Cumulative Workload Research Articles

Cumulative workload comparison between lifting routes of heavy lifting tasks from a biomechanical perspective

Heavy lifting tasks at work sites can cause musculoskeletal disorders. One strategy to avoid such disorders is to choose a work motion with low cumulative physical workload during the work. This study investigated whether the cumulative workload between different work motions can be compared from a biomechanical perspective. The work motions were analyzed biomechanically by a digital human model. The cumulative workload was calculated using the time integrated value of the product of the torque and the amount of motion at each joint, i.e., an index proposed by a previous study. The results showed that the lifting operation with the shortest route is not always optimal in terms of cumulative workload on the whole body. This indicates the importance of selecting work motions based on a cumulative workload index obtained by mechanical analysis.

A numerical evaluation of real-time workloads for ramp controller through optimization of multi-type feature combinations derived from eye tracker, respiratory, and fatigue patterns.

Ramp controllers are required to manage their workloads effectively while handling complex operational tasks, a crucial part of improving aviation safety. The ability to detect their instantaneous workload is vital for ensuring operational effectiveness and preventing hazardous incidents. This paper introduces a novel methodology aimed at enhancing the evaluation of the ramp controller's cumulative workload by incorporating and optimizing the feature combination from eye movement, respiratory, and fatigue characteristics. Specifically, a 90-minute simulated experiment related to ramp control tasks, using real data from Shanghai Hongqiao Airport, is conducted to collect multi-type data from 8 controllers. Following data construction and the extraction of multi-type, the workloads of all samples are categorized through unsupervised learning. Subsequently, supervised learning techniques are used to calculate feature weights and train classifiers after data alignment. The optimal feature combination is established by calculating feature weights, and the best classification accuracy is over 98%, achieved by the KNN classifier. Furthermore, numerical evaluation and threshold calculations for different workload levels are interpreted. It is promising to provide insights into future works towards human-centered data construction, processing, and interpretation to promote the progress of workload assessment within the aviation industry.

Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis.

Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players from physiological and mechanical load variables. Twenty-five professional male soccer players with a first-time, non-contact muscle injury were included in this study. Recordings of external load (speed, distance, and acceleration/deceleration data) and internal load (heart rate) were obtained during all training sessions and official matches over a 4-year period. Machine learning model training and evaluation features were calculated for each of nine different metrics for a 28-day period prior to the injury and an equal-length baseline epoch. The acute surge in the values of each workload metric was quantified by the deviation of maximum values from the average, while the variations of cumulative workload over the last four weeks preceding injury were also calculated. Seven features were selected by the model as prominent estimators of injury incidence. Three of the features concerned acute load deviations (number of sprints, training load score-incorporating heart rate and muscle load- and time of heart rate at the 90-100% of maximum). The four cumulative load features were (total distance, high speed and sprint running distance and training load score). The accuracy of the muscle injury risk assessment model was 0.78, with a sensitivity of 0.73 and specificity of 0.85. Our model achieved high performance in injury risk detection using a limited number of training load variables. The inclusion, for the first time, of heart rate related variables in an injury risk assessment model highlights the importance of physiological overload as a contributor to muscle injuries in soccer. By identifying the important parameters, coaches may prevent muscle injuries by controlling surges of training load during training and competition.

Increased Utilization of Pitchers in NCAA Women’s Softball Compared to Men’s Baseball

