Adaptive P-Splines for challenging filtering problems in biomechanics

Suppression of noise from recorded signals is a critically important data processing step for biomechanical analyses. While a wide variety of filtering or smoothing spline methods are available, the majority of these are not well suited for the analysis of signals with rapidly changing derivatives such as the processing of motion data for impact-like events. This is because commonly used low-pass filtering approaches or smoothing splines typically assume a single fixed cut-off frequency or regularization penalty which fails to describe rapid changes in the underlying function. To overcome these limitations we examine a class of adaptive penalized splines (APS) that extend commonly used penalized spline smoothers by inferring temporal adaptations in regularization penalty from observed data. Three variations of APS are examined in which temporal variation of spline penalization is described via either a series of independent random variables, an autoregressive process or a smooth cubic spline. Comparing the performance of APS on simulated datasets is promising with APS reducing RMSE by 48%-183% compared to a widely used Butterworth filtering approach. When inferring acceleration from noisy measurements describing the position of a pendulum impacting a barrier we observe between a 13% (independent variables) to 28% (spline) reduction in RMSE when compared to a 4th order Butterworth filter with optimally selected cut-off frequency. In addition to considerable improvement in RMSE, APS can provide estimates of uncertainty for fitted curves and generated quantities such as peak accelerations or durations of stationary periods. As a result, we suggest that researchers should consider the use of APS if features such as impact peaks, rates of loading, or periods of negligible acceleration are of interest.

Use of subject-specific models to detect fatigue-related changes in running biomechanics: a random forest approach

Running biomechanics are affected by fatiguing or prolonged runs. However, no evidence to date has conclusively linked this effect to running-related injury (RRI) development or performance implications. Previous investigations using subject-specific models in running have demonstrated higher accuracy than group-based models, however, this has been infrequently applied to fatigue. In this study, two experiments were conducted to determine whether subject-specific models outperformed group-based models to classify running biomechanics during non-fatigued and fatigued conditions. In the first experiment, 16 participants performed four treadmill runs at or around the maximal lactate steady state. In the second experiment, nine participants performed five prolonged runs using commercial wearable devices. For each experiment, two segments were extracted from each trial from early and late in the run. For each participant, a random forest model was applied with a leave-one-run-out cross-validation to classify between the early (non-fatigued) and late (fatigued) segments. Additionally, group-based classifiers with a leave-one-subject-out cross validation were constructed. For experiment 1, mean classification accuracies for the single-subject and group-based classifiers were 68.2 ± 8.2% and 57.0 ± 8.9%, respectively. For experiment 2, mean classification accuracies for the single-subject and group-based classifiers were 68.9 ± 17.1% and 61.5 ± 11.7%, respectively. Variable importance rankings were consistent within participants, but these rankings differed from each participant to those of the group. Although the classification accuracies were relatively low, these findings highlight the advantage of subject-specific classifiers to detect changes in running biomechanics with fatigue and indicate the potential of using big data and wearable technology approaches in future research to determine possible connections between biomechanics and RRI.

Perceptions and Attitudes Toward the Use of Wearable Technology in the Dance Studio Environment

Introduction: Wearable technology (WT) has become common place in sport. Increased affordability has allowed WT to reach the wrists and bodies of grassroots and community athletes. While WT is commonly used by sport populations to monitor training load, the use of WT among dancers and dance teachers is unknown. Therefore, the purpose of this study is to explore the perspectives of dancers, dance teachers, and dance parents on using WT in the dance studio environment. Methods: Dancers (aged 14+), dance teachers (aged 18+), and dance parents (with a child <18 years registered in a dance program) were recruited from local dance studios (including those offering vocational programs and/or professional training opportunities), and dance retail stores. Participants provided informed consent/assent and completed a one-time online survey about their attitudes, self-efficacy, motivations, barriers, and current practices of using WT in the studio. Results: Sixty-seven participants (19 dancers, 32 dance teachers, and 16 dance parents) completed the survey. Attitudes toward using WT were similar across all groups (mean score range = 34-38/45). Thirteen dancers (68%), 29 teachers (91%), and 7 dance parents reporting on behalf of their children (47%) were permitted to use WT in the studio. Smartwatches were the most common WT used in the studio by dancers (7/9) and teachers (13/17), while dance parents reported that their children primarily used wristband activity trackers (3/4). Among all groups, the primary reason for using WT was to track personalized training data, with calories, total duration, and heart rate being the most important perceived metrics for improving dancing. Conclusion: Across all groups, attitudes toward WT were modest. Prevalence of WT use in the dance studio varied, with wrist-based gadgets being the most common. As WT research continues in dance populations, it will be important for future studies to consider studio permissions as well as participants' existing WT use practices.

Relationships Between Common Preseason Screening Measures and Dance-Related Injuries in Preprofessional Ballet Dancers

OBJECTIVE: To examine modifiable and nonmodifiable factors for associations with dance-related injury among preprofessional ballet dancers over 5 academic years. DESIGN: Prospective cohort study. METHODS: Full-time preprofessional ballet dancers (n = 452; 399 female; median age [range], 15 years [11-21]) participated across 5 academic years at a vocational school. Participants completed baseline screening and online weekly injury questionnaires including dance exposure (hours/week). Zero-inflated Poisson regression models were used to examine associations between potential risk factors measured at baseline and self-reported dance-related injury. RESULTS: In count model coefficients, left one leg standing score (log coefficient estimate, -0.249 [95% CI: -0.478, -0.02]; P = .033) and right unipedal dynamic balance time (log coefficient estimate, -0.0294 [95% CI: -0.048, -0.01]; P>.001) carried a protective effect with increased years of training when adjusted for Athletic Coping Skills Inventory (ACSI) score. A significant association was found for left unipedal dynamic balance time and dance-related injury (log coefficient estimate, 0.013 [95% CI: 0.000, 0.026]; P = .045) when adjusted for years of training and ACSI score. There were no significant associations between dance-related injury and ankle and hip range of motion, active straight leg raise, or Y Balance Test measures. CONCLUSION: When adjusted for years of previous dance training and psychological coping skills, there was a significant association between limb-specific lumbopelvic control and dynamic balance tasks, as well as self-reported dance-related injury in preprofessional ballet. J Orthop Sports Phys Ther 2023;53(11):703-711. Epub 3 October 2023. doi:10.2519/jospt.2023.11835.

Differences in Situational Patterns During Change of Direction Movements Greater than 90° in Youth Male and Female Soccer Players

Change of direction (COD) maneuvers in soccer create tactical advantages, but also expose the player to an increased risk of injury. COD ability is commonly tested with pre-planned drills including cuts greater than 90°. These tests do not take into consideration positional differences players encounter during games. This case-series study used principal component analysis (PCA) to examine situational differences during COD movements between playing positions in youth soccer games. For each of the four teams included (26 females, 27 males), one game was analyzed using video-analysis. Two independent reviewers identified situational patterns and a PCA was used to examine differences between playing positions. Three principal components explained 89% of the variation in the data and were categorized as the total quantity of CODs, attacking/goal-scoring and defensive reacting types of CODs. One-way ANOVA on the individual principal component (PC) scores showed significant differences (p < 0.05) between centre midfielders, goalkeepers, and centrebacks in the quantity of CODs (PC1), and between wingers and fullbacks and centre backs in attacking/goal-scoring CODs (PC2), whereas PC3 was not different between playing positions. Differences between playing positions suggest that training and testing protocols in soccer could be enhanced to better match the individual and playing position-based needs.

