A data-driven approach to detect upper limb functional use during daily life in breast cancer survivors using wrist-worn sensors.

To gain insights into the impact of upper limb (UL) dysfunctions after breast cancer treatment, this study aimed to develop a temporal convolutional neural network (TCN) to detect functional daily UL use in breast cancer survivors using data from a wrist-worn accelerometer. A pre-existing dataset of 10 breast cancer survivors was used that contained raw 3-axis acceleration data and simultaneously recorded video data, captured during four daily life activities. The input of our TCN consists of a 3-axis acceleration sequence with a receptive field of 243 samples. The 4 ResNet TCN blocks perform dilated temporal convolutions with a kernel of size 3 and a dilation rate that increases by a factor of 3 after each iteration. Outcomes of interest were functional UL use (minutes) and percentage UL use. We found strong agreement between the video and predicted data for functional UL use (ICC = 0.975) and moderately strong agreement for %UL use (ICC = 0.794). The TCN model overestimated the functional UL use by 0.71 min and 3.06%. Model performance showed good accuracy, f1, and AUPRC scores (0.875, 0.909, 0.954, respectively). In conclusion, using wrist-worn accelerometer data, the TCN model effectively identified functional UL use in daily life among breast cancer survivors.

Upper-Limb Movement Quality before and after Surgery in Women with Breast Cancer: An Exploratory Study.

(1) Background: This study aimed to describe upper-limb (UL) movement quality parameters in women after breast cancer surgery and to explore their clinical relevance in relation to post-surgical pain and disability. (2) Methods: UL movement quality was assessed in 30 women before and 3 weeks after surgery for breast cancer. Via accelerometer data captured from a sensor located at the distal end of the forearm on the operated side, various movement quality parameters (local dynamic stability, movement predictability, movement smoothness, movement symmetry, and movement variability) were investigated while women performed a cyclic, weighted reaching task. At both test moments, the Quick Disabilities of the Arm, Shoulder, and Hand (Quick DASH) questionnaire was filled out to assess UL disability and pain severity. (3) Results: No significant differences in movement quality parameters were found between the pre-surgical and post-surgical time points. No significant correlations between post-operative UL disability or pain severity and movement quality were found. (4) Conclusions: From this study sample, no apparent clinically relevant movement quality parameters could be derived for a cyclic, weighted reaching task. This suggests that the search for an easy-to-use, quantitative analysis tool for UL qualitative functioning to be used in research and clinical practice should continue.

Prognostic factors for the development of upper limb dysfunctions after breast cancer: the UPLIFT-BC prospective longitudinal cohort study protocol.

INTRODUCTION: Upper limb (UL) dysfunctions are highly prevalent in people after breast cancer and have a great impact on performing activities in daily living. To improve care, a more comprehensive understanding of the development and persistence of UL dysfunctions is needed. Therefore, the UPLIFT-BC study will primarily examine the prognostic value of different factors at the body functions and structures, environmental and personal level of the International Classification of Functioning, Disability and Health (ICF) framework at 1-month post-surgery for persisting UL dysfunctions at 6 months after finishing cancer treatment. METHODS AND ANALYSIS: A prospective longitudinal cohort study, running from 1-week pre-surgery to 6 months post-local cancer treatment, is performed in a cohort of 250 women diagnosed with primary breast cancer. Different potentially prognostic factors to UL dysfunctions, covering body functions and structures, environmental and personal factors of the ICF, are assessed pre-surgically and at different time points post-surgery. The primary aim is to investigate the prognostic value of these factors at 1-month post-surgery for subjective UL function (ie, QuickDASH) at 6 months post-cancer treatment, that is, 6 months post-radiotherapy or post-surgery (T3), depending on the individuals' cancer treatment trajectory. In this, factors with relevant prognostic value pre-surgery are considered as well. Similar analyses are performed with an objective measure for UL function (ie, accelerometry) and a composite score of the combination of subjective and objective UL function. Second, in the subgroup of participants who receive radiotherapy, the prognostic value of the same factors is explored at 1-month post-radiotherapy and 6 months post-surgery. A forward stepwise selection strategy is used to obtain these multivariable prognostic models. ETHICS AND DISSEMINATION: The study protocol was approved by the Ethics Committee of UZ/KU Leuven (reference number s66248). The results of this study will be published in peer-reviewed journals and will be presented at several research conferences. TRIAL REGISTRATION NUMBER: NCT05297591.