BACKGROUND: Pitch count and rest time guidelines are widely employed in youth baseball to mitigate injury risk among pitchers. Similar recommendations are lacking in youth softball, despite evidence of potential overuse injuries from the underhand windmill pitching motion. This disparity in regulation may heighten injury susceptibility. Additionally, the composition of collegiate softball and baseball rosters differs significantly, with softball teams typically featuring fewer pitchers. This discrepancy may lead to increased pitching demands for individual softball pitchers, potentially exacerbating injury risks. Therefore, understanding the relationship between the number of pitchers, game appearances, and injury incidence is crucial for informing injury prevention strategies in both baseball and softball. We hypothesize that individual softball pitchers will have significantly more game appearances per season and pitch more innings per game and season as compared to baseball pitchers, and that softball rosters will average significantly fewer pitchers per team than baseball rosters. METHODS: A retrospective analysis was conducted of National Collegiate Athletic Association Division I softball and baseball College World Series teams from 2016 to 2021. Individual data consisting of game appearances and season innings pitched facilitated calculations for innings per game. Team data included the number of athletes and pitchers per team. Statistical analysis comparing baseball and softball team continuous variables utilized Student’s t-test. Individual data, characterized by non-normal distribution, was evaluated with the Wilcoxon-Mann-Whitney Test. A significance level of p < 0.05 was applied to all analyses. RESULTS: Softball pitchers had a significantly greater number of innings pitched per season (median 84.1 (21.0-143.2) vs 29.2 (10.0-56.1) p<0.0001), game appearances per season (median 23.0 (10.0-32.0) vs 16.0 (9.0-21.0), p<0.0001), and innings pitched per game (median 3.6 (2.2-4.7) vs 1.6 (1.1-2.8), p<0.0001). Baseball rosters averaged significantly more total players (35.7 ± 3.9 vs 21.5 ± 2.8, p<0.0001), more pitchers (15.7 ± 2.3 vs 4.5 ± 1.1, p<0.0001), and a greater percentage of their roster was made up of pitchers (44.2% ± 6.4% vs 21.0% ± 4.4%, p<0.0001). CONCLUSION: This study highlights a notable difference in collegiate softball pitcher utilization compared to baseball pitchers. Fewer pitchers on softball teams led to a higher cumulative workload per pitcher, with increased innings pitched per season, per game, and more game appearances. While the relationship between pitch count and injury risk is established in baseball, further research is needed to understand its implications for softball pitchers.

Frequency of breaks, amount of muscular rest, and sustained muscle activity related to neck pain in a pooled dataset.

Neck pain remains a persistent challenge in modern society and is frequently encountered across a wide range of occupations, particularly those involving repetitive and monotonous tasks. It might be expected that patterns of trapezius muscle activity at work, characterized by few breaks and prolonged periods of sustained muscle activity, are linked to neck pain. However, previous cross-sectional studies have generally failed to establish a definitive association. While some longitudinal studies have suggested that extended periods of heightened muscle activity could be a risk factor for neck pain, these findings often relied on limited participant numbers or specific professional groups. This study aimed to investigate the relationship between trapezius muscle activity and neck pain by pooling data from seven Scandinavian research institutes encompassing a diverse range of occupational backgrounds. Electromyographic (EMG) data for the upper trapezius muscle, collected during working hours, were coupled with questionnaire responses pertaining to neck pain, individual characteristics, and potential confounding variables for a total of 731 subjects. Additionally, longitudinal data from 258 subjects were available. The various EMG datasets were consolidated into a standardized format, and efforts were made to harmonize inquiries about neck pain. Regression analyses, adjusting for sex and height, were conducted to explore the associations between muscle activity variables and neck pain. An exposure index was devised to quantify the cumulative neck load experienced during working hours and to differentiate between various occupational categories. The cross-sectional data displayed a distinct pattern characterized by positive associations for brief periods of sustained muscle activity (SUMA) and negative associations for prolonged SUMA-periods and neck pain. The longitudinal data exhibited a contrasting trend, although it was not as pronounced as the cross-sectional findings. When employing the exposure index, notable differences in cumulative muscle load emerged among occupational groups, and positive associations with longitudinal neck pain were identified. The results suggest that individuals with neck pain experience higher cumulative workloads and extended periods of muscle activity over the long term. In the short term, they appear to compensate by taking frequent short breaks, resulting in a lower cumulative workload. Regardless of their occupation, it is crucial to distribute work breaks throughout the workday to ensure that the cumulative load remains manageable.

An Exploratory Investigation Evaluating the Impact of Fatigue-Induced Stride Length Compensations on Ankle Biomechanics among Skilled Baseball Pitchers.

Altered propulsive and bracing ground reaction forces from lower-body fatigue significantly impact stride length to increase weakness in dynamic elbow stabilizers and risk of medial elbow injury in baseball pitchers. This work investigated altered stride length on three-dimensional ankle joint dynamics to illustrate fatigue-induced changes in ankle motion that can also be impacted by coaching errors. Nineteen pitchers (15 collegiate and 4 high school) were randomized in a crossover design study that encouraged fatigue by throwing two simulated 80-pitch games at ±25% of their desired stride length. An integrated motion-capture system with two force plates and radar gun tracked each throw. Retrospective analysis using pairwise comparisons, including effect size calculations, were undertaken to identify differences in ankle dynamics between stride length conditions for both the drive and stride leg. Longer strides were found to be more effective in drive ankle propulsion and stride-bracing mechanics. Conversely, shorter strides delayed bracing dynamics by demonstrating continued drive ankle plantar flexion moments after stride-foot contact to extend pitchers' time in propulsion (p < 0.001, d > 0.8). Additionally, heightened braking effects were seen during the acceleration phase of throwing with greater stride knee extension power when pitching with shorter strides (p < 0.001, d > 0.8). The knowledge gained from this work offers new insight into compensatory stride length adaptation that impacts systemic and throwing arm-specific fatigue to maintain ball velocity, as bilateral ankle joint dynamics can be significantly affected in response to cumulative workload.

