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Accurate detection of accelerometer non-wear time is crucial for calculating physical activity summary statistics. In this study, we evaluated three epoch-based non-wear algorithms (Hecht, Troiano, and Choi) and one raw-based algorithm (Hees). In addition, we performed a sensitivity analysis to provide insight into the relationship between the algorithms' hyperparameters and classification performance, as well as to generate tuned hyperparameter values to better detect episodes of wear and non-wear time. We used machine learning to construct a gold-standard dataset by combining two accelerometers and electrocardiogram recordings. The Hecht and Troiano algorithms achieved poor classification performance, while Choi exhibited moderate performance. Meanwhile, Hees outperformed all epoch-based algorithms. The sensitivity analysis and hyperparameter tuning revealed that all algorithms were able to achieve increased classification performance by employing larger intervals and windows, while more stringently defining artificial movement. These classification gains were associated with the ability to lower the false positives (type I error) and do not necessarily indicate a more accurate detection of the total non-wear time. Moreover, our results indicate that with tuned hyperparameters, epoch-based non-wear algorithms are able to perform just as well as raw-based non-wear algorithms with respect to their ability to correctly detect true wear and non-wear episodes.

Purpose: This study aimed to investigate whether prospectively reallocating time away from sedentary behavior (SB) into different physical activity intensities is associated with 12-month change to cardiometabolic health in a cohort at high risk of type 2 diabetes (T2DM)., Methods: Participants with known risk factors for T2DM were recruited from primary care (Leicestershire, United Kingdom) as part of the Walking Away from Type 2 Diabetes trial (n = 808). Participants were followed up at 12, 24, and 36 months. SB, light-intensity physical activity (LPA) and moderate-to-vigorous intensity physical activity (MVPA) were measured objectively by accelerometer. Postchallenge glucose, triglycerides, HDL cholesterol, systolic blood pressure, and waist circumference were analyzed individually and combined into a clustered cardiometabolic risk score (CMRS). Associations of changing SB over each consecutive 12-month period were analyzed taking account of repeated measures., Results: Reallocating 30 min from SB to LPA was associated with 0.21-cm (95% confidence interval, 0.03-0.38 cm) reduction in waist circumference, 0.09-mmol·L (0.04-0.13 mmol·L) reduction in 2-h glucose, 0.02-mmol·L (0.00-0.04 mmol·L) reduction in triglycerides, and 0.02 (0.01-0.03) reduction in CMRS. Every 30-min reallocation from SB to MVPA was associated with 1.23-cm (0.68-1.79 cm) reduction in waist circumference, 0.23-mmol·L (0.10-0.36 mmol·L) reduction in 2-h glucose, 0.04-mmol·L (0.00-0.09 mmol·L) reduction in triglycerides, and 0.07 (0.04-0.11) reduction in CMRS. Reallocating 30 min from LPA into MVPA was also associated with 1.02-cm (0.43-1.60 cm) reduction in waist circumference, 0.16-mmol·L (0.02-0.30 mmol·L) reduction in 2-h glucose, and 0.05 (0.01-0.09) reduction in CMRS., Conclusion: Over 12 months, reallocating time away from SB into LPA or MVPA was associated with improved cardiometabolic health in a population at risk of T2DM, with the greatest benefits observed for MVPA.

The purpose of this study was to describe the mechanical characteristics of a trampoline circus act and its individual tracks performed in training and shows using a tri-axial accelerometer. A track is an artist's specific role within a choreographed act. Seven male acrobats performed their trampoline act during training and shows while wearing a triaxial accelerometer and reported ratings of perceived exertion (RPE) after each trial. Average acceleration (AVG), root mean square (RMS), root mean to the fourth (RM4), time spent in specific acceleration ranges and RPE were measured/recorded from training and show acts. Paired t -tests compared dependent variables between training and show. Acceleration AVG, RMS and RM4 were significantly higher ( p  < 0.05) in training than show. RPE was significantly higher ( p  < 0.05) in show than training. No significant differences existed in time spent in any of the acceleration ranges between training and show. GPS devices have been used to manage workloads in field sports but are inoperable in theatres. But, inertial measurements may be an effective alternative to describe mechanical demands in theatre or arena environments. Wearable technology may be useful to coaches to improve understanding of track demands to manage artist workloads.

Purpose: The present study estimated the long-term reproducibility of accelerometer-based measures over 6 months in adults and the implications for statistical power, and attenuation in regression coefficients for future activity-disease studies., Methods: We used data from 914 adults in the Interactive Diet and Activity Tracking in AARP study. Participants wore an activPAL 3 (AP) and an ActiGraph GT3X (AG) twice, 6 months apart. AP measures included time spent sitting or lying, standing, and stepping, whereas AG measures included time spent in sedentary, light, and moderate-to-vigorous physical activity (PA). Reproducibility of each metric and implications for epidemiological studies were determined based on intraclass correlation coefficients (ICC; 95% confidence interval)., Results: The ICC values for AP estimates were 0.58 (95% confidence interval, 0.53-0.63) for sitting, 0.62 (0.57-0.67) for standing, and 0.57 (0.51-0.62) for stepping. The ICC values for AG were 0.56 (0.50-0.61) for sedentary, 0.54 (0.49-0.60) for light PA, and 0.58 (0.52-0.63) for moderate-to-vigorous PA. Modeling showed that increasing the number of replicate administrations to two or three resulted in the most noticeable increases in ICC values, statistical power, and reductions in attenuation coefficients. For example, administering the AP twice reduced within-subject variability by half and resulted in an increase in the ICC associated with sitting time from 0.58 to 0.74. Similar comparisons for AG and measure of sedentary time resulted in an increase in ICC values from 0.56 to 0.72. Increasing the number of replicate administrations from one to two reduced the attenuation in activity-outcome associations from 40% to 25%., Conclusion: Accelerometer-based classifications of activity are moderately stable over time, but there is considerable within-subject variability that needs to be considered when estimating usual activity in future studies.

Aim: To describe the characteristics of objectively measured physical activity (PA) during the 2nd and 3rd trimesters of pregnancy using a accelerometer., Methods: This was a longitudinal observational study wherein PA in pregnant women in the 2nd and 3rd trimesters was measured for seven consecutive days using a accelerometer (Silmee W10, TDK, Japan)., Results: A total of 34 primiparous women were examined. There was no statistically significant difference in the amount of PA during the 2nd and 3rd trimesters of pregnancy. Sub-group analysis demonstrated that PA in the full-time housewife group increased significantly from the 2nd to the 3rd trimester, while total PA and moderate and vigorous PA of the stopped-working group, decreased significantly over that time. There was no difference in the PA of the currently working group between trimesters. In the full-time housewife group, the amount of PA for each hour significantly increased at 12:00, 18:00, and 22:00 hr. In the currently working group, the amount of PA for each hour significantly increased at 9:00, 10:00, and 16:00 hr. In the stopped-working group, the amount of PA for each hour significantly decreased at 7:00, 8:00, and 18:00 hr., Conclusion: Objective measurements using an accelerometer did not identify any significant changes in PA during the different trimesters of pregnancy. Sub-group analysis revealed clear patterns in PA change correlating with different lifestyles during pregnancy. Future research may enable the development of personalized health guidance by identifying the relationship between PA and pregnancy outcomes., (© 2019 Japan Academy of Nursing Science.)

For total knee replacement (TKR) patients, rehabilitation after the surgery is key toregaining mobility. This study proposes a sensor-based system for effectively monitoringrehabilitation progress after TKR. The system comprises a hardware module consisting of thetriaxial accelerometer and gyroscope, a microcontroller, and a Bluetooth module, and a softwareapp for monitoring the motion of the knee joint. Three indices, namely the number of swings, themaximum knee flexion angle, and the duration of practice each time, were used as metrics tomeasure the knee rehabilitation progress. The proposed sensor device has advantages such asusability without spatiotemporal constraints and accuracy in monitoring the rehabilitation progress.The performance of the proposed system was compared with the measured range of motion of theCybex isokinetic dynamometer (or Cybex) professional rehabilitation equipment, and the resultsrevealed that the average absolute errors of the measured angles were between 1.65° and 3.27° forthe TKR subjects, depending on the swing speed. Experimental results verified that the proposedsystem is effective and comparable with the professional equipment.

This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space).