Systematic review of methods used to measure training load in dance

Dance is a popular physical activity. Increased dance training has been associated with an increased risk of injury. Given the established association between training load (TL) and injury in sport, knowledge of how TL is currently being measured in dance is critical. The objective of this study is to summarise published literature examining TL monitoring in dance settings. Six prominent databases (CINAHL, EMBASE, Medline, ProQuest, Scopus, SportDiscus) were searched and nine dance-specific journals were handsearched up to May 2022. Selected studies met inclusion criteria, where original TL data were collected from at least one dancer in a class, rehearsal and/or performance. Studies were excluded if TL was not captured in a dance class, rehearsal or performance. Two reviewers independently assessed each record for inclusion at title, abstract and full-text screening stages. Study quality was assessed using Joanna Briggs Institute Critical Appraisal Tool checklists for each study design. The 199 included studies reported on female dancers (61%), ballet genre (55%) and the professional level (31%). Dance hours were the most common tool used to measure TL (90%), followed by heart rate (20%), and portable metabolic systems (9%). The most common metric for each tool was mean weekly hours (n=381; median=9.5 hours, range=0.2-48.7 hours), mean heart rate (n=143) and mean oxygen consumption (n=93). Further research on TL is needed in dance, including a consensus on what tools and metrics are best suited for TL monitoring in dance.

Pre-Season Screening Assessments: Normative Data for Pre-Professional Ballet Dancers

OBJECTIVES

Pre-professional dance is high-risk, with injury incidence up to 4.7 injuries/1000 dance hours. Pre-season screening measures have been utilized to assess risk factors for dance-related injury, however normative values haven't been established for a pre-professional ballet population. The purpose of this study was to establish normative values of ankle and hip joint range of motion (ROM), lumbopelvic control, and dynamic balance pre-season screening measures for pre-professional ballet dancers.

METHODS

498 adolescent pre-professional ballet dancers [n = 219 junior division (194 female, 25 male; mean age: 12.9±0.9 year); n = 281 senior division (238 female, 41 male; mean age: 16.8±1.5 year)] participated in baseline screening tests across 5 seasons (2015-2019). Baseline measures took place at the beginning of each academic year: ankle ROM [dorsiflexion (deg); plantarflexion (PF) (deg)], total active turnout (TAT) (deg), lumbopelvic control [active straight leg raise (ASLR) (score); one leg standing test (OLS) (score)], and dynamic balance [unipedal balance (sec); Y-Balance Test (cm)].

RESULTS

Percentiles for ankle dorsiflexion ranged from 28.2° (male senior division, 10th percentile) to 63.3° (female junior division, 100th percentile). For PF, percentiles ranged from 77.5 to 111.8° (male junior division, 10th percentile; male senior division, 100th percentile). Percentiles for TAT for all participants ranged between 121.1° and 131.0°. For the ASLR, the proportion of participants moving with compensation (pelvis shifting) was between 64.0% and 82.2%. For OLS, 19.7% to 56.1% of dancers had a positive score (hip hiking). Percentiles for dynamic balance ranged from 3.5 to 17.1 seconds (unipedal dynamic balance) and 75.8 to 103.3 cm (YBT composite reach score) across all groups.

CONCLUSION

The establishment of normative values of pre-season screening measures among a pre-professional ballet population can be used to determine areas to target during training, recognize individuals with possible injury risk, and inform return to dance protocols following injury. Comparison with other dancer/athletic populations will also provide insight into the performance of dancers and identify areas in need of improvement.

Evaluation of a Restoration Algorithm Applied to Clipped Tibial Acceleration Signals

Wireless accelerometers with various operating ranges have been used to measure tibial acceleration. Accelerometers with a low operating range output distorted signals and have been found to result in inaccurate measurements of peaks. A restoration algorithm using spline interpolation has been proposed to restore the distorted signal. This algorithm has been validated for axial peaks within the range of 15.0-15.9 g. However, the accuracy of peaks of higher magnitude and the resultant peaks have not been reported. The purpose of the present study is to evaluate the measurement agreement of the restored peaks using a low-range accelerometer (±16 g) against peaks sampled using a high-range accelerometer (±200 g). The measurement agreement of both the axial and resultant peaks were examined. In total, 24 runners were equipped with 2 tri-axial accelerometers at their tibia and completed an outdoor running assessment. The accelerometer with an operating range of ±200 g was used as reference. The results of this study showed an average difference of -1.40 ± 4.52 g and -1.23 ± 5.48 g for axial and resultant peaks. Based on our findings, the restoration algorithm could skew data and potentially lead to incorrect conclusions if used without caution.

Differences in kinetic variables between injured and uninjured rearfoot runners: A hierarchical cluster analysis

Running is a popular form of physical activity with a high incidence of running-related injuries. However, the etiology of running-related injuries remains elusive, possibly due to the heterogeneity of movement patterns. The purpose of this study was to investigate whether different clusters existed within a large group of injured and uninjured runners based on their kinetic gait patterns. A sample of 134 injured and uninjured runners were acquired from an existing database and 12 discrete kinetic and spatiotemporal variables which are commonly associated with running injuries were extracted from the ground reaction force waveforms. A principal components analysis followed by an unsupervised hierarchical cluster analysis was performed. The results revealed two distinct clusters of runners which were not associated with injury status (OR = 1.14 [0.57, 2.30], χ²  = 0.143, p = 0.706) or sex (OR = 1.72 [0.85, 3.49], χ²  = 2.3258, p = 0.127). These results suggest that while there appeared to be evidence for two distinct clusters within a large sample of injured and uninjured runners, there is no association between the kinetic variables and running related injuries.

Sex differences in the regularity and symmetry of gait in older adults with and without knee osteoarthritis

BACKGROUND

Three-dimensional (3D) motion measured at the lower back during walking can describe the regularity and symmetry of gait that may be related to osteoarthritis (OA) and functional status. However, gait speed and inherent sex differences, regardless of the presence of OA, may confound these measures. Therefore, there is a need to understand the effect of OA separately among males and females, without the confounding influence of gait speed.

OBJECTIVE

To investigate the difference in 3D gait regularity and symmetry measures between gait speed-matched males and females with and without knee OA.

METHOD

Gait regularity and symmetry were computed as autocorrelations of pelvic accelerations during treadmill walking in four groups of older adults: healthy asymptomatic females (AsymF; n = 44), healthy asymptomatic males (AsymM; n = 45), females diagnosed with knee OA (OAF; n = 44), and males diagnosed with knee OA (OAM; n = 45). Data were obtained from a larger research database, allowing for the matching of gait speed between groups. The main effect of OA, sex, and interaction effect between them was examined for the 3D gait regularity and symmetry measures at an alpha level of 0.05.

RESULTS

There was no main effect of OA on any variable, but there was a significant main effect of sex on mediolateral and anteroposterior gait regularity measures. Specifically, females demonstrated significantly greater gait regularity, most notably in the mediolateral directions compared to males.

