Systemic DNA Damage and Repair Activity Vary by Race in Breast Cancer Survivors.

Non-Hispanic Black breast cancer survivors have poorer outcomes and higher mortality rates than White survivors, but systemic biological mechanisms underlying these disparities are unclear. We used circulating leukocytes as a surrogate for measuring systemic mechanisms, which might be different from processes in the target tissue (e.g., breast). We investigated race-based differences in DNA damage and repair, using a novel CometChip assay, in circulating leukocytes from breast cancer survivors who had completed primary cancer therapy and were cancer free. We observed novel race-based differences in systemic DNA damage and repair activity in cancer survivors, but not in cells from healthy volunteers. Basal DNA damage in leukocytes was higher in White survivors, but Black survivors showed a much higher induction after bleomycin treatment. Double-strand break repair activity was also significantly different between the races, with cells from White survivors showing more sustained repair activity compared to Black leukocytes. These results suggest that cancer and cancer therapy might have long-lasting effects on systemic DNA damage and repair mechanisms that differ in White survivors and Black survivors. Findings from our preliminary study in non-cancer cells (circulating leukocytes) suggest systemic effects beyond the target site, with implications for accelerated aging-related cancer survivorship disparities.

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress.

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Hamstrings are stretched more and faster during accelerative running compared to speed-matched constant speed running.

Background: Hamstring strain injuries are associated with significant time away from sport and high reinjury rates. Recent evidence suggests that hamstring injuries often occur during accelerative running, but investigations of hamstring mechanics have primarily examined constant speed running on a treadmill. To help fill this gap in knowledge, this study compares hamstring lengths and lengthening velocities between accelerative running and constant speed overground running. Methods: We recorded 2 synchronized videos of 10 participants (5 female, 5 male) during 6 accelerative running trials and 6 constant speed running trials. We used OpenCap (a markerless motion capture system) to estimate body segment kinematics for each trial and a 3-dimensional musculoskeletal model to compute peak length and step-average lengthening velocity of the biceps femoris (long head) muscle-tendon unit. To compare running conditions, we used linear mixed regression models with running speed (normalized by the subject-specific maximum) as the independent variable. Results: At running speeds below 75% of top speed accelerative running resulted in greater peak lengths than constant speed running. For example, the peak hamstring muscle-tendon length when a person accelerated from running at only 50% of top speed was equivalent to running at a constant 88% of top speed. Lengthening velocities were greater during accelerative running at all running speeds. Differences in hip flexion kinematics primarily drove the greater peak muscle-tendon lengths and lengthening velocities observed in accelerative running. Conclusion: Hamstrings are subjected to longer muscle-tendon lengths and faster lengthening velocities in accelerative running compared to constant speed running. This provides a biomechanical explanation for the observation that hamstring strain injuries often occur during acceleration. Our results suggest coaches who monitor exposure to high-risk circumstances (long lengths, fast lengthening velocities) should consider the accelerative nature of running in addition to running speed.

A simulation framework to determine optimal strength training and musculoskeletal geometry for sprinting and distance running.

Musculoskeletal geometry and muscle volumes vary widely in the population and are intricately linked to the performance of tasks ranging from walking and running to jumping and sprinting. As an alternative to experimental approaches, where it is difficult to isolate factors and establish causal relationships, simulations can be used to independently vary musculoskeletal geometry and muscle volumes, and develop a fundamental understanding. However, our ability to understand how these parameters affect task performance has been limited due to the high computational cost of modelling the necessary complexity of the musculoskeletal system and solving the requisite multi-dimensional optimization problem. For example, sprinting and running are fundamental to many forms of sport, but past research on the relationships between musculoskeletal geometry, muscle volumes, and running performance has been limited to observational studies, which have not established cause-effect relationships, and simulation studies with simplified representations of musculoskeletal geometry. In this study, we developed a novel musculoskeletal simulator that is differentiable with respect to musculoskeletal geometry and muscle volumes. This simulator enabled us to find the optimal body segment dimensions and optimal distribution of added muscle volume for sprinting and marathon running. Our simulation results replicate experimental observations, such as increased muscle mass in sprinters, as well as a mass in the lower end of the healthy BMI range and a higher leg-length-to-height ratio in marathon runners. The simulations also reveal new relationships, for example showing that hip musculature is vital to both sprinting and marathon running. We found hip flexor and extensor moment arms were maximized to optimize sprint and marathon running performance, and hip muscles the main target when we simulated strength training for sprinters. Our simulation results provide insight to inspire future studies to examine optimal strength training. Our simulator can be extended to other athletic tasks, such as jumping, or to non-athletic applications, such as designing interventions to improve mobility in older adults or individuals with movement disorders.

AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization.

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subject's skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

OpenCap: Human movement dynamics from smartphone videos.

Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.

Simulating Muscle-Level Energetic Cost Savings When Humans Run with a Passive Assistive Device.

Connecting the legs with a spring attached to the shoelaces, called an exotendon, can reduce the energetic cost of running, but how the exotendon reduces the energetic burden of individual muscles remains unknown. We generated muscle-driven simulations of seven individuals running with and without the exotendon to discern whether savings occurred during the stance phase or the swing phase, and to identify which muscles contributed to energy savings. We computed differences in muscle-level energy consumption, muscle activations, and changes in muscle-fiber velocity and force between running with and without the exotendon. The seven of nine participants who reduced energy cost when running with the exotendon reduced their measured energy expenditure rate by 0.9 W/kg (8.3%). Simulations predicted a 1.4 W/kg (12.0%) reduction in the average rate of energy expenditure and correctly identified that the exotendon reduced rates of energy expenditure for all seven individuals. Simulations showed most of the savings occurred during stance (1.5 W/kg), though the rate of energy expenditure was also reduced during swing (0.3 W/kg). The energetic savings were distributed across the quadriceps, hip flexor, hip abductor, hamstring, hip adductor, and hip extensor muscle groups, whereas no changes were observed in the plantarflexor or dorsiflexor muscles. Energetic savings were facilitated by reductions in the rate of mechanical work performed by muscles and their estimated rate of heat production. By modeling muscle-level energetics, this simulation framework accurately captured measured changes in whole-body energetics when using an assistive device. This is a useful first step towards using simulation to accelerate device design by predicting how humans will interact with assistive devices that have yet to be built.

Validation Study of Physical Activity and Sedentary Behavior in African-American Men and Women.

BACKGROUND: Self-reported data of physical activity are practical and inexpensive ways to collect data, although, subject to significant measurement errors. Most physical activity questionnaires used in the USA have been predominately validated among non-Hispanic White American populations with limited attention paid to the validity of the measures among racial/ethnic minorities. Additionally, there are limited studies that have evaluated factors related to over- and under-reporting errors linked to self-reported physical activity data, particularly among African Americans. The primary objectives of this study were to validate self-reported levels of physical activity and sedentary behavior among African-American men and women against objective measurements and to identify the factors related to under- and over-reporting. METHODS: This study was a 7-day, cross-sectional study conducted on African-American men and women (n = 56) who were between 21-70 years of age. Participants were required to attend two study visits for the collection of self-reported and objective measurements of physical activity and sedentary behavior (VO2max, DEXA scan, anthropometrics, ActivPal accelerometer, resting metabolic rate (RMR) and International Physical Activity Questionnaire (IPAQ) questionnaire. RESULTS: Overall, energy expenditure measured by ActivPal was 24.1 MET/hr/week whereas self-reported (IPAQ) energy expenditure was 52.66 MET/hr/week. Self-reported sedentary time was 40.37 h/week, whereas sedentary time measured by ActivPal was 63.03 h/week. Obese participants tended to over-report their physical activity levels more so than non-obese participants (Obese, Activpal-23.89 MET/hr/week vs IPAQ-58.98 MET/hr/week; Non-obese, Activpal - 24.48 MET/hr/week vs IPAQ - 42.55 MET/hr/week). Both obese and non-obese participants underestimated their sedentary time (Obese, Activpal - 66.89 h/week vs IPAQ-43.92 h/week; Non-obese, Activpal -56.07 h/week vs IPAQ - 33.98 h/week). CONCLUSIONS: The results of this study found that the ActivPal validated physical activity and sedentary behavior among African-Americans. Self-reported data were found to be highly variable, whereas the objective assessments of physical activity and sedentary behavior had limited variability. It was also found that obese individuals over-estimated their self-reported physical activity levels and under-estimated sedentary behavior in comparison to the ActivPal. These findings strongly support the need to measure physical activity and sedentary behaviors objectively, particularly among African-Americans.

AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization.

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subjects skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

How Connecting the Legs with a Spring Improves Human Running Economy.

