Hip muscle activity in people with hip-related pain compared to asymptomatic controls: A systematic review
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Cited by 3 publications
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Muscle Fat and Volume Differences in People With Hip‐Related Pain Compared With Controls: A Machine Learning Approach
BackgroundHip‐related pain (HRP) affects young to middle‐aged active adults and impacts physical activity, finances and quality of life. HRP includes conditions like femoroacetabular impingement syndrome and labral tears. Lateral hip muscle dysfunction and atrophy in HRP are more pronounced in advanced hip pathology, with limited evidence in younger populations. While MRI use for assessing hip muscle morphology is increasing, with automated deep‐learning techniques showing promise, studies assessing their accuracy are limited. Therefore, we aimed to compare hip intramuscular fat infiltrate (MFI) and muscle volume, in individuals with and without HRP as well as assess the reliability and accuracy of automated machine‐learning segmentations compared with human‐generated segmentation.MethodsThis cross‐sectional study included sub‐elite/amateur football players (Australian football and soccer) with a greater than 6‐month history of HRP [n = 180, average age 28.32, (standard deviation 5.88) years, 19% female] and a control group of sub‐elite/amateur football players without pain [n = 48, 28.89 (6.22) years, 29% female]. Muscle volume and MFI of gluteus maximus, medius, minimis and tensor fascia latae were assessed using MRI. Associations between muscle volume and group were explored using linear regression models, controlling for body mass index, age, sport and sex. A convolutional neural network (CNN) machine‐learning approach was compared with human‐performed muscle segmentations in a subset of participants (n = 52) using intraclass correlation coefficients and Sorensen–Dice index.ResultsWhen considering adjusted estimates of muscle volume, there were significant differences observed between groups for gluteus medius (adjusted mean difference 23 858 mm3 [95% confidence interval 7563, 40 137]; p = 0.004) and tensor fascia latae (6660 mm3 [2440, 13 075]; p = 0.042). No differences were observed between groups for gluteus maximus (18 265 mm3 [−21 209, 50 782]; p = 0.419) or minimus (3893 mm3 [−2209, 9996]; p = 0.21). The CNN was trained for 30 000 iterations and assessed its accuracy and reliability on an independent testing dataset, achieving high segmentation accuracy (mean Sorenson–Dice index >0.900) and excellent muscle volume and MFI reliability (ICC2,1 > 0.900). The CNN outperformed manual raters, who had slightly lower interrater accuracy (Sorensen–Dice index >0.800) and reliability (ICC2,1 > 0.800).ConclusionsThe increased muscle volumes in the symptomatic group compared with controls could be associated with increased myofibrillar size, sarcoplasmic hypertrophy or both. These changes may facilitate greater muscular efficiency for a given load, enabling the athlete to maintain their normal level of function. In addition, the CNNs for muscle segmentation was more efficient and demonstrated excellent reliability in comparison to manual segmentations.
Muscle Fat and Volume Differences in People With Hip‐Related Pain Compared With Controls: A Machine Learning Approach
BackgroundHip‐related pain (HRP) affects young to middle‐aged active adults and impacts physical activity, finances and quality of life. HRP includes conditions like femoroacetabular impingement syndrome and labral tears. Lateral hip muscle dysfunction and atrophy in HRP are more pronounced in advanced hip pathology, with limited evidence in younger populations. While MRI use for assessing hip muscle morphology is increasing, with automated deep‐learning techniques showing promise, studies assessing their accuracy are limited. Therefore, we aimed to compare hip intramuscular fat infiltrate (MFI) and muscle volume, in individuals with and without HRP as well as assess the reliability and accuracy of automated machine‐learning segmentations compared with human‐generated segmentation.MethodsThis cross‐sectional study included sub‐elite/amateur football players (Australian football and soccer) with a greater than 6‐month history of HRP [n = 180, average age 28.32, (standard deviation 5.88) years, 19% female] and a control group of sub‐elite/amateur football players without pain [n = 48, 28.89 (6.22) years, 29% female]. Muscle volume and MFI of gluteus maximus, medius, minimis and tensor fascia latae were assessed using MRI. Associations between muscle volume and group were explored using linear regression models, controlling for body mass index, age, sport and sex. A convolutional neural network (CNN) machine‐learning approach was compared with human‐performed muscle segmentations in a subset of participants (n = 52) using intraclass correlation coefficients and Sorensen–Dice index.ResultsWhen considering adjusted estimates of muscle volume, there were significant differences observed between groups for gluteus medius (adjusted mean difference 23 858 mm3 [95% confidence interval 7563, 40 137]; p = 0.004) and tensor fascia latae (6660 mm3 [2440, 13 075]; p = 0.042). No differences were observed between groups for gluteus maximus (18 265 mm3 [−21 209, 50 782]; p = 0.419) or minimus (3893 mm3 [−2209, 9996]; p = 0.21). The CNN was trained for 30 000 iterations and assessed its accuracy and reliability on an independent testing dataset, achieving high segmentation accuracy (mean Sorenson–Dice index >0.900) and excellent muscle volume and MFI reliability (ICC2,1 > 0.900). The CNN outperformed manual raters, who had slightly lower interrater accuracy (Sorensen–Dice index >0.800) and reliability (ICC2,1 > 0.800).ConclusionsThe increased muscle volumes in the symptomatic group compared with controls could be associated with increased myofibrillar size, sarcoplasmic hypertrophy or both. These changes may facilitate greater muscular efficiency for a given load, enabling the athlete to maintain their normal level of function. In addition, the CNNs for muscle segmentation was more efficient and demonstrated excellent reliability in comparison to manual segmentations.
