Different clinical subgroups in patellofemoral pain syndrome

NewsBot · Sep 27, 2013

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Targeted interventions for patellofemoral pain syndrome (TIPPS): classification of clinical subgroups.
Selfe J, Callaghan M, Witvrouw E, Richards J, Dey MP, Sutton C, Dixon J, Martin D, Stokes M, Janssen J, Ritchie E, Turner D.
BMJ Open. 2013 Sep 23;3(9):e003795.

INTRODUCTION:
Patellofemoral pain (PFP) can cause significant pain leading to limitations in societal participation and physical activity. An international expert group has highlighted the need for a classification system to allow targeted intervention for patients with PFP; we have developed a work programme systematically investigating this. We have proposed six potential subgroups: hip abductor weakness, quadriceps weakness, patellar hypermobility, patellar hypomobility, pronated foot posture and lower limb biarticular muscle tightness. We could not uncover any evidence of the relative frequency with which patients with PFP fell into these subgroups or whether these subgroups were mutually exclusive. The aim of this study is to provide information on the clinical utility of our classification system.
METHODS AND ANALYSIS:
150 participants will be recruited over 18 months in four National Health Services (NHS) physiotherapy departments in England. Inclusion criteria: adults 18-40 years with PFP for longer than 3 months, PFP in at least two predesignated functional activities and PFP elicited by clinical examination. Exclusion criteria: prior or forthcoming lower limb surgery; comorbid illness or health condition; and lower limb training or pregnancy. We will record medical history, demographic details, pain, quality of life, psychomotor movement awareness and knee temperature. We will assess hip abductor and quadriceps weakness, patellar hypermobility and hypomobility, foot posture and lower limb biarticular muscle tightness. The primary analytic approach will be descriptive. We shall present numbers and percentages of participants who meet the criteria for membership of (1) each of the subgroups, (2) none of the subgroups and (3) multiple subgroups. Exact (binomial) 95% CIs for these percentages will also be presented.
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Craig Payne · Sep 27, 2013

There was a patellofemoral pain research retreat last week (I can't recall where!) and I did notice this tweet from @Bill_Vicenzino from the conference:

Theme emerging @ #ipfrr PFP is not a single entity but rather likely to be a number of sub groups with different profiles
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Admin2 · Sep 27, 2013

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NewsBot · Nov 21, 2015

Identification of strata for a trial of a targeted multimodal physiotherapy intervention in patellofemoral pain patients
Chris Sutton et al
Trials 2015, 16(Suppl 2):p158 doi:10.1186/1745-6215-16-S2-P158

Background
Patellofemoral Pain (PFP) is a musculoskeletal disorder causing significant pain and dysfunction around the knee, commonly leading to long term limitations. People with PFP are commonly referred for physiotherapy, although current multimodal physiotherapy approaches tend to be rather ad-hoc and are failing in the long term. Identification of patient strata (or subgroups) who may respond differentially to interventions has been recognised as an international priority. It has been proposed that there are PFP patient strata, classified based on clinical tests, who would respond to modes of interventions targeted at the individual's stratum.

Methods
Participants with PFP underwent clinical assessment of muscle weakness, muscle length, patellar mobility and foot posture. The presence of strata was explored using two classification techniques: hierarchical cluster analysis and latent profile analysis (LPA); for LPA, the Bayesian Information Criterion was used to guide model selection.

Results
One hundred and twenty seven (of 130) recruited participants had complete assessment data. A three strata solution appeared optimal from modelling and clinical perspectives. The three suggested strata were characterised as: stronger; weaker and tighter; weaker with pronated feet. However, although membership of the stronger stratum was consistent, there were substantial differences in the other composition of the other two strata between the two classification approaches.

Conclusion
PFP patients can be classified into clinical strata, although further investigation is needed to identify thresholds upon which stratification should be performed. Interventions are currently being mapped onto the strata and feasibility and evaluation trials of these interventions should then be performed.
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NewsBot · Dec 1, 2015

Are there three main subgroups within the patellofemoral pain population? A detailed characterisation study of 127 patients to help develop targeted Intervention (TIPPs)
Janssen, Jessie, Callaghan, Michael, Witvrouw, Erik, Sutton, Chris, Richards, Jim, Stokes, Maria, Denis, Denis, Dixon, John, Hogarth, Russell, Baltzopoulos, Vasilios, Ritchie, Elizabeth, Arden, Nigel and Dey, Paola
British Journal of Sports Medicine, 1-23. (In Press).

