lowest value was 1.9 MPa during walking at the speed of 1.4 m/s. Most studies applied methods which use data from previous studies. However, methods which use data from their own participants for most parts of the calculation might be preferred to minimize potential errors. When direct measures are not possible, a standard method could be applied to facilitate comparisons among studies.

Introduction

Patellar malalignment is a common finding in people with patellofemoral joint (PFJ) pain [1] and PFJ osteoarthritis [2], the two main PFJ dysfunctions. The association between patellar malalignment and the resultant force from quadriceps muscle and patellar tendon actions [3] may contribute to the progression of PFJ dysfunctions. Due to changes on PFJ contact area from the malalignment, PFJ reaction force may not be dissipated adequately [4]. Consequently, interferences on the relationship between patellar forces and patellar alignment may be related to pain and morphological changes such as osteophytes and loss of cartilage [2,5].

The relationship between patellar forces and contact area has been commonly investigated by measuring the stress these forces can create in the PFJ [[6], [7], [8], [9], [10]]. For PFJ stress calculations, the force imposed in the patella is divided by the contact area between the patella and femur [6,8,11,12]. PFJ stress measure has been reported during different activities such as running [13,14] and squatting [15,16]; also in different populations, such as those with PFJ pain [6] and those with PFJ osteoarthritis [17].

As PFJ stress cannot be directly measured in vivo, studies present data on PFJ stress based on mathematical models currently available in the literature [6,18,19]. The majority of the studies underwent the steps below described and presented in Fig. 1 in order to calculate PFJ stress:

1st step: kinematic and kinetic data are obtained from participants during some activity, usually with the use of cameras and force platforms. The knee flexion angle and knee extensor moment are measured and used in the PFJ stress mathematical model. Knee angles and moments are calculated indirectly through biomechanical models [20]. Although these measurements are considered reliable, there is the potential of miscalculating them [20]. In the sagittal plane knee angle calculations could have approximately five degrees of error during gait measurement [21] and knee extensor moment could have approximately 10 Nm of error during vertical drop jumps [22].

2nd step: quadriceps muscle effective lever arm (Leff) is calculated. This is a step to estimate quadriceps force. Studies commonly use data previously published to develop a formula in which knee flexion angle is the dependent variable [6,18]. Data for the development of the formulas is based on measures from images of the knee in the sagittal plane, from radiography or magnetic resonance imaging (MRI), and its accuracy is uncertain [9,23].

3rd step: quadriceps muscle force is calculated. This is a step to obtain PFJ reaction force. Knee extensor moment obtained during an activity is divided by the calculated Leff. This step is required to isolate the force generated by the quadriceps muscle [24].

4th step: coefficient k is calculated. This is also a step for the PFJ reaction force calculation. Coefficient k is a constant that defines the relation between quadriceps force and PFJ reaction force as a function of knee flexion angle [9]. Studies usually use data previously published to develop a formula in which knee flexion angle is the dependent variable [25,26]. However it is difficult to know whether the theoretical approach used in some of the studies are likely to generate significant error in the estimates [9].

5th step: PFJ reaction force is calculated. The calculated quadriceps muscle force is multiplied by the calculated coefficient k. The estimated error in this measure is approximately 50N [27].

6th step: calculation of PFJ contact area. Most studies used previously published data to develop a formula in which knee flexion angle is the dependent variable [18,28]. Methods were developed based on the contact area of the PFJ of cadavers, healthy people and people with knee injuries [29,30]. The estimated error for PFJ contact area, measured by MRI, is approximately 40 mm2 [31].

7th step: final step where PFJ stress is calculated by dividing PFJ reaction force by PFJ contact area.

Interestingly, although the majority of studies have reported to follow these steps, there are different mathematical models to reach values of PFJ stress. Some studies used a wider range of data from their participants as opposed to using data from previous studies as described above. These studies collected data via MRI and used the new collected data from their own participants to calculate PFJ contact area, Leff and coefficient k [19,23]. The assumption of cocontraction of knee flexors and extensors is another difference among mathematical models to calculate PFJ stress [32,33].

Given the complexity of the methods and potential error associated to each method, it is difficult to compare results from studies that used different methods. This was demonstrated in the study by Kernozek et al. [16], where two methods to estimate quadriceps muscle force were compared and results showed a significant difference in PFJ stress of approximately 7 MPa [16]. For that reason, slight differences in the used methods for PFJ stress calculation could potentially lead to misinterpretation of the findings. Studies that used PFJ stress calculation might have been performed without the required attention which compromises the findings and potentially misleads readers who are interested in the PFJ stress field. It also highlights the importance of using consistent methods to calculate PFJ stress. Furthermore, some statements and hypotheses on PFJ dysfunctions, based on PFJ stress, indicate that some clinicians and researchers may be unfamiliar with the available methods used to calculate PFJ stress. For example, some studies suggest that the presence of excessive dynamic knee valgus during activities could increase the PFJ stress [34,35]. This statement may be true; however, no factor related to frontal plane is applied in the methods to calculate PFJ stress and therefore such statement is in disagreement with the present literature. Therefore, the aim of the current study was to systematically review the mathematical methods used in the literature to calculate PFJ stress and to potentially identify the best method to calculate PFJ stress. We also aimed at addressing the complexity of the methods by highlighting the differences among the methods.

Section snippets

Methods

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement recommendations [36].

Study selection

A total of 12,670 titles were retrieved from the searches. In the initial search in five databases, a total of 9667 titles were found, of which 42 papers met the eligibility criteria (Supplementary material 2 – Flow diagram). After inclusion of the references of included papers and citation checking 3003 titles were further screened and 11 more papers met the eligibility criteria. These studies were not part of the databases searched in the current study or were in press.

Of the 53 included

Discussion

After analyzing the PFJ stress results reported by the included studies, a large variability was noticed. For example, studies with similar activities and population presented PFJ stress differences larger than 15 MPa for the running activity in healthy people. These results are alarming, because the variability noticed in the present review is greater than the difference in PFJ stress presented by studies comparing healthy and affected populations [6,17]. Brechter and Powers [6] reported that

Conclusion

PFJ stress calculation used by the majority of the studies has many indirect calculations; however methods with more data from participants might be preferred as direct measures are more likely to minimize the potential errors in the indirect calculations. When direct measures are not possible, based on the studies analyzed in the current systematic review, the model that seems to be the most appropriated is:

  • -

    Quadriceps Muscle Effective Lever Arm (Leff) = 8.0E − 05x3–0.013x2 + 0.28x + 0.046;

  • -

Conflicts of interest

The authors declare no conflict of interest.

Acknowledgments

The authors would like to acknowledge the São Paulo Research Foundation – FAPESP (process 2015/01704-7 and 2016/09438-7).

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