Connecting the legs with a spring attached to the shoelaces reduces the energy cost of running, but how the spring reduces the energy burden of individual muscles remains unknown. We generated muscle-driven simulations of seven individuals running with and without the spring to discern whether savings occurred during the stance phase or the swing phase, and to identify which muscles contributed to energy savings. We computed differences in muscle-level energy consumption, muscle activations, and changes in muscle-fiber velocity and force between running with and without the spring. Across participants, running with the spring reduced the measured rate of energy expenditure by 0.9 W/kg (8.3%). Simulations predicted a 1.4 W/kg (12.0%) reduction in the average rate of energy expenditure and correctly identified that the spring reduced rates of energy expenditure for all participants. Simulations showed most of the savings occurred during stance (1.5 W/kg), though the rate of energy expenditure was also reduced during swing (0.3 W/kg). The energetic savings were distributed across the quadriceps, hip flexor, hip abductor, hamstring, hip adductor, and hip extensor muscle groups, whereas no changes in the rate of energy expenditure were observed in the plantarflexor or dorsiflexor muscles. Energetic savings were facilitated by reductions in the rate of mechanical work performed by muscles and their estimated rate of heat production. The simulations provide insight into muscle-level changes that occur when utilizing an assistive device and the mechanisms by which a spring connecting the legs improves running economy.

Smartphone videos of the sit-to-stand test predict osteoarthritis and health outcomes in a nationwide study.

Physical function decline due to aging or disease can be assessed with quantitative motion analysis, but this currently requires expensive laboratory equipment. We introduce a self-guided quantitative motion analysis of the widely used five-repetition sit-to-stand test using a smartphone. Across 35 US states, 405 participants recorded a video performing the test in their homes. We found that the quantitative movement parameters extracted from the smartphone videos were related to a diagnosis of osteoarthritis, physical and mental health, body mass index, age, and ethnicity and race. Our findings demonstrate that at-home movement analysis goes beyond established clinical metrics to provide objective and inexpensive digital outcome metrics for nationwide studies.

A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation.

Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Laboratory-based biomechanical assessment can evaluate ACL injury risk and rehabilitation progress after ACLR; however, lab-based measurements are expensive and inaccessible to most people. Portable sensors such as wearables and cameras can be deployed during sporting activities, in clinics, and in patient homes. Although many portable sensing approaches have demonstrated promising results during various assessments related to ACL injury, they have not yet been widely adopted as tools for out-of-lab assessment. The purpose of this review is to summarize research on out-of-lab portable sensing applied to ACL and ACLR and offer our perspectives on new opportunities for future research and development. We identified 49 original research articles on out-of-lab ACL-related assessment; the most common sensing modalities were inertial measurement units, depth cameras, and RGB cameras. The studies combined portable sensors with direct feature extraction, physics-based modeling, or machine learning to estimate a range of biomechanical parameters (e.g., knee kinematics and kinetics) during jump-landing tasks, cutting, squats, and gait. Many of the reviewed studies depict proof-of-concept methods for potential future clinical applications including ACL injury risk screening, injury prevention training, and rehabilitation assessment. By synthesizing these results, we describe important opportunities that exist for clinical validation of existing approaches, using sophisticated modeling techniques, standardization of data collection, and creation of large benchmark datasets. If successful, these advances will enable widespread use of portable-sensing approaches to identify ACL injury risk factors, mitigate high-risk movements prior to injury, and optimize rehabilitation paradigms.

An 8-week exercise study to improve cancer treatment related fatigue and QOL among African American breast cancer patients undergoing radiation treatment: A pilot randomized clinical trial.

BACKGROUND: Cancer treatment related fatigue (CTRF) is one of the most debilitating side effects of adjuvant radiation therapy (RT). Several studies have found that physical activity (PA) may be an effective intervention to decrease fatigue and enhance QOL in cancer survivors. The primary objective of the PEDLAR study is to test the feasibility of an easily administered 8-week structured moderate-intensity PA intervention, delivered concurrently with RT, in reducing CTRF and improving health-related QOL among African-American breast cancer patients. This study is also designed to provide pilot data on the acceptability and adherence of PA interventions in African-American women with breast cancer. METHODS: It is a prospective, 2-arm, 8-week feasibility trial. Participants are randomized to either a structured, moderate-intensity aerobic training exercise regimen concurrent with radiotherapy or a control group. RESULTS: Participants in intervention group reported high satisfaction with exercise and adherence was >75% for exercise sessions. CONCLUSIONS: African-American breast cancer patients in a moderate-intensity 75 min/wk aerobic exercise intervention had marginally lower fatigue at 8-wk follow-up compared to baseline. The control group participants had marginally higher fatigue at 8-wk follow-up compared to baseline. Participants in the intervention group reported slightly better quality of life at 8-wk follow-up compared to baseline (P = 0.06).