CONCLUSION

Older adult females were found to display significantly greater mediolateral gait regularity as compared to males, regardless of the presence of OA. Further, this difference exists among matched gait speeds, suggesting it is not the result of gait speed. Overall, these results highlight the importance of sex-specific analyses and considering gait speed when examining gait acceleration patterns near the center of mass for both cross sectional and longitudinal gait assessments.

Between-Day Reliability of Commonly Used IMU Features during a Fatiguing Run and the Effect of Speed

The purpose of this study was to determine if fatigue-related changes in biomechanics derived from an inertial measurement unit (IMU) placed at the center of mass (CoM) are reliable day-to-day. Sixteen runners performed two runs at maximal lactate steady state (MLSS) on a treadmill, one run 5% above MLSS speed, and one run 5% below MLSS speed while wearing a CoM-mounted IMU. Trials were performed to volitional exhaustion or a specified termination time. IMU features were derived from each axis and the resultant. Feature means were calculated for each subject during non-fatigued and fatigued states. Comparisons were performed between the two trials at MLSS and between all four trials. The only significant fatigue state × trial interaction was the 25th percentile of the results when comparing all trials. There were no main effects for trial for either comparison method. There were main effects for fatigue state for most features in both comparison methods. Reliability, measured by an intraclass coefficient (ICC), was good-to-excellent for most features. These results suggest that fatigue-related changes in biomechanics derived from a CoM-mounted IMU are reliable day-to-day when participants ran at or around MLSS and are not significantly affected by slight deviations in speed.

Predicting knee adduction moment response to gait retraining with minimal clinical data

Knee osteoarthritis is a progressive disease mediated by high joint loads. Foot progression angle modifications that reduce the knee adduction moment (KAM), a surrogate of knee loading, have demonstrated efficacy in alleviating pain and improving function. Although changes to the foot progression angle are overall beneficial, KAM reductions are not consistent across patients. Moreover, customized interventions are time-consuming and require instrumentation not commonly available in the clinic. We present a regression model that uses minimal clinical data—a set of six features easily obtained in the clinic—to predict the extent of first peak KAM reduction after toe-in gait retraining. For such a model to generalize, the training data must be large and variable. Given the lack of large public datasets that contain different gaits for the same patient, we generated this dataset synthetically. Insights learned from a ground-truth dataset with both baseline and toe-in gait trials (N = 12) enabled the creation of a large (N = 138) synthetic dataset for training the predictive model. On a test set of data collected by a separate research group (N = 15), the first peak KAM reduction was predicted with a mean absolute error of 0.134% body weight * height (%BW*HT). This error is smaller than the standard deviation of the first peak KAM during baseline walking averaged across test subjects (0.306%BW*HT). This work demonstrates the feasibility of training predictive models with synthetic data and provides clinicians with a new tool to predict the outcome of patient-specific gait retraining without requiring gait lab instrumentation.

Gait retraining is a conservative intervention for knee osteoarthritis shown to reduce pain and improve function. Although customizing a treatment plan for each patient results in a better therapeutic response, customization cannot yet be performed outside of the gait laboratory, preventing research advances from becoming part of clinical practice. Our work aimed to build a model that accurately predicts whether a patient with knee osteoarthritis will benefit from non-invasive gait retraining using measures that can be easily collected in the clinic. To overcome the lack of large datasets required to train predictive models, we generated data synthetically (N = 138) based on limited ground-truth examples, and we provide experimental evidence for the model’s ability to generalize to real data (N = 15). Our results contribute toward a future in which clinicians can use data collected in the clinic to easily identify patients who would respond to therapeutic gait retraining.

Why machine learning (ML) has failed physical activity research and how we can improve

Measuring physical activity is a critical issue for our understanding of the health benefits of human movement. Machine learning (ML), using accelerometer data, has become a common way to measure physical activity. ML has failed physical activity measurement research in four important ways. First, as a field, physical activity researchers have not adopted and used principles from computer science. Benchmark datasets are common in computer science and allow the direct comparison of different ML approaches. Access to and development of benchmark datasets are critical components in advancing ML for physical activity. Second, the priority of methods development focused on ML has created blind spots in physical activity measurement. Methods, other than cut-point approaches, may be sufficient or superior to ML but these are not prioritised in our research. Third, while ML methods are common in published papers, their integration with software is rare. Physical activity researchers must continue developing and integrating ML methods into software to be fully adopted by applied researchers in the discipline. Finally, training continues to limit the uptake of ML in applied physical activity research. We must improve the development, integration and use of software that allows for ML methods’ broad training and application in the field.

Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis

Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments.

Evaluation of COVID-19 Restrictions on Distance Runners' Training Habits Using Wearable Trackers

The COVID-19 pandemic caused widespread disruption to many individuals' lifestyles. Social distancing restrictions implemented during this global pandemic may bring potential impact on physical activity habits of the general population. However, running is one of the most popular forms of physical activity worldwide and one in which it could be maintained even during most COVID-19 restrictions. We aimed to determine the impact of COVID-19 restrictions on runners' training habits through analyzing the training records obtained from their GPS enabled wearable trackers. Retrospective and prospective data were collected from an online database (https://wetrac.ucalgary.ca). Runners' training habits, including frequency, intensity and duration of training, weekly mileage and running locations were analyzed and compared 9 months before and after the start of COVID-19 restrictions in March 2020. We found that runners ran 3 km per week more (p = 0.05, Cohen's d = 0.12) after the start of COVID-19 restrictions, and added 0.3 training sessions per week (p = 0.03, Cohen's d = 0.14). Moreover, runners ran an additional 0.4 sessions outdoors (p < 0.01, Cohen's d = 0.21) but there was no significant change in the intensity or duration of training sessions. Our findings suggested that runners adopted slightly different training regimen as a result of COVID-19 restrictions. Our results described the collective changes, irrespective of differences in response measures adopted by various countries or cities during the COVID-19 pandemic.

Wearable Fitness Trackers to Predict Clinical Deterioration in Maintenance Hemodialysis: A Prospective Cohort Feasibility Study

Rationale & Objective

People receiving hemodialysis often require urgent care or hospitalizations. It is possible that reductions in a patient’s level of physical activity may serve as an “early warning” of clinical deterioration, allowing timely clinical intervention. We explored whether step count could serve as a trigger for deterioration.

Study Design

Prospective observational cohort feasibility study.

Setting & Participants

We recruited consenting adult participants from outpatient dialysis clinics in Calgary, AB, between June 28, 2019, and October 10, 2019.

Exposure and Outcomes

Participants wore a wristband fitness tracker for 4 weeks. Activity data from the trackers were imported weekly into the study database. Demographic, clinical management, functional impairment, and frailty were assessed at baseline. Clinical events (urgent care and emergency department visits and hospitalizations) were monitored during the observation period.

Analytical Approach

Box and whisker plots and line plots were used to describe each participant’s daily steps. Adjusted rate ratios (and 95 % confidence intervals) were calculated to assess the associations between the number of steps taken each day and potential predictors.

Results

Data from 46 patients were included; their median age was 64 years (range, 22 to 85), and 63 % were men. The median number of steps taken per day was 3,133 (range, 248-13,753). Fourteen events among 11 patients were reported. Within patients, step count varied considerably; it was not possible to identify a patient-specific normal range for daily step count. There was no association between step count and the occurrence of clinical events, although the number of events was very small.