Association between 2D:4D ratios and sprinting, change of direction ability, aerobic fitness, and cumulative workloads in elite youth soccer players

BackgroundThe aim of this study was two-fold: (i) to determine the correlation between 2D:4D, maximal oxygen uptake (VO2max), body fat percentage (BF%), maximum heart rate (HRmax), change of direction (COD), and accumulated acute and chronic workload variables; (ii) to verify if the length of the second digit divided by fourth digit (2D:4D) can explain fitness variables and accumulated training load.MethodsTwenty elite young football players (age: 13.26 ± 0.19 years; height: 165.8 ± 11.67 cm; body mass: 50.70 ± 7.56 kg; VO2max, 48.22 ± 2.29 ml.kg− 1.min− 1) participated in the present study. Anthropometric and body composition variables (e.g., height, body mass, sitting height, age, BF%, body mass index, right and left finger 2D:4D ratios) were measured. The following fitness tests were also conducted: 30 − 15 Intermittent Fitness Test (VO2max and HRmax), COD (5-0-5 agility test), and speed (10-30msprint test. HRmax and the training load were also measured and monitored using the Rate of Perceived Exertion during the 26 weeks.ResultsThere were associations between HRmax and VO2max, between 2D and 4D lengths and Left and Right hand ratios. Also, in AW with Right and Left 4D. The CW and de ACWR with the Right 4D. There were other associations between physical test variables and workload variables.ConclusionsUnder-14 soccer players with low right and left-hand 2D:4D ratios did not perform better in the selected fitness tests to assess VO2max, COD, or sprint ability. However, it cannot be ruled out that the absence of statistically significant results may be related to the small sample size and the maturational heterogeneity of the participants.

Variations in cumulative workload and anaerobic power in adolescent elite male football players: associations with biological maturation

BackgroundIt is considered that the maturity condition of young football players is related to their physical performance during short high-intensity efforts dependent on anaerobic power ability. Hence, the aim was to determine possible relationships between maturity status and training intensity by measuring the footballer´s peak height velocity (PHV), maturity offset and anaerobic power.MethodsSeventeen youth male players of different playing positions participated in the study and performed tests at three different stages of the season (early-, mid- and end-season) All the anthropometric parameters and biomarkers related to maturation were controlled during the season. The training intensity and load was monitored and the anaerobic power of the players was assessed by a running-based anaerobic sprint test (RAST).ResultsThe analysis of all the recorded data revealed a positive relationship between PHV and anaerobic power in the early- and end-season stages (p < 0.05). Maturity offset and anaerobic power (mean RAST) showed an absolute correlation in early- and end -season stages (r: − 0.39 to 0.91). The multiple linear regression analysis revealed that min RAST was the best predictor for both PHV and maturity offset. The analysis of the training intensity and workloads showed a positive effect on the performance in the fitness test (p < 0.05). Additionally, players experienced less fatigue at the end-season stage than at the early-season.ConclusionsThe results show that coaches might benefit from monitoring training loads and the maturity status of the players in order to enhance their physical performance during the season.

Development of algorithm for work intensity evaluation using excess overwork index of construction workers with real-time heart rate measurement device.

The construction workers are vulnerable to fatigue due to high physical workload. This study aimed to investigate the relationship between overwork and heart rate in construction workers and propose a scheme to prevent overwork in advance. We measured the heart rates of construction workers at a construction site of a residential and commercial complex in Seoul from August to October 2021 and develop an index that monitors overwork in real-time. A total of 66 Korean workers participated in the study, wearing real-time heart rate monitoring equipment. The relative heart rate (RHR) was calculated using the minimum and maximum heart rates, and the maximum acceptable working time (MAWT) was estimated using RHR to calculate the workload. The overwork index (OI) was defined as the cumulative workload evaluated with the MAWT. An appropriate scenario line (PSL) was set as an index that can be compared to the OI to evaluate the degree of overwork in real-time. The excess overwork index (EOI) was evaluated in real-time during work performance using the difference between the OI and the PSL. The EOI value was used to perform receiver operating characteristic (ROC) curve analysis to find the optimal cut-off value for classification of overwork state. Of the 60 participants analyzed, 28 (46.7%) were classified as the overwork group based on their RHR. ROC curve analysis showed that the EOI was a good predictor of overwork, with an area under the curve of 0.824. The optimal cut-off values ranged from 21.8% to 24.0% depending on the method used to determine the cut-off point. The EOI showed promising results as a predictive tool to assess overwork in real-time using heart rate monitoring and calculation through MAWT. Further research is needed to assess physical workload accurately and determine cut-off values across industries.