Assessing Upper Limb Function in Breast Cancer Survivors Using Wearable Sensors and Machine Learning in a Free-Living Environment.

(1) Background: Being able to objectively assess upper limb (UL) dysfunction in breast cancer survivors (BCS) is an emerging issue. This study aims to determine the accuracy of a pre-trained lab-based machine learning model (MLM) to distinguish functional from non-functional arm movements in a home situation in BCS. (2) Methods: Participants performed four daily life activities while wearing two wrist accelerometers and being video recorded. To define UL functioning, video data were annotated and accelerometer data were analyzed using a counts threshold method and an MLM. Prediction accuracy, recall, sensitivity, f1-score, 'total minutes functional activity' and 'percentage functionally active' were considered. (3) Results: Despite a good MLM accuracy (0.77-0.90), recall, and specificity, the f1-score was poor. An overestimation of the 'total minutes functional activity' and 'percentage functionally active' was found by the MLM. Between the video-annotated data and the functional activity determined by the MLM, the mean differences were 0.14% and 0.10% for the left and right side, respectively. For the video-annotated data versus the counts threshold method, the mean differences were 0.27% and 0.24%, respectively. (4) Conclusions: An MLM is a better alternative than the counts threshold method for distinguishing functional from non-functional arm movements. However, the abovementioned wrist accelerometer-based assessment methods overestimate UL functional activity.

ß Band Rhythms Influence Reaction Times.

Despite their involvement in many cognitive functions, ß oscillations are among the least understood brain rhythms. Reports on whether the functional role of ß is primarily inhibitory or excitatory have been contradictory. Our framework attempts to reconcile these findings and proposes that several ß rhythms co-exist at different frequencies. ß Frequency shifts and their potential influence on behavior have thus far received little attention. In this human magnetoencephalography (MEG) experiment, we asked whether changes in ß power or frequency in auditory cortex and motor cortex influence behavior (reaction times) during an auditory sweep discrimination task. We found that in motor cortex, increased ß power slowed down responses, while in auditory cortex, increased ß frequency slowed down responses. We further characterized ß as transient burst events with distinct spectro-temporal profiles influencing reaction times. Finally, we found that increased motor-to-auditory ß connectivity also slowed down responses. In sum, ß power, frequency, bursting properties, cortical focus, and connectivity profile all influenced behavioral outcomes. Our results imply that the study of ß oscillations requires caution as ß dynamics are multifaceted phenomena, and that several dynamics must be taken into account to reconcile mixed findings in the literature.

The effect of myofascial and physical therapy on trunk, shoulder, and elbow movement patterns in women with pain and myofascial dysfunctions after breast cancer surgery: Secondary analyses of a randomized controlled trial.

INTRODUCTION: Secondary upper limb dysfunctions are common after breast cancer treatment. Myofascial treatment may be a valuable physical therapy modality for this problem. OBJECTIVE: To investigate the effect of myofascial therapy in addition to physical therapy on shoulder, trunk, and elbow movement patterns in women with pain and myofascial dysfunctions at the upper limb after breast cancer surgery. DESIGN: A double-blinded randomized controlled trial. SETTING: Rehabilitation unit of a university hospital. PARTICIPANTS: Forty-eight women with persistent pain after finishing breast cancer treatment. INTERVENTIONS: Over 3 months, all participants received a standard physical therapy program. The experimental (n = 24) and control group (n = 24) received 12 additional sessions of myofascial therapy or placebo therapy, respectively. MAIN OUTCOME MEASURES: Outcomes of interest were movement patterns of the humerothoracic joint, scapulothoracic joint, trunk, and elbow, measured with an optoelectronic measurement system during the performance of a forward flexion and scaption task. Statistical parametric mapping (SPM) analyses were used for assessing the effect of treatment on movement patterns between both groups (group × time interaction effect). RESULTS: A significantly decreased protraction and anterior tilting was found after experimental treatment. No beneficial effects on movement patterns of the humerothoracic joint, trunk, or elbow were found. CONCLUSION: Myofascial therapy in addition to a 12-week standard physical therapy program can decrease scapular protraction and anterior tilting (scapulothoracic joint) during arm movements. Given the exploratory nature of these secondary analyses, the clinical relevance of these results needs to be investigated further.

