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.

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.

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.

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.

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.

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.

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.

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.

Injury epidemiology in pre-professional ballet dancers: A 5-year prospective cohort study.

INTRODUCTION: Pre-professional ballet dancers are at high-risk for injury, with injury rates ranging from 1.4 to 4.7 injuries/1000 dance-hours. In dance injury epidemiology, multi-year studies are limited, and findings are inconsistent. Thus, the extent to which injury estimates range from year to year in a pre-professional ballet program is currently unknown. OBJECTIVE: The aim of this study was to establish the extent and characteristics of injuries in pre-professional adolescent ballet dancers across five academic training years. METHODS: 452 female and male pre-professional ballet dancers (median age, 15 years; range, 11-20 years) participated across five academic years at a vocational ballet school. Participants completed an online weekly injury questionnaire (OSTRC-Q) and self-reported dance hours questionnaire. RESULTS: Questionnaire response was 91.4%. Depending on the definition of injury, yearly injury prevalence ranged from 32.1% (145/452; time-loss) to 67.4% (305/452; all-complaints) across the 5 years. Yearly injury rates ranged from 0.76 (95%CI: 0.66, 0.86; time-loss) to 2.54 (95%CI: 2.37, 2.73; all-complaints) per 1000 dance-hours. The ankle was the most reported injury location (range: 16-33%). CONCLUSIONS: Injury prevalence and injury rate estimates remained high across five academic years in a pre-professional ballet population. Injury estimates were highest when an all-complaints definition was employed. LEVEL OF EVIDENCE: Therapy/Prevention, Aetiology/Harm, level 2b.

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.

A generalised smoothing approach for continuous, planar, inverse kinematics problems.

Bayesian methods have recently been proposed to solve inverse kinematics problems for marker based motion capture. The objective is to find the posterior distribution, a probabilistic summary of our knowledge and corresponding uncertainty about the model parameters such as joint angles, segment angles, segment translations, and marker positions. To date, Bayesian inverse kinematics models have focused on a frame by frame solution, which if repeatedly applied gives estimates that are discontinuous in time. We propose to overcome this limitation for continuous, planar inverse kinematics problems via the use of finite basis representations to model latent kinematic quantities as smooth, continuous functions. Our generalised smoothing approach is able to accurately approximate the solution to planar inverse kinematics problems defined by simple systems of ordinary differential equations in addition to considerably more complex systems such as a planar analysis of human gait. Improvements in accuracy are considerable with a decrease in average RMSE of 0.025 rad observed when estimating ankle joint angle for a randomly selected running stride with the proposed generalised smoothing approach compared to previous time-independent approaches. In addition, the generalised smoothing approach is able to effectively estimate kinematic parameters in the presence of missing data along with derivatives of kinematic quantities without the need for prior filtering or gap-filling of data.

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.

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.

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.

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.

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.

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.

Kinematic and Coordination Variability in Individuals With Acute and Chronic Patellofemoral Pain.

Altered gait variability occurs in those with patellofemoral pain and may be relevant to pain progression. We examined gait kinematic and coordination variability between individuals with acute and chronic patellofemoral pain and healthy controls. Eighty-three patellofemoral pain runners (37 men and 46 women) and 142 healthy controls (52 men and 90 women) ran on a treadmill while 3-dimensional lower limb kinematic data were collected. Patellofemoral pain runners were split into acute (n = 22) and chronic (n = 61) subgroups based on pain duration (< and ≥3 mo, respectively). Approximate entropy assessed continuous hip, knee, and ankle kinematic variability. Vector coding calculated coordination variability for select joint couplings. Variability measures were compared between groups using 1-way analysis of variance and post hoc comparisons with Cohen d effect sizes. The chronic patellofemoral pain subgroup displayed higher frontal plane knee kinematic variability compared with controls (P = .0004, d = 0.550). No statistically significant effects for any coordination variability couplings were identified. Minimal differences in gait variability were detected between those with acute and chronic patellofemoral pain and healthy controls.