Urinary incontinence (UI) is experienced by an estimated 51% of women in the U.S. and often results from impaired function or weakening of the pelvic floor muscles. Pelvic floor muscle training (PFMT) is a frontline nonsurgical treatment, yet a number of symptomatic individuals cannot accurately perform a pelvic floor muscle contraction with simple verbal or written instruction. Long-term adherence to PFMT regimens is often a barrier to resolution of symptoms. Various biofeedback tools have been utilized to aid correct pelvic floor muscle performance and adherence. One novel device, the leva® Pelvic Digital Health System, utilizes an intravaginal probe embedded with MEMS accelerometer sensors that allow real-time visualization of the shape and motion of the vagina during PFMT. Early positive results with this device prompted design of a wearable version. The purpose of this study was to design a wearable, wireless clinical research device to optimize MEMS accelerometer sensor placement to detect maximal movement during a pelvic floor muscle exercise (PFME) and to test the form factor for retention and user acceptability. The device comprised a ring designed to sit at the fornix with an extension following the length of the vagina. This paper presents design components and results from clinical testing of 10 subjects. It was determined that a ring form factor alone, similar to other vaginal rings (pessaries, estrogen rings) provided less accurate visual information about PFME performance. By contrast, we determined that a ring with an extension allowed for device retention and improved real-time detection of vaginal shape and motion during PFMT.

Background: Accelerometers have become common for evaluating the efficacy of rehabilitation for patients with neurologic disorders. For example, metrics like use ratio (UR) and magnitude ratio (MR) have been shown to differentiate movement patterns of children with cerebral palsy (CP) compared to typically-developing (TD) peers. However, these metrics are calculated from "activity counts" - a measure based on proprietary algorithms that approximate movement duration and intensity from raw accelerometer data. Algorithms used to calculate activity counts vary between devices, limiting comparisons of clinical and research results. The goal of this research was to develop complementary metrics based on raw accelerometer data to analyze arm movement after neurologic injury., Method: We calculated jerk, the derivative of acceleration, to evaluate arm movement from accelerometer data. To complement current measures, we calculated jerk ratio (JR) as the relative jerk magnitude of the dominant (non-paretic) and non-dominant (paretic) arms. We evaluated the JR distribution between arms and calculated the 50th percentile of the JR distribution (JR50). To evaluate these metrics, we analyzed bimanual accelerometry data for five children with hemiplegic CP who underwent Constraint-Induced Movement Therapy (CIMT) and five typically developing (TD) children. We compared JR between the CP and TD cohorts, and to activity count metrics., Results: The JR50 differentiated between the CP and TD cohorts (CP = 0.578 ± 0.041 before CIMT, TD = 0.506 ± 0.026), demonstrating increased reliance on the dominant arm for the CP cohort. Jerk metrics also quantified changes in arm use during and after therapy (e.g., JR50 = 0.378 ± 0.125 during CIMT, 0.591 ± 0.057 after CIMT). The JR was strongly correlated with UR and MR (r = - 0.92, 0.89) for the CP cohort. For the TD cohort, JR50 was repeatable across three data collection periods with an average similarity of 0.945 ± 0.015., Conclusions: Acceleration-derived jerk captured differences in motion between TD and CP cohorts and correlated with activity count metrics. The code for calculating and plotting JR is open-source and available for others to use and build upon. By identifying device-independent metrics that can quantify arm movement in daily life, we hope to facilitate collaboration for rehabilitation research using wearable technologies.

Racquet sports can provide positive benefits for human healthcare. A reliable detection device that can effectively distinguish movement with similar sub-features is therefore needed. In this paper, a racquet sports recognition wristband system and a multilayer hybrid clustering model are proposed to achieve reliable activity recognition and perform number counting. Additionally, a Bluetooth mesh network enables communication between a phone and wristband, and sets-up the connection between multiple devices. This allows users to track their exercise through the phone and share information with other players and referees. Considering the complexity of the classification algorithm and the user-friendliness of the measurement system, the improved multi-layer hybrid clustering model applies three-level K-means clustering to optimize feature extraction and segmentation and then uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to determine the feature center of different movements. The model can identify unlabeled and noisy data without data calibration and is suitable for smartwatches to recognize multiple racquet sports. The proposed system shows better recognition results and is verified in practical experiments.

In pedestrian inertial navigation, multi-sensor fusion is often used to obtain accurate heading estimates. As a widely distributed signal source, the geomagnetic field is convenient to provide sufficiently accurate heading angles. Unfortunately, there is a broad presence of artificial magnetic perturbations in indoor environments, leading to difficulties in geomagnetic correction. In this paper, by analyzing the spatial distribution model of the magnetic interference field on the geomagnetic field, two quantitative features have been found to be crucial in distinguishing normal magnetic data from anomalies. By leveraging these two features and the classification and regression tree (CART) algorithm, we trained a decision tree that is capable of extracting magnetic data from distorted measurements. Furthermore, this well-trained decision tree can be used as a reject gate in a Kalman filter. By combining the decision tree and Kalman filter, a high-precision indoor pedestrian navigation system based on a magnetically assisted inertial system is proposed. This system is then validated in a real indoor environment, and the results show that our system delivers state-of-the-art positioning performance. Compared to other baseline algorithms, an improvement of over 70% in the positioning accuracy is achieved.

Accelerometer cut points are an important consideration for distinguishing the intensity of activity into categories such as moderate and vigorous. It is well-established in the literature that these cut points depend on a variety of factors, including age group, device, and wear location. The Actigraph GT9X is a newer model accelerometer that is used for physical activity research, but existing cut points for this device are limited since it is a newer device. Furthermore, there is not existing data on cut points for the GT9X at the ankle or foot locations, which offers some potential benefit for activities that do not involve arm and/or core motion. A total of N = 44 adults completed a four-stage treadmill protocol while wearing Actigraph GT9X sensors at four different locations: foot, ankle, wrist, and hip. Metabolic Equivalent of Task (MET) levels assessed by indirect calorimetry along with Receiver Operating Characteristic (ROC) curves were used to establish cut points for moderate and vigorous intensity for each wear location of the GT9X. Area under the ROC curves indicated high discrimination accuracy for each case.

Background: Cerebellar ataxia is characterized by difficulty in the planning of movement and lack of anticipatory postural adjustments, which can result in deficits of balance. Being able to have quantitative measurements in clinical practice, to detect any improvements on balance resulting from new rehabilitation treatments or experimental drugs is very important., Aim: The purpose of this study was to develop an application (APP) able to assess static and dynamic balance in patients with cerebellar ataxias (CA). The APP that works by a wearable device (smartphone) placed at the breastbone level and immobilized by an elastic band, measures the body sway by means of a triaxial accelerometer., Methods: We investigated 40 CA patients and 80 healthy subjects. All patients were clinically evaluated using the "Berg Balance Scale" (BBS) and the "Scale for the Assessment and Rating of Ataxia" (SARA). Balance impairment was quantitatively assessed using a validated static balance evaluating systems, i.e., Techno-body Pro-Kin footboard. All participants underwent static and dynamic balance assessments using the new APP., Results: We observed a strong correlation between the APP measurements and the score obtained with the BBS, SARA, and Pro-Kin footboard. The intra-rater reliability and the test-retest reliability of the APP measurements, estimated by intraclass correlation coefficient, were excellent. The standard error of measurement and the minimal detectable change were small. No learning effect was observed., Conclusions: We can state that the APP is an easy, reliable, and valid evaluating system to quantify the trunk sway in a static position and during the gait.

Objective: To determine the accuracy of wrist and arm-worn activity monitors' estimates of energy expenditure (EE)., Data Sources: SportDISCUS (EBSCOHost), PubMed, MEDLINE (Ovid), PsycINFO (EBSCOHost), Embase (Ovid) and CINAHL (EBSCOHost)., Design: A random effects meta-analysis was performed to evaluate the difference in EE estimates between activity monitors and criterion measurements. Moderator analyses were conducted to determine the benefit of additional sensors and to compare the accuracy of devices used for research purposes with commercially available devices., Eligibility Criteria: We included studies validating EE estimates from wrist-worn or arm-worn activity monitors against criterion measures (indirect calorimetry, room calorimeters and doubly labelled water) in healthy adult populations., Results: 60 studies (104 effect sizes) were included in the meta-analysis. Devices showed variable accuracy depending on activity type. Large and significant heterogeneity was observed for many devices (I 2 >75%). Combining heart rate or heat sensing technology with accelerometry decreased the error in most activity types. Research-grade devices were statistically more accurate for comparisons of total EE but less accurate than commercial devices during ambulatory activity and sedentary tasks., Conclusions: EE estimates from wrist and arm-worn devices differ in accuracy depending on activity type. Addition of physiological sensors improves estimates of EE, and research-grade devices are superior for total EE. These data highlight the need to improve estimates of EE from wearable devices, and one way this can be achieved is with the addition of heart rate to accelerometry., Prosperoregistration Number: CRD42018085016., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose : This study examined the extent to which fundamental motor skill competence (FMSC; locomotor and object control skill competence) and perceived physical competence (PPC) predicted physical activity levels and sedentary behaviors during free-play time at preschool. Method : A total of 72 children (girls n = 33, boys n = 39; M age = 4.38, SD = .85 years) were recruited from two preschools. Fundamental motor skill competence was measured using the Test of Gross Motor Development - 2nd edition, and PPC was assessed using the physical competence subscale of the Pictorial Scale of Perceived Competence and Social Acceptance. Physical activity was measured using accelerometers. Results : Children engaged in light physical activity 19.66%, and moderate to vigorous physical activity (MVPA) 36.41%, and sedentary behaviors 43.94% of their free-play time. Since no correlation was observed in light physical activity with any variables, a hierarchical multiple linear regression was run for MVPA and sedentary behaviors. The results illustrated 38.9% of the variance in MVPA during free-play time was predicted by FMSC and PPC, after controlling for age and sex, with locomotor skill competence as a significant predictor ( t = 2.98, p < .05). For sedentary behaviors, the 32.9% of the variance during free-play time was predicted by FMSC and PPC, after controlling age and sex, with locomotor skill competence as a significant predictor ( t = 2.72, p < .05). Conclusion : The findings highlight the importance of developing FMSC and positive PPC during early childhood to enhance physical activity engagement during free-play.