Sex And Position Differences In Head Impact Kinematics Over The Course Of A Bobsled World Cup: A Pilot Study

PURPOSE: To determine if there are differences in average workload, peak linear acceleration (PLA, g), peak linear velocity (PLV, m/s) between positions and sex during the 2021 - 2022 World Cup Tour. METHODS: Twelve elite bobsled athletes (6 Females; 3 pilots, 9 push athletes; 30 ± 5 y; F 173 ± 8 cm, 75 ± 5 kg; M 183 ± 5 cm, 101 ± 5 kg) wore an accelerometer-enabled mouthguard during all training sessions and competitions occurring over ten consecutive weeks to quantify head impact kinematics. The number of impacts, PLA, and PLV, and workload (J) were calculated from the raw accelerometer waveforms. A head impact was defined as an instant where PLA exceeded 8 g. Summary data, including number of days and runs captured, and number of head impacts were determined. Average values for each metric were determined. Two separate generalized linear mixed models via PQL were used by using a fixed effect of sex or position and random effects of run number, training or competition session, athlete. An effect was considered significant if p < 0.05. RESULTS: 48 training days and 53 competition days were recorded, with a mean of 2 (range, 1 - 3) runs per day. A total of 1900 head impacts were recorded over the season with a mean of 11 ± 8 impacts recorded per run. Mean head impacts per run were similar between males (11 ± 8) and females (11 ± 8, p = 0.96) as well as between position (pilots, 11 ± 6; push, 10 ± 11.; p = 0.27). PLA (F, 11.9 ± 3.5 g; M, 12.4 ± 3.9 g; β = 0.05, p = 0.96), PLV (F, 1.1 ± 0.3 m/s; M, 1.1 ± 0.3 m/s; β = 0.05, p = 0.41), and workload (F, 3.0 ± 1.9 J; M, 3.3 ± 2.2 J; β = 0.08, p = 0.49) were similar between males and females. Push athletes experienced significantly higher workload than pilots (pilot, 2.9 ± 2.0 J; push, 3.9 ± 2.2 J; β = -0.19, p = 0.04). PLA (pilot, 12.5 ± 3.9 g; push, 11.5 ± 3.4 g; β = 0.09, p = 0.07) and PLV (pilot, 1.0 ± 0.3 m/s; push, 1.2 ± 0.3 m/s; β = -0.10, p = 0.07) did not reach the threshold for significant difference. CONCLUSION: Cumulative workload was greater in push athletes compared to pilots throughout a bobsled World Cup season. This work provides a foundational understanding of the repetitive head impacts that occur in bobsled athletes. Future work should determine the effects of repetitive head impacts on neurocognitive function to determine methods to optimize acute and long-term health outcomes among bobsled athletes.

Artificial Intelligence Technology Based on Deep Learning in Building Construction Management System Modeling

In order to explore the application of AI technology in construction management system modeling, the author proposed the application of a deep learning-based AI technology in construction management system modeling. The 3 D reconstruction deep learning model is first introduced, and then the model idea of the construction progress reliability control system is designed based on BIM (building information model). Second, the construction process of the 4dbim model is described, and the construction method is introduced. The construction of the model provides data information for the construction schedule reliability control system. Finally, the three functional modules of progress monitoring, progress reliability early warning, and progress prediction are realized by combining the S-curve comparison method, and the work of the system is described through case simulation. The early warning result is from June 7 to June 11, the progress deviation is between (−2%, 2%), and the progress is basically controlled. On June 13, the planned workload was 81.099%, and the actual cumulative workload was 7.099%, which was 4% less than the planned workload. The project progress was out of date, so it needs to be closely tracked. On June 15, the planned workload was 85.511%, and the actual cumulative workload was 80.899%, 4.5% less than the planned workload. The forecast result is the line forecast of the actual cumulative completion percentage on June 17. After calculation, the forecast result on June 17 is 84.311%. The progress deviation on June 17 was 5.21%. If no timely delay is taken on June 15, the delay will get worse. In addition, the system can predict the completion period of the project. When the actual percentage of cumulative completion is greater than or equal to 100%, it indicates that the project has been completed. Therefore, we can calculate the completion period of the three-storey project, and the construction period is about June 29 or 30. When the simulation can be carried out, the simulation number is set as 1000 times, the completion probability of the project is only 40%, and the completion probability is not too high. Artificial intelligence technology can realize progress monitoring, progress reliability early warning, and progress prediction. This system model prepares for software development and is conducive to improving the progress reliability control level of construction enterprises. Starting from the schedule planning subsystem and the schedule control subsystem, this paper studies the application of the artificial intelligence technology based on deep learning in the modeling of a building construction management system. The results show that this technology can effectively improve the efficiency of the building construction schedule management. Compared with the existing management methods, it shows great advantages in terms of operating costs and ease of use. It also promotes the application of artificial intelligence technology in the construction phase.