Limitations

The number of participants was relatively small, and there were too few events to model to examine whether step count could predict clinical deterioration.

Conclusions

Within-patient variation in daily step count was too high to generate a normal range for patients. However, patient-specific norms over a longer period (3- or 7-day periods) appear potentially worthy of future study in a larger sample and/or over a longer follow-up.

Using wearable and mobile technology to measure and promote healthy sleep behaviors in adolescents: a scoping review protocol

OBJECTIVE

The objective of this scoping review is to map the evidence related to how consumer-targeted wearable and mobile technology is being used to measure and/or promote sleep among adolescents.

INTRODUCTION

Sleep is a key component of physical and mental health and is required for healthy development in adolescence. Efforts to improve insufficient and poor-quality sleep among adolescents have resulted in limited and temporary enhancements in sleep habits. Since good sleep hygiene is established through the development of daily routines, wearable technology offers a potential solution by providing real-time feedback, allowing adolescents to monitor and manage their sleep habits.

INCLUSION CRITERIA

Studies that focus on adolescents between 13 and 24 years who use mobile or wearable technology to measure and/or promote sleep health will be considered for inclusion.

METHODS

Using a scoping methodology, the authors will conduct a review of studies on the use of commercially available, wearable technology or mobile devices designed to measure and/or improve sleep among adolescents. Literature searched will include published primary studies, reviews, and dissertations from database inception to present. Databases searched will include MEDLINE, Embase, PsycINFO, CINAHL, CENTRAL, SPORTDiscus, JBI Evidence Synthesis, Cochrane Database of Systematic Reviews, Scopus, and ProQuest Dissertations and Theses. The search will be conducted using identified keywords and indexed terms, and studies will be limited to the English language. Data extracted will include study population, methods, description of sleep technology reported, sleep outcomes, and strategies used to promote healthy sleep behaviors. Quality assessment of included studies will be conducted to facilitate data mapping and synthesis.

Comparing the performance of Bayesian and least-squares approaches for inverse kinematics problems

Bayesian inference has recently been identified as an approach for estimating a subjects' pose from noisy marker position data. Previous research suggests that Bayesian inference markedly reduces error for inverse kinematic problems relative to traditional least-squares approaches with estimators having reduced variance despite both least-squares and Bayesian estimators being unbiased. This result is surprising as Bayesian estimators are typically similar to least-squares approaches unless highly informative prior distributions are used. As a result the purpose of this work was to examine the sensitivity of Bayesian inverse kinematics solutions to the prior distribution. Our results highlight that Bayesian solutions to inverse kinematics are sensitive to the choice of prior and that the previously reported superior performance of Bayesian inference is likely due to an overly informative prior distribution which unrealistically uses knowledge of the true kinematic pose. When more realistic, 'weakly-informative' priors, which do not use the known kinematic pose are used then any improvements in estimator accuracy are minimal when compared to the traditional least squares approach. However, with appropriate priors, Bayesian inference can propagate uncertainties related to marker position to uncertainty in joint angles, a valuable contribution for kinematic analyses. When using Bayesian methods, we recommend researchers use weakly-informative priors and conduct a sensitivity analysis to highlight the effects of prior choice on analysis outcomes.

Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework

The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in uncontrolled settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings. We trained deep neural networks on a large set of synthetic inertial data derived from a clinical marker-based motion-tracking database of hundreds of subjects. We used data augmentation techniques and an automated calibration approach to reduce error due to variability in sensor placement and limb alignment. On left-out subjects, lower extremity kinematics could be predicted with a mean (±STD) root mean squared error of less than 1.27° (±0.38°) in flexion/extension, less than 2.52° (±0.98°) in ad/abduction, and less than 3.34° (±1.02°) internal/external rotation, across walking and running trials. Errors decreased exponentially with the amount of training data, confirming the need for large datasets when training deep neural networks. While this framework remains to be validated with true inertial measurement unit data, the results presented here are a promising advance toward convenient estimation of gait kinematics in natural environments. Progress in this direction could enable large-scale studies and offer new perspective into disease progression, patient recovery, and sports biomechanics.

Wearable activity trackers and mobilization after major head and neck cancer surgery: You can't improve what you don't measure

Major surgery involving resection and free flap reconstruction is a mainstay of head and neck cancer (HNC) treatment, but postoperative morbidity and complications are common. One of the foundations for better surgical outcomes is early mobilization, which is included in enhanced recovery guidelines for all surgical specialties. However, a major unsolved challenge with early mobilization after surgery is quantifying how much a patient moves. To date, mobilization after major HNC surgery has been reported as the time to mobilization, i.e. the interval between the date of surgery and the date of the initial meaningful mobilization. Other data on postoperative mobilization in these patients are limited. Although clinicians can document mobilization via multidisciplinary progress notes, an estimate of mobilization for each postoperative day would be subjective and based on observations from several clinicians and/or the recall of the patient. Advancing research on postoperative mobilization requires the ability to objectively measure patient activity, particularly ambulatory activity, without placing a further burden on the inpatient team. Wearable activity trackers may provide a solution. Data from other surgical specialties indicate that such objective monitoring of patient ambulation in real-time to support interventions to increase mobilization may provide opportunities to improve clinical care. Objective measurement of step counts after HNC surgery would lead to an understanding of the dose-response relationship (the required quantity and frequency of mobilization that is safe and beneficial). In conclusion, integration of wearable activity trackers in the care plan for patients undergoing HNC surgery will facilitate the measurement and improvement of postoperative mobilization to reduce complications, improve surgical outcomes and enhance patient recovery.

0917 Designing a Wearable Technology-Based Sleep Intervention To Support Sleep Health Among Adolescents: Using a Participatory Design Approach

Introduction

Sleep problems during adolescence are increasingly common and have been associated with adverse physical and psychological health outcomes. Efforts to improve insufficient sleep among adolescents have resulted in increased sleep knowledge and temporary enhancements in sleep hygiene. Good sleep hygiene is established through the development of daily routines that support healthy sleep. Wearable technology offers a potential solution whereby adolescents can acquire and manage healthy sleep habits. In this study, we are co-designing with adolescents a prototype intervention using wearable technology to promote sustained improvements in their sleep hygiene.

Methods

Guided by participatory design approaches, the ongoing multi-phase mixed methods study is currently being conducted in a metropolitan area in western Canada. In phase 1, sleep data is being collected from a sample of 30 adolescent-parent dyads using wearable sensors (Actigraphy watches) and self-report sleep measures (questionnaires about sleep quality, hygiene, and beliefs and attitudes, as well as their general health) over a 10-day period. In phases 2 and 3, individual interviews and iterative user interface design sessions will be conducted with 25 adolescents.

Results

To date, thirteen adolescents-parent dyads (13-17 years, 9 females; 39-56 years, 11 females) have completed phase 1 of our study. Data analysis is currently being conducted to evaluate sleep onset/offset, total sleep time, wake after sleep onset, sleep efficiency, and sleep schedule differences between adolescents and their parents. Ten adolescents have completed individual interviews in phase 2 of the study. Preliminary qualitative data suggests that youth are aware of the importance of sleep to their overall health. However, they struggle with identifying credible information to act on from the various and sometimes conflicting sources (e.g. online, friends, family).