Background: Aspects of physical functioning, including balance and gait, are affected after surgery for lower limb musculoskeletal tumors. These are not routinely measured but likely are related to how well patients function after resection or amputation for a bone or soft tissue sarcoma. Small, inexpensive portable accelerometers are available that might be clinically useful to assess balance and gait in these patients, but they have not been well studied., Questions/purposes: In patients treated for lower extremity musculoskeletal tumors, we asked: (1) Are accelerometer-based body-worn monitor assessments of balance, gait, and timed up-and-go tests (TUG) feasible and acceptable? (2) Do these accelerometer-based body-worn monitor assessments produce clinically useful data (face validity), distinguish between patients and controls (discriminant validity), reflect findings obtained using existing clinical measures (convergent validity) and standard manual techniques in clinic (concurrent validity)?, Methods: This was a prospective cross-sectional study. Out of 97 patients approached, 34 adult patients treated for tumors in the femur/thigh (19), pelvis/hip (3), tibia/leg (9), or ankle/foot (3) were included in this study. Twenty-seven had limb-sparing surgery and seven underwent amputation. Patients performed standard activities while wearing a body-worn monitor on the lower back, including standing, walking, and TUG tests. Summary measures of balance (area [ellipsis], magnitude [root mean square {RMS}], jerkiness [jerk], frequency of postural sway below which 95% of power of acceleration power spectrum is observed [f95 of postural sway]), gait [temporal outcomes, step length and velocity], and TUG time were derived. Body-worn monitor assessments were evaluated for feasibility by investigating data loss and patient-reported acceptability and comfort. In addition, outcomes in patients were compared with datasets of healthy participants collected in parallel studies using identical methods as in this study to assess discriminant validity. Body-worn monitor assessments were also investigated for their relationships with routine clinical scales (the Musculoskeletal Tumour Society Scoring system [MSTS], the Toronto Extremity Salvage Score [TESS], and the Quality of life-Cancer survivors [QoL-CS)] to assess convergent validity and their agreement with standard manual techniques (video and stopwatch) to assess concurrent validity., Results: Although this was a small patient group, there were initial indications that body-worn monitor assessments were well-tolerated, feasible to perform, acceptable to patients who responded (95% [19 of 20] of patients found the body-worn monitor acceptable and comfortable and 85% [17 of 20] found it user-friendly), and produced clinically useful data comparable with the evidence. Balance and gait measures distinguished patients and controls (discriminant validity), for instance balance outcome (ellipsis) in patients (0.0475 m/s [95% confidence interval 0.0251 to 0.0810]) was affected compared with controls (0.0007 m/s [95% CI 0.0003 to 0.0502]; p = 0.001). Similarly gait outcome (step time) was affected in patients (0.483 seconds [95% CI 0.451 to 0.512]) compared with controls (0.541 seconds [95% CI 0.496 to 0.573]; p < 0.001). Moreover, body-worn monitor assessments showed relationships with existing clinical scales (convergent validity), for instance ellipsis with MSTS (r = -0.393; p = 0.024). Similarly, manual techniques showed excellent agreement with body-worn monitor assessments (concurrent validity), for instance stopwatch time 22.28 +/- 6.93 seconds with iTUG time 21.18 +/- 6.23 seconds (intraclass correlation coefficient agreement = 0.933; p < 0.001). P < 0.05 was considered statistically significant., Conclusions: Although we had a small, heterogeneous patient population, this pilot study suggests that body-worn monitors might be useful clinically to quantify physical functioning in patients treated for lower extremity tumors. Balance and gait relate to disability and quality of life. These measurements could provide clinicians with useful novel information on balance and gait, which in turn could guide rehabilitation strategies., Level of Evidence: Level III, diagnostic study.

Background: This study examined the volume and patterns of physical activity (PA) and sedentary time (ST) across different segments of the week among boys and girls., Methods: A total of 188 children aged 7-12 years wore a wrist-mounted ActiGraph GT3X+ accelerometer for 7 days. Time spent in PA and ST was calculated using ActiLife software. The mean number of minutes of light PA, moderate PA, vigorous PA, moderate-to-vigorous PA (MVPA), and ST were calculated per weekday (before school, during school, and after school) and per weekend day (morning and afternoon-evening)., Results: After school represented the greatest accumulation of ST compared with before school and during school segments. Boys engaged in 225.4 min/day of ST (95% confidence interval (CI): 216-235), and girls engaged in 222.2 min/day of ST (95%CI: 213-231). During school, boys engaged in significantly more MVPA than girls (46.1 min/day (95%CI: 44-48) vs. 40.7 min/day (95%CI: 39-43)). Across the whole weekday, boys participated in significantly more MVPA than girls (103.9 min/day (95%CI: 99-109) vs. 95.7 min/day (95%CI: 90-101)). The weekend afternoon-evening segment represented the larger accumulation of ST, where boys were significantly more sedentary than girls (367.5 min/day (95%CI: 353-382) vs. 339.8 min/day (95%CI: 325-355), respectively)., Conclusion: Our findings suggest that children are highly sedentary and spend little of their time in school in MVPA, especially girls. Routine breaks in school elicit increases in light PA and MVPA. Future work should consider the use of more active breaks within school time to encourage PA and reduce ST., (© 2019 Published by Elsevier B.V. on behalf of Shanghai University of Sport.)

Background: Gait impairment is a hallmark of multiple sclerosis (MS). InertiaLocoGraphy, the quantification of gait with inertial measurement units (IMUs), has been found useful to detect early changes in gait in MS. Still, the potential use of IMUs as a reliable biomarker of disease severity in MS remains unknown., Objective: This systematic review and meta-analysis of observational studies aimed to describe IMU protocols used to assess gait in MS patients and calculate the effect sizes of IMU features associated with disease severity scale measures., Methods: We searched MEDLINE, Cochrane Central, EMBASE and grey literature to identify articles published before May 2, 2018 that measured gait in MS patients by using IMUs and correlated IMU parameters with disease severity scale measures. We excluded from the meta-analysis articles that did not provide enough data to evaluate the association between IMU parameters and disease severity scale measures. The study was registered with the International Prospective Register of Systematic Reviews on May 2, 2018 (Registration: CRD42018092651) and the protocol was published in Systematic Reviews on January 8, 2019., Results: We included 36 articles in the systematic review and pooled 12 for the meta-analysis. The risk of bias was moderate, with only 2 articles (none included in the meta-analysis) showing a bias score<50%. Among protocols tested, 2 were predominant (the Timed Up and Go test and 6-min walk test). Speed, step length and step time with IMUs were significantly correlated with the Expanded Disability Status Scale (EDSS) score, and speed and step length were significantly correlated with the Multiple Sclerosis Walking Scale-12 score., Conclusion: IMU measurement has the potential to increase the sensitivity of clinical and performance tests to identify evolution in gait alteration in MS. Kinematic parameters easily accessible with IMUs, such as speed, step length and step duration, can help follow up disease severity in MS individuals with low to medium EDSS score (1.0-4.5)., (Copyright © 2019 Elsevier Masson SAS. All rights reserved.)

In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson's disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment. However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario. This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics. The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system. It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Satyabrata Aich et al.)