Paper 22: Increased Lower Extremity Injury Associated with Player Load and Distance in Collegiate Women’s Soccer

Objectives:High cumulative workloads and spikes in player workloads have been associated with an increased risk of injury in soccer and other sports. These studies, however, have all been conducted with data from male soccer players or male athletes in other sports, with few studies investigating female athletes. Using metrics collected from GPS units to monitor cumulative load and distance may be valuable for injury prevention. The purpose of this study was to examine the relationship between injury risk and workload as collected from wearable GPS units in NCAA Division I women’s soccer players.Methods:Lower extremity injury incidence and GPS workload data (player load, total distance, and high-speed distance) of 65 NCAA Division I women’s soccer players were collected over 3 seasons. Accumulated 1-, 2-, 3- and 4-weekly loads and acute: chronic workload ratios (ACWR) were classified into discrete ranges by z-scores. ACWR was calculated using rolling averages and exponentially weighted moving averages (EWMA) models. Binary logistic regression models were used to compare the 7:28 rolling average and EWMA ACWRs between injured and non-injured players for all GPS/accelerometer variables. The prior 1-, 2-, 3-, and 4-weekly accumulated loads for all GPS/accelerometer variables were compared between the injured and uninjured cohorts using two-sample t-tests with an alpha level of 0.05 set as significant.Results:There were a total of 53 lower extremity injuries that resulted in lost time recorded (5.76/1000 hours ‘on-field’ exposure time; 34 non-contact and 19 contact injuries). The prior 2-week (7,242 vs 6,613; p=0.02), 3-week (10,533 vs 9,718; p=0.02), and 4-week (13,819 vs 12,892; p=0.04) accumulated player loads and 2-week (62.40 vs 57.25 km; p=0.04), 3-week (90.97 vs 84.10 km; p=0.03), and 4-week (119.31 vs 111.38 km; p=0.05) accumulated total distances were significantly higher for injured players compared to non-injured players during the same timeframes. There were no significant differences in player load, total distance, or high-speed distance ACWR between injured and non-injured players for both the rolling averages and EWMA calculations.Conclusions:Higher accumulated player load and total distance, but not ACWR, are associated with injury in women’s soccer players.

A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players

Abstract Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between modifiable and non-modifiable risk factors, with the final goal of predicting non-contact injuries. Twenty-three young soccer players were monitored during an entire season, with a total of fifty-seven non-contact injuries identified. Anthropometric data were collected, and the maturity offset was calculated for each player. To quantify internal training/match load and recovery status of the players, we daily employed the session-RPE method and the total quality recovery (TQR) scale. Cumulative workloads and the acute: chronic workload ratio (ACWR) were calculated. To explore the relationship between the various risk factors and the onset of non-contact injuries, we performed a classification tree analysis. The classification tree model exhibited an acceptable discrimination (AUC=0.76), after receiver operating characteristic curve (ROC) analysis. A low state of recovery, a rapid increase in the training load, cumulative workload, and maturity offset were recognized by the data mining algorithm as the most important injury risk factors.