The biomechanical fingerprint of hip and knee osteoarthritis patients during activities of daily living.

BACKGROUND: Osteoarthritis is a highly prevalent disease affecting the hip and knee joint and is characterized by load-mediated pain and decreased quality of life. Dependent on involved joint, patients present antalgic movement compensations, aiming to decrease loading on the involved joint. However, the associated alterations in mechanical loading of the ipsi- and contra-lateral lower limb joints, are less documented. Here, we documented the biomechanical fingerprint of end-stage hip and knee osteoarthritis patients in terms of ipsilateral and contralateral hip and knee loading during walking and stair ambulation. METHODS: Three-dimensional motion-analysis was performed in 20 hip, 18 knee osteoarthritis patients and 12 controls during level walking and stair ambulation. Joint contact forces were calculated using a standard musculoskeletal modelling workflow in Opensim. Involved and contralateral hip and knee joint loading was compared against healthy controls using independent t-tests (p < 0.05). FINDINGS: Both hip and knee cohorts significantly decreased loading of the involved joint during gait and stair ambulation. Hip osteoarthritis patients presented no signs of ipsilateral knee nor contralateral leg overloading, during walking and stair ascending. However, knee osteoarthritis patients significantly increased loading at the ipsilateral hip, and contralateral hip and knee joints during stair ambulation compared to controls. INTERPRETATION: The biomechanical fingerprint in knee and hip osteoarthritis patients confirmed antalgic movement strategies to unload the involved leg during gait. Only during stair ambulation in knee osteoarthritis patients, movement adaptations were confirmed that induced unbalanced intra- and inter-limb loading conditions, which are known risk factors for secondary osteoarthritis.

Altered upper limb motor behavior in breast cancer survivors and its relation to pain: A narrative review.

Breast cancer is the most commonly diagnosed cancer among women and many women suffer from persistent physical and psychological complaints following their cancer treatment. Altered motor behavior at the shoulder region and upper limb, that is, alterations in movement patterns, spatiotemporal movement characteristics and muscle activation patterns, is a common physical consequence of breast cancer treatment, that can have a clear impact on daily life functioning and quality of life. Furthermore, altered upper limb motor behavior is suggested to relate to upper limb pain, which is very commonly reported in breast cancer survivors (BCS). This review, prepared according to the SANRA guidelines, looks into the current understanding of alterations in motor behavior at shoulder and upper limb in BCS, by discussing the factors related to this altered behavior. In this, we specifically focus on the relation between motor behavior and pain. Results of our search show that cancer treatment modality is predictive for shoulder range of motion. Furthermore, single prospective studies show depressive symptoms, living alone, being non-white and low physical activity levels as predicting factors for reduced shoulder range of motion. Pain as factor related to altered motor behavior is only assessed in cross-sectional research, limiting its interpretation in context of being cause or consequence of motor behavioral adaptations, and on the underlying mechanism explaining their relation. It is concluded that studies which explain the mechanisms how and in which subgroup of BCS motor behavioral alterations are associated with pain at the upper limb, are necessary in future.

Inertial Sensor-to-Segment Calibration for Accurate 3D Joint Angle Calculation for Use in OpenSim.

Inertial capture (InCap) systems combined with musculoskeletal (MSK) models are an attractive option for monitoring 3D joint kinematics in an ecological context. However, the primary limiting factor is the sensor-to-segment calibration, which is crucial to estimate the body segment orientations. Walking, running, and stair ascent and descent trials were measured in eleven healthy subjects with the Xsens InCap system and the Vicon 3D motion capture (MoCap) system at a self-selected speed. A novel integrated method that combines previous sensor-to-segment calibration approaches was developed for use in a MSK model with three degree of freedom (DOF) hip and knee joints. The following were compared: RMSE, range of motion (ROM), peaks, and R2 between InCap kinematics estimated with different calibration methods and gold standard MoCap kinematics. The integrated method reduced the RSME for both the hip and the knee joints below 5°, and no statistically significant differences were found between MoCap and InCap kinematics. This was consistent across all the different analyzed movements. The developed method was integrated on an MSK model workflow, and it increased the sensor-to-segment calibration accuracy for an accurate estimate of 3D joint kinematics compared to MoCap, guaranteeing a clinical easy-to-use approach.