Conclusion

We anticipate that co-designing a wearable solution with adolescents will lead to a sleep intervention that is more relevant, persuasive, and useful in supporting their sleep health.

Support

This work is supported by the Sensor Technology in Monitoring Movement STiMM Program.

Distance running stride-to-stride variability for sagittal plane joint angles

Recent research indicates that distance running stride-to-stride variability (SSV) is related to performance and injury. Previous studies have primarily focused on stride characteristics (stride length and time). We assessed SSV for sagittal plane joint angles with the primary purpose of testing for significant differences among the lower body joints. The secondary purpose was to determine if strong correlations exist among joint SSV measures. Thirty recreational adult runners participated in the study (8 females, 22 males, 39 ± 10 years; 53.1 ± 25.7 km/week). A 6-camera motion capture system (200 Hz) collected kinematic data during treadmill running at a preferred pace. A 2 by 3 repeated measures factorial ANOVA (phase-stance, swing; joint-hip, knee, ankle) was run (p = 0.05). There was a significant interaction effect (p < 0.001) and post hoc analysis revealed knee swing to be the most variable condition by far. For all three joints, there were strong correlations between stance and swing SSV (r = 0.80 to r = 0.88) and correlations among the joints were moderate to strong (r = 0.55 to 0.86). This study helps to better understand the joints/phases that contribute most to variability in the overall stride. Also, the strong correlations suggest that runners appear to have an overall SSV pattern that is similar across joints/phases.

New Considerations for Collecting Biomechanical Data Using Wearable Sensors: The Effect of Different Running Environments

Traditionally, running biomechanics analyses have been conducted using 3D motion capture during treadmill or indoor overground running. However, most runners complete their runs outdoors. Since changes in running terrain have been shown to influence running gait mechanics, the purpose of this study was to use a machine learning approach to objectively determine relevant accelerometer-based features to discriminate between running patterns in different environments and determine the generalizability of observed differences in running patterns. Center of mass accelerations were recorded for recreational runners in treadmill-only (n = 28) and sidewalk-only (n = 25) environments, and an independent group (n = 16) ran in both treadmill and sidewalk environments. A feature selection algorithm was used to develop a training dataset from treadmill-only and sidewalk-only running. A binary support vector machine model was trained to classify treadmill and sidewalk running. Classification accuracy was determined using 10-fold cross-validation of the training dataset and an independent testing dataset from the runners that ran in both environments. Nine features related to the consistency and variability of center of mass accelerations were selected. Specifically, there was greater ratio of vertical acceleration during treadmill running and a greater ratio of anterior-posterior acceleration during sidewalk running in both the training and testing dataset. Step and stride regularity were significantly greater in the treadmill condition for the vertical axis in both the training and testing dataset, and in the medial-lateral axis for the testing dataset. During sidewalk running, there was significantly greater variability in the magnitude of the vertical and anterior-posterior accelerations for both datasets. The classification accuracy based on 10-fold cross-validation of the training dataset (M = 93.17%, SD = 2.43%) was greater than the classification accuracy of the independent testing dataset (M = 83.81%, SD = 3.39%). This approach could be utilized in future analyses to identify relevant differences in running patterns using wearable technology.

Validity and reliability of a smartphone motion analysis app for lower limb kinematics during treadmill running

OBJECTIVE

To investigate the validity and reliability of a smartphone application for selected lower-limb kinematics during treadmill running.

DESIGN

Validity and reliability study.

SETTING

Biomechanics laboratory.

PARTICIPANTS

Twenty healthy female runners.

MAIN OUTCOME MEASURE(S)

Sagittal-plane hip, knee, and ankle angle and rearfoot eversion were assessed using the Coach's Eye Smartphone application and a 3D motion capture system. Paired t-test and intraclass correlation coefficients (ICC) established criterion validity of Coach's Eye; ICC determined test-retest and intrarater/interrater reliability. Standard error of measurement (SEM) and minimal detectable change (MDC) were also reported.

RESULTS

Significant differences were found between Coach's Eye and 3D measurements for ankle angle at touchdown and knee angle at toe-off (p < 0.05). ICCs for validity of Coach's Eye were excellent for rearfoot eversion at touchdown (ICC = 0.79) and fair-to-good for the other kinematics (range 0.51-0.74), except for hip at touchdown, which was poor (ICC = 0.36). Test-retest (range 0.80-0.92), intrarater (range 0.95-0.99) and interrater (range 0.87-0.94) ICC results were excellent for all selected kinematics.

CONCLUSION

Coach's Eye can be used as a surrogate for 3D measures of knee and rearfoot in/eversion at touchdown, and hip, ankle, and rearfoot in/eversion at toe-off, but not for hip and ankle at touchdown or knee at toe-off. Reliable running kinematics were obtained using Coach's Eye, making it suitable for repeated measures.

The effects of midfoot strike gait retraining on impact loading and joint stiffness

OBJECTIVE

To assess the biomechanical changes following a systematic gait retraining to modify footstrike patterns from rearfoot strike (RFS) to midfoot strike (MFS).

DESIGN

Pre-post interventional study. All participants underwent a gait retraining program designed to modify footstrike pattern to MFS.

SETTING

Research laboratory.

PARTICIPANTS

Twenty habitual RFS male runners participated.

MAIN OUTCOME MEASURES

Gait evaluations were conducted before and after the training. Footstrike pattern, vertical loading rates, ankle and knee joint stiffness were compared.

RESULTS

Participants' footstrike angle was reduced (p < 0.001, Cohen's d = 1.65) and knee joint stiffness was increased (p = 0.003, Cohen's d = 0.69). No significant difference was found in the vertical loading rates (p > 0.155). Further subgroup analyses were conducted on the respondents (n = 8, 40% of participants) who exhibited MFS for over 80% of their footfalls during the post-training evaluation. Apart from the increased knee joint stiffness (p = 0.005, Cohen's d = 1.14), respondents exhibited a significant reduction in the ankle joint stiffness (p = 0.019, Cohen's d = 1.17) when running with MFS.

CONCLUSIONS

Gait retraining to promote MFS was effective in reducing runners' footstrike angle, but only 40% of participants responded to this training program. The inconsistent training effect on impact loading suggests a need to develop new training protocols in an effort to prevent running injuries.

Walking with head-mounted virtual and augmented reality devices: Effects on position control and gait biomechanics

What was once a science fiction fantasy, virtual reality (VR) technology has evolved and come a long way. Together with augmented reality (AR) technology, these simulations of an alternative environment have been incorporated into rehabilitation treatments. The introduction of head-mounted displays has made VR/AR devices more intuitive and compact, and no longer limited to upper-limb rehabilitation. However, there is still limited evidence supporting the use of VR and AR technology during locomotion, especially regarding the safety and efficacy relating to walking biomechanics. Therefore, the objective of this study is to explore the limitations of such technology through gait analysis. In this study, thirteen participants walked on a treadmill in normal, virtual and augmented versions of the laboratory environment. A series of spatiotemporal parameters and lower-limb joint angles were compared between conditions. The center of pressure (CoP) ellipse area (95% confidence ellipse) was significantly different between conditions (p = 0.002). Pairwise comparisons indicated a significantly greater CoP ellipse area for both the AR (p = 0.002) and VR (p = 0.005) conditions when compared to the normal laboratory condition. Furthermore, there was a significant difference in stride length (p<0.001) and cadence (p<0.001) between conditions. No statistically significant difference was found in the hip, knee and ankle joint kinematics between the three conditions (p>0.082), except for maximum ankle plantarflexion (p = 0.001). These differences in CoP ellipse area indicate that users of head-mounted VR/AR devices had difficulty maintaining a stable position on the treadmill. Also, differences in the gait parameters suggest that users walked with an unusual gait pattern which could potentially affect the effectiveness of gait rehabilitation treatments. Based on these results, position guidance in the form of feedback and the use of specialized treadmills should be considered when using head-mounted VR/AR devices.