Background: Lumbar spinal disease causes disabilities in performing daily activities. Operative treatments are aimed at pain relief and rapid return to routine activity. Patient-based outcome measures are used to evaluate pathologies and therapeutic effects associated with lumbar spinal disease. Nevertheless, it remains unknown as to how much such treatment improves activity levels. The purpose of the current study was to measure changes in activity levels before and after lumbar spinal surgery using a wearable activity tracker and to analyze the differences between results and patient-based outcomes., Methods: Sixty patients who underwent lumbar surgery were studied. The physical activity of participants was objectively evaluated using a wearable Micro-Motion logger system (Actigraph). We measured the amount of activity before and at 1, 3, 6, and 12 months after the surgery to evaluate postoperative changes. The Japanese Orthopaedic Association Back Pain Evaluation Questionnaire, Oswestry Disability Index, Roland-Morris Disability Questionnaire and visual analog scale were used to assess patient-based outcomes of pain and activities of daily living-related scores; we analyzed the relationships between scores and actual activity levels., Results: The amount of actual activity decreased significantly 1 month after the surgery compared to that during the preoperative period, which then improved after 3 months postoperatively (p < 0.01). Furthermore, there was a significant improvement 6 months after the surgery compared to that during the preoperative period (p < 0.05). The changes in activity for each period were strongly correlated, regardless of the period. In contrast, a significant improvement was observed at 1 month after the surgery in almost all items of the patient-based questionnaires (p < 0.05)., Conclusions: The objective activity tracker demonstrated that lumbar surgery results in the amount of activity decreasing 1 month just after surgery followed by gradual postoperative recovery within 3 months. By contrast, patient-based outcomes showed improvement in 1 month that was significantly different from the change in actual activity, indicating a gap between patient-oriented clinical scores and their actual activities.

Purpose: The purpose of this study was to evaluate the correlation between tibial acceleration parameters measured by the KiRA device and the clinical grade of pivot shift. The secondary objective was to report the risk factors for pre-operative high-grade pivot shift., Methods: Two-hundred and ninety-five ACL deficient patients were examined under anesthesia. The pivot shift tests were performed twice by an expert surgeon. Clinical grading was performed using the International Knee Documentation Committee (IKDC) scale and tibial acceleration data was recorded using a triaxial accelerometer system (KiRA). The difference in the tibial acceleration range between injured and contralateral limbs was used in the analysis. Correlation coefficients were calculated using linear regression. Multivariate logistic regression was used to identify risk factors for high grade pivot shift., Results: The clinical grade of pivot shift and the side-to-side difference in delta tibial acceleration determined by KiRA were significantly correlated (r = 0.57; 95% CI 0.513-0.658, p < 0.0001). The only risk factor identified to have a significant association with high grade pivot shift was an antero-posterior side to side laxity difference > 6 mm (OR = 2.070; 95% CI (1.259-3.405), p = 0.0042)., Conclusion: Side-to-side difference in tibial acceleration range, as measured by KiRA, is correlated with the IKDC pivot shift grade in anaesthetized patients. Side-to-side A-P laxity difference greater than 6 mm is reported as a newly defined risk factor for high grade pivot shift in the ACL injured knee., Diagnostic Study: Level II.

The purpose of this study was to compare energy expenditure (EE) estimates from 5 consumer physical activity monitors (PAMs) to indirect calorimetry in a sample of youth. Eighty-nine youth (mean (SD); age, 12.3 (3.4) years; 50% female) performed 16 semi-structured activities. Activities were performed in duplicate across 2 visits. Participants wore a Cosmed K4b 2 (criterion for EE), an Apple Watch 2 (left wrist), Mymo Tracker (right hip), and Misfit Shine 2 devices (right hip; right shoe). Participants were randomized to wear a Samsung Gear Fit 2 or a Fitbit Charge 2 on the right wrist. Oxygen consumption was converted to EE by subtracting estimated basal EE (Schofield's equation) from the measured gross EE. EE from each visit was summed across the 2 visit days for comparison with the total EE recorded from the PAMs. All consumer PAMs estimated gross EE, except for the Apple Watch 2 (net Active EE). Paired t tests were used to assess differences between estimated (PAM) and measured (K4b 2 ) EE. Mean absolute percent error (MAPE) was used to assess individual-level error. The Mymo Tracker was not significantly different from measured EE and was within 15.9 kcal of measured kilocalories ( p = 0.764). Mean percent errors ranged from 3.5% (Mymo Tracker) to 48.2% (Apple Watch 2). MAPE ranged from 16.8% (Misfit Shine 2 - right hip) to 49.9% (Mymo Tracker). Novelty Only the Mymo Tracker was not significantly different from measured EE but had the greatest individual error. The Misfit Shine 2 - right hip had the lowest individual error. Caution is warranted when using consumer PAMs in youth for tracking EE.

Purpose: To establish whether associations between sedentary behavior and cardiometabolic health differ when assessed by thigh-worn and waist-worn accelerometry., Methods: Participants were recruited from several areas in the United Kingdom. Sedentary behavior was assessed using the activPAL worn on the thigh and ActiGraph worn on the waist. Average total (TST), prolonged (bouts ≥30 min; PST) and breaks (BST) in sedentary time were calculated. Cardiometabolic health markers included: adiposity (body fat) and surrogate markers of adiposity ((waist circumference, body mass index [BMI]), lipids (total, low density lipoprotein, and high-density lipoprotein [HDL] cholesterol, triglycerides), blood pressure, and glucose (fasting, 2 h and glycated hemoglobin A1c). A clustered cardiometabolic risk score was calculated. Linear regression analysis examined the associations with cardiometabolic health., Results: There were 1457 participants (mean age [± standard deviation], 59.38 ± 11.85 yr; 51.7% male; mean BMI, 30.19 ± 5.59 kg·m) included in the analyses. ActivPAL and ActiGraph sedentary variables were moderately correlated (0.416-0.511, P < 0.01); however, all variables were significantly different from each other (P < 0.05). Consistency was observed across devices in the direction and magnitude of associations of TST and PST with adiposity, surrogate markers of adiposity, HDL, triglycerides, and cardiometabolic risk score and for BST with adiposity, surrogate markers of adiposity, and cardiometabolic risk. Differences across devices were observed in associations of TST and PST with diastolic blood pressure, for TST with 2-h glucose and for BST with HDL. No other associations were observed for any other health marker for either device., Conclusions: Results suggest that associations with cardiometabolic health are largely comparable across the two common assessments of sedentary behavior but some small differences may exist for certain health markers.

Importance: Patient-generated health data captured from smartphone sensors have the potential to better quantify the physical outcomes of surgery. The ability of these data to discriminate between postoperative trends in physical activity remains unknown., Objective: To assess whether physical activity captured from smartphone accelerometer data can be used to describe postoperative recovery among patients undergoing cancer operations., Design, Setting, and Participants: This prospective observational cohort study was conducted from July 2017 to April 2019 in a single academic tertiary care hospital in the United States. Preoperatively, adults (age ≥18 years) who spoke English and were undergoing elective operations for skin, soft tissue, head, neck, and abdominal cancers were approached. Patients were excluded if they did not own a smartphone., Exposures: Study participants downloaded an application that collected smartphone accelerometer data continuously for 1 week preoperatively and 6 months postoperatively., Main Outcomes and Measures: The primary end points were trends in daily exertional activity and the ability to achieve at least 60 minutes of daily exertional activity after surgery among patients with vs without a clinically significant postoperative event. Postoperative events were defined as complications, emergency department presentations, readmissions, reoperations, and mortality., Results: A total of 139 individuals were approached. In the 62 enrolled patients, who were followed up for a median (interquartile range [IQR]) of 147 (77-179) days, there were no preprocedural differences between patients with vs without a postoperative event. Seventeen patients (27%) experienced a postoperative event. These patients had longer operations than those without a postoperative event (median [IQR], 225 [152-402] minutes vs 107 [68-174] minutes; P < .001), as well as greater blood loss (median [IQR], 200 [35-515] mL vs 25 [5-100] mL; P = .006) and more follow-up visits (median [IQR], 2 [2-4] visits vs 1 [1-2] visits; P = .002). Compared with mean baseline daily exertional activity, patients with a postoperative event had lower activity at week 1 (difference, -41.6 [95% CI, -75.1 to -8.0] minutes; P = .02), week 3 (difference, -40.0 [95% CI, -72.3 to -3.6] minutes; P = .03), week 5 (difference, -39.6 [95% CI, -69.1 to -10.1] minutes; P = .01), and week 6 (difference, -36.2 [95% CI, -64.5 to -7.8] minutes; P = .01) postoperatively. Fewer of these patients were able to achieve 60 minutes of daily exertional activity in the 6 weeks postoperatively (proportions: week 1, 0.40 [95% CI, 0.31-0.49]; P < .001; week 2, 0.49 [95% CI, 0.40-0.58]; P = .003; week 3, 0.39 [95% CI, 0.30-0.48]; P < .001; week 4, 0.47 [95% CI, 0.38-0.57]; P < .001; week 5, 0.51 [95% CI, 0.42-0.60]; P < .001; week 6, 0.73 [95% CI, 0.68-0.79] vs 0.43 [95% CI, 0.33-0.52]; P < .001)., Conclusions and Relevance: Smartphone accelerometer data can describe differences in postoperative physical activity among patients with vs without a postoperative event. These data help objectively quantify patient-centered surgical recovery, which have the potential to improve and promote shared decision-making, recovery monitoring, and patient engagement.