Background
Current multimodal approaches for the management of non-specific patellofemoral pain are not optimal, however, targeted intervention for subgroups could improve patient outcomes. This study explores whether subgrouping of non-specific patellofemoral pain patients, using a series of low cost simple clinical tests, is possible.

Method
The exclusivity and clinical importance of potential subgroups was assessed by applying ? priori test thresholds (1 SD) from seven clinical tests in a sample of adult patients with non-specific patellofemoral pain. Hierarchical clustering and latent profile analysis, were used to gain additional insights into subgroups using data from the same clinical tests.

Results
One hundred and thirty participants were recruited, 127 had complete data: 84 (66%) female, mean age 26 years (SD 5.7) and mean BMI 25.4 (SD 5.83), median (IQR) time between onset of pain and assessment was 24 (7-60) months. Potential subgroups defined by the ? priori test thresholds were not mutually exclusive and patients frequently fell into multiple subgroups. Using hierarchical clustering and latent profile analysis three subgroups were identified using 6 of the 7 clinical tests. These subgroups were given the following nomenclature: (i) ‘strong’, (ii) ‘weak and tighter’, and (iii) ‘weak and pronated foot’.

Conclusions
We conclude that three subgroups of patellofemoral patients may exist based on the results of six clinical tests which are feasible to perform in routine clinical practice. Further research is needed to validate these findings in other datasets and, if supported by external validation, to see if targeted interventions for these subgroups improve patient outcomes.
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NewsBot · Aug 7, 2016

Determination of patellofemoral pain sub-groups and development of a method for predicting treatment outcome using running gait kinematics
Ricky Watari, Dylan Kobsar, Angkoon Phinyomark, Sean Osis, Reed Ferber
Clinical Biomechanics; 7 August 2016

Highlights
•3D kinematics can be used to classify on treatment response in patellofemoral pain.
•Non-responders present greater knee abduction angles during swing phase of running.
•Non-responders are more similar to pain free controls than Responders.
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Background
Not all patients with patellofemoral pain exhibit successful outcomes following exercise therapy. Thus, the ability to identify patellofemoral pain subgroups related to treatment response is important for the development of optimal therapeutic strategies to improve rehabilitation outcomes. The purpose of this study was to use baseline running gait kinematic and clinical outcome variables to classify patellofemoral pain patients on treatment response retrospectively.

Methods
Forty-one individuals with patellofemoral pain that underwent a 6-week exercise intervention program were sub-grouped as treatment Responders (n = 28) and Non-responders (n = 13) based on self-reported measures of pain and function. Baseline three-dimensional running kinematics, and self-reported measures underwent a linear discriminant analysis of the principal components of the variables to retrospectively classify participants based on treatment response. The significance of the discriminant function was verified with a Wilk's lambda test (α = 0.05).

Findings.
The model selected 2 gait principal components and had a 78.1% classification accuracy. Overall, Non-responders exhibited greater ankle dorsiflexion, knee abduction and hip flexion during the swing phase and greater ankle inversion during the stance phase, compared to Responders.

Interpretation.
This is the first study to investigate an objective method to use baseline kinematic and self-report outcome variables to classify on patellofemoral pain treatment outcome. This study represents a significant first step towards a method to help clinicians make evidence-informed decisions regarding optimal treatment strategies for patients with patellofemoral pain.
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NewsBot · Mar 1, 2017

An article in LER:
Patellofemoral pain subgroups: A critical first step toward personalized clinical intervention

NewsBot · Jun 29, 2017

This clinical trial was just registered:
Clinical Subgroups in Patellofemoral Pain Syndrome

Patellofemoral pain syndrome (PFPS) is a multifactorial pathology characterized by diffuse retropatellar and peripatellar pain in the knee joint, exacerbated by overloading activities on the patellofemoral joint. However, this disease showed high degree of patients not responsive to therapeutic strategies. This condition occurred because several factors is related to disease such as: (1) proximal factors (involving trunk and hip), (2) local factors (surrounding and or within the patellofemoral joint) and (3) distal factors (involving ankle and foot). Thus, the identification of clinical subgroups based in anatomic changes (proximal, local and distal factors) is a recent strategy that could help in the therapeutic strategies focused on the etiology of the disease, improve responsiveness to treatment, clinical and functional benefits.
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NewsBot · Apr 26, 2018