The effect of running speed on joint coupling coordination and its variability in recreational runners

The purpose of this study was to examine the effect of speed on coordination and its variability in running gait using vector coding analysis. Lower extremity kinematic data were collected for thirteen recreational runners while running at three different speeds in random order: preferred speed, 15% faster and 15% lower than preferred speed. A dynamical systems approach, using vector coding and circular statistics, were used to quantify coordination and its variability for selected hip-knee and knee-ankle joint couplings. The influence of running speed was calculated from the continuous data sets of the running cycle, allowing for the identification of time percentages where differences existed. Results indicate that increases in running speed produced moderate alterations in the frequency of movement patterns which were not enough to alter classification of coordination. No effects of speed on coordination variability were observed. This study has demonstrated that coordination and coordination variability is generally stable in the range of ±15% around of preferred speed in recreational runners.

New Considerations for Collecting Biomechanical Data Using Wearable Sensors: How Does Inclination Influence the Number of Runs Needed to Determine a Stable Running Gait Pattern?

As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a ‘typical’ or ‘stable’ gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics of running gait, this study sought to determine the number of runs needed to establish a stable running pattern during level, downhill, and uphill conditions for both univariate and multivariate analyses of running biomechanical data collected using a single wearable IMU device. Pelvic drop, ground contact time, braking, vertical oscillation, pelvic rotation, and cadence, were recorded from thirty-five recreational runners running in three elevation conditions: level, downhill, and uphill. Univariate and multivariate normal distributions were estimated from differing numbers of runs and stability was defined when the addition of a new run resulted in less than a 5% change in the 2.5 and 97.5 quantiles of the 95% probability density function for each individual runner. This stability point was determined separately for each runner and each IMU variable (univariate and multivariate). The results showed that 2–4 runs were needed to define a stable running pattern for univariate, and 4–5 days were necessary for multivariate analysis across all inclination conditions. Pearson’s correlation coefficients were calculated to cross-validate differing elevation conditions and showed excellent correlations (r = 0.98 to 1.0) comparing the training and testing data within the same elevation condition and good to very good correlations (r = 0.63–0.88) when comparing training and testing data from differing elevation conditions. These results suggest that future research involving wearable technology should collect multiple days of data in order to build reliable and accurate representations of an individual’s stable gait pattern.

Classification of higher- and lower-mileage runners based on running kinematics

BACKGROUND

Running-related overuse injuries can result from the combination of extrinsic (e.g., running mileage) and intrinsic risk factors (e.g., biomechanics and gender), but the relationship between these factors is not fully understood. Therefore, the first purpose of this study was to determine whether we could classify higher- and lower-mileage runners according to differences in lower extremity kinematics during the stance and swing phases of running gait. The second purpose was to subgroup the runners by gender and determine whether we could classify higher- and lower-mileage runners in male and female subgroups.

METHODS

Participants were allocated to the "higher-mileage" group (≥32 km/week; n = 41 (30 females)) or to the "lower-mileage" group (≤25 km; n = 40 (29 females)). Three-dimensional kinematic data were collected during 60 s of treadmill running at a self-selected speed (2.61 ± 0.23 m/s). A support vector machine classifier identified kinematic differences between higher- and lower-mileage groups based on principal component scores.

RESULTS

Higher- and lower-mileage runners (both genders) could be separated with 92.59% classification accuracy. When subgrouping by gender, higher- and lower-mileage female runners could be separated with 89.83% classification accuracy, and higher- and lower-mileage male runners could be separated with 100% classification accuracy.

CONCLUSION

These results demonstrate there are distinct kinematic differences between subgroups related to both mileage and gender, and that these factors need to be considered in future research.

Automated Accelerometer-Based Gait Event Detection During Multiple Running Conditions

The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this study, algorithms for identifying gait events were developed for accelerometers that were placed on the foot and low back and validated against a gold standard force plate gait event detection method. These algorithms were automated to enable the processing of large quantities of data by accommodating variability in running patterns. An evaluation of the accuracy of the algorithms was done by comparing the magnitude and variability of the difference between the back and foot methods in different running conditions, including different speeds, foot strike patterns, and outdoor running surfaces. The results show the magnitude and variability of the back-foot difference was consistent across running conditions, suggesting that the gait event detection algorithms can be used in a variety of settings. As wearable technology allows for running gait analyses to move outside of the laboratory, the use of automated accelerometer-based gait event detection methods may be helpful in the real-time evaluation of running patterns in real world conditions.

Patellofemoral joint stress measured across three different running techniques

BACKGROUND

Patellofemoral pain (PFP) is the most common running-related injury. It has been shown in previous studies that gait retraining may have a beneficial effect on patellofemoral joint stress (PFJS).

RESEARCH QUESTION

Is there a reduction of PFJS across 4 running conditions: 1. runner's typical rearfoot strike pattern, 2. forefoot landing, 3. step rate increase by 10% and 4. forward trunk lean?

METHODS

Nineteen healthy runners (28.05 ± 5.03 years; 26.58 ± 8.85 km/week, 6.00 ± 4.51 years of running experience) completed one running trial for each condition, at the same subject-specific comfortable speed on a treadmill. Kinetic and kinematic data were collected and measures of hip, knee and ankle joint moments and PFJS were calculated.

RESULTS

Compared to rearfoot strike condition, peak PFJS and PFJS-time integral per step were significantly (P < 0.01) lower during forefoot landing and step rate increase conditions. PFJS per kilometer was significantly reduced for forefoot landing (17.01%; P < 0.01) and increased step rate (12.90%; P = 0.003). Forward trunk lean technique showed no significant differences in peak PFJS (P = 0.187), PFJS-time integral per step (P = 0.815) and PFJS per kilometer (P = 0.077) compared to rearfoot strike pattern.

INTERPRETATION

The comparison between techniques revealed greater reductions on PFJS by forefoot landing, followed by 10% step rate increase condition. These changes were the result of different lower limb movement strategies across the 2 running conditions. We conclude that compared to a rearfoot strike pattern, both a forefoot landing and step rate increase result in lower cumulative PFJS joint stress in healthy runners, with the forefoot landing being the most effective. These running technique modifications could be recommended to reduce PFJS loads and may have implications for PFP prevention.