Background and Objective: Fall detection is an important problem for vulnerable sectors of the population such as elderly people, who frequently live alone. Note that a fall can be very dangerous for them if they cannot ask for help. Hence, in those situations, an automatic system that detected and informed to emergency services about the fall and subject identity could help to save lives. This way, they would know not only when but also who to help. Thus, our objective is to develop a new approach, based on deep learning, for fall detection and people identification that can be used in different datasets without any fine-tuning of the model parameters., Methods: We present a dataset-independent deep learning-based model that, by employing a multi-task learning approach, uses raw inertial information as input to solve simultaneously two tasks: fall detection and subject identification. By this way, our approach is able to automatically learn the best representations without any constraint introduced by the pre-processed features., Results: Our cross-dataset classifier is able to detect falls with more than a 98% of accuracy in four datasets recorded under different conditions (i.e. accelerometer device, sampling rate, sequence length, age of the subjects, etc.). Moreover, the number of false positives is very low - on average less than 1.6% - establishing a new state-of-the-art. Finally, our classifier is also capable of correctly identifying people with an average accuracy of 79.6%., Conclusions: The presented approach performs both tasks (fall detection and people identification) by using a single model and achieving real-time execution. The obtained results allow us to assert that a single model can be used for both fall detection and people identification under different conditions, easing its real implementation, as it is not necessary to train the model for new subjects., Competing Interests: Declaration of Competing Interest None., (Copyright © 2019. Published by Elsevier B.V.)

López-Laval, I, Sitko, S, Muñiz-Pardos, B, Cirer-Sastre, R, and Calleja-González, J. Relationship between bench press strength and punch performance in male professional boxers. J Strength Cond Res 34(2): 308-312, 2020-This study investigated the relationship between punching performance and the velocity at which different loads were lifted during the bench press (BP) exercise in 12 professional boxers (age = 22.6 ± 4 years; height = 177.7 ± 5 cm; body mass 70.6 ± 6.43 kg; years of boxing experience = 6.5 ± 3.5 years; weight class = from light to super welterweight). To determine the maximal punching velocity (PVmax) during both rear arm (RA) and lead arm (LA) punching, an accelerometer (Crossbow; Willow Technologies, Sussex, United Kingdom) was placed inside the boxing glove while executing 3 jabs at a maximal velocity with each arm. Upper-body strength was assessed through the direct 1-repetition maximum (1RM) BP test, and the maximum velocity at different percentages of 1RM was obtained with a linear encoder. The main finding was that RA PVmax was correlated with the BP velocity at all submaximal intensities (p < 0.05). Nevertheless, LA PVmax did not correlate with BP velocity at any intensity. When the correlated BP submaximal intensities were introduced in linear regression models, the velocity at 80% 1RM was the only predictor of RA PVmax (r = 0.75; p < 0.01) in professional boxers. Additional body mass adjustment to the regression model significantly affected the predictive value (r = 0.65; p < 0.005). Results encourage coaches and trainers to use BP exercise with high loads (i.e., 80% of 1RM) because this could be a reliable predictor of performance during the specific boxing action. Future research is needed to determine exercises and intensities that could explain LA PVmax because significant associations were not found.

Physical activity (PA) is essential for development of fundamental motor skills (FMS) in children, but it is uncertain which PA intensities are most influential. A limitation to current evidence is the reliance on analytic approaches that cannot handle collinearity. The aim of this study was to determine the PA signature related to FMS in preschoolers, by investigating the association pattern for the whole spectrum of PA intensities using multivariate pattern analysis. We used a sample of N = 1081 Norwegian preschoolers (4.7 yr; 52% boys) who provided valid accelerometer (ActiGraph GT3X+) and FMS data (TGMD-3, modified version). We created 33 PA variables (from 0-100 to ≥15,000 counts per minute [cpm]), and used partial least squares regression to analyse the associations between PA and FMS, after controlling for potential covariates. PA was positively associated with locomotor- and object control skills (explained variances for vertical axes; R 2  = 9.7% and 3.9%, respectively). The strongest associations were found for PA between 5000-8000 cpm. No association pattern was found for PA and balance skills. This study is the first to determine the multivariate PA intensity signature related to FMS. This approach shows that PA within the vigorous range is strongest related to FMS in preschoolers. Abbreviations: FMS: fundamental motor skills; PA: Physical activity; TPA: total physical activity; SED: Sedentary behaviour; LPA: Light physical activity; MPA: Moderate physical activity; VPA: Vigorous physical activity; MVPA: Moderate-to-vigorous physical activity; Min: minutes; cpm: counts per minute; SD: standard deviation; SES: Socioeconomic status; BMI: Body Mass Index.

Objective: Lumbar mobilization is a standard intervention for the management of low back pain, yet ways to quantify lumbar mobilization are limited. An inertial measurement unit (IMU) is a small and inexpensive device that can be used to quantify lumbar mobilization. The objective of this study was to determine the validity and reliability of an IMU in measuring the amplitude of displacement of a clinician's hand movement during oscillatory lumbar mobilization., Methods: An IMU was secured on a clinician's hand during application of mobilization forces at the L4 segment of 16 healthy participants. The validity of the IMU was tested against common laboratory methods of measurements (force plate and motion capture system). The reliability of the IMU measurements was determined between 2 clinicians (inter-rater reliability) and between 2 sessions (intra-rater reliability) by calculating percent error of measurement (%e) and limits of agreement (LOA). The reliability was considered high when |%e| ≤ 10% and |LOA| ≤ 20%; moderate when |%e| 10% to 20% and |LOA| 21% to 40%; and non-acceptable when |%e| > 20% and |LOA| > 40%., Results: The IMU measurements had high correlation with the force plate measurements (r s  = 0.94) and high agreement with the motion capture system measurements (%e = 4%, LOA = -11% and 20%). Both the inter-rater reliability (%e = 6%, LOA = -25% and 37%) and the intrarater reliability (%e = -1%, LOA = -29% and 27%) of IMU measurements were moderate., Conclusion: The IMU seems to be a valid device to measure the amplitude of a clinician's hand movement. The moderate reliability found in this study may not reflect poor reliability of the IMU as much as inconsistency in reapplication of lumbar mobilization., (Copyright © 2020. Published by Elsevier Inc.)

Gómez-Carmona, CD, Bastida-Castillo, A, González-Custodio, A, Olcina, G, and Pino-Ortega, J. Using an inertial device (WIMU PRO) to quantify neuromuscular load in running: reliability, convergent validity, and influence of type of surface and device location. J Strength Cond Res 34(2): 365-373, 2020-Currently, the use of accelerometers in sport is increasing, and thus, the devices are required to be valid and reliable. This study tested (a) the reliability and validity of WIMU PRO accelerometers to measure PlayerLoad (PL) and (b) the influence of speed, inertial device location, and type of surface where the incremental test is performed. Twenty resistance-trained men (age: 27.32 ± 6.65 years; height: 1.74 ± 0.03 m; body mass: 68.96 ± 4.37 kg; and body mass index: 22.76 ± 1.11 kg·m) volunteered to participate in the study that lasted 5 weeks. Four progressive incremental tests were performed in treadmill and athletic track conditions. External load variable (PL) and physiological variables (heart rate [HR] and SmO2) were recorded by 4 WIMU PRO inertial devices (scapulae, center of mass, knee, and ankle), a GARMIN HR band, and a MOXY near-infrared spectroscopy device, respectively. High reliability was found on both types of surface, showing the best values at the ankle (treadmill: intraclass correlation coefficient [ICC] = 0.99, coefficient of variation [CV] = 4.65%; track: ICC = 0.96, CV = 6.54%). A nearly perfect convergent validity was shown with HRAVG (r = 0.99) and a moderate one with SmO2 (r = -0.69). Significant differences in the PL variable between surfaces were reported in all locations except the scapulae (p = 0.173), and the higher values were found on the track. In the analysis per location, the ankle location reported the highest values at all speeds and on the 2 surfaces analyzed. Assessment needs to be individualized, due to the great variability of gait biomechanics among subjects. The accelerometer location should be chosen according to the purpose of the measurement, with the ankle location being recommended for neuromuscular load analysis in running.