Anterior knee pain subgroups: the first step towards a personalized treatment
James Selfe et al
Annals of Joint

Identification of subgroups within the patellofemoral pain (PFP) population has gained a lot of interest and attention from the research community in recent years due to the recognition of the relatively poor patient outcomes associated with the multimodal approach and following the success of subgrouping approaches used in the management of low back pain. This paper reviews early attempts at PFP subgrouping and introduces readers to some of the modern methodological approaches employed to derive subgroups. Summaries of the results of two research projects illustrating the use of these more robust methods to derive subgroups in the PFP population are provided. In conclusion, it appears there are probably 3 or 4 discrete subgroups within the PFP population that may require a more personalised approach to treatment. However, to date no definitive randomized controlled trials (RCTs) have been conducted to evaluate the potential benefits of targeted interventions for PFP subgroups in terms of improved patient outcomes so this warrants further research.
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NewsBot · May 6, 2018

Stratification of patellofemoral pain using clinical, biomechanical and imaging features
Drew, Benjamin Timothy (2018)
PhD thesis, University of Leeds

Patellofemoral pain (PFP) is a common musculoskeletal complaint and the efficacy of current therapies aimed at PFP is limited. The aetiology of PFP is widely considered to be multifactorial and as a result the clinical presentation is often heterogeneous. In an attempt to address this issue, an international PFP consensus statement, published in 2013, highlighted the need to sub-group patients with PFP to enable more stratified interventions.

A multi-methodological approach was used in this thesis. A systematic review of the existing imaging literature in PFP demonstrated that PFP is associated with a number of imaging features in particular MRI bisect offset and CT congruence angle and that some of these features should be modifiable with conservative treatment. A retrospective analysis investigating the overall 3D shape and 3D equivalents of commonly used PFJ imaging features demonstrated no differences between a group with and without PFP, challenging the current perceptions on the structural associations to PFP. A cross-sectional cluster analysis using modifiable clinical, biomechanical and imaging features identified four subgroups that are present in PFP cohort with a Weak group showing the worst prognosis at 12 months. Lastly, a pragmatic randomised controlled feasibility study comparing matched treatment to usual care management showed that matching treatment to a specific subgroup is feasible in terms of adherence, retention and conversion to consent.

In summary, the findings of this thesis improves our understanding of the structural associations to PFP; the subgroups that exist within the PFP population; the natural prognosis of these PFP subgroups; and the feasibility of targeting treatment at PFP subgroups within a clinical trial.
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NewsBot · Jul 4, 2018

Two-dimensional frontal plane projection angle can identify subgroups of patellofemoral pain patients who demonstrate dynamic knee valgus☆
Craig R.GwynneSarah A.Curran
Clinical Biomechanics 3 July 2018

Highlights
•Subjects with patellofemoral pain exhibit increased frontal plane knee alignment during activity.
•Two-dimensional frontal plane projection angle may be employed as a clinical assessment tool.
•Increased frontal plane knee alignment is a fair predictor of patellofemoral pain.
•Subgroup of patients may exist who exhibit dynamic knee valgus.
•Patients with similar modifiable factors may undergo individualised management programmes.
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Background
Identifying individuals with patellofemoral pain who demonstrate similar modifiable factors including dynamic knee valgus may be useful in establishing subgroups of patients that can undergo individualised management strategies. However, a lack of objective assessment criteria means that the findings are of limited value to clinicians aiming to distinguish between patients with and without altered frontal plane knee kinematics. Therefore, the aim of the study was to investigate dynamic knee valgus in individuals with and without patellofemoral pain by determining frontal plane knee alignment during functional activity.

Methods
Thirty recreationally active individuals with patellofemoral pain and 30 non-injured individuals had frontal plane knee alignment assessed via two-dimensional analysis of the frontal plane projection angle during single limb stance and single limb squats to 60° of knee flexion.