Validity of a novel method to measure vertical oscillation during running using a depth camera

Recent advancements in low-cost depth cameras may provide a clinically accessible alternative to conventional three-dimensional (3D) multi-camera motion capture systems for gait analysis. However, there remains a lack of information on the validity of clinically relevant running gait parameters such as vertical oscillation (VO). The purpose of this study was to assess the validity of measures of VO during running gait using raw depth data, in comparison to a 3D multi-camera motion capture system. Sixteen healthy adults ran on a treadmill at a standard speed of 2.7 m/s. The VO of their running gait was simultaneously collected from raw depth data (Microsoft Kinect v2) and 3D marker data (Vicon multi-camera motion capture system). The agreement between the VO measures obtained from the two systems was assessed using a Bland-Altman plot with 95% limits of agreement (LOA), a Pearson's correlation coefficient (r), and a Lin's concordance correlation coefficient (r_c). The depth data from the Kinect v2 demonstrated excellent results across all measures of validity (r = 0.97; r_c = 0.97; 95% LOA = -8.0 mm - 8.7 mm), with an average absolute error and percent error of 3.7 (2.1) mm and 4.0 (2.0)%, respectively. The findings of this study have demonstrated the ability of a low cost depth camera and a novel tracking method to accurately measure VO in running gait.

Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions

Running-related overuse injuries can result from a combination of various intrinsic (e.g., gait biomechanics) and extrinsic (e.g., running surface) risk factors. However, it is unknown how changes in environmental weather conditions affect running gait biomechanical patterns since these data cannot be collected in a laboratory setting. Therefore, the purpose of this study was to develop a classification model based on subject-specific changes in biomechanical running patterns across two different environmental weather conditions using data obtained from wearable sensors in real-world environments. Running gait data were recorded during winter and spring sessions, with recorded average air temperatures of -10° C and +6° C, respectively. Classification was performed based on measurements of pelvic drop, ground contact time, braking, vertical oscillation of pelvis, pelvic rotation, and cadence obtained from 66,370 strides (~11,000/runner) from a group of recreational runners. A non-linear and ensemble machine learning algorithm, random forest (RF), was used to classify and compute a heuristic for determining the importance of each variable in the prediction model. To validate the developed subject-specific model, two cross-validation methods (one-against-another and partitioning datasets) were used to obtain experimental mean classification accuracies of 87.18% and 95.42%, respectively, indicating an excellent discriminatory ability of the RF-based model. Additionally, the ranked order of variable importance differed across the individual runners. The results from the RF-based machine-learning algorithm demonstrates that processing gait biomechanical signals from a single wearable sensor can successfully detect changes to an individual's running patterns based on data obtained in real-world environments.

Use of baseline pelvic acceleration during running for classifying response to muscle strengthening treatment in patellofemoral pain: A preliminary study

BACKGROUND

Objectively identifying patients at baseline who may not respond well to a generic muscle strengthening intervention could improve clinical practice by optimizing treatment strategies. The purpose of this study was to determine whether pelvic acceleration measures during running, and clinical and demographic variables could classify patellofemoral pain patients according to their response to a 6-week hip/core and knee exercise-based rehabilitation protocol.

METHODS

Forty-one individuals with patellofemoral pain participated in a 6-week exercise intervention program and were sub-grouped into treatment Responders (n = 28) and Non-responders (n = 13) based on self-reported pain and function measures. Baseline pelvic acceleration measures were reduced using a principal component analysis and combined with patient reported outcome measures and demographic variables in a support vector machine to retrospectively classify patient treatment response.

FINDINGS

The final classification model had 85.4% classification accuracy, which was significantly better than treatment success rate, with excellent detection rates for Responders (recall: 96.4%), but 23.1% of misclassifications among Non-responders (precision: 90.0%). Thus, it resulted in an F1-score of 0.93 and a Matthews correlation coefficient of 0.69.

INTERPRETATION

Overall, the classifier successfully separated patellofemoral pain patients into exercise-based treatment Responders and Non-responders based on a combination of three components of the pelvic accelerations. While this model requires independent validation, it has the potential for further development and to be applied in clinical practice and improve treatment strategies for patellofemoral pain.

Running patterns for male and female competitive and recreational runners based on accelerometer data

The purpose of this study was to classify runners in sex-specific groups as either competitive or recreational based on center of mass (CoM) accelerations. Forty-one runners participated in the study (25 male and 16 female), and were labeled as competitive or recreational based on age, sex, and race performance. Three-dimensional acceleration data were collected during a 5-minute treadmill run, and 24 features were extracted. Support vector machine classification models were used to examine the utility of the features in discriminating between competitive and recreational runners within each sex-specific subgroup. Competitive and recreational runners could be classified with 82.63 % and 80.4 % in the male and female models, respectively. Dominant features in both models were related to regularity and variability, with competitive runners exhibiting more consistent running gait patterns, but the specific features were slightly different in each sex-specific model. Therefore, it is important to separate runners into sex-specific competitive and recreational subgroups for future running biomechanical studies. In conclusion, we have demonstrated the ability to analyze running biomechanics in competitive and recreational runners using only CoM acceleration patterns. A runner, clinician, or coach may use this information to monitor how running patterns change as a result of training.

Treatment Success of Hip and Core or Knee Strengthening for Patellofemoral Pain: Development of Clinical Prediction Rules

CONTEXT

  Patellofemoral pain (PFP) is a common injury that interferes with quality of life and physical activity. Clinical subgroups of patients may exist, one of which is caused by proximal muscle dysfunction.

OBJECTIVES

  To develop clinical prediction rules that predict a positive outcome after either a hip and core- or knee-focused strengthening program for individuals with PFP.

DESIGN

  Secondary analysis of data from a randomized control trial.

SETTING

  Four university laboratories.

PATIENTS OR OTHER PARTICIPANTS

  A total of 199 participants with PFP.

INTERVENTION(S)

  Participants were randomly allocated to either a hip and core-focused (n = 111) or knee-focused (n = 88) rehabilitation group for a 6-week program.

MAIN OUTCOME MEASURE(S)

  Demographics, self-reported knee pain (visual analog scale) and function (Anterior Knee Pain Scale), hip strength, abdominal muscle endurance, and hip range of motion were evaluated at baseline. Treatment success was defined as a decrease in visual analog scale score by ≥2 cm or an increase in the Anterior Knee Pain Scale score by ≥8 points or both. Bivariate relationships between the outcome (treatment success) and the predictor variables were explored, followed by a forward stepwise logistic regression to predict a successful outcome.

RESULTS

  Patients with more pain, better function, greater lateral core endurance, and less anterior core endurance were more likely to have a successful outcome after hip and core strengthening (88% sensitivity and 54% specificity). Patients with lower weight, weaker hip internal rotation, stronger hip extension, and greater trunk-extension endurance were more likely to have success after knee strengthening (82% sensitivity and 58% specificity).

CONCLUSION

  The patients with PFP who have more baseline pain and yet maintain a high level of function may experience additional benefit from hip and core strengthening. The clinical prediction rules from this study remain in the developmental phase and should be applied with caution until externally validated.

The use of wearable devices for walking and running gait analysis outside of the lab: A systematic review

BACKGROUND

Quantitative gait analysis is essential for evaluating walking and running patterns for markers of pathology, injury, or other gait characteristics. It is expected that the portability, affordability, and applicability of wearable devices to many different populations will have contributed advancements in understanding the real-world gait patterns of walkers and runners. Therefore, the purpose of this systematic review was to identify how wearable devices are being used for gait analysis in out-of-lab settings.

METHODS

A systematic search was conducted in the following scientific databases: PubMed, Medline, CINAHL, EMBASE, and SportDiscus. Each of the included articles was assessed using a custom quality assessment. Information was extracted from each included article regarding the participants, protocol, sensor(s), and analysis.