Independently ambulatory children with spina bifida experience near-typical knee and ankle joint moments and forces during walking.

BACKGROUND: Spina bifida, a neurological defect, can result in lower-limb muscle weakness. Altered ambulation and reduced musculoskeletal loading can yield decreased bone strength in individuals with spina bifida, yet individuals who remain ambulatory can exhibit normal bone outcomes. RESEARCH QUESTION: During walking, how do lower-limb joint kinematics and moments and tibial forces in independently ambulatory children with spina bifida differ from those of children with typical development? METHODS: We retrospectively analyzed data from 16 independently ambulatory children with spina bifida and 16 children with typical development and confirmed that tibial bone strength was similar between the two groups. Plantar flexor muscle strength was measured by manual muscle testing, and 14 of the children with spina bifida wore activity monitors for an average of 5 days. We estimated tibial forces at the knee and ankle using motion capture data and musculoskeletal simulations. We used Statistical Parametric Mapping t-tests to compare lower-limb joint kinematic and kinetic waveforms between the groups with spina bifida and typical development. Within the group with spina bifida, we examined relationships between plantar flexor muscle strength and peak tibial forces by calculating Spearman correlations. RESULTS: Activity monitors from the children with spina bifida reported typical daily steps (9656 [SD 3095]). Despite slower walking speeds (p = 0.004) and altered lower-body kinematics (p < 0.001), children with spina bifida had knee and ankle joint moments and forces similar to those of children with typical development, with no detectable differences during stance. Plantar flexor muscle weakness was associated with increased compressive knee force (p = 0.002) and shear ankle force (p = 0.009). SIGNIFICANCE: High-functioning, independently ambulatory children with spina bifida exhibited near-typical tibial bone strength and near-typical step counts and tibial load magnitudes. Our results suggest that the tibial forces in this group are of sufficient magnitudes to support the development of normal tibial bone strength.

Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective.

Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology-and the unprecedented scope and quantity of data it generates-has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as anexemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors.

Dietary fiber intake and metabolic syndrome in postmenopausal African American women with obesity.

Fiber intake may be associated with lower risk of metabolic syndrome (MetS) but data from metabolically unhealthy African American women is sparse. We examined the association of dietary fiber intake and MetS among postmenopausal African American women with obesity. Baseline cross-sectional data from the Focused Intervention on Exercise to Reduce CancEr (FIERCE) trial of 213 women (mean age 58.3 years) were used. Dietary intake was assessed by Food Frequency Questionnaires (FFQs). Multivariate linear and logistic regressions were performed to estimate associations of MetS with fiber intake and adherence to dietary fiber intake guidelines, respectively. Mean daily fiber intake was (10.33 g/1000kcal) in women with impaired metabolic health. We observed an inverse association of total fiber intake with MetS. One unit increase in energy-adjusted fiber intake was associated with a 0.10 unit decrease in the MetS z-score (p = 0.02). Similar results were obtained for both soluble and insoluble fiber. In multivariate-adjusted analyses, participants not adherent to fiber intake recommendations were more likely to have MetS as compared to those reporting intakes in the recommended range (adjusted odds ratio 4.24, 95% CI: 1.75, 10.30). Of the MetS components, high fasting glucose and high triglycerides were all associated with lower intake of fiber. Study participants who consumed a higher amount of fiber had a better overall metabolic profile and were less likely to have MetS in our cross-sectional analysis of postmenopausal African American women with obesity and unhealthy metabolic profiles.

Running in the wild: Energetics explain ecological running speeds.

Human runners have long been thought to have the ability to consume a near-constant amount of energy per distance traveled, regardless of speed, allowing speed to be adapted to particular task demands with minimal energetic consequence.1-3 However, recent and more precise laboratory measures indicate that humans may in fact have an energy-optimal running speed.4-6 Here, we characterize runners' speeds in a free-living environment and determine if preferred speed is consistent with task- or energy-dependent objectives. We analyzed a large-scale dataset of free-living runners, which was collected via a commercial fitness tracking device, and found that individual runners preferred a particular speed that did not change across commonly run distances. We compared the data from lab experiments that measured participants' energy-optimal running speeds with the free-living preferred speeds of age- and gender-matched runners in our dataset and found the speeds to be indistinguishable. Human runners prefer a particular running speed that is independent of task distance and is consistent with the objective of minimizing energy expenditure. Our findings offer an insight into the biological objectives that shape human running preferences in the real world-an important consideration when examining human ecology or creating training strategies to improve performance and prevent injury.

OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations.

BACKGROUND: The ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate and capable of assessing and mitigating drift. METHODS: We computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-min trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subject's RMS differences over time. RESULTS: IMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3 and 6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60-0.87). We observed minimal drift in the RMS differences over 10 min; the average slopes of the linear fits to these data were near zero (- 0.14-0.17 deg/min). CONCLUSIONS: Our workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, which may obviate the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches when studying similar activities. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.

Coupled exoskeleton assistance simplifies control and maintains metabolic benefits: A simulation study.

Assistive exoskeletons can reduce the metabolic cost of walking, and recent advances in exoskeleton device design and control have resulted in large metabolic savings. Most exoskeleton devices provide assistance at either the ankle or hip. Exoskeletons that assist multiple joints have the potential to provide greater metabolic savings, but can require many actuators and complicated controllers, making it difficult to design effective assistance. Coupled assistance, when two or more joints are assisted using one actuator or control signal, could reduce control dimensionality while retaining metabolic benefits. However, it is unknown which combinations of assisted joints are most promising and if there are negative consequences associated with coupled assistance. Since designing assistance with human experiments is expensive and time-consuming, we used musculoskeletal simulation to evaluate metabolic savings from multi-joint assistance and identify promising joint combinations. We generated 2D muscle-driven simulations of walking while simultaneously optimizing control strategies for simulated lower-limb exoskeleton assistive devices to minimize metabolic cost. Each device provided assistance either at a single joint or at multiple joints using massless, ideal actuators. To assess if control could be simplified for multi-joint exoskeletons, we simulated different control strategies in which the torque provided at each joint was either controlled independently or coupled between joints. We compared the predicted optimal torque profiles and changes in muscle and total metabolic power consumption across the single joint and multi-joint assistance strategies. We found multi-joint devices-whether independent or coupled-provided 50% greater metabolic savings than single joint devices. The coupled multi-joint devices were able to achieve most of the metabolic savings produced by independently-controlled multi-joint devices. Our results indicate that device designers could simplify multi-joint exoskeleton designs by reducing the number of torque control parameters through coupling, while still maintaining large reductions in metabolic cost.

Mindset is associated with future physical activity and management strategies in individuals with knee osteoarthritis.

BACKGROUND: Despite the benefits of physical activity for individuals with knee osteoarthritis (KOA), physical activity levels are low in this population. OBJECTIVES: We conducted a repeated cross-sectional study to compare mindset about physical activity among individuals with and without KOA and to investigate whether mindset relates to physical activity. METHODS: Participants with (n = 150) and without (n = 152) KOA completed an online survey at enrollment (T1). Participants with KOA repeated the survey 3 weeks later (T2; n = 62). The mindset questionnaire, scored from 1 to 4, assessed the extent to which individuals associate the process of exercising with less appeal-focused qualities (e.g., boring, painful, isolating, and depriving) versus appeal-focused (e.g., fun, pleasurable, social, and indulgent). Using linear regression, we examined the relationship between mindset and having KOA, and, in the subgroup of KOA participants, the relationship between mindset at T1 and self-reported physical activity at T2. We also compared mindset between people who use medication for management and those who use exercise. RESULTS: Within the KOA group, a more appeal-focused mindset was associated with higher future physical activity (ß=38.72, p = 0.006) when controlling for demographics, health, and KOA symptoms. Individuals who used exercise with or without pain medication or injections had a more appeal-focused mindset than those who used medication or injections without exercise (p<0.001). A less appeal-focused mindset regarding physical activity was not significantly associated with KOA (ß = -0.14, p = 0.067). Further, the mindset score demonstrated strong internal consistency (α = 0.92; T1; n = 150 and α = 0.92; T2; n = 62) and test-retest reliability (intraclass correlation coefficient (ICC) > 0.84, p < 0.001) within the KOA sample. CONCLUSIONS: In individuals with KOA, mindset is associated with future physical activity levels and relates to the individual's management strategy. Mindset is a reliable and malleable construct and may be a valuable target for increasing physical activity and improving adherence to rehabilitation strategies involving exercise among individuals with KOA.