Findings
Individuals with patellofemoral pain demonstrated excessive frontal plane knee alignment (P = .003; ES = 0.68) compared to uninjured participants during single limb squats. In addition, assessing frontal plane knee alignment using two-dimensional analysis had fair specificity and sensitivity of discriminating patellofemoral pain injury.

Interpretation
Cinical quantification of two-dimensional frontal plane knee alignment may be utilised to subgroup patients with patellofemoral pain that display dynamic knee valgus during single limb squats. Furthermore, this may be a useful clinical tool to determine individuals that may be at risk of developing pain in the future.
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NewsBot · Dec 2, 2018

PATELLOFEMORAL SUBGROUPS, A LOW COST CLINICAL APPROACH WITH A SURPRISE OR TWO!
Selfe, James
ISBS Proceedings Archive: Vol. 36 : Iss. 1 , Article 261. 2018

Identification of subgroups within the patellofemoral pain (PFP) population has gained a lot of interest and attention from the research community in recent years due to the recognition of the relatively poor patient outcomes associated with the traditional multimodal approach. This presentation summarises the results of the Targeted Interventions for Patellofemoral Pain (TIPPs) programme of research that has led to the development of a robust simple hierarchical algorithm. The TIPPs algorithm uses objective data generated by six low cost clinical tests to categorise patients into one of three subgroups: a) Strong (22% of participants); b) Weak and Tight (39% of participants); c) Weak and Pronated (39% of participants). To many in the field the emergence of the two weak groups was not a great surprise and these two weak subgroups are consistent with contemporary thinking on the causes and subsequent management of PFP. However, the strong subgroup was a surprise and is a novel previously unrecognised group that falls outside the current treatment recommendations as no weakness in muscle strength or shortening in muscle length was identified in this group of PFP sufferers. The people in the strong subgroup also reported higher levels of function and quality-of-life compared to the other two subgroups. It is currently our hypothesis that this group may be overloading their patellofemoral joint due to reduced motor control. We speculate whether proprioceptive and neuromuscular retraining should be the focus of any future rehabilitation strategy for this subgroup of patients, rather than strengthening exercises. It would seem illogical and potentially detrimental, due to possible overload/overuse of an already painful joint, to offer this subgroup of patients strengthening exercises. Indeed this may be one of the key factors leading to the current high failure rate of the multimodal approach as virtually all PFP patients are prescribed quadriceps strengthening exercises as a standard component of their rehabilitation, increasingly this is also in combination with hip strengthening. Due to the low cost nature of the clinical tests employed, the TIPPs approach has high clinical utility. This makes it potentially viable for widespread future roll out into primary care and physiotherapy clinics internationally. Further work to support the clinical roll out has led to the development of Appatella, which is a mobile and tablet app. https://digitallabs.mmu.ac.uk/what-we-do/products/appatella/. Appatella has three components: i) the TIPPs algorithm for clinicians to subgroup patients, ii) videos, outcome measures and diary functions to guide patients through their rehabilitation programme, iii) an online GDPR compliant remote searchable relational database which over time will build into a key resource providing ‘Big Data’. However, despite some preliminary positive data (unpublished) to date no definitive RCTs have so far been conducted currently therefore it is still unknown if targeted treatment directed at the three subgroups will actually lead to improved patient outcomes. Future research should investigate the prognostic implications of these subgroups and establish the level of efficacy of targeted intervention.
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NewsBot · Mar 9, 2019

Towards the development of data-driven diagnostic subgroups for people with patellofemoral pain using modifiable clinical, biomechanical and imaging features.
Drew, BT, Conaghan, PG , Smith, TO et al
Journal of Orthopaedic and Sports Physical Therapy. ISSN 0190-6011 (In Press)

Background. Unfavourable treatment outcomes for people with patellofemoral pain (PFP) have been attributed to the potential existence of subgroups that respond differently to treatment.

Objectives: This study aimed to identify subgroups within PFP by combining modifiable clinical, biomechanical and imaging features and exploring the prognosis of these subgroups.

Methods. Longitudinal cohort with baseline cluster analyses. Baseline data were analysed using a two-stage cluster analysis; 10 features were analysed within health domains before being combined at the second stage. Prognosis of the subgroups was assessed at 12-months with subgroup differences in the Global Rating of Change Scale analysed using an exploratory logistic regression adjusted for known confounders.