Comparison of Elbow Varus Torque During Interval Throwing Programs and Gameday Pitching in High School Baseball Pitchers (125)

Objectives:Completion of an interval throwing program (ITP) is a common benchmark for return to full competition following an injury to the dominant extremity of an overhead throwing athlete. While workload management for overhead athletes has evolved, the general structure of the ITP remains relatively unexamined. Furthermore, the daily and cumulative workload of ITPs is generally unknown. An ideal ITP would allow for a gradual increase in workload that eventually approximates, but does not exceed, workload measurements attained during competition. It is currently unknown if ITPs achieve this critically important objective. Therefore, the current study sought to 1) determine the daily and cumulative workload for common ITPs using elbow varus torque (EVT), and 2) compare EVT experienced during completion of ITPs to game pitching EVT values.Methods:A retrospective review identified high school pitchers with at least 50 throws at distances of 90, 120, 150, and 180 feet plus game pitches while wearing a MotusBASEBALL sensor. Averages for EVT per throw and torque per minute were calculated at each distance. Three throwing programs were created using a template of one phase at each distance with two steps per phase (Table 1). Programs varied only by number of throws per set (20, 25, and 30 throws for Programs A, B, and C, respectively). Total EVT for each step, phase, and program were calculated using average EVT values for each distance. Total torque for each step and program was converted to an average inning pitched equivalent (IPE) and maximum pitch count equivalent (MPE), respectively, using pitching EVT values and expected average pitch counts (16 pitches/inning and maximum 105 pitches/game).Results:3,447 throws were analyzed from 7 pitchers with an average age of 16.7 yrs (0.8 yrs SD). EVT progressively increased with distance (range 36.9-45.5 N·m), comparable to game pitching (45.7 N·m). Average torque per minute was highest for 90 ft throws (193.4 N·m/min) and lowest for game pitches (125.0 N·m/min). Program A demonstrated the lowest range of IPE per step (2.0-3.7), and Program C had the highest range (3.0-5.6) (Figure 1). The phases of Program A never exceeded 1MPE. Program B exceeded this threshold after phase 1, and Program C exceeded 1MPE at every phase (Figure 2). Total program MPE ranged from 3.5 (Program A) to 5.2 (Program C).Conclusions:Performing long-toss throwing led to greater torque per minute compared to gameday pitching. Additionally, ITPs requiring 25 or more throws per set led to increased cumulative EVT, especially at distances greater than 150 ft, which can exceed typical values from gameday pitching. ITPs should be adjusted accordingly to encourage a slower pace of long-toss throws and less than 25 throws per set, especially at distances greater than 120 ft. Most ITPs currently recommend one rest day between steps. However, cumulative EVT at longer distances can exceed 5 IPE. Most pitch count rules require more than one rest day after a pitching outing that exceeds multiple innings pitched. Therefore, days off between steps and phases of an ITP should reflect these demands. We advocate for multiple days off between steps that require more than 3 IPE. Table 2 presents a novel ITP based on the findings of the current study.

PD58-11 BEYOND EFFICIENCY: SURFACE ELECTROMYOGRAPHY ENABLES FURTHER INSIGHTS INTO THE QUALITY OF SURGICAL MOVEMENT OF EXPERT UROLOGISTS

You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment (PD58)1 Sep 2021PD58-11 BEYOND EFFICIENCY: SURFACE ELECTROMYOGRAPHY ENABLES FURTHER INSIGHTS INTO THE QUALITY OF SURGICAL MOVEMENT OF EXPERT UROLOGISTS Perry Xu, Daniel Haik, Pengbo Jiang, Andrew Brevik, Akhil Peta, Zhamshid Okhunov, Shlomi Tapiero, Emmanuel John, Rahul Soangra, Jaime Landman, and Ralph Clayman Perry XuPerry Xu More articles by this author , Daniel HaikDaniel Haik More articles by this author , Pengbo JiangPengbo Jiang More articles by this author , Andrew BrevikAndrew Brevik More articles by this author , Akhil PetaAkhil Peta More articles by this author , Zhamshid OkhunovZhamshid Okhunov More articles by this author , Shlomi TapieroShlomi Tapiero More articles by this author , Emmanuel JohnEmmanuel John More articles by this author , Rahul SoangraRahul Soangra More articles by this author , Jaime LandmanJaime Landman More articles by this author , and Ralph ClaymanRalph Clayman More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002092.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The advent of laparoscopic and robotic surgery has required urologists to hone multiple skillsets that involve complex movements. Surface electromyography (EMG) accurately records aspects of complex movements. Our objective was to use EMG to capture the differences of surgical movement between expert urologists and trainees while performing laparoscopic and robotic surgical tasks. METHODS: Based on surgical experience, 26 participants were divided into three groups: novice trainees (no surgical training; n=10), intermediate trainees (resident; n=11), and experts (faculty; n=5). With 12 surface EMG electrodes affixed to the upper extremities, participants performed 2 tasks: laparoscopic peg transfer (LPT) and robotic suturing (RS) (Figure 1). Performance EMG data were computed based on muscular activation and workload. These variables were compared across groups using ANOVA statistical analysis; significance was defined as p<0.05 and differences were confirmed with posthoc Tukey analysis. RESULTS: Novices took longer to complete the LPT and RS tasks (Table 1). There was no significant difference across skill level for both tasks in muscle contraction range, root mean square (variability in linear motion), and average work per second. Experts had higher muscle contraction frequency compared to both novices and intermediate trainees in RS (p<0.01). Intermediate trainees had higher muscle contraction frequency compared to novices in LPT (p=0.03). Experts had lower cumulative workload in RS compared to novices (p=0.04). CONCLUSIONS: Contrary to open surgery where speed is often regarded as a primary indicator of skill, among a range of personnel there was no significant difference in efficiency in task completion. Instead, experts in robotic surgery were distinguished by a higher muscle contraction frequency indicating a more concentrated effort expended on fine muscle control and microadjustments. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1021-e1021 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Perry Xu More articles by this author Daniel Haik More articles by this author Pengbo Jiang More articles by this author Andrew Brevik More articles by this author Akhil Peta More articles by this author Zhamshid Okhunov More articles by this author Shlomi Tapiero More articles by this author Emmanuel John More articles by this author Rahul Soangra More articles by this author Jaime Landman More articles by this author Ralph Clayman More articles by this author Expand All Advertisement Loading ...