Prefabricated contoured foot orthoses to reduce pain and increase physical activity in people with hip osteoarthritis: A randomised feasibility trial
BackgroundHip osteoarthritis (OA) is a prevalent and burdensome condition that leads to impaired quality of life and a substantial economic burden. Encouraging physical activity, particularly walking, is crucial for OA management, but many individuals with hip OA fail to meet recommended activity levels. Prefabricated contoured foot orthoses have shown promise in improving hip muscle efficiency during walking in laboratory settings, but their real‐world feasibility and efficacy remain uncertain.ObjectiveThe aim of this study was to assess the feasibility of conducting a fully powered randomised controlled trial (RCT) to evaluate the effectiveness of prefabricated contoured foot orthoses, prescribed via telehealth, in people with hip OA.MethodsThis feasibility trial randomised 27 participants with hip OA into two groups: prefabricated contoured foot orthoses or flat shoe inserts. Feasibility outcomes were assessed, including recruitment rate, adherence, logbook completion, and dropout rate. Patient‐reported outcomes and accelerometer‐measured physical activity were collected as secondary outcomes.ResultsWhile the recruitment rate was low (0.88 people/week), adherence to the intervention (59%), logbook completion (93%), and dropout rates (7%) met or exceeded our predefined feasibility parameters. Participants found the intervention acceptable, and practicality was demonstrated with minor adverse events. Preliminary efficacy testing indicated that prefabricated contoured foot orthoses positively affected physical activity (adjusted mean difference = 2590 [260 to 4920] steps/day), with comparable outcomes for hip‐related quality of life and pain.ConclusionThis trial supports proceeding to a fully powered RCT to assess the effect of teleheath prescribed prefabricated contoured foot orthoses on physical activity in people with hip OA.Study Registration NumberNational Institutes of Health Trial Registry (NCT05138380).
Hip Flexor Muscle Activation During Common Rehabilitation and Strength Exercises
Background/Objectives: The iliopsoas muscle plays an essential role in lumbopelvic and hip anterior stability, which is particularly important in the presence of limited osseous acetabular coverage anteriorly as in hip dysplasia and/or hip micro-instability. The purpose of this systematic review is to (1) describe iliopsoas activation levels during common rehabilitation exercises and (2) provide an evidence-based exercise progression for strengthening the iliopsoas based on electromyography (EMG) studies. Methods: In total, 109 healthy adult participants ranging from ages 20 to 40 were included in nine studies. PubMed, CINAHL, and Embase databases were systematically searched for EMG studies of the psoas, iliacus, or combined iliopsoas during specific exercise. The Modified Downs and Black Checklist was used to perform a risk of bias assessment. PROSPERO guidelines were followed. Results: Nine studies were included. Findings suggest that the iliopsoas is increasingly activated in ranges of hip flexion of 30–60°, particularly with leg lowering/raising exercises. Briefly, >60% MVIC activity of the iliopsoas was reported in the active straight leg raise (ASLR) in ranges around 60° of hip flexion, as well as with supine hip flexion and leg lifts. In total, 40–60% MVIC was found in exercises including the mid-range of the ASLR around 45° of hip flexion and lifting a straight trunk while in a hip flexed position. Conclusions: The findings suggest that exercises in increased hip flexion provide greater activation of the iliopsoas compared to exercises where the trunk is moving on the lower extremity. Iliopsoas activation can be incrementally progressed from closed to open kinetic chain exercises, and eventually to the addition of external loads. The proposed exercise program interprets the results and offers immediate translation into clinical practice.
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