Can the Output of a Learned Classification Model Monitor a Person's Functional Recovery Status Post-Total Knee Arthroplasty?

Osteoarthritis is a common musculoskeletal disorder. Classification models can discriminate an osteoarthritic gait pattern from that of control subjects. However, whether the output of learned models (probability of belonging to a class) is usable for monitoring a person's functional recovery status post-total knee arthroplasty (TKA) is largely unexplored. The research question is two-fold: (I) Can a learned classification model's output be used to monitor a person's recovery status post-TKA? (II) Is the output related to patient-reported functioning? We constructed a logistic regression model based on (1) pre-operative IMU-data of level walking, ascending, and descending stairs and (2) 6-week post-operative data of walking, ascending-, and descending stairs. Trained models were deployed on subjects at three, six, and 12 months post-TKA. Patient-reported functioning was assessed by the KOOS-ADL section. We found that the model trained on 6-weeks post-TKA walking data showed a decrease in the probability of belonging to the TKA class over time, with moderate to strong correlations between the model's output and patient-reported functioning. Thus, the LR-model's output can be used as a screening tool to follow-up a person's recovery status post-TKA. Person-specific relationships between the probabilities and patient-reported functioning show that the recovery process varies, favouring individual approaches in rehabilitation.

Movement Quality Parameters during Gait Assessed by a Single Accelerometer in Subjects with Osteoarthritis and Following Total Joint Arthroplasty.

This study's aim is threefold: (I) Evaluate movement quality parameters of gait in people with hip or knee osteoarthritis (OA) compared to asymptomatic controls from a single trunk-worn 3D accelerometer. (II) Evaluate the sensitivity of these parameters to capture changes at 6-weeks, 3-, 6-, and 12-months following total knee arthroplasty (TKA). (III) Investigate whether observed changes in movement quality from 6-weeks and 12-months post-TKA relates to changes in patient-reported outcome measures (PROMs). We invited 20 asymptomatic controls, 20 people with hip OA, 18 people pre- and post-TKA to our movement lap. They wore a single trunk-worn accelerometer and walked at a self-selected speed. Movement quality parameters (symmetry, complexity, smoothness, and dynamic stability) were calculated from the 3D acceleration signal. Between groups and between timepoints comparisons were made, and changes in movement quality were correlated with PROMs. We found significant differences in symmetry and stability in both OA groups. Post-TKA, most parameters reflected an initial decrease in movement quality at 6-weeks post-TKA, which mostly normalised 6-months post-TKA. Finally, improved movement quality relates to improvements in PROMs. Thus, a single accelerometer can characterise movement quality in both OA groups and post-TKA. The correlation shows the potential to monitor movement quality in a clinical setting to inform objective, data-driven personalised rehabilitation.

Joint kinematics alone can distinguish hip or knee osteoarthritis patients from asymptomatic controls with high accuracy.

Osteoarthritis (OA) is one of the leading musculoskeletal disabilities worldwide, and several interventions intend to change the gait pattern in OA patients to more healthy patterns. However, an accessible way to follow up the biomechanical changes in a clinical setting is still missing. Therefore, this study aims to evaluate whether we can use biomechanical data collected from a specific activity of daily living to help distinguish hip OA patients from controls and knee OA patients from controls using features that potentially could be measured in a clinical setting. To achieve this goal, we considered three different classes of statistical models with different levels of data complexity. Class 1 is kinematics based only (clinically applicable), class 2 includes joint kinetics (semi-applicable under the condition of access to a force plate or prediction models), and class 3 uses data from advanced musculoskeletal modeling (not clinically applicable). We used a machine learning pipeline to determine which classification model was best. We found 100% classification accuracy for KneeOA-vs-Asymptomatic and 93.9% for HipOA-vs-Asymptomatic using seven features derived from the lumbar spine and hip kinematics collected during ascending stairs. These results indicate that kinematical data alone can distinguish hip or knee OA patients from asymptomatic controls. However, to enable clinical use, we need to validate if the classifier also works with sensor-based kinematical data and whether the probabilistic outcome of the logistic regression model can be used in the follow-up of patients with OA.