Physiological and Perceptual Recovery-Stress Responses to an Elite Netball Tournament.

To investigate and explore the relationships between physiological and perceptual recovery and stress responses to elite netball tournament workloads. Nine elite female netballers were observed across a 3-day (T1-3), 4-match tournament. Participants provided salivary samples for cortisol and alpha-amylase analysis, completed the Short Recovery Stress Scale (SRSS), and reported session ratings of perceived exertion. Inertial measurement units and heart-rate monitors determined player load, changes of direction (COD), summated heart-rate zones, and jumps. Analysis revealed 6 significant SRSS time effects: (1)decreased recovery markers of physical performance (P = .042), emotional balance (P = .034), and overall recovery (P = .001) and (2)increased perceptual stress markers of muscular stress (P = .001), negative emotional state (P = .026), and overall stress (P = .010). Salivary cortisol decreased over the tournament (T1-3) before progressively increasing posttournament with greater salivary samples for cortisol on T+2 compared with T3 (P = .014, ES = -1.29; -2.24 to -0.22]) and T+1 (P = .031, ES = -1.54; -2.51 to -0.42). SRSS overall recovery moderately negatively correlated with COD (r = -.41, P = .028) and session ratings of perceived exertion (r = -.40, P = .034). Cumulative workload did not relate to posttournament perceptual or salivary responses. Percentage change in salivary variables related (P < .05) to total player load, total COD, and overall recovery across specific cumulative time periods. During and after an elite netball tournament, athletes indicated increased perceptual stress and lack of recovery. The SRSS is a valuable tool for recovery-stress monitoring in elite tournament netball. It is recommended that practitioners monitor COD due to its negative influence on perceived overall recovery.

How does Gender Influence Sustainable Return to Work Following Prolonged Work Disability? An Interpretive Description Study.

A sustainable return to work (S-RTW) following prolonged work disability poses different challenges, depending on gender. This article provides a synthesis of gender differences in the issues and factors influencing the S-RTW of workers following such a disability. Using an interpretive description method, an integrative review was conducted of the literature on gender differences in S-RTW issues and factors associated with four major causes of work disability. The initial review concerned the 2000-2016 literature; it was subsequently updated for November 2016-March 2020. To explore and contextualise the results, four focus groups were held with stakeholders representing the workplace, insurance, and healthcare systems and workers. Qualitative thematic analysis was performed. A total of 47 articles were reviewed, and 35 stakeholders participated in the focus groups. The prevailing traditional gender roles were found to have a major gender-specific influence on the attitudes, behaviours, processes and outcomes associated with S-RTW. These differences related to the (1) cumulative workload, (2) work engagement, and (3) expressed and addressed needs. The results highlight the importance of taking into account both professional and personal aspects when integrating gender issues into the assessment of workers' needs and subsequently into interventions.

Effect of Workload After ACL Reconstruction on Rerupture Rates in NBA Players.