Results. 70 participants were included (mean age 31 years; 43 (61%) female). Cluster analysis revealed 4 subgroups: ‘Strong’, ‘Pronation & Malalignment’, ‘Weak’ and ‘Active & Flexible’. Descriptively, compared to the Strong subgroup (55% favourable), the odds of a favourable outcome were lower in the Weak subgroup (31% favourable; adjusted odds ratio [OR] 0.30; 95% confidence intervals [CI] 0.07, 1.36) and Pronation & Malalignment subgroup (50%; OR 0.64; 95% CI 0.11, 3.66),

and higher in the Active & Flexible subgroup (63%; OR 1.24 (95% CI 0.20, 7.51). Page 5 of 61 JOSPT, 1033 N. Fairfax St., Suite 304, Alexandria, VA 22314, ph. 877-766-3450 Journal of Orthopaedic & Sports Physical Therapy Review Copy Data-driven diagnostic subgroups in patellofemoral pain After adjustment, compared to the Strong subgroup, differences between some subgroups remained substantive but none were statistically significant

Conclusion. In this relatively small cohort, 4 PFP subgroups were identified which show potentially different outcomes at 12 months. Further research is required to determine whether a stratified treatment approach using these subgroups would improve outcomes for people with PFP
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NewsBot · Jul 2, 2019

Toward the Development of Data-Driven Diagnostic Subgroups for People With Patellofemoral Pain Using Modifiable Clinical, Biomechanical, and Imaging Features
Journal of Orthopaedic & Sports Physical Therapy, 2019 Volume:49 Issue:7 Pages:536–547 DOI:

Background
Unfavorable treatment outcomes for people with patellofemoral pain (PFP) have been attributed to the potential existence of subgroups that respond differently to treatment.

Objectives
This study aimed to identify subgroups within PFP by combining modifiable clinical, biomechanical, and imaging features and exploring the prognosis of these subgroups.

Methods
This was a longitudinal cohort study, with baseline cluster analyses. Baseline data were analyzed using a 2-stage cluster analysis; 10 features were analyzed within 4 health domains before being combined at the second stage. Prognosis of the subgroups was assessed at 12 months, with subgroup differences reported as global rating of change and analyzed with an exploratory logistic regression adjusted for known confounders.

Results
Seventy participants were included (mean age, 31 years; 43 [61%] female). Cluster analysis revealed 4 subgroups: “strong,” “pronation and malalignment,” “weak,” and “active and flexible.” Descriptively, compared to the strong subgroup (55% favorable), the odds of a favorable outcome were lower in the weak subgroup (31% favorable; adjusted odds ratio [OR] = 0.30; 95% confidence interval [CI]: 0.07, 1.36) and the pronation and malalignment subgroup (50%; OR = 0.64; 95% CI: 0.11, 3.66), and higher in the active and flexible subgroup (63%; OR = 1.24; 95% CI: 0.20, 7.51). After adjustment, compared to the strong subgroup, differences between some subgroups remained substantive, but none were statistically significant.

Conclusion
In this relatively small cohort, 4 PFP subgroups were identified that show potentially different outcomes at 12 months. Further research is required to determine whether a stratified treatment approach using these subgroups would improve outcomes for people with PFP.
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NewsBot · Aug 19, 2019

Targeted Treatment Protocol in Patellofemoral Pain (TIPPs): Does Treatment Designed
According to Subgroups Improve Clinical Outcomes in Patients Unresponsive to
Multimodal Treatment?
Hayri Baran Yosmaoğlu, Emel Sonmezer, Manolya Ozkoslu, Ezgi Sahin, Senay Çerezci, Jim
Richards, James Selfe, Jessie Janssen
Available at http://clok.uclan.ac.uk/29355/