No behavioural evidence for rhythmic facilitation of perceptual discrimination.

It has been hypothesized that internal oscillations can synchronize (i.e., entrain) to external environmental rhythms, thereby facilitating perception and behaviour. To date, evidence for the link between the phase of neural oscillations and behaviour has been scarce and contradictory; moreover, it remains an open question whether the brain can use this tentative mechanism for active temporal prediction. In our present study, we conducted a series of auditory pitch discrimination tasks with 181 healthy participants in an effort to shed light on the proposed behavioural benefits of rhythmic cueing and entrainment. In the three versions of our task, we observed no perceptual benefit of purported entrainment: targets occurring in-phase with a rhythmic cue provided no perceptual benefits in terms of discrimination accuracy or reaction time when compared with targets occurring out-of-phase or targets occurring randomly, nor did we find performance differences for targets preceded by rhythmic versus random cues. However, we found a surprising effect of cueing frequency on reaction time, in which participants showed faster responses to cue rhythms presented at higher frequencies. We therefore provide no evidence of entrainment, but instead a tentative effect of covert active sensing in which a faster external rhythm leads to a faster communication rate between motor and sensory cortices, allowing for sensory inputs to be sampled earlier in time.

Towards the Monitoring of Functional Status in a Free-Living Environment for People with Hip or Knee Osteoarthritis: Design and Evaluation of the JOLO Blended Care App.

(1) Background: Joint loading is an important parameter in patients with osteoarthritis (OA). However, calculating joint loading relies on the performance of an extensive biomechanical analysis, which is not possible to do in a free-living situation. We propose the concept and design of a novel blended-care app called JOLO (Joint Load) that combines free-living information on activity with lab-based measures of joint loading in order to estimate a subject's functional status. (2) Method: We used an iterative design process to evaluate the usability of the JOLO app through questionnaires. The user interfaces that resulted from the iterations are described and provide a concept for feedback on functional status. (3) Results: In total, 44 people (20 people with OA and 24 health-care providers) participated in the testing of the JOLO app. OA patients rated the latest version of the JOLO app as moderately useful. Therapists were predominantly positive; however, their intention to use JOLO was low due to technological issues. (4) Conclusion: We can conclude that JOLO is promising, but further technological improvements concerning activity recognition, the development of personalized joint loading predictions and a more comfortable means to carry the device are needed to facilitate its integration as a blended-care program.

A Machine Learning Approach to Estimate Hip and Knee Joint Loading Using a Mobile Phone-Embedded IMU.

Hip osteoarthritis patients exhibit changes in kinematics and kinetics that affect joint loading. Monitoring this load can provide valuable information to clinicians. For example, a patient's joint loading measured across different activities can be used to determine the amount of exercise that the patient needs to complete each day. Unfortunately, current methods for measuring joint loading require a lab environment which most clinicians do not have access to. This study explores employing machine learning to construct a model that can estimate joint loading based on sensor data obtained solely from a mobile phone. In order to learn such a model, we collected a dataset from 10 patients with hip osteoarthritis who performed multiple repetitions of nine different exercises. During each repetition, we simultaneously recorded 3D motion capture data, ground reaction force data, and the inertial measurement unit data from a mobile phone attached to the patient's hip. The 3D motion and ground reaction force data were used to compute the ground truth joint loading using musculoskeletal modeling. Our goal is to estimate the ground truth loading value using only the data captured by the sensors of the mobile phone. We propose a machine learning pipeline for learning such a model based on the recordings of a phone's accelerometer and gyroscope. When evaluated for an unseen patient, the proposed pipeline achieves a mean absolute error of 29% for the left hip and 36% for the right hip. While our approach is a step in the direction of using a minimal number of sensors to estimate joint loading outside the lab, developing a tool that is accurate enough to be applicable in a clinical context still remains an open challenge. It may be necessary to use sensors at more than one location in order to obtain better estimates.