Accelerometers are often placed on the tibia to measure segmental accelerations, and external mechanical load during running. However, in applied sport settings it is sometimes preferable to place accelerometers on the dorsal foot to avoid tibial impact injuries. This study aimed to quantify the differences in accelerations measured at the dorsal foot compared with the distal tibia during running. Sixteen recreationally active participants performed a sprint protocol on a non-motorised treadmill. Accelerometers were positioned bilaterally on the medial tibia (TIB Left and TIB Right ), and bilateral dorsal foot surfaces (DOR Left and DOR Right ). Continuous acceleration signal waveform analysis was performed using one-dimensional statistical parametric mapping (1DSPM). Resultant accelerations were greater for DOR Left than TIB Left for 60% of the gait cycle ( p < 0.001) and greater for DOR Right than TIB Right for 50% of the gait cycle ( p < 0.003). The larger accelerations at the dorsal foot than the tibia can be explained by movement at the ankle joint, and the placement location relative to the hip. The dorsal foot location can be used to effectively measure accelerations and external mechanical load when it is not feasible to place the accelerometer on the tibia, however results between the two locations should not be compared.

Resolving intra-stride accelerations from training and game data routinely collected by athlete tracking devices is rarely attempted, even though these data can provide important insights into the physical condition of athletes. This study describes in detail a novel analysis tool which uses accelerometer-based measures of step variability to assess longitudinal consistency of stride technique. The performance of the analysis tool was examined by combining results from the analysis of data collected over the course of an Australian Football League season with instances of missed or modified training. Results demonstrated increased gait-related trunk acceleration variability when training was modified due to load or accumulated game and training activity (p = 0.02) and reduced trunk acceleration variability in the week following a training modification due to any injury described as "ankle", "heel", or any other description that would loosely refer to a joint or bone in the leg (p = 0.01). These results support the findings of recent research which used a slightly modified version of the same analysis tool to demonstrate an increase in gait-related trunk acceleration variability with the increased acute physical load.

As long-distance races have substantially increased in popularity over the last few years, the improvement of training programs has become a matter of concern to runners, coaches and health professionals. Triaxial accelerometers have been proposed as a one of the most accurate tools to evaluate physical activity during free-living conditions. In this study, eighty-eight recreational marathon runners, aged 30-45 years, completed a marathon wearing a GENEActiv accelerometer on their non-dominant wrist. Energy consumed by each runner during the marathon was estimated based on both running speed and accelerometer output data, by applying the previously established GENEActiv cut-points for discriminating the six relative-intensity activity levels. Since accelerometry allowed to perform an individualized estimation of energy consumption, higher interpersonal differences in the number of calories consumed by a runner were observed after applying the accelerometry-based approach as compared to the speed-based method. Therefore, pacing analyses should include information of effort intensity distribution in order to adjust race pacing appropriately to achieve the marathon goal time. Several biomechanical and physiological parameters (maximum oxygen uptake, energy cost of running and running economy) were also inferred from accelerometer output data, which is of great value for coaches and doctors.

Purpose: The aim of this study was to analyze the effect of epoch length on intensity classification during continuous and intermittent activities., Methods: Ten active students exercised under controlled conditions on a treadmill for four 5-min bouts by combining two effort intensities (running and walking) and two physical activity (PA) patterns (continuous or intermittent). The testing session was designed to generate a known level of moderate to vigorous PA (MVPA) for each condition. These PA levels were used as criterion measures to compare with the accelerometer measures. Data obtained from the accelerometer were reintegrated into 1-sec, 10-sec, 30-sec and 60-sec epochs. Equivalence testing was used to examine measurement agreements between MVPA values obtained with the different epochs and the reference values. Mean absolute percent errors (MAPE) were also calculated to provide an indicator of overall measurement error., Results: During the intermittent conditions, only the value obtained with the 1-sec epoch was significantly equivalent to the reference value. With longer epochs the difference increased for both intermittent conditions but in an opposite way: with longer epochs, MVPA decreased during walking but increased during running. Regarding the measurement accuracy, the pattern of variations according to the epoch length selected during the intermittent conditions was identical between walking and running: MAPE increased with the increase in epoch length. MAPE remained low only for the 1-sec epoch (7.6% and 2.7% for walking and running, respectively), increased at 31.3% and 34% for the 10-sec epoch and until near 100% with the 30- and 60-sec epoch lengths., Conclusion: This study highlighted the misclassification of exercise intensity based on accelerometer measurement and described for the first time the extent and the direction of this misclassification. Moreover, we can confirm that the shorter epochs are more accurate to measure the real exercise intensity during intermittent PA whatever the intensity., Competing Interests: The authors have declared that no competing interests exist.

For canid species, scent marking plays a critical role in territoriality, social dynamics, and reproduction. However, due in part to human dependence on vision as our primary sensory modality, research on olfactory communication is hampered by a lack of tractable methods. In this study, we leverage a powerful biologging approach, using accelerometers in concert with GPS loggers to monitor and describe scent-marking events in time and space. We performed a validation experiment with domestic dogs, monitoring them by video concurrently with the novel biologging approach. We attached an accelerometer to the pelvis of 31 dogs (19 males and 12 females), detecting raised-leg and squat posture urinations by monitoring the change in device orientation. We then deployed this technique to describe the scent marking activity of 3 guardian dogs as they defend livestock from coyote depredation in California, providing an example use-case for the technique. During validation, the algorithm correctly classified 92% of accelerometer readings. High performance was partly due to the conspicuous signatures of archetypal raised-leg postures in the accelerometer data. Accuracy did not vary with the weight, age, and sex of the dogs, resulting in a method that is broadly applicable across canid species' morphologies. We also used models trained on each individual to detect scent marking of others to emulate the use of captive surrogates for model training. We observed no relationship between the similarity in body weight between the dog pairs and the overall accuracy of predictions, although models performed best when trained and tested on the same individual. We discuss how existing methods in the field of movement ecology can be extended to use this exciting new data type. This paper represents an important first step in opening new avenues of research by leveraging the power of modern-technologies and machine-learning to this field.

Background: Gait disturbances are typical of persons with idiopathic normal pressure hydrocephalus (iNPH) without signs distinctive from other neurodegenerative and vascular conditions. Cerebrospinal fluid tap-test (CSF-TT) is expected to improve the motor performance of iNPH patients and is a prognostic indicator in their surgical management. This observational prospective study aims to determine which spatio-temporal gait parameter(s), measured during instrumented motor tests, and clinical scale(s) may provide a relevant contribution in the evaluation of motor performance pre vs. post CSF-TT on iNPH patients with and without important vascular encephalopathy., Methods: Seventy-six patients (20 with an associated vascular encephalopathy) were assessed before, and 24 and 72 h after the CSF-TT by a timed up and go test (TUG) and an 18 m walking test (18 mW) instrumented using inertial sensors. Tinetti Gait, Tinetti Balance, Gait Status Scale, and Grading Scale were fulfilled before and 72 h after the CSF-TT. Stride length, cadence and total time were selected as the outcome measures. Statistical models with mixed effects were implemented to determine the relevant contribution to response variables of each quantitative gait parameter and clinical scales., Results and Conclusion: From baseline to 72 h post CSF-TT patients improved significantly by increasing cadence in 18 mW and TUG (on average of 1.7 and 2.4 strides/min respectively) and stride length in 18 mW (on average of 3.1 cm). A significant reduction of gait apraxia was reflected by modifications in double support duration and in coordination index. Tinetti Gait, Tinetti Balance and Gait Status Scale were able to explain part of the variability of response variables not covered by instrumental data, especially in TUG. Grading Scale revealed the highest affinity with TUG total time and cadence when considering clinical scales alone. Patients with iNPH and an associated vascular encephalopathy showed worst performances compared to pure iNPH but without statistical significance. Gait improvement following CSF-TT was comparable in the two groups. Overall these results suggest that, in order to augment CSF-TT accuracy, is key to assess the gait pattern by analyzing the main spatio-temporal parameters and set post evaluation at 72 h., Trial Registration: Approved by ethics committee: CE 14131 23/02/2015.

Upper trunk (UT) kinematics in runners and its relationship with lower limbs has been poorly investigated, although it is acknowledged that dynamic stability of the upper body is a primary objective of human locomotion. This study aimed to explore UT kinematics according to gender and level of training and in relation to lower limb run patterns described through the presence of: overstriding, crossover, excessive protonation, and pelvic drop. Lower body variables chosen to describe running pattern were those that are frequently modified during gait-retraining with the goal of reducing injury risk. Eighty-seven recreational runners (28 females and 59 males, age 41 ± 10 years) performed a one minute run test on a treadmill at self-selected speed. UT kinematics was measured using an inertial measurement unit, while run features were assessed through an optoelectronic system and video analysis. Accelerations and root-mean-square on mediolateral and anteroposterior axes, normalized using the vertical component of the acceleration, were estimated to describe UT stability. Results showed no significant differences in the normalized UT acceleration root-mean-square according to gender and level of training as well as according to the presence of overstriding, crossover, and excessive protonation. The only running strategy studied in this work that showed a significant relationship with UT stability was the presence of excessive pelvic drop. The latter was significantly associated ( p =0.020) to a decrease in the normalized acceleration root-mean-square along the mediolateral direction. Although the excessive pelvic drop seemed to have a positive effect in stabilizing the upper body, concerns remain on the effect of a poor control of the pelvis on the biomechanics of lower limbs. Results obtained confirm the hypothesis that the lower body is able to respond to varying impact load conditions to maintain UT stability., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2020 Laura Simoni et al.)