Background: Targeted intervention for subgroups is a promising approach for the management
of patellofemoral pain.
Hypothesis: Treatment designed according to subgroups improves clinical outcomes in
patients unresponsive to multimodal treatment.
Study Design: A prospective crossover intervention.
Level of Evidence: Level III
Methods: PFP patients (n=61, mean age: 27±9 years) were enrolled. PFP patients received
standard multimodal treatment three times a week for 6 weeks. Patients not responding to
multimodal treatment were then classified into one of 3 subgroups “strong”, “weak and tight”
and “weak and pronated foot” using six simple clinical tests. They subsequently were
administered a further 6 weeks of targeted intervention designed according to subgroup
characteristics. Visual Analog Scale (VAS), Perception of Recovery Scale (PRS), EQ-5D-5L,
and S-LANSS were used to assess pain, knee function and quality of life before and after the
interventions.
Results: 36% of the patients (21 patients) demonstrated recovery following multimodal
treatment. However, over 70% (29 patients) of these non-responders demonstrated recovery
after targeted treatment. The VAS, PRS, S-LANSS, and EQ-5D-5L scores improved
significantly after targeted intervention compared to after multimodal treatment (p<0.001). The
VAS score at rest was significantly lower in the weak and pronated foot, and weak and tight
subgroups (p=0.011, p=0.008) respectively. Post-treatment pain intensity on activity was
significantly lower in the “strong” subgroup (p=0.006).
Conclusion: Targeted treatment designed according to subgroup characteristics improves
clinical outcomes in patients unresponsive to multimodal treatment.
Clinical Relevance: Targeted intervention could be easily implemented following six simple
clinical assessment tests to subgroup patients into one of three subgroups (strong, weak and
tight, weak and pronated foot). Targeted interventions applied according to the characteristics
of these subgroups have more beneficial treatment effects than a current multimodal treatment
program.
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NewsBot · Nov 23, 2019

Targeted Treatment Protocol in Patellofemoral Pain: Does Treatment Designed According to Subgroups Improve Clinical Outcomes in Patients Unresponsive to Multimodal Treatment?
Yosmaoğlu HB et al
Sports Health. 2019 Nov 21

BACKGROUND:
Targeted intervention for subgroups is a promising approach for the management of patellofemoral pain.

HYPOTHESIS:
Treatment designed according to subgroups will improve clinical outcomes in patients unresponsive to multimodal treatment.

STUDY DESIGN:
Prospective crossover intervention.

LEVEL OF EVIDENCE:
Level 3.

METHODS:
Patients with patellofemoral pain (PFP; n = 61; mean age, 27 ± 9 years) were enrolled. Patients with PFP received standard multimodal treatment 3 times a week for 6 weeks. Patients not responding to multimodal treatment were then classified into 1 of 3 subgroups (strong, weak and tight, and weak and pronated foot) using 6 simple clinical tests. They were subsequently administered 6 further weeks of targeted intervention, designed according to subgroup characteristics. Visual analog scale (VAS), perception of recovery scale (PRS), 5-Level European Quality 5 Dimensions (EQ-5D-5L), and self-reported version of the Leeds Assessment of Neuropathic Symptoms and Signs scale (S-LANSS) were used to assess pain, knee function, and quality of life before and after the interventions.

RESULTS:
In total, 34% (n = 21) of patients demonstrated recovery after multimodal treatment. However, over 70% (n = 29/40) of nonresponders demonstrated recovery after targeted treatment. The VAS, PRS, S-LANSS, and EQ-5D-5L scores improved significantly after targeted intervention compared with after multimodal treatment (P < 0.001). The VAS score at rest was significantly lower in the "weak and pronated foot" and the "weak and tight" subgroups (P = 0.011 and P = 0.008, respectively). Posttreatment pain intensity on activity was significantly lower in the "strong" subgroup (P = 0.006).

CONCLUSION:
Targeted treatment designed according to subgroup characteristics improves clinical outcomes in patients unresponsive to multimodal treatment.

CLINICAL RELEVANCE:
Targeted intervention could be easily implemented after 6 simple clinical assessment tests to subgroup patients into 1 of 3 subgroups (strong, weak and tight, and weak and pronated foot). Targeted interventions applied according to the characteristics of these subgroups have more beneficial treatment effects than a current multimodal treatment program.
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NewsBot · Jun 1, 2020

Classifying individuals with and without Patellofemoral Pain Syndrome using ground force profiles – development of a method using functional data boosting
Bernard X.W.Liew et al
Gait & Posture; 25 May 2020

Highlights
•Prognostic models are critical for the management of patellofemoral pain syndrome.
•Many physiological predictors are functional in nature.
•Functional predictors cannot be used in traditional modelling methods.
•Simple ground reach force signatures provide excellent classification performance.
•FDboost may be useful for modelling when functional predictors are involved.
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Background
Predictors of recovery in patellofemoral pain syndrome (PFPS) currently used in prognostic models are scalar in nature, despite many physiological measures originally lying on the functional scale. Traditional modelling techniques cannot harness the potential predictive value of functional physiological variables.