Background:Rupture of the anterior cruciate ligament (ACL) is a common and potentially career-altering injury sustained by players in the National Basketball Association (NBA). Strategies have been employed by the league to prevent reinjury of players after ACL reconstruction (ACLR), including minute restrictions and rest games; however, it remains unknown whether workload metrics after ACLR influence the risk for reinjury and revision surgery.Purpose:To evaluate whether workload changes after return to play (RTP) from primary ACLR influences the risk of rerupture in NBA players.Study Design:Case-control study; Level of evidence, 3.Methods:We identified NBA players from 1975 to 2018 who underwent primary ACLR as well as those who required revision ACLR. Primary outcomes included workload measures such as games played, games started, and minutes per game. Secondary outcomes included in-game performance statistics. Statistical analysis was used to compare relative workload and performance 3 years before and 3 years after undergoing primary ACLR. Workload was also compared between the control group of NBA players who underwent primary ACLR and those who required revision ACLR.Results:A total of 68 players who underwent primary ACLR were included, 8 of whom subsequently required revision ACLR. In their first season upon RTP, control players (primary ACLR) demonstrated a significant reduction in all workload metrics relative to the season before injury (P < .001), while the revision group demonstrated an unchanged to increased workload. In a comparison between the primary and revision groups during the first season after RTP, the primary group demonstrated significantly fewer games started (mean ± SD, 22.2 ± 3.0 vs 35.8 ± 8.3; P = .039) and minutes per game (20.5 ± 1.1 vs 27.0 ± 3.1; P = .048) than revision players. The primary ACLR group demonstrated reduced cumulative workload trends for the first 3 years after RTP relative to 3 years before injury, which was not demonstrated in the revision ACLR group, albeit statistically insignificant.Conclusion:Our study found that after ACLR, a reduction in workload parameters relative to preinjury baseline was associated with players who did not sustain rerupture. Further study is required to determine if workload measures following RTP from primary ACLR should be individualized relative to preinjury baseline.

To what extent is severe osteoarthritis preventable? Occupational and non-occupational risk factors for knee and hip osteoarthritis.

To explore the relative contribution of cumulative physical workload, sociodemographic and lifestyle factors, as well as prior injury to hospitalization due to knee and hip OA. We examined a nationally representative sample of persons aged 30-59 years, who participated in a comprehensive health examination (the Health 2000 Study). A total of 4642 participants were followed from mid-2000 to end-2015 for the first hospitalization due to knee or hip OA using the National Hospital Discharge Register. We examined the association of possible risk factors with the outcome using a competing risk regression model (death was treated as competing risk) and calculated population attributable fractions for statistically significant risk factors. Baseline age and BMI as well as injury were associated with the risk of first hospitalization due to knee and hip OA. Composite cumulative workload was associated with a dose-response pattern with hospitalizations due to knee OA and with hospitalizations due to hip OA at a younger age only. Altogether, prior injury, high BMI and intermediate to high composite cumulative workload accounted for 70% of hospitalizations due to knee OA. High BMI alone accounted for 61% and prior injury only for 6% of hospitalizations due to hip OA. Our results suggest that overweight/obesity, prior injury and cumulative physical workload are the most important modifiable risk factors that need to be targeted in the prevention of knee OA leading to hospitalization. A substantial proportion of hospitalizations due to hip OA can be reduced by controlling excess body weight.

Harmonizing Protocol Complexity with Resource Management and Capacity Planning at Clinical Research Sites.

Clinical research sites conduct trials with diverse complexities, timelines, and ever-changing workloads. Though the principal investigator (PI) is ultimately responsible for the content and conduct of trials, they rely heavily on site staff to successfully enroll and complete studies following good clinical practice (GCP) Guidelines. The mainstays of the site workforce arethe clinical research coordinators (CRCs) to whom the trials are assigned. These CRCs work on many studies concurrently. Managing study assignments and workload is a difficult task that requires knowledge of the trial complexity, expected enrollment, and many other factors affecting performance. Traditional methods for allocating workload to site staff quantitate trial complexity and estimate work hours by factoring in the number of trial participants. However, this does not account for the effects of associated workload or variability in staff attributes. It also neglects other factors that affect performance and assumes maximum enrollment and completion of the trial by all participants. This article introduces a novel approach that determines the effects of protocol complexity on CRC productivity without effort tracking. These metrics permit an assessment of how the CRC's performance is affected by the number of studies assigned. By understanding the effects of workload allocation on CRC productivity and capacity, the site manager can use an algorithmic approach toward improving performance. The process takes into account factors that are both within and outside the control of the site manager. Sites may benefit from analytics that measures how CRCs adapt to the effects of study complexity on cumulative workloads over time. Optimizing productivity also means conforming to GCP Guidelines and avoiding staff burnout. As studies become increasingly difficult, site managers need tools to manage complexity and balance workloads among staff.