Excessive forces and/or loading rates during landing may place ballet dancers at risk for overuse injury. The ability to estimate and monitor the landing forces of ballet dancers could help to improve injury prevention and rehabilitation; however, force platforms are not conducive to testing outside of a laboratory. Fortunately, it may be possible to indirectly assess landing forces via a wearable accelerometer. The purposes of this study were to examine the relationship between impact accelerations, recorded via a pelvis-worn accelerometer, and the peak forces and loading rates during performance of a common ballet manoeuvre, and to examine if a wearable accelerometer is sensitive to fatigue-related changes in landing forces. Fifteen ballet dancers continuously performed a ballet manoeuvre until self-determined exhaustion while impact accelerations and landing forces were simultaneously recorded using an accelerometer and force platforms. We observed very strong, positive relationships between the impact accelerations and the peak forces and loading rates during the landings. In addition, the changes in impact accelerations with fatigue paralleled the changes in the peak forces and loading rates. As a result, it appears that a wearable accelerometer could be used to estimate and monitor landing forces in ballet dancers.

Introduction: Evaluation of total energy expenditure (TEE) and physical activity level (PAL) is important for treatment of patients with type 2 diabetes mellitus (T2DM). However, the validity of accelerometers (ACC) and physical activity questionnaires (PAQ) for estimating TEE and PAL remains unknown in elderly populations with T2DM. We evaluated the accuracy of TEE and PAL results estimated by an ACC (TEEACC and PALACC) and a PAQ (TEEPAQ and PALPAQ) in elderly patients with T2DM., Methods: Fifty-one elderly patients with T2DM (aged 61-79 years) participated in this study. TEEACC was calculated with PALACC using a triaxial ACC (Active style Pro HJA-750c) over 2 weeks and predicted basal metabolic rate (BMR) by Ganpule's equation. TEEPAQ was estimated using predicted BMR and the PALPAQ from the -Japan Public Health Center Study-Long questionnaire. We compared the results to TEEDLW measured with the doubly labeled water (DLW) method and PALDLW calculated with BMR using indirect calorimetry., Results: TEEDLW was 2,165 ± 365 kcal/day, and TEEACC was 2,014 ± 339 kcal/day; TEEACC was strongly correlated with TEEDLW (r = 0.87, p < 0.01) but significantly underestimated (-150 ± 183 kcal/day, p < 0.05). There was no significant difference in TEEPAQ and TEEDLW (-49 ± 284 kcal/day), while the range of difference seemed to be larger than TEEACC. PALDLW, PALACC, and PALPAQ were calculated to be 1.71 ± 0.17, 1.69 ± 0.16, and 1.78 ± 0.24, respectively. -PALACC was strongly correlated with PALDLW (r = 0.71, p < 0.01), and there was no significant difference between the 2 values. PALPAQ was moderately correlated with PALDLW (r = 0.43, p < 0.01) but significantly overestimated. Predicted BMR was significantly lower than the BMR -measured by indirect calorimetry (1,193 ± 186 vs. 1,262 ± 155 kcal/day, p < 0.01)., Conclusions: The present ACC and questionnaire showed acceptable correlation of TEE and PAL compared with DLW method in elderly patients with T2DM. Systematic errors in estimating TEE may be improved by the better equation for predicting BMR., (© 2020 S. Karger AG, Basel.)

Application of machine learning techniques has the potential to yield unseen insights into movement and permits visualisation of complex behaviours and tangible profiles. The aim of this study was to identify profiles of relative motor competence (MC) and movement behaviours in pre-school children using novel analytics. One-hundred and twenty-five children (4.3 ± 0.5y, 1.04 ± 0.05 m, 17.8 ± 3.2 kg, BMI: 16.2 ± 1.9 kg . m 2 ) took part in this study. Measures included accelerometer-derived 24-h activity, MC (Movement Assessment Battery for Children second edition), height, weight and waist circumference, from which zBMI were derived. Self-Organised Map (SOM) analysis was used to classify participants' profiles and a k-means cluster analysis was used to classify the neurons into larger groups according to the input variables. These clusters were used to describe the individuals' characteristics according to their MC and PA compositions. The SOM analysis indicated five profiles according to MC and PA. One cluster was identified as having both the lowest MC and MVPA (profile 2), whilst profiles 4 and 5 show moderate-high values of PA and MC. We present a novel pathway to profiling complex tenets of human movement and behaviour, which has never previously been implemented in pre-school children, highlighting that the focus should change from obesity monitoring, to "moving well". Abbreviations: MC: Motor competence; PA: Physical activity; MVPA: Moderate-to-vigorous physical activity; SOM: Self-organized map; BMI: Body mass index; MABC2: Movement assessment battery for children 2 nd edition; MANOVA: Multiple analysis of variance.

Background: Physical activity, assessed by accelerometers, has been proposed as a quantitative outcome measure for patients with DMD, but research is limitedObjective:To assess the total amount and patterns of physical activity in patients with DMD using accelerometers., Methods: Physical activity was assessed in patients with DMD (n = 49, 13.6±4.0-year-old) and age- and sex-matched healthy controls (n = 15, 14.0±2.3-year-old) using wrist- and ankle-worn accelerometers. To assess the amount of activity, accelerometer recordings were converted into acceleration estimates (counts/min). Patterns of activity were assessed as the time that participants spent in sedentary, low-intensity, and moderate-to-vigorous physical activity categories. The sedentary category was divided into three (sedentary -1, -2, and -3) and the low-intensity into two (low-intensity-1, and -2) subcategories., Results: Physical activity across intensity categories differed between study groups (p < 0.001). Patients with DMD spent on average 98.8% of their daytime in the sedentary and low-intensity categories. Compared to non-ambulatory, ambulatory patients spent more time in sedentary-3 and low-intensity-2 subcategories (p < 0.001). Amount of activity was lower in all patients than controls (p < 0.05) and in non-ambulatory than ambulatory patients and controls (p < 0.001), but similar between ambulatory patients and controls. Activity measures in patients were significantly affected by age and ambulation status (p < 0.05) but not corticosteroid use., Conclusion: Patients with DMD spent most of their daytime in sedentary and low-intensity activities. Dividing these intensities into three and two subcategories, respectively, allows better characterization of activity patterns in DMD. Ambulation status and age but not corticosteroid use affected activity measures in patients with DMD.

Balloch, AS, Meghji, M, Newton, RU, Hart, NH, Weber, JA, Ahmad, I, and Habibi, D. Assessment of a novel algorithm to determine change-of-direction angles while running using inertial sensors. J Strength Cond Res 34(1): 134-144, 2020-The ability to detect and quantify change-of-direction (COD) movement may offer a unique approach to load-monitoring practice. Validity and reliability of a novel algorithm to calculate COD angles for predetermined COD movements ranging from 45 to 180° in left and right directions was assessed. Five recreationally active men (age: 29.0 ± 0.5 years; height: 181.0 ± 5.6 cm; and body mass: 79.4 ± 5.3 kg) ran 5 consecutive predetermined COD trials each, at 4 different angles (45, 90, 135, and 180°), in each direction. Participants were fitted with a commercially available microtechnology unit where inertial sensor data were extracted and processed using a novel algorithm designed to calculate precise COD angles for direct comparison with a high-speed video (remotely piloted, position-locked aircraft) criterion measure. Validity was assessed using Bland-Altman 95% limits of agreement and mean bias. Reliability was assessed using typical error (expressed as a coefficient of variation [CV]). Concurrent validity was present for most angles. Left: (45° = 43.8 ± 2.0°; 90° = 88.1 ± 2.0°; 135° = 136.3 ± 2.1°; and 180° = 181.8 ± 2.5°) and Right: (45° = 46.3 ± 1.6°; 90° = 91.9 ± 2.2°; 135° = 133.4 ± 2.0°; 180° = 179.2 ± 5.9°). All angles displayed excellent reliability (CV < 5%) while greater mean bias (3.6 ± 5.1°, p < 0.001), weaker limits of agreement, and reduced precision were evident for 180° trials when compared with all other angles. High-level accuracy and reliability when detecting COD angles further advocates the use of inertial sensors to quantify sports-specific movement patterns.