Research question
What is the classification performance of PFPS status of a statistical model when using functional ground reaction force (GRF) time-series?

Methods
Thirty-one individuals (control = 17, PFPS = 14) performed maximal countermovement jumps, on two force plates. The three-dimensional components of the GRF profiles were time-normalized between the start of the eccentric phase and take-off, and used as functional predictors. A statistical model was developed using functional data boosting (FDboost), for binary classification of PFPS statuses (control vs PFPS). The area under the Receiver Operating Characteristic curve (AUC) was used to quantify the model’s ability to discriminate the two groups.

Results
The three predictors of GRF waveform achieved an average out-of-bag AUC of 93.7%. A 1% increase in applied medial force reduced the log odds of being in the PFPS group by 0.68 at 87% of jump cycle. In the AP direction, a 1% reduction in applied posterior force increased the log odds of being classified as PFPS by 1.10 at 70% jump cycle. For the vertical GRF, a 1% increase in applied force reduced the log odds of being classified in the PFPS group by 0.12 at 44% of the jump cycle.

Significance
Using simple functional GRF variables collected during functionally relevant task, in conjunction with FDboost, produced clinically interpretable models that retain excellent classification performance in individuals with PFPS. FDboost may be an invaluable tool to be used in longitudinal cohort prognostic studies, especially when scalar and functional predictors are collected
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NewsBot · Jan 17, 2024

Interaction of Biomechanical, Anthropometric, and Demographic Factors Associated with Patellofemoral Pain in Rearfoot Strike Runners: A Classification and Regression Tree Approach
José Roberto de Souza Júnior, Logan Walter Gaudette, Caleb D. Johnson, João Paulo Chieregato Matheus, Thiago Vilela Lemos, Irene S. Davis & Adam S. Tenforde
Sports Medicine - Open volume 10, Article number: 5 (2024)

Background
Patellofemoral pain (PFP) is among the most common injuries in runners. While multiple risk factors for patellofemoral pain have been investigated, the interactions of variables contributing to this condition have not been explored. This study aimed to classify runners with patellofemoral pain using a combination of factors including biomechanical, anthropometric, and demographic factors through a Classification and Regression Tree analysis.

Results
Thirty-eight runners with PFP and 38 healthy controls (CON) were selected with mean (standard deviation) age 33 (16) years old and body mass index 22.3 (2.6) kg/m2. Each ran at self-selected speed, but no between-group difference was identified (PFP = 2.54 (0.2) m/s x CON = 2.55 (0.1) m/s, P = .660). Runners with patellofemoral pain had different patterns of interactions involving braking ground reaction force impulse, contact time, vertical average loading rate, and age. The classification and regression tree model classified 84.2% of runners with patellofemoral pain, and 78.9% of healthy controls. The prevalence ratios ranged from 0.06 (95% confidence interval: 0.02–0.23) to 9.86 (95% confidence interval: 1.16–83.34). The strongest model identified runners with patellofemoral pain as having higher braking ground reaction force impulse, lower contact times, higher vertical average loading rate, and older age. The receiver operating characteristic curve demonstrated high accuracy at 0.83 (95% confidence interval: 0.74–0.93; standard error: 0.04; P < .001).

Conclusions
The classification and regression tree model identified an influence of multiple factors associated with patellofemoral pain in runners. Future studies may clarify whether addressing modifiable biomechanical factors may address this form of injury.

Key Points
This study highlights interactions between multiple biomechanical factors associated with patellofemoral pain.

Interactions between braking ground reaction force impulse, contact time, vertical average loading rate, and age correctly identified runners with patellofemoral pain and controls.

Step rate, sex, and body mass index were not predictors of patellofemoral pain in runners when a classification and regression tree analysis was used
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