RESULTS

A total of 61 articles were reviewed: 47 involved gait analysis during walking, 13 involved gait analysis during running, and one involved both walking and running. Most studies performed adequately on measures of reporting, and external and internal validity, but did not provide a sufficient description of power. Small, unobtrusive wearable devices have been used in retrospective studies, producing unique measures of gait quality. Walking, but not running, studies have begun to use wearable devices for gait analysis among large numbers of participants in their natural environment.

CONCLUSIONS

Despite the advantages provided by the portability and accessibility of wearable devices, more studies monitoring gait over long periods of time, among large numbers of participants, and in natural walking and running environments are needed to analyze real-world gait patterns, and would facilitate prospective, subject-specific, and subgroup investigations. The development of wearables-specific metrics for gait analysis provide insights regarding the quality of gait that cannot be determined using traditional components of in-lab gait analyses. However, guidelines for the usability of wearable devices and the validity of wearables-based measurements of gait quality need to be established.

Runners with patellofemoral pain demonstrate sub-groups of pelvic acceleration profiles using hierarchical cluster analysis: an exploratory cross-sectional study

Background

Previous studies have suggested that distinct and homogenous sub-groups of gait patterns exist among runners with patellofemoral pain (PFP), based on gait analysis. However, acquisition of 3D kinematic data using optical systems is time consuming and prone to marker placement errors. In contrast, axial segment acceleration data can represent an overall running pattern, being easy to acquire and not influenced by marker placement error. Therefore, the purpose of this study was to determine if pelvic acceleration patterns during running could be used to classify PFP patients into homogeneous sub-groups. A secondary purpose was to analyze lower limb kinematic data to investigate the practical implications of clustering these subjects based on 3D pelvic acceleration data.

Methods

A hierarchical cluster analysis was used to determine sub-groups of similar running profiles among 110 PFP subjects, separately for males (n = 44) and females (n = 66), using pelvic acceleration data (reduced with principal component analysis) during treadmill running acquired with optical motion capture system. In a secondary analysis, peak joint angles were compared between clusters (α = 0.05) to provide clinical context and deeper understanding of variables that separated clusters.

Results

The results reveal two distinct running gait sub-groups (C1 and C2) for female subjects and no sub-groups were identified for males. Two pelvic acceleration components were different between clusters (PC1 and PC5; p < 0.001). While females in C1 presented similar acceleration patterns to males, C2 presented greater vertical and anterior peak accelerations. All females presented higher and delayed mediolateral acceleration peaks than males. Males presented greater ankle eversion (p < 0.001), lower knee abduction (p = 0.007) and hip adduction (p = 0.002) than all females, and lower hip internal rotation than C1 (p = 0.007).

Conclusions

Two distinct and homogeneous kinematic PFP sub-groups were identified for female subjects, but not for males. The results suggest that differences in running gait patterns between clusters occur mainly due to sex-related factors, but there are subtle differences among female subjects. This study shows the potential use of pelvic acceleration patterns, which can be acquired with accessible wearable technology (i.e. accelerometers).

Gait Kinematics in Individuals with Acute and Chronic Patellofemoral Pain

PURPOSE

This study aimed to identify the discriminating kinematic gait characteristics between individuals with acute and chronic patellofemoral pain (PFP) and healthy controls.

METHODS

Ninety-eight runners with PFP (39 male, 59 female) and 98 healthy control runners (38 male, 60 female) ran on a treadmill at a self-selected speed while three-dimensional lower limb kinematic data were collected. Runners with PFP were split into acute (n = 25) and chronic (n = 73) subgroups on the basis of whether they had been experiencing pain for less or greater than 3 months, respectively. Principal component analysis and linear discriminant analysis were used to determine the combination of kinematic gait characteristics that optimally separated individuals with acute PFP and chronic PFP and healthy controls.

RESULTS

Compared with controls, both the acute and chronic PFP subgroups exhibited greater knee flexion across stance and greater ankle dorsiflexion during early stance. The acute PFP subgroup demonstrated greater transverse plane hip motion across stance compared with healthy controls. In contrast, the chronic PFP subgroup demonstrated greater frontal plane hip motion, greater knee abduction, and reduced ankle eversion/greater ankle inversion across stance when compared with healthy controls.

CONCLUSIONS

This study identified characteristics that discriminated between individuals with acute and chronic PFP when compared with healthy controls. Certain discriminating characteristics were shared between both the acute and chronic subgroups when compared with healthy controls, whereas others were specific to the duration of PFP.

The use of real-time feedback to improve kinematic marker placement consistency among novice examiners

Marker placement deviation has been shown to be the largest source of error in gait kinematic data, limiting the ability of clinicians and researchers to conduct between-day or between-center investigations. Prior marker-placement standardization methods are either impractical for a clinical setting or rely on expert marker placement. However, a recently developed, real-time feedback tool has been developed and shown to improve marker placement and downstream kinematic calculations. The purpose of this study was to determine whether this real-time marker-placement tool could improve the consistency of gait kinematic data collected by a group of novice examiners. Twelve novice examiners identified anatomical landmarks and placed retroreflective markers on a single subject. For each examiner, a running trial was analyzed separately using two static trials: (1) PRE and (2) POST implementation of the feedback tool. The protocol was repeated a second time, one week later. Between-examiner consistency was represented by the 95% confidence interval (CI) of the mean joint angles for the entire stride, and compared between the PRE and POST conditions. The POST feedback trials showed an average 27.45% reduction of the 95%CI range for eight out of nine joint angles on day one, and an average 24.73% for five out of nine joint angles on day two, compared to POST. The results indicate a real-time feedback tool improves the consistency in marker placement for novice users.

Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis

Background

Muscle strengthening exercises consistently demonstrate improvements in the pain and function of adults with knee osteoarthritis, but individual response rates can vary greatly. Identifying individuals who are more likely to respond is important in developing more efficient rehabilitation programs for knee osteoarthritis. Therefore, the purpose of this study was to determine if pre-intervention multi-sensor accelerometer data (e.g., back, thigh, shank, foot accelerometers) and patient reported outcome measures (e.g., pain, symptoms, function, quality of life) can retrospectively predict post-intervention response to a 6-week hip strengthening exercise intervention in a knee OA cohort.

Methods

Thirty-nine adults with knee osteoarthritis completed a 6-week hip strengthening exercise intervention and were sub-grouped as Non-Responders, Low-Responders, or High-Responders following the intervention based on their change in patient reported outcome measures. Pre-intervention multi-sensor accelerometer data recorded at the back, thigh, shank, and foot and Knee Injury and Osteoarthritis Outcome Score subscale data were used as potential predictors of response in a discriminant analysis of principal components.

Results

The thigh was the single best placement for classifying responder sub-groups (74.4%). Overall, the best combination of sensors was the back, thigh, and shank (81.7%), but a simplified two sensor solution using the back and thigh was not significantly different (80.0%; p = 0.27).

Conclusions

While three sensors were best able to identify responders, a simplified two sensor array at the back and thigh may be the most ideal configuration to provide clinicians with an efficient and relatively unobtrusive way to use to optimize treatment.

Electronic supplementary material

The online version of this article (10.1186/s12984-017-0309-z) contains supplementary material, which is available to authorized users.