Sedentary time (ST) has been inconsistently associated with adiposity and cardiorespiratory fitness in children in previous studies. We studied cross-sectional associations of ST, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) with fat mass index (FMI) and cardiorespiratory fitness (estimated VO 2 max). Associations were evaluated with and without considering pattern of ST by bout length. We measured ST and activity by a wrist-worn accelerometer, FMI by bioelectrical impedance, and VO 2 max by Pacer test in 443 children (51.2% girls, 10.2 ± 0.6 years). Isotemporal substitution regression models estimated the effects of substituting ST, LPA, and MVPA on FMI and VO 2 max. Further models repeated analyses separating ST into short (<10 min) and long (≥10 min) bouts. Only replacing ST or LPA with MVPA was consistently associated with lower FMI and greater VO 2 max. When separated by bout length, only one unique association was found where replacing long ST bouts with short ST bouts was associated with lower FMI in girls only. In conclusion, activity pattern is associated with adiposity in girls and fitness in boys and girls. Separating ST into long and short ST bouts may be of minimal importance when assessing associations with adiposity and fitness using wrist-worn accelerometry in children.

Balance and mobility issues are common non-resolving symptoms following mild traumatic brain injury (mTBI). Current approaches for evaluating balance and mobility following an mTBI can be subjective and suboptimal as they may not be sensitive to subtle deficits, particularly in those with chronic mTBI. Wearable inertial measurement units (IMU) allow objective quantification of continuous mobility outcomes in natural free-living environments. This study aimed to explore free-living mobility (physical activity and turning) of healthy and chronic mild traumatic brain injury (mTBI) participants using a single IMU. Free-living mobility was examined in 23 healthy control (48.56 ± 23.07 years) and 29 symptomatic mTBI (40.2 ± 12.1 years) participants (average 419 days post-injury, persistent balance complaints) over 1 week, using a single IMU placed at the waist. Free-living mobility was characterized in terms of macro (physical activity volume, pattern and variability) and micro-level (discrete measures of turning) features. Macro-level outcomes showed those with chronic mTBI had similar quantities of mobility compared with controls. Micro-level outcomes within walking bouts showed that chronic mTBI participants had impaired quality of mobility. Specifically, people with chronic mTBI made larger turns, had longer turning durations, slower average and peak velocities (all p  < 0.001), and greater turn variability compared with controls. Results highlighted that the quality rather than quantity of mobility differentiated chronic mTBI from controls. Our findings support the use of free-living IMU continuous monitoring to enhance understanding of specific chronic mTBI-related mobility deficits. Future work is required to develop an optimal battery of free-living measures across the mTBI spectrum to aid application within clinical practice.

Objectives: In this study, we investigated the differences in strategic constructs of the Transtheoretical Model (TTM) across accelerometer-determined sedentary time. Methods: A total of 201 college students participated in a TTM questionnaire for sedentary behavior and wore an accelerometer for 7 consecutive days to assess sedentary time. Multivariate analyses of variances (MANOVA) with post hoc pairwise comparisons were conducted to determine mean differences in the strategic constructs across quintiles of sedentary time. Tests for linear trends were conducted using orthogonal polynomial coefficients. Results: Compared with participants in higher quintiles of sitting time, 8 out of 10 processes of change (eg, mostly consciousness raising [η² p = .09]) were used significantly more frequently by those in the lowest quintile (p < .05) with negative linear trends (p trend < .05). We found no statistically significant differences in the constructs of self-efficacy and decisional balance across the quintiles. Conclusions: Based on this preliminary analysis it appears that several intervention methods such as awareness raising, incentivization, self-motivation, and social norm building would be more beneficial to reduce sitting time or to protect their current sitting time from relapse.

Background: Kinematic gait analysis employing multi-segment foot models has been mainly conducted in laboratories by means of optical motion capture systems. This type of process requires considerable setup time and is constrained by a limited capture space. A procedure involving the use of multiple inertial measurement units (IMUs) is proposed to overcome these limitations., Research Question: This study presents a new approach for the estimation of the trajectories of a multi-segment foot model by means of multiple IMUs., Methods: To test the proposed method, a system consisting of four IMUs attached to the shank, heel, dorsum and toes segments of the foot, was considered. The performance of the proposed method was compared to that of a conventional method using IMUs adopted from the literature. In addition, an optical motion capture system was used as a reference to assess the performance of the implemented methods., Results: Employing the suggested method, all trajectory directions of the shank, heel and dorsum segments, as well as the Z (yaw) direction of the toes segment, have exhibited an error reduction varying between 8% and 55%. However, X (roll) and Y (pitch) direction of the toes segment presented an error increase of 17% and 26%, respectively. The estimation of the vertical displacement, corresponding to the foot clearance, was improved for all segments, resulting in a final mean accuracy and precision of 3.5 ± 2.8 cm, 2.7 ± 2.1 cm, 0.8 ± 0.7 cm and 1.1 ± 0.9 cm for the shank, heel, dorsum and toes segments, respectively., Significance: It has been demonstrated that as an alternative to tracking each foot segment separately, the fusion of multiple IMU measurements using kinematic equations, considerably improves the estimated trajectories, especially when considering vertical foot displacements. The proposed method could complement the use of smaller and cheaper sensors, while still matching the same performance of other published methods, making the suggested approach very attractive for real life applications., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Objective: Tools to measure physical activity, such as pedometers, have become more prevalent and attracted popular interest in recent years. Despite this trend, research has not yet quantified pedometer-measured physical activity among Emergency Physicians. This study aims to provide the first characterization of physical activity among on-duty Emergency Physicians in terms of step count., Methods: Emergency Physicians wore Empatica E4 research-grade accelerometers while performing routine clinical care in the Emergency Department. A publicly available algorithm was used to estimate the number of steps taken., Results: Fifty-one Emergency Physicians, including thirty-four residents and seventeen attending physicians, contributed over 1500 h of accelerometer data. On average, this cohort took 577 steps per hour (SD: 72.6), totaling 4950 steps per recorded shift (SD: 737.8), which is approximately 2.6 miles (SD: 0.31). Residents walked more than attending physicians (595.9 steps per hour (SD: 99.7) vs 563.0 steps per hour (SD: 89.0), respectively; p = 0.02)., Conclusion: The average emergency physician in this cohort walked roughly half the daily recommended number of steps during their recorded shift. Residents walk significantly more than attending physicians., (Copyright © 2019 Elsevier Inc. All rights reserved.)

This study investigated whether using an inertial measurement unit (IMU) can identify different walking conditions, including level walking (LW), descent (DC) and ascent (AC) slope walking as well as downstairs (DS) and upstairs (US) walking. Thirty healthy participants performed walking under five conditions. The IMU was stabilised on the exterior of the left shoe. The data from IMU were used to establish a customised prediction model by cut point and a prediction model by using deep learning method. The accuracy of both prediction models was evaluated. The customised prediction model combining the angular velocity of dorsi-plantar flexion in the heel-strike (HS) and toe-off (TO) phases can distinctly determine real conditions during DC and AC slope, DS, and LW (accuracy: 86.7-96.7%) except for US walking (accuracy: 60.0%). The prediction model established by deep learning using the data of three-axis acceleration and three-axis gyroscopes can also distinctly identify DS, US, and LW with 90.2-90.7% accuracy and 84.8% and 82.4% accuracy for DC and AC slope walking, respectively. In conclusion, inertial measurement units can be used to identify walking patterns under different conditions such as slopes and stairs with customised prediction model and deep learning prediction model.

Objective: The complex nature of Parkinson's disease (PD) makes difficult to rate its severity, mainly based on the visual inspection of motor impairments. Wearable sensors have been demonstrated to help overcoming such a difficulty, by providing objective measures of motor abnormalities. However, up to now, those sensors have been used on advanced PD patients with evident motor impairment. As a novelty, here we report the impact of wearable sensors in the evaluation of motor abnormalities in newly diagnosed, untreated, namely de novo, patients., Methods: A network of wearable sensors was used to measure motor capabilities, in 30 de novo PD patients and 30 healthy subjects, while performing five motor tasks. Measurement data were used to determine motor features useful to highlight impairments and were compared with the corresponding clinical scores. Three classifiers were used to differentiate PD from healthy subjects., Results: Motor features gathered from wearable sensors showed a high degree of significance in discriminating the early untreated de novo PD patients from the healthy subjects, with 95% accuracy. The rates of severity obtained from the measured features are partially in agreement with the clinical scores, with some highlighted, though justified, exceptions., Conclusion: Our findings support the feasibility of adopting wearable sensors in the detection of motor anomalies in early, untreated, PD patients., Significance: This work demonstrates that subtle motor impairments, occurring in de novo patients, can be evidenced by means of wearable sensors, providing clinicians with instrumental tools as suitable supports for early diagnosis